diff --git a/notebooks/SBI.ipynb b/notebooks/SBI.ipynb
index bbde891..596b8e8 100644
--- a/notebooks/SBI.ipynb
+++ b/notebooks/SBI.ipynb
@@ -32,7 +32,7 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "from torch.utils.data import DataLoader, TensorDataset\n",
-    "#from src.scripts import utils"
+    "import numpy as np"
    ]
   },
   {
@@ -56,58 +56,40 @@
      "text": [
       "0.2.01\n"
      ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'STOP' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdeepbench\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mphysics_object\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Pendulum\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(deepbench\u001b[38;5;241m.\u001b[39m__version__)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mSTOP\u001b[49m\n\u001b[1;32m      6\u001b[0m true_L \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m      7\u001b[0m true_theta \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpi \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'STOP' is not defined"
-     ]
     }
    ],
    "source": [
     "import deepbench\n",
     "from deepbench.physics_object import Pendulum\n",
-    "print(deepbench.__version__)\n"
+    "print(deepbench.__version__)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "1080e951",
+   "execution_count": 11,
+   "id": "7bfe9a7f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# how should this error propagate?\n",
-    "\n",
-    "def calc_error_prop(true_L, true_theta, true_a, dthing, time = 0.5, wrt = 'theta_0'):\n",
-    "    if wrt == 'theta_0':\n",
-    "        dx_dthing = true_L * np.cos(true_theta * np.cos(np.sqrt(true_a / true_L) * one_time)) * \\\n",
-    "              np.cos(np.sqrt(true_a / true_L) * one_time) * dthing\n",
-    "    if wrt == 'L':\n",
-    "\n",
-    "        dx_dthing = (0.5 * true_theta * time * np.sqrt(true_a / true_L) * np.sin(time * np.sqrt(true_a / true_L)) * \\\n",
-    "             np.cos(true_theta * np.cos(time * np.sqrt(true_a / true_L))) + \\\n",
-    "             np.sin(true_theta * np.cos(time * np.sqrt(true_a / true_L)))) * dthing\n",
-    "    return dx_dthing"
+    "import sbi\n",
+    "# from sbi import inference\n",
+    "from sbi.inference import SNPE, simulate_for_sbi, prepare_for_sbi\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "import torch"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7bfe9a7f",
+   "id": "ff8152c0-94f3-4a7c-a50b-dff13517305b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sbi import utils, inference, analysis\n",
-    "# from sbi import inference\n",
-    "from sbi.inference import SNPE, simulate_for_sbi, prepare_for_sbi\n",
-    "from sbi.inference.base import infer\n",
-    "import torch"
+    "# this is necessary to import modules from this repo\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "from src.scripts import models, utils, train"
    ]
   },
   {
@@ -128,7 +110,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([6.1716, 0.0338, 4.2549])\n"
+      "tensor([3.4324, 0.0128, 8.4127])\n"
      ]
     }
    ],
@@ -138,7 +120,8 @@
     "low_bounds = torch.tensor([1, np.pi/500, 1])\n",
     "high_bounds = torch.tensor([10, 3*np.pi/200, 10])\n",
     "\n",
-    "prior = utils.BoxUniform(low = low_bounds, high = high_bounds)\n",
+    "prior = sbi.utils.BoxUniform(low = low_bounds, high = high_bounds)\n",
+    "# sample from the prior\n",
     "print(prior.sample())"
    ]
   },
@@ -208,45 +191,32 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e86df6e27fb94cea86c5617b8e24f20b",
+       "model_id": "0016983ebf36487d82074e9b92c0999f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Running 10000 simulations.:   0%|          | 0/10000 [00:00<?, ?it/s]"
+       "Running 1000 simulations.:   0%|          | 0/1000 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "ename": "OSError",
-     "evalue": "[Errno 24] Too many open files",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m posterior \u001b[38;5;241m=\u001b[39m \u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSNPE\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_simulations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/deepuq-DRzT0TL8-py3.9/lib/python3.9/site-packages/sbi/inference/base.py:72\u001b[0m, in \u001b[0;36minfer\u001b[0;34m(simulator, prior, method, num_simulations, num_workers)\u001b[0m\n\u001b[1;32m     69\u001b[0m simulator, prior \u001b[38;5;241m=\u001b[39m prepare_for_sbi(simulator, prior)\n\u001b[1;32m     71\u001b[0m inference \u001b[38;5;241m=\u001b[39m method_fun(prior\u001b[38;5;241m=\u001b[39mprior)\n\u001b[0;32m---> 72\u001b[0m theta, x \u001b[38;5;241m=\u001b[39m \u001b[43msimulate_for_sbi\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m    \u001b[49m\u001b[43msimulator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msimulator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproposal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprior\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_simulations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_simulations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     76\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     78\u001b[0m _ \u001b[38;5;241m=\u001b[39m inference\u001b[38;5;241m.\u001b[39mappend_simulations(theta, x)\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m     79\u001b[0m posterior \u001b[38;5;241m=\u001b[39m inference\u001b[38;5;241m.\u001b[39mbuild_posterior()\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/deepuq-DRzT0TL8-py3.9/lib/python3.9/site-packages/sbi/inference/base.py:495\u001b[0m, in \u001b[0;36msimulate_for_sbi\u001b[0;34m(simulator, proposal, num_simulations, num_workers, simulation_batch_size, show_progress_bar)\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Returns ($\\theta, x$) pairs obtained from sampling the proposal and simulating.\u001b[39;00m\n\u001b[1;32m    467\u001b[0m \n\u001b[1;32m    468\u001b[0m \u001b[38;5;124;03mThis function performs two steps:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[38;5;124;03mReturns: Sampled parameters $\\theta$ and simulation-outputs $x$.\u001b[39;00m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    493\u001b[0m theta \u001b[38;5;241m=\u001b[39m proposal\u001b[38;5;241m.\u001b[39msample((num_simulations,))\n\u001b[0;32m--> 495\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43msimulate_in_batches\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m    \u001b[49m\u001b[43msimulator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_batch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m theta, x\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/deepuq-DRzT0TL8-py3.9/lib/python3.9/site-packages/sbi/simulators/simutils.py:88\u001b[0m, in \u001b[0;36msimulate_in_batches\u001b[0;34m(simulator, theta, sim_batch_size, num_workers, seed, show_progress_bars)\u001b[0m\n\u001b[1;32m     86\u001b[0m         simulation_outputs \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     87\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m batches:\n\u001b[0;32m---> 88\u001b[0m             simulation_outputs\u001b[38;5;241m.\u001b[39mappend(\u001b[43msimulator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     89\u001b[0m             pbar\u001b[38;5;241m.\u001b[39mupdate(sim_batch_size)\n\u001b[1;32m     91\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(simulation_outputs, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/deepuq-DRzT0TL8-py3.9/lib/python3.9/site-packages/sbi/utils/user_input_checks.py:551\u001b[0m, in \u001b[0;36mbatch_loop_simulator\u001b[0;34m(theta)\u001b[0m\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/deepuq-DRzT0TL8-py3.9/lib/python3.9/site-packages/sbi/utils/user_input_checks.py:515\u001b[0m, in \u001b[0;36mpytorch_simulator\u001b[0;34m(theta)\u001b[0m\n",
-      "Cell \u001b[0;32mIn[6], line 7\u001b[0m, in \u001b[0;36msimulator\u001b[0;34m(thetas)\u001b[0m\n\u001b[1;32m      3\u001b[0m length, theta, a_g \u001b[38;5;241m=\u001b[39m thetas\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m#print('heres what were inputting', thetas, a_g)\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#length_percent_error_all, theta_percent_error_all, a_g_percent_error_all = \\\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m#    percent_errors\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m pendulum \u001b[38;5;241m=\u001b[39m \u001b[43mPendulum\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpendulum_arm_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlength\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstarting_angle_radians\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43macceleration_due_to_gravity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ma_g\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnoise_std_percent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpendulum_arm_length\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstarting_angle_radians\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43macceleration_due_to_gravity\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m output \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(pendulum\u001b[38;5;241m.\u001b[39mcreate_object(np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m100\u001b[39m), noiseless\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m#torch.tensor(pendulum.create_object(0.75, noiseless=False))\u001b[39;00m\n",
-      "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/deepuq-DRzT0TL8-py3.9/lib/python3.9/site-packages/deepbench/physics_object/pendulum.py:135\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, pendulum_arm_length, starting_angle_radians, noise_std_percent, acceleration_due_to_gravity, big_G_newton, phi_planet, mass_pendulum_bob, coefficient_friction)\u001b[0m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.9/logging/__init__.py:1135\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, filename, mode, encoding, delay, errors)\u001b[0m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.9/posixpath.py:380\u001b[0m, in \u001b[0;36mabspath\u001b[0;34m(path)\u001b[0m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 24] Too many open files"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 94 epochs."
      ]
     }
    ],
    "source": [
-    "posterior = infer(simulator, prior, \"SNPE\", num_simulations=10000)"
+    "posterior = infer(simulator, prior, \"SNPE\", num_simulations=1000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "4102596e",
    "metadata": {},
    "outputs": [
@@ -254,29 +224,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.07625158  0.08049497  0.09278156  0.06236312  0.08333401  0.08372527\n",
-      "  0.07612885  0.08864967  0.06947212  0.08427258  0.07112986  0.07345919\n",
-      "  0.08093297  0.08278143  0.07708453  0.0896376   0.07609298  0.0623881\n",
-      "  0.08728749  0.06844008  0.06962766  0.08282833  0.06933933  0.07545184\n",
-      "  0.06952057  0.06881789  0.0761766   0.06252401  0.06426313  0.0582699\n",
-      "  0.04921441  0.05879513  0.05600547  0.0541257   0.06640297  0.05142542\n",
-      "  0.04955162  0.05071783  0.05250058  0.05361158  0.05015727  0.04254812\n",
-      "  0.03757062  0.04678548  0.03337447  0.0382374   0.03071208  0.04042319\n",
-      "  0.03530869  0.02688927  0.02644155  0.02156865  0.02267736  0.02447217\n",
-      "  0.02161515  0.0180927   0.01433271  0.01479866  0.01585307  0.01208199\n",
-      "  0.01115776  0.00803297  0.00607459  0.0056249   0.00291435  0.00118301\n",
-      " -0.00046837 -0.00260291 -0.0034015  -0.00562956 -0.00700255 -0.00974571\n",
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+      "  0.07879793  0.05868415  0.06223347  0.06609198  0.06532925  0.07618247\n",
+      "  0.0649434   0.0603592   0.05569079  0.0693472   0.06219306  0.05140238\n",
+      "  0.06909498  0.05091801  0.05732841  0.05429712  0.0547272   0.05604888\n",
+      "  0.04509002  0.04387677  0.04521991  0.05154561  0.0471123   0.04789744\n",
+      "  0.03978025  0.04386657  0.03761613  0.03613892  0.03583977  0.03415337\n",
+      "  0.03012392  0.02778596  0.02931387  0.02480714  0.02374231  0.02169891\n",
+      "  0.02035453  0.01591178  0.01810248  0.01670907  0.01044349  0.01370839\n",
+      "  0.0111382   0.00921387  0.00668215  0.00507055  0.00334703  0.00129868\n",
+      " -0.00059254 -0.00228121 -0.00439478 -0.00668649 -0.00766331 -0.0118025\n",
+      " -0.01137257 -0.01182074 -0.01700982 -0.01698029 -0.0194498  -0.02165009\n",
+      " -0.02472339 -0.02293032 -0.03174761 -0.02933469 -0.02754536 -0.03221995\n",
+      " -0.03103672 -0.03457344 -0.03413206 -0.03585147 -0.0448898  -0.04131922\n",
+      " -0.04336628 -0.03953229 -0.04753358 -0.04484064 -0.05179059 -0.0559457\n",
+      " -0.05100568 -0.05426347 -0.05852063 -0.04605235]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2956b2e38a74b73b03922af08ffd849",
+       "model_id": "c2d2f117110d4ee49e0673845b8350a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -289,7 +259,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 500x500 with 9 Axes>"
       ]
@@ -314,28 +284,10 @@
     ")\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "053817fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.031415926535897934\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.pi/100)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "23d40aa4-5e55-496e-a32b-dec65d90fb58",
+   "id": "5ba2e8ba-c69b-4cb0-8f6c-f39043933da0",
    "metadata": {},
    "outputs": [],
    "source": []