diff --git a/docs/notebooks/dwi_gp.ipynb b/docs/notebooks/dwi_gp.ipynb
new file mode 100644
index 00000000..0f91ecd8
--- /dev/null
+++ b/docs/notebooks/dwi_gp.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Gaussian process notebook",
+   "id": "486923b289155658"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "import tempfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel\n",
+    "\n",
+    "from eddymotion import model\n",
+    "from eddymotion.data.dmri import DWI\n",
+    "from eddymotion.data.splitting import lovo_split\n",
+    "\n",
+    "datadir = Path(\"../../test\")  # Adapt to your local path or download to a temp location using wget\n",
+    "\n",
+    "kernel = DotProduct() + WhiteKernel()\n",
+    "\n",
+    "dwi = DWI.from_filename(datadir / \"dwi.h5\")\n",
+    "\n",
+    "_dwi_data = dwi.dataobj\n",
+    "# Use a subset of the data for now to see that something is written to the\n",
+    "# output\n",
+    "# bvecs = dwi.gradients[:3, :].T\n",
+    "bvecs = dwi.gradients[:3, 10:13].T  # b0 values have already been masked\n",
+    "# bvals = dwi.gradients[3:, 10:13].T  # Only for inspection purposes: [[1005.], [1000.], [ 995.]]\n",
+    "dwi_data = _dwi_data[60:63, 60:64, 40:45, 10:13]\n",
+    "\n",
+    "# ToDo\n",
+    "# Provide proper values/estimates for these\n",
+    "a = 1\n",
+    "h = 1  # should be a NIfTI image\n",
+    "\n",
+    "num_iterations = 5\n",
+    "gp = model.GaussianProcessModel(\n",
+    "    dwi=dwi, a=a, h=h, kernel=kernel, num_iterations=num_iterations\n",
+    ")\n",
+    "indices = list(range(bvecs.shape[0]))\n",
+    "# ToDo\n",
+    "# This should be done within the GP model class\n",
+    "# Apply lovo strategy properly\n",
+    "# Vectorize and parallelize\n",
+    "result_mean = np.zeros_like(dwi_data)\n",
+    "result_stddev = np.zeros_like(dwi_data)\n",
+    "for idx in indices:\n",
+    "    lovo_idx = np.ones(len(indices), dtype=bool)\n",
+    "    lovo_idx[idx] = False\n",
+    "    X = bvecs[lovo_idx]\n",
+    "    for i in range(dwi_data.shape[0]):\n",
+    "        for j in range(dwi_data.shape[1]):\n",
+    "            for k in range(dwi_data.shape[2]):\n",
+    "                # ToDo\n",
+    "                # Use a mask to avoid traversing background data\n",
+    "                y = dwi_data[i, j, k, lovo_idx]\n",
+    "                gp.fit(X, y)\n",
+    "                pred_mean, pred_stddev = gp.predict(\n",
+    "                    bvecs[idx, :][np.newaxis]\n",
+    "                )  # Can take multiple values X[:2, :]\n",
+    "                result_mean[i, j, k, idx] = pred_mean.item()\n",
+    "                result_stddev[i, j, k, idx] = pred_stddev.item()"
+   ],
+   "id": "da2274009534db61",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot the data",
+   "id": "77e77cd4c73409d3"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "from matplotlib import pyplot as plt \n",
+    "%matplotlib inline\n",
+    "\n",
+    "s = dwi_data[1, 1, 2, :]\n",
+    "s_hat_mean = result_mean[1, 1, 2, :]\n",
+    "s_hat_stddev = result_stddev[1, 1, 2, :]\n",
+    "x = np.asarray(indices)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, s_hat_mean, c=\"orange\", label=\"predicted\")\n",
+    "plt.fill_between(\n",
+    "    x.ravel(),\n",
+    "    s_hat_mean - 1.96 * s_hat_stddev,\n",
+    "    s_hat_mean + 1.96 * s_hat_stddev,\n",
+    "    alpha=0.5,\n",
+    "    color=\"orange\",\n",
+    "    label=r\"95% confidence interval\",\n",
+    ")\n",
+    "plt.scatter(x, s, c=\"b\", label=\"ground truth\")\n",
+    "ax.set_xlabel(\"bvec indices\")\n",
+    "ax.set_ylabel(\"signal\")\n",
+    "ax.legend()\n",
+    "plt.title(\"Gaussian process regression on dataset\")\n",
+    "\n",
+    "plt.show()"
+   ],
+   "id": "4e51f22890fb045a",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "Plot the DWI signal for a given voxel\n",
+    "Compute the DWI signal value wrt the b0 (how much larger/smaller is and add that delta to the unit sphere?) for each bvec direction and plot that?"
+   ],
+   "id": "694a4c075457425d"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# from mpl_toolkits.mplot3d import Axes3D\n",
+    "# fig, ax = plt.subplots()\n",
+    "# ax = fig.add_subplot(111, projection='3d')\n",
+    "# plt.scatter(xx, yy, zz)"
+   ],
+   "id": "bb7d2aef53ac99f0",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot the DWI signal brain data\n",
+   "id": "62d7bc609b65c7cf"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "# plot_dwi(dmri_dataset.dataobj, dmri_dataset.affine, gradient=data_test[1], black_bg=True)",
+   "id": "edb0e9d255516e38",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot the predicted DWI signal",
+   "id": "1a52e2450fc61dc6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "# plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);",
+   "id": "66150cf337b395e0",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/dwi_simulated_gp.ipynb b/docs/notebooks/dwi_simulated_gp.ipynb
new file mode 100644
index 00000000..09073dbe
--- /dev/null
+++ b/docs/notebooks/dwi_simulated_gp.ipynb
@@ -0,0 +1,2037 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define a method to create a DWI signal using a single tensor",
+   "id": "28be0e6f9dbd2c9d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T00:51:12.970699Z",
+     "start_time": "2024-09-25T00:51:12.966380Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from dipy.core.gradients import gradient_table\n",
+    "from dipy.core.sphere import disperse_charges, HemiSphere, Sphere\n",
+    "\n",
+    "def create_single_tensor_signal(_hsph_dirs, _evals, _evecs, _bval_shell, _S0, _snr):\n",
+    "    \"\"\"Create a multi-tensor dMRI signal for simulation purposes. It adds a b0 volume.\"\"\"\n",
+    "\n",
+    "    # Create the gradient table placing random points on a hemisphere\n",
+    "    rng = np.random.default_rng(1234)\n",
+    "    theta = np.pi * rng.random(_hsph_dirs)\n",
+    "    phi = 2 * np.pi * rng.random(_hsph_dirs)\n",
+    "    hsph_initial = HemiSphere(theta=theta, phi=phi)\n",
+    "\n",
+    "    # Move the points so that the electrostatic potential energy is minimized\n",
+    "    iterations = 5000\n",
+    "    hsph_updated, potential = disperse_charges(hsph_initial, iterations)\n",
+    "    # Create a sphere\n",
+    "    sph = Sphere(xyz=np.vstack((hsph_updated.vertices, -hsph_updated.vertices)))\n",
+    "\n",
+    "    # Create the gradients\n",
+    "    vertices = sph.vertices\n",
+    "    values = np.ones(vertices.shape[0])\n",
+    "    bvecs = vertices\n",
+    "    bvals = _bval_shell * values\n",
+    "\n",
+    "    # Add a b0 value to the gradient table\n",
+    "    bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)\n",
+    "    bvals = np.insert(bvals, 0, 0)\n",
+    "    _gtab = gradient_table(bvals, bvecs)\n",
+    "\n",
+    "    # Create the signal\n",
+    "    _signal = single_tensor(_gtab, S0=_S0, evals=_evals, evecs=_evecs, snr=_snr, rng=None)\n",
+    "\n",
+    "    return _signal, _gtab"
+   ],
+   "id": "962dd0e463ccf0e",
+   "outputs": [],
+   "execution_count": 31
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Create the DWI signal using a single tensor.",
+   "id": "7d5b5cbebaa82e19"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T00:51:16.944050Z",
+     "start_time": "2024-09-25T00:51:12.981115Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "from dipy.core.geometry import sphere2cart\n",
+    "from dipy.sims.voxel import single_tensor, all_tensor_evecs\n",
+    "\n",
+    "# Polar coordinates (theta, phi) of the principal axis of the tensor\n",
+    "angles = np.array([0, 0])\n",
+    "sticks = np.array(sphere2cart(1, np.deg2rad(angles[0]), np.deg2rad(angles[1])))\n",
+    "evecs = all_tensor_evecs(sticks)\n",
+    "\n",
+    "# Eigenvalues of the tensor\n",
+    "evals1 = [0.0015, 0.0003, 0.0003]\n",
+    "\n",
+    "# Half the number of the gradient vectors\n",
+    "hsph_dirs = 90\n",
+    "\n",
+    "# Single shell: b = 1000 s/mm^2\n",
+    "bval_shell = 1000\n",
+    "\n",
+    "S0 = 100\n",
+    "# Noise-free signal\n",
+    "snr = None\n",
+    "\n",
+    "signal, gtab = create_single_tensor_signal(hsph_dirs, evals1, evecs, bval_shell, S0, snr)"
+   ],
+   "id": "e9545781fe5cf3b8",
+   "outputs": [],
+   "execution_count": 32
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Define a method to plot the fODFs of the signal",
+   "id": "61c7bd2fa7406c07"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T00:51:16.961400Z",
+     "start_time": "2024-09-25T00:51:16.958847Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from dipy.viz import window, actor\n",
+    "\n",
+    "def plot_dwi_fodf(_odf, _sphere, background=(255, 255, 255)):\n",
+    "\n",
+    "    scene = window.Scene()\n",
+    "    scene.SetBackground(background)\n",
+    "    \n",
+    "    odf_actor = actor.odf_slicer(_odf[None, None, None, :], sphere=_sphere, colormap=\"plasma\")\n",
+    "    odf_actor.RotateX(90)\n",
+    "    \n",
+    "    scene.add(odf_actor)\n",
+    "    _scene_array = window.snapshot(scene, offscreen=True)\n",
+    "\n",
+    "    return _scene_array"
+   ],
+   "id": "ce7bcfbd73447d81",
+   "outputs": [],
+   "execution_count": 33
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot the fODFs of the signal",
+   "id": "1e429c27665565a4"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "We now define the Gaussian process model instance with a spherical kernel.",
+   "id": "37e6f400ed44f2d9"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T00:51:17.003878Z",
+     "start_time": "2024-09-25T00:51:17.001352Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from eddymotion.model._dipy import GaussianProcessModel\n",
+    "\n",
+    "kernel_model = \"spherical\"\n",
+    "lambda_s = 2.0\n",
+    "a = 1.0\n",
+    "sigma_sq = 0.5\n",
+    "\n",
+    "gp_model = GaussianProcessModel(kernel_model=kernel_model, lambda_s=lambda_s, a=a, sigma_sq=sigma_sq)"
+   ],
+   "id": "a66400cc9ee4c084",
+   "outputs": [],
+   "execution_count": 34
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "We fit the Gaussian process with the diffusion-encoding gradient vectors: walk all directions leaving out one direction at a time from the training data and fit the Gaussian process. Predict on the direction that has been left out from the training data. The b0 volume is excluded from the computations.",
+   "id": "12b7491f09d7ea74"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T00:51:33.343762Z",
+     "start_time": "2024-09-25T00:51:17.047317Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "y_pred = []\n",
+    "pred_x_idx = list(range(1, len(gtab.bvals)))\n",
+    "for idx in pred_x_idx:\n",
+    "\n",
+    "    # Create the train mask\n",
+    "    # Exclude the 0 index (b0) as well\n",
+    "    loo = [0, idx]\n",
+    "    train_mask = np.ones(len(gtab.bvals), dtype=bool)\n",
+    "    train_mask[loo] = False\n",
+    "    gpfit = gp_model.fit(signal[train_mask], gtab[train_mask])\n",
+    "\n",
+    "    # Predict the signal on the excluded diffusion-encoding gradient vector\n",
+    "    X_qry = gtab.bvecs[~train_mask][-1]\n",
+    "    _y_pred = gpfit.predict(X_qry[np.newaxis, :])\n",
+    "\n",
+    "    # Check whether the hyperparameters of the kernel have been optimized\n",
+    "    # gp_model.kernel_\n",
+    "\n",
+    "    y_pred.append(_y_pred.item())"
+   ],
+   "id": "b64f511b41456359",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
+      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "  _check_optimize_result(\"lbfgs\", opt_res)\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter a is close to the specified upper bound 3.141592653589793. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter lambda_s is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n",
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter sigma_sq is close to the specified upper bound 10000.0. Increasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Plot the training data and the predictions from the Gaussian process.",
+   "id": "8913c3c3ef7d50f7"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T00:51:33.579777Z",
+     "start_time": "2024-09-25T00:51:33.432761Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# There is only a single voxel in the simulated DWI signal, so we add 3 axes before the diffusion-encoding gradient axis to appropriately model the voxel.\n",
+    "voxel_idx = [0, 0, 0]\n",
+    "dwi_data = signal[np.newaxis, np.newaxis, np.newaxis, :]\n",
+    "\n",
+    "# Exclude the b0s for the plot\n",
+    "y = dwi_data[voxel_idx[0], voxel_idx[1], voxel_idx[2], :][~gtab.b0s_mask]\n",
+    "x = np.asarray(range(len(gtab.bvals)))[~gtab.b0s_mask]\n",
+    "\n",
+    "# Account for the fact that the query index is obtained in the [1, len(gtab)] range in the indices\n",
+    "_pred_x_idx = list(map(lambda _x: _x-1, pred_x_idx))\n",
+    "                   \n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y, c=\"orange\")\n",
+    "plt.scatter(x, y, c=\"b\", label=\"ground truth\")\n",
+    "# plt.scatter(x[pred_x_idx], y_pred[pred_x_idx], c=\"r\", label=\"predicted\")\n",
+    "plt.scatter(x[_pred_x_idx], y_pred, c=\"r\", label=\"predicted\")\n",
+    "ax.set_xlabel(\"bvec indices\")\n",
+    "ax.set_ylabel(\"signal\")\n",
+    "ax.legend(loc=\"lower right\")\n",
+    "plt.title(\"Gaussian process regression on dataset\")\n",
+    "\n",
+    "plt.show()"
+   ],
+   "id": "58f01aeb70aff1c1",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 36
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}