From 93d641ef66805649d820e25aa0c553af02bb587c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Mon, 4 Nov 2024 19:25:50 -0500
Subject: [PATCH 01/17] ENH: Add GP estimation notebook

Add GP estimation notebook.
---
 docs/notebooks/dwi_gp_estimation.ipynb | 3423 ++++++++++++++++++++++++
 1 file changed, 3423 insertions(+)
 create mode 100644 docs/notebooks/dwi_gp_estimation.ipynb

diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
new file mode 100644
index 00000000..263511a5
--- /dev/null
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -0,0 +1,3423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d11e5969ed6af8a5",
+   "metadata": {},
+   "source": [
+    "Estimate a DWI signal using the eddymotion Gaussian Process (GP) regressor estimator."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3476a8e9cfefd4b8",
+   "metadata": {},
+   "source": [
+    "Download the \"Sherbrooke 3-shell\" dataset using DIPY and select the b=1000 s/mm^2 shell data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "69a3bc6b4fbe7036",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dipy.data as dpd\n",
+    "import nibabel as nib\n",
+    "import numpy as np\n",
+    "from dipy.core.gradients import get_bval_indices\n",
+    "from dipy.io import read_bvals_bvecs\n",
+    "from dipy.segment.mask import median_otsu\n",
+    "\n",
+    "seed = 1234\n",
+    "rng = np.random.default_rng(seed)\n",
+    "\n",
+    "name = \"sherbrooke_3shell\"\n",
+    "\n",
+    "dwi_fname, bval_fname, bvec_fname = dpd.get_fnames(name=name)\n",
+    "dwi_data = nib.load(dwi_fname).get_fdata()\n",
+    "bvals, bvecs = read_bvals_bvecs(bval_fname, bvec_fname)\n",
+    "\n",
+    "_, brain_mask = median_otsu(dwi_data, vol_idx=[0])\n",
+    "\n",
+    "bval = 1000\n",
+    "indices = get_bval_indices(bvals, bval, tol=20)\n",
+    "\n",
+    "bvecs_shell = bvecs[indices]\n",
+    "shell_data = dwi_data[..., indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bd417117afaad49",
+   "metadata": {},
+   "source": [
+    "Visualize a slice of the data for a given DWI volume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d8547475686958f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot a slice\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "dwi_vol_idx = len(indices) // 2\n",
+    "slice_idx = list(map(int, np.divide(dwi_data.shape[:-1], 2)))\n",
+    "\n",
+    "x_slice = dwi_data[slice_idx[0], :, :, dwi_vol_idx]\n",
+    "y_slice = dwi_data[:, slice_idx[1], :, dwi_vol_idx]\n",
+    "z_slice = dwi_data[:, :, slice_idx[2], dwi_vol_idx]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "    axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
+    "    axes[i].set_axis_off()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dcab811fe667617",
+   "metadata": {},
+   "source": [
+    "Define the EddyMotionGPR instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7d5d9562339bc849",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from eddymotion.model.gpr import EddyMotionGPR, SphericalKriging\n",
+    "\n",
+    "beta_a = 1.38\n",
+    "beta_l = 1 / 2.1\n",
+    "kernel = SphericalKriging(beta_a=beta_a, beta_l=beta_l)\n",
+    "\n",
+    "alpha = 0.1\n",
+    "disp = True\n",
+    "optimizer = None\n",
+    "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=optimizer)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea5cc8036fa0ab48",
+   "metadata": {},
+   "source": [
+    "Do not optimize the parameters in the fitting. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7e93b99c3b072d99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>EddyMotionGPR(alpha=0.1, disp=True,\n",
+       "              kernel=SphericalKriging (a=1.38, λ=0.47619047619047616),\n",
+       "              optimizer=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;EddyMotionGPR<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>EddyMotionGPR(alpha=0.1, disp=True,\n",
+       "              kernel=SphericalKriging (a=1.38, λ=0.47619047619047616),\n",
+       "              optimizer=None)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "EddyMotionGPR(alpha=0.1, disp=True,\n",
+       "              kernel=SphericalKriging (a=1.38, λ=0.47619047619047616),\n",
+       "              optimizer=None)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train = bvecs_shell\n",
+    "# Consider only brain voxels\n",
+    "dwi_mask = np.repeat(brain_mask[..., np.newaxis], shell_data.shape[-1], axis=-1)\n",
+    "y_train = shell_data[dwi_mask].reshape((X_train.shape[0], -1))\n",
+    "gpr.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfdd82afbdb22790",
+   "metadata": {},
+   "source": [
+    "Predict on a randomly picked diffusion-encoding gradient direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ae3407b31b14928d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 20.177777097314834\n",
+      "Number of RMSE values above 10: 97093 (61.20%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pick a direction to predict\n",
+    "idx = rng.integers(0, len(indices))\n",
+    "X_test = bvecs_shell[idx][np.newaxis, :]\n",
+    "y_pred = gpr.predict(X_test)\n",
+    "\n",
+    "rmse = np.sqrt(np.mean(np.square(y_train[idx, ...] - y_pred.squeeze())))\n",
+    "_rmse_element = np.sqrt(np.square(y_train[idx, ...] - y_pred.squeeze()))\n",
+    "\n",
+    "print(f\"RMSE: {rmse}\")\n",
+    "threshold = 10\n",
+    "n_error_thr = len(_rmse_element[_rmse_element > threshold])\n",
+    "ratio = n_error_thr / len(_rmse_element) * 100\n",
+    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74b040c05621f2d9",
+   "metadata": {},
+   "source": [
+    "Visualize the prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a130de2a03dff2b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Reshape the predicted data array to the image shape\n",
+    "brain_mask_idx = np.where(brain_mask)\n",
+    "_y_pred = np.zeros((shell_data.shape[:-1]), dtype=y_train.dtype)\n",
+    "_y_pred[brain_mask_idx] = y_pred.squeeze()\n",
+    "\n",
+    "x_slice = _y_pred[slice_idx[0], :, :]\n",
+    "y_slice = _y_pred[:, slice_idx[1], :]\n",
+    "z_slice = _y_pred[:, :, slice_idx[2]]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
+    "     axes[i].set_axis_off()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ea9cf5ae629f489",
+   "metadata": {},
+   "source": [
+    "Select a number of brain voxels at random to perform the GP parameter optimization. The same voxels will be selected across each DWI volume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "24a72a024e3f2e37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_voxels = 10000\n",
+    "true_indices = np.argwhere(brain_mask)\n",
+    "\n",
+    "# Sample indices randomly\n",
+    "num_samples = min(n_voxels, len(true_indices))\n",
+    "sampled_indices = true_indices[np.random.choice(len(true_indices), num_samples, replace=False)]\n",
+    "\n",
+    "sampled_dwi = shell_data[sampled_indices[:, 0], sampled_indices[:, 1], sampled_indices[:, 2], :].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4697b2182f5178d7",
+   "metadata": {},
+   "source": [
+    "Use a k-fold cross-validation and a grid search to find the best parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e92ebf1204c7f889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  4.00084D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23889D+05    |proj g|=  6.95102D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23693D+05    |proj g|=  6.92318D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90777D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.47262D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  9.89565D-03\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   9.896D-03   7.237D+05\n",
+      "  F =   723658.65193438344     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.93671D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23926D+05    |proj g|=  6.95426D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23700D+05    |proj g|=  6.92476D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90796D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90879D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  1.50953D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   1.510D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.92653D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23948D+05    |proj g|=  6.95605D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23704D+05    |proj g|=  6.92564D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90806D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90879D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  1.88219D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   1.882D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.94029D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23917D+05    |proj g|=  6.95348D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23698D+05    |proj g|=  6.92437D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90791D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90879D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  1.36717D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   1.367D-02   7.237D+05\n",
+      "  F =   723658.65193438344     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  4.00075D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.27412D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.38150D+05    |proj g|=  6.95660D+00\n",
+      "\n",
+      "At iterate    3    f=  7.37895D+05    |proj g|=  6.92592D+00\n",
+      "\n",
+      "At iterate    4    f=  7.37848D+05    |proj g|=  6.90810D+00\n",
+      "\n",
+      "At iterate    5    f=  7.37848D+05    |proj g|=  6.90879D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.37848D+05    |proj g|=  2.05235D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   2.052D-02   7.378D+05\n",
+      "  F =   737848.03726643079     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.08723D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.83932D+05    |proj g|=  8.32144D+00\n",
+      "\n",
+      "At iterate    2    f=  7.76269D+05    |proj g|=  7.82081D+00\n",
+      "\n",
+      "At iterate    3    f=  7.71380D+05    |proj g|=  7.71301D+00\n",
+      "\n",
+      "At iterate    4    f=  7.67603D+05    |proj g|=  6.31005D+00\n",
+      "\n",
+      "At iterate    5    f=  7.67425D+05    |proj g|=  6.42738D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Bad direction in the line search;\n",
+      "   refresh the lbfgs memory and restart the iteration.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.65625D+05    |proj g|=  7.62520D+00\n",
+      "\n",
+      "At iterate    7    f=  7.54900D+05    |proj g|=  6.54833D+00\n",
+      "\n",
+      "At iterate    8    f=  7.54427D+05    |proj g|=  6.69883D+00\n",
+      "\n",
+      "At iterate    9    f=  7.54314D+05    |proj g|=  6.74652D+00\n",
+      "\n",
+      "At iterate   10    f=  7.53938D+05    |proj g|=  6.86533D+00\n",
+      "\n",
+      "At iterate   11    f=  7.53839D+05    |proj g|=  6.97181D+00\n",
+      "\n",
+      "At iterate   12    f=  7.53682D+05    |proj g|=  7.03698D+00\n",
+      "\n",
+      "At iterate   13    f=  7.53640D+05    |proj g|=  7.19362D+00\n",
+      "\n",
+      "At iterate   14    f=  7.52778D+05    |proj g|=  7.31218D+00\n",
+      "\n",
+      "At iterate   15    f=  7.40147D+05    |proj g|=  7.25559D+00\n",
+      "\n",
+      "At iterate   16    f=  7.20996D+05    |proj g|=  7.03999D+00\n",
+      "\n",
+      "At iterate   17    f=  7.18577D+05    |proj g|=  6.96253D+00\n",
+      "\n",
+      "At iterate   18    f=  7.18126D+05    |proj g|=  6.91793D+00\n",
+      "\n",
+      "At iterate   19    f=  7.18110D+05    |proj g|=  6.90834D+00\n",
+      "\n",
+      "At iterate   20    f=  7.18110D+05    |proj g|=  6.90740D+00\n",
+      "\n",
+      "At iterate   21    f=  7.18110D+05    |proj g|=  3.62404D+00\n",
+      "\n",
+      "At iterate   22    f=  7.18110D+05    |proj g|=  2.86270D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     22     80     25     0     0   2.863D+00   7.181D+05\n",
+      "  F =   718110.04019295715     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.07626D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.82949D+05    |proj g|=  8.31102D+00\n",
+      "\n",
+      "At iterate    2    f=  7.72844D+05    |proj g|=  7.73986D+00\n",
+      "\n",
+      "At iterate    3    f=  7.67712D+05    |proj g|=  7.59657D+00\n",
+      "\n",
+      "At iterate    4    f=  7.62885D+05    |proj g|=  6.65611D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Warning:  more than 10 function and gradient\n",
+      "   evaluations in the last line search.  Termination\n",
+      "   may possibly be caused by a bad search direction.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    5    f=  7.62885D+05    |proj g|=  6.65611D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      5     30      6     0     0   6.656D+00   7.629D+05\n",
+      "  F =   762884.93959453050     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.07316D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.82422D+05    |proj g|=  8.31001D+00\n",
+      "\n",
+      "At iterate    2    f=  7.71894D+05    |proj g|=  7.75235D+00\n",
+      "\n",
+      "At iterate    3    f=  7.66951D+05    |proj g|=  7.60726D+00\n",
+      "\n",
+      "At iterate    4    f=  7.61447D+05    |proj g|=  6.58793D+00\n",
+      "  ys=-1.027E+03  -gs= 8.482E+03 BFGS update SKIPPED\n",
+      "\n",
+      "At iterate    5    f=  7.36278D+05    |proj g|=  7.19324D+00\n",
+      "\n",
+      "At iterate    6    f=  7.31568D+05    |proj g|=  6.91931D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Bad direction in the line search;\n",
+      "   refresh the lbfgs memory and restart the iteration.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.25311D+05    |proj g|=  7.04881D+00\n",
+      "\n",
+      "At iterate    8    f=  7.20388D+05    |proj g|=  6.97588D+00\n",
+      "\n",
+      "At iterate    9    f=  7.18070D+05    |proj g|=  6.99080D+00\n",
+      "\n",
+      "At iterate   10    f=  7.17117D+05    |proj g|=  6.91651D+00\n",
+      "\n",
+      "At iterate   11    f=  7.17103D+05    |proj g|=  6.90916D+00\n",
+      "\n",
+      "At iterate   12    f=  7.17103D+05    |proj g|=  6.90864D+00\n",
+      "\n",
+      "At iterate   13    f=  7.17103D+05    |proj g|=  6.90856D+00\n",
+      "\n",
+      "At iterate   14    f=  7.17103D+05    |proj g|=  1.67323D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     14     50     17     1     0   1.673D+00   7.171D+05\n",
+      "  F =   717102.79139089293     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.07599D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.82132D+05    |proj g|=  8.31081D+00\n",
+      "\n",
+      "At iterate    2    f=  7.74290D+05    |proj g|=  7.79325D+00\n",
+      "\n",
+      "At iterate    3    f=  7.69580D+05    |proj g|=  7.68928D+00\n",
+      "\n",
+      "At iterate    4    f=  7.65643D+05    |proj g|=  6.30492D+00\n",
+      "\n",
+      "At iterate    5    f=  7.64819D+05    |proj g|=  6.44921D+00\n",
+      "\n",
+      "At iterate    6    f=  7.64800D+05    |proj g|=  6.47049D+00\n",
+      "\n",
+      "At iterate    7    f=  7.64782D+05    |proj g|=  6.49304D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Bad direction in the line search;\n",
+      "   refresh the lbfgs memory and restart the iteration.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    8    f=  7.62784D+05    |proj g|=  7.59947D+00\n",
+      "\n",
+      "At iterate    9    f=  7.46995D+05    |proj g|=  6.68758D+00\n",
+      "\n",
+      "At iterate   10    f=  7.46722D+05    |proj g|=  6.73618D+00\n",
+      "\n",
+      "At iterate   11    f=  7.46606D+05    |proj g|=  6.98182D+00\n",
+      "\n",
+      "At iterate   12    f=  7.46408D+05    |proj g|=  7.09454D+00\n",
+      "\n",
+      "At iterate   13    f=  7.45521D+05    |proj g|=  7.30952D+00\n",
+      "\n",
+      "At iterate   14    f=  7.27416D+05    |proj g|=  7.14360D+00\n",
+      "\n",
+      "At iterate   15    f=  7.19297D+05    |proj g|=  7.02035D+00\n",
+      "\n",
+      "At iterate   16    f=  7.17592D+05    |proj g|=  6.94187D+00\n",
+      "\n",
+      "At iterate   17    f=  7.17439D+05    |proj g|=  6.91330D+00\n",
+      "\n",
+      "At iterate   18    f=  7.17435D+05    |proj g|=  6.90797D+00\n",
+      "\n",
+      "At iterate   19    f=  7.17435D+05    |proj g|=  6.90808D+00\n",
+      "\n",
+      "At iterate   20    f=  7.17435D+05    |proj g|=  6.90810D+00\n",
+      "\n",
+      "At iterate   21    f=  7.17435D+05    |proj g|=  4.47102D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     21     81     24     0     0   4.471D+00   7.174D+05\n",
+      "  F =   717435.20438548480     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.09352D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.99555D+05    |proj g|=  8.30971D+00\n",
+      "\n",
+      "At iterate    2    f=  7.86760D+05    |proj g|=  7.75215D+00\n",
+      "\n",
+      "At iterate    3    f=  7.81719D+05    |proj g|=  7.60277D+00\n",
+      "\n",
+      "At iterate    4    f=  7.75959D+05    |proj g|=  6.60307D+00\n",
+      "  ys=-1.526E+03  -gs= 8.984E+03 BFGS update SKIPPED\n",
+      "\n",
+      "At iterate    5    f=  7.48905D+05    |proj g|=  7.17000D+00\n",
+      "\n",
+      "At iterate    6    f=  7.43399D+05    |proj g|=  6.91770D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Bad direction in the line search;\n",
+      "   refresh the lbfgs memory and restart the iteration.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.38452D+05    |proj g|=  7.03452D+00\n",
+      "\n",
+      "At iterate    8    f=  7.33912D+05    |proj g|=  6.96037D+00\n",
+      "\n",
+      "At iterate    9    f=  7.31901D+05    |proj g|=  6.97224D+00\n",
+      "\n",
+      "At iterate   10    f=  7.31319D+05    |proj g|=  6.91288D+00\n",
+      "\n",
+      "At iterate   11    f=  7.31313D+05    |proj g|=  6.90865D+00\n",
+      "\n",
+      "At iterate   12    f=  7.31312D+05    |proj g|=  6.90821D+00\n",
+      "\n",
+      "At iterate   13    f=  7.31312D+05    |proj g|=  6.90820D+00\n",
+      "\n",
+      "At iterate   14    f=  7.31312D+05    |proj g|=  1.67795D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     14     50     17     1     0   1.678D+00   7.313D+05\n",
+      "  F =   731312.48190047638     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  7.83924D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23957D+05    |proj g|=  6.95679D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23706D+05    |proj g|=  6.92601D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90811D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90880D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  2.05783D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     23      7     0     1   2.058D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  7.74503D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.24458D+05    |proj g|=  6.98701D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23820D+05    |proj g|=  6.94262D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23661D+05    |proj g|=  6.91082D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90898D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  3.50473D-01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  1.18141D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     24      8     0     1   1.181D-04   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  7.70901D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.24805D+05    |proj g|=  7.00221D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23916D+05    |proj g|=  6.95208D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23663D+05    |proj g|=  6.91287D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90919D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  1.00854D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  6.50553D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     24      8     0     1   6.506D-04   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  7.73957D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.24552D+05    |proj g|=  6.99141D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23845D+05    |proj g|=  6.94528D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23661D+05    |proj g|=  6.91137D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90903D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  4.84570D-01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  1.99262D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     24      8     0     1   1.993D-04   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  7.86753D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.27412D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.38926D+05    |proj g|=  6.99855D+00\n",
+      "\n",
+      "At iterate    3    f=  7.38084D+05    |proj g|=  6.94973D+00\n",
+      "\n",
+      "At iterate    4    f=  7.37852D+05    |proj g|=  6.91233D+00\n",
+      "\n",
+      "At iterate    5    f=  7.37848D+05    |proj g|=  6.90913D+00\n",
+      "\n",
+      "At iterate    6    f=  7.37848D+05    |proj g|=  8.09551D-01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.37848D+05    |proj g|=  4.50810D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     24      8     0     1   4.508D-04   7.378D+05\n",
+      "  F =   737848.03726642986     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.81537D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.15845D+05    |proj g|=  8.64108D+00\n",
+      "\n",
+      "At iterate    2    f=  8.11262D+05    |proj g|=  8.52536D+00\n",
+      "\n",
+      "At iterate    3    f=  7.87008D+05    |proj g|=  8.47550D+00\n",
+      "\n",
+      "At iterate    4    f=  7.84765D+05    |proj g|=  8.46851D+00\n",
+      "\n",
+      "At iterate    5    f=  7.81554D+05    |proj g|=  8.43520D+00\n",
+      "\n",
+      "At iterate    6    f=  7.81113D+05    |proj g|=  8.43266D+00\n",
+      "\n",
+      "At iterate    7    f=  7.77957D+05    |proj g|=  8.35509D+00\n",
+      "\n",
+      "At iterate    8    f=  7.74189D+05    |proj g|=  8.22224D+00\n",
+      "\n",
+      "At iterate    9    f=  7.73008D+05    |proj g|=  8.11866D+00\n",
+      "\n",
+      "At iterate   10    f=  7.73006D+05    |proj g|=  8.12047D+00\n",
+      "\n",
+      "At iterate   11    f=  7.73003D+05    |proj g|=  5.70008D+00\n",
+      "\n",
+      "At iterate   12    f=  7.73003D+05    |proj g|=  5.69958D+00\n",
+      "\n",
+      "At iterate   13    f=  7.73003D+05    |proj g|=  1.22630D+00\n",
+      "\n",
+      "At iterate   14    f=  7.73003D+05    |proj g|=  1.72622D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     14     21     15     0     0   1.726D-02   7.730D+05\n",
+      "  F =   773002.95537064306     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.79584D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.17379D+05    |proj g|=  8.63632D+00\n",
+      "\n",
+      "At iterate    2    f=  8.11757D+05    |proj g|=  8.51390D+00\n",
+      "\n",
+      "At iterate    3    f=  7.87536D+05    |proj g|=  8.46399D+00\n",
+      "\n",
+      "At iterate    4    f=  7.82105D+05    |proj g|=  8.44328D+00\n",
+      "\n",
+      "At iterate    5    f=  7.81215D+05    |proj g|=  8.42562D+00\n",
+      "\n",
+      "At iterate    6    f=  7.73883D+05    |proj g|=  8.16463D+00\n",
+      "\n",
+      "At iterate    7    f=  7.73622D+05    |proj g|=  5.67161D+00\n",
+      "\n",
+      "At iterate    8    f=  7.73360D+05    |proj g|=  5.75040D+00\n",
+      "\n",
+      "At iterate    9    f=  7.73114D+05    |proj g|=  5.70863D+00\n",
+      "\n",
+      "At iterate   10    f=  7.73113D+05    |proj g|=  5.70543D+00\n",
+      "\n",
+      "At iterate   11    f=  7.73113D+05    |proj g|=  8.11146D+00\n",
+      "\n",
+      "At iterate   12    f=  7.73113D+05    |proj g|=  1.66588D+00\n",
+      "\n",
+      "At iterate   13    f=  7.73113D+05    |proj g|=  3.51131D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     13     21     15     0     0   3.511D-02   7.731D+05\n",
+      "  F =   773112.55499294470     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.78721D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.15842D+05    |proj g|=  8.63439D+00\n",
+      "\n",
+      "At iterate    2    f=  8.08320D+05    |proj g|=  8.51193D+00\n",
+      "\n",
+      "At iterate    3    f=  7.82670D+05    |proj g|=  8.45740D+00\n",
+      "\n",
+      "At iterate    4    f=  7.81250D+05    |proj g|=  8.45066D+00\n",
+      "\n",
+      "At iterate    5    f=  7.79503D+05    |proj g|=  8.42573D+00\n",
+      "\n",
+      "At iterate    6    f=  7.79062D+05    |proj g|=  8.41728D+00\n",
+      "\n",
+      "At iterate    7    f=  7.73891D+05    |proj g|=  8.28218D+00\n",
+      "\n",
+      "At iterate    8    f=  7.71309D+05    |proj g|=  8.09634D+00\n",
+      "\n",
+      "At iterate    9    f=  7.71271D+05    |proj g|=  8.14819D+00\n",
+      "\n",
+      "At iterate   10    f=  7.71115D+05    |proj g|=  8.11885D+00\n",
+      "\n",
+      "At iterate   11    f=  7.71099D+05    |proj g|=  5.71150D+00\n",
+      "\n",
+      "At iterate   12    f=  7.71099D+05    |proj g|=  8.10673D+00\n",
+      "\n",
+      "At iterate   13    f=  7.71099D+05    |proj g|=  2.01602D-01\n",
+      "\n",
+      "At iterate   14    f=  7.71099D+05    |proj g|=  2.72880D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     14     19     15     0     0   2.729D-02   7.711D+05\n",
+      "  F =   771098.55727667443     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.79428D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.15648D+05    |proj g|=  8.63616D+00\n",
+      "\n",
+      "At iterate    2    f=  8.10268D+05    |proj g|=  8.51631D+00\n",
+      "\n",
+      "At iterate    3    f=  7.84863D+05    |proj g|=  8.46250D+00\n",
+      "\n",
+      "At iterate    4    f=  7.83265D+05    |proj g|=  8.45692D+00\n",
+      "\n",
+      "At iterate    5    f=  7.80449D+05    |proj g|=  8.42655D+00\n",
+      "\n",
+      "At iterate    6    f=  7.80020D+05    |proj g|=  8.42096D+00\n",
+      "\n",
+      "At iterate    7    f=  7.78613D+05    |proj g|=  8.39050D+00\n",
+      "\n",
+      "At iterate    8    f=  7.73916D+05    |proj g|=  8.25263D+00\n",
+      "\n",
+      "At iterate    9    f=  7.72095D+05    |proj g|=  8.15110D+00\n",
+      "\n",
+      "At iterate   10    f=  7.71819D+05    |proj g|=  8.12659D+00\n",
+      "\n",
+      "At iterate   11    f=  7.71812D+05    |proj g|=  5.72583D+00\n",
+      "\n",
+      "At iterate   12    f=  7.71776D+05    |proj g|=  5.71155D+00\n",
+      "\n",
+      "At iterate   13    f=  7.71775D+05    |proj g|=  8.10931D+00\n",
+      "\n",
+      "At iterate   14    f=  7.71774D+05    |proj g|=  8.10769D+00\n",
+      "\n",
+      "At iterate   15    f=  7.71774D+05    |proj g|=  3.32346D-01\n",
+      "\n",
+      "At iterate   16    f=  7.71774D+05    |proj g|=  7.29946D-03\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     16     21     17     0     0   7.299D-03   7.718D+05\n",
+      "  F =   771774.43666481553     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.82325D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.33796D+05    |proj g|=  8.63462D+00\n",
+      "\n",
+      "At iterate    2    f=  8.23219D+05    |proj g|=  8.51151D+00\n",
+      "\n",
+      "At iterate    3    f=  7.97031D+05    |proj g|=  8.45674D+00\n",
+      "\n",
+      "At iterate    4    f=  7.95640D+05    |proj g|=  8.45010D+00\n",
+      "\n",
+      "At iterate    5    f=  7.93950D+05    |proj g|=  8.42594D+00\n",
+      "\n",
+      "At iterate    6    f=  7.91928D+05    |proj g|=  8.38281D+00\n",
+      "\n",
+      "At iterate    7    f=  7.87436D+05    |proj g|=  8.25046D+00\n",
+      "\n",
+      "At iterate    8    f=  7.85736D+05    |proj g|=  8.14948D+00\n",
+      "\n",
+      "At iterate    9    f=  7.85545D+05    |proj g|=  8.11674D+00\n",
+      "\n",
+      "At iterate   10    f=  7.85538D+05    |proj g|=  8.11199D+00\n",
+      "\n",
+      "At iterate   11    f=  7.85536D+05    |proj g|=  5.70797D+00\n",
+      "\n",
+      "At iterate   12    f=  7.85536D+05    |proj g|=  5.70647D+00\n",
+      "\n",
+      "At iterate   13    f=  7.85536D+05    |proj g|=  1.52837D+00\n",
+      "\n",
+      "At iterate   14    f=  7.85536D+05    |proj g|=  2.17409D-03\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     14     22     15     0     0   2.174D-03   7.855D+05\n",
+      "  F =   785535.80548623193     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.24840D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.24399D+05    |proj g|=  6.99386D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23799D+05    |proj g|=  6.93143D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23660D+05    |proj g|=  6.90138D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91799D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  2.59693D-01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  7.24672D-05\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     23      8     0     1   7.247D-05   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.21021D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.24785D+05    |proj g|=  7.01132D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23900D+05    |proj g|=  6.94199D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23663D+05    |proj g|=  6.90360D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91821D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  8.87429D-01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  5.26542D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     23      8     0     1   5.265D-04   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.19964D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.25031D+05    |proj g|=  7.02087D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23972D+05    |proj g|=  6.94817D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23665D+05    |proj g|=  6.90509D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91838D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  1.59565D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  1.35821D-03\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     23      8     0     1   1.358D-03   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.20595D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.24935D+05    |proj g|=  7.01728D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23943D+05    |proj g|=  6.94581D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23664D+05    |proj g|=  6.90451D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91831D+00\n",
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  1.28708D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    7    f=  7.23659D+05    |proj g|=  9.59853D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      7     23      8     0     1   9.599D-04   7.237D+05\n",
+      "  F =   723658.65193438309     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  3.26019D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.27413D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.37904D+05    |proj g|=  6.93865D+00\n",
+      "\n",
+      "At iterate    3    f=  7.37854D+05    |proj g|=  6.90436D+00\n",
+      "\n",
+      "At iterate    4    f=  7.37848D+05    |proj g|=  6.89791D+00\n",
+      "\n",
+      "At iterate    5    f=  7.37848D+05    |proj g|=  5.18311D-01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.37848D+05    |proj g|=  1.56000D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     21      7     0     1   1.560D-04   7.378D+05\n",
+      "  F =   737848.03726642986     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.05212D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.73327D+05    |proj g|=  8.26664D+00\n",
+      "\n",
+      "At iterate    2    f=  8.00507D+05    |proj g|=  7.95926D+00\n",
+      "\n",
+      "At iterate    3    f=  7.63187D+05    |proj g|=  7.54976D+00\n",
+      "\n",
+      "At iterate    4    f=  7.55543D+05    |proj g|=  6.75243D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Bad direction in the line search;\n",
+      "   refresh the lbfgs memory and restart the iteration.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    5    f=  7.54887D+05    |proj g|=  7.52081D+00\n",
+      "\n",
+      "At iterate    6    f=  7.37759D+05    |proj g|=  7.25689D+00\n",
+      "\n",
+      "At iterate    7    f=  7.32275D+05    |proj g|=  6.99577D+00\n",
+      "\n",
+      "At iterate    8    f=  7.26958D+05    |proj g|=  6.98589D+00\n",
+      "\n",
+      "At iterate    9    f=  7.20211D+05    |proj g|=  7.04582D+00\n",
+      "\n",
+      "At iterate   10    f=  7.18098D+05    |proj g|=  6.92617D+00\n",
+      "\n",
+      "At iterate   11    f=  7.18086D+05    |proj g|=  6.91764D+00\n",
+      "\n",
+      "At iterate   12    f=  7.18085D+05    |proj g|=  6.89946D+00\n",
+      "\n",
+      "At iterate   13    f=  7.18085D+05    |proj g|=  6.89922D+00\n",
+      "\n",
+      "At iterate   14    f=  7.18085D+05    |proj g|=  2.25798D+00\n",
+      "\n",
+      "At iterate   15    f=  7.18085D+05    |proj g|=  9.33477D-01\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     15     44     19     0     0   9.335D-01   7.181D+05\n",
+      "  F =   718085.40298732324     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.04261D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.72456D+05    |proj g|=  8.25643D+00\n",
+      "\n",
+      "At iterate    2    f=  7.99243D+05    |proj g|=  7.92417D+00\n",
+      "\n",
+      "At iterate    3    f=  7.59970D+05    |proj g|=  7.51890D+00\n",
+      "\n",
+      "At iterate    4    f=  7.50732D+05    |proj g|=  6.65020D+00\n",
+      "\n",
+      "At iterate    5    f=  7.50185D+05    |proj g|=  6.85873D+00\n",
+      "\n",
+      "At iterate    6    f=  7.49802D+05    |proj g|=  6.95852D+00\n",
+      "\n",
+      "At iterate    7    f=  7.48823D+05    |proj g|=  7.09653D+00\n",
+      "\n",
+      "At iterate    8    f=  7.47349D+05    |proj g|=  7.22217D+00\n",
+      "\n",
+      "At iterate    9    f=  7.38608D+05    |proj g|=  7.24551D+00\n",
+      "\n",
+      "At iterate   10    f=  7.21570D+05    |proj g|=  7.07431D+00\n",
+      "\n",
+      "At iterate   11    f=  7.18547D+05    |proj g|=  6.97867D+00\n",
+      "\n",
+      "At iterate   12    f=  7.18070D+05    |proj g|=  6.93066D+00\n",
+      "\n",
+      "At iterate   13    f=  7.18050D+05    |proj g|=  6.91910D+00\n",
+      "\n",
+      "At iterate   14    f=  7.18049D+05    |proj g|=  6.89780D+00\n",
+      "\n",
+      "At iterate   15    f=  7.18049D+05    |proj g|=  6.89818D+00\n",
+      "\n",
+      "At iterate   16    f=  7.18049D+05    |proj g|=  6.89800D+00\n",
+      "\n",
+      "At iterate   17    f=  7.18049D+05    |proj g|=  1.46904D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     17     31     19     0     0   1.469D+00   7.180D+05\n",
+      "  F =   718049.33988406940     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.03950D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.71848D+05    |proj g|=  8.25518D+00\n",
+      "\n",
+      "At iterate    2    f=  7.97381D+05    |proj g|=  7.92739D+00\n",
+      "\n",
+      "At iterate    3    f=  7.59092D+05    |proj g|=  7.53010D+00\n",
+      "\n",
+      "At iterate    4    f=  7.49630D+05    |proj g|=  6.64430D+00\n",
+      "\n",
+      "At iterate    5    f=  7.49465D+05    |proj g|=  6.81840D+00\n",
+      "\n",
+      "At iterate    6    f=  7.48950D+05    |proj g|=  7.00058D+00\n",
+      "\n",
+      "At iterate    7    f=  7.47672D+05    |proj g|=  7.08044D+00\n",
+      "\n",
+      "At iterate    8    f=  7.42528D+05    |proj g|=  7.22874D+00\n",
+      "\n",
+      "At iterate    9    f=  7.30933D+05    |proj g|=  7.21534D+00\n",
+      "\n",
+      "At iterate   10    f=  7.18401D+05    |proj g|=  7.01430D+00\n",
+      "\n",
+      "At iterate   11    f=  7.17236D+05    |proj g|=  6.95199D+00\n",
+      "\n",
+      "At iterate   12    f=  7.17082D+05    |proj g|=  6.92255D+00\n",
+      "\n",
+      "At iterate   13    f=  7.17079D+05    |proj g|=  6.91803D+00\n",
+      "\n",
+      "At iterate   14    f=  7.17079D+05    |proj g|=  6.89793D+00\n",
+      "\n",
+      "At iterate   15    f=  7.17079D+05    |proj g|=  6.89800D+00\n",
+      "\n",
+      "At iterate   16    f=  7.17079D+05    |proj g|=  4.39164D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     16     33     18     0     0   4.392D+00   7.171D+05\n",
+      "  F =   717079.11894415319     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.04185D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.71437D+05    |proj g|=  8.25541D+00\n",
+      "\n",
+      "At iterate    2    f=  7.97348D+05    |proj g|=  7.93616D+00\n",
+      "\n",
+      "At iterate    3    f=  7.69474D+05    |proj g|=  7.70185D+00\n",
+      "\n",
+      "At iterate    4    f=  7.58212D+05    |proj g|=  7.50010D+00\n",
+      "\n",
+      "At iterate    5    f=  7.52139D+05    |proj g|=  6.71860D+00\n",
+      "\n",
+      "At iterate    6    f=  7.51847D+05    |proj g|=  6.80502D+00\n",
+      "\n",
+      "At iterate    7    f=  7.50910D+05    |proj g|=  7.02185D+00\n",
+      "\n",
+      "At iterate    8    f=  7.50190D+05    |proj g|=  7.09594D+00\n",
+      "\n",
+      "At iterate    9    f=  7.46711D+05    |proj g|=  7.23406D+00\n",
+      "\n",
+      "At iterate   10    f=  7.36828D+05    |proj g|=  7.25652D+00\n",
+      "\n",
+      "At iterate   11    f=  7.20708D+05    |proj g|=  7.07099D+00\n",
+      "\n",
+      "At iterate   12    f=  7.17871D+05    |proj g|=  6.97659D+00\n",
+      "\n",
+      "At iterate   13    f=  7.17431D+05    |proj g|=  6.92933D+00\n",
+      "\n",
+      "At iterate   14    f=  7.17412D+05    |proj g|=  6.91801D+00\n",
+      "\n",
+      "At iterate   15    f=  7.17412D+05    |proj g|=  6.89906D+00\n",
+      "\n",
+      "At iterate   16    f=  7.17412D+05    |proj g|=  6.89927D+00\n",
+      "\n",
+      "At iterate   17    f=  7.17412D+05    |proj g|=  6.89896D+00\n",
+      "\n",
+      "At iterate   18    f=  7.17412D+05    |proj g|=  2.78471D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     18     36     20     0     0   2.785D+00   7.174D+05\n",
+      "  F =   717411.52134795184     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.05923D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  8.88773D+05    |proj g|=  8.25485D+00\n",
+      "\n",
+      "At iterate    2    f=  8.12846D+05    |proj g|=  7.92712D+00\n",
+      "\n",
+      "At iterate    3    f=  7.73505D+05    |proj g|=  7.52751D+00\n",
+      "\n",
+      "At iterate    4    f=  7.63776D+05    |proj g|=  6.66304D+00\n",
+      "\n",
+      "At iterate    5    f=  7.63456D+05    |proj g|=  6.85988D+00\n",
+      "\n",
+      "At iterate    6    f=  7.62806D+05    |proj g|=  6.92725D+00\n",
+      "\n",
+      "At iterate    7    f=  7.60245D+05    |proj g|=  7.05954D+00\n",
+      "\n",
+      "At iterate    8    f=  7.57024D+05    |proj g|=  7.21945D+00\n",
+      "\n",
+      "At iterate    9    f=  7.47805D+05    |proj g|=  7.22857D+00\n",
+      "\n",
+      "At iterate   10    f=  7.32973D+05    |proj g|=  7.02599D+00\n",
+      "\n",
+      "At iterate   11    f=  7.31501D+05    |proj g|=  6.95735D+00\n",
+      "\n",
+      "At iterate   12    f=  7.31292D+05    |proj g|=  6.92361D+00\n",
+      "\n",
+      "At iterate   13    f=  7.31287D+05    |proj g|=  6.91783D+00\n",
+      "\n",
+      "At iterate   14    f=  7.31287D+05    |proj g|=  6.89829D+00\n",
+      "\n",
+      "At iterate   15    f=  7.31287D+05    |proj g|=  6.89835D+00\n",
+      "\n",
+      "At iterate   16    f=  7.31287D+05    |proj g|=  6.12573D+00\n",
+      "\n",
+      "At iterate   17    f=  7.31287D+05    |proj g|=  1.05089D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     17     31     19     0     0   1.051D+00   7.313D+05\n",
+      "  F =   731286.97671517858     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  5.42643D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23966D+05    |proj g|=  6.96698D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23705D+05    |proj g|=  6.91704D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89908D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  2.05885D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   2.059D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  5.39364D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23966D+05    |proj g|=  6.96699D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23705D+05    |proj g|=  6.91704D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89908D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  2.05985D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   2.060D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  5.36936D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23968D+05    |proj g|=  6.96715D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23705D+05    |proj g|=  6.91712D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89909D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  2.09840D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   2.098D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  5.37698D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.23971D+05    |proj g|=  6.96739D+00\n",
+      "\n",
+      "At iterate    3    f=  7.23706D+05    |proj g|=  6.91724D+00\n",
+      "\n",
+      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89911D+00\n",
+      "\n",
+      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.23659D+05    |proj g|=  2.15845D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   2.158D-02   7.237D+05\n",
+      "  F =   723658.65193438367     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  5.47557D+06    |proj g|=  9.21034D+00\n",
+      "\n",
+      "At iterate    1    f=  2.27413D+06    |proj g|=  1.38155D+01\n",
+      "\n",
+      "At iterate    2    f=  7.38164D+05    |proj g|=  6.96722D+00\n",
+      "\n",
+      "At iterate    3    f=  7.37896D+05    |proj g|=  6.91716D+00\n",
+      "\n",
+      "At iterate    4    f=  7.37848D+05    |proj g|=  6.89910D+00\n",
+      "\n",
+      "At iterate    5    f=  7.37848D+05    |proj g|=  6.91785D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    6    f=  7.37848D+05    |proj g|=  2.15751D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2      6     22      7     0     1   2.158D-02   7.378D+05\n",
+      "  F =   737848.03726643079     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.67246D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.05370D+05    |proj g|=  8.59454D+00\n",
+      "\n",
+      "At iterate    2    f=  7.93673D+05    |proj g|=  8.45570D+00\n",
+      "\n",
+      "At iterate    3    f=  7.86233D+05    |proj g|=  8.44013D+00\n",
+      "\n",
+      "At iterate    4    f=  7.83307D+05    |proj g|=  8.43184D+00\n",
+      "\n",
+      "At iterate    5    f=  7.79600D+05    |proj g|=  8.39439D+00\n",
+      "\n",
+      "At iterate    6    f=  7.79094D+05    |proj g|=  8.39220D+00\n",
+      "\n",
+      "At iterate    7    f=  7.76140D+05    |proj g|=  8.31471D+00\n",
+      "\n",
+      "At iterate    8    f=  7.72357D+05    |proj g|=  8.12355D+00\n",
+      "\n",
+      "At iterate    9    f=  7.72270D+05    |proj g|=  8.13581D+00\n",
+      "\n",
+      "At iterate   10    f=  7.72143D+05    |proj g|=  5.71824D+00\n",
+      "\n",
+      "At iterate   11    f=  7.72139D+05    |proj g|=  5.72605D+00\n",
+      "\n",
+      "At iterate   12    f=  7.72132D+05    |proj g|=  5.27579D+00\n",
+      "\n",
+      "At iterate   13    f=  7.72132D+05    |proj g|=  8.09885D+00\n",
+      "\n",
+      "At iterate   14    f=  7.72132D+05    |proj g|=  3.58664D+00\n",
+      "\n",
+      "At iterate   15    f=  7.72132D+05    |proj g|=  4.12909D-01\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     15     21     16     0     0   4.129D-01   7.721D+05\n",
+      "  F =   772131.57504821080     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.65785D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.06921D+05    |proj g|=  8.58986D+00\n",
+      "\n",
+      "At iterate    2    f=  7.94113D+05    |proj g|=  8.44316D+00\n",
+      "\n",
+      "At iterate    3    f=  7.82970D+05    |proj g|=  8.41911D+00\n",
+      "\n",
+      "At iterate    4    f=  7.79739D+05    |proj g|=  8.39908D+00\n",
+      "\n",
+      "At iterate    5    f=  7.79120D+05    |proj g|=  8.38395D+00\n",
+      "\n",
+      "At iterate    6    f=  7.77640D+05    |proj g|=  8.33776D+00\n",
+      "\n",
+      "At iterate    7    f=  7.75123D+05    |proj g|=  5.65579D+00\n",
+      "\n",
+      "At iterate    8    f=  7.74933D+05    |proj g|=  5.79235D+00\n",
+      "\n",
+      "At iterate    9    f=  7.73943D+05    |proj g|=  5.84809D+00\n",
+      "\n",
+      "At iterate   10    f=  7.72319D+05    |proj g|=  8.08401D+00\n",
+      "\n",
+      "At iterate   11    f=  7.72270D+05    |proj g|=  8.10031D+00\n",
+      "\n",
+      "At iterate   12    f=  7.72264D+05    |proj g|=  8.09639D+00\n",
+      "\n",
+      "At iterate   13    f=  7.72264D+05    |proj g|=  5.72206D+00\n",
+      "\n",
+      "At iterate   14    f=  7.72264D+05    |proj g|=  5.72246D+00\n",
+      "\n",
+      "At iterate   15    f=  7.72264D+05    |proj g|=  2.72379D+00\n",
+      "\n",
+      "At iterate   16    f=  7.72264D+05    |proj g|=  4.80516D+00\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     16     21     17     0     0   4.805D+00   7.723D+05\n",
+      "  F =   772263.56102596270     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.65010D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.05251D+05    |proj g|=  8.58763D+00\n",
+      "\n",
+      "At iterate    2    f=  7.90267D+05    |proj g|=  8.44090D+00\n",
+      "\n",
+      "At iterate    3    f=  7.80465D+05    |proj g|=  8.41894D+00\n",
+      "\n",
+      "At iterate    4    f=  7.78623D+05    |proj g|=  8.38258D+00\n",
+      "\n",
+      "At iterate    5    f=  7.77485D+05    |proj g|=  8.38786D+00\n",
+      "\n",
+      "At iterate    6    f=  7.75156D+05    |proj g|=  8.32756D+00\n",
+      "\n",
+      "At iterate    7    f=  7.73379D+05    |proj g|=  8.27125D+00\n",
+      "\n",
+      "At iterate    8    f=  7.72138D+05    |proj g|=  8.22278D+00\n",
+      "\n",
+      "At iterate    9    f=  7.70865D+05    |proj g|=  8.15223D+00\n",
+      "\n",
+      "At iterate   10    f=  7.70367D+05    |proj g|=  5.72297D+00\n",
+      "\n",
+      "At iterate   11    f=  7.70363D+05    |proj g|=  5.75719D+00\n",
+      "\n",
+      "At iterate   12    f=  7.70297D+05    |proj g|=  5.74445D+00\n",
+      "\n",
+      "At iterate   13    f=  7.70261D+05    |proj g|=  5.72885D+00\n",
+      "\n",
+      "At iterate   14    f=  7.70260D+05    |proj g|=  8.08915D+00\n",
+      "\n",
+      "At iterate   15    f=  7.70260D+05    |proj g|=  2.48853D+00\n",
+      "\n",
+      "At iterate   16    f=  7.70260D+05    |proj g|=  4.51512D-01\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     16     25     18     0     0   4.515D-01   7.703D+05\n",
+      "  F =   770260.40219358355     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.65518D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.05091D+05    |proj g|=  8.58931D+00\n",
+      "\n",
+      "At iterate    2    f=  7.92902D+05    |proj g|=  8.44556D+00\n",
+      "\n",
+      "At iterate    3    f=  7.85838D+05    |proj g|=  8.43163D+00\n",
+      "\n",
+      "At iterate    4    f=  7.82649D+05    |proj g|=  8.42338D+00\n",
+      "\n",
+      "At iterate    5    f=  7.78607D+05    |proj g|=  8.38534D+00\n",
+      "\n",
+      "At iterate    6    f=  7.78067D+05    |proj g|=  8.38289D+00\n",
+      "\n",
+      "At iterate    7    f=  7.74956D+05    |proj g|=  8.30304D+00\n",
+      "\n",
+      "At iterate    8    f=  7.71309D+05    |proj g|=  8.13716D+00\n",
+      "\n",
+      "At iterate    9    f=  7.71005D+05    |proj g|=  8.11919D+00\n",
+      "\n",
+      "At iterate   10    f=  7.70983D+05    |proj g|=  5.74874D+00\n",
+      "\n",
+      "At iterate   11    f=  7.70920D+05    |proj g|=  5.72889D+00\n",
+      "\n",
+      "At iterate   12    f=  7.70919D+05    |proj g|=  8.09015D+00\n",
+      "\n",
+      "At iterate   13    f=  7.70919D+05    |proj g|=  8.53290D-01\n",
+      "\n",
+      "At iterate   14    f=  7.70919D+05    |proj g|=  1.92460D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     14     20     16     0     0   1.925D-02   7.709D+05\n",
+      "  F =   770918.73107949912     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.68299D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  9.22966D+05    |proj g|=  8.58775D+00\n",
+      "\n",
+      "At iterate    2    f=  8.04756D+05    |proj g|=  8.44012D+00\n",
+      "\n",
+      "At iterate    3    f=  7.93080D+05    |proj g|=  8.41035D+00\n",
+      "\n",
+      "At iterate    4    f=  7.92176D+05    |proj g|=  8.39009D+00\n",
+      "\n",
+      "At iterate    5    f=  7.91645D+05    |proj g|=  8.38014D+00\n",
+      "\n",
+      "At iterate    6    f=  7.87514D+05    |proj g|=  8.26959D+00\n",
+      "\n",
+      "At iterate    7    f=  7.84861D+05    |proj g|=  8.10394D+00\n",
+      "\n",
+      "At iterate    8    f=  7.84810D+05    |proj g|=  8.12825D+00\n",
+      "\n",
+      "At iterate    9    f=  7.84691D+05    |proj g|=  8.10053D+00\n",
+      "\n",
+      "At iterate   10    f=  7.84684D+05    |proj g|=  5.72978D+00\n",
+      "\n",
+      "At iterate   11    f=  7.84680D+05    |proj g|=  5.72422D+00\n",
+      "\n",
+      "At iterate   12    f=  7.84680D+05    |proj g|=  1.12844D+00\n",
+      "\n",
+      "At iterate   13    f=  7.84680D+05    |proj g|=  1.83845D-01\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     13     17     15     0     0   1.838D-01   7.847D+05\n",
+      "  F =   784680.17670340673     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  1.37170D+06    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  1.10457D+06    |proj g|=  8.30646D+00\n",
+      "\n",
+      "At iterate    2    f=  9.64302D+05    |proj g|=  7.83671D+00\n",
+      "\n",
+      "At iterate    3    f=  9.62637D+05    |proj g|=  7.83024D+00\n",
+      "\n",
+      "At iterate    4    f=  9.62178D+05    |proj g|=  5.98021D+00\n",
+      "\n",
+      "At iterate    5    f=  9.62166D+05    |proj g|=  5.97630D+00\n",
+      "\n",
+      "At iterate    6    f=  9.62148D+05    |proj g|=  7.85827D+00\n",
+      "\n",
+      "At iterate    7    f=  9.62145D+05    |proj g|=  7.85437D+00\n",
+      "\n",
+      "At iterate    8    f=  9.62145D+05    |proj g|=  5.96242D+00\n",
+      "\n",
+      "At iterate    9    f=  9.62145D+05    |proj g|=  1.34110D+00\n",
+      "\n",
+      "At iterate   10    f=  9.62145D+05    |proj g|=  3.49965D-02\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     10     17     11     0     0   3.500D-02   9.621D+05\n",
+      "  F =   962145.18997697858     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
+      "Best parameters found: {'alpha': 0.01, 'kernel__beta_a': 0.7853981633974483, 'kernel__beta_l': 0.47619047619047616}\n",
+      "Best cross-validation score: 1011.0595625257647\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV, KFold\n",
+    "\n",
+    "# Define the hyperparameter grid\n",
+    "param_grid = {\n",
+    "    \"kernel__beta_a\": np.linspace(np.pi/4, np.pi/2, 2),\n",
+    "    \"kernel__beta_l\": np.linspace(0.1, 1 / 2.1, 2),\n",
+    "    \"alpha\": np.linspace(1e-3, 1e-2, 2)\n",
+    "}\n",
+    "\n",
+    "optimizer = \"fmin_l_bfgs_b\"\n",
+    "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=optimizer)\n",
+    "\n",
+    "# Define k-fold cross-validation\n",
+    "cv = KFold(n_splits=5, shuffle=True, random_state=seed)\n",
+    "\n",
+    "# Perform grid search with cross-validation\n",
+    "grid_search = GridSearchCV(estimator=gpr, param_grid=param_grid, cv=cv, scoring=\"neg_mean_squared_error\")\n",
+    "grid_search.fit(X_train, sampled_dwi)\n",
+    "\n",
+    "print(f\"Best parameters found: {grid_search.best_params_}\")\n",
+    "print(f\"Best cross-validation score: {-grid_search.best_score_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9249e76858c166e7",
+   "metadata": {},
+   "source": [
+    "Train the GP leaving out a randomly picked diffusion-encoding gradient direction and predict on it using the optimized parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83166ebc22702475",
+   "metadata": {},
+   "source": [
+    "Define the GP instances with the optimized hiperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b7e1ca2ccc899189",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel = SphericalKriging(beta_a=grid_search.best_params_[\"kernel__beta_a\"], beta_l=grid_search.best_params_[\"kernel__beta_l\"])\n",
+    "gpr = EddyMotionGPR(kernel=kernel, alpha=grid_search.best_params_[\"alpha\"], disp=disp, optimizer=optimizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ebe09c2e2fef822",
+   "metadata": {},
+   "source": [
+    "Pick a random diffusion-encoding gradient direction and call fit/predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ba7e22252116b374",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RUNNING THE L-BFGS-B CODE\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Machine precision = 2.220D-16\n",
+      " N =            2     M =           10\n",
+      "\n",
+      "At X0         0 variables are exactly at the bounds\n",
+      "\n",
+      "At iterate    0    f=  2.51647D+07    |proj g|=  7.64969D+00\n",
+      "\n",
+      "At iterate    1    f=  1.78650D+07    |proj g|=  8.43930D+00\n",
+      "\n",
+      "At iterate    2    f=  1.61450D+07    |proj g|=  7.97417D+00\n",
+      "\n",
+      "At iterate    3    f=  1.56409D+07    |proj g|=  7.61023D+00\n",
+      "\n",
+      "At iterate    4    f=  1.53457D+07    |proj g|=  7.20265D+00\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Bad direction in the line search;\n",
+      "   refresh the lbfgs memory and restart the iteration.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "At iterate    5    f=  1.44974D+07    |proj g|=  6.81551D+00\n",
+      "\n",
+      "At iterate    6    f=  1.43453D+07    |proj g|=  6.87952D+00\n",
+      "\n",
+      "At iterate    7    f=  1.42177D+07    |proj g|=  7.00259D+00\n",
+      "\n",
+      "At iterate    8    f=  1.41897D+07    |proj g|=  6.94118D+00\n",
+      "\n",
+      "At iterate    9    f=  1.41844D+07    |proj g|=  6.92042D+00\n",
+      "\n",
+      "At iterate   10    f=  1.41830D+07    |proj g|=  6.89929D+00\n",
+      "\n",
+      "At iterate   11    f=  1.41824D+07    |proj g|=  6.89793D+00\n",
+      "\n",
+      "At iterate   12    f=  1.41820D+07    |proj g|=  6.89811D+00\n",
+      "\n",
+      "At iterate   13    f=  1.41818D+07    |proj g|=  6.91827D+00\n",
+      "\n",
+      "At iterate   14    f=  1.41818D+07    |proj g|=  6.89926D+00\n",
+      "\n",
+      "At iterate   15    f=  1.41818D+07    |proj g|=  6.91796D+00\n",
+      "\n",
+      "At iterate   16    f=  1.41818D+07    |proj g|=  1.70266D+00\n",
+      "\n",
+      "At iterate   17    f=  1.41818D+07    |proj g|=  3.99657D-04\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "Tit   = total number of iterations\n",
+      "Tnf   = total number of function evaluations\n",
+      "Tnint = total number of segments explored during Cauchy searches\n",
+      "Skip  = number of BFGS updates skipped\n",
+      "Nact  = number of active bounds at final generalized Cauchy point\n",
+      "Projg = norm of the final projected gradient\n",
+      "F     = final function value\n",
+      "\n",
+      "           * * *\n",
+      "\n",
+      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
+      "    2     17     50     20     0     0   3.997D-04   1.418D+07\n",
+      "  F =   14181772.726816090     \n",
+      "\n",
+      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
+     ]
+    }
+   ],
+   "source": [
+    "idx = rng.integers(0, len(indices))\n",
+    "idx_mask = np.zeros(len(indices), dtype=bool)\n",
+    "idx_mask[idx] = True\n",
+    "\n",
+    "X_train = bvecs_shell[~idx_mask]\n",
+    "_dwi_mask = np.repeat(brain_mask[..., np.newaxis], X_train.shape[0], axis=-1)\n",
+    "y_train = shell_data[..., ~idx_mask][_dwi_mask].reshape((X_train.shape[0], -1))\n",
+    "\n",
+    "gpr_fit = gpr.fit(X_train, y_train)\n",
+    "\n",
+    "X_test = bvecs_shell[idx_mask]\n",
+    "y_pred = gpr_fit.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc93122f8370d2cf",
+   "metadata": {},
+   "source": [
+    "Plot the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e5c8e303b4639b8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_slice = shell_data[..., idx][slice_idx[0], :, :]\n",
+    "y_slice = shell_data[..., idx][:, slice_idx[1], :]\n",
+    "z_slice = shell_data[..., idx][:, :, slice_idx[2]]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect=\"equal\")\n",
+    "     axes[i].set_axis_off()\n",
+    "\n",
+    "plt.suptitle(\"Data\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Reshape the predicted data array to the image shape\n",
+    "brain_mask_idx = np.where(brain_mask)\n",
+    "_y_pred = np.zeros((shell_data.shape[:-1]), dtype=y_train.dtype)\n",
+    "_y_pred[brain_mask_idx] = y_pred.squeeze()\n",
+    "\n",
+    "x_slice = _y_pred[slice_idx[0], :, :]\n",
+    "y_slice = _y_pred[:, slice_idx[1], :]\n",
+    "z_slice = _y_pred[:, :, slice_idx[2]]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect=\"equal\")\n",
+    "     axes[i].set_axis_off()\n",
+    "\n",
+    "plt.suptitle(\"GP prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e9c1fd2f4d2bfd8",
+   "metadata": {},
+   "source": [
+    "Compute the RMSE and plot it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fd54cb47f4e2fbf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 20.108829839123334\n",
+      "Number of RMSE values above 10: 133201 (13.549906412760418%)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3vvOd/MRP/MTp7MvS0hLDw8Nn7GdZFtVqlaWlpad9bJn2K4QQQmwR/zDDcTbHAJidnT1jyMZ13bM6bhiG/NiP/Rhaaz70oQ+d1bG+EwlIhBBCiOegYrH4jGtIvptvByMnT57k1ltvPeO4o6OjrKysnLF/FEXUajVGR0ef9nPIkI0QQgixRSQY52Q7l74djBw+fJgvfelLDAwMnHH/ddddR71e59577z1926233kqSJFx77bVP+3kkQyKEEEJsEbFWxGc5ZPNMH99utzly5Mjpfx8/fpwHHniAarXK2NgYr3vd67jvvvv4zGc+QxzHp+tCqtUqjuOwf/9+Xvayl/GWt7yFD3/4w4RhyNve9jZe//rXP+0ZNiDTfoUQQohN9+1pv//+9n91Tqb9fugFn3ja036/+tWv8sIXvvApt7/pTW/i13/919m+fft3fNxXvvIVbrjhBiBtjPa2t72Nv/u7v8MwDF772tfygQ98gHw+/7TPWzIkQgghxBZxLotan64bbriBfyo38XTyFtVqlY997GPP6Hn/MQlIhBBCiC1Cn4PVfrUsrieEEEII8b2RDIkQQgixRcQo4me4ON53Osb5SAISIYQQYotI9DOvAflOxzgfyZCNEEIIITadZEiEEEKILSI5B0WtZ/v4zSIBiRBCCLFFJCiSs6wBOdvHbxYJSIQQQogtYjM6tW4V52deRwghhBDPKZIhEUIIIbYIqSERQgghxKZLOAet48/TGpLzM4wSQgghxHOKZEiEEEKILUKfg1k2+jzNkEhAIoQQQmwRm7Ha71YhQzZCCCGE2HSSIRFCCCG2CJllI4QQQohNJ0M2QgghhBCbSDIkQgghxBYha9kIIYQQYtNdyEM2EpAIIYQQW8SFHJBIDYkQQgghNp1kSIQQQogt4kLOkEhAIoQQQmwRF3JAIkM2QgghhNh0kiERQgghtgjN2U/b1efmVJ51EpAIIYQQW4QM2QghhBBCbCLJkAghhBBbxIWcIZGARAghhNgiLuSARIZshBBCCLHpJEMihBBCbBEXcoZEAhIhhBBii9Baoc8yoDjbx28WCUiEEEKILSJBnXUfkrN9/GaRGhIhhBBCbDrJkAghhBBbhNSQCCGEEGLTXcg1JDJkI4QQQohNJwGJEEIIsUV8e8jmbLdn4rbbbuPVr3414+PjKKX41Kc+dcb9Wmve/e53MzY2RiaT4cYbb+Tw4cNn7FOr1bj55pspFouUy2Xe/OY30263n9F5SEAihBBCbBHfHrI52+2Z6HQ6XHbZZXzwgx/8jvf/3u/9Hh/4wAf48Ic/zF133UUul+OlL30p/X7/9D4333wzjz76KF/84hf5zGc+w2233cZb3/rWZ3QeSmt9vq5ULIQQQjwnNJtNSqUSV/7tf8DKuWd1rKjjc+9r/4BGo0GxWHxGj1VK8clPfpKbbroJSLMj4+PjvP3tb+eXfumXAGg0GoyMjPCRj3yE17/+9Rw8eJCLLrqIu+++m6uuugqAW265hVe84hXMzc0xPj7+tJ5bMiRCCCHEFqHPwXDNtzMkzWbzjM33/Wd8PsePH2dpaYkbb7zx9G2lUolrr72WO+64A4A77riDcrl8OhgBuPHGGzEMg7vuuutpP5cEJEIIIcQWoQGtz3J78lhTU1OUSqXT23ve855nfD5LS0sAjIyMnHH7yMjI6fuWlpYYHh4+437LsqhWq6f3eTpk2q8QQgjxHDQ7O3vGkI3rnt1Q0L80CUiEEGKLerHxo5t9CuIc+2Ly8X/y/gSFOket44vF4jOuIfnHRkdHAVheXmZsbOz07cvLy1x++eWn91lZWTnjcVEUUavVTj/+6ZAhGyGEEGKL2IxZNv+U7du3Mzo6ype//OXTtzWbTe666y6uu+46AK677jrq9Tr33nvv6X1uvfVWkiTh2muvfdrPJRkSIYQQYotItEI9y63j2+02R44cOf3v48eP88ADD1CtVpmenuYXfuEX+K3f+i12797N9u3bede73sX4+PjpmTj79+/nZS97GW95y1v48Ic/TBiGvO1tb+P1r3/9055hAxKQCCGEEBe0e+65hxe+8IWn//2Lv/iLALzpTW/iIx/5CL/8y79Mp9PhrW99K/V6nec///nccssteJ53+jEf/ehHedvb3saLXvQiDMPgta99LR/4wAee0XlIHxIhhNiipIbkuee71ZB8uw/Jgb96B2b27IpP467Poz/+n7+nPiSbSTIkQgghxBYhi+sJIYQQQmwiyZAIIYQQW8SFnCGRgEQIIYTYIjZjls1WIUM2QgghhNh0kiERQgghtohvr0dztsc4H0lAIoQQQmwRaUBytjUk5+hknmUSkAghhBBbxIVc1Co1JEIIIYTYdJIhEUIIIbYI/eR2tsc4H0lAIoQQQmwRMmQjhBBCCLGJJEMihBBCbBUX8JiNBCRCCCHEVnEOhmyQIRshhBBCiO+NZEiEEEKILUI6tQohhBBi013Is2wkIBFCCLF5DBMj46E8F5RKf94nmnhjY7PPTDzLJCARQgjxrDAKBXQQoIMAI5NBZTySHROceGWBYFePiaE6bd9hY7XAvp8/iHIctO+jLAscG4Ck3kBH0Sa/kn9BWp19UapkSIQQQogzmZUKynXQcULvyhm8pQ7G7BLBpTM0ZlxaMwrrkgaZu0qsehmCUkLlsELvmaE7mSOz0KG1PY9fNNAmDH91GdXz0X5AvLZ2/hZMfBdSQ/I0vNj40X/J8xAXiC8mH3/Wn1M+u+Jc2IzP7vnKrFTQUyMYrR7BdBVtKLRSWJ0Io9lFWxb9AZvsWkxQtOgeKRJPxJQfMygeVzithCRrk7/nFFgW3WuKZFcSsot9kmKGjetGMANN8cgI6pEjJP3+Zr/kc+cC7kMi036FEEKcG0phFoswOkhzX5n2gRHMZoB7ZAVnvYu93IRuj2jHGJGnMIKE7ErCxNciKo8aVB/3Gbx9gTCriDImuu+jXRtvQ2P6GrPto8KYysE2varB0vVFjJEhUAojl9vsVy/OkgzZCCGEOHtKYU1OQJIQ5z28tRBtgNEPCKcHsVaaRMNFVDlLVLApnPQx2wHeYoyxsoHZm0SbCm0a+CUDq2/h6YTm5cOgIbPUJRjMoRKNe2INbRYoHw1JCjmMbBajkEd5LvF6bbOvxFmRWTYXKHNkmGjnGIYfgZEmixLbQEUJKvk/OS/Vj9AZm8QyiDMWKtHpPrFGxQkqTIjzDs6xZaKFxfN3AE9cUAzPwxgdBkDnMiRZB/XYMZJOZ5PPTJxvrG1TECforAcr65grdRKrirXeJq7mWL84w/CdPmajj7ZNvBMb0GiRjA/R3VZETRTInqjTPFDFO65xmwnFh1bpXr+H7qBBdjVBRQlOrUdvIoc5WqZ0PCJ7ZIOkmIEoIqk3UI6DOThAvLa+2Zfk7Fygf0IujIBEKazpSfADcB0IQjAMOpdNMH+DhdVWaBO0oYkzGqurUJFCJWBEYLehNwSxq4nKMSRg9A1MX2F1FVYHohyMW6NYa+voIMAsFVH5PFgmhBHR/MJmXwVxgTFyOfT+7aA1xqkVlGmk0yo9F7RG2xbNi4dQiaY7ZFK7LGHHJ/biHpwH10l/eXb7kCQARCdnJdgWT2GNjeJvH8Je67JxeZnqnTFEMWbHJ8l59Ac9vA1NZ6ZA7mgTs9ZEd/ugE1SSEOQNjEiTZB2CnMHcq8cxA42KYqKsAQp6AwaFJ9L9rU5MZzJD6b5ltG0RlByM6w/gzNVR3T7YFqzX5LN6HnruBiSGiTJNMBRGxuOJt02SWVH0hzTOhkJbEGU0KoYor9GmJrEAU6P7CiyN2VVkl9IPdWcCUGDXzXRGlQKzr/BWNdnVhOIXD6JnxlH5HMQe7R/cy9qlFn41wdkwmHlvHR3HkGiUaZAEISTxZl4h8RykbCft5wBEz9vFzX/8WWpRnv/5Ry8j9iCxobMtwuwZDN0Dbj3GCBPygeaXf/mvef/uF7H85Z30BzWXXHeE+x/bjtkxSCzY9xttdN8Hw0inbvr+Jr9asdniFz6P+Ws8xm/voD2LzqgB145QONnH9GPMtSb1HyiRX4jpDpm0xyoMPuRirXdQjRaJbdKcMSieTDDXWyR2kfb2hN1/0SYcr5Cb6+GtW9i1Hto2UX5Cd8Shvtug+LBFkndpbncY/soSjeeNUL5rnu6+EezxCtZam/jQ0c2+RM+YDNk8B6kr9rF+WRG/kr4xbg38AY3VURhxGjznaorCfER3yGTjAFQeVbS3KZymwm5qvI0Ebz3Er1hYbZOwpLFaityCJrsaY3ViVKKJMibBlbuwmj7NF+2h+Fgdpxkx8q0Eux1R35Xh8T84QGbWxlvTdCZh29/3UN94YHMvknjOWfvJKzFuWsM2Y2p3ZvhPX72JbTtWCAuQWJA4YAQGTs0gt9jHPVXj4NtHmPgy/D+feANvesVX+LPxYfInDR75xi6mvxHjrXVpzWRZeMM+utd3qJY69L44zOgffHOzX67YRKv//jrGblnA+Q8es94A059v07m0D3gU5hSrz8vj1nOUj0XkvnUCCjlYr5O0WuiL91C/Yju9AYPxr/eoXeRRu36cobvrDN8eok/OYe2YRjXaGJUiAL3pHLMvUQzeoxi+NySq5mjuzFA52CMpZSnffoJ4YhBtQuxZ6LEilrOP5JHHN/U6PWMX8Cyb51xAsvj26/ErGqeuyC0l5Oc0q1ekQUjulMLqa1QC7QlFUIRkWdEdVXjLiuGPPshIPkcymVZt94czrFzp4g9oyo9rjFOA0vQrCrehcJoJfsWmPWqitIURuKBg8YVViqci3PWA2DUpnvRxmzbdIU2mlpBf1Bj9EK6+hO54BpWA93ff2uxLJ85j9lfHiN9WYvhTRzg5uJvu9pDBU5rBBxWBO8pwLWDpWofcgsZ5AhJL05pysdc89v3qIR7/zT1oK+Z/fOaFDD2miR1NUFYERYPsbMjyK328gxk4nqVTyzHyQB915QGe+Okc7kCPbT/28GZfAvFsUorRW1eh16f4oWG8UsLKVXmox5ROxNgPHmOoPUntkiIr1xhMRtvIzLdQDQtzaoKg5BIUFFEG7FoXp+mSWQ0xml3Wrx9jIAjRc0tQKREOZbHaIe66z8z/tnBXu2jDIM7Z2F2N1ezDoRMwUKU7maVwzzyrL5omdqF0wsRbeg7UlFwgnhMBibIsajdfTWKDX9ZE2TTo6GoDFYG3BkFF05nUZBfToCQ/r/E2YnInWhQOQ1RwUdsmIIoxeiH98QLtMQsUFI5DdjUms9TDXG8RjpQw+hGJZ6ENRaamyS76BGWbzFwH7ZqQaIxuiPIDdMbBalsU72/S2zOM1Q4xWn2SnIcRaJROf234ZYURwfT/PEq0tLzZl1VsccZl+3E/UOPR+TGi4ybmf0yY/rNpZv5qge6eIZa+T7F2bcK+P2qjTi4wc2IQkoRgssLGHpfuqMKvVCkfKZA7ZdIdSxi+L6H44Cqq3WXYdcAPiCcG8R7LMHxfgDff5om3lDl1VYT9YImBezUDjygW3nE97Yt89v92jfjI8c2+NOJfkLId1IFddKby5B5PyD00D5dPUDjWYezjy9Ru3EHzxn1klgOcdkJcijFiUL0APTJAfzRHc7tD5fE+aE1rT5l+xaCxw2WiX6E1bVB9wKN55X6KD67izG6AbdG6uIrdTqgfKFF5oEacs0lM0K6NOVDF3z1K/hvHibaPYoaaKKM48WqT6tQehv7XQ+dRsfaTNQFnfYzzz3kdkFjbt7Fx7RjFI22cdoLpa5yWQVBUWL203V13xMCIwFtThDnwNhLcRoKKNN5aHzW/gh4fIsrbOCc66DCEchGlweprvFXIbCREnqI/6GEWHKKcid02sdohuZNtjG4AiUabRbRrYi1uoF0Hf7JMb7iM04rJPbJEUsphNwOs5UZaaAhkT0VoUxG7RYzAJMoq2ldtw62NYoQJRrtPfPDwZl9qsYU0/vX3sXKNxggVxoKTjhcnisI3M7SmNGZYITPbZLKTJXEM0BqVzxNVc1gnV9LZD30ontD0q4owb+LWNJkfXKdzdIjMagmjlyNxLayWj9HsMf3ZiN5EgfmXVlFxgvVwnsGHI7Knmhi1FrmFLCpxwbEJXnoVy9c4oGHqt2RY5znn0t20ZvKsHzCZOeXS3T2A3Y6IszbGSBUzBL9gEOQ8iqd89n7IJ87YJOUczZ05yl8+it0ewzwyT7h/muL9i+ROFVh7XhGzHzH0QAhByOqP9ih+pQ6jQ8QFl+LhFnHOwerFEIQYUUJuwUc9fgLyOdqTDjljmsQxMH1N8WRMb9gmzoBRLp0/AYkM2Zx/zJFhOhcN09hh4K25mL7GXffJHe/jj+Sx2gFGEON08jSnLfoD4NYhsxrhLXWIcw5x1kKND6HCGKfWR3e6qGwGf7xImDfTGTY9jVsL0abCWevij+RIbIWKNMZGG1od6PVJdk9hr3Xxx/NYszEq8TH7EXbbxF1PCwGDwRz2Rh9tW6gwSmc6mIq44OE0IrxVH20atKdcwlwGy0+w2g6OdwAMiLMOxjcekmLYC5S68gDt7XnWLlPoQoh93MVcydEdT8guGQw82qc94aANBWGE2Q4wDdCORW/fKNoysOZMvIUWVidDnLXojLmEGUVhNsRwfXprCVHGIqo6WN0Yq5GQFDx46DB28SLCvI0RKgqzmvwjS2jbIhkskV8IyKwZaMsgLJiEOU1Ujdh403VU/vyOzb504hwxKxVi26S5zaS/3SdxLUw/QRuK/oCNvQYq1jjt9LvNr6TrzzSnXfyqonw4YvlHduFtJJSX89jraZBgrrfILeUISy65hxfQWY/Cl3K0fmA3ZpCgYsic7EHWJsxbhPuG6FdMBr94jCRJaFy/DZWA+/ApOtdup/jgCkkpy9SXbYKyTbhtCGNlDR0Gm3n5xD/jvAtIzL27IEmIhou0Ry20gjhjEjsKI4xheQ2r4KLiBGO9Sb7dI3JHaG+DzKqBW/Mxai20U6FfdkisHN6xNQzXhuEBwmqO1qRLYgMalAYVJRiBxmj1cGwTI3Swa12oN9F+gHJsgopH5tg62igAoHs97FNr2LMq7TZYLYGCOGej4icDCsskyTqEBTudYz/XhFaHTGYav2SiDUVQsmjsKKazgbKK8eWZdFrb/BJxvbFp74N4ln3fpawdyNEbVmRWIfeATfF4h/6Qi92xGHywh9kLyS+QzkhwbIKhDCrSmH5Mc8ah8ngP7TmwuoHd6aFnBnHrmu6YonwoZKFZYPxUj8Q1ib20zXdUyoDW2BkP8+QaA49MsLHXJLE0STlPdzJPZrGD0Y+x17v0xwtY3YTSEYPmTouNi2DA81A7ptNMn0zFPK/FuydpzWQZvr+P1XUxNzZQiaY/ksGtRWjbJH+sTVRyMcKE+q4MKrHwy4rumKZ0HLqjCqet6OwfxlvsomyTYCBLr2pSaobpd2ocM/KZ4xz6he24NZORu32iag7Dj4iyBoVjbZTOoMMQlfGo7zLJrGqoFPHLBt5gAXOjS1R0yR5voLp94ot3wwOPbf3PoGRIzg9GocD8y4cx+5rMeoLbTNCWQb9s4tXjtFmZbZPYJv2xDNZghsyxGuX7V+kNjKTDalGC9hy0qbCbIfZqG531qF1Swu4l+EWD2AWtFCgwQk1YsPErJvHuLNVHmti1Xrq4k2mishko5jH9hGiwgN0M0FGEbndIWm0M10WViuAHuEtt2rtKOLPraM9F2ybmeptMrUM4UgTDQGUzeCc2UJNlgrKFXzTojim8NbB6mtXnD9MbUkx8zcV4+Cg6CCXqf45SloU5MYbudHn8J7IMPAjb/nYZ1emljfyiCKtepHBfl2R1DTU1jtMN0JZBXHQxggT3yDLdA2NoE5yTa8SDJVTOI87YxK7JyO3rPP6zFTpTGTqrMWFBYzdDvH5M4posX+0x9ekVmBhBzy9T+maESrbRGzRY/r4SfkWx7Y5F1OQIan6F+guqFE9GGCHYTcXUl1oYI0OceuUgkyfm0tVdm235zJ6HzHKJXsXFWwuxaz3GvlCjt2sQnmwi6c5uoLNumpEbdggzCreVkJ3t0KsUqRyJcNd9ioUcYUaRPxWiXZP2ZB63FmH5aaG1NbgDpxXjzbcw+4rqExFGmKDCtL6v9OAamAbZQ10II/SOSRIL8vMh7YsGyC6FAATj6eyccCgHSdpW/rxYK0VW+z0PGCYbP3yAyhMhVi/GWWig5xYpAmpyDBXFNJ43gj2aI3OsRpSrUt/p0Nw2gtNKp+maoaY3kcOtWRhhgtXzIY6JSxlKx3qYrT6rP14hs6QYu72Oml1CT43SncqjYrASTXN3AacRk5nXGN0euttDt9o4UUxv5yCZJ5bR5SJGNpP+Z/EcwpESZqtP8shh8stlkl4fNTGK6vpQb6EKOexD89DrozMe/oEpopxJdzANRowAvFqCNsCrxxROJTS3ZzGmLqZwpJ1G/cqQoZznGGP3dtb/i6Zx5xQ7P97FPngKgPqL9uAXFMO3rxBWs9iAXkowun10LkN/qohftjBiTWd8CiPUVA75JNUC2jUx1rpY602S7cPMvmKQqc9HNKdNtn06JvvQHDoMiXZP0ht2GPt6B72wjH/dXtR4Ea0gf6xJ4eE+SSlLZ1ue5dfuZfgvHoRcjvFPHCNpNMlctIMoU2DlqjyjX+pQORRhDA8y+9pJxr/agPvOg1+q4gyqUiYsmGSW06BXtzpY7RJm28dodNLP3lgelWh6Awqvplm7xMT0s5SP9bEfm0NPDNEfUPgV6A1nyS5p8gshccagcKLLxt4c868K2ff+Hq09ZUbuifBW+1hz6+DY9HYM4K50UP0QFSdQLZOYisFHItrjNokD0XaLic9sYCqF6vTQpTxRKYN1aJb4PPjMyWq/W5yyLFbffDUjt6+zftUA7QkbI/aw2umKj/nFCBVD/ngbbZt09qRFVmNf3oA4Jpwo45xYY+6mKRIXKofAaUSoxMJw01bwRttn6flVYkfT3JXQGy5ROFmifNTH2QjoDWTojCnijKZ4zCJ7NCRe38Ao5lHDA6z8wChePcHrdglnhgiLFt5CF/XEcaxGm+DiKZxqGSol9MwY9COSrI2/ZyAtsF1sExc8rCdmcZfbuMtr5JSBKuTQzRbR7kmCskP2aA09v0RmaIBotEx/NEvw49cSFBQD/5+M1T9XWNumaFxUhY8p/KsTahdlyA7vpvDIGqWvHaN+ww4e/9US039lYj+6ChfvpjOaxV3vk31oDnd1DSObJbxsJ/ZSg+6eAey7TrL85ssZeMzGueMgDjCYHaM9buNXYG67ya6VIcyVOtpU9KqK1SuybDN3YnUi2pMelp/gnQqg0cZwnXRdkabGf/5FaEOx8HyLsW9GZE61KB8LiTJG+odDga43GLmryvplRcr5yzG+dv9mX2bxNJmDA7QvHiE318Ns9FB+iNYJ9sIGyeo6/jV7CfMW2dk2/mCG6kEfI4jpjOUw+wnOoUXieoPetdvpjmoyq4rekMaIFZUnQjoTHip2ae5QDA61qF0y8OREhQSz2Sceq9KdyOCtpEPu4bYh7Pknu7EaaRErQHYlIcwp6lcOE3mK0vE+rUmX6t2rMDxA54W7yf3NXZt8NcV3s+UDErNYpPaaixj5xBEo5ekNKvrDCe6aQX4jwe4maKWo77Ywp4sMPtTBaYX4FZv+4ABGpOmMmIw87jP5V0cJdo3RH3IJ8xbuuo9Z7xKXsyzeUAGgcFLhNKHyWAvj0CkwFIwN4w65qNggykB/QNG8dJCiacDKOtgWTiuhVzEwrt+Fu+aTe2QJ7djEF+/Eml3FPbEOuSy0uxi1OrrXx8x4ZDfKYFvo2UXsYoG43sAwTYgT0BEkGcID0xi9iNz9p0habdTEKEQx1nwNc93BmizTGfNYf8t1DP7Z3ego2tw3TZy1pFKgvssku6Qx+5DYisJtR0gaLZRpUDzUIigUcWpdtO9jLKyiJ2ewFjeIxwcIL5rAWe9h9iNUt4/hJxx592VkViEoWqhr96UrrT44y9plO+lNRhRGW6BNkkIuDX5XEwYf9vErDrnbHqcyV6J9yRg642B0bKKCS3fIZPiTh/AvnaE1YzP2zQgjSGjtLdEvG2BA58YxGrthz8EhNvZlaE8pesMZBnNX437u7s2+1OLpME2MUGPNrRONVzEBykXa+4dwmgO4x9dIdg2ReBbeyQ26uweYfY2DNhNG7wyJ12pw+V66QyZxIaE/08N+OM/Ey07yxI4JigdN8osKqwu1jRzTK2GarUvSYXiyDtmTHYyuz+qLprF8TfmxFuHFM9jLTbIL67SmtlHbbzLwWESYMUhs6A06OJ3kdKakcKRFstnX8p8jNSRbkzk0RP+yaZxWAoZi4+oRrJ5m5E7ILvexOiFx1iLMW1g9TXYtxtzoYsYxzrF0HDEaq9AaL9D6/u0Ubj+G/ehJOi/ZS1AwsDpWegEMxcTfL4Np0NpfpTNiUN+bpxxPoY6cIil4FL5+DD0+SJxLfxVaixsAJFOjGI0O5XuWTufJdLsDuSwqijFPLpO02ujJnTR35CjfehRVLJBMj6EBs9FBN+voXdO0J3JklgdQK/W0CBag5+McW02fq9XGGKyiV2vo4YF0pvnaBm6rw1hrCNWPMCbGiOcXJSg5TxnZLP7zL6I9ZjN8f0Bz2mbHJztYs2vgeZiZTLq2zNI6I7fUIYqgUkYHIbljDRrXTJBd6OM+eALtB5j5HMuv2k75iM/Ov21DlI7FKz9AtbtoP2D0jh7mrSFHfqJEYvewmj1yj6+iophwokpmsZMWUiea/MOLJIUcOuNiHplnuFEl2T6O3fQJ8y7eap/uRIbEVuRWYvplk8ar29gKMBSZWkJhNibOGAQFE++qi9H3PLLZl138M3S7Q+b4BrrXwx/08OIE5ZvknliH1RqUCqxe4TLxgUdQw0NkTzYpnBigtSOhsStL9/uvYvIvjjDU7lO7bICw7VBY1eworHPg6kX+d/FS2isexcOgHspQ2w9xZpr2mMno/3oc4jJGp093dzXNnASa3rW7WLrGZsefLKD9gOxqTHfYojVhYQaayqE+sW1gNwNQCtXzYW5psy/lP09qSLYeZVmorEfsmaAg2jVO5CkGH+yiDYURxBjtPkbXoDtcpvpYF7MToBotgl1jRHkbuxFgLzcYetAkypjokQGMegu7k6BNA22lH1LrRJuk2UJZFsWeT2asQm/Moz+aJRNO0J3MkouHMU6twK5xzLUWeqOR/moA8AOSkSr60cMYO2fSL+8oXWBKOQ5MjRO5JvnZHsoyiYaKdCeymH6C41mEpSEypxpYvZjeSAaz4uKs9jA3WuheH2VZ6THjGN1sQxyjmu30QumEpNnC6JXTcd18FkwTa3QE3e9Lh8LzyOIvXk/18RA05JZCvOM1OqMjJK4JQOP7JvELBtWDHcyji+maNaaBdh30eg3l+3jDuXQFastCWRb1F8wQZRTusVW0bdG+eIjugElxNiTzcJf2D+wmu9BDPXKE3Ozz8Adc7OUGutFEV0oERZvsE7Osv2Ivg99YTOuhOj64DtGeSYKSg7fSJfFsKodCVC8kOw/aVCg/xup4rKxnMPLpDwS7GeEttsEPCEdL1PflyZWuxPryvZt56cU/wZqaZPGDOQp/UiJT8OgOWWRPhGjDoP68ISrfikmWVsgtjGMUiwQzQ9iPzzHxiTbtKyboDZhkVjT9y6bJHFwi8RKKgx3qL/B4cH0cgCQysEZ6NHWG0iFF/Tqf3JKDSqD5oj0EOYPsWpbYNeiMmnTHNYMPanLzmt7FkySuQeGeeZpT2ygfCajvdOiMuZiBJnENrFWLuJzHyLjEI7swv3LfJl9V8Z1s3aJj0wQjXQXSCNOup4X5ELPpo3Q6fTYcyBGVPJxWgn1yFRWkWQGzF6INiHIWJAnWoXm8EzUwQBeyuBsBmZUQu+5DkqCrpbT63/dJllaw59bJLvQwwoTuTJHGjEl36skq7ShJZzhMjhJctp3WFWMEu8eJCi5GuUQ0mCeYLKNtK12IzDSIBnLYGz2so4voXAbDj3A3QuxOlDaZ6kSojSbuYgt33ScopH+AMAz0SJVkqIx+cmaFymVR1XI6lTgIUJlMOtPHD4jKHsFwDuU4RJMDxDvGMYeGNusdFM+Auupi+oOaMGdgt0Lc9T4AbjPG8OP0vY/T1adVlKDyWTr7h2lfOpZOH/d9GBvGagUY/ZBksEL/wCTtcQOzr9GuA46N1U1wWxqtIJoaor7LQpsKY2yE1vaE9QMW4UgJqmX8bVUSVxHtmcTqJyR5j8Qz0Y5FVM7Qns7Q2G7TncoR5SwyJ+tElQyJZWA2epi1JvZGH2/RwljwnhzrT8APUH6I2QuxO5raXhezXNrkd0B8V1FE/k9LOI2QUy8r0tgNa9cMoG2T0mN12hcN03nJxVQeacBwld6wQ3DxFMlgCbsdEWbTX+sbuxw6l45RecAk6wbYxzJstLPUvjkKTZuwY+NOtRm5fYPRzzq0Jg0y6wnFg3ViD5ausSk8ssbgIz2mb+mTP9Fh+BOP41cslq+0iceqZNYTvPk2A4/1yayEFB5dJ8waaMvEWFgFpZi90d3kC/pPU/rcbE9XHMe8613vYvv27WQyGXbu3Mlv/uZvov9BZazWmne/+92MjY2RyWS48cYbOXz43Dfs3HoZEqUw8nmU56UNxKIEIwRzZYPM4hr+vgmirEnspiuQGjFkln1wbKJqDtM0MeZWyW1kSYpZtGOj+j7J3CLsmSHJuVj1HtZKmKbwgO62ErlWFxrNdLVeK52Oa/Q92jsKRNknT81zUX5IOFygP+zSmjQJSlA8blCY9UmmRtGWojviYLUyGI1WGlQYCpbXQBnEgwXM40s4c1H6XEA0u0DiuagkwTw5j126CCOISLIe/bE8ditErdcwS0XioTJGEEG7k9aZODYU81BrwGiJKG/hmAbaNIjyJuZQBdVsysqsW9zKVQVG7k7IH2+h/JDEc4gGC3jLPtZ8DR0EFB5bhyRBtbskA2X6VZMgr7DbedzOJJ1dFZyNAENr+sNZ2hMWucUEv6To7q7iLXRxF9p4JyOSvEt3KocRQm/UgxGP3HSTtpejd9jDMxWdMRu7o1m94snZM8MVrFZA4qQZR3S6evD6RRZj3+xDFBNlTRI7bYZl93xUGGMGkJtPX2dQtjH7eYgS4qyD3YowBwySnZMYB0OSbncT3wXxjxm5HCf+6wAzb19C5zJUnnDw1iPc2Q386QrHb7IoHjIpzMecfFWFmb9eIrMS4MxvEFfzNLe5lI8GZGabJK6NOnQC/zWXwP8YIlvWBN0igw+HLF9tk100sLsWK9erNPgOYflf+ZT+nz6Z9QSzb4BpQJJm4HpjGVrX7AcNmVVNlLMpP1SnvadE8d4FAIKZQfyywdKLRqgcruDONYgyW7zA4lmuIfnd3/1dPvShD/Hnf/7nHDhwgHvuuYef+qmfolQq8fM///MA/N7v/R4f+MAH+PM//3O2b9/Ou971Ll760pfy2GOP4XneWZ7s/7HlMiTKcWD7BPHOMZJSGgmEBZPWVZPoMMRqB3SHLHoDBkacpoDruzMEk1WinEVvWyFdHA8wVuuobp9kpErS77NxSZnOVA78ANY20J20IDB3cJmk3sDIZtMW8K32k2OOAcX7lxi908db8dF9nyTrsLE3g18wqBwK2fbJGgNfnSXKWOliTwsNegMG/aEMemwQf7qKESWoXBY9MURrJpu+RqXSgCJOMHJZdBihlCLp9bE6EYQRRqtD5lgN8+FjmPkcqlTAaHVhaRWiKO130vdJsi7KsXFOrJJ7bAUdhNhz62QemYOFZcyJMZTtbObbKv4phom3ofFqAWp2ifjxI5i1JmHRxj6xTH/3CMn0CFE1l35uAKPRpnr3GmOfX2D+BzyWXzxJ5mQLI0xo7C3Q2GHjNhIq35glzCu0UiRZm7mXV5l/xTCtnQWcRkTlUMjcixR+yWT4v2ZwVi3MXpJm9PoabUJhLqK/fwKOzREWXfST4/LZ5YDccoIRgPv4PGhN5o5DZJZ7BFUHf2YQFYSYPgzfWUM12qw8z2L1inyarTQVsy9xGHioTXsmj5oYxSgUTr9Gsfl0EOJ9oUhSykEQUnqsjrOeBo1hwWTkm4riqYi1i03G7vTRWZfZGzMkxSz+YIb8XIC73qe1r0Lt0iJGtUKYU+Rn+/SGFN3JmNo+m4nb+jhtTelIl5Hb1+i+vEV/UDP4dx7+zAClr59g4N4NomqO2n6P9oSDEWpySzHDd7conoxwjywTF1wKT9TRnS7rL5hg+coM5SM+Y586xrEfNVn9/iFG79AYl+7b5Cu7dXzzm9/kh3/4h3nlK1/JzMwMr3vd63jJS17Ct76VLviqteZ973sfv/qrv8oP//APc+mll/I//sf/YGFhgU996lPn9Fy2XIZE+z5G14esQ1By6A/aWP2EzohJ8PI9DHzlFANhTGN/ic6IgZO1MX2N/dhJbIDRoTQb4brorAdak7jpeHp+IaAz6rBx9Qj52RLOXA2d9Vh/XhVvfYTcwWV0rZ7WbARhWqTqB2SOrlG/eoxo5y6qDzcY/vijqGqZYNsAvekCGSBzfCOdcTM+TGKB3Y1Qi2u4jQz0fVrXTJN/dJXSZ2dJdk4RZwfSbFAQYzo20XARfXgOa2wEjq/Q2zeKX7HIrIU4q+uoaiXNmEwWyNzfQWWzKEOBH2AsrJK0OxjFAt0DY2QyLv2xAlYrwFppoD2H6PsvxvyqjJtuOUph7t9FdiUgzFs4joOy7PT2ICEZrrD4sz65zxboTCgGH3bxVvJYjR4sLKMTzfb/ZZOcmEW5Lp0f2kd2OaR6+zJEEa1rpkFB/pElkmKWyS/4hJUMvWGH9QMeQQFGv55Q+cYpVl80TWJBa8rCXXco3bNIUs7jD2dRiSa6fBfaUqxckiH2oHAqoXLfGsVPzpLs20GSsWGwQJSzaU5Z9IZtykccJm5ZJX78CMaBvYzcE5I9skFSzBBlTHZ/ZD3NUH7iW6jxMdov2k/h3gWi2bnNfmcEYORzDH2rSXe6QG1vBbeuGXikQzSYpTVuYXc02eWYmfc+gpqZxB8rYHVh7Xklykf7OEdXIIpQk1miLPi7hhm6cwOj1QEjy/b9i5wMxtlou2RqCf6Aix70GPwL6A5B6RP3kzxvH/HkEFHeoTvikF+MceohsWuQq/m0Z3Lk5vvorIcRJaxfWWXgzojsUojdTds6BLvHmfiiwupF5B5d4uibJ9n20GZf3e/iHBa1NpvNM252XRfXPXPI6vrrr+dP/uRPOHToEHv27OHBBx/k61//Or//+78PwPHjx1laWuLGG288/ZhSqcS1117LHXfcwetf//qzO9d/YEsFJPV/cx2Vx1pwchkr0US5gbR+JNQ4Lc3q8xT96gxjt9Yof+UYlIv4EyUa2x1qL99DYioSG3JLJdx1H6MfpcMgxxZo/vCVFB+r4Z4I0M0WSbtDDJiVMpWHDNafV0HpETInXfTSGslGHaNYJNo1TnNHBoDKX96NGqiiclmSfBar4acrUcYx8cIyxrYJ9NGTTDbahNODRDvGsBc3wLLIfu4Bksv3snHdKOUn2thLaeGgchw6l0+RPZUOF639wCTlJ9oktoEZaFSYwNAA2jDwx/NkH1kAyyKpbaRDSJkMdLokl+wkNg2yjy6CY+OerKHzGYJtAwC4R5aReTdbjGFiDg2weMMAQRHCkqZS3c7AvUWSk/M49QaqVMT70iRRRjHz8RUwDYKRPHHsYvR9jJEhOjsq2EN5zBOrBHmD2DFwqgWMtQZ2O6b6WFpDcvx1FSoHNUYMzRmD3nBC8aiiN2CQ3zZEmFPs/HiDlWuKHHlDjuxCgemPncQsuPRGHHJzPQAmPz1PMFFh/ZIMjcsGKZ6cQy2s4V81Q1A0KR7rMHAwob/skDvVZu7lQ0y1uiSWQa9q0bt2KF2l1YCwUMVuxfReczW5o028ZR8twzZbRv2leyke7eDWAkzfInYUUdamPe4Q5cDqgztXR5WKNPdVyM51yc/ZdEcMusMOZjMdYl47YOG0wHnoBKpUJByvMn6bz4nSONrRbHx/QPCoR5QxKR7TaW+oAOKr99PckUn7LD3WJ3+qR+2iLM5GQObYOv2ZAex2TH/IwShXyR7ZILPq0Z+p4K50ceoGRsenvbeC6Wu0odCNJjv/dH7rfh+ewyGbqampM27+tV/7NX7913/9jNt+5Vd+hWazyb59+zBNkziO+e3f/m1uvvlmAJaW0plJIyMjZzxuZGTk9H3nytYISAyTuXdei7f+ZIu64SqrV1VpbVNUD8bkDq5iTVcJD7lUDvVo7i/RvaGK09QM3jaP90T60WpeM4VXC7DXuqhaA93rgeui8jlys126O8popYiyY5h+grcWYCw34NAJBpTCWK6hCzkYGyIazhPl0rn32dUoHcpJNMFFk0+mC8Fpxni2iXH4FMqxYa1G8zWXp90sZ3tYq810WCYMMbZNsL4/j9NKSDIW/nQVq5HDWFgle+cRcGw6V20jKKp0waogSVvWDzo4J/w0+g8TdCGLdm2i7cMYUdo0SJkGxqPH6f3APkgGUVpjrrUwNlo4G610aKpaxn/51bi33HP+tvF7jjHzOeo37MBpaqqPB0QZk8x8B20YsGsaDTT2FBn738fBc9GeQ5Jz6VdtbNck4zjoXIbc/afAc3niF6bY8Yke/oDL8vVlcstF8kebHLm5zO77muz84//zh75wtEJnWx6rG7N0rY19YoX2a7ZRPpph4LE+gw9pjCBm5SXTNLdD6SisvCZHdlExYA/iVyy0oSg+vpEW3BZy5J5YJbtWQ5WKBHtHiF1F4liM39ZE93poq0L5cAdrpYluNIl3TzL3wjzbPrXOkTcOMt3IYvgxenIE0zCJV1c3780RANR3G2SX0pqgwnyM6SconQ6tFI8r/EEHvbhC7/q9aROzehdvw6MzYRC7iv54lu6gRZTTzHx8Bd3pEu2bRsUJ7qOzbLOmWb3cob07oTecdgjp/EiLzkqO6c+mkwi0gvLREHu9SziQJbsWE+VtzGqexDHIPb7K4Z8eY+azPVSzTeJWyR5q0N5Txm7GOP2I/KNrxJUc3cksDFbp7B3EPXFqMy/ts2J2dpZisXj63/84OwLw13/913z0ox/lYx/7GAcOHOCBBx7gF37hFxgfH+dNb3rTs3m6WyMgUYYicaF8JKC9PY/VS0gccDeg9OAaycISViVHdsXCnq9T6Oax+hlUnHbp075P//IZEgusej+dcmiakGiUbRMPFumNZ7BbMYltoBV4awH2iZV0Cu34KH4lg7tukZRzqF6Ie2INx7EJhwtoy0DFmvgHL8NZaFLZcFBhTFTyaM1kcaq7yT6+jO720+dwFbX9WdS+LPn5MO3CmnepPNrEXGuiO10wTZRhQMZDD5RJMjaZ+Q6Gn8GZq0HfxxmpEpXcdEy9VkeNFQkHctjLTZzVOrgO2jLR3T5qfITsPSeJdo1jHllEWRY666GzLtHUAEaQnpdZLhPX6xKUbDJlO6hCnuLRNo3deexaH2+jhb99kKBokViKMGvQG1Z0fnQH7kb6RyAs2jitGHepDa5LXPQwTsxx8qd3Mv71iLVLs1g9KJ6IMGLN2vPKzHy2T7RzDOuJWVCK8MA0QSGdcbOxx6Z4TKMzLlZPUdvnYAaa0rGQzKkOveECwVhAcsomPwuxC0HZIrvYp3A0RHV9wqv3Yj90goV/vZ+x27PEjkl31MYvGmSXTexTq+iBCka9QzRcJBouYpoG1tw6058LCQfzTN4a4Dw6Czqtq5Li1s135H3fx+g3YsxuSG8sQ32HyfB9EVbTx1xYJ9gzhopAjY8QlCy8tZBTPzLC+O0dBh4Bqxtj9iP8kokRKVSrA6Ui/UGH7HyXZHoEuxnQH7CpjjbonRokLGhGii2ONz2yp7rox45SjffS2p7DW0jXGMs/tobOOMQFj+yhVXRtg+LxMayNLrqc1kaxvIbaWaY3ZBOUSuQPNwiLDp1hg2Kc4NRDMMytudzGOcyQFIvFMwKS7+Qd73gHv/Irv3J66OWSSy7h5MmTvOc97+FNb3oTo6OjACwvLzM2Nnb6ccvLy1x++eVneaJn2vSiViOXo/3DV5I/ma5K2hswaE1aGAEU5mKigTy9F11C4tnpB229jrnawFvu4dQDtG3ByCCdMYfsSoC2TVQ/BMtMF7Wz0lVzY1vhntogc6pB8VAL+9QautMBK/2lZ9e66HYbFaQrVoZjFeJyFqvZBwXOXA271iMayKH8ENXuYq13yJ/s4i220yCjUkwX7WvFZGoJ2dUYpxGAYdAfcjGavXTRvUYrLXKtlkBrVKuL0eiiuj6ZwyvpazJNjHobezVtSqVyWYxehFXvo1odknojrXOxLSgX6M2UIY6xah10q43udFBxQuJaxFmLKO+QO94imRlPi2rFptO+jzm3iluPqV9UoHHlGAvP99jYYxFlVNqN8mhEUICV62M29mdpTdmYvRg1v4IyDYKSgzEyRGZVkzvWRBuK1e+LWXmejbvSxYhh+coMcz+UI9k+TnhgmjBrYbcjzH5MmIeB+2qEY2ViRxMW0lWl67sdFl84hBGAvWbTnoLesKK5P6Q5lc6wMZbWUVGMitJvPxWTrvLaDyk/3qZyJMAv2+hijt72CsoPseq9dNaZY6N7PdTSOnYt/T+UbGzAUBU9PYo5MYb5z3yRin9ZKlRkVwI2Lspjt2OyKxrvxDpBxSOaGiLMWRiRRgUhmZUAdzGtV7CPLZE7uIL7+DxGP6J4rIvZV/j7xll5zU5UAubSBlHOxmz0GLpfU1ssMfhwhN1Q5OwAHRjpDMuMB0mCGSQYzS5mL0KFEetXVNLPURDC0AAqBtVos/p9g9R3uTA6hBFqMmshXi0gyTv4VZvR29ZZuWGcxDHwX/a8Tb7C34U+R9vT1O12MYwzQwHTNEmSNGO1fft2RkdH+fKXv3z6/mazyV133cV11133vbzC72rTMyQqm2XlaoPxr0UYfoTVS3+FGZHG9BMS1yQomDj1ELOfDs1ox8bohqg4RntpC2unnWCt94hLHuQ8ksH86Y6qZr2LNZJJ/7B3epgbzbRHiGWly7E7Nkarh7adNJjxbOKMlRactvpEWRM3jDDWm/QmxzG7Ydr0rNPDXF4F0yTZPo7R6qNNMIJ0+iZJQpxz6U7lCbNPrulRLGBms8SVdMqv2TVAaVQUox0DgpB4pJwuDNXuono+cTlP4lkYQVoTg+dimCa6mCPJOhhJkmaFclnwQ1SlnHZq1RrDj3A2IM5aGCsbBLvGcCbGSBaWSPr9zXzrL1jKdTEKeVQhj7Yt/EoaNAd5hTbB9CGzFuOu9TB6ISvPq5JZsIgy4DYSoqyJsXMClWjsVkg0VGT4jg1UPyS7ltBIFEE1ISq55OcCNl4esWt0lSO5bRSPAAaEWRsUZJZ1Ov3dyGJ1FE4LVARBGYKiJregKD0BmdcvMbtQZWq8xtL6KEHZwSrkQGus5QYql6V0PMSq90myacBr9mLaYzYFAKXQro12LLRtkGQdzFIxLTpXCqPdxRioElWy9Idc7FYWL0ngHxXliWdPZleD9niRjX1QfbjP4JEVAMwwbSzpLXYxN1q0LhvF6iUoPyS3oJn7iZ2MfbWBKmaJMzZmy08/172I2PWIXcXaD01TOtzFHy+ydL3GXbTJnaiz+H0lriyf4mGmOPhzJfb/QUxY9sg/tk537zB2O2L9+eMkFpx6mUd2fhudKU0y3Ucb2xi6p8HJV5dp7Slj+gl2M8CvumRW2hQfC9LPugL3xDqt54+zJTuSPMudWl/96lfz27/920xPT3PgwAHuv/9+fv/3f59/+2//LQBKKX7hF36B3/qt32L37t2np/2Oj49z0003nd15/iObHpBgKGJXkzgKY3aFkmul0WvFJvaMNNI+odG5DNoyYHSQqJrDWmtDrYGeHEYlmvzhBvrJJdX7gw5hzsBtxJjHfdhoYE2V6ewsk5mzMTpPdj9NErRt0R8vYHUzQDntANvs4R3rgFIkpRyJrQh2DmOvdbE6MURp/w+tNYQulIssX1Nk7G9XQFVRcRrNhyMlageyBEVF8WScruZbLdGbKmL1IsxmQFLKEeecNPhZa6IrRRLHxEyS9PkLGfpjWYwgwelHdLeVcBppc7Wo4Ka/Io6tk3FtSDRJOYMu5TCCCB2EGPV2OrW4lAfPxV5rE0xUsJstkIBkUxjlEnpskCjnEJRt1i9V7Prvy2CZVA56KA1GN0ib3Q0XCcoJe/+0Re2SEpVHmjT2Flm71KF0LKb0jRNEMyPp8KVlkl302fFxRViw6Iy5lO9bJV4cYseudfa8ZIWvLl5De0pz0TXHqPWyOO+t0HjeCMUHVnDrGbyNhCCvMAKF3Vb0hjWF2YRf2/Vp3rL4kyzViqgEusMWRlhJu64GIdpz8Nb6qPkV1m/aQ2c8XRPKbmlYq5NJNNpz6Y3lUBrCrIWVs3EWGulMta/Nwu5t6f/rIZewYOLmMpv9Vl2wjn7scnRHMX60Q3OmwNoVRUY+36BzyRjechej7cNaDbJZ8o/X0AvLHPvFS5j8cpfqp45iDA2gPQd7bp2kkmfith7mWouh+w1UGBNUPXpjHkHOYHDnGqtLJWqXlIizCR/51vdzxb4TPL4yAosruGs16i/eQ+mROmq1Rt6ZxOyEOO0cq1dC5TGo4zFy6yIA41/30qJ+Q6ESjZm3CQfz+AM2uSPQG1Ic+bfj7PzAEbbggM2z7g//8A9517vexc/+7M+ysrLC+Pg4/+7f/Tve/e53n97nl3/5l+l0Orz1rW+lXq/z/Oc/n1tuueWc9iCBzQ5IlErbXyuIPCMdhtBjWHUfZ66O6gdgW9SvGiV/sos1u0r3sinc1W5a+5HLYK7WUYMlOruKeKsB/QGL7EpA7qSP0eySPNnszOxGeI8vEuwYebLpmJsGOEBn3CEounjrCbGrKB2xsJrpL73YsyjcPUf7ikmsu07i1sroXAZ/W5V+NV0zwa2HDBzsozIeG7stJuZ7aNOgO+7hlxWjd/XQpgLXgdlFWtdXaey0cBoZhh4IyT00n94Xxehjp7CZBsskHirTH8vSr5iU/ypdGdXfdxVOA1Ss046dGjBMVKePdm3QGnNlA13Kp9fYf3Idh0Y7vRZKYZ1aIO5JMLJZVDZDYirsk6uE+THcdYV2HaKBDPZKm3AwT2NXLi0KtWDgfmj9v33aHQWUcFsxQ/eHZB6Zw98/gVX3OfbjVcZvC8g8sUxS28AKQjKGgozHvt/vc/z3B1n/wSlcM529cHx1B1EWohs1TlORP5EjurHO2qFSGjAMhFRGmowXmzw6OMX7515M/mGX6kGD9lg6fKNNB3dFsX79CIOfO4K/axA3GqU7qvBHYozQpHQihqEKSc7FODJHdn4ZRgfpT5XS/xPrG2RNI83qzS6hO11yGYewkknvF5vCPJZh53sfQ1UrzPyvJqs/OI7Oenir/fT7pO+TTI7QmSoQZQ3sXRW2/806+sQcynPTDHAChsphrDexDIO17x8ltxyROdXFTTRGyWX9DT70XLyTDtWH67SnyqjEZHmqQH8pR+cH92G3IjpjBlavSM4AZ6FJ85IBooxi29/72Ktdhr7UYv2GKVau0VQeMRiuZWjuLaFiTWbZx/Rj8refBMOkeKJMfq4Pg2XCy6axv7S1li14pp1Wv9sxnq5CocD73vc+3ve+93334ynFb/zGb/Abv/EbZ3di/4xNDUjU5Rfx+JsKFA8ZFI82MabGMU+uovNZkmKGeLREnDEpPtHAaPfRxXTaqw5DkskhGnsKdEcNYiftGFk6ZlA43sNq9lHNDrrVQnkuydQoKk6IV9bQu0epX1Kh9HgTY3aFaOcYXi2mX7UoHu/S2pZl9YosSmdxGppMLYI9o+QfW0F7Lu2rplm9zEKbYPXSboIqtjD7GntiHJXA0dflSZwcVjtdvfL4azwmbosJLh9lff8kUU6jgMSB9rhFZr6Iml9h+XV7GfnLZjpDKAwx4pjcSg5vuII5OoIuZCl94n50GGFMT+CsxelMouEBkpyLeuIkSbsNl+3HqLdJVtbQgFGtoPt9jGqZ6OTs1i3mukBEJ05hrOXRU2P0hiym/vgR9PYJiDWNSwZozhiYfRi+p8PqFTna05rCp0YotDQDXztFvLSMUSigRweJMiZWS7HtM22s2VV030flcxjWk/+1XQeCkGRtnepdDk/87AgM+TiHMxSPaVavTpj+zUc4/OuX8pZdX+KW/EXMrlRxj2TwvlRhxavyyrfdx2fvvYz9n1lBtbs49SFizyJ2DeZfXKV8NEJlPTLffIKFN11M4mjsDQOnCYUHlkjKeYzjC6hKiWR1HY7PYpcyrF+cg5m9ZNdjtFEls+xj9EPWLy1i9TWVlfbmvlEXKHOgSjjtE100g9n26U0VKJ70SYoZjCNz6G1jBFMDzL44y8AjMa03NBh5rwtLq6hslmRmlNZMluLhFqrdTbsLL66RG82RfXQR7dqYnR71vRP81L6v8j+PXEN3X5cTqoxzxQat2SJG32X4DkX+vlma10wx8WePknTS+iPlumSrOYr1LsoP0ZZJf88oYU5hdhVKw9yLK5SPxTRmTKyejX3HSTrP301jxsL0NZU71wknB9jY4zL8pc2+4v+IrPa7OVQcY/ae/BX04CGC77+YxDEI8yaRp9Ipt0tpoareaKDKRXQxR+PiKirRFI92qN7ZPL1YXWvSpL47h1PPMXqHg9lqkWyfpDudI3/bYdT4CBGQm+ujlaJ7zQzrByyqByNySwnmRpdys483XsCINYYfkzgmYcGi9tIx8gvDhFmD0rGE4rEuJJr2TA5vLcQ7vEzj2gkmP/I4DFRQfoB2HTp7B6jts9Lx9AmH/LymPaFQCThNjeVrutNFclFC6XjI/JsvJj+fYHdiMidbqEab3mQOz35yfZChQVZfNE3xpI/VDugPjeNXTPKzPtH1e9GGwurFdPcVUck4udke6tAsWBbRSBm1tII5PESwbRB7qUF85PhmfgQuSOa+XTQvquIXDcK84ug7D1B9VNMvG7iNhMEHA+xmSOKaWD1NNByw7/nH2Pi/xtL+OLu3p7VDJ5fILq2hKiXo9khGB1DtHiqMSAo5orKH1Q44cVOZya8M0686VA4qvG861HdC41VtCncUeNN9j/Hr/+syblm6iKFMm8pUl8rOHjt/ZJUExZeX9/KT132dL9z6A8QOlB9tERZsmtsspj4xTzRUJJyoUr9hkjAPw/fF6VLxQUQ8WOTIL1rs+Zn0D4dRLdO6fIyNPRZWD8Y/O4/OuPQniyy8PWTkgx7DtxwnHh1g/aoB/BuvZ+QD39zst+yCcvA/76BSbhEWixz/4SzZBcXkX8+CYxNcPEN/0MGth2z/eA3lBxSfcDAa69R/aDe9AYPhe1oUDzZQpxbQrguDZfTcEplTDXTGRXV6kCS0Jw3+6Js/hOobVLZv4PtZOofL7PlYE7PWZuHVFR5/+zQTX0vQ02OoBJSfThKwji1CuUj9yhHK9y6jtKb6eJ+RL9fBdejsKJHYipF7e9gLDTrP343Zi5n41AKNaybQfR+r1mHsy20ZttlCNjcg6fpkF9MlyZOr9hNlTLKnmiQzRcweZE/UYWkNgGTHOCRg1pqUHlxLh2z6QbruzPEl3OwUubkuzR1psWh/JINR3ZOm6r72BOHF27HueRxjcgCz0U97mQybDD0QkrnnGO3n76R21SC9IYOxb7QwjszBYIXGFcN0Rg3y8zGZxR6FlQbxQIGw7JHYBpnVALMXsf4Dk3gbMXpqFH8ww/rFLv0BjRErElOzsS8tn3LrCZlVKB0PsJsBQckhypn0xwssXe0wfH9I7BnU9tl4gxUG7ofsqRb9sTzdvXmKJ/o47QRnuYXaaJJt5PDyGcKqR2fUpvpYG+PYPM7DkEyPEZVcVCFPPL+ElfGIgWS9hlUtoMIt2xroOWv556+nO6qpPA5Dty8RjpWxlxrQbLN80066owb5hQTr8BwMD1DtR6z/oMWhj+xjePEoarBKc1+V2FFUVtLmevWrRumMGhRPRXRG0i9ir5ZQONlHtXvs+OMaB391G9rUVO+DwpEWdjvHqZEcwRV9/uOtP0qhCe2PjTP34h7JskfpCcUjnXS83a9obr91gIwOcI8sE8ykSzMMPtils3+Y2lvbTP6befxrL2bszj7ukRWSaoHVa6tsHNAYJxUn/2yKbf+3T3ffCNn5LvnDQTr1PIpR3T6ZIwHm7eP0BhPCa6fxVgO8Wkxz++aXuV1o3Dmb3skBGi9LuOkFd3HX71xN99JJvMU2vRGHzojJypUG05/XzP1QlW2fbuDvGKL00DpFz6Z2WRmtYABQQUjjkgFKgDYMdMHG2GiiJ4bwq2kxa/VgQmNxkOyKprM9orM9T+H4XFok3bXY2Gvh1jPU9noMPtCmP5Ihd8zEH82TP9ll9qZxBh4NyJyok5RzrF9aYPjLc+nEhV4fVSmxcqVFZsmkNzxJ9Rvz6GKe+ZcMMf7/PbDJV1v8Q5v3v/2aS1jfl2fo/h4qSrAXNwgqI6h6i9wDLeLxAbozZeyBHPYT8xhzq1AqkNTqKNdJ13IxTRiooFfXcQ7OoQwDeyRDmDew2xH2erosOhOj2IcXYNskZtvHWK9TfAK89Vzan2PbKEaYZitMXxO7JmY+R5J1SUzIrCWUvjVPsGMI+j6d6TG6QwZmAF5doXIWxWM9WjMZYreANgEFpq9w65BbTOgOGww+1KOxM0OYV7THHfSkQ5hX5BdivIUWw/cZ9AdMwpwidiAoKTrbi7i1kChjkFmPsNc7BGWbqJLFbnWhVsdQCtuATNakN5ohd8pCd3uoOF0pVtfqmMODJOU86y+cSM8t0FQeBk5u2ifggtS4JMTwIuLjHp39Q/TLJv4VGQYe9QnzabZw7WKPYmkn+duPYLWyDH15ChVrHv+/dzD9hZjciTaJZxFOD5I4wyy+QFN9MM3Rlo+kfR1UrNPiwakKRlAiM2cSlHTa36TiYTcDJr/iYnUUph9w4tUmrb0RRqLInzCoHEozcNUHQzAMmnuKtMcMJk9amL2Q7rhHb9CieKJP9m9KYCxgN9MOm/FohbDgYPqa3KxB+WhE+GABFbTxljoY602ScoHedAGVlMkcXUf1AyY+u0x/uoy70sHYaGPVMkw28pv5dl1w/FdcTVBOGLzPoO4qbjmxn5KlWLnSYeiBAoVDDYqPJUSVLH7Fob8twOj0cdo91q4fwWvERBmwuoCpSHIepYfX0xXSLQOUIto9jhHETH0xwGkEqF5Ic1uVxm7IHbPRRgwTo2S++hjZyTHmXjVMa9KlerBPY3eO2FFkjyuc1Q7hQJaJL9ToTReISxnWL8uTWU+IRsqYnQBVb9G6YoziMU2Yhc6oQcW20BmHiVtW0saZYYQOg82+9KcpzkENyTk5k2ffpv78MH2NM7tOOFGFvo82VdrMa2kVRitpYNFVKMtEB08WZ357UTrbQZkGNNvgpOPkeqCM0wjpjHqYgY2znGAtbqAzbppR8QPC0QLOskadWsTtDRCOlwjLHtpQFI608NY9jCAmHi7RH84SeYrSCR+iiH7VIb50ivpuk8QEt64JIoPEVPjltL2xuaIJMwaFUzEqAW2CuxFhBiYAkZfeFuZJU5CxJrGgvbuM1UvIz6V/ULwNlbbWzhqgbOxWhNWLUT2f7MlmOqtCKZTjkGTSxfqyx5tEAxmUbaMGM2g/wt5ok0QRlPJ0p/IYcTqtul8xiHPOefvBPV+ZDZPY0PglReRZNC4NUYFB5QlFfi4h8hRmqLG6CQynSwaoRGOGYPUUG3tsBnwPd61HVHKxWgHFwx4Dj7TpjXq4cw1UUqS5zaV/SZ7sWpz2kFjSDByM6ZfMdDr8Whtro5tmIMeHiUZNSBTmKY/cUoIRJsRZG20aWKtN7FaOYpR2Uo6KaaBu99JvzYGvL9D8oX3kl6J0jD+McWo9Ys+gXzVwmiH5uxfSGWSuhb9vFCPW2K0oXbW7lKX2/SNU//I+MkE6pV77AcpQWC2bZJPfswvJqR9LKN1tUzreo3hCUT9WJDffI8xl6IxaRNkSxYP1dKp3mMdazZOUshiNLtXH2nQm0+/MwlwIpMPy/liRxE5Xj7Z6mpVroPSEYujeJiqMMeotxr6Z4fgPu/Qu7jN+WwJRDLu3oTZaFGYTrF6C/fgcXn6G1rQFloHyQ2LXxN9dIjfXpbE7h7eRflr6wxlyR/voYg4j0DjthOp9DYz1OjoMUe4gJAn9q3eSObRCdAF0bT0fbFpjNGt2ldKj9TS4AMIdo6hYEw0WMAarqCghu/jkWjGGcbrJGcMDqHwOlcuknUrDEBw7zZrYFlajh93RRJ4iKmXS9WK6faiUIIrpjLnoUgFVLpGUsiSmQWKnl0HNr+IenE97jxRcgpKJNkAbinD7CFYvoTuUxnBOC9yGxu5orL6mN2CQWddkF/tpf4h2jLsRYoQ6XQTq8TU64y5+VWGEYERgdzRuU9OvGmzssQjzJs58Haub4K2HacdaO12p1VtsY3aCtBHQ0VmYXUxTkp6LdizCoouxtoHZDNJfqGPltMdDvQGmCVoT5gyqD2xQOhqQX4wx2/4mvfsXroEHFZkTDmYAdluza8cS2VkTuxVSfrhGbjkiP+/jLrWJCx5hJcPaZWmzvenP+5g9TWvawR/MYK91MZo9hu7rYi1upE+QJBh+jF9W1C/SqBjsWp/cUkT+G8fJrkUkpoF20nbgemKEYCiHPetQfNBh4GFNbsnH8COU1kQ5i3iwQJwx8NYD4oEC/aqNStKVtmPXJCnnWfx+hVMPseo+xtJ6OsPNSdeWAtD9Pq39VcKiQ3vSoTfkPLnwpUFnKkd7UmFMT6B7fTANVNYjGimzcbE0R3tWdSxiDww/pj9oM3hvE/vwApUnepQP9yk+uEqSdYhGSljLdXb+TTttn5B1SRyTwqE6Y99oYnVjtGVgNDqoROMtdcgtpUPETt0gyiiMVjpjp3XlBO1JFxUrUJC4ZlqDZxloz6V03zLumk+0axy7FaZN0MIY4oTsEyuYfkKcTb+XY1vRHjNZutakvbeCdiy8lR65U530/tEBVLGAVopgspx+v1rmpl3u7+jbfUjOdjsPbVqGJNmop829SnnsU2ssvXyKoftaafX+QAFzaQPzsVUSz037NlgmRDHRcPF0rxCAeKSMtdIgWd9AdXuosWGqdywQDZeIszZMjREVXMz1dlqJnVUE40X8ik2QM8iuRsSewuxrKBegnxZNaUOlGRwNzWkHt2lR+sYJ7O2jZFfTnh9OI8DYaKM9l/JXa+huDyOXxStN05q00+xIQ5Or++j5JdRlQ/SHEgrHDcwnYwG/qOiNKnJzmvyRBu0Dg9T2Wel/uhisrqZ4zIelVQzbJqk3MAaqaZfZMIDATntWFBx0qUB/PEtn2CK/GGGEWQw1AusbqFaX3GIeo9XDLHgUv3qCeG19s97+C1b1/g2yK3nsVoi91ODgFePs+++HCfdNojo9VFSgvsPDazgUvnEcY3QAa1uM3U6D7bFPzbNxw3YaOx28RWhcPkS/rCi7w+ln2DIxghirq09/wRt+mK6XZBrYjZDagQzmlEPlYJvOVJb8yQ47PzBLcPEUKtJYjR5Gs4vu9bGDEMaGmHuLg73iUTrskVmPiR1Fe9KhdLzPxoEicS4hKNvkji2howh/ZoDukEn5aIS91ILxEVauMNj+qT6lMKG+06P+gizaALcBE7f3WL9uhOzKAJljNbRjU7s4z8ZFmtJfbPa7duHInTQZut8nsQyCnMGpV5aY+oKB2fbTjGo/oL53mMRSDK7Y+AMe2oDeoEXpcDedgGAZWK2AsORhHJ3D8KsY9TbBniJLz9fs+8AS7YsGWbt+mPLhHv2KSXtCEQ0ElAs9YjePv32QyDPJBBGdHVUa2y3Gv1ijsysNUIPhHM5y2gOnvtNm9I4exWMx/oCL24gpzCtix2D1mjKZ9SQNSEyFsbCa/sD10qC49PB6ukr7ViKzbJ59KpclHq3Qmc4R5KuMfvYk0cIizsw0ut1FA+bIULooV6tFsrJKdN0B3KMrdC4ZQw14mEFCba9LlCsw9QmFduy0UDOMMNs+/ZEMG3uqDN3XQrsOsWdgRBBlTOxmhEpMukMWXj3G7Mc0LxuiXzKwe2lKPSgq7DYMPdAl9kx6l06xfKWDtsBuQ+GURc5U9EY8CvUW4eUTmL2QzHyL7IkY1eyQrNcAMEaHyZ/oYFxVwAg0xeN9wqJFULCoPpbgNGNUrLFb6R+fKAPFUzH5o23CqgeGmS78N1yhsadE8eF1iGPCoWKaFbrtQaKrD5B7aBEuGSN7cAmd9ejuqpDsr2L2EjIPnKJ/YApnpZP+EhXPOpUkZA+tEs/Ok1gW+38/QesE4xsPofftpDdkYzxZa9y5ega7E1H5lEOYjzG7JuGOUfySYuMiTe2KEqO3QW45pjtik10OURtNlDtAZj3BiAy6Qya5Q0na78dzaU959AdU2qE1TggKBv2RDN2Ld6ESQIM2PYywjFeL8Za6tHfkuejX5gi2D+PM1ehvH8Rb8bEXN9j4vnSq++QXFCvPs5hoj9Gc9sgthwx//DGSXVM0Lhtk2//vCXb8e4NgMEfsGVSe6DF4T5+44BF7JsducikeVaAttFGlO2yxcUATl6Tw+tli7tnJxFda6dIYpxYZOp5j7XdGSW63wFBYqy2isQp+yaByOMCfrqBNyB6uYfVKWEcWWH3VLrx6gtVNOw03XrIfgPKcpnC8w8rVBVS7S+woagfAqzu0pxTe1euEj1dJHq7irjdp7ciTXUgzKFopDB+MegunniUxFf2qjeFnsHs+A4/5dKZzFO9bxK+MsLHPZuBhn/z8Bq39VfolA3M4gzYUnmth1tqE1SzZZR+W1kh8yRRvFZtXQ2KYoBRmL6F6tEnn8glyShFX8ximAWs1klYLlcmg+z5mpYz+2v0wM0328Hq6UBMw3BpCRUlag5LLUL98ELuTrkjpNENG758nnB7EanTJLHRYvK5EYlnYnTSEdNoJQd5g8XqXykFOFxZavbTWI3YhzFu0pmzWr4kgjrBaJrGbBjbt8Twqgeb0NrQBrR0upq9wNhRGNISKZxh60Md98ARqaYXdi1Xq3z9NYhs49ZCiBm0qVq6wcWeqDN3XpnjCxO6ki1gZ7R7uiQV0EBDvGsdab1N8eB3VbKMLOcxemEb4polx3+NEl+0hezJtja+A3GMrxAtLmOOjJK021q330nvF1eQ2SiSdzma9+xesqJIlHi3gWiasbRANl1h9+QSjf/kYaE31riWIY5JSjnAgi/PAcU79uylKX8jRHi8y+GCL4W9sMHSvhdELUbV0SG71xmlO/GTCzJ9N0h9Il4mvHOxy9MczmP4gA3dEJMurlA7lyc85mL2QxLHwy2kX4cbLfab/yKRfdegNGFg9cNf7RGWXjTe0CQrTDN1yjN7Fk3jzTfyxImsvm6B0IuLUS032/tpBsgsz9Ac9CrM+UcZk/k0HaG9L2PFJn/W3T3Hix3Ps+vAsSW0Do1xKh1oX1zEnhtj33kWSwRLdqQJ+2SQ/F1A8oWlPyLpLz5qNJmanR7B9GKvvc/LfH2D3h9qYT8zSuHEPxW6ANb9OZnsOFWnmb3BQiaL/6gF2/HUMg2UKswGxZ9KatGmP20Q5hd3SJANFgqrHtr/3wbHJzfXJTufInuww0s9yYluBkYc0kQut7TmCnKJ8YpV4KK2tK51M0KU8fsXG3YjwDi3Ru2gMs5ChMeNQOdInnl8ia1soPYB3osb69aN0hxXtXRGtFYuRu2MMP6LxvBG6gwZOWzN4NIsxPJAu83H42Ga/AynJkDz74tVVjH6f7MggenkN7/GA1ssvw1sNMGsJOnnyipoGanIMohizVESvb8DkKHx7+KLZIxwrYs+FqPllKp0ecTWftlRfb6L7fbRpoLp9VBAy9WWP1rRLv6KIsorwySHq6qMJQUFh9XS61kEzAQyCMUX28BruVxfJrl7G3IsUmWWFu6Hxy9CZ0LgbCm9N09gL+VMGdlvjtBK0AWhw1rpEuyexlxvEpRylB1bTyN+zydZ76OOzbLsjA5USST5dxdg9vgZ+QDJURg2UUY6N1ezjT5Xxjq2hq6V0jNUyiA5MY913BGOgQmMqizZz2J0i7mofa7WJUSxCL/21wTWXkLn9caK2NJ3aDFatw/xNQ/g/XcR5bJRtn67RekGP0a8Oo3o+ut5AhxEql6E94dC/eD+F3BrDX2mw+LJxagcKeI0YuxljGQoKwwQlJ20MWOoSvjNg4/axdLn3n53F/393YbcjiGPqr7mEyr2rmAvr6CTBCEImagPElSw/uvd+eD98/T9+H9VH0wUlreU6llLYX5tk8FOPgufhbKRZjc64TW9EobSFNhMe/539TNwKrQmTMG9RfSJm/KsbbFxSwnrgCI+/by97/rjN/E3TjNxVwViuA2ltSXt7gdorykx/rk52tkV+rYEu5tC2Sfkbj5yv363nn3KBYLxEa8ql0pyhNxGjwpik3aF89yK6VodshvJ9K5x83Sj2JXU6jQz7f3uDudeMMr7hoBKo7bfIrGjsnia/EBKULGZfVmb6bxZJyjkIQmoHsmRWNfUDBbojBgN3akpHOhx/TY6dH2vgj+ZYeck0gx+7H6NyAHd2g3C0RPGBJbRp0LlsHLsRkmQdhv/3IY784h52zU/Q21ElM9uk8bwR6nugcEIz8d+6GK0+qtmmfeU0QV5hBhoVa3Qhy4nXDeHUYWSLBCTPdqfWrWRTZ9novo+qN9OiqCCddmUfWyLZqKe/+AeqRFODtKcyFI61MQHd7qTjlJUnMyndPvbjLfTkCAsvLDPxd4t0tuXplwwK8zkyD82ma+PsGaU/kC65PnDPOsFInvoOF5TCCKA9aWB1INNIaE8YxJ5JYS5i4P4WyfIqOghwayFDd3u0tgE6bW5m9RVRFrpjKi3KzULsKPrVNNNit6E/miNzdB3d6mCs1ehdvxe7FWJtdFHdPjpJQGt0xsE4tUg2GCIcqzy5zo9Jf7CMt/Zk1XqiCUfLJJ6JVmmNQFCyMH5wP5nPP0BJKXo7BwEw/AiCEOU6aD/AKBYw1lrEvV7aA0I869avGsTd0Li3ZagfiDlxU5Vdv7uB6vYJZgZRY5V0LaQgZuDrC2jHZtYbIa42yC/E5A9toG2TOJ+uqquWa2SqJTpjg+T/uIgRavLjmjCrmP8vuykc3WD2FYNUSmOUH63Dyho60SS7p2juLpBdCnAOztGMPL5wbB/lqkn2RBdqDch4JMUsY7fWeOIPd7DvV9foTuXInWijEuiPRrgbFrk5k+5oQuGRVVoTIwwcjMgdaaLd9DN6+NcO4J0y0HZEUIaNi/JUE43R7NF+8UVk/695VtcrPDFSZN+H12lfMUm/YlKY9TG30HTM57x6i3hbBaun2bikzJ7/3iIYyuDt2Z7OfBweOF0MrRJor2f5mWu/xm3BPib/fo3+ZBEjTJj8fI1Tr6xiBorJB+uY40WWr3YJx8pY9z5BcPVehj91CJXLsvDKKWInrZc7/poc4WjI8vMrtGZg9+88ysn/8DyyS5rhoyHWRo+VF45jdzTeRnR6sVWVybD7vy3S2T+EiiHOOWSXfUbuSc81Kjg4h079/9u77yjL7urA99/fSffcXDdU3cpdnYNaOUsgBAgEBpaxZex5g8eY8bOfZwRjYGbsNTMeM8vjB7bfW8BzII6tx4yNA8NgDJgokGShVupGqXOunG+OJ/zeH6dpuzH2M+6W6rZ6f9Y6a3WdunXrnFtp9/7t3960b9tBfLFF8mQXgpDWtjwLrxmk9GS0g7NvSIZkYyg3Fo0bP3EWa9MEyTMNdK+HDs79gW42seYM0t0s5moVLBMVi+GnYmhLYXsBzC2htcYo1xk4Hm1Bi617oKPCU5TCPblCd6qI7yriix6slIlVGxQrOfxsDLPjs3xDCqehCU3F0IEuVtOLtuTGbdSeLZjNLtbsOmpyhNLTPlbbx49beCkDs6PRFsy/0oCGOretV+NUFenZAHepBdUGFAcoX1cgtBRxBVolMBMORrMNXtSRVlkW2jYx613ChE2QtTF8TXvQprrFIHMmJLHkYQQhhhdgtjxiiwFeLo6RTUeNpjSYLT+aXGxb0S+RTBK9VkEvraAD6U24UYxAk54LcVc6hHaK5GJAt5Rk7e4coQ3Dj7ewar1oC7wV7TbIHfPpDCVIHVqlcVURsx1iNX26Q0nchTVYreDU8ySOrcFqGXd0iM5I1L9D2ybtG1q4a3GSgJ4ao7orAxpSMx3Wd8UxNm3lHvdrDPxlkuVbNaFVJHfYRVsG9kKFtTtHUIuK+bdMRNvWjTRoGDhokT/cZeGOGJv/sod2bYIY1CYs6mN5elnwE5r0GYPq9hBruUanFCN9Jhotr+MOsXWP2W9OMjAd7Sir787jVHxiFgQxgz7b//Cy1ts7QXPEofjQDGEhw8rNWUpfn6V+/QjuQBx7oRINFA1CJj/2Aiqb4RO/8UqMdzvs/PA0i/cV2fxHUefe7OmQ5FwXVWvgVGoMjmzBXmvC1DiqFzL/z3dSv6VN/HnY9JdrqEqd1vAUXgnK1wRkjlpU3riHrW84ReVDkxCEdEdTaBOcRoB5rou2vVglGM4xc08aw4eRx1r08i7l7Tb5w13iJ1bQtoVKJqhssRmsRRN/O1sHST43j1MdpLItTmpOAt9+sLFtEIOor4YOoi1cqtmBUhEznULXG9G21moNo9cDN0aYSWAslwltgyBmYBsGYauFiseh28NqBxitHnag8eNRy3dz2wjaVNQnYrjl6I+1Sieh1caotzDtqILcaiexmyFOPcA9uYKu1qEwgDcygOn1CDIuVruLWwlIHF0Gz8eOx4g7NmHCQZuKgSNJYtWQ+riBl4HAhV7aoD2WxCzEMTsBS7drMscN3IrCy9j0cg7xYIgg42C2fHQpD36I6nkYloHd9LGb4CUtkvOKWDXAWW6ieh7KD6DbQ3e7OK0UulQkVAqz5WOt1qNR21pHjX+ScXSjKXUjGyw108Wqd1HNDsmlBJmjVdrjaayOxkfhp2zMjo/q+KgghCDqwWC2A1iv0CqUcKsKu+ETxE0YSNPaXoi2S6bimK0OgWvRy1qElsKdDzCm4ziNkNamaH2yXTCIr4bYZ1dI5saoTVr87mOvZddzFZqjecxeGG3Z9KLaLKcRMvhdg05O0RoLaU5C+rTB4DMdzLZPfMkhdnqFtVeM0S1qYqsKswtmFxKLUPhuFW1k0YsrJKeHyZxpY6xWCQsZQscgPROSmu9hdANmX5tkeJ9PZ8DEG7cY+toGf8GuINGE3ALNa0bwEtH3SHPPcFRoP1/GGx6gMRln4OtHYWSI0LaInXTpjPo0bhhn6pMnorbuy1UGyk3aW/Is/+QWrI5GheAPxAljJs5sBS+ZRJcd9C1VaicGMHtZ2ju75J5wKF8T0BrRoAxqX9nCgB3Q3V7Cjxu0hhS+azN0wMPwQsKVNQzXYeIbmrNvzmDPl1l66xjp6YDGuENtaozCcw1MpSg+12Lu7hSjj0RTgIPSAH7cJHAhttrun343kiHZGGGrRXhmGsN1CdfLsHmCIB2DXAJr2UKtV1CJRFTQOZgltE1UsxX9QvftqOdCKhkVvg6kqW52KB1pEkwU6eZMOgMGzWGX0FZ4Scic8ehlbWAAa92O+ncMnGvpXg8xexr39Ho0b8EPorqVRi/aKpZOEgykSBxdjZY7LDPqb9Lz6I2l6aVNhr69GLWzv2MCFRiENnQKitaQjZ+AiW820Wmfbs6kt2pgdTTaBD8bo7YpRmF/Ga+YIHZiiTAf7ZW3F+to28Jag8RjC6jcAOHSCiTi0f9WQo1KJcHz8bMuRtfHWqlFQ61ME2wrClrmFiUY6QPO8XmIuwTFTFRjZBjEVjsknlqk/sottIsmWsWJz9ShEwWU3QGL9HoH5bokl6PsVjfn0MmZxIpp1vbY5I77NLamcLMxmiMO7aJBrKrRlsGmL3fQpmJ9V4zUYkDueA+7Ej134uHD9N58Fdsf6OEVEkz+8ZkoeD23Yy0YLZB+ZpHm7hK5p1doD5Vgdx1vKY293qY1mSZ3rIs3XqAxYeDHQzIVSM37UTO/Z06gXJdYOYNRzDP2jSrG7DJhs4lKJ2gNWbQHDVTgkJrpEMSiQtbqVtDmZfpb9TKlLZPAiYrtteGQemaexnWjxJ46Q2/XBM6ZFQZqKRgdwsvFaY65bPnDs4TFLKs3ZEnms3SH08ROr6KT0S7I0lNNtG3QGIthNro0x7LUJ0oE8ahWz3okS2iFdPIGuX0moaVIj9QZ2VHn2IkRhr5jsfCjPZzjcbQJoa1JLENrOEb6ZB3lumjbZPGODFs+cQq0ZvRbZdrjKexmFGKEcQttpTj5E3G2/3GV9b0Z8gfrhK5N/IVZ7NoQanZpg1/9vyE1JBvJMDEGi/gjOcxyE2tuiXBqNGo1bJhRcduZMqrnY80sEzSaGEfOYCcSkE3h7d1M6JgYXojd1ASjBTpFFz8WLZ0YPRja36KXsWmNxKKMQWBhLQeohWXcWRPd6ZI55dK5YXPULMdUOGUn6u9hGXSuncT59nOY4yOESytRkzato8LagRR2rcfKNSmcegH31BrZA0tkqw10q4WRSdO4cZLF203s+TKpg+O0h0Oq2wyyJ0Oyh+sY8ysUHq9FdTOZbdEEX5VBaY1qtFC2RVDMEO6YRHkhRqtNsKkU9WNZqUSdaM+tgZprdTAMgvFB/LRDL2OhDUXi809s8BdaAPiLS+jbr6WyMxH180g6LN+QYGypwtpuk8SixuyGaNfC3zQEQGdAUX1thomvhSQPr1C/eoilWw28QY/UnMXkH59CN1vM/MLec7VTPqnZLt2CzeJdeQaO9+hlTMwuuCs9rOXauZqlGMr3WXpDj85TSToF2LyUojeUolOMmp/Vx0zsRpRZiT+8wNZPdQhG8hBWIYh+69WmYmRPd8gdC3DXDLykYuVaG6duM7I+imq0+MSvf4R/8dH3Mvk/5whbLYzBAl4uQWLZJ/dCC2NxDWIOm76cZ+mWJPFlGHymvWFfpytRc0c0CHTyT+cxv1sm3DZJ6pl5dC5Lt+hgH/dQtSbt3cNUtthRwBuP0S3GGfzrJWrXDOKueeh6AxVzMF2Lbi5Gq2Sx8koPs5slOddh+cYkXjokPtKg9Cc2jQkXpx41l4y9aZlWzyZm+cRyHVZvSDIyWKXzkEv5Ko1dN0gu+JR3WKSPh1GDzI7H0FNNMM2oC2urS/Jwm/a2IuVtDlbbwq2GFJ5VEEJhfxntWvSyNnr7KN2CTZxRkL5MG27jA5IwIFhYZPpfTpI5nSJ3MA6Adm2MZBzWq/jXbMOqdaI5NqaJ9n1Ut4tOFmlMuOSeK4PnM7B/AWOoSGp+jVQQoP2oqJOhAtayor53kGbJIIgpurkhrHaR+EL0S89s9rCaPvVNLpXtBoHrkDsEhadXWbk7zdi3oTeZx14rozMpwrSL6vmgNZUdSbpFzdk3WLgrIyTnNbmjTVQ3wItbJB89SmZwD/7wABP/7SBrb9mD6YWkptuY1SY4DhowSoOEoYZiHqPWojeep71tgvhiF3uphp6ZRwch6z91A35cYbU0TiNFbN3DOnAMu5oiHC7Qy8dpjtrEqiGpgysy0bfP2EtVso6Bl7I4/g6bzEGY/bEJsqdClm+GtdsViVMZBr/r0Ri1KH7qcY597GbO/qrB8CcL0XPUFKmzNvYj+wkAc2yY4cdbeBkbbYDhBSTPdCnvGCB+pkLlniGCGJz4Pwyc08PkD4e46z5mK0sy0+Ff/OJD/N6Buznx0wUyJ6Pr7OYVAycDMk/NUb5zHEyTMz+zidakT/6AyfDX59BmhuZbaqysJBl+RFH6+mxUeD5eorIny+y9BUr7E/zHN/8M/AjRz+PWCWZfk6e20ye2YhJbH0CbA6RmQ0xPM/bNNfSZWcnovcTic01KjVjU+2nPFoyTc9EORdsittojnCydH14auIrEsR7hQJLaZof4wRZe0sBLxCieSUGtgUq71CctOnmFapos/FiP8T+NkZn2CWIW3U6Gbi6IApFKSKcAaTMgtBXLzRTBmRTu1hpx22NtQpE9BuVrfco7Lcw2hK6Nmq6CMrBMA2+iiNHx6Q7FSRxdJjbfYKhis3p9ChVoCvvLUb2eUoRmHDS0h6Ll9jBubVzb8u93KTqtSqfWi+NlQuoTBsnFOLHlFn7GJSglcWdr2NMrdHaN4JhbUEvr0O0SbpvATznkHzpD8/oJrFZAd2+R9CMnoJCLRlx7UTASnplB7dmG0hptKQxfkzlax1wug2NDp0tYrcG126lsMwjimsxxiK/51HflGX6yTfve6wgdhT0+TGc8Q2ypiQo0latzrF0NTk2ROQHtIahtUdSnUmRPhvgxxXc++02Wgi/z5o/8MuNrBQr7FgmKafyEjbe9iJ8waQ5PMPy/TmKebEI2Fa3dn17GXktGUzJn5qMMSjpN5nSH+qSLUw9Jniyj2l2ad+2hOWwRq4ckZ9vk99dgeY3gXGM20T/8U2ewa3WMbWPs/u061JsoyyIcHCD3rVXKr9nC0h0Bs3mTwrMQ3nUdIw8ZNEcy9NIBqekWdjNGeZuDvn4n1twana2DxF6YoXfLFGgIXIvWSJzAhvbveVQPhez63SWW68PkjrSxDhxDmSYql8Wx4vzuo/dw896THEwO461kSc2FuNWQzFNz+LNzBM4EJ//TXrb9jzUqV+dYvRYa//sYo4/6TP37JmEqoLp7gJn7JmhsDkifMCk92cT0XOwjc6y8aSvpmZDW3lHW9jq4a5rtf9SjM+iQmGly9BcTTPz5At5EgZk3FsgfyeJ+6cmN/lJdUYyuh+HZBFvHoh16QwX02Vl6ezcxfW+MbQ8sEzgp/ARoA1rDNplGj25WoXu9aA5X0QbPR6cSmKs1QjNDa4uHtW7h90xmXmcw8c2QzNmA+NPRxoHEsqI7YDD4nM/i7gx7Rhc5tG8L4XiHViXO5NhZThZHGXxGU74Gmte1KXwzWqppvmIn8YUm3WKcdtEi/+QyejhBmE1i1NsYrQ7pGZfYagftWnSGihi9gPqUS+Zkm8TZNpVr8ngJi9hGfwG+R2pINpb2fXb8xlFOv3s3lS02QysK5/Ry1CCq0URvGcc9tkSwuIzKpqm/ehdWO+p4iWURn2+ycnOWxErA2ht3kD3ZwgoC9EA6qszeWqC6xSY1HzC4v4nSGrPajNqvdy1ULIYOAqxah+LzLmt7TRJr0Rp4fdzFXdEkT5bxiin8gTjxE6sEuahPQmq6zfKNCQI3mhsy9u0m9pmlaB3ejeEVE1z3iV8iNaMxEzD3I8MUn+9GRVVxg8AxsFoBww+XCUcHaY8niS+0CEtZOkWHXsrA9MDceg3Jb7xAODWMtVwjd2I+Kgq2LIi7uCtt4g+firZQByFBEIDumzIt8X2CtXXMnoe/dzNWuUrzpk0knjxD9e4trF2rMAsd3HiPVTPD2rUxGOliTLvkD4eEtoHV9Ck91aVdimOlRjjzsyG7fi1O6ulpvC3DEGoyB1bwkmM0/mQU+94mh3+5yKapBU6cGSR+17VRZ1ZgMDZPOVDsSC3z1MEt2EMau6bIH+mwdO8ETn2c9HSXgSMep34qTy8fkn9WUfpKNJCsftMYyQcPs/jzWYpPa7KnwV1pYS+UyZ6JRjEM/q9D6PER1PQ8OXtXNI17ehU/XsJPx9jxB11WXz1BJ68Y+3YVXjhxuf5OvWyFCYfWSAzfVbSGDEYfqWE4Ds5yg9R0DJ2IkZrrnRuaaNIuGuS+2yFWTdF4xTZS3z5CLO6iu13C4RyVXWn8FDjLFvFFRXdLl5HPaGKrbXqFOMoPKe+Mk571aIyZ1B0Lr6E5+aWtJDowfMsyE8kKDx7ZibNmYrZ7pE86tEaNaCdhvYMbhJjza8SMQapbbChXsToD+BkXp9aCMCRxPJo2rOMO7rFFyq+YYPlmMHtxEpZBYCvC2OWZUXi56YuABCAol9n8mQXCbAJztYputqIlF8+L1rsTLsaOzSzfnsdPKPKHfHStDoA620TfksXoamIEeBkHw0thrtaIH6pH83JqLp1SjKXbkhhdKD2lMYOA9s4ShBp3Ok5vMElyukF1cxYAZ3qV4Rl1vmeHVTZRS9Fat1luQhBghprNXzJYvDWOtjTLNyXx7t5CfCUauuclFe5qNBk1tCFzNmD16hh2Q5OZ8XCXW3gZJ9peHGgaoybtfJr4ejQtOL7qk3h2Bq01QbuNefQsutcj7J3rS2KaYCjU6jphq7UxXzzxw9OaoF7HOjpD97rN9FIm/qu2UNlmEoy1SSa6NM9kGTisaEzA2AMGht/FWWkSOhbaNlBegNKabt5i5C+gfm2J1Asr2CcXCIcLVG8exY8ptv3LoxxeKeG5FsV4g7N6EKsddSFubfaodWJoJ2S6nUPFQtJnwOzB9OsT9KY63LbtNE9PT7Lt/S0Gn0lgeJr4YoOwmKU5lcLoadp37WL0IVi8Dca/FWIfPAupJO2rx+mlTebf4mPFfNLfvgqnoTE7ijCbwvA1s692iVVg4IRP4aky+uyctPPeAMs3p0kuhmROd8g/WYf1CoyVCGM2I1+ZIxxIYXZ87LJP4tEZVDJBWMpTv6dJ42iS9P407T0jhKZi4U6LXjFg4DkDuwbVnSHjn47jpRUYCeLPzxKMFjCCONUtNn4cWnt7bJpcZWlulOSrl7kpP82B8gSGHdId9Fm9xqE3oAlyPrUph+R8kjBmYHTyqIMnSU5cTf2ubaQPr4NSrLxqlMSKT+JMjdZkhuShRU793Ca2fOIkyzduQQUh1a0uzRFFr2oR3+gvwDlS1NonghOn4Zarae6NtuoC+HFFL2Xgneuu18kr4qsaI9BQKuJnXbqFGAMnz0XuAyaJJT8qdE24MLeI8n3sdYVVzWDXox4KoWUQjuYxvBCr3qO1PU9spUNzU4r0bIi70ov6dxgG2rYwKnV6pSRWwsZaqqDrTQgDOLcTJnsy6lJY2WoSOtH6e89XmD1Iz0TrnokFTTcbbafTpsJLmYSmS2PcIrBjGIGm8EIbo+tHwwPDENX18Bf/pgI8qNUueM10GO260PIL/PKjNcF6mdhMjiCWx0uZxFc14Yk47bhLalGRWA0YOOFjtn2CePTj2hlNEMQMMs8skWj3WHz1IMXnWsy8LomXKEU/J7noZ8ZqaiwVkoj1qFczHHhmK4VnDAIXmte3MZdjNFfyxFuKxypXkd5eoTUcIzmr0aZm89gqvzjybb6cuI7Hd94SzbBZqqETMbqDCRJnm+iYSRgzIbQoPaWwOgFqIIO2TMrbHcLXlXF9k27Hxk8o7GZUuKuCAG1E86Kyp3xSLywRzMxF/xERL7n6JugUTaY+10LPLnDml69j05dqNCeSuCkHs9XDnokKPzs3b6cx6lA4sE7qW1nKe6PeUZ28hbvuM/x4VAuyvgvau7tMfNZi7SoLswujD9XRvR5GvUP2pENjIkZ8NSSI28zEcpAJec3IMT7zzC3ojonRMhjbs0z11DBeOiRx3KG1xWPmHofRRwNC1+LU+69l6ksd7GfPFUBNjKAV+HGD+o4BVKhp7h1m9NEuq6/bwvY/rqJaXep7i8RXNYlvPifbfvtAXwUkEHUXbYxadAbVuRHnGm0qOkWIrSviy5pYPWpg5uUTeGkr6upY9+lmTUITzO65xmq2CUGIbrZQjg3zS7j1VlR0N1aivSmN7xq4GupjFr1UktaQQemJOtZCGZ1J4hUTWJUOOpsitI3zz6kSLtqOXr4wESNzuExrc5bUfDQlWBugDYVTD4jPN1m9egC3ElAfN0nNB7SGTLxQEVsPcGomXoJoMuuTh9BeD2lddoXQmuDYSRKmQWvzAFqZWG1N4ChAYzcCYk8fp/a63ee3MaoADE+D54NtYXWgPhUndDStQYMgHmU/rFa07Xy9m6DTsxk4ZJCeDYgv1Fm6LcPYUIWl6WEGvxtSnTIZfTRgzsqSbIDT1DgVg0rb5dv1PTy9NsnSzSZONUFiycXwwfA17pkWaqWHTsZZfsMQxeejwNgfzKDCqD13u2PjPJOiOK9xGgFWK8SqdVBdLxrTUNakHjmOXy5v4BdC2HUV9ZOM2ZhDRQaOhZhzq6TbHn7GxWj1WHvVOIGjUAEM/vUC3U15hr9wivWbJ6jePkFytoMKNTpt08nbOLetY3wnT3k7FJ/zUBqueeAQD33odgpfOIgaTFOfNBh6ukv2uEO7Eqe+zedbCztIHorR3NPFLJvMnxxk9J5FNrltjtemMFwfv3Du5wDwBz28lEX79bvJHFxHNVoMPdwlTLh0h5NYLR/r0BmIxYgtDKA6PU7/81HcVc3IV2bxOzJstB/0XUASPnuY9MjNeGkbq6UZeryMV0jQXraJr3goremlo5bAZtvDqrZJHumw9OoR/KQic9bH8KLW26oZtWVXjoNKJMBQNK8aJnlkBbVWwRhNsX6didkzUQFUd2jSZ0B5YbT11o0Ge6n5Vfzto8Snq7C8hjZNwuEh/JSD0Q1QQYhab9MYKZA508NdaEQBkRHVbQeZGHaDaJpvE7ykQS+tSKwEmA8dIP237v8yDWzFRQoOH8cq3YCXMjF8CE3oZaIR6qqQQ5uKxLGo62T8xCqq00PHY6zfVCQ93WP2tQ5OVWEEEIZR0OInwd/R4o7CKf7gyF3sfngN5pfQ4yMQwuxi9L9RqxnS2B4y/s0m2/9IYbQ9/IE43WyC9bkBPl2+DWs2RvaGNbbnVnh2cRTvaIbUtMK/tcTAnx9AXbWN5rjGT8QYOtDDqmuCuE3x+TalfR5erkPs+CJ6IB0tgXo++ns/X2G0ZCs21sT/+Rjm9i2gFN5YnnbRQOcyqFqTxt4cTs6hl1Lkj3QxvBCvlMVZrEPMwUr4lLc7xJcUS7ckyEwHmF3NwMfSWI0W9uFp/B0T2NMrfO7Q9Wx/vkq4dQJrtYEKEtQ2OQw9vMji64bZ+uc+zZEivb2a+NEYiUWN04A5VWQ+6ZNeVSQOxtAGxPcfR3d7jH15D3ajh9IW/kCCcDhNZWuM0jdmadyYw2lYZE/H6W0dxl6q0d5SoJsP8VOK6s2jJM/ObPTL/zcuwZLN5fqHpO8CEgDnq08x9kQOlUmhbYvKLTlS8z5+0qQ1aJF/voa5WAbLRNsWulwlNT9IZbtNaygKLpKrDVSnh3/tNrr5GE6lhzOzhp8w6EwVcE+tEH9uhpFgnKWbY8SXNcUXfFb3OqzckiE5mcRqRnkKJ+ZgPnMcJkbpXjtFbKGOuVrDXIm+6tq2WH7NOL2MYvmmGIYXw3ehN6AJEyFGRxFke/gJB6cKo587RWaxfxrxiP5gPnSAFOC9/iaWb3AILZi+L2DcGSa+0oNWm9b1k1FwMlNHNTs0Rww6+RiBo/mJtz/EdysTrHcSTKbL3Jo9TT1weXB5J4NPmFEQE2qMaoPESpZqxUGnfabfbJIcbEAYYi6Wqb5iCrOryR/q4P5YlYWjQ3i5gFtLZzlaG8I7msFdVTQnNLUY5J7dQmc4ydgjAYania11UD0fu9lF1ZrM3TdF8fkO5VdOknt0Bp2ME6bi0WiEmEH+D/dt9Esv/jbLJHQMxv78JDqbRqcTZI9UUR0Pp5qK/rNlGlj1aGBnuF5h6POjzL/eoz4dw/CiLHXmiTm8qaGoeaPjYNU6BEM5hj/voGbO0rt2M6uvyjL6hmlO7Z+g9HWPka/M0RvPk/vWKZbu3Ezu+jXKBwbpFTWJsxYT32xjVCoAzL15mNrd28gcKhM4KlpSWqoRpl2a4y7JxYDajaO0SwqUcX46evW6QTp5g9KTmoEDywTHTm7s6/39ZMmm/wTlMka7jVEsMPgnz9G7bRf1cQcVQG17muCqDK2Soj2kKTw3jOlpAjdqhOalDHqlNHokQ3mHQxBXjH1xBd3qkJxtY6410OsVwm4X96hFPjVG6vAazZ0FtAmlR1ZpTQ3QHHXIP1clrNaY//nrcNc1xYdmaF49guFncNY7GO2o6NbqaBopRWJJ0xyL+kS4a4qeZ5A5DfUpm/EPPAaArJCLf4j99acZ+3r0byOR4Mj/cxW5AzGc0c0kVnzmb7fgZyx6rTx37DjEgflxDM/k/z1wO1dtnqfRdfjO9HYe6+1EdRVhMuCX/v1X+MbD17L49qswemB6YNchtqnJ5EAFS4U89+/G2PaxGJkHj4FpEGwZZX3fMPEe/Mo7/ifvf+ytWK7P1a84gaE05W6Ct4w8x+Bb6vzJwi2MxGt854vXYjds3HWNWw6ojxdRAdhrLSp3uNQnNp3rkwIDJ33iX5CtvX2lXGX2Z3bi1DX+3q2MfHsdHbdpTiRw13pUp1wSyz7uagfV8ehM5XDnlwgchVE3aQ8ajH9umvbOEmEug3VsjtYtU1grcVSticqnMXxN/a7tdHIGVltz7PgoE98JCIpZukNx4tNV5v63beSe06zXB/EKAdfvOc0z3haWbk6TOxEjNBXKh9TZFr2hFF5SUdmVJv9X8yi3RHK+S22zC0D2ZIDVCVm9YwgvEbV9qG6DgaOKhdeXGJKApG/0bUACEHY6UK3RfN1VVLZajP/VCvgBK68s4ScUqbmQ9DQ0hw1UqCg+51HZYrN8I2SPx8gf7pA97ZE4uECYT6NsC3O9SXcyh51NAFDdlCQ13SLIJ/HjBkEMeqU0yWdmSMZdgnwK2m2Gn2hgnVkCpUg+v4BOJwhj5yZfNtvEqgGpGYWfUIS2xstEdSRKQ3wtpPjA/sv1e0RsoLDVYue/fvb8QMRjH72RWL6B71ns3TzHXDPLW7c+x2Rsjd965E0cnh1mYqhMJ9emdzbF1utmuaN4it/76hsYvlFTeL6Nvd4ijNv4bobkzR0OnR1BNy22/1GXys4ExpaddPIGjUmNU4OdbzzOZ+ZvxU13MYyQrNNhzK1gZkL2xOb45MKrOHRwktq2ZfyUJr4c1bCs7rXo5qM6kpP/PMd1rzhK/RcH0UdOAMiQx37k+wQxSB/0qE/YNLZn8V2F3QoxugHxtYBOwcTsOtiAs95BT41RePA01W1biZU17d3DLF/voIIYE38VkjhRjkZqAGpuGbuYoJe1aJWion+7bJI6topqdYifmUdvGqFd0igftv3BIkuvHebZ9Djuokm3AFXfRmlILoV0hlwaIxbZUz1aJZtwapTeYBytoPDoPJVbRmkOm+SOB3Szisx0QG2Tyfb/sc7xdwyQPinbfftJXwckAGGjQfLBw6SfzdPYWyJ5ssLgvlVqe/LUJ0zsuiZ/1KM5bLF8g83AiZD80XNbYoOQxJFVvE2DWEdn8HeMY3R9nLV2NAW1FKeXNjCaXRbuLtIuQWpG4yyfG+zXbqNW1ghDjdH20IUBwoSDUW4QJhz8lINVBbWyhlPO0xq0aI1oYmVFYEPmTEj+m6eioXayc0D8E2nvbyaR7vmvs9FYBa0JHBc35bLwySxDTg0n12EkV6PZc2hXXMxQcezsMEppcjvXmUsMMPllB+fMCsaaR6nt4T+fwf23PToVh8p/apH6sIX7zBkW37aDrTfMcPLABM/u38pb7nqaWwtneL46iheafPnsVVSW0zy/c5SZWo7dv3EWYg6p9kn43o4vy4qmV2uNUorGf8ugz56SXTR9LKhUCVxYud5h8otr1HYPoE0wOyFrVyUZONUlvtjDy8ZojkdbwDs5k2zSwWqBl1Kc+ekQy25iHk1S253D7GqSp6vRrkHLxD00hz1aoDmUITPj48cNmltzxBeaGJ0uRrVJ+nSWTlGxfPcwg/vrmJ001e0ava1JUEkRXw1xqj5WKyBX8/FSFvnHoq3J7kwVP5fAH8pi9kLScyFWw6M5aRFfVYx9bYXuSIbsUUXxk49t9Ev+d8i2336mNWG9jm63ab9qlF46T+7ZMtkDi8SX81S3uqztsbGbmsyZEBVqKlsdUJA9CebqOmY2SVipos1JUApjaQ2dSWGnot4fYcJBW5A5pckfrKNaHbSh0L0QI5uhfdcuemmTWCXA7AQYdoZuPurrZ3Z8wuu2U96ZILnkU99kY7Vg8FiP+KEF/KXlDX4BxcuJPzd/wdvKsjj969dzwt3DxLpHECuy+mMGt159gqVWmoLb5BX5E/z+F98IAwGzrzGwbp9EBaDNqMN00i5jTRvorYrGuMP6np3UN4eUj41ihWCMtPj66V1kP5ciVg1YBwqtgFKzQzm/iYyn8ReObcwLIi651IxG/+ga6k/alHcUGH3tDJ3fGyW+HlLbFCN3xMc9sUzjrnFCC9xKiPICrBbEKiHxZJd23cUb9Vj3bTrDASOP5EhNtzBXK4RTwwSuReFwhyBm4rY8QlPRGk9iDCeoj1sUv9tg+o1pyns1g0+HJBc9QttmPZ0gd6xHbLVNezSJUe1FmettI7S3D2HXelFLCA21rUkSyx6NUYfEjE/uoKKTV3TGMgSuyfA3F2T5vM/0f0ByjvZ9ckeadAZdtGWgwhB7rUkqZhBfM+mlox0tKgS7qTF8sBseuudh1tuEloXyQ8yVKrrdRpkmdtnBSDooPwQdRffaMsAPUK6Lsm3wfcxOiGUp7LqHtdoAI0rzadNAK0VjIo7V1YSOYvhJD7vWwzopwYh48WnfJ/ZXT51/2wQm3Ft4fnoXhg/LJjyf3I4yIV5sobUiFe/iBwa1ehzD0NSbLrEYbM+t8NSeAtoMcVcMBr8boM2A+ukkblmT+V8HLsjWaMB56W9ZvMiGPneERXsX3mgLqwmnD4xTTGpilWgienlXkqxtYrdCqptNBp8ow/wSvddehdlRJGIerZqLWY3+vMTnTdLHq4QJG4oDlHenCC0YemiBxp4hzJZPZVec9GyAU/HwdtlY8+ts+suQE29Pc+q+LIPPhsTXQia/HpI4vhoNhgw09akE4bbNuOsB3QETsxMwf1eaxJImd6gGSqGGbRZekSVwwV3TOJUu5noD/9SZjX2hxd9x2QQkADz+HKmJccJsKhrfbpuY3RB3oYGbjlHflMCu+SSnuxitHqpci/p5hCEqncZaaxAurWAMZNGeh7FaBbIQhqTmA5rDJn7Cxoyfm2qgFKrnEZuvEfOj7b30PDANjJRLN+PgJQ26WUVyKcBqhzjffk76iIgNFf/Ck4x/4cJz8//uDpqJVNRbJJ5ChZBYjSZim72oGPzp6UnsmiKxBPlDLdR3ngHAPfccl2kWWPyQgnKZdklh9HzaJc3Egz5mJ6Ax5mD4UNsCaJf0TI/SmkdnPIMTOzcdegsMWj4D+2MYPY3Z1aTmejQ3p0CDkXdYvUEzsmsZvtLBSxrE1jVWG6xmgFVuMfw4Ud8n10Ib4Kc1WkFti8ngM3603f36HNv+9REOfmYPXhJW3tBl9HMG63viNDcHdIsGhSe76LhD9mSL1RsSBMkQ4/Y64eEYup+DESlqvXz4M7OY7ha0m6Cbi7G+20ZbcSb/xylyc1ZUvOqHqPUqYaWKcmPRtrOES7iwjMpm0IUBVLONrjcwKibeWJ7Mt46RdWNgWXjjBcx6F6U17akchJrYviMwOUpr9yChpahNWrSHox8Ud1XR0iaFTz15uX4fiJe50f/7H7FW/vsv/nWIy8Pm3zvKzDt34q5Cu2iilcX6Xgjcc8FDV7O2N0ZyITjXoTpDeiYke6zBbH2Y/KyPVorEXOvc3C6T1IFZTv38FIl5qJ8eJuMfZ+C5NfB88m0fc2EVbBtrrUL3qgli3z3F9j8ZZ+nWNKvXadwVsOo9GtuzVLcY7Du1mbSKeu5o32D2R0IGR9exDxTJH9acedsQ7qpm6IkahWcUlZ0m5nM5jKf7e4OB1JBcZoLjpwhfdT2r19p4KY3VVJRfNUWs7NMctik+voLudDAGi+hMEmpNdKOJkR+gcfUIhhcSuFn8uIHVDkkfmCeYGgXLQB06hZVw0a4NbY/4czPobg/cGHg+67tsOkWN2Yk6x1otjVMPyf7R4xv9sgghxCURrK7Rvr7F9n95BOU4MFaiur2I1TRwqpA5WqOdz5I5VkNNLxK/ZgpnpszZt41itmHhJ3ts/l1NdUeS7IkWyaNr6G6Xqd88gN67jblXZwjHh1BHT6MsCzW/RPkt12B1Q4yeppc2cVNJzJUq3Vwao6sYfaiCUW2ihuJ0xj0MDem5qPu16QRYp2JUl4pkTkGsHJA5TdSfZLVK4avLLL9yis1/0ZYxG33ssgxIAIyHv8ummSkWXj9CY0pT2Wrgrtm0RhT1qSGG9udJ7jsBrRa9vZvoDtjEv7if+GCW0Daxa2DVOrC8TlgqYJyehZEhVDGPXliGLeNUrxukmx0iVtVkjlRYujOHl4T8wSin1s0Y2E0kGBFCvOxsffuz+K+4DqvcxismGH3Uw2r6WCt10JrRL9dYvWsc744B4qshq+9zmfy3C/hDGYIXLLysQXPEwF2L0dmaIP9dEzWY5+R9aQxPs3pThqKxGf3CCZTjkFjqUd0cY/DJdRIzC1DI0ZvM41ShOaZRXkDt+mG8hGLs65rYuoF7bJbOwCR+1SGzpEnNB5S3Wxh+9KfNfNsKx3dOEF9Q7P7IKsHh4xv8qv4jXaYZjot12QYkAP7psww9sEDJcai9YQ+NUROrBU5NR417bJuwmMN64jB2NkNoWxin5wl3TRK4Jr2BDMGOAdLPL6M9H51wUNPz0bbEwyfJHFGo8RHm3jRC5piB4UUV6PVJAzQUX/CJf/WZK/V7RwjxcqY1Vq1D+boB8l87GRXz57NQrdPbM45V75H/3LPU33g1mUdPkz2Uo721QOLQIjbQvGaE0Iq6tiYXw6gmr9pg2weXoinCKRd1bBpjbARvOIufNBk41YnGCkyOsvCqfPS7fC1E+Qb1nQP0kgadYtQUrZcyWLx1E2YXjK5BL6No9UycajR81exp5s7mYchjeJ9BePzM+cntfe0KriExNvoCLorW6G6XsF4n+81jtEc0rfGA5hisXpOkfc0EKgjAMPC3jWJMjsHwIGaji3tyhfhcnW7GYO7NIzTu3QuGgZoaxxwdRl+/E/+mXWCajH57nfZIEi+pQEEvo8kdC0j+9dELdh0IIcTLiT54PJqtYpkopVBdj3CyRC9r0R5OYJQGWd9j0rh9Cm0ZxJ+IMhD1G0dZ22Mzsq9D7Onj+HGTxvYsvW0lGq/ehXYsCEO6t+6gM1UADe5ii9aQg/IDusNJOoOwer0mvhZQPNhm9RqT6jZFaEPgQmNC0Zrw8W6vM/wY5I/4tEvRhOzWkEEnZ6ASPu60Q+qx0/K7+jJweQckf0tQLrPpS22cdZNYWWG1weiFqHINo5BHeUE0d8G1oqF7yTid0TSBG/VjcFd6dAZdvFwcb2SA0DKix6ddjPU6yzfY+Mmob8PYIz7ZJ+cIKtWNvm0hhHjR6CAg93yF8qumCCaG0OtlzMUy7ZzJ7GsNKjcNow2oj1sYy2VUOg2eR+p4lcIhj8A18fduJj7XZOE2E6Mb0EsbrF87QOhYxJabxA7OYM+vY5abDOybpbspj7PWIXXTKmZXYXghla0uvUxIb6pDfFlTerpD/lCAChSdWozKNoPpt0TdXeMLbQIXqtshedAldVZDePnse/xeUevFHpejl01AAmA8+gx289yk0zj0BqyoGNUyMSstlB9ACCiFNhVmNyC5FJA5G+CcWcEp9zCbHmati73ewl5tYLR6BMM5grgmVtYMnOySePQo/szsRt+uEEK8uLRGHz5FfMWjMRmN28AyKbzQIHXWoDtgkFjQKF/jTwyCaYBt42dckgeXQGvWdycIXQunpvBSNn48midjnVoAQCUToBS02mBbODNlMBWhBndFUZ9wWLsxZNu1s1jzMdxygNEJiK/2sGsGqXwLd1WTfcGm+IKHWW6SnI/+Ig893aX4Z88SrK1v1Cv4w9OX6LgMXdY1JD9IbE3TKSq6OUUva2H0SsRWOxgtD52IoW0Dr5TB8EPscgd7LYy6tVaq2J4HcRfdaqMS8eiHJAioX5XDXVE49RD78UMEnc5G36YQQrwktNcj9tRx7O0TYJqEmQR+yiY9E+DHFWZXs/AWj8Jhi/aOIXppE7OnMfwsVivA6lrMvyoJGjoFm8LzTSo7kgSbh+nmYyRO+lFtRzJO9aocdjMguX+a8ukphudCFl+pcUtN5r8yydCZgF7KwKmZxE4sU3pqlOZ0lsILLfyUjbPegfUK+QMOqdkksYMzBK3WRr+EPxTZ9vsyUviDfdR/6jbW9iraQ7CQsQidFNljYLejRj3NkomfgFhVM3CiG0Xknkdv+yjKD9FWHi99rkq7E+K7BtqEpTtD8l+OQ78HJIaJMk3g3ACzyyhdKYToP0GthrVWZ+2NO4iv+MRPrtEYK+HHFJlTbdIHkqxeBYPPNLEaHtZaM2og2egQy9pUdigypyA13UZ5ASrQdIouidkGfj6JVW2jWh16SUW7YOHOF4mtm1gdn8IBk9BKU/rrFbRp0prKYDU8vIkCoaVIrISs3JDEXQ9xz7QhCNBHT2EfVwSyxfey8rJasvme0Aazq3AqCqsVFaJWd0K7aLB2tUm7FD3OqWmMbsDa7cMYg0Vaww7aMTAbPdyVTvRxWx38ONR3+JdNGszaNI5/516C267CmprY6MsRQrwMhGtlDB/agxZBPkV5lyK16GPWuphdzfCj6wRxi17WQTWjAEN1PZJHVxn/dpf8wQZmq4cKNHZbo03wci7aVLSmsvTG8xSfKpNaCJh/dZbckZBOzsSph5QeWibIxlE9DxVo/JSN0Qtw1zycSvS7uZtRLL9qiGDHJNrrXb79RjZgyWZubo6f/umfplAoEI/Hufrqq3n66af/5pK05td+7dcYGRkhHo9zzz33cPz4pd9C/Y/OkHwj/Owl/+SXpV/c6AsQPyz53hXi4oX1OtkvH2T1J/aydnUKq6mY/hGDia9kKD7XJkjGCG2D+VdajIUjJI6tRHUhqQRaKczVGtp1wPNJH/HxcwlO/ZjL1v/ZorLVxm5YuBWH9KE1KluHWLtakZhXFJ9YRzXbBKNZGEiQOFNh9kcGaY457Pz4CtgW9oRDZTeUnggxDp8h3OgX62K8xNt+y+Uyd955J69+9av5yle+wuDgIMePHyeXy51/zG//9m/zO7/zO3z6059m8+bN/Of//J+59957OXToEK7r/gPP/sN52S3ZCCGEeHGE9TrFP3sWAO+WXcy9yqU5oknM9ujl47izdWLlPO2iheEVWbw1RumpLu5MFV2uoJJJdKcDStHdOsDQU6D8kNE/OgymwfobtrNy5yADJ3x81yBW8UApmteOEZ9rsHLzAIXnoux2p6iYe1OJkbecpfHtAtt+8yBhu0N4uWZGNshv/dZvMTExwQMPPHD+3ObNm8//W2vNRz7yEX71V3+VH/3RHwXgv//3/06pVOIv/uIv+Gf/7J9dsmt5WS7ZCCGEeHGErRZhq4X58HeJL2lCExbvGGBtb4wT/yIX9QlxFO0hm8SiplOwIQjwd09RuWOC9s1b6dywGafcY+D5dYxaG4YKqHTq3A5JRepElfIuA20qWKuQOLGOsbTO0F+vEMYs1q8LKTynGf+Tk8x8cxOTv/k0QaV6+S7T/C2XcttvrVa74Oj+gNfnL//yL7npppt429vextDQENdffz2f+tSnzr//9OnTLC4ucs8995w/l81mufXWW9m3b98lvXcJSIQQQvzwtGbw4/soPt+mti3Ev7tKclax6c8X6GUVnZwie6ZL+myLuTePcPJtcbIHy0zfa2JXutgrDVZvLuCVMnhDafzBDPnH5hj5+iKsltn851ErBn/bKJXrB6m8coqluwfxMha7PzhLarZLWG8w/oHHXl5Nzy5hDcnExATZbPb88cEPfvDvfLpTp07xsY99jO3bt/O1r32Nf/Wv/hX/5t/8Gz796U8DsLi4CECpVLrg40ql0vn3XSqyZCOEEOKfTH3nGbZ9B1o/div+LyzBVyBW1igNsdOrrLxqjPhKiLumUO0uW/+8jXHwFMHVW0ks+8y+Js74gy20ZaCTccKEQ/n2Er20ovhsi/KuOO1BRXs0YOuftbGX63R2DBN77gxBs7nRt9/XZmZmyGQy59+OxWJ/5zFhGHLTTTfxgQ98AIDrr7+eF154gY9//OO84x3veMmuFSRDIoQQ4hJIfP4JMm88ifJ82kMKbYButil+Z5Hkgkf2RIvD7x3GOjGPURrErHdpFy02332GlesTGN2oH4lRadJLKZJLAfZynaG/XmHyy+ts+nKI9exJgmMnsb61n2B1baNv+cVxCTMkmUzmguMHBSQjIyPs2bPngnO7d+9menoagOHhYQCWlpYueMzS0tL5910qEpAIIYS4ZPyzM4z+X49R3LcC+Syq0SK21CBIWCRnTFQ6SXtbkfnXFFh7Q4dQKxqTmvm7syy/oog3MoAKNemvHYoG8nW6GI0OzlefIqzXN/r2XnQvdev4O++8k6NHj15w7tixY2zatAmIClyHh4d58MEHz7+/VqvxxBNPcPvtt1+Se/4eWbIRQghxyQVHT/zNG4tLWAdh7GELncvhlNPYDRv7eJzZ5zcRD0H5UPrqDP7MLIN/HU354OjLPwDZaO9973u54447+MAHPsBP/uRP8uSTT/LJT36ST37ykwAopXjPe97Db/zGb7B9+/bz235HR0d561vfekmvRQISIYQQLwnt+wQrK7CyQv4pyH/f+/0Nuao+8xL3Ibn55pv5/Oc/z3/4D/+BX//1X2fz5s185CMf4e1vf/v5x/zyL/8yzWaTX/iFX6BSqfCKV7yCr371q5e0BwmA0lpfJv1HhRDiyvI6420bfQniEvv7GjXWajWy2Sy73/UBzNjF/aEPuh0O/95/pFqtXlDU2u8kQyKEEEL0i5c4Q9JPpKhVCCGEEBtOMiRCCCFEv7iCMyQSkAghhBB9Qp07LvY5LkeyZCOEEEKIDScZEiGEEKJfyJKNEEIIITbaD9tp9e97jsuRLNkIIYQQYsNJhkQIIYToF7JkI4QQQoi+cJkGFBdLlmyEEEIIseEkQyKEEEL0iSu5qFUCEiGEEKJfSA2JEEIIITaaZEiEEEL0nb9vVL0QL0cSkAghhBD9QpZshBBCCLHRruQlG9n2K4QQQogNJxkSIYQQol/Iko0QQgghNtwVHJDIko0QQgghNpxkSIQQQog+cSUXtUpAIoQQQvQLWbIRQgghhNg4kiERQggh+oTSGqUvLsVxsR+/USQgEUIIIfrFFbxkIwGJEEII0Seu5KJWqSERQgghxIaTDIkQQgjRL2TJRgghhBAbTZZshBBCCCE2kGRIhBBCiH4hSzZCCCGE2GiyZCOEEEIIsYEkQyKEEEL0C1myEUIIIUQ/uFyXXC6WLNkIIYQQYsNJhkQIIYToF1pHx8U+x2VIMiRCCCFEn/jeLpuLPf6pfvM3fxOlFO95z3vOn+t0Otx///0UCgVSqRT33XcfS0tLF3+z30cCEiGEEKJf6Et0/BM89dRTfOITn+Caa6654Px73/tevvjFL/LZz36Whx9+mPn5eX78x3/8n/ZJ/gESkAghhBBXuEajwdvf/nY+9alPkcvlzp+vVqv8wR/8AR/60Id4zWtew4033sgDDzzAY489xuOPP35Jr0ECEiGEEKJPqPDSHAC1Wu2Co9vt/r2f9/777+dNb3oT99xzzwXn9+/fj+d5F5zftWsXk5OT7Nu375LeuwQkQgghRL+4hEs2ExMTZLPZ88cHP/jBH/gp//RP/5QDBw78wPcvLi7iOA4DAwMXnC+VSiwuLl7kzV5IdtkIIYQQL0MzMzNkMpnzb8disR/4mF/6pV/iG9/4Bq7rvpSX93dIhkQIIYToE5dyl00mk7ng+EEByf79+1leXuaGG27Asiwsy+Lhhx/md37nd7Asi1KpRK/Xo1KpXPBxS0tLDA8PX9J7lwyJEEII0S9e4j4kr33ta3n++ecvOPfOd76TXbt28Su/8itMTExg2zYPPvgg9913HwBHjx5lenqa22+//eKu8/tIQCKEEEJcodLpNHv37r3gXDKZpFAonD//cz/3c7zvfe8jn8+TyWR497vfze23385tt912Sa9FAhIhhBCiT1xsY7PvPcel9OEPfxjDMLjvvvvodrvce++9fPSjH720nwRQWl+mPWaFEEKIl4larUY2m+XWN/9XLPviikt9r8MTX/rPVKvVC4pa+50UtQohhBBiw8mSjRBCCNEn+nHJ5qUiAYkQQgjRL67gab8SkAghhBB94krOkEgNiRBCCCE2nGRIhBBCiH7xt2bRXNRzXIYkIBFCCCH6hCzZCCGEEEJsIMmQCCGEEP0i1NFxsc9xGZKARAghhOgXV3ANiSzZCCGEEGLDSYZECCGE6BOKS1DUekmu5KUnAYkQQgjRL67gTq2yZCOEEEKIDScZEiGEEKJPXMl9SCQgEUIIIfrFFbzLRgISIYQQok8orVEXWQNysR+/UaSGRAghhBAbTjIkQgghRL8Izx0X+xyXIQlIhBBCiD4hSzZCCCGEEBtIMiRCCCFEv5BdNkIIIYTYcNKpVQghhBBi40iGRAghhOgT0qlVCCGEEBtPlmyEEEIIITaOZEiEEEKIPqHC6LjY57gcSUAihBBC9IsreMlGAhIhhBCiX1zBfUikhkQIIYQQG04yJEIIIUSfuJJn2UhAIoQQQvSLK7iGRJZshBBCCLHhJEMihBBC9AsNXOy23cszQSIBiRBCCNEvruQaElmyEUIIIcSGkwyJEEII0S80l6Co9ZJcyUtOMiRCCCFEv/jeLpuLPf6RPvjBD3LzzTeTTqcZGhrirW99K0ePHr3gMZ1Oh/vvv59CoUAqleK+++5jaWnpUt+5BCRCCCHElerhhx/m/vvv5/HHH+cb3/gGnufx+te/nmazef4x733ve/niF7/IZz/7WR5++GHm5+f58R//8Ut+LUrry7T6RQghhHiZqNVqZLNZXnP1r2CZsYt6Lj/o8q3nf4tqtUomk/mhPnZlZYWhoSEefvhh7rrrLqrVKoODg3zmM5/hJ37iJwA4cuQIu3fvZt++fdx2220Xda1/m2RIhBBCiD7xvV02F3tAFOT87aPb7f7/fv5qtQpAPp8HYP/+/Xiexz333HP+Mbt27WJycpJ9+/Zd0nuXgEQIIYToF5ewhmRiYoJsNnv++OAHP/gPfuowDHnPe97DnXfeyd69ewFYXFzEcRwGBgYueGypVGJxcfGS3rrsshFCCCFehmZmZi5YsonF/uGloPvvv58XXniBRx999MW+tB9IAhIhhBCiX1zCWTaZTOYfXUPyrne9iy996Us88sgjjI+Pnz8/PDxMr9ejUqlckCVZWlpieHj44q7z+8iSjRBCCNEvXuJtv1pr3vWud/H5z3+eb33rW2zevPmC9994443Yts2DDz54/tzRo0eZnp7m9ttvv2S3DZIhEUIIIa5Y999/P5/5zGf4whe+QDqdPl8Xks1micfjZLNZfu7nfo73ve995PN5MpkM7373u7n99tsv6Q4bkIBECCGE6B8hoC7Bc/wjfexjHwPg7rvvvuD8Aw88wM/+7M8C8OEPfxjDMLjvvvvodrvce++9fPSjH73Ii/y7pA+JEEIIscG+14fknh3vuyR9SL557EP/pD4kG0lqSIQQQgix4WTJRgghhOgXl3CXzeVGAhIhhBCiX4Qa1EUGFOHlGZDIko0QQgghNpxkSIQQQoh+IUs2QgghhNh4lyAgQQISIYQQQlyMKzhDIjUkQgghhNhwkiERQggh+kWouegll8t0l40EJEIIIUS/0GF0XOxzXIZkyUYIIYQQG04yJEIIIUS/uIKLWiUgEUIIIfrFFVxDIks2QgghhNhwkiERQggh+oUs2QghhBBiw2kuQUBySa7kJSdLNkIIIYTYcJIhEUIIIfqFLNkIIYQQYsOFIXCRjc3Cy7MxmgQkQgghRL+4gjMkUkMihBBCiA0nGRIhhBCiX1zBGRIJSIQQQoh+IZ1ahRBCCCE2jmRIhBBCiD6hdYjWF7dL5mI/fqNIQCKEEEL0C60vfsnlMq0hkSUbIYQQQmw4yZAIIYQQ/UJfgqLWyzRDIgGJEEII0S/CENRF1oBcpjUksmQjhBBCiA0nGRIhhBCiX8iSjRBCCCE2mg5D9EUu2ci2XyGEEEJcnCs4QyI1JEIIIYTYcJIhEUIIIfpFqEFdmRkSCUiEEEKIfqE1cLHbfi/PgESWbIQQQgix4SRDIoQQQvQJHWr0RS7ZaMmQCCGEEOKi6PDSHD+k3//932dqagrXdbn11lt58sknX4Sb+4dJQCKEEEJcwf7sz/6M973vfbz//e/nwIEDXHvttdx7770sLy+/pNchAYkQQgjRJ3SoL8nxw/jQhz7Ez//8z/POd76TPXv28PGPf5xEIsEf/uEfvkh3+YNJQCKEEEL0i5d4yabX67F//37uueee8+cMw+Cee+5h3759L8Yd/r2kqFUIIYToEz7eRTdq9fEAqNVqF5yPxWLEYrELzq2urhIEAaVS6YLzpVKJI0eOXNyF/JAkIBFCCCE2mOM4DA8P8+jiX12S50ulUkxMTFxw7v3vfz//5b/8l0vy/C8GCUiEEEKIDea6LqdPn6bX612S59Nao5S64Nz3Z0cAisUipmmytLR0wfmlpSWGh4cvybX8Y0lAIoQQQvQB13VxXfcl/ZyO43DjjTfy4IMP8ta3vhWAMAx58MEHede73vWSXosEJEIIIcQV7H3vex/veMc7uOmmm7jlllv4yEc+QrPZ5J3vfOdLeh0SkAghhBBXsJ/6qZ9iZWWFX/u1X2NxcZHrrruOr371q3+n0PXFpvTl2mNWCCGEEC8b0odECCGEEBtOAhIhhBBCbDgJSIQQQgix4SQgEUIIIcSGk4BECCGEEBtOAhIhhBBCbDgJSIQQQgix4SQgEUIIIcSGk4BECCGEEBtOAhIhhBBCbDgJSIQQQgix4SQgEUIIIcSG+/8AJJnE2ICmNsIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Multiply the data by the brain mask to remove spurious values that were not predicted\n",
+    "rmse = np.sqrt(np.mean(np.square(shell_data[..., idx]*brain_mask - _y_pred)))\n",
+    "_rmse_element = np.sqrt(np.square(shell_data[..., idx]*brain_mask - _y_pred))\n",
+    "\n",
+    "print(f\"RMSE: {rmse}\")\n",
+    "threshold = 10\n",
+    "n_error_thr = len(_rmse_element[_rmse_element > threshold])\n",
+    "ratio = n_error_thr / np.prod(_rmse_element.shape) * 100\n",
+    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio}%)\")\n",
+    "\n",
+    "# Plot the RSME\n",
+    "x_slice = _rmse_element[slice_idx[0], :, :]\n",
+    "y_slice = _rmse_element[:, slice_idx[1], :]\n",
+    "z_slice = _rmse_element[:, :, slice_idx[2]]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "images = []\n",
+    "for i, _slice in enumerate(slices):\n",
+    "     images.append(axes[i].imshow(_slice.T, cmap=\"viridis\", origin=\"lower\", aspect=\"equal\"))\n",
+    "     axes[i].set_axis_off()\n",
+    "\n",
+    "plt.colorbar(images[-1])\n",
+    "plt.suptitle(\"RMSE\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 03b3e54d2597864a58fb9793059cecf45e3b6bfc Mon Sep 17 00:00:00 2001
From: Oscar Esteban <code@oscaresteban.es>
Date: Wed, 6 Nov 2024 00:16:36 +0100
Subject: [PATCH 02/17] fix: address indexing problems and simplify the story

---
 docs/notebooks/dwi_gp_estimation.ipynb | 3302 +++---------------------
 1 file changed, 312 insertions(+), 2990 deletions(-)

diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
index 263511a5..68cd8189 100644
--- a/docs/notebooks/dwi_gp_estimation.ipynb
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -30,6 +30,9 @@
     "from dipy.io import read_bvals_bvecs\n",
     "from dipy.segment.mask import median_otsu\n",
     "\n",
+    "from scipy.ndimage import binary_dilation\n",
+    "from skimage.morphology import ball\n",
+    "\n",
     "seed = 1234\n",
     "rng = np.random.default_rng(seed)\n",
     "\n",
@@ -40,6 +43,7 @@
     "bvals, bvecs = read_bvals_bvecs(bval_fname, bvec_fname)\n",
     "\n",
     "_, brain_mask = median_otsu(dwi_data, vol_idx=[0])\n",
+    "brain_mask = binary_dilation(brain_mask, ball(8))\n",
     "\n",
     "bval = 1000\n",
     "indices = get_bval_indices(bvals, bval, tol=20)\n",
@@ -64,7 +68,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -125,506 +129,85 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea5cc8036fa0ab48",
+   "id": "b3015aaf",
    "metadata": {},
    "source": [
-    "Do not optimize the parameters in the fitting. "
+    "Let's leave one direction out:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7e93b99c3b072d99",
+   "id": "4ea84424",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>EddyMotionGPR(alpha=0.1, disp=True,\n",
-       "              kernel=SphericalKriging (a=1.38, λ=0.47619047619047616),\n",
-       "              optimizer=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;EddyMotionGPR<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>EddyMotionGPR(alpha=0.1, disp=True,\n",
-       "              kernel=SphericalKriging (a=1.38, λ=0.47619047619047616),\n",
-       "              optimizer=None)</pre></div> </div></div></div></div>"
-      ],
-      "text/plain": [
-       "EddyMotionGPR(alpha=0.1, disp=True,\n",
-       "              kernel=SphericalKriging (a=1.38, λ=0.47619047619047616),\n",
-       "              optimizer=None)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "X_train = bvecs_shell\n",
-    "# Consider only brain voxels\n",
-    "dwi_mask = np.repeat(brain_mask[..., np.newaxis], shell_data.shape[-1], axis=-1)\n",
-    "y_train = shell_data[dwi_mask].reshape((X_train.shape[0], -1))\n",
-    "gpr.fit(X_train, y_train)"
+    "# Prepare a train/test mask (False for all directions except the left-out where it's true)\n",
+    "train_test_mask = np.zeros(bvecs_shell.shape[0], dtype=bool)\n",
+    "train_test_mask[dwi_vol_idx] = True\n",
+    "\n",
+    "# Generate train/test bvecs\n",
+    "X_train = bvecs_shell[~train_test_mask, :]\n",
+    "X_test = bvecs_shell[train_test_mask, :]\n",
+    "\n",
+    "# Select voxels within brain mask\n",
+    "y = shell_data[brain_mask]\n",
+    "\n",
+    "# Generate train/test data\n",
+    "y_train = y[:, ~train_test_mask]\n",
+    "y_test = y[:, train_test_mask]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dfdd82afbdb22790",
+   "id": "ea5cc8036fa0ab48",
    "metadata": {},
    "source": [
-    "Predict on a randomly picked diffusion-encoding gradient direction."
+    "Do not optimize the parameters in the fitting. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "ae3407b31b14928d",
+   "id": "09068d0f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RMSE: 20.177777097314834\n",
-      "Number of RMSE values above 10: 97093 (61.20%)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Pick a direction to predict\n",
-    "idx = rng.integers(0, len(indices))\n",
-    "X_test = bvecs_shell[idx][np.newaxis, :]\n",
-    "y_pred = gpr.predict(X_test)\n",
+    "gpr = gpr.fit(X_train, y_train.T)\n",
     "\n",
-    "rmse = np.sqrt(np.mean(np.square(y_train[idx, ...] - y_pred.squeeze())))\n",
-    "_rmse_element = np.sqrt(np.square(y_train[idx, ...] - y_pred.squeeze()))\n",
+    "y_sim = np.squeeze(gpr.predict(X_test))\n",
     "\n",
-    "print(f\"RMSE: {rmse}\")\n",
-    "threshold = 10\n",
-    "n_error_thr = len(_rmse_element[_rmse_element > threshold])\n",
-    "ratio = n_error_thr / len(_rmse_element) * 100\n",
-    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio:.2f}%)\")"
+    "dwi_sim = np.zeros_like(shell_data[..., 0])\n",
+    "dwi_sim[brain_mask] = y_sim"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "74b040c05621f2d9",
+   "id": "49c72e31",
    "metadata": {},
    "source": [
-    "Visualize the prediction."
+    "And now, visualize:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "a130de2a03dff2b5",
+   "id": "7e93b99c3b072d99",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -634,14 +217,10 @@
     }
    ],
    "source": [
-    "# Reshape the predicted data array to the image shape\n",
-    "brain_mask_idx = np.where(brain_mask)\n",
-    "_y_pred = np.zeros((shell_data.shape[:-1]), dtype=y_train.dtype)\n",
-    "_y_pred[brain_mask_idx] = y_pred.squeeze()\n",
     "\n",
-    "x_slice = _y_pred[slice_idx[0], :, :]\n",
-    "y_slice = _y_pred[:, slice_idx[1], :]\n",
-    "z_slice = _y_pred[:, :, slice_idx[2]]\n",
+    "x_slice = dwi_sim[slice_idx[0], :, :]\n",
+    "y_slice = dwi_sim[:, slice_idx[1], :]\n",
+    "z_slice = dwi_sim[:, :, slice_idx[2]]\n",
     "slices = [x_slice, y_slice, z_slice]\n",
     "\n",
     "fig, axes = plt.subplots(1, len(slices))\n",
@@ -649,9 +228,54 @@
     "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
     "     axes[i].set_axis_off()\n",
     "\n",
+    "plt.show()\n",
+    "\n",
+    "x_slice = dwi_data[slice_idx[0], :, :, dwi_vol_idx]\n",
+    "y_slice = dwi_data[:, slice_idx[1], :, dwi_vol_idx]\n",
+    "z_slice = dwi_data[:, :, slice_idx[2], dwi_vol_idx]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "    axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
+    "    axes[i].set_axis_off()\n",
+    "\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dfdd82afbdb22790",
+   "metadata": {},
+   "source": [
+    "Predict on a randomly picked diffusion-encoding gradient direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ae3407b31b14928d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 20.092110897117276\n",
+      "Number of RMSE values above 10: 199698 (67.21%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse = np.sqrt((y_test.squeeze() - y_sim) ** 2)\n",
+    "\n",
+    "print(f\"RMSE: {rmse.mean()}\")\n",
+    "threshold = 10\n",
+    "n_error_thr = len(rmse[rmse > threshold])\n",
+    "ratio = n_error_thr / len(rmse) * 100\n",
+    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio:.2f}%)\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8ea9cf5ae629f489",
@@ -662,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "24a72a024e3f2e37",
    "metadata": {},
    "outputs": [],
@@ -679,16 +303,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4697b2182f5178d7",
+   "id": "5c92d1b8",
    "metadata": {},
    "source": [
-    "Use a k-fold cross-validation and a grid search to find the best parameters."
+    "Let's now allow our GP to fit a and λ on the full dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "e92ebf1204c7f889",
+   "execution_count": 14,
+   "id": "06375052",
    "metadata": {},
    "outputs": [
     {
@@ -704,19 +328,11 @@
       "\n",
       "At X0         0 variables are exactly at the bounds\n",
       "\n",
-      "At iterate    0    f=  4.00084D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
+      "At iterate    0    f=  4.16597D+07    |proj g|=  7.64969D+00\n",
       "\n",
-      "At iterate    2    f=  7.23889D+05    |proj g|=  6.95102D+00\n",
+      "At iterate    1    f=  3.02802D+07    |proj g|=  8.01781D+00\n",
       "\n",
-      "At iterate    3    f=  7.23693D+05    |proj g|=  6.92318D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90777D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.47262D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  9.89565D-03\n",
+      "At iterate    2    f=  2.96130D+07    |proj g|=  7.92633D+00\n",
       "\n",
       "           * * *\n",
       "\n",
@@ -731,2402 +347,232 @@
       "           * * *\n",
       "\n",
       "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   9.896D-03   7.237D+05\n",
-      "  F =   723658.65193438344     \n",
+      "    2      2      4      3     0     0   7.926D+00   2.961D+07\n",
+      "  F =   29612979.200755097     \n",
       "\n",
       "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=\"fmin_l_bfgs_b\", ftol=0.1)\n",
+    "gpr = gpr.fit(bvecs_shell, y.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b03e1be",
+   "metadata": {},
+   "source": [
+    "Now we check the fitted parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "20a0f4ee",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "(SphericalKriging (a=0.8841585520664049, λ=2.769244783648302),\n",
+       " SphericalKriging (a=1.38, λ=0.47619047619047616))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gpr.kernel_, gpr.kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "700542ad",
+   "metadata": {},
+   "source": [
+    "Let's create a new GPR with the new parameters (we should check this, I don't understand why the fitted parameters are not set back)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fa17538d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpr = EddyMotionGPR(kernel=gpr.kernel_, alpha=alpha, disp=disp, optimizer=None)\n",
+    "gpr = gpr.fit(X_train, y_train.T)\n",
+    "y_sim2 = np.squeeze(gpr.predict(X_test))\n",
+    "\n",
+    "dwi_sim2 = np.zeros_like(shell_data[..., 0])\n",
+    "dwi_sim2[brain_mask] = y_sim2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74935ed2",
+   "metadata": {},
+   "source": [
+    "Let's check RMSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ad5a4554",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.93671D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23926D+05    |proj g|=  6.95426D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23700D+05    |proj g|=  6.92476D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90796D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90879D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
+      "RMSE: 21.310449692859184\n",
+      "Number of RMSE values above 10: 204399 (68.79%)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "rmse = np.sqrt((y_test.squeeze() - y_sim2) ** 2)\n",
+    "\n",
+    "print(f\"RMSE: {rmse.mean()}\")\n",
+    "threshold = 10\n",
+    "n_error_thr = len(rmse[rmse > threshold])\n",
+    "ratio = n_error_thr / len(rmse) * 100\n",
+    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ea4f92f",
+   "metadata": {},
+   "source": [
+    "And now we visualize the new simulated orientation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "90129831",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  1.50953D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   1.510D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.92653D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23948D+05    |proj g|=  6.95605D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23704D+05    |proj g|=  6.92564D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90806D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90879D+00\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  1.88219D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   1.882D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.94029D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23917D+05    |proj g|=  6.95348D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23698D+05    |proj g|=  6.92437D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90791D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90879D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  1.36717D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   1.367D-02   7.237D+05\n",
-      "  F =   723658.65193438344     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  4.00075D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.27412D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.38150D+05    |proj g|=  6.95660D+00\n",
-      "\n",
-      "At iterate    3    f=  7.37895D+05    |proj g|=  6.92592D+00\n",
-      "\n",
-      "At iterate    4    f=  7.37848D+05    |proj g|=  6.90810D+00\n",
-      "\n",
-      "At iterate    5    f=  7.37848D+05    |proj g|=  6.90879D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.37848D+05    |proj g|=  2.05235D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   2.052D-02   7.378D+05\n",
-      "  F =   737848.03726643079     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.08723D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.83932D+05    |proj g|=  8.32144D+00\n",
-      "\n",
-      "At iterate    2    f=  7.76269D+05    |proj g|=  7.82081D+00\n",
-      "\n",
-      "At iterate    3    f=  7.71380D+05    |proj g|=  7.71301D+00\n",
-      "\n",
-      "At iterate    4    f=  7.67603D+05    |proj g|=  6.31005D+00\n",
-      "\n",
-      "At iterate    5    f=  7.67425D+05    |proj g|=  6.42738D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Bad direction in the line search;\n",
-      "   refresh the lbfgs memory and restart the iteration.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.65625D+05    |proj g|=  7.62520D+00\n",
-      "\n",
-      "At iterate    7    f=  7.54900D+05    |proj g|=  6.54833D+00\n",
-      "\n",
-      "At iterate    8    f=  7.54427D+05    |proj g|=  6.69883D+00\n",
-      "\n",
-      "At iterate    9    f=  7.54314D+05    |proj g|=  6.74652D+00\n",
-      "\n",
-      "At iterate   10    f=  7.53938D+05    |proj g|=  6.86533D+00\n",
-      "\n",
-      "At iterate   11    f=  7.53839D+05    |proj g|=  6.97181D+00\n",
-      "\n",
-      "At iterate   12    f=  7.53682D+05    |proj g|=  7.03698D+00\n",
-      "\n",
-      "At iterate   13    f=  7.53640D+05    |proj g|=  7.19362D+00\n",
-      "\n",
-      "At iterate   14    f=  7.52778D+05    |proj g|=  7.31218D+00\n",
-      "\n",
-      "At iterate   15    f=  7.40147D+05    |proj g|=  7.25559D+00\n",
-      "\n",
-      "At iterate   16    f=  7.20996D+05    |proj g|=  7.03999D+00\n",
-      "\n",
-      "At iterate   17    f=  7.18577D+05    |proj g|=  6.96253D+00\n",
-      "\n",
-      "At iterate   18    f=  7.18126D+05    |proj g|=  6.91793D+00\n",
-      "\n",
-      "At iterate   19    f=  7.18110D+05    |proj g|=  6.90834D+00\n",
-      "\n",
-      "At iterate   20    f=  7.18110D+05    |proj g|=  6.90740D+00\n",
-      "\n",
-      "At iterate   21    f=  7.18110D+05    |proj g|=  3.62404D+00\n",
-      "\n",
-      "At iterate   22    f=  7.18110D+05    |proj g|=  2.86270D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     22     80     25     0     0   2.863D+00   7.181D+05\n",
-      "  F =   718110.04019295715     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.07626D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.82949D+05    |proj g|=  8.31102D+00\n",
-      "\n",
-      "At iterate    2    f=  7.72844D+05    |proj g|=  7.73986D+00\n",
-      "\n",
-      "At iterate    3    f=  7.67712D+05    |proj g|=  7.59657D+00\n",
-      "\n",
-      "At iterate    4    f=  7.62885D+05    |proj g|=  6.65611D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Warning:  more than 10 function and gradient\n",
-      "   evaluations in the last line search.  Termination\n",
-      "   may possibly be caused by a bad search direction.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    5    f=  7.62885D+05    |proj g|=  6.65611D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      5     30      6     0     0   6.656D+00   7.629D+05\n",
-      "  F =   762884.93959453050     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.07316D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.82422D+05    |proj g|=  8.31001D+00\n",
-      "\n",
-      "At iterate    2    f=  7.71894D+05    |proj g|=  7.75235D+00\n",
-      "\n",
-      "At iterate    3    f=  7.66951D+05    |proj g|=  7.60726D+00\n",
-      "\n",
-      "At iterate    4    f=  7.61447D+05    |proj g|=  6.58793D+00\n",
-      "  ys=-1.027E+03  -gs= 8.482E+03 BFGS update SKIPPED\n",
-      "\n",
-      "At iterate    5    f=  7.36278D+05    |proj g|=  7.19324D+00\n",
-      "\n",
-      "At iterate    6    f=  7.31568D+05    |proj g|=  6.91931D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Bad direction in the line search;\n",
-      "   refresh the lbfgs memory and restart the iteration.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.25311D+05    |proj g|=  7.04881D+00\n",
-      "\n",
-      "At iterate    8    f=  7.20388D+05    |proj g|=  6.97588D+00\n",
-      "\n",
-      "At iterate    9    f=  7.18070D+05    |proj g|=  6.99080D+00\n",
-      "\n",
-      "At iterate   10    f=  7.17117D+05    |proj g|=  6.91651D+00\n",
-      "\n",
-      "At iterate   11    f=  7.17103D+05    |proj g|=  6.90916D+00\n",
-      "\n",
-      "At iterate   12    f=  7.17103D+05    |proj g|=  6.90864D+00\n",
-      "\n",
-      "At iterate   13    f=  7.17103D+05    |proj g|=  6.90856D+00\n",
-      "\n",
-      "At iterate   14    f=  7.17103D+05    |proj g|=  1.67323D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     14     50     17     1     0   1.673D+00   7.171D+05\n",
-      "  F =   717102.79139089293     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.07599D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.82132D+05    |proj g|=  8.31081D+00\n",
-      "\n",
-      "At iterate    2    f=  7.74290D+05    |proj g|=  7.79325D+00\n",
-      "\n",
-      "At iterate    3    f=  7.69580D+05    |proj g|=  7.68928D+00\n",
-      "\n",
-      "At iterate    4    f=  7.65643D+05    |proj g|=  6.30492D+00\n",
-      "\n",
-      "At iterate    5    f=  7.64819D+05    |proj g|=  6.44921D+00\n",
-      "\n",
-      "At iterate    6    f=  7.64800D+05    |proj g|=  6.47049D+00\n",
-      "\n",
-      "At iterate    7    f=  7.64782D+05    |proj g|=  6.49304D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Bad direction in the line search;\n",
-      "   refresh the lbfgs memory and restart the iteration.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    8    f=  7.62784D+05    |proj g|=  7.59947D+00\n",
-      "\n",
-      "At iterate    9    f=  7.46995D+05    |proj g|=  6.68758D+00\n",
-      "\n",
-      "At iterate   10    f=  7.46722D+05    |proj g|=  6.73618D+00\n",
-      "\n",
-      "At iterate   11    f=  7.46606D+05    |proj g|=  6.98182D+00\n",
-      "\n",
-      "At iterate   12    f=  7.46408D+05    |proj g|=  7.09454D+00\n",
-      "\n",
-      "At iterate   13    f=  7.45521D+05    |proj g|=  7.30952D+00\n",
-      "\n",
-      "At iterate   14    f=  7.27416D+05    |proj g|=  7.14360D+00\n",
-      "\n",
-      "At iterate   15    f=  7.19297D+05    |proj g|=  7.02035D+00\n",
-      "\n",
-      "At iterate   16    f=  7.17592D+05    |proj g|=  6.94187D+00\n",
-      "\n",
-      "At iterate   17    f=  7.17439D+05    |proj g|=  6.91330D+00\n",
-      "\n",
-      "At iterate   18    f=  7.17435D+05    |proj g|=  6.90797D+00\n",
-      "\n",
-      "At iterate   19    f=  7.17435D+05    |proj g|=  6.90808D+00\n",
-      "\n",
-      "At iterate   20    f=  7.17435D+05    |proj g|=  6.90810D+00\n",
-      "\n",
-      "At iterate   21    f=  7.17435D+05    |proj g|=  4.47102D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     21     81     24     0     0   4.471D+00   7.174D+05\n",
-      "  F =   717435.20438548480     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.09352D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.99555D+05    |proj g|=  8.30971D+00\n",
-      "\n",
-      "At iterate    2    f=  7.86760D+05    |proj g|=  7.75215D+00\n",
-      "\n",
-      "At iterate    3    f=  7.81719D+05    |proj g|=  7.60277D+00\n",
-      "\n",
-      "At iterate    4    f=  7.75959D+05    |proj g|=  6.60307D+00\n",
-      "  ys=-1.526E+03  -gs= 8.984E+03 BFGS update SKIPPED\n",
-      "\n",
-      "At iterate    5    f=  7.48905D+05    |proj g|=  7.17000D+00\n",
-      "\n",
-      "At iterate    6    f=  7.43399D+05    |proj g|=  6.91770D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Bad direction in the line search;\n",
-      "   refresh the lbfgs memory and restart the iteration.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.38452D+05    |proj g|=  7.03452D+00\n",
-      "\n",
-      "At iterate    8    f=  7.33912D+05    |proj g|=  6.96037D+00\n",
-      "\n",
-      "At iterate    9    f=  7.31901D+05    |proj g|=  6.97224D+00\n",
-      "\n",
-      "At iterate   10    f=  7.31319D+05    |proj g|=  6.91288D+00\n",
-      "\n",
-      "At iterate   11    f=  7.31313D+05    |proj g|=  6.90865D+00\n",
-      "\n",
-      "At iterate   12    f=  7.31312D+05    |proj g|=  6.90821D+00\n",
-      "\n",
-      "At iterate   13    f=  7.31312D+05    |proj g|=  6.90820D+00\n",
-      "\n",
-      "At iterate   14    f=  7.31312D+05    |proj g|=  1.67795D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     14     50     17     1     0   1.678D+00   7.313D+05\n",
-      "  F =   731312.48190047638     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  7.83924D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23957D+05    |proj g|=  6.95679D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23706D+05    |proj g|=  6.92601D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.90811D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90880D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  2.05783D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     23      7     0     1   2.058D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  7.74503D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.24458D+05    |proj g|=  6.98701D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23820D+05    |proj g|=  6.94262D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23661D+05    |proj g|=  6.91082D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90898D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  3.50473D-01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  1.18141D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     24      8     0     1   1.181D-04   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  7.70901D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.24805D+05    |proj g|=  7.00221D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23916D+05    |proj g|=  6.95208D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23663D+05    |proj g|=  6.91287D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90919D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  1.00854D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  6.50553D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     24      8     0     1   6.506D-04   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  7.73957D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.24552D+05    |proj g|=  6.99141D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23845D+05    |proj g|=  6.94528D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23661D+05    |proj g|=  6.91137D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.90903D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  4.84570D-01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  1.99262D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     24      8     0     1   1.993D-04   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  7.86753D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.27412D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.38926D+05    |proj g|=  6.99855D+00\n",
-      "\n",
-      "At iterate    3    f=  7.38084D+05    |proj g|=  6.94973D+00\n",
-      "\n",
-      "At iterate    4    f=  7.37852D+05    |proj g|=  6.91233D+00\n",
-      "\n",
-      "At iterate    5    f=  7.37848D+05    |proj g|=  6.90913D+00\n",
-      "\n",
-      "At iterate    6    f=  7.37848D+05    |proj g|=  8.09551D-01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.37848D+05    |proj g|=  4.50810D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     24      8     0     1   4.508D-04   7.378D+05\n",
-      "  F =   737848.03726642986     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.81537D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.15845D+05    |proj g|=  8.64108D+00\n",
-      "\n",
-      "At iterate    2    f=  8.11262D+05    |proj g|=  8.52536D+00\n",
-      "\n",
-      "At iterate    3    f=  7.87008D+05    |proj g|=  8.47550D+00\n",
-      "\n",
-      "At iterate    4    f=  7.84765D+05    |proj g|=  8.46851D+00\n",
-      "\n",
-      "At iterate    5    f=  7.81554D+05    |proj g|=  8.43520D+00\n",
-      "\n",
-      "At iterate    6    f=  7.81113D+05    |proj g|=  8.43266D+00\n",
-      "\n",
-      "At iterate    7    f=  7.77957D+05    |proj g|=  8.35509D+00\n",
-      "\n",
-      "At iterate    8    f=  7.74189D+05    |proj g|=  8.22224D+00\n",
-      "\n",
-      "At iterate    9    f=  7.73008D+05    |proj g|=  8.11866D+00\n",
-      "\n",
-      "At iterate   10    f=  7.73006D+05    |proj g|=  8.12047D+00\n",
-      "\n",
-      "At iterate   11    f=  7.73003D+05    |proj g|=  5.70008D+00\n",
-      "\n",
-      "At iterate   12    f=  7.73003D+05    |proj g|=  5.69958D+00\n",
-      "\n",
-      "At iterate   13    f=  7.73003D+05    |proj g|=  1.22630D+00\n",
-      "\n",
-      "At iterate   14    f=  7.73003D+05    |proj g|=  1.72622D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     14     21     15     0     0   1.726D-02   7.730D+05\n",
-      "  F =   773002.95537064306     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.79584D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.17379D+05    |proj g|=  8.63632D+00\n",
-      "\n",
-      "At iterate    2    f=  8.11757D+05    |proj g|=  8.51390D+00\n",
-      "\n",
-      "At iterate    3    f=  7.87536D+05    |proj g|=  8.46399D+00\n",
-      "\n",
-      "At iterate    4    f=  7.82105D+05    |proj g|=  8.44328D+00\n",
-      "\n",
-      "At iterate    5    f=  7.81215D+05    |proj g|=  8.42562D+00\n",
-      "\n",
-      "At iterate    6    f=  7.73883D+05    |proj g|=  8.16463D+00\n",
-      "\n",
-      "At iterate    7    f=  7.73622D+05    |proj g|=  5.67161D+00\n",
-      "\n",
-      "At iterate    8    f=  7.73360D+05    |proj g|=  5.75040D+00\n",
-      "\n",
-      "At iterate    9    f=  7.73114D+05    |proj g|=  5.70863D+00\n",
-      "\n",
-      "At iterate   10    f=  7.73113D+05    |proj g|=  5.70543D+00\n",
-      "\n",
-      "At iterate   11    f=  7.73113D+05    |proj g|=  8.11146D+00\n",
-      "\n",
-      "At iterate   12    f=  7.73113D+05    |proj g|=  1.66588D+00\n",
-      "\n",
-      "At iterate   13    f=  7.73113D+05    |proj g|=  3.51131D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     13     21     15     0     0   3.511D-02   7.731D+05\n",
-      "  F =   773112.55499294470     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.78721D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.15842D+05    |proj g|=  8.63439D+00\n",
-      "\n",
-      "At iterate    2    f=  8.08320D+05    |proj g|=  8.51193D+00\n",
-      "\n",
-      "At iterate    3    f=  7.82670D+05    |proj g|=  8.45740D+00\n",
-      "\n",
-      "At iterate    4    f=  7.81250D+05    |proj g|=  8.45066D+00\n",
-      "\n",
-      "At iterate    5    f=  7.79503D+05    |proj g|=  8.42573D+00\n",
-      "\n",
-      "At iterate    6    f=  7.79062D+05    |proj g|=  8.41728D+00\n",
-      "\n",
-      "At iterate    7    f=  7.73891D+05    |proj g|=  8.28218D+00\n",
-      "\n",
-      "At iterate    8    f=  7.71309D+05    |proj g|=  8.09634D+00\n",
-      "\n",
-      "At iterate    9    f=  7.71271D+05    |proj g|=  8.14819D+00\n",
-      "\n",
-      "At iterate   10    f=  7.71115D+05    |proj g|=  8.11885D+00\n",
-      "\n",
-      "At iterate   11    f=  7.71099D+05    |proj g|=  5.71150D+00\n",
-      "\n",
-      "At iterate   12    f=  7.71099D+05    |proj g|=  8.10673D+00\n",
-      "\n",
-      "At iterate   13    f=  7.71099D+05    |proj g|=  2.01602D-01\n",
-      "\n",
-      "At iterate   14    f=  7.71099D+05    |proj g|=  2.72880D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     14     19     15     0     0   2.729D-02   7.711D+05\n",
-      "  F =   771098.55727667443     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.79428D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.15648D+05    |proj g|=  8.63616D+00\n",
-      "\n",
-      "At iterate    2    f=  8.10268D+05    |proj g|=  8.51631D+00\n",
-      "\n",
-      "At iterate    3    f=  7.84863D+05    |proj g|=  8.46250D+00\n",
-      "\n",
-      "At iterate    4    f=  7.83265D+05    |proj g|=  8.45692D+00\n",
-      "\n",
-      "At iterate    5    f=  7.80449D+05    |proj g|=  8.42655D+00\n",
-      "\n",
-      "At iterate    6    f=  7.80020D+05    |proj g|=  8.42096D+00\n",
-      "\n",
-      "At iterate    7    f=  7.78613D+05    |proj g|=  8.39050D+00\n",
-      "\n",
-      "At iterate    8    f=  7.73916D+05    |proj g|=  8.25263D+00\n",
-      "\n",
-      "At iterate    9    f=  7.72095D+05    |proj g|=  8.15110D+00\n",
-      "\n",
-      "At iterate   10    f=  7.71819D+05    |proj g|=  8.12659D+00\n",
-      "\n",
-      "At iterate   11    f=  7.71812D+05    |proj g|=  5.72583D+00\n",
-      "\n",
-      "At iterate   12    f=  7.71776D+05    |proj g|=  5.71155D+00\n",
-      "\n",
-      "At iterate   13    f=  7.71775D+05    |proj g|=  8.10931D+00\n",
-      "\n",
-      "At iterate   14    f=  7.71774D+05    |proj g|=  8.10769D+00\n",
-      "\n",
-      "At iterate   15    f=  7.71774D+05    |proj g|=  3.32346D-01\n",
-      "\n",
-      "At iterate   16    f=  7.71774D+05    |proj g|=  7.29946D-03\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     16     21     17     0     0   7.299D-03   7.718D+05\n",
-      "  F =   771774.43666481553     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.82325D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.33796D+05    |proj g|=  8.63462D+00\n",
-      "\n",
-      "At iterate    2    f=  8.23219D+05    |proj g|=  8.51151D+00\n",
-      "\n",
-      "At iterate    3    f=  7.97031D+05    |proj g|=  8.45674D+00\n",
-      "\n",
-      "At iterate    4    f=  7.95640D+05    |proj g|=  8.45010D+00\n",
-      "\n",
-      "At iterate    5    f=  7.93950D+05    |proj g|=  8.42594D+00\n",
-      "\n",
-      "At iterate    6    f=  7.91928D+05    |proj g|=  8.38281D+00\n",
-      "\n",
-      "At iterate    7    f=  7.87436D+05    |proj g|=  8.25046D+00\n",
-      "\n",
-      "At iterate    8    f=  7.85736D+05    |proj g|=  8.14948D+00\n",
-      "\n",
-      "At iterate    9    f=  7.85545D+05    |proj g|=  8.11674D+00\n",
-      "\n",
-      "At iterate   10    f=  7.85538D+05    |proj g|=  8.11199D+00\n",
-      "\n",
-      "At iterate   11    f=  7.85536D+05    |proj g|=  5.70797D+00\n",
-      "\n",
-      "At iterate   12    f=  7.85536D+05    |proj g|=  5.70647D+00\n",
-      "\n",
-      "At iterate   13    f=  7.85536D+05    |proj g|=  1.52837D+00\n",
-      "\n",
-      "At iterate   14    f=  7.85536D+05    |proj g|=  2.17409D-03\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     14     22     15     0     0   2.174D-03   7.855D+05\n",
-      "  F =   785535.80548623193     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.24840D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.24399D+05    |proj g|=  6.99386D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23799D+05    |proj g|=  6.93143D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23660D+05    |proj g|=  6.90138D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91799D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  2.59693D-01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  7.24672D-05\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     23      8     0     1   7.247D-05   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.21021D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.24785D+05    |proj g|=  7.01132D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23900D+05    |proj g|=  6.94199D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23663D+05    |proj g|=  6.90360D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91821D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  8.87429D-01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  5.26542D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     23      8     0     1   5.265D-04   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.19964D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.25031D+05    |proj g|=  7.02087D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23972D+05    |proj g|=  6.94817D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23665D+05    |proj g|=  6.90509D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91838D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  1.59565D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  1.35821D-03\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     23      8     0     1   1.358D-03   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.20595D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.24935D+05    |proj g|=  7.01728D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23943D+05    |proj g|=  6.94581D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23664D+05    |proj g|=  6.90451D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91831D+00\n",
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  1.28708D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    7    f=  7.23659D+05    |proj g|=  9.59853D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      7     23      8     0     1   9.599D-04   7.237D+05\n",
-      "  F =   723658.65193438309     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  3.26019D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.27413D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.37904D+05    |proj g|=  6.93865D+00\n",
-      "\n",
-      "At iterate    3    f=  7.37854D+05    |proj g|=  6.90436D+00\n",
-      "\n",
-      "At iterate    4    f=  7.37848D+05    |proj g|=  6.89791D+00\n",
-      "\n",
-      "At iterate    5    f=  7.37848D+05    |proj g|=  5.18311D-01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.37848D+05    |proj g|=  1.56000D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     21      7     0     1   1.560D-04   7.378D+05\n",
-      "  F =   737848.03726642986     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.05212D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.73327D+05    |proj g|=  8.26664D+00\n",
-      "\n",
-      "At iterate    2    f=  8.00507D+05    |proj g|=  7.95926D+00\n",
-      "\n",
-      "At iterate    3    f=  7.63187D+05    |proj g|=  7.54976D+00\n",
-      "\n",
-      "At iterate    4    f=  7.55543D+05    |proj g|=  6.75243D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Bad direction in the line search;\n",
-      "   refresh the lbfgs memory and restart the iteration.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    5    f=  7.54887D+05    |proj g|=  7.52081D+00\n",
-      "\n",
-      "At iterate    6    f=  7.37759D+05    |proj g|=  7.25689D+00\n",
-      "\n",
-      "At iterate    7    f=  7.32275D+05    |proj g|=  6.99577D+00\n",
-      "\n",
-      "At iterate    8    f=  7.26958D+05    |proj g|=  6.98589D+00\n",
-      "\n",
-      "At iterate    9    f=  7.20211D+05    |proj g|=  7.04582D+00\n",
-      "\n",
-      "At iterate   10    f=  7.18098D+05    |proj g|=  6.92617D+00\n",
-      "\n",
-      "At iterate   11    f=  7.18086D+05    |proj g|=  6.91764D+00\n",
-      "\n",
-      "At iterate   12    f=  7.18085D+05    |proj g|=  6.89946D+00\n",
-      "\n",
-      "At iterate   13    f=  7.18085D+05    |proj g|=  6.89922D+00\n",
-      "\n",
-      "At iterate   14    f=  7.18085D+05    |proj g|=  2.25798D+00\n",
-      "\n",
-      "At iterate   15    f=  7.18085D+05    |proj g|=  9.33477D-01\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     15     44     19     0     0   9.335D-01   7.181D+05\n",
-      "  F =   718085.40298732324     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.04261D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.72456D+05    |proj g|=  8.25643D+00\n",
-      "\n",
-      "At iterate    2    f=  7.99243D+05    |proj g|=  7.92417D+00\n",
-      "\n",
-      "At iterate    3    f=  7.59970D+05    |proj g|=  7.51890D+00\n",
-      "\n",
-      "At iterate    4    f=  7.50732D+05    |proj g|=  6.65020D+00\n",
-      "\n",
-      "At iterate    5    f=  7.50185D+05    |proj g|=  6.85873D+00\n",
-      "\n",
-      "At iterate    6    f=  7.49802D+05    |proj g|=  6.95852D+00\n",
-      "\n",
-      "At iterate    7    f=  7.48823D+05    |proj g|=  7.09653D+00\n",
-      "\n",
-      "At iterate    8    f=  7.47349D+05    |proj g|=  7.22217D+00\n",
-      "\n",
-      "At iterate    9    f=  7.38608D+05    |proj g|=  7.24551D+00\n",
-      "\n",
-      "At iterate   10    f=  7.21570D+05    |proj g|=  7.07431D+00\n",
-      "\n",
-      "At iterate   11    f=  7.18547D+05    |proj g|=  6.97867D+00\n",
-      "\n",
-      "At iterate   12    f=  7.18070D+05    |proj g|=  6.93066D+00\n",
-      "\n",
-      "At iterate   13    f=  7.18050D+05    |proj g|=  6.91910D+00\n",
-      "\n",
-      "At iterate   14    f=  7.18049D+05    |proj g|=  6.89780D+00\n",
-      "\n",
-      "At iterate   15    f=  7.18049D+05    |proj g|=  6.89818D+00\n",
-      "\n",
-      "At iterate   16    f=  7.18049D+05    |proj g|=  6.89800D+00\n",
-      "\n",
-      "At iterate   17    f=  7.18049D+05    |proj g|=  1.46904D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     17     31     19     0     0   1.469D+00   7.180D+05\n",
-      "  F =   718049.33988406940     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.03950D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.71848D+05    |proj g|=  8.25518D+00\n",
-      "\n",
-      "At iterate    2    f=  7.97381D+05    |proj g|=  7.92739D+00\n",
-      "\n",
-      "At iterate    3    f=  7.59092D+05    |proj g|=  7.53010D+00\n",
-      "\n",
-      "At iterate    4    f=  7.49630D+05    |proj g|=  6.64430D+00\n",
-      "\n",
-      "At iterate    5    f=  7.49465D+05    |proj g|=  6.81840D+00\n",
-      "\n",
-      "At iterate    6    f=  7.48950D+05    |proj g|=  7.00058D+00\n",
-      "\n",
-      "At iterate    7    f=  7.47672D+05    |proj g|=  7.08044D+00\n",
-      "\n",
-      "At iterate    8    f=  7.42528D+05    |proj g|=  7.22874D+00\n",
-      "\n",
-      "At iterate    9    f=  7.30933D+05    |proj g|=  7.21534D+00\n",
-      "\n",
-      "At iterate   10    f=  7.18401D+05    |proj g|=  7.01430D+00\n",
-      "\n",
-      "At iterate   11    f=  7.17236D+05    |proj g|=  6.95199D+00\n",
-      "\n",
-      "At iterate   12    f=  7.17082D+05    |proj g|=  6.92255D+00\n",
-      "\n",
-      "At iterate   13    f=  7.17079D+05    |proj g|=  6.91803D+00\n",
-      "\n",
-      "At iterate   14    f=  7.17079D+05    |proj g|=  6.89793D+00\n",
-      "\n",
-      "At iterate   15    f=  7.17079D+05    |proj g|=  6.89800D+00\n",
-      "\n",
-      "At iterate   16    f=  7.17079D+05    |proj g|=  4.39164D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     16     33     18     0     0   4.392D+00   7.171D+05\n",
-      "  F =   717079.11894415319     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.04185D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.71437D+05    |proj g|=  8.25541D+00\n",
-      "\n",
-      "At iterate    2    f=  7.97348D+05    |proj g|=  7.93616D+00\n",
-      "\n",
-      "At iterate    3    f=  7.69474D+05    |proj g|=  7.70185D+00\n",
-      "\n",
-      "At iterate    4    f=  7.58212D+05    |proj g|=  7.50010D+00\n",
-      "\n",
-      "At iterate    5    f=  7.52139D+05    |proj g|=  6.71860D+00\n",
-      "\n",
-      "At iterate    6    f=  7.51847D+05    |proj g|=  6.80502D+00\n",
-      "\n",
-      "At iterate    7    f=  7.50910D+05    |proj g|=  7.02185D+00\n",
-      "\n",
-      "At iterate    8    f=  7.50190D+05    |proj g|=  7.09594D+00\n",
-      "\n",
-      "At iterate    9    f=  7.46711D+05    |proj g|=  7.23406D+00\n",
-      "\n",
-      "At iterate   10    f=  7.36828D+05    |proj g|=  7.25652D+00\n",
-      "\n",
-      "At iterate   11    f=  7.20708D+05    |proj g|=  7.07099D+00\n",
-      "\n",
-      "At iterate   12    f=  7.17871D+05    |proj g|=  6.97659D+00\n",
-      "\n",
-      "At iterate   13    f=  7.17431D+05    |proj g|=  6.92933D+00\n",
-      "\n",
-      "At iterate   14    f=  7.17412D+05    |proj g|=  6.91801D+00\n",
-      "\n",
-      "At iterate   15    f=  7.17412D+05    |proj g|=  6.89906D+00\n",
-      "\n",
-      "At iterate   16    f=  7.17412D+05    |proj g|=  6.89927D+00\n",
-      "\n",
-      "At iterate   17    f=  7.17412D+05    |proj g|=  6.89896D+00\n",
-      "\n",
-      "At iterate   18    f=  7.17412D+05    |proj g|=  2.78471D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     18     36     20     0     0   2.785D+00   7.174D+05\n",
-      "  F =   717411.52134795184     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.05923D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  8.88773D+05    |proj g|=  8.25485D+00\n",
-      "\n",
-      "At iterate    2    f=  8.12846D+05    |proj g|=  7.92712D+00\n",
-      "\n",
-      "At iterate    3    f=  7.73505D+05    |proj g|=  7.52751D+00\n",
-      "\n",
-      "At iterate    4    f=  7.63776D+05    |proj g|=  6.66304D+00\n",
-      "\n",
-      "At iterate    5    f=  7.63456D+05    |proj g|=  6.85988D+00\n",
-      "\n",
-      "At iterate    6    f=  7.62806D+05    |proj g|=  6.92725D+00\n",
-      "\n",
-      "At iterate    7    f=  7.60245D+05    |proj g|=  7.05954D+00\n",
-      "\n",
-      "At iterate    8    f=  7.57024D+05    |proj g|=  7.21945D+00\n",
-      "\n",
-      "At iterate    9    f=  7.47805D+05    |proj g|=  7.22857D+00\n",
-      "\n",
-      "At iterate   10    f=  7.32973D+05    |proj g|=  7.02599D+00\n",
-      "\n",
-      "At iterate   11    f=  7.31501D+05    |proj g|=  6.95735D+00\n",
-      "\n",
-      "At iterate   12    f=  7.31292D+05    |proj g|=  6.92361D+00\n",
-      "\n",
-      "At iterate   13    f=  7.31287D+05    |proj g|=  6.91783D+00\n",
-      "\n",
-      "At iterate   14    f=  7.31287D+05    |proj g|=  6.89829D+00\n",
-      "\n",
-      "At iterate   15    f=  7.31287D+05    |proj g|=  6.89835D+00\n",
-      "\n",
-      "At iterate   16    f=  7.31287D+05    |proj g|=  6.12573D+00\n",
-      "\n",
-      "At iterate   17    f=  7.31287D+05    |proj g|=  1.05089D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     17     31     19     0     0   1.051D+00   7.313D+05\n",
-      "  F =   731286.97671517858     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  5.42643D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23966D+05    |proj g|=  6.96698D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23705D+05    |proj g|=  6.91704D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89908D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  2.05885D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   2.059D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  5.39364D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23966D+05    |proj g|=  6.96699D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23705D+05    |proj g|=  6.91704D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89908D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  2.05985D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   2.060D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  5.36936D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23968D+05    |proj g|=  6.96715D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23705D+05    |proj g|=  6.91712D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89909D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  2.09840D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   2.098D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  5.37698D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.23039D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.23971D+05    |proj g|=  6.96739D+00\n",
-      "\n",
-      "At iterate    3    f=  7.23706D+05    |proj g|=  6.91724D+00\n",
-      "\n",
-      "At iterate    4    f=  7.23659D+05    |proj g|=  6.89911D+00\n",
-      "\n",
-      "At iterate    5    f=  7.23659D+05    |proj g|=  6.91785D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.23659D+05    |proj g|=  2.15845D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   2.158D-02   7.237D+05\n",
-      "  F =   723658.65193438367     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  5.47557D+06    |proj g|=  9.21034D+00\n",
-      "\n",
-      "At iterate    1    f=  2.27413D+06    |proj g|=  1.38155D+01\n",
-      "\n",
-      "At iterate    2    f=  7.38164D+05    |proj g|=  6.96722D+00\n",
-      "\n",
-      "At iterate    3    f=  7.37896D+05    |proj g|=  6.91716D+00\n",
-      "\n",
-      "At iterate    4    f=  7.37848D+05    |proj g|=  6.89910D+00\n",
-      "\n",
-      "At iterate    5    f=  7.37848D+05    |proj g|=  6.91785D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jhlegarreta/.virtualenvs/eddymotion/lib/python3.10/site-packages/sklearn/gaussian_process/kernels.py:445: ConvergenceWarning: The optimal value found for dimension 0 of parameter beta_a is close to the specified lower bound 0.1. Decreasing the bound and calling fit again may find a better value.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    6    f=  7.37848D+05    |proj g|=  2.15751D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2      6     22      7     0     1   2.158D-02   7.378D+05\n",
-      "  F =   737848.03726643079     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.67246D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.05370D+05    |proj g|=  8.59454D+00\n",
-      "\n",
-      "At iterate    2    f=  7.93673D+05    |proj g|=  8.45570D+00\n",
-      "\n",
-      "At iterate    3    f=  7.86233D+05    |proj g|=  8.44013D+00\n",
-      "\n",
-      "At iterate    4    f=  7.83307D+05    |proj g|=  8.43184D+00\n",
-      "\n",
-      "At iterate    5    f=  7.79600D+05    |proj g|=  8.39439D+00\n",
-      "\n",
-      "At iterate    6    f=  7.79094D+05    |proj g|=  8.39220D+00\n",
-      "\n",
-      "At iterate    7    f=  7.76140D+05    |proj g|=  8.31471D+00\n",
-      "\n",
-      "At iterate    8    f=  7.72357D+05    |proj g|=  8.12355D+00\n",
-      "\n",
-      "At iterate    9    f=  7.72270D+05    |proj g|=  8.13581D+00\n",
-      "\n",
-      "At iterate   10    f=  7.72143D+05    |proj g|=  5.71824D+00\n",
-      "\n",
-      "At iterate   11    f=  7.72139D+05    |proj g|=  5.72605D+00\n",
-      "\n",
-      "At iterate   12    f=  7.72132D+05    |proj g|=  5.27579D+00\n",
-      "\n",
-      "At iterate   13    f=  7.72132D+05    |proj g|=  8.09885D+00\n",
-      "\n",
-      "At iterate   14    f=  7.72132D+05    |proj g|=  3.58664D+00\n",
-      "\n",
-      "At iterate   15    f=  7.72132D+05    |proj g|=  4.12909D-01\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     15     21     16     0     0   4.129D-01   7.721D+05\n",
-      "  F =   772131.57504821080     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.65785D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.06921D+05    |proj g|=  8.58986D+00\n",
-      "\n",
-      "At iterate    2    f=  7.94113D+05    |proj g|=  8.44316D+00\n",
-      "\n",
-      "At iterate    3    f=  7.82970D+05    |proj g|=  8.41911D+00\n",
-      "\n",
-      "At iterate    4    f=  7.79739D+05    |proj g|=  8.39908D+00\n",
-      "\n",
-      "At iterate    5    f=  7.79120D+05    |proj g|=  8.38395D+00\n",
-      "\n",
-      "At iterate    6    f=  7.77640D+05    |proj g|=  8.33776D+00\n",
-      "\n",
-      "At iterate    7    f=  7.75123D+05    |proj g|=  5.65579D+00\n",
-      "\n",
-      "At iterate    8    f=  7.74933D+05    |proj g|=  5.79235D+00\n",
-      "\n",
-      "At iterate    9    f=  7.73943D+05    |proj g|=  5.84809D+00\n",
-      "\n",
-      "At iterate   10    f=  7.72319D+05    |proj g|=  8.08401D+00\n",
-      "\n",
-      "At iterate   11    f=  7.72270D+05    |proj g|=  8.10031D+00\n",
-      "\n",
-      "At iterate   12    f=  7.72264D+05    |proj g|=  8.09639D+00\n",
-      "\n",
-      "At iterate   13    f=  7.72264D+05    |proj g|=  5.72206D+00\n",
-      "\n",
-      "At iterate   14    f=  7.72264D+05    |proj g|=  5.72246D+00\n",
-      "\n",
-      "At iterate   15    f=  7.72264D+05    |proj g|=  2.72379D+00\n",
-      "\n",
-      "At iterate   16    f=  7.72264D+05    |proj g|=  4.80516D+00\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     16     21     17     0     0   4.805D+00   7.723D+05\n",
-      "  F =   772263.56102596270     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.65010D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.05251D+05    |proj g|=  8.58763D+00\n",
-      "\n",
-      "At iterate    2    f=  7.90267D+05    |proj g|=  8.44090D+00\n",
-      "\n",
-      "At iterate    3    f=  7.80465D+05    |proj g|=  8.41894D+00\n",
-      "\n",
-      "At iterate    4    f=  7.78623D+05    |proj g|=  8.38258D+00\n",
-      "\n",
-      "At iterate    5    f=  7.77485D+05    |proj g|=  8.38786D+00\n",
-      "\n",
-      "At iterate    6    f=  7.75156D+05    |proj g|=  8.32756D+00\n",
-      "\n",
-      "At iterate    7    f=  7.73379D+05    |proj g|=  8.27125D+00\n",
-      "\n",
-      "At iterate    8    f=  7.72138D+05    |proj g|=  8.22278D+00\n",
-      "\n",
-      "At iterate    9    f=  7.70865D+05    |proj g|=  8.15223D+00\n",
-      "\n",
-      "At iterate   10    f=  7.70367D+05    |proj g|=  5.72297D+00\n",
-      "\n",
-      "At iterate   11    f=  7.70363D+05    |proj g|=  5.75719D+00\n",
-      "\n",
-      "At iterate   12    f=  7.70297D+05    |proj g|=  5.74445D+00\n",
-      "\n",
-      "At iterate   13    f=  7.70261D+05    |proj g|=  5.72885D+00\n",
-      "\n",
-      "At iterate   14    f=  7.70260D+05    |proj g|=  8.08915D+00\n",
-      "\n",
-      "At iterate   15    f=  7.70260D+05    |proj g|=  2.48853D+00\n",
-      "\n",
-      "At iterate   16    f=  7.70260D+05    |proj g|=  4.51512D-01\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     16     25     18     0     0   4.515D-01   7.703D+05\n",
-      "  F =   770260.40219358355     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.65518D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.05091D+05    |proj g|=  8.58931D+00\n",
-      "\n",
-      "At iterate    2    f=  7.92902D+05    |proj g|=  8.44556D+00\n",
-      "\n",
-      "At iterate    3    f=  7.85838D+05    |proj g|=  8.43163D+00\n",
-      "\n",
-      "At iterate    4    f=  7.82649D+05    |proj g|=  8.42338D+00\n",
-      "\n",
-      "At iterate    5    f=  7.78607D+05    |proj g|=  8.38534D+00\n",
-      "\n",
-      "At iterate    6    f=  7.78067D+05    |proj g|=  8.38289D+00\n",
-      "\n",
-      "At iterate    7    f=  7.74956D+05    |proj g|=  8.30304D+00\n",
-      "\n",
-      "At iterate    8    f=  7.71309D+05    |proj g|=  8.13716D+00\n",
-      "\n",
-      "At iterate    9    f=  7.71005D+05    |proj g|=  8.11919D+00\n",
-      "\n",
-      "At iterate   10    f=  7.70983D+05    |proj g|=  5.74874D+00\n",
-      "\n",
-      "At iterate   11    f=  7.70920D+05    |proj g|=  5.72889D+00\n",
-      "\n",
-      "At iterate   12    f=  7.70919D+05    |proj g|=  8.09015D+00\n",
-      "\n",
-      "At iterate   13    f=  7.70919D+05    |proj g|=  8.53290D-01\n",
-      "\n",
-      "At iterate   14    f=  7.70919D+05    |proj g|=  1.92460D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     14     20     16     0     0   1.925D-02   7.709D+05\n",
-      "  F =   770918.73107949912     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.68299D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  9.22966D+05    |proj g|=  8.58775D+00\n",
-      "\n",
-      "At iterate    2    f=  8.04756D+05    |proj g|=  8.44012D+00\n",
-      "\n",
-      "At iterate    3    f=  7.93080D+05    |proj g|=  8.41035D+00\n",
-      "\n",
-      "At iterate    4    f=  7.92176D+05    |proj g|=  8.39009D+00\n",
-      "\n",
-      "At iterate    5    f=  7.91645D+05    |proj g|=  8.38014D+00\n",
-      "\n",
-      "At iterate    6    f=  7.87514D+05    |proj g|=  8.26959D+00\n",
-      "\n",
-      "At iterate    7    f=  7.84861D+05    |proj g|=  8.10394D+00\n",
-      "\n",
-      "At iterate    8    f=  7.84810D+05    |proj g|=  8.12825D+00\n",
-      "\n",
-      "At iterate    9    f=  7.84691D+05    |proj g|=  8.10053D+00\n",
-      "\n",
-      "At iterate   10    f=  7.84684D+05    |proj g|=  5.72978D+00\n",
-      "\n",
-      "At iterate   11    f=  7.84680D+05    |proj g|=  5.72422D+00\n",
-      "\n",
-      "At iterate   12    f=  7.84680D+05    |proj g|=  1.12844D+00\n",
-      "\n",
-      "At iterate   13    f=  7.84680D+05    |proj g|=  1.83845D-01\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     13     17     15     0     0   1.838D-01   7.847D+05\n",
-      "  F =   784680.17670340673     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  1.37170D+06    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  1.10457D+06    |proj g|=  8.30646D+00\n",
-      "\n",
-      "At iterate    2    f=  9.64302D+05    |proj g|=  7.83671D+00\n",
-      "\n",
-      "At iterate    3    f=  9.62637D+05    |proj g|=  7.83024D+00\n",
-      "\n",
-      "At iterate    4    f=  9.62178D+05    |proj g|=  5.98021D+00\n",
-      "\n",
-      "At iterate    5    f=  9.62166D+05    |proj g|=  5.97630D+00\n",
-      "\n",
-      "At iterate    6    f=  9.62148D+05    |proj g|=  7.85827D+00\n",
-      "\n",
-      "At iterate    7    f=  9.62145D+05    |proj g|=  7.85437D+00\n",
-      "\n",
-      "At iterate    8    f=  9.62145D+05    |proj g|=  5.96242D+00\n",
-      "\n",
-      "At iterate    9    f=  9.62145D+05    |proj g|=  1.34110D+00\n",
-      "\n",
-      "At iterate   10    f=  9.62145D+05    |proj g|=  3.49965D-02\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     10     17     11     0     0   3.500D-02   9.621D+05\n",
-      "  F =   962145.18997697858     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
-      "Best parameters found: {'alpha': 0.01, 'kernel__beta_a': 0.7853981633974483, 'kernel__beta_l': 0.47619047619047616}\n",
-      "Best cross-validation score: 1011.0595625257647\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "from sklearn.model_selection import GridSearchCV, KFold\n",
+    "x_slice = dwi_sim[slice_idx[0], :, :]\n",
+    "y_slice = dwi_sim[:, slice_idx[1], :]\n",
+    "z_slice = dwi_sim[:, :, slice_idx[2]]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
     "\n",
-    "# Define the hyperparameter grid\n",
-    "param_grid = {\n",
-    "    \"kernel__beta_a\": np.linspace(np.pi/4, np.pi/2, 2),\n",
-    "    \"kernel__beta_l\": np.linspace(0.1, 1 / 2.1, 2),\n",
-    "    \"alpha\": np.linspace(1e-3, 1e-2, 2)\n",
-    "}\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
+    "     axes[i].set_axis_off()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "x_slice = dwi_data[slice_idx[0], :, :, dwi_vol_idx]\n",
+    "y_slice = dwi_data[:, slice_idx[1], :, dwi_vol_idx]\n",
+    "z_slice = dwi_data[:, :, slice_idx[2], dwi_vol_idx]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "    axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
+    "    axes[i].set_axis_off()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "x_slice = dwi_sim2[slice_idx[0], :, :]\n",
+    "y_slice = dwi_sim2[:, slice_idx[1], :]\n",
+    "z_slice = dwi_sim2[:, :, slice_idx[2]]\n",
+    "slices = [x_slice, y_slice, z_slice]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(slices))\n",
+    "for i, _slice in enumerate(slices):\n",
+    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
+    "     axes[i].set_axis_off()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c77d954",
+   "metadata": {},
+   "source": [
+    "I'm not sure the cross-validation effort and the rest is worth trying right now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4697b2182f5178d7",
+   "metadata": {},
+   "source": [
+    "## Cross-validation\n",
     "\n",
-    "optimizer = \"fmin_l_bfgs_b\"\n",
-    "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=optimizer)\n",
+    "Use a k-fold cross-validation and a grid search to find the best parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e92ebf1204c7f889",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is doubling-down the parameter optimization\n",
+    "# from sklearn.model_selection import GridSearchCV, KFold\n",
+    "\n",
+    "# Define the hyperparameter grid\n",
+    "# param_grid = {\n",
+    "#     \"kernel__beta_a\": np.linspace(np.pi/4, np.pi/2, 2),\n",
+    "#     \"kernel__beta_l\": np.linspace(0.1, 1 / 2.1, 2),\n",
+    "#     \"alpha\": np.linspace(1e-3, 1e-2, 2)\n",
+    "# }\n",
     "\n",
     "# Define k-fold cross-validation\n",
-    "cv = KFold(n_splits=5, shuffle=True, random_state=seed)\n",
+    "# cv = KFold(n_splits=5, shuffle=True, random_state=seed)\n",
     "\n",
-    "# Perform grid search with cross-validation\n",
-    "grid_search = GridSearchCV(estimator=gpr, param_grid=param_grid, cv=cv, scoring=\"neg_mean_squared_error\")\n",
-    "grid_search.fit(X_train, sampled_dwi)\n",
+    "# # Perform grid search with cross-validation\n",
+    "# grid_search = GridSearchCV(estimator=gpr, param_grid=param_grid, cv=cv, scoring=\"neg_mean_squared_error\")\n",
+    "# grid_search.fit(X_train, sampled_dwi)\n",
     "\n",
-    "print(f\"Best parameters found: {grid_search.best_params_}\")\n",
-    "print(f\"Best cross-validation score: {-grid_search.best_score_}\")"
+    "# print(f\"Best parameters found: {grid_search.best_params_}\")\n",
+    "# print(f\"Best cross-validation score: {-grid_search.best_score_}\")"
    ]
   },
   {
@@ -3147,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "b7e1ca2ccc899189",
    "metadata": {},
    "outputs": [],
@@ -3166,94 +612,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "ba7e22252116b374",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RUNNING THE L-BFGS-B CODE\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Machine precision = 2.220D-16\n",
-      " N =            2     M =           10\n",
-      "\n",
-      "At X0         0 variables are exactly at the bounds\n",
-      "\n",
-      "At iterate    0    f=  2.51647D+07    |proj g|=  7.64969D+00\n",
-      "\n",
-      "At iterate    1    f=  1.78650D+07    |proj g|=  8.43930D+00\n",
-      "\n",
-      "At iterate    2    f=  1.61450D+07    |proj g|=  7.97417D+00\n",
-      "\n",
-      "At iterate    3    f=  1.56409D+07    |proj g|=  7.61023D+00\n",
-      "\n",
-      "At iterate    4    f=  1.53457D+07    |proj g|=  7.20265D+00\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Bad direction in the line search;\n",
-      "   refresh the lbfgs memory and restart the iteration.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "At iterate    5    f=  1.44974D+07    |proj g|=  6.81551D+00\n",
-      "\n",
-      "At iterate    6    f=  1.43453D+07    |proj g|=  6.87952D+00\n",
-      "\n",
-      "At iterate    7    f=  1.42177D+07    |proj g|=  7.00259D+00\n",
-      "\n",
-      "At iterate    8    f=  1.41897D+07    |proj g|=  6.94118D+00\n",
-      "\n",
-      "At iterate    9    f=  1.41844D+07    |proj g|=  6.92042D+00\n",
-      "\n",
-      "At iterate   10    f=  1.41830D+07    |proj g|=  6.89929D+00\n",
-      "\n",
-      "At iterate   11    f=  1.41824D+07    |proj g|=  6.89793D+00\n",
-      "\n",
-      "At iterate   12    f=  1.41820D+07    |proj g|=  6.89811D+00\n",
-      "\n",
-      "At iterate   13    f=  1.41818D+07    |proj g|=  6.91827D+00\n",
-      "\n",
-      "At iterate   14    f=  1.41818D+07    |proj g|=  6.89926D+00\n",
-      "\n",
-      "At iterate   15    f=  1.41818D+07    |proj g|=  6.91796D+00\n",
-      "\n",
-      "At iterate   16    f=  1.41818D+07    |proj g|=  1.70266D+00\n",
-      "\n",
-      "At iterate   17    f=  1.41818D+07    |proj g|=  3.99657D-04\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "Tit   = total number of iterations\n",
-      "Tnf   = total number of function evaluations\n",
-      "Tnint = total number of segments explored during Cauchy searches\n",
-      "Skip  = number of BFGS updates skipped\n",
-      "Nact  = number of active bounds at final generalized Cauchy point\n",
-      "Projg = norm of the final projected gradient\n",
-      "F     = final function value\n",
-      "\n",
-      "           * * *\n",
-      "\n",
-      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
-      "    2     17     50     20     0     0   3.997D-04   1.418D+07\n",
-      "  F =   14181772.726816090     \n",
-      "\n",
-      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "idx = rng.integers(0, len(indices))\n",
     "idx_mask = np.zeros(len(indices), dtype=bool)\n",
@@ -3279,31 +641,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "e5c8e303b4639b8c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "x_slice = shell_data[..., idx][slice_idx[0], :, :]\n",
     "y_slice = shell_data[..., idx][:, slice_idx[1], :]\n",
@@ -3347,29 +688,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "fd54cb47f4e2fbf3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RMSE: 20.108829839123334\n",
-      "Number of RMSE values above 10: 133201 (13.549906412760418%)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Multiply the data by the brain mask to remove spurious values that were not predicted\n",
     "rmse = np.sqrt(np.mean(np.square(shell_data[..., idx]*brain_mask - _y_pred)))\n",
@@ -3415,7 +737,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

From 82749c4bcd89553e52dddb596ba79a6444ecb651 Mon Sep 17 00:00:00 2001
From: Oscar Esteban <code@oscaresteban.es>
Date: Wed, 6 Nov 2024 11:03:30 +0100
Subject: [PATCH 03/17] enh: use nireports for plotting

---
 docs/notebooks/dwi_gp_estimation.ipynb | 266 +++++++++----------------
 1 file changed, 96 insertions(+), 170 deletions(-)

diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
index 68cd8189..777f288d 100644
--- a/docs/notebooks/dwi_gp_estimation.ipynb
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "69a3bc6b4fbe7036",
+   "id": "8fcb3c77",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,6 +33,16 @@
     "from scipy.ndimage import binary_dilation\n",
     "from skimage.morphology import ball\n",
     "\n",
+    "from nireports.reportlets.modality.dwi import plot_dwi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "69a3bc6b4fbe7036",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "seed = 1234\n",
     "rng = np.random.default_rng(seed)\n",
     "\n",
@@ -52,6 +62,39 @@
     "shell_data = dwi_data[..., indices]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b3015aaf",
+   "metadata": {},
+   "source": [
+    "Let's leave one direction out:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4ea84424",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dwi_vol_idx = len(indices) // 2\n",
+    "\n",
+    "# Prepare a train/test mask (False for all directions except the left-out where it's true)\n",
+    "train_test_mask = np.zeros(bvecs_shell.shape[0], dtype=bool)\n",
+    "train_test_mask[dwi_vol_idx] = True\n",
+    "\n",
+    "# Generate train/test bvecs\n",
+    "X_train = bvecs_shell[~train_test_mask, :]\n",
+    "X_test = bvecs_shell[train_test_mask, :]\n",
+    "\n",
+    "# Select voxels within brain mask\n",
+    "y = shell_data[brain_mask]\n",
+    "\n",
+    "# Generate train/test data\n",
+    "y_train = y[:, ~train_test_mask]\n",
+    "y_test = y[:, train_test_mask]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9bd417117afaad49",
@@ -62,15 +105,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "d8547475686958f3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<nilearn.plotting.displays._slicers.OrthoSlicer at 0x7f0d03563e10>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -78,26 +131,8 @@
     }
    ],
    "source": [
-    "# Plot a slice\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "dwi_vol_idx = len(indices) // 2\n",
-    "slice_idx = list(map(int, np.divide(dwi_data.shape[:-1], 2)))\n",
-    "\n",
-    "x_slice = dwi_data[slice_idx[0], :, :, dwi_vol_idx]\n",
-    "y_slice = dwi_data[:, slice_idx[1], :, dwi_vol_idx]\n",
-    "z_slice = dwi_data[:, :, slice_idx[2], dwi_vol_idx]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "    axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
-    "    axes[i].set_axis_off()\n",
-    "\n",
-    "plt.show()"
+    "affine = nib.load(dwi_fname).affine\n",
+    "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))"
    ]
   },
   {
@@ -110,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "7d5d9562339bc849",
    "metadata": {},
    "outputs": [],
@@ -127,48 +162,18 @@
     "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=optimizer)\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b3015aaf",
-   "metadata": {},
-   "source": [
-    "Let's leave one direction out:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4ea84424",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Prepare a train/test mask (False for all directions except the left-out where it's true)\n",
-    "train_test_mask = np.zeros(bvecs_shell.shape[0], dtype=bool)\n",
-    "train_test_mask[dwi_vol_idx] = True\n",
-    "\n",
-    "# Generate train/test bvecs\n",
-    "X_train = bvecs_shell[~train_test_mask, :]\n",
-    "X_test = bvecs_shell[train_test_mask, :]\n",
-    "\n",
-    "# Select voxels within brain mask\n",
-    "y = shell_data[brain_mask]\n",
-    "\n",
-    "# Generate train/test data\n",
-    "y_train = y[:, ~train_test_mask]\n",
-    "y_test = y[:, train_test_mask]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "ea5cc8036fa0ab48",
    "metadata": {},
    "source": [
-    "Do not optimize the parameters in the fitting. "
+    "1. Fit and do not optimize the parameters in the fitting.\n",
+    "2. Predict on a single diffusion-encoding gradient direction."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "09068d0f",
    "metadata": {},
    "outputs": [],
@@ -191,25 +196,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "7e93b99c3b072d99",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<nilearn.plotting.displays._slicers.OrthoSlicer at 0x7f0d0235db90>"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<Figure size 660x350 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -217,43 +222,12 @@
     }
    ],
    "source": [
-    "\n",
-    "x_slice = dwi_sim[slice_idx[0], :, :]\n",
-    "y_slice = dwi_sim[:, slice_idx[1], :]\n",
-    "z_slice = dwi_sim[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "x_slice = dwi_data[slice_idx[0], :, :, dwi_vol_idx]\n",
-    "y_slice = dwi_data[:, slice_idx[1], :, dwi_vol_idx]\n",
-    "z_slice = dwi_data[:, :, slice_idx[2], dwi_vol_idx]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "    axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
-    "    axes[i].set_axis_off()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfdd82afbdb22790",
-   "metadata": {},
-   "source": [
-    "Predict on a randomly picked diffusion-encoding gradient direction."
+    "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "ae3407b31b14928d",
    "metadata": {},
    "outputs": [
@@ -276,31 +250,6 @@
     "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio:.2f}%)\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8ea9cf5ae629f489",
-   "metadata": {},
-   "source": [
-    "Select a number of brain voxels at random to perform the GP parameter optimization. The same voxels will be selected across each DWI volume."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "24a72a024e3f2e37",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_voxels = 10000\n",
-    "true_indices = np.argwhere(brain_mask)\n",
-    "\n",
-    "# Sample indices randomly\n",
-    "num_samples = min(n_voxels, len(true_indices))\n",
-    "sampled_indices = true_indices[np.random.choice(len(true_indices), num_samples, replace=False)]\n",
-    "\n",
-    "sampled_dwi = shell_data[sampled_indices[:, 0], sampled_indices[:, 1], sampled_indices[:, 2], :].T"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5c92d1b8",
@@ -311,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "id": "06375052",
    "metadata": {},
    "outputs": [
@@ -369,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "20a0f4ee",
    "metadata": {},
    "outputs": [
@@ -380,7 +329,7 @@
        " SphericalKriging (a=1.38, λ=0.47619047619047616))"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -399,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "fa17538d",
    "metadata": {},
    "outputs": [],
@@ -422,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "ad5a4554",
    "metadata": {},
    "outputs": [
@@ -455,15 +404,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "90129831",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<nilearn.plotting.displays._slicers.OrthoSlicer at 0x7f0cec6b2050>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -471,9 +430,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<Figure size 660x350 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -481,9 +440,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
+       "<Figure size 660x350 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -491,42 +450,9 @@
     }
    ],
    "source": [
-    "x_slice = dwi_sim[slice_idx[0], :, :]\n",
-    "y_slice = dwi_sim[:, slice_idx[1], :]\n",
-    "z_slice = dwi_sim[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "x_slice = dwi_data[slice_idx[0], :, :, dwi_vol_idx]\n",
-    "y_slice = dwi_data[:, slice_idx[1], :, dwi_vol_idx]\n",
-    "z_slice = dwi_data[:, :, slice_idx[2], dwi_vol_idx]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "    axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
-    "    axes[i].set_axis_off()\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "x_slice = dwi_sim2[slice_idx[0], :, :]\n",
-    "y_slice = dwi_sim2[:, slice_idx[1], :]\n",
-    "z_slice = dwi_sim2[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect='equal')\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.show()"
+    "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
+    "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
+    "plot_dwi(dwi_sim2, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))"
    ]
   },
   {

From 6dc7e898b8288896db033fa72ac88b8410751514 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 9 Nov 2024 13:40:26 -0500
Subject: [PATCH 04/17] ENH: Save the DWI GP estimation notebook figures

Save the DWI GP estimation notebook figures.
---
 docs/notebooks/dwi_gp_estimation.ipynb | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
index 777f288d..b4e01e00 100644
--- a/docs/notebooks/dwi_gp_estimation.ipynb
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -132,7 +132,7 @@
    ],
    "source": [
     "affine = nib.load(dwi_fname).affine\n",
-    "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))"
+    "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_data.svg\")"
    ]
   },
   {
@@ -221,9 +221,7 @@
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))"
-   ]
+   "source": "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_gp_noopt_pred.svg\")"
   },
   {
    "cell_type": "code",
@@ -452,7 +450,7 @@
    "source": [
     "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
     "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
-    "plot_dwi(dwi_sim2, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))"
+    "plot_dwi(dwi_sim2, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_gp_opt_pred.svg\")"
    ]
   },
   {

From bca7e8a5ee47949fa5f13cbd917a6415da2887f0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 9 Nov 2024 13:41:45 -0500
Subject: [PATCH 05/17] ENH: Remove cross-validation and RMSE computation/plot
 from GP notebook

Remove the cross-validation and RMSE computation and plot cells from the
GP estimation notebook.
---
 docs/notebooks/dwi_gp_estimation.ipynb | 191 -------------------------
 1 file changed, 191 deletions(-)

diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
index b4e01e00..2cce4b3e 100644
--- a/docs/notebooks/dwi_gp_estimation.ipynb
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -452,197 +452,6 @@
     "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
     "plot_dwi(dwi_sim2, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_gp_opt_pred.svg\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c77d954",
-   "metadata": {},
-   "source": [
-    "I'm not sure the cross-validation effort and the rest is worth trying right now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4697b2182f5178d7",
-   "metadata": {},
-   "source": [
-    "## Cross-validation\n",
-    "\n",
-    "Use a k-fold cross-validation and a grid search to find the best parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e92ebf1204c7f889",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is doubling-down the parameter optimization\n",
-    "# from sklearn.model_selection import GridSearchCV, KFold\n",
-    "\n",
-    "# Define the hyperparameter grid\n",
-    "# param_grid = {\n",
-    "#     \"kernel__beta_a\": np.linspace(np.pi/4, np.pi/2, 2),\n",
-    "#     \"kernel__beta_l\": np.linspace(0.1, 1 / 2.1, 2),\n",
-    "#     \"alpha\": np.linspace(1e-3, 1e-2, 2)\n",
-    "# }\n",
-    "\n",
-    "# Define k-fold cross-validation\n",
-    "# cv = KFold(n_splits=5, shuffle=True, random_state=seed)\n",
-    "\n",
-    "# # Perform grid search with cross-validation\n",
-    "# grid_search = GridSearchCV(estimator=gpr, param_grid=param_grid, cv=cv, scoring=\"neg_mean_squared_error\")\n",
-    "# grid_search.fit(X_train, sampled_dwi)\n",
-    "\n",
-    "# print(f\"Best parameters found: {grid_search.best_params_}\")\n",
-    "# print(f\"Best cross-validation score: {-grid_search.best_score_}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9249e76858c166e7",
-   "metadata": {},
-   "source": [
-    "Train the GP leaving out a randomly picked diffusion-encoding gradient direction and predict on it using the optimized parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83166ebc22702475",
-   "metadata": {},
-   "source": [
-    "Define the GP instances with the optimized hiperparameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b7e1ca2ccc899189",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kernel = SphericalKriging(beta_a=grid_search.best_params_[\"kernel__beta_a\"], beta_l=grid_search.best_params_[\"kernel__beta_l\"])\n",
-    "gpr = EddyMotionGPR(kernel=kernel, alpha=grid_search.best_params_[\"alpha\"], disp=disp, optimizer=optimizer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ebe09c2e2fef822",
-   "metadata": {},
-   "source": [
-    "Pick a random diffusion-encoding gradient direction and call fit/predict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba7e22252116b374",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx = rng.integers(0, len(indices))\n",
-    "idx_mask = np.zeros(len(indices), dtype=bool)\n",
-    "idx_mask[idx] = True\n",
-    "\n",
-    "X_train = bvecs_shell[~idx_mask]\n",
-    "_dwi_mask = np.repeat(brain_mask[..., np.newaxis], X_train.shape[0], axis=-1)\n",
-    "y_train = shell_data[..., ~idx_mask][_dwi_mask].reshape((X_train.shape[0], -1))\n",
-    "\n",
-    "gpr_fit = gpr.fit(X_train, y_train)\n",
-    "\n",
-    "X_test = bvecs_shell[idx_mask]\n",
-    "y_pred = gpr_fit.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc93122f8370d2cf",
-   "metadata": {},
-   "source": [
-    "Plot the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e5c8e303b4639b8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_slice = shell_data[..., idx][slice_idx[0], :, :]\n",
-    "y_slice = shell_data[..., idx][:, slice_idx[1], :]\n",
-    "z_slice = shell_data[..., idx][:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect=\"equal\")\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.suptitle(\"Data\")\n",
-    "plt.show()\n",
-    "\n",
-    "# Reshape the predicted data array to the image shape\n",
-    "brain_mask_idx = np.where(brain_mask)\n",
-    "_y_pred = np.zeros((shell_data.shape[:-1]), dtype=y_train.dtype)\n",
-    "_y_pred[brain_mask_idx] = y_pred.squeeze()\n",
-    "\n",
-    "x_slice = _y_pred[slice_idx[0], :, :]\n",
-    "y_slice = _y_pred[:, slice_idx[1], :]\n",
-    "z_slice = _y_pred[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect=\"equal\")\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.suptitle(\"GP prediction\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e9c1fd2f4d2bfd8",
-   "metadata": {},
-   "source": [
-    "Compute the RMSE and plot it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fd54cb47f4e2fbf3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Multiply the data by the brain mask to remove spurious values that were not predicted\n",
-    "rmse = np.sqrt(np.mean(np.square(shell_data[..., idx]*brain_mask - _y_pred)))\n",
-    "_rmse_element = np.sqrt(np.square(shell_data[..., idx]*brain_mask - _y_pred))\n",
-    "\n",
-    "print(f\"RMSE: {rmse}\")\n",
-    "threshold = 10\n",
-    "n_error_thr = len(_rmse_element[_rmse_element > threshold])\n",
-    "ratio = n_error_thr / np.prod(_rmse_element.shape) * 100\n",
-    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio}%)\")\n",
-    "\n",
-    "# Plot the RSME\n",
-    "x_slice = _rmse_element[slice_idx[0], :, :]\n",
-    "y_slice = _rmse_element[:, slice_idx[1], :]\n",
-    "z_slice = _rmse_element[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "images = []\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     images.append(axes[i].imshow(_slice.T, cmap=\"viridis\", origin=\"lower\", aspect=\"equal\"))\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.colorbar(images[-1])\n",
-    "plt.suptitle(\"RMSE\")\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {

From cbff151f3b03e966f52497291a116b49ad67faf6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 9 Nov 2024 13:47:15 -0500
Subject: [PATCH 06/17] STYLE: Lint DWI GP estimation notebook

Lint DWI GP estimation notebook.

Take advantage of the commit to remove the unused random number
generator.
---
 docs/notebooks/dwi_gp_estimation.ipynb | 45 +++++++++++++++-----------
 1 file changed, 26 insertions(+), 19 deletions(-)

diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
index 2cce4b3e..4ef57d3a 100644
--- a/docs/notebooks/dwi_gp_estimation.ipynb
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -29,23 +29,9 @@
     "from dipy.core.gradients import get_bval_indices\n",
     "from dipy.io import read_bvals_bvecs\n",
     "from dipy.segment.mask import median_otsu\n",
-    "\n",
     "from scipy.ndimage import binary_dilation\n",
     "from skimage.morphology import ball\n",
     "\n",
-    "from nireports.reportlets.modality.dwi import plot_dwi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "69a3bc6b4fbe7036",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seed = 1234\n",
-    "rng = np.random.default_rng(seed)\n",
-    "\n",
     "name = \"sherbrooke_3shell\"\n",
     "\n",
     "dwi_fname, bval_fname, bvec_fname = dpd.get_fnames(name=name)\n",
@@ -131,8 +117,15 @@
     }
    ],
    "source": [
+    "from nireports.reportlets.modality.dwi import plot_dwi\n",
+    "\n",
     "affine = nib.load(dwi_fname).affine\n",
-    "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_data.svg\")"
+    "plot_dwi(\n",
+    "    shell_data[..., dwi_vol_idx],\n",
+    "    affine,\n",
+    "    gradient=np.concatenate((np.squeeze(X_test), [1000])),\n",
+    "    output_file=\"sherbrooke_3shell_b1k_data.svg\",\n",
+    ")"
    ]
   },
   {
@@ -159,7 +152,7 @@
     "alpha = 0.1\n",
     "disp = True\n",
     "optimizer = None\n",
-    "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=optimizer)\n"
+    "gpr = EddyMotionGPR(kernel=kernel, alpha=alpha, disp=disp, optimizer=optimizer)"
    ]
   },
   {
@@ -221,7 +214,14 @@
      "output_type": "display_data"
     }
    ],
-   "source": "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_gp_noopt_pred.svg\")"
+   "source": [
+    "plot_dwi(\n",
+    "    dwi_sim,\n",
+    "    affine,\n",
+    "    gradient=np.concatenate((np.squeeze(X_test), [1000])),\n",
+    "    output_file=\"sherbrooke_3shell_b1k_gp_noopt_pred.svg\",\n",
+    ")"
+   ]
   },
   {
    "cell_type": "code",
@@ -449,8 +449,15 @@
    ],
    "source": [
     "plot_dwi(dwi_sim, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
-    "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
-    "plot_dwi(dwi_sim2, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_gp_opt_pred.svg\")"
+    "plot_dwi(\n",
+    "    shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000]))\n",
+    ")\n",
+    "plot_dwi(\n",
+    "    dwi_sim2,\n",
+    "    affine,\n",
+    "    gradient=np.concatenate((np.squeeze(X_test), [1000])),\n",
+    "    output_file=\"sherbrooke_3shell_b1k_gp_opt_pred.svg\",\n",
+    ")"
    ]
   }
  ],

From b89edf5a62c24f8858289dffdd26860cb1036450 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 18:56:06 -0500
Subject: [PATCH 07/17] DOC: Fix typos in GPR model documentation

Fix typos in GPR model documentation.
---
 src/eddymotion/model/gpr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index 6798d4bd..f738b9dd 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -80,10 +80,10 @@ class EddyMotionGPR(GaussianProcessRegressor):
 
     In principle, Scikit-Learn's implementation normalizes the training data
     as in [Andersson15]_ (see
-    `FSL's souce code <https://git.fmrib.ox.ac.uk/fsl/eddy/-/blob/2480dda293d4cec83014454db3a193b87921f6b0/DiffusionGP.cpp#L218>`__).
+    `FSL's source code <https://git.fmrib.ox.ac.uk/fsl/eddy/-/blob/2480dda293d4cec83014454db3a193b87921f6b0/DiffusionGP.cpp#L218>`__).
     From their paper (p. 167, end of first column):
 
-        Typically one just substracts the mean (:math:`\bar{\mathbf{f}}`)
+        Typically one just subtracts the mean (:math:`\bar{\mathbf{f}}`)
         from :math:`\mathbf{f}` and then add it back to
         :math:`f^{*}`, which is analogous to what is often done in
         "traditional" regression.

From 57e2eb60800f9dd4644855ae0bcbfcc7b62b12be Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 18:56:58 -0500
Subject: [PATCH 08/17] DOC: Expand GP acronym when introduced for the first
 time in GPR model

Expand GP acronym when introduced for the first time in GPR model
module.
---
 src/eddymotion/model/gpr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index f738b9dd..16949cfd 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -64,7 +64,7 @@
 
 class EddyMotionGPR(GaussianProcessRegressor):
     r"""
-    A GP regressor specialized for eddymotion.
+    A Gaussian process (GP) regressor specialized for eddymotion.
 
     This specialization of the default GP regressor is created to allow
     the following extended behaviors:

From bb212ab737d01f441bb190b601d6974346d1b1c7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 18:58:07 -0500
Subject: [PATCH 09/17] DOC: Prefer impersonal style in `EddyMotionGPR` hpm
 optimization comment

Prefer impersonal style in `EddyMotionGPR` hyperparameter optimization
comment.
---
 src/eddymotion/model/gpr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index 16949cfd..34174b57 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -94,8 +94,8 @@ class EddyMotionGPR(GaussianProcessRegressor):
     through gradient-descent with analytical gradient calculations
     would not work (the derivative of the kernel w.r.t. alpha is zero).
 
-    I believe this is overlooked in [Andersson15]_, or they actually did not
-    use analytical gradient-descent:
+    This might have been overlooked in [Andersson15]_, or else they actually did
+    not use analytical gradient-descent:
 
         *A note on optimisation*
 

From 34d1ff0e2021f76f359ac94c43202f381bf8d0ff Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 19:03:19 -0500
Subject: [PATCH 10/17] DOC: Do not introduce unnecessary line breaks within
 paragraph

Do not introduce unnecessary line breaks within paragraph.

Take advantage of the commit to use literal highlighting syntax for
the `alpha` parameter for the sake of consistency.
---
 src/eddymotion/model/gpr.py | 20 +++++++++-----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index 34174b57..b189dcbf 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -89,10 +89,9 @@ class EddyMotionGPR(GaussianProcessRegressor):
         "traditional" regression.
 
     Finally, the parameter :math:`\sigma^2` maps on to Scikit-learn's ``alpha``
-    of the regressor.
-    Because it is not a parameter of the kernel, hyperparameter selection
-    through gradient-descent with analytical gradient calculations
-    would not work (the derivative of the kernel w.r.t. alpha is zero).
+    of the regressor. Because it is not a parameter of the kernel, hyperparameter
+    selection through gradient-descent with analytical gradient calculations
+    would not work (the derivative of the kernel w.r.t. ``alpha`` is zero).
 
     This might have been overlooked in [Andersson15]_, or else they actually did
     not use analytical gradient-descent:
@@ -105,13 +104,12 @@ class EddyMotionGPR(GaussianProcessRegressor):
         The reason for that is that such methods typically use fewer steps, and
         when the cost of calculating the derivatives is small/moderate compared
         to calculating the functions itself (as is the case for Eq. (12)) then
-        execution time can be much shorter.
-        However, we found that for the multi-shell case a heuristic optimisation
-        method such as the Nelder-Mead simplex method (Nelder and Mead, 1965) was
-        frequently better at avoiding local maxima.
-        Hence, that was the method we used for all optimisations in the present
-        paper.
-    
+        execution time can be much shorter. However, we found that for the
+        multi-shell case a heuristic optimisation method such as the Nelder-Mead
+        simplex method (Nelder and Mead, 1965) was frequently better at avoiding
+        local maxima. Hence, that was the method we used for all optimisations
+        in the present paper.
+
     **Multi-shell regression (TODO).**
     For multi-shell modeling, the kernel :math:`k(\textbf{x}, \textbf{x'})`
     is updated following Eq. (14) in [Andersson15]_.

From 6b9a1fa3854a31721b703fb78fa9d1f6b2fffe58 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 19:06:33 -0500
Subject: [PATCH 11/17] DOC: Use literal highlighting syntax for `a` parameter

Use literal highlighting syntax for the `a` parameter in `gpr` module
`ExponentialKriging` initialization method.
---
 src/eddymotion/model/gpr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index b189dcbf..3888602d 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -271,7 +271,7 @@ def __init__(
         beta_l : :obj:`float`, optional
             The :math:`\lambda` hyperparameter.
         a_bounds : :obj:`tuple`, optional
-            Bounds for the a parameter.
+            Bounds for the ``a`` parameter.
         l_bounds : :obj:`tuple`, optional
             Bounds for the :math:`\lambda` hyperparameter.
 

From a5ccc0fc6c1c7bd4927d0408494a85df61d88f3f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 19:09:22 -0500
Subject: [PATCH 12/17] DOC: Use `obj` Sphinx role to reference `NumPy` arrays
 consistently

Use `obj` Sphinx role to reference `NumPy` arrays consistently in `gpr`
module docstrings.
---
 src/eddymotion/model/gpr.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index 3888602d..d225d1e6 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -308,10 +308,10 @@ def __call__(
 
         Returns
         -------
-        K : ndarray of shape (n_samples_X, n_samples_Y)
+        K : :obj:`~numpy.ndarray` of shape (n_samples_X, n_samples_Y)
             Kernel k(X, Y)
 
-        K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\
+        K_gradient : :obj:`~numpy.ndarray` of shape (n_samples_X, n_samples_X, n_dims),\
                 optional
             The gradient of the kernel k(X, X) with respect to the log of the
             hyperparameter of the kernel. Only returned when `eval_gradient`
@@ -339,12 +339,12 @@ def diag(self, X: np.ndarray) -> np.ndarray:
 
         Parameters
         ----------
-        X : ndarray of shape (n_samples_X, n_features)
+        X : :obj:`~numpy.ndarray` of shape (n_samples_X, n_features)
             Left argument of the returned kernel k(X, Y)
 
         Returns
         -------
-        K_diag : ndarray of shape (n_samples_X,)
+        K_diag : :obj:`~numpy.ndarray` of shape (n_samples_X,)
             Diagonal of kernel k(X, X)
         """
         return self.beta_l * np.ones(X.shape[0])
@@ -414,10 +414,10 @@ def __call__(
 
         Returns
         -------
-        K : ndarray of shape (n_samples_X, n_samples_Y)
+        K : :obj:`~numpy.ndarray` of shape (n_samples_X, n_samples_Y)
             Kernel k(X, Y)
 
-        K_gradient : ndarray of shape (n_samples_X, n_samples_X, n_dims),\
+        K_gradient : :obj:`~numpy.ndarray` of shape (n_samples_X, n_samples_X, n_dims),\
                 optional
             The gradient of the kernel k(X, X) with respect to the log of the
             hyperparameter of the kernel. Only returned when ``eval_gradient``
@@ -450,12 +450,12 @@ def diag(self, X: np.ndarray) -> np.ndarray:
 
         Parameters
         ----------
-        X : ndarray of shape (n_samples_X, n_features)
+        X : :obj:`~numpy.ndarray` of shape (n_samples_X, n_features)
             Left argument of the returned kernel k(X, Y)
 
         Returns
         -------
-        K_diag : ndarray of shape (n_samples_X,)
+        K_diag : :obj:`~numpy.ndarray` of shape (n_samples_X,)
             Diagonal of kernel k(X, X)
         """
         return self.beta_l * np.ones(X.shape[0])

From 0d50d7e28c800810b61636d6223d5f02fe660bbe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 19:19:38 -0500
Subject: [PATCH 13/17] STYLE: Make Kriging hyperparameter property naming
 consistent

Make Kriging hyperparameter property naming consistent: use
`hyperparameter_l` instead of `hyperparameter_beta_l` for the `beta_l`
hyperparameter, much like `hyperparameter_a` is being used for the
`beta_a` hyperparameter.
---
 src/eddymotion/model/gpr.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index 6798d4bd..8f098a20 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -288,7 +288,7 @@ def hyperparameter_a(self) -> Hyperparameter:
         return Hyperparameter("beta_a", "numeric", self.a_bounds)
 
     @property
-    def hyperparameter_beta_l(self) -> Hyperparameter:
+    def hyperparameter_l(self) -> Hyperparameter:
         return Hyperparameter("beta_l", "numeric", self.l_bounds)
 
     def __call__(
@@ -394,7 +394,7 @@ def hyperparameter_a(self) -> Hyperparameter:
         return Hyperparameter("beta_a", "numeric", self.a_bounds)
 
     @property
-    def hyperparameter_beta_l(self) -> Hyperparameter:
+    def hyperparameter_l(self) -> Hyperparameter:
         return Hyperparameter("beta_l", "numeric", self.l_bounds)
 
     def __call__(

From a0c8ec2c1ad0e71622159b8f4d74f3e8a141c578 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 20:25:47 -0500
Subject: [PATCH 14/17] DOC: Include special members in documentation

Include the `__call__` and `__len__` special members when generating the
documentation.
---
 docs/conf.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/conf.py b/docs/conf.py
index 6826697d..2d30e874 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -234,6 +234,13 @@
 apidoc_separate_modules = True
 apidoc_extra_args = ["--module-first", "-d 1", "-T"]
 
+
+# -- Options for autodoc extension -------------------------------------------
+autodoc_default_options = {
+    "special-members": "__call__, __len__",
+}
+
+
 # -- Options for intersphinx extension ---------------------------------------
 
 # Example configuration for intersphinx: refer to the Python standard library.

From 5d10d90414618dbeabc30d2e22de55048515ea40 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Sat, 7 Dec 2024 21:14:05 -0500
Subject: [PATCH 15/17] DOC: Add `__init__` documentation to class
 documentation

Add `__init__` documentation to class documentation:
- Add a function to the documentation configuration file that processes
  the signatures and adds the `__init__` method signature to the class
  signatures.
- Make the class documentation contain the documentation from both the
  class and the `__ini__` method setting the `autoclass_content` option
  to `both`.

Remove the short description of the `__init__` methods so that the class
documentation reads seamlessly.
---
 docs/conf.py                | 27 +++++++++++++++++++++++++++
 src/eddymotion/model/gpr.py |  2 --
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 6826697d..e9cd3542 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -234,6 +234,11 @@
 apidoc_separate_modules = True
 apidoc_extra_args = ["--module-first", "-d 1", "-T"]
 
+
+# -- Options for autodoc extension -------------------------------------------
+autoclass_content = "both"
+
+
 # -- Options for intersphinx extension ---------------------------------------
 
 # Example configuration for intersphinx: refer to the Python standard library.
@@ -253,3 +258,25 @@
 
 # -- Options for versioning extension ----------------------------------------
 scv_show_banner = True
+
+
+# -- Special functions -------------------------------------------------------
+import inspect
+
+
+def autodoc_process_signature(app, what, name, obj, options, signature, return_annotation):
+    """Replace the class signature by the signature from cls.__init__"""
+
+    if what == "class" and hasattr(obj, "__init__"):
+        try:
+            init_signature = inspect.signature(obj.__init__)
+            # Convert the Signature object to a string
+            return str(init_signature), return_annotation
+        except ValueError:
+            # Handle cases where `inspect.signature` fails
+            return signature, return_annotation
+    return signature, return_annotation
+
+
+def setup(app):
+    app.connect("autodoc-process-signature", autodoc_process_signature)
diff --git a/src/eddymotion/model/gpr.py b/src/eddymotion/model/gpr.py
index 6798d4bd..1d1c4da6 100644
--- a/src/eddymotion/model/gpr.py
+++ b/src/eddymotion/model/gpr.py
@@ -264,7 +264,6 @@ def __init__(
         l_bounds: tuple[float, float] = BOUNDS_LAMBDA,
     ):
         r"""
-        Initialize an exponential Kriging kernel.
 
         Parameters
         ----------
@@ -370,7 +369,6 @@ def __init__(
         l_bounds: tuple[float, float] = BOUNDS_LAMBDA,
     ):
         r"""
-        Initialize a spherical Kriging kernel.
 
         Parameters
         ----------

From e26943b5cdf3d1d61077edfe7bfd75c36cfad715 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Wed, 27 Nov 2024 08:46:57 -0500
Subject: [PATCH 16/17] DOC: Fix cut passages from `README` in documentation
 index file

Fix cut passages from `README` in documentation index file: add comments
around the flowchart image in the `README` file and change the
`start-line` and `end-line` directive pair for the `end-before` and
`start-after` pair to skip the flowchart image only.

The `start-line` and `end-line` directives were making such that
relevant passages of the `README` file providing context for the
tool were not being included. Thus the text shown in the documentation
index file had some missing parts, and was not making sense.
---
 README.rst     | 4 ++++
 docs/index.rst | 5 ++---
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index a90f0d37..56aad44c 100644
--- a/README.rst
+++ b/README.rst
@@ -47,9 +47,13 @@ More recently, Cieslak et al. [#r3]_ integrated both approaches in *SHORELine*,
 the work of ``eddy`` and *SHORELine*, while generalizing these methods to multiple acquisition schemes
 (single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY [#r5]_.
 
+.. BEGIN FLOWCHART
+
 .. image:: https://raw.githubusercontent.com/nipreps/eddymotion/507fc9bab86696d5330fd6a86c3870968243aea8/docs/_static/eddymotion-flowchart.svg
    :alt: The eddymotion flowchart
 
+.. END FLOWCHART
+
 .. [#r1] S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic
    Resonance in Medicine 67:1694–1702 (2012)
 .. [#r2] J. L. R. Andersson. et al., An integrated approach to correction for off-resonance effects and subject movement
diff --git a/docs/index.rst b/docs/index.rst
index 8f3de245..0467a8e4 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,9 +1,8 @@
 .. include:: links.rst
 .. include:: ../README.rst
-   :end-line: 29
+   :end-before: BEGIN FLOWCHART
 .. include:: ../README.rst
-   :start-line: 34
-
+   :start-after: END FLOWCHART
 
 .. image:: _static/eddymotion-flowchart.svg
    :alt: The eddymotion flowchart

From 3019beeb46e271e95001b8cb657db8f0b9a1ad76 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?=
 <jon.haitz.legarreta@gmail.com>
Date: Fri, 13 Dec 2024 19:51:04 -0500
Subject: [PATCH 17/17] DOC: Fix installation documentation Python version

Fix installation documentation Python version: the tool supports Python
greater than or equal to 3.10.
---
 docs/installation.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.rst b/docs/installation.rst
index 86e5bf8c..02fccc59 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -5,7 +5,7 @@ Installation
 ============
 Make sure all of *eddymotion*' `External Dependencies`_ are installed.
 
-On a functional Python 3.7 (or above) environment with ``pip`` installed,
+On a functional Python 3.10 (or above) environment with ``pip`` installed,
 *eddymotion* can be installed using the habitual command ::
 
     $ python -m pip install eddymotion