diff --git a/notebooks/1_explore-sampling-methods/4_simple-unfirom.ipynb b/notebooks/1_explore-sampling-methods/4_simple-unfirom.ipynb
new file mode 100644
index 0000000..69997a6
--- /dev/null
+++ b/notebooks/1_explore-sampling-methods/4_simple-unfirom.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8233267b-e98b-44be-b9aa-116d0e67a94b",
+   "metadata": {},
+   "source": [
+    "# Compute Energies of Random Offsets\n",
+    "Vary every coordinate uniformly between -step_size and step_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6a28419-6831-4197-8973-00c5591e19cb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from jitterbug.utils import make_calculator\n",
+    "from ase.io import write, read\n",
+    "from ase.db import connect\n",
+    "from ase import Atoms\n",
+    "from pathlib import Path\n",
+    "from tqdm import tqdm \n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cec456a7-3c13-4b00-936a-abc31c898262",
+   "metadata": {},
+   "source": [
+    "Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6be56c5-a460-4acd-9b89-8c3d9c812f5f",
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "starting_geometry = '../data/exact/caffeine_pm7_None.xyz'\n",
+    "method = 'hf/def2-svpd'\n",
+    "threads = min(os.cpu_count(), 12)\n",
+    "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7010df09-73b2-4d58-be03-15a5f0d04b4c",
+   "metadata": {},
+   "source": [
+    "Derived"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b6794cd-477f-45a1-b96f-2332804ddb20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "relax_name = Path(starting_geometry).name[:-4]\n",
+    "name, relax_method, relax_basis = relax_name.split(\"_\")\n",
+    "method, basis = method.split(\"/\")\n",
+    "run_name = f'{name}_{method}_{basis}_at_{relax_method}_{relax_basis}'\n",
+    "print(f'Run name: {run_name}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf9ff792-6b5b-46ce-9a78-78912e372912",
+   "metadata": {},
+   "source": [
+    "## Load in the Relaxed Structure\n",
+    "We generated a relaxed structure in the previous notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad9fd725-b1ba-4fec-ae41-959be0e540b3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "atoms = read(starting_geometry)\n",
+    "atoms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2284056b-ddf2-4a3b-88ca-b1c6dc84a2d5",
+   "metadata": {},
+   "source": [
+    "## Compute many random energies\n",
+    "Compute $3N + 3N(3N+1)/2 + 1$ energies with displacements sampled [on the unit sphere](https://mathoverflow.net/questions/24688/efficiently-sampling-points-uniformly-from-the-surface-of-an-n-sphere). This is enough to fit the Jacobian and Hessian exactly plus a little more"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad4c5d8e-96d4-4bb6-9bf2-6474d7563448",
+   "metadata": {},
+   "source": [
+    "Prepare the output directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23502eea-0974-4248-8f19-e85447069c61",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "out_dir = Path('data') / 'simple-uniform'\n",
+    "out_dir.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf1366fc-d9a7-4a98-b9c9-cb3a0209b406",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "db_path = out_dir / f'{run_name}_d={step_size:.2e}.db'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "004158dc-3fe9-47a6-99dd-268aa69bb27b",
+   "metadata": {},
+   "source": [
+    "Add the relaxed geometry if needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4f21e81-5ec3-4877-a4d1-402077be2ee8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "if not db_path.is_file():\n",
+    "    with connect(db_path) as db:\n",
+    "        db.write(atoms)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ebf431-75a0-44d5-8e18-43f2898d6dab",
+   "metadata": {},
+   "source": [
+    "Make the calculator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0915595d-133a-43df-84fc-4ff6a3b538ea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "calc = make_calculator(method, basis, num_threads=threads)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e9e5ff2-3728-459b-b3d3-09acba0f71bc",
+   "metadata": {},
+   "source": [
+    "Generate the energies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2a28593-2634-4bb7-ae5b-8f557937bda1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "n_atoms = len(atoms)\n",
+    "to_compute = 3 * n_atoms + 3 * n_atoms * (3 * n_atoms + 1) // 2 + 1\n",
+    "print(f'Need to run {to_compute} calculations for full accuracy.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf40523-dcaa-4046-a9c6-74e35178e87f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "with connect(db_path) as db:\n",
+    "    done = len(db)\n",
+    "print(f'Already done {done}. {to_compute - done} left to do.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6fa1b33-defc-4b35-895d-052eb64453fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pbar = tqdm(total=to_compute)\n",
+    "pbar.update(done)\n",
+    "for i in range(to_compute - done):\n",
+    "    # Sample a perturbation\n",
+    "    disp = np.random.normal(-step_size, step_size, size=(n_atoms, 3))\n",
+    "\n",
+    "    # Make the new atoms\n",
+    "    new_atoms = atoms.copy()\n",
+    "    new_atoms.positions += disp\n",
+    "\n",
+    "    # Compute the energy and store in the db\n",
+    "    new_atoms.calc = calc\n",
+    "    new_atoms.get_potential_energy()\n",
+    "    with connect(db_path) as db:\n",
+    "        db.write(new_atoms)\n",
+    "\n",
+    "    pbar.update(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "785add47-39b5-4d7e-9d92-0375c8128171",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/1_explore-sampling-methods/run-all-methods.sh b/notebooks/1_explore-sampling-methods/run-all-methods.sh
index 8bcca29..fc27b81 100644
--- a/notebooks/1_explore-sampling-methods/run-all-methods.sh
+++ b/notebooks/1_explore-sampling-methods/run-all-methods.sh
@@ -3,23 +3,23 @@
 xyz=../data/exact/caffeine_pm7_None.xyz
 method='pm7/None'
 
-for step_size in 0.02; do
+for step_size in 0.005 0.01 0.02; do
     # Do the randomized methods
-    for notebook in 0_random-directions-same-distance.ipynb 1_random-directions-variable-distance.ipynb; do
+    for notebook in 0_random-directions-same-distance.ipynb 1_random-directions-variable-distance.ipynb 4_simple-unfirom.ipynb; do
         papermill -p starting_geometry $xyz -p method $method -p step_size $step_size $notebook last.ipynb
     done
     
     # Test with different reductions for "along axes"
     notebook=2_displace-along-axes.ipynb
-    for n in 2 4 8; do
+    for n in 4; do
         papermill -p starting_geometry $xyz -p method $method -p perturbs_per_evaluation $n -p step_size $step_size $notebook last.ipynb
     done
 done
 
 # Test with the vibrational modes
 notebook=3_displace-along-vibrational-modes.ipynb
-for step_size in 0.001 0.002; do  # These step sizes are energy scales in eV, not distances in Angstrom as above
-    for n in 16 32 64; do
+for step_size in 0.0025; do  # These step sizes are energy scales in eV, not distances in Angstrom as above
+    for n in 32; do
         papermill -p starting_geometry $xyz -p method $method -p perturbs_per_evaluation $n -p step_size $step_size $notebook last.ipynb
     done
 done
diff --git a/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb b/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb
index 7776ede..e8cd619 100644
--- a/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb
+++ b/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb
@@ -95,7 +95,7 @@
     {
      "data": {
       "text/plain": [
-       "<ase.vibrations.data.VibrationsData at 0x7f5d24a5f0a0>"
+       "<ase.vibrations.data.VibrationsData at 0x7fed406b4730>"
       ]
      },
      "execution_count": 4,
@@ -129,7 +129,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 11 approximate Hessians\n"
+      "Found 22 approximate Hessians\n"
      ]
     }
    ],
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "2056b382-8566-4f2e-a574-961a3268d3c3",
    "metadata": {
     "tags": []
@@ -150,23 +150,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                                                                                                                                                                                              | 0/11 [00:00<?, ?it/s]/home/lward/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pmutt/statmech/vib.py:87: RuntimeWarning: overflow encountered in sinh\n",
+      "  0%|                                                                                                                                                                                                                                                              | 0/22 [00:00<?, ?it/s]/home/lward/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pmutt/statmech/vib.py:87: RuntimeWarning: overflow encountered in sinh\n",
       "  (0.5 * vib_dimless)**2 * (1. / np.sinh(vib_dimless / 2.))**2\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:02<00:00,  3.80it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 176 approximate hessians\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      " 41%|████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                                                 | 9/22 [00:02<00:03,  4.05it/s]"
      ]
     }
    ],
@@ -211,42 +197,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "c3aa94ce-1f23-4007-8766-3da998db628e",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "path               ../2_testing-fitting-strategies/data/mbtr/caff...\n",
-       "sampling_method                                           along-axes\n",
-       "options                                               d=2.00e-02-N=2\n",
-       "size                                                               5\n",
-       "d                                                           2.00e-02\n",
-       "N                                                                  2\n",
-       "scale_factor                                                     1.0\n",
-       "zpe                                                         5.058148\n",
-       "zpe_error                                                -104.407702\n",
-       "cp                 [0.014146793803042134, 0.01771395512622287, 0....\n",
-       "cp_error           [-0.010065824804868617, -0.01212553988407947, ...\n",
-       "h                  [5.072098696119492, 5.11863818857305, 5.175908...\n",
-       "h_error            [104.39707523324437, 104.36530161384731, 104.3...\n",
-       "temps              [1.0, 3.9291338582677167, 6.858267716535433, 9...\n",
-       "vib_freqs          [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
-       "vib_errors         [218.79974021444124, 187.1848443090143, 148.53...\n",
-       "vib_mae                                                  1119.440878\n",
-       "maxstep                                                          NaN\n",
-       "lower                                                            NaN\n",
-       "Name: 0, dtype: object"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "all_results.iloc[0]"
    ]
@@ -261,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "2d315d44-7564-468f-bd03-8bbedf8b424c",
    "metadata": {
     "tags": []
@@ -274,27 +230,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "a238ef1e-eae0-4195-a254-7c34fc63cc8d",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "along-vibrational-modes     6\n",
-       "along-axes                  3\n",
-       "random-dir-same-dist        1\n",
-       "random-dir-variable-dist    1\n",
-       "Name: sampling_method, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "all_results.drop_duplicates('path')['sampling_method'].value_counts()"
    ]
@@ -319,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "45dd474a-22e7-49d7-bf7e-81321c1c470c",
    "metadata": {
     "tags": []
@@ -334,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "cc0ca444-01d5-4a25-80e7-6676bb90f3bc",
    "metadata": {
     "tags": []
@@ -346,26 +287,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "83063556-ea68-42d6-8feb-0bf863f8882f",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_along-axes_d=2.00e-02-N=4-increment.json'),\n",
-       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_along-vibrational-modes_d=1.00e-03-N=32-maxstep=6.00e-02-lower=xtb+None-increment.json'),\n",
-       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_random-dir-same-dist_d=2.00e-02-increment.json'),\n",
-       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_random-dir-variable-dist_d=2.00e-02-increment.json')}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "best_for_sampling_method"
    ]
@@ -380,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "38d7ff93-3cd6-4489-96fb-5a1f6a917465",
    "metadata": {
     "tags": []
@@ -392,23 +319,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "16cdf7cf-4d09-415e-8996-3222ee33307b",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x250 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(3.5, 2.5))\n",
     "\n",
@@ -418,6 +334,7 @@
     "ax.legend()\n",
     "\n",
     "ax.set_yscale('log')\n",
+    "ax.set_ylim([10, 1000])\n",
     "\n",
     "ax.set_xlabel('Training Size')\n",
     "ax.set_ylabel('Vibration MAE (cm$^{-1}$)')\n",
@@ -427,23 +344,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "7dc273cc-6b8e-47a9-8f58-31bd385368da",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x250 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(3.5, 2.5))\n",
     "\n",
@@ -483,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "edbf9938-54b0-4182-b9e2-8de6ec96404e",
    "metadata": {
     "tags": []
@@ -495,23 +401,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "b0e1309b-a6dd-4728-9583-e8ec5d8a765e",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x380 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(3, 1, figsize=(3.5, 3.8), sharex=True)\n",
     "\n",
@@ -545,27 +440,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "bc042000-02a6-4b07-ade7-849c74a4fadf",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "single positional indexer is out-of-bounds",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m best_model \u001b[38;5;241m=\u001b[39m \u001b[43mbest_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43md==0.01\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msort_values\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msize\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtail\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pandas/core/indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pandas/core/indexing.py:1625\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1622\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1624\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[0;32m-> 1625\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1627\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
-      "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pandas/core/indexing.py:1557\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1555\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[1;32m   1556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[0;32m-> 1557\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "best_model = best_strategy.query('d==0.01').sort_values('size').tail().iloc[0]"
    ]