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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0e588be-6a7a-486b-814e-0ccc5666ee8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a913cd4d-2231-4b75-b264-f3ca9f7c224a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ompy as om\n",
+    "import numpy as np\n",
+    "import pymc3 as pm\n",
+    "import arviz as az\n",
+    "import theano\n",
+    "import theano.tensor as tt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import logging\n",
+    "from scipy.interpolate import interp1d\n",
+    "from model_op import calculate_FG, LogLike, LogLike2, FG_loglike, loglike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a786c35d-dfcd-4818-9f3b-a431906fede9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6a686c416db4128a690bd72cd007172",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<matplotlib.collections.QuadMesh at 0x139e10d00>,\n",
+       " <AxesSubplot:xlabel='$\\\\gamma$-ray energy $E_{\\\\gamma}$', ylabel='Excitation energy $E_{x}$'>,\n",
+       " <Figure size 640x480 with 2 Axes>)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw = om.example_raw('Dy164')\n",
+    "\n",
+    "raw.cut_diagonal(E1=(800, 0), E2=(7500, 7300))\n",
+    "raw.cut('Ex', 0, 8400)\n",
+    "\n",
+    "raw.values = np.around(raw.values)\n",
+    "raw.remove_negative()\n",
+    "raw.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1108ecc3-15e2-4b38-8fbb-d0cf0c73a144",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01a60d6b193f49989fef9f8b7a91be0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<matplotlib.collections.QuadMesh at 0x138298310>,\n",
+       " <AxesSubplot:xlabel='$\\\\gamma$-ray energy $E_{\\\\gamma}$', ylabel='Excitation energy $E_{x}$'>,\n",
+       " <Figure size 640x480 with 2 Axes>)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "folderpath = \"../OCL_response_functions/nai2012_for_opt13\"\n",
+    "Eg = raw.Eg\n",
+    "\n",
+    "# Experimental relative FWHM at 1.33 MeV of resulting array\n",
+    "fwhm_abs = 90.44 # (90/1330 = 6.8%)\n",
+    "\n",
+    "# Magne recommends 1/10 of the actual resolution for unfolding purposes\n",
+    "response = om.Response(folderpath)\n",
+    "R_ompy_unf, R_tab_unf = response.interpolate(Eg, fwhm_abs=fwhm_abs/10, return_table=True)\n",
+    "R_ompy_view, _ = response.interpolate(Eg, fwhm_abs=fwhm_abs, return_table=True)\n",
+    "fthreshold = interp1d([30., 80., 122., 183., 244., 294., 344., 562., 779., 1000.],\n",
+    "                      [0.0, 0.0, 0.0, 0.06, 0.44, 0.60, 0.87, 0.99, 1.00, 1.00],\n",
+    "                      fill_value=\"extrapolate\")\n",
+    "\n",
+    "def apply_detector_threshold(response, table, fthreshold):\n",
+    "    thres = fthreshold(response.Eg)\n",
+    "    response.values = response.values * thres\n",
+    "    # renormalize\n",
+    "    response.values = om.div0(response.values, response.values.sum(axis=1)[:, np.newaxis])\n",
+    "    table[\"eff_tot\"] *= thres\n",
+    "apply_detector_threshold(R_ompy_unf, R_tab_unf, fthreshold)\n",
+    "R_ompy_view.plot(scale='log', vmin=1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cbf55e7d-1864-40ea-bdd5-5137eeeefe0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# With compton subtraction and all tweaks\n",
+    "unfolder= om.Unfolder(response=R_ompy_unf)\n",
+    "unfolder.response_tab = R_tab_unf\n",
+    "# Magne suggests some \"tweaks\" for a better unfolding performance. Default is 1 for all.\n",
+    "unfolder.FWHM_tweak_multiplier = {\"fe\": 1., \"se\": 1.1,\n",
+    "                                     \"de\": 1.3, \"511\": 0.9}\n",
+    "firstgen = om.FirstGeneration()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "86ee14f3-1329-4958-9b43-a897d62440f3",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-11-12 14:48:18,745 - ompy.ensemble - INFO - Start normalization with 7 cpus\n",
+      "2021-11-12 14:48:18,819 - ompy.ensemble - INFO - Generating/loading 0\n",
+      "2021-11-12 14:48:18,851 - ompy.ensemble - INFO - Generating/loading 1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ca2291bcbdb437d85066cfce69b0c91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/25 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-11-12 14:48:18,889 - ompy.ensemble - INFO - Generating/loading 2\n",
+      "2021-11-12 14:48:18,925 - ompy.ensemble - INFO - Generating/loading 3\n",
+      "2021-11-12 14:48:18,961 - ompy.ensemble - INFO - Generating/loading 4\n",
+      "2021-11-12 14:48:18,999 - ompy.ensemble - INFO - Generating/loading 5\n",
+      "2021-11-12 14:48:19,036 - ompy.ensemble - INFO - Generating/loading 6\n",
+      "2021-11-12 14:48:23,031 - ompy.ensemble - INFO - Generating/loading 7\n",
+      "2021-11-12 14:48:23,093 - ompy.ensemble - INFO - Generating/loading 8\n",
+      "2021-11-12 14:48:23,147 - ompy.ensemble - INFO - Generating/loading 9\n",
+      "2021-11-12 14:48:23,259 - ompy.ensemble - INFO - Generating/loading 10\n",
+      "2021-11-12 14:48:23,299 - ompy.ensemble - INFO - Generating/loading 11\n",
+      "2021-11-12 14:48:23,341 - ompy.ensemble - INFO - Generating/loading 12\n",
+      "2021-11-12 14:48:23,378 - ompy.ensemble - INFO - Generating/loading 13\n",
+      "2021-11-12 14:48:28,157 - ompy.ensemble - INFO - Generating/loading 14\n",
+      "2021-11-12 14:48:28,315 - ompy.ensemble - INFO - Generating/loading 15\n",
+      "2021-11-12 14:48:28,363 - ompy.ensemble - INFO - Generating/loading 16\n",
+      "2021-11-12 14:48:28,407 - ompy.ensemble - INFO - Generating/loading 17\n",
+      "2021-11-12 14:48:28,469 - ompy.ensemble - INFO - Generating/loading 18\n",
+      "2021-11-12 14:48:28,514 - ompy.ensemble - INFO - Generating/loading 19\n",
+      "2021-11-12 14:48:28,555 - ompy.ensemble - INFO - Generating/loading 20\n",
+      "2021-11-12 14:48:32,931 - ompy.ensemble - INFO - Generating/loading 21\n",
+      "2021-11-12 14:48:33,073 - ompy.ensemble - INFO - Generating/loading 22\n",
+      "2021-11-12 14:48:33,138 - ompy.ensemble - INFO - Generating/loading 23\n",
+      "2021-11-12 14:48:33,185 - ompy.ensemble - INFO - Generating/loading 24\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62db238dbe4b4e79a4f9ba2f431514a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/25 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "logger = om.introspection.get_logger('ensemble', 'INFO')\n",
+    "\n",
+    "# Tell the `Ensemble` class which raw spectrum, what kind of undolfer and first\n",
+    "# generations method to use.\n",
+    "# Note: This will have the same setting as above. We could for example have\n",
+    "# set the first generations method to use a different \"valley_collection\", or a\n",
+    "# differnt type of \"multiplicity_estimation\"\n",
+    "ensemble = om.Ensemble(raw=raw)\n",
+    "ensemble.unfolder = unfolder\n",
+    "ensemble.first_generation_method = firstgen\n",
+    "# Generates N perturbated members;\n",
+    "# the `regernerate` flag ensures, that we don't load from disk; which might result in expected results\n",
+    "# if we have changed something in the input `raw` matrix.\n",
+    "ensemble.generate(25, regenerate=True)\n",
+    "E_rebinned = np.arange(100., 8500, 200)\n",
+    "ensemble.rebin(E_rebinned, member=\"firstgen\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e0516a17-9ad2-486d-ace2-922c781621fc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "firstgen = ensemble.get_firstgen(0)\n",
+    "firstgen = om.Matrix(Ex=firstgen.Ex, Eg=firstgen.Eg, values=np.mean([mat.values for mat in ensemble.get_firstgen(range(25))], axis=0))\n",
+    "firstgen_std = om.Matrix(Ex=firstgen.Ex, Eg=firstgen.Eg, values=np.std([mat.values for mat in ensemble.get_firstgen(range(25))], axis=0))\n",
+    "firstgen.trapezoid(4400, 7700, 1300, 7900, inplace=True)\n",
+    "firstgen_std.trapezoid(4400, 7700, 1300, 7900, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "394fcdce-9d7c-4b70-b321-645e3ccc0467",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bcc90d81eb940c19f807fd52843b69f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65afe39df2a344a0856930fc51f78ff6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<matplotlib.collections.QuadMesh at 0x1382ae970>,\n",
+       " <AxesSubplot:xlabel='$\\\\gamma$-ray energy $E_{\\\\gamma}$', ylabel='Excitation energy $E_{x}$'>,\n",
+       " <Figure size 640x480 with 2 Axes>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "firstgen.plot()\n",
+    "firstgen_std.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "86d9b1ab-0efa-4989-bab8-e9c058728f94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We run the extractor on the ensemble just to have a reference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5978d341-d93c-4c63-bf4a-300fcf684415",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05719e8ec60e48a3b2a2f45a2d5efcc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/25 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #12 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #13 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #14 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #15 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #16 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #17 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #18 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #19 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #20 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #21 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #22 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #23 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #24 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #12 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #13 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #14 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #15 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #16 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #17 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #18 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #19 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #20 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #21 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #22 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #23 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #24 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "trapezoid_cut = om.Action('matrix')\n",
+    "trapezoid_cut.trapezoid(Ex_min=4400, Ex_max=7700, Eg_min=1300, Eg_max=7900, inplace=True)\n",
+    "extractor = om.Extractor()\n",
+    "extractor.trapezoid = trapezoid_cut\n",
+    "extractor.extract_from(ensemble)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "08a2f2d2-c84a-4558-82fc-4aae8944d296",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200.0\n",
+      "3200.0\n",
+      "16.0\n",
+      "(17, 34)\n",
+      "(17, 34)\n",
+      "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  34\n",
+      "  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  68\n",
+      "  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86\n",
+      " 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119\n",
+      " 120 121 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151\n",
+      " 152 153 154 155 156 170 171 172 173 174 175 176 177 178 179 180 181 182\n",
+      " 183 184 185 186 187 188 189 190 191 204 205 206 207 208 209 210 211 212\n",
+      " 213 214 215 216 217 218 219 220 221 222 223 224 225 226 238 239 240 241\n",
+      " 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259\n",
+      " 260 261 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
+      " 288 289 290 291 292 293 294 295 296 306 307 308 309 310 311 312 313 314\n",
+      " 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 340\n",
+      " 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358\n",
+      " 359 360 361 362 363 364 365 366 374 375 376 377 378 379 380 381 382 383\n",
+      " 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401\n",
+      " 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425\n",
+      " 426 427 428 429 430 431 432 433 434 435 436 442 443 444 445 446 447 448\n",
+      " 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466\n",
+      " 467 468 469 470 471 476 477 478 479 480 481 482 483 484 485 486 487 488\n",
+      " 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506\n",
+      " 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527\n",
+      " 528 529 530 531 532 533 534 535 536 537 538 539 540 541 544 545 546 547\n",
+      " 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565\n",
+      " 566 567 568 569 570 571 572 573 574 575 576]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dE = firstgen.Ex[1] - firstgen.Ex[0]\n",
+    "print(dE)\n",
+    "print(firstgen.Ex[0] - firstgen.Eg[0])\n",
+    "print((firstgen.Ex[0] - firstgen.Eg[0])/dE)\n",
+    "\n",
+    "k0 = int((firstgen.Ex[0] - firstgen.Eg[0])/dE)\n",
+    "\n",
+    "Ef = []\n",
+    "for ex in firstgen.Ex:\n",
+    "    for eg in firstgen.Eg:\n",
+    "        Ef.append(ex-eg)\n",
+    "# We only keep those that are larger than one\n",
+    "Ef = np.array(Ef)\n",
+    "Ef = Ef[Ef >= 0]\n",
+    "# Keep only unique\n",
+    "Ef = np.unique(Ef)\n",
+    "\n",
+    "# Next we can get the index for NLDs\n",
+    "index_NLD = []\n",
+    "for i, ex in enumerate(firstgen.Ex):\n",
+    "    idx = []\n",
+    "    for j, eg in enumerate(firstgen.Eg):\n",
+    "        if ex - eg < 0:\n",
+    "            idx.append(len(Ef))\n",
+    "            continue\n",
+    "        idx.append(i-j+k0)\n",
+    "    index_NLD.append(idx)\n",
+    "index_NLD = np.array(index_NLD)\n",
+    "\n",
+    "# Lastly we can make the index for gSFs\n",
+    "index_GSF = []\n",
+    "for i, ex in enumerate(firstgen.Ex):\n",
+    "    idx = []\n",
+    "    for j, eg in enumerate(firstgen.Eg):\n",
+    "        if eg > ex:\n",
+    "            idx.append(len(firstgen.Eg)-1)\n",
+    "            continue\n",
+    "        idx.append(j)\n",
+    "    index_GSF.append(idx)\n",
+    "index_GSF = np.array(index_GSF)\n",
+    "    \n",
+    "index_keep = []\n",
+    "for i, ex in enumerate(firstgen.Ex):\n",
+    "    for j, eg in enumerate(firstgen.Eg):\n",
+    "        if eg > ex:\n",
+    "            continue\n",
+    "        index_keep.append(i*len(firstgen.Eg) + j)\n",
+    "index_keep = np.array(index_keep)\n",
+    "\n",
+    "\n",
+    "print(index_NLD.shape)\n",
+    "print(index_GSF.shape)\n",
+    "print(index_keep)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ebd312ab-6962-4327-b289-fec3f7d967df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.  0.2 0.4 0.6 0.8 1.  1.2 1.4 1.6 1.8 2.  2.2 2.4 2.6 2.8 3.  3.2 3.4\n",
+      " 3.6 3.8 4.  4.2 4.4 4.6 4.8 5.  5.2 5.4 5.6 5.8 6.  6.2 6.4]\n",
+      "33\n",
+      "[0 1 2]\n",
+      "[3 4 5 6 7]\n",
+      "[ 8  9 10 11 12 13 14]\n",
+      "[15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31]\n",
+      "[32]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa26347171e7456fbbe301e6eb5efd23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot:xlabel='Energy [MeV]'>)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nld0 = extractor.nld[0].copy()\n",
+    "gsf0 = extractor.gsf[0].copy()\n",
+    "\n",
+    "nld0_no_nan = nld0.copy()\n",
+    "nld0_no_nan.cut_nan()\n",
+    "nld0_no_nan.to_MeV()\n",
+    "\n",
+    "\n",
+    "gsf0_no_nan = gsf0.copy()\n",
+    "gsf0_no_nan.cut_nan()\n",
+    "gsf0_no_nan.to_MeV()\n",
+    "\n",
+    "E_nld = nld0_no_nan.E.copy()\n",
+    "print(E_nld)\n",
+    "\n",
+    "Sn = 7.658 # MeV\n",
+    "rhoSn = [1.916E+06, 3.503E+05]\n",
+    "Γ_γ_obs = [113., 13.]\n",
+    "nld_range_low = [0.6, 1.7]\n",
+    "nld_range_high = [3., 6.5]\n",
+    "\n",
+    "N_ρ = len(nld0_no_nan)\n",
+    "N_T = len(firstgen.Eg)-1\n",
+    "print(len(gsf0_no_nan))\n",
+    "\n",
+    "# bins that we compare with the discrete\n",
+    "idx_discrete = np.array(range(nld0_no_nan.index(nld_range_low[0]), nld0_no_nan.index(nld_range_low[1])), dtype=int)\n",
+    "idx_model = np.array(range(nld0_no_nan.index(nld_range_high[0]), nld0_no_nan.index(nld_range_high[1])), dtype=int)\n",
+    "idx_pre = np.array(range(idx_discrete[0]), dtype=int)\n",
+    "idx_mid = np.array(range(idx_discrete[-1]+1, idx_model[0]), dtype=int)\n",
+    "idx_end = np.array(range(idx_model[-1]+1, N_ρ), dtype=int)\n",
+    "\n",
+    "print(idx_pre)\n",
+    "print(idx_discrete)\n",
+    "print(idx_mid)\n",
+    "print(idx_model)\n",
+    "print(idx_end)\n",
+    "\n",
+    "nld_discrete = om.normalizer_nld.load_levels_discrete(\"../example_data/discrete_levels_Dy164.txt\", nld0_no_nan.E)\n",
+    "nld_discrete.plot(kind='step')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8496f135-83c3-4476-b37f-84533ad2d4c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Initializing NUTS failed. Falling back to elementwise auto-assignment.\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "Slice: [x]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='0' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      0.00% [0/8000 00:00<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fg_norm, fg_std_norm = om.extractor.normalize(firstgen, firstgen_std)\n",
+    "logl = FG_loglike(firstgen, firstgen_std, nld0.E)\n",
+    "fg_op = LogLike2(logl)\n",
+    "\n",
+    "pre_points = np.ones(len(nld0.E) - N_ρ, dtype=float)\n",
+    "\n",
+    "with pm.Model() as model:\n",
+    "    \n",
+    "    # Define ρ\n",
+    "    #σ_ρ = om.FermiDirac(\"σ_ρ\", mu=1.2, lam=10., shape=N_ρ)\n",
+    "    \n",
+    "    #ρ0_pre = pm.HalfFlat(\"ρ0_pre\", shape=len(idx_pre))\n",
+    "    #ρ0_mid = pm.HalfFlat(\"ρ0_mid\", shape=len(idx_mid))\n",
+    "    #ρ0_end = pm.HalfFlat(\"ρ0_end\", shape=len(idx_end))\n",
+    "    \n",
+    "    #ρ_pre = pm.Normal(\"ρ_pre\", mu=ρ0_pre, sigma=σ_ρ[idx_pre]*ρ0_pre, shape=len(idx_pre))\n",
+    "    #ρ_mid = pm.Normal(\"ρ_mid\", mu=ρ0_mid, sigma=σ_ρ[idx_mid]*ρ0_mid, shape=len(idx_mid))\n",
+    "    #ρ_end = pm.Normal(\"ρ_end\", mu=ρ0_end, sigma=σ_ρ[idx_end]*ρ0_end, shape=len(idx_end))\n",
+    "    \n",
+    "    #Temp = om.FermiDirac(\"Temp\", mu=2., lam=10.)\n",
+    "    #E0 = pm.Normal(\"E0\", mu=0, sigma=10.)\n",
+    "    \n",
+    "    #ρ_CT = pm.math.exp((E_nld[idx_model] - E0)/Temp)/Temp\n",
+    "    \n",
+    "    \n",
+    "    #ρ_discrete = pm.Normal(\"ρ_discrete\", mu=nld_discrete.values[idx_discrete], \n",
+    "    #                       sigma=nld_discrete.values[idx_discrete]*σ_ρ[idx_discrete], shape=len(idx_discrete))\n",
+    "    \n",
+    "    #ρ_model = pm.Normal(\"ρ_model\", mu=ρ_CT, sigma=ρ_CT*σ_ρ[idx_model], shape=len(idx_model))\n",
+    "    \n",
+    "    # Next we define ρ proper\n",
+    "    #ρ = pm.math.concatenate((ρ_pre, ρ_discrete, ρ_mid, ρ_model, ρ_end))\n",
+    "    #ρ = pm.HalfFlat(\"ρ\", shape=N_ρ)\n",
+    "    \n",
+    "    # Define transmission coefficients\n",
+    "    #σ_T = om.FermiDirac(\"σ_ρ\", mu=1.2, lam=10., shape=N_T)\n",
+    "    #T = pm.HalfFlat(\"T\", shape=N_T)\n",
+    "    \n",
+    "    # Models\n",
+    "    \n",
+    "    #Γ_γ_mu = pm.math.sum(T) # At the moment we will use a super hacky solution. We do not implement the integral just yet\n",
+    "    \n",
+    "    #ρ_mat = pm.math.concatenate((theano.tensor.as_tensor_variable(pre_points), ρ))\n",
+    "    #T_mat = pm.math.concatenate((T, theano.tensor.as_tensor_variable(np.array([0], dtype=float))))\n",
+    "    x = pm.HalfFlat(\"x\", shape=len(firstgen.Eg)+len(nld0))\n",
+    "    #x = pm.math.concatenate((T_mat, ρ_mat))\n",
+    "    \n",
+    "    #Pth = fg_op(x)\n",
+    "    #Pth = ρ_mat * T_mat\n",
+    "    #Pth /= pm.math.sum(Pth, axis=1, keepdims=True)\n",
+    "    \n",
+    "    # We flatten and remove all that are not\n",
+    "    #mu_FG = pm.math.flatten(Pth)[index_keep]\n",
+    "    \n",
+    "    # Observables\n",
+    "    \n",
+    "    #Γ_γ = pm.Normal(\"Γ_γ\", mu=Γ_γ_mu, sigma=Γ_γ_obs[1], observed=Γ_γ_obs[0])\n",
+    "    #ρ_Sn = pm.Normal(\"ρ_Sn\", mu=pm.math.exp((Sn - E0)/Temp)/Temp, sigma=rhoSn[1], observed=rhoSn[0])\n",
+    "    #P = pm.Normal(\"P\", mu=mu_FG, sigma=fg_std_norm.flatten()[index_keep], observed=fg_norm.flatten()[index_keep])\n",
+    "    P = pm.DensityDist(\"P\", lambda x: fg_op(x), observed={'x': x})\n",
+    "    \n",
+    "    # Sample\n",
+    "    trace = pm.sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7eb51c06-d0d2-4158-b054-408d5e00d64c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Initializing NUTS failed. Falling back to elementwise auto-assignment.\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">Slice: [c]\n",
+      ">Slice: [m]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='16000' class='' max='16000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [16000/16000 00:06<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 16 seconds.\n"
+     ]
+    },
+    {
+     "ename": "MissingInputError",
+     "evalue": "Input 0 of the graph (indices start from 0), used to compute sigmoid(c_interval__), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMissingInputError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/cb/ly7f8x1n48q9lkmqt6c6lyv00000gn/T/ipykernel_27971/1192511093.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDensityDist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"likelihood\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlogl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndraws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnburn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiscard_tuned_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)\u001b[0m\n\u001b[1;32m    637\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0midata_kwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m             \u001b[0mikwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midata_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m         \u001b[0midata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marviz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pymc3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mikwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcompute_convergence_checks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/arviz/data/io_pymc3.py\u001b[0m in \u001b[0;36mfrom_pymc3\u001b[0;34m(trace, prior, posterior_predictive, log_likelihood, coords, dims, model, save_warmup, density_dist_obs)\u001b[0m\n\u001b[1;32m    561\u001b[0m     \u001b[0mInferenceData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m     \"\"\"\n\u001b[0;32m--> 563\u001b[0;31m     return PyMC3Converter(\n\u001b[0m\u001b[1;32m    564\u001b[0m         \u001b[0mtrace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0mprior\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprior\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/arviz/data/io_pymc3.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, trace, prior, posterior_predictive, log_likelihood, predictions, coords, dims, model, save_warmup, density_dist_obs)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdensity_dist_obs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdensity_dist_obs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmulti_observations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_observations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfind_observations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/arviz/data/io_pymc3.py\u001b[0m in \u001b[0;36mfind_observations\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdensity_dist_obs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m                     \u001b[0mmulti_observations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"eval\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobservations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_observations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/graph/basic.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, inputs_to_values)\u001b[0m\n\u001b[1;32m    552\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs_to_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fn_cache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fn_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minputs_to_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/compile/function/__init__.py\u001b[0m in \u001b[0;36mfunction\u001b[0;34m(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0;31m# note: pfunc will also call orig_function -- orig_function is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0;31m#      a choke point that all compilation must pass through\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m         fn = pfunc(\n\u001b[0m\u001b[1;32m    338\u001b[0m             \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/compile/function/pfunc.py\u001b[0m in \u001b[0;36mpfunc\u001b[0;34m(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[1;32m    522\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 524\u001b[0;31m     return orig_function(\n\u001b[0m\u001b[1;32m    525\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m         \u001b[0mcloned_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/compile/function/types.py\u001b[0m in \u001b[0;36morig_function\u001b[0;34m(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[1;32m   1968\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0mMaker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"function_maker\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFunctionMaker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1970\u001b[0;31m         m = Maker(\n\u001b[0m\u001b[1;32m   1971\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1972\u001b[0m             \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/compile/function/types.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys, name)\u001b[0m\n\u001b[1;32m   1582\u001b[0m             \u001b[0;31m# make the fgraph (copies the graph, creates NEW INPUT AND\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1583\u001b[0m             \u001b[0;31m# OUTPUT VARIABLES)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1584\u001b[0;31m             \u001b[0mfgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madditional_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstd_fgraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_inplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1585\u001b[0m             \u001b[0mfgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1586\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/compile/function/types.py\u001b[0m in \u001b[0;36mstd_fgraph\u001b[0;34m(input_specs, output_specs, accept_inplace)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0morig_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mspec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutput_specs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m     \u001b[0mfgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFunctionGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate_mapping\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mupdate_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/graph/fg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, inputs, outputs, features, clone, update_mapping)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_var\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"init\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclients\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"output\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/graph/fg.py\u001b[0m in \u001b[0;36mimport_var\u001b[0;34m(self, var, reason)\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0;31m# Imports the owners of the variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m         elif (\n\u001b[1;32m    332\u001b[0m             \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/site-packages/theano/graph/fg.py\u001b[0m in \u001b[0;36mimport_node\u001b[0;34m(self, apply_node, check, reason)\u001b[0m\n\u001b[1;32m    381\u001b[0m                             \u001b[0;34m\"for more information on this error.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m                         )\n\u001b[0;32m--> 383\u001b[0;31m                         \u001b[0;32mraise\u001b[0m \u001b[0mMissingInputError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mMissingInputError\u001b[0m: Input 0 of the graph (indices start from 0), used to compute sigmoid(c_interval__), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error."
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "def my_model(theta, x):\n",
+    "    m, c = theta\n",
+    "    return m*x + c\n",
+    "\n",
+    "# set up our data\n",
+    "N = 10  # number of data points\n",
+    "sigma = 1.0  # standard deviation of noise\n",
+    "x = np.linspace(0.0, 9.0, N)\n",
+    "\n",
+    "mtrue = 0.4  # true gradient\n",
+    "ctrue = 3.0  # true y-intercept\n",
+    "\n",
+    "truemodel = my_model([mtrue, ctrue], x)\n",
+    "\n",
+    "# make data\n",
+    "np.random.seed(716742)  # set random seed, so the data is reproducible each time\n",
+    "data = sigma * np.random.randn(N) + truemodel\n",
+    "\n",
+    "ndraws = 3000  # number of draws from the distribution\n",
+    "nburn = 1000  # number of \"burn-in points\" (which we'll discard)\n",
+    "\n",
+    "# create our Op\n",
+    "logl = LogLike(loglike, data, x, sigma)\n",
+    "\n",
+    "# use PyMC3 to sampler from log-likelihood\n",
+    "with pm.Model():\n",
+    "    # uniform priors on m and c\n",
+    "    m = pm.Uniform(\"m\", lower=-10.0, upper=10.0)\n",
+    "    c = pm.Uniform(\"c\", lower=-10.0, upper=10.0)\n",
+    "\n",
+    "    # convert m and c to a tensor vector\n",
+    "    theta = tt.as_tensor_variable([m, c])\n",
+    "\n",
+    "    # use a DensityDist (use a lamdba function to \"call\" the Op)\n",
+    "    pm.DensityDist(\"likelihood\", lambda v: logl(v), observed={\"v\": theta})\n",
+    "\n",
+    "    trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb86dd80-036f-403f-8b17-16b139af7257",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FG_loglike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "e7183e98-821d-4df0-86ab-10dac4f10197",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51c102b5ef0f41aeb6421c60c0b63b04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test a toy model to make sure it works...\n",
+    "nld_values = np.concatenate((np.mean(trace['ρ_pre'], axis=0), np.mean(trace['ρ_discrete'], axis=0),\n",
+    "                             np.mean(trace['ρ_mid'], axis=0), np.mean(trace['ρ_model'], axis=0),\n",
+    "                             np.mean(trace['ρ_end'], axis=0)))\n",
+    "nld_std = np.concatenate((np.std(trace['ρ_pre'], axis=0), np.std(trace['ρ_discrete'], axis=0),\n",
+    "                          np.std(trace['ρ_mid'], axis=0), np.std(trace['ρ_model'], axis=0),\n",
+    "                          np.std(trace['ρ_end'], axis=0)))\n",
+    "\n",
+    "nld_normed = om.Vector(E=E_nld, values=nld_values, std=nld_std)\n",
+    "fig, ax = nld_normed.plot()\n",
+    "nld_discrete.plot(ax=ax, kind='step')\n",
+    "ax.errorbar(Sn, rhoSn[0], yerr=rhoSn[1], fmt=\"o\")\n",
+    "ax.plot(np.linspace(3, Sn, 1001), np.exp((np.linspace(3, Sn, 1001) - np.mean(trace['E0']))/np.mean(trace['Temp']))/np.mean(trace['']))\n",
+    "ax.semilogy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3789a2ee-2efa-42d6-a986-af2e708e1d9f",
+   "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/model_op.py b/notebooks/model_op.py
new file mode 100644
index 00000000..9ef169fe
--- /dev/null
+++ b/notebooks/model_op.py
@@ -0,0 +1,194 @@
+import numpy as np
+import ompy as om
+import theano.tensor as tt
+import pymc3 as pm
+
+def diagonal_resolution(matrix, resolution_Ex):
+    """Detector resolution at the Ex=Eg diagonal
+
+    Uses gaussian error propagations which assumes independence of
+    resolutions along Ex and Eg axis.
+
+    Args:
+        matrix (Matrix): Matrix for which the sesoluton shall be calculated
+
+    Returns:
+        resolution at Ex = Eg.
+    """
+    def resolution_Eg(matrix):
+        """Resolution along Eg axis for each Ex. Defaults in this class are for OSCAR.
+
+        Args:
+            matrix (Matrix): Matrix for which the sesoluton shall be calculated
+
+        Returns:
+            resolution
+        """
+        def fFWHM(E, p):
+            return np.sqrt(p[0] + p[1] * E + p[2] * E**2)
+        fwhm_pars = np.array([73.2087, 0.50824, 9.62481e-05])
+        return fFWHM(matrix.Ex, fwhm_pars)
+    
+    dEx = matrix.Ex[1] - matrix.Ex[0]
+    dEg = matrix.Eg[1] - matrix.Eg[0]
+    assert dEx == dEg
+
+    dE_resolution = np.sqrt(resolution_Ex**2
+                            + resolution_Eg(matrix)**2)
+    return dE_resolution
+
+
+
+
+# Ops to calculate the FG matrix from the NLDs and Ts
+class calculate_FG(tt.Op):
+    
+    itypes = [tt.dvector]  # expects a vector of parameter values when called
+    otypes = [tt.dscalar]  # outputs a single scalar value (the log likelihood)
+    
+    def __init__(self, matrix, std, E_nld):
+        
+        self.matrix = matrix.copy()
+        self.std = std.copy()
+        self.resolution = diagonal_resolution(matrix, 150.)
+        self.E_nld = E_nld.copy(order='C')
+        
+        self.matrix.values, self.std.values = om.extractor.normalize(self.matrix, self.std)
+        
+        self.std.values = std.values.copy(order='C')
+        self.matrix.Ex = self.matrix.Ex.copy(order='C')
+        self.matrix.Eg = self.matrix.Eg.copy(order='C')
+        self.matrix.values = self.matrix.values.copy(order='C')
+        
+        
+    def perform(self, node, inputs, outputs):
+        
+        (x,) = inputs
+        T = x[:self.matrix.Eg.size]
+        nld = x[self.matrix.Eg.size:]
+        
+        fg_th = om.decomposition.nld_T_product(nld, T, self.resolution, self.E_nld,
+                                               self.matrix.Eg, self.matrix.Ex)
+        
+        z = -0.5*np.array(om.decomposition.chisquare_diagonal(self.matrix.values, fg_th,
+                                                              self.std.values, self.resolution,
+                                                              self.matrix.Eg, self.matrix.Ex))
+        outputs[0][0] = z
+        print(z)
+        #return outputs[0][0]
+
+class FG_loglike:
+    def __init__(self, matrix, std, E_nld):
+        
+        self.matrix = matrix.copy()
+        self.std = std.copy()
+        self.resolution = diagonal_resolution(matrix, 150.)
+        self.E_nld = E_nld.copy(order='C')
+        
+        self.matrix.values, self.std.values = om.extractor.normalize(self.matrix, self.std)
+        
+        self.std.values = std.values.copy(order='C')
+        self.matrix.Ex = self.matrix.Ex.copy(order='C')
+        self.matrix.Eg = self.matrix.Eg.copy(order='C')
+        self.matrix.values = self.matrix.values.copy(order='C')
+    
+    def __call__(self, x):
+        T = x[:self.matrix.Eg.size]
+        nld = x[self.matrix.Eg.size:]
+        
+        fg_th = om.decomposition.nld_T_product(nld, T, self.resolution, self.E_nld,
+                                               self.matrix.Eg, self.matrix.Ex)
+        
+        return om.decomposition.chisquare_diagonal(self.matrix.values, fg_th,
+                                                   self.std.values, self.resolution,
+                                                   self.matrix.Eg, self.matrix.Ex)
+
+class LogLike2(tt.Op):
+    """
+    Specify what type of object will be passed and returned to the Op when it is
+    called. In our case we will be passing it a vector of values (the parameters
+    that define our model) and returning a single "scalar" value (the
+    log-likelihood)
+    """
+
+    itypes = [tt.dvector]  # expects a vector of parameter values when called
+    otypes = [tt.dscalar]  # outputs a single scalar value (the log likelihood)
+
+    def __init__(self, loglike):
+        """
+        Initialise the Op with various things that our log-likelihood function
+        requires. Below are the things that are needed in this particular
+        example.
+
+        Parameters
+        ----------
+        loglike:
+            The log-likelihood (or whatever) function we've defined
+        data:
+            The "observed" data that our log-likelihood function takes in
+        x:
+            The dependent variable (aka 'x') that our model requires
+        sigma:
+            The noise standard deviation that our function requires.
+        """
+
+        # add inputs as class attributes
+        self.likelihood = loglike
+
+    def perform(self, node, inputs, outputs):
+        # the method that is used when calling the Op
+        (theta,) = inputs  # this will contain my variables
+
+        # call the log-likelihood function
+        logl = self.likelihood(theta)
+
+        outputs[0][0] = np.array(logl)  # output the log-likelihood
+
+def loglike(theta, x, data, sigma):
+    return -0.5*np.sum(theta)
+        
+# define a theano Op for our likelihood function
+class LogLike(tt.Op):
+
+    """
+    Specify what type of object will be passed and returned to the Op when it is
+    called. In our case we will be passing it a vector of values (the parameters
+    that define our model) and returning a single "scalar" value (the
+    log-likelihood)
+    """
+
+    itypes = [tt.dvector]  # expects a vector of parameter values when called
+    otypes = [tt.dscalar]  # outputs a single scalar value (the log likelihood)
+
+    def __init__(self, loglike, data, x, sigma):
+        """
+        Initialise the Op with various things that our log-likelihood function
+        requires. Below are the things that are needed in this particular
+        example.
+
+        Parameters
+        ----------
+        loglike:
+            The log-likelihood (or whatever) function we've defined
+        data:
+            The "observed" data that our log-likelihood function takes in
+        x:
+            The dependent variable (aka 'x') that our model requires
+        sigma:
+            The noise standard deviation that our function requires.
+        """
+
+        # add inputs as class attributes
+        self.likelihood = loglike
+        self.data = data
+        self.x = x
+        self.sigma = sigma
+
+    def perform(self, node, inputs, outputs):
+        # the method that is used when calling the Op
+        (theta,) = inputs  # this will contain my variables
+
+        # call the log-likelihood function
+        logl = self.likelihood(theta, self.x, self.data, self.sigma)
+
+        outputs[0][0] = np.array(logl)  # output the log-likelihood
\ No newline at end of file
diff --git a/notebooks/uncertanty_by_counts.ipynb b/notebooks/uncertanty_by_counts.ipynb
new file mode 100644
index 00000000..b7c368c0
--- /dev/null
+++ b/notebooks/uncertanty_by_counts.ipynb
@@ -0,0 +1,7874 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0e6a22c9-5631-443b-98dd-e25c0cc05cf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79d7254a-6b71-460d-96f0-400bf769a436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import ompy as om\n",
+    "import logging\n",
+    "import pymc3 as pm\n",
+    "import arviz as az"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cd12b7cd-7248-4ba8-bf64-6a98b86167f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.1.0.dev0+02f42f6'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "om.__full_version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c085a2be-0201-4ecb-a972-94ec65cb5b44",
+   "metadata": {},
+   "source": [
+    "# Global settings of notebook\n",
+    "In the cell below there are a nuber of global settings that will affect the notebook from here. This is to make it easier to do a short PoC without having it to run for hours and hours.\n",
+    "\n",
+    "* `ensemble_size` controls the number of ensemble members that gets generated. Depending on the operation done the runtime may be linear or N^(N-1).\n",
+    "* `multinest_livepoints` controls the number of live points used by PyMultinest. It is recomended to use at least 400 when accuracy is important. For debugging purposes this can be lowered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dd94bf0d-0ad4-4c46-a90e-4dd30911f037",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ensemble_size = 10\n",
+    "multinest_livepoints = 300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7c5ff2cd-da69-4d52-bca1-7e723fff8c15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_random(matrix: om.Matrix, count: int, **kwargs) -> om.Matrix:\n",
+    "    \"\"\" Simple function to generate a matrix with N counts\n",
+    "    where the probability of drawing a particular bin is given\n",
+    "    by the number of counts in the input matrix.\n",
+    "    Args:\n",
+    "        matrix: (Matrix) Matrix giving the probability\n",
+    "        count: (int) Number of counts to draw\n",
+    "        **kwargs: Keyword arguments to the random number generator.\n",
+    "    Returns:\n",
+    "        A matrix-type with bin contents randomly drawn with the\n",
+    "        same distribution as the input matrix.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Step 1, make a giant array with number of entries equal to the number of counts in each bin in the matrix.\n",
+    "    coords = []\n",
+    "    probability = []\n",
+    "    for i in range(len(matrix.Ex)):\n",
+    "        for j in range(len(matrix.Eg)):\n",
+    "            coords.append([i,j])\n",
+    "            probability.append(int(matrix.values[i,j]))\n",
+    "    \n",
+    "    coords = np.array(coords)\n",
+    "    probability = np.array(probability, dtype=float)\n",
+    "    probability /= np.sum(probability)\n",
+    "    \n",
+    "    # Step 2, draw 'draws' number of elements from the list\n",
+    "    rng = np.random.default_rng(**kwargs)\n",
+    "    drawed = rng.choice(coords, size=int(count), p=probability)\n",
+    "    mat = matrix.copy()\n",
+    "    mat.values *= 0\n",
+    "    for i,j in drawed:\n",
+    "        mat.values[i,j] += 1\n",
+    "    return mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cbbf6ed-aeae-49f8-b87b-561ce1cdb208",
+   "metadata": {},
+   "source": [
+    "# Loading data\n",
+    "In this notebook we will be looking at the same data-set as in the [Getting Started](https://ompy.readthedocs.io/en/latest/getting_started.html) tutorial, but will do the analysis with three different statistics levels. To simulate low statistics we will use the `draw_random` function declared above. We will be looking at the full statistics case (5,279,152 counts), a low count case (100,000 counts) and a medium count case (250,000 counts).\n",
+    "\n",
+    "The $^{164}\\mathrm{Dy}$ data used below has been gathered from following experiment: Nyhus, H. T. *et al.* (2010). DOI: [10.1103/physrevc.81.024325](https://doi.org/10.1103/PhysRevC.81.024325)\n",
+    "and is reanalyzed in Renstrøm, T. *et al.* (2018). DOI: [10.1103/physrevc.98.054310](https://doi.org/10.1103/PhysRevC.98.054310)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ed0784d7-9111-4519-a940-4f5e33e0592c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of counts in raw matrix: 5279151.998904223\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb0a934117f249349701a8dd2b95a723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import raw matrix into instance of om.Matrix() and plot it\n",
+    "raw = om.example_raw('Dy164')\n",
+    "# To use you own data, uncomment/adapt the line below instead\n",
+    "# raw = om.Matrix(path=\"/path/to/matrix.ending\")\n",
+    "\n",
+    "print(f\"Total number of counts in raw matrix: {np.sum(raw.values)}\")\n",
+    "\n",
+    "# Cut the diagonal\n",
+    "raw.cut_diagonal(E1=(800, 0), E2=(7500, 7300))\n",
+    "raw.cut('Ex', 0, 8400)\n",
+    "\n",
+    "# Plot the entire matrix\n",
+    "raw_org = raw.copy() # workaround due to execution order in jupyter notebook\n",
+    "                     # (calculations are performed before plotting, but we make a cut to raw further down)\n",
+    "raw_org.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c653219-7b91-4e22-95e0-75c51efd0b49",
+   "metadata": {},
+   "source": [
+    "## Declare and define used classes\n",
+    "We are declaring all our analysis classes in the cells bellow as we only need to do this once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c4fd9521-72c7-403b-ae06-b1fb390802fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 09:56:07,851 - ompy.response - INFO - Note: Spectra outside of 200.0 and 20000.0 are extrapolation only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "logger = om.introspection.get_logger('response', 'INFO')\n",
+    "# Then do the same using OMpy functionality:\n",
+    "# You may need to adpot this to whereever you response matrices are stored\n",
+    "folderpath = \"../OCL_response_functions/oscar2017_scale1.15\"\n",
+    "\n",
+    "# Energy calibration of resulting response matrix:\n",
+    "Eg = raw.Eg\n",
+    "\n",
+    "# Experimental relative FWHM at 1.33 MeV of resulting array\n",
+    "fwhm_abs = 30 # (30/1330 = 2.25% )\n",
+    "\n",
+    "# Magne recommends 1/10 of the actual resolution for unfolding purposes\n",
+    "response = om.Response(folderpath)\n",
+    "R_ompy_unf, R_tab_unf = response.interpolate(Eg, fwhm_abs=fwhm_abs/10, return_table=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "62cb066b-0b79-4932-a69b-41dfef2e69c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# With compton subtraction and all tweaks\n",
+    "unfolder= om.Unfolder(response=R_ompy_unf)\n",
+    "firstgen = om.FirstGeneration()\n",
+    "unfolder.use_compton_subtraction = True # default\n",
+    "unfolder.response_tab = R_tab_unf\n",
+    "# Magne suggests some \"tweaks\" for a better unfolding performance. Default is 1 for all.\n",
+    "unfolder.FWHM_tweak_multiplier = {\"fe\": 1., \"se\": 1.1,\n",
+    "                                     \"de\": 1.3, \"511\": 0.9}\n",
+    "\n",
+    "trapezoid_cut = om.Action('matrix')\n",
+    "trapezoid_cut.trapezoid(Ex_min=4000, Ex_max=7000, Eg_min=1000, Eg_max=7000+200, inplace=True)\n",
+    "E_rebinned = np.arange(100., 8500, 200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da8405e4-6667-4da2-b30e-5717be7f72c4",
+   "metadata": {},
+   "source": [
+    "### Normalization parameters\n",
+    "See [getting started]() for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "99350fc0-4246-411b-af81-c7451ffff09a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "norm_pars = om.NormalizationParameters(name=\"164Dy\")\n",
+    "norm_pars.D0 = [6.8, 0.6]  # eV\n",
+    "norm_pars.Sn = [7.658, 0.001] # MeV\n",
+    "norm_pars.Gg = [113., 13.]  #meV\n",
+    "norm_pars.spincutModel = 'Disc_and_EB05'  # see eg. Guttormsen et al., 2017, PRC 96, 024313\n",
+    "norm_pars.spincutPars = {\"mass\":164, \"NLDa\":18.12, \"Eshift\":0.31,\n",
+    "                         \"Sn\": norm_pars.Sn[0], \"sigma2_disc\":[1.5, 3.6]}\n",
+    "norm_pars.Jtarget = 5/2 # A-1 nucleus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1933ac94-54f0-430e-a4d2-a539c7797352",
+   "metadata": {},
+   "source": [
+    "### Setup useful loggers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f9f34016-82b5-4eb3-b736-68d9660cb3ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nld_log = om.introspection.get_logger('normalizer_nld', 'INFO')\n",
+    "gsf_log = om.introspection.get_logger('normalizer_gsf', 'INFO')\n",
+    "sim_log = om.introspection.get_logger('normalizer_simultan', 'INFO')\n",
+    "ens_log = om.introspection.get_logger('ensembleNormalizer', 'INFO')\n",
+    "logger = om.introspection.get_logger('error_finder', 'DEBUG')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0a22ee-97fa-4e0b-a15e-8db8b90a0ea3",
+   "metadata": {},
+   "source": [
+    "## Declare ErrorFinder\n",
+    "This declares the error estimator using the logarithmic model. This is the more stable model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "83dbc654-9179-44fa-8caa-6661db38b7d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error_estimator = om.error_finder.ErrorFinder(algorithm='log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc7d94d1-93ff-4840-9cf6-d8f27941758f",
+   "metadata": {},
+   "source": [
+    "## Test dependence on statistics\n",
+    "In the next few cells we will generate input matrices with various number of counts. This is to simulate the effect of statistics has on the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "96b883fc-66f6-4c95-8c76-6ece9f000c69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_orig = raw.copy()\n",
+    "raw_orig.fill_and_remove_negative(window_size=2)\n",
+    "\n",
+    "counts = [25000, 50000, 75000, 100000, 150000, 200000, 250000, 500000, 750000, 1000000, 1500000, 2000000]\n",
+    "raw_matrices = [draw_random(raw_orig, count) for count in counts]\n",
+    "raw_matrices.append(raw_orig.copy())\n",
+    "counts.append(int(raw_orig.counts))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f9f6d2-347d-48e1-b921-b6860ddce67a",
+   "metadata": {},
+   "source": [
+    "### Setup the ensembles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f7f32cc8-8074-4959-88ce-c008954ad525",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a891a1cf71644fae9304b8606cadc53d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+    },
+    {
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+    },
+    {
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+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
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+     "output_type": "display_data"
+    },
+    {
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+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
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+    },
+    {
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+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
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+     "metadata": {},
+     "output_type": "display_data"
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+       "model_id": "edcc6efe59454cd291bc8b00366f7560",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "450d605417bd4b19b277824744d12e45",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e70bd2e6e0154f888fe585b7063c0017",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
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+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d315dfe6886249f5ba3a8cce3a7aae72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a34247510d1431c8461c1cab37ce18d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ad573addbf14673b52780491c5447d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ad611a219c149feb8bfe50c0e20aac5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c79ac81e871147a9a85f0af91e965b46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f1d6fcf31024348bf3e4c4ba089496b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ensembles = [om.Ensemble(raw=raw) for raw in raw_matrices]\n",
+    "for ensemble in ensembles:\n",
+    "    ensemble.unfolder = unfolder\n",
+    "    ensemble.first_generation_method = firstgen\n",
+    "    ensemble.generate(ensemble_size, regenerate=True)\n",
+    "    ensemble.rebin(E_rebinned, member=\"firstgen\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdce5960-a2aa-4c10-8879-97df3525f027",
+   "metadata": {},
+   "source": [
+    "### Setup extractors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1fb1aaba-2350-4cea-8cd7-71884eed65d8",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5343d3753814dcea4edf586d445389c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a198120d47c4d25b4efcba966c0ef2a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c49f6c1689684ac6a80d20ca898db245",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a5b5d0fe85c41a28fdda1ee7eedbb88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dc24e35038349aebb87fdb2a6654774",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d4c58d2a0d6416eab596c6126b6f95b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78de5519e5624725b425665dee5ebfa4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3bd211fc87b4968911f7cd574c97b4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d286e674b04241ebb419b3936d56d589",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d20d142a7ec4a9eb98275b95a805201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbf093143ca34bda8b057731876ad0d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d28d2cf4096442f1a9f6019b47b9ff08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e843ce81885e4501b71c0cd0969b93b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "extractors = [om.Extractor() for raw in raw_matrices]\n",
+    "for extractor, ensemble in zip(extractors, ensembles):\n",
+    "    extractor.trapezoid = trapezoid_cut\n",
+    "    extractor.extract_from(ensemble, regenerate=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54927ecd-ee65-4dc9-9f13-55c575340daf",
+   "metadata": {},
+   "source": [
+    "### Estimate relative uncertanties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "52ca7f94-a8ea-4729-8eef-c1a0629b91b8",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:15:42,736 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:15:42,737 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:15:42,739 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:15:42,740 - ompy.error_finder - WARNING - Some members of the ensemble have different lengths. Consider re-binning or changing limits.\n",
+      "2021-08-12 10:15:42,743 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 27 GSF values\n",
+      "2021-08-12 10:15:42,745 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:05<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 76 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:17:25,444 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.27 │ 0.00 ± 0.17 │ -0.000 ± 0.049 │ 0.276 ± 0.022 │ 0.168 ± 0.015 │ 0.0492 ± 0.0043 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 37.0 ± 2.9   │ 13.2 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 32.5 ± 2.5   │ 11.39 ± 0.98 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 46.5 ± 3.7   │ 10.11 ± 0.87 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 20.6 ± 1.7   │ 15.1 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 18.0 ± 1.5   │ 9.25 ± 0.78  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 22.7 ± 1.8   │ 9.57 ± 0.79  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 18.7 ± 1.5   │ 12.20 ± 0.98 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 12.2 ± 1.0   │ 14.6 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 15.3 ± 1.1   │ 18.7 ± 1.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 9.61 ± 0.82  │ 5.11 ± 0.57  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 13.8 ± 1.1   │ 11.37 ± 0.92 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 10.21 ± 0.82 │ 12.2 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 5.53 ± 0.56  │ 14.0 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 11.42 ± 0.88 │ 9.08 ± 0.80  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 10.62 ± 0.84 │ 17.0 ± 1.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 12.33 ± 1.00 │ 13.6 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 10.08 ± 0.82 │ 26.6 ± 2.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 10.51 ± 0.86 │ 20.6 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 16.0 ± 1.3   │ 42.3 ± 3.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 17.9 ± 1.4   │ 34.4 ± 2.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 13.3 ± 1.1   │ 54.1 ± 4.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 13.7 ± 1.2   │ 39.6 ± 3.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 14.6 ± 1.2   │ 88.1 ± 6.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 13.9 ± 1.1   │ 56.9 ± 4.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 17.8 ± 1.4   │ 61.2 ± 4.7   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 29.5 ± 2.2   │ 82.3 ± 6.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 15.1 ± 1.3   │ 68.6 ± 5.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 27.0 ± 2.1   │              │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 52.3 ± 4.2   │              │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 44.4 ± 3.5   │              │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:17:25,462 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:17:25,463 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:17:25,467 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:17:25,468 - ompy.error_finder - WARNING - Some members of the ensemble have different lengths. Consider re-binning or changing limits.\n",
+      "2021-08-12 10:17:25,471 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 28 GSF values\n",
+      "2021-08-12 10:17:25,473 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
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+       "                background-size: auto;\n",
+       "            }\n",
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+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:07<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 78 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:18:55,744 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.33 │ -0.00 ± 0.31 │ 0.000 ± 0.067 │ 0.332 ± 0.026 │ 0.309 ± 0.023 │ 0.0679 ± 0.0054 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 165 ± 11     │ 13.4 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 12.3 ± 1.0   │ 5.29 ± 0.58  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 14.7 ± 1.2   │ 8.79 ± 0.73  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 11.7 ± 1.0   │ 12.10 ± 0.95 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 13.2 ± 1.1   │ 11.97 ± 0.95 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 11.68 ± 0.98 │ 10.04 ± 0.85 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 13.8 ± 1.1   │ 11.85 ± 0.94 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 17.1 ± 1.4   │ 6.85 ± 0.63  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 18.0 ± 1.4   │ 7.35 ± 0.64  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 11.96 ± 0.93 │ 8.41 ± 0.71  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 7.29 ± 0.63  │ 11.66 ± 0.91 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 11.37 ± 0.91 │ 10.07 ± 0.81 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 6.73 ± 0.59  │ 9.23 ± 0.77  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 7.71 ± 0.63  │ 13.4 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 9.27 ± 0.74  │ 12.8 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 10.91 ± 0.84 │ 12.7 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 7.62 ± 0.65  │ 11.93 ± 0.97 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 13.9 ± 1.1   │ 12.7 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 6.07 ± 0.56  │ 16.1 ± 1.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 7.68 ± 0.66  │ 14.0 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 9.43 ± 0.77  │ 27.7 ± 2.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 14.6 ± 1.1   │ 44.9 ± 3.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 18.4 ± 1.5   │ 31.8 ± 2.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 14.7 ± 1.2   │ 25.9 ± 2.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 20.6 ± 1.6   │ 49.1 ± 3.8   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 16.4 ± 1.3   │ 39.6 ± 3.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 27.1 ± 2.0   │ 251 ± 16     │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 17.5 ± 1.5   │ 108.8 ± 7.5  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 33.2 ± 2.5   │              │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 38.6 ± 3.0   │              │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:18:55,759 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:18:55,760 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:18:55,761 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:18:55,762 - ompy.error_finder - WARNING - Some members of the ensemble have different lengths. Consider re-binning or changing limits.\n",
+      "2021-08-12 10:18:55,765 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 28 GSF values\n",
+      "2021-08-12 10:18:55,767 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
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+       "                border: none;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:11<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 82 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:20:28,338 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.28 │ 0.00 ± 0.18 │ -0.000 ± 0.068 │ 0.289 ± 0.022 │ 0.185 ± 0.015 │ 0.0690 ± 0.0057 │\n",
+      "└──────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 8.35 ± 0.72  │ 8.80 ± 0.73  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 10.91 ± 0.88 │ 7.14 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 11.41 ± 0.93 │ 10.56 ± 0.85 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 11.15 ± 0.89 │ 9.76 ± 0.80  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 16.1 ± 1.2   │ 4.90 ± 0.49  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 8.49 ± 0.69  │ 4.58 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 13.2 ± 1.1   │ 10.88 ± 0.83 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 14.7 ± 1.1   │ 10.60 ± 0.86 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 8.45 ± 0.68  │ 7.18 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 12.42 ± 0.97 │ 9.61 ± 0.76  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 3.96 ± 0.37  │ 11.02 ± 0.87 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 6.51 ± 0.53  │ 10.08 ± 0.79 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 4.68 ± 0.41  │ 6.97 ± 0.59  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 6.32 ± 0.52  │ 9.84 ± 0.78  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 8.88 ± 0.70  │ 7.20 ± 0.60  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 7.13 ± 0.58  │ 12.09 ± 0.97 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 9.00 ± 0.69  │ 13.1 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 8.25 ± 0.66  │ 11.26 ± 0.89 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 15.9 ± 1.2   │ 12.5 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 10.93 ± 0.87 │ 15.2 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 8.41 ± 0.70  │ 21.0 ± 1.7   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 9.89 ± 0.80  │ 25.4 ± 2.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 15.6 ± 1.2   │ 19.5 ± 1.5   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 8.90 ± 0.70  │ 41.2 ± 3.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 16.3 ± 1.3   │ 11.40 ± 0.98 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 6.92 ± 0.68  │ 22.7 ± 1.8   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 27.5 ± 2.2   │ 50.6 ± 3.9   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 27.1 ± 2.0   │ 55.5 ± 4.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 43.8 ± 3.3   │              │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 45.0 ± 3.5   │              │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:20:28,354 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:20:28,355 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:20:28,356 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:20:28,357 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:20:28,359 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:11<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
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+      ],
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+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 81 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:22:02,477 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.28 │ -0.00 ± 0.21 │ 0.000 ± 0.066 │ 0.288 ± 0.023 │ 0.216 ± 0.017 │ 0.0671 ± 0.0052 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 10.89 ± 0.93 │ 6.94 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 10.74 ± 0.88 │ 5.37 ± 0.50  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 10.79 ± 0.87 │ 8.28 ± 0.68  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 11.38 ± 0.94 │ 11.25 ± 0.89 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 11.86 ± 0.97 │ 10.91 ± 0.86 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 10.77 ± 0.85 │ 5.14 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 12.8 ± 1.0   │ 10.58 ± 0.84 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 7.45 ± 0.64  │ 6.96 ± 0.56  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 11.98 ± 0.94 │ 6.10 ± 0.51  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 10.83 ± 0.86 │ 6.64 ± 0.55  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 8.95 ± 0.72  │ 8.04 ± 0.66  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 8.12 ± 0.65  │ 6.54 ± 0.54  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 8.44 ± 0.68  │ 9.41 ± 0.76  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 6.12 ± 0.51  │ 8.61 ± 0.69  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 8.22 ± 0.67  │ 5.35 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 10.93 ± 0.85 │ 11.18 ± 0.87 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 11.09 ± 0.89 │ 14.2 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 5.20 ± 0.47  │ 14.1 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 9.91 ± 0.77  │ 15.3 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 8.42 ± 0.68  │ 13.2 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 9.19 ± 0.78  │ 14.0 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 12.29 ± 0.96 │ 16.7 ± 1.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 9.64 ± 0.79  │ 15.1 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 10.71 ± 0.85 │ 28.8 ± 2.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 10.58 ± 0.88 │ 17.8 ± 1.5   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 12.46 ± 0.99 │ 21.6 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 20.4 ± 1.6   │ 49.9 ± 3.8   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 18.4 ± 1.4   │ 62.5 ± 4.9   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 16.3 ± 1.3   │ 54.9 ± 4.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 58.4 ± 4.3   │ 66.1 ± 5.0   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:22:02,494 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:22:02,495 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:22:02,497 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:22:02,497 - ompy.error_finder - WARNING - Some members of the ensemble have different lengths. Consider re-binning or changing limits.\n",
+      "2021-08-12 10:22:02,500 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 29 GSF values\n",
+      "2021-08-12 10:22:02,502 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:14<00:00 Sampling 4 chains, 0 divergences]\n",
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 84 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:23:39,646 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.29 │ -0.00 ± 0.21 │ 0.000 ± 0.057 │ 0.297 ± 0.023 │ 0.216 ± 0.017 │ 0.0578 ± 0.0046 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 13.7 ± 1.1   │ 15.0 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 5.71 ± 0.58  │ 10.84 ± 0.85 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 12.8 ± 1.0   │ 10.36 ± 0.81 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 16.9 ± 1.4   │ 9.92 ± 0.78  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 9.14 ± 0.77  │ 6.45 ± 0.54  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 7.29 ± 0.61  │ 4.78 ± 0.42  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 6.12 ± 0.54  │ 7.51 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 9.69 ± 0.79  │ 7.16 ± 0.57  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 7.11 ± 0.59  │ 8.24 ± 0.66  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 6.48 ± 0.53  │ 2.47 ± 0.30  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 5.77 ± 0.45  │ 4.42 ± 0.40  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 8.74 ± 0.70  │ 7.79 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 4.02 ± 0.37  │ 10.98 ± 0.85 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 3.98 ± 0.38  │ 8.38 ± 0.65  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 5.55 ± 0.47  │ 9.24 ± 0.73  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 8.45 ± 0.67  │ 7.56 ± 0.62  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 10.47 ± 0.81 │ 6.20 ± 0.51  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 13.6 ± 1.1   │ 5.99 ± 0.53  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 6.83 ± 0.56  │ 11.54 ± 0.91 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 17.1 ± 1.3   │ 15.6 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 12.28 ± 0.94 │ 17.3 ± 1.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 8.87 ± 0.73  │ 17.2 ± 1.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 8.64 ± 0.72  │ 18.7 ± 1.5   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 18.2 ± 1.4   │ 29.0 ± 2.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 12.03 ± 0.99 │ 15.6 ± 1.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 9.97 ± 0.85  │ 17.8 ± 1.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 17.5 ± 1.4   │ 44.9 ± 3.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 25.6 ± 1.9   │ 71.0 ± 5.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 22.5 ± 1.7   │ 52.1 ± 4.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 46.4 ± 3.5   │              │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:23:39,664 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:23:39,665 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:23:39,666 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:23:39,667 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:23:39,668 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "      100.00% [8000/8000 01:22<00:00 Sampling 4 chains, 0 divergences]\n",
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+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 93 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:25:25,352 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.25 │ -0.00 ± 0.23 │ 0.000 ± 0.061 │ 0.258 ± 0.020 │ 0.231 ± 0.018 │ 0.0617 ± 0.0049 │\n",
+      "└─────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 8.81 ± 0.73  │ 5.59 ± 0.50  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 9.00 ± 0.73  │ 8.70 ± 0.71  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 6.67 ± 0.58  │ 7.57 ± 0.60  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 12.36 ± 0.97 │ 5.81 ± 0.49  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 8.48 ± 0.68  │ 4.94 ± 0.42  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 7.28 ± 0.60  │ 8.18 ± 0.69  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 9.92 ± 0.79  │ 5.57 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 11.70 ± 0.92 │ 4.97 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 9.33 ± 0.72  │ 5.85 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 6.06 ± 0.51  │ 4.03 ± 0.35  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 3.13 ± 0.31  │ 8.89 ± 0.71  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 7.59 ± 0.60  │ 11.54 ± 0.91 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 5.97 ± 0.49  │ 6.97 ± 0.55  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 8.45 ± 0.67  │ 5.94 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 6.03 ± 0.49  │ 9.60 ± 0.76  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 3.59 ± 0.32  │ 9.45 ± 0.73  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 6.43 ± 0.52  │ 11.11 ± 0.88 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 5.78 ± 0.47  │ 5.69 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 7.46 ± 0.60  │ 4.11 ± 0.41  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 10.10 ± 0.81 │ 9.68 ± 0.76  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 6.14 ± 0.52  │ 14.6 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 9.45 ± 0.75  │ 21.5 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 9.41 ± 0.75  │ 12.74 ± 0.98 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 11.51 ± 0.91 │ 14.0 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 10.63 ± 0.87 │ 20.8 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 10.27 ± 0.86 │ 24.2 ± 1.9   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 15.4 ± 1.2   │ 16.5 ± 1.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 15.1 ± 1.2   │ 26.2 ± 2.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 15.9 ± 1.2   │ 37.8 ± 3.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 37.9 ± 2.9   │ 107.4 ± 7.2  │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:25:25,371 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:25:25,372 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:25:25,374 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:25:25,374 - ompy.error_finder - WARNING - Some members of the ensemble have different lengths. Consider re-binning or changing limits.\n",
+      "2021-08-12 10:25:25,377 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 29 GSF values\n",
+      "2021-08-12 10:25:25,378 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:13<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 84 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:27:03,190 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.25 │ -0.00 ± 0.23 │ 0.000 ± 0.050 │ 0.251 ± 0.019 │ 0.233 ± 0.018 │ 0.0502 ± 0.0039 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 7.74 ± 0.65  │ 5.80 ± 0.52  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 7.71 ± 0.64  │ 5.16 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 6.03 ± 0.51  │ 7.43 ± 0.60  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 7.55 ± 0.63  │ 8.40 ± 0.68  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 8.26 ± 0.68  │ 7.28 ± 0.59  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 9.02 ± 0.73  │ 5.78 ± 0.51  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 5.43 ± 0.46  │ 5.69 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 12.64 ± 0.98 │ 5.43 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 7.77 ± 0.62  │ 6.89 ± 0.56  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 5.22 ± 0.42  │ 8.54 ± 0.70  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 5.20 ± 0.43  │ 10.04 ± 0.77 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 5.88 ± 0.48  │ 5.97 ± 0.50  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 5.42 ± 0.44  │ 7.41 ± 0.58  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 8.37 ± 0.63  │ 8.78 ± 0.72  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 6.30 ± 0.51  │ 11.10 ± 0.86 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 5.65 ± 0.45  │ 8.09 ± 0.66  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 6.69 ± 0.54  │ 6.47 ± 0.55  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 4.46 ± 0.40  │ 7.41 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 7.88 ± 0.62  │ 9.96 ± 0.82  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 11.97 ± 0.91 │ 14.8 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 5.03 ± 0.46  │ 11.17 ± 0.89 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 4.08 ± 0.42  │ 12.8 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 9.43 ± 0.76  │ 12.09 ± 0.97 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 13.7 ± 1.1   │ 19.3 ± 1.5   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 13.0 ± 1.0   │ 20.2 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 14.3 ± 1.2   │ 20.5 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 14.7 ± 1.1   │ 20.1 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 11.37 ± 0.92 │ 30.7 ± 2.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 29.5 ± 2.3   │ 61.1 ± 4.8   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 24.4 ± 1.9   │              │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:27:03,209 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:27:03,211 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:27:03,213 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:27:03,214 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:27:03,217 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 88 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:28:44,238 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.27 │ 0.00 ± 0.26 │ -0.000 ± 0.060 │ 0.273 ± 0.021 │ 0.266 ± 0.020 │ 0.0611 ± 0.0047 │\n",
+      "└──────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 7.03 ± 0.57  │ 5.15 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 6.36 ± 0.53  │ 5.85 ± 0.49  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 4.85 ± 0.43  │ 8.68 ± 0.68  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 4.81 ± 0.42  │ 5.02 ± 0.42  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 6.86 ± 0.55  │ 5.09 ± 0.42  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 5.03 ± 0.41  │ 4.17 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 5.72 ± 0.47  │ 4.46 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 6.18 ± 0.49  │ 4.66 ± 0.39  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 5.90 ± 0.49  │ 5.40 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 5.94 ± 0.47  │ 5.95 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 3.08 ± 0.28  │ 4.41 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 3.76 ± 0.32  │ 7.15 ± 0.56  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 6.78 ± 0.54  │ 5.51 ± 0.45  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 4.80 ± 0.39  │ 7.55 ± 0.61  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 6.66 ± 0.52  │ 5.37 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 5.38 ± 0.43  │ 8.77 ± 0.69  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 6.71 ± 0.52  │ 8.42 ± 0.66  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 5.38 ± 0.45  │ 6.16 ± 0.51  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 6.20 ± 0.50  │ 7.66 ± 0.63  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 7.71 ± 0.61  │ 11.61 ± 0.93 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 5.06 ± 0.43  │ 6.47 ± 0.53  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 4.66 ± 0.42  │ 10.73 ± 0.84 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 7.49 ± 0.59  │ 12.72 ± 1.00 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 10.23 ± 0.84 │ 21.5 ± 1.6   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 12.8 ± 1.0   │ 15.1 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 9.91 ± 0.78  │ 21.5 ± 1.7   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 10.05 ± 0.79 │ 11.83 ± 0.95 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 9.32 ± 0.74  │ 27.7 ± 2.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 15.9 ± 1.2   │ 23.2 ± 1.8   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 24.6 ± 1.9   │ 39.1 ± 2.9   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:28:44,255 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:28:44,256 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:28:44,257 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:28:44,258 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:28:44,259 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:12<00:00 Sampling 4 chains, 0 divergences]\n",
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 83 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:30:19,612 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.28 │ 0.00 ± 0.25 │ -0.000 ± 0.070 │ 0.287 ± 0.022 │ 0.256 ± 0.020 │ 0.0709 ± 0.0054 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 3.92 ± 0.38  │ 6.39 ± 0.50  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 5.01 ± 0.43  │ 9.85 ± 0.75  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 6.02 ± 0.49  │ 4.82 ± 0.41  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 14.4 ± 1.1   │ 5.66 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 7.86 ± 0.63  │ 3.61 ± 0.31  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 5.53 ± 0.43  │ 2.93 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 6.85 ± 0.55  │ 5.49 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 7.72 ± 0.61  │ 2.18 ± 0.21  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 10.03 ± 0.76 │ 1.99 ± 0.19  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 5.47 ± 0.44  │ 3.02 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 3.54 ± 0.30  │ 6.09 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 4.13 ± 0.34  │ 3.43 ± 0.28  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 4.08 ± 0.34  │ 4.61 ± 0.38  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 4.23 ± 0.34  │ 4.93 ± 0.39  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 3.22 ± 0.27  │ 5.77 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 3.68 ± 0.30  │ 6.53 ± 0.52  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 4.40 ± 0.36  │ 6.57 ± 0.52  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 4.99 ± 0.39  │ 6.79 ± 0.53  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 3.43 ± 0.30  │ 5.68 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 6.10 ± 0.47  │ 7.53 ± 0.60  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 4.91 ± 0.40  │ 8.97 ± 0.69  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 5.45 ± 0.45  │ 11.10 ± 0.87 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 6.74 ± 0.56  │ 9.76 ± 0.77  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 12.57 ± 0.97 │ 6.78 ± 0.56  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 5.65 ± 0.48  │ 12.15 ± 0.93 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 8.14 ± 0.64  │ 14.1 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 9.20 ± 0.74  │ 13.0 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 13.2 ± 1.0   │ 13.6 ± 1.1   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 14.6 ± 1.1   │ 26.9 ± 2.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 19.4 ± 1.5   │ 59.4 ± 4.5   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:30:19,635 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:30:19,636 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:30:19,639 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:30:19,640 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:30:19,643 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:11<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+       "<IPython.core.display.HTML object>"
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+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 80 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:31:52,349 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.27 │ 0.00 ± 0.27 │ -0.000 ± 0.068 │ 0.270 ± 0.021 │ 0.272 ± 0.021 │ 0.0687 ± 0.0054 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 3.33 ± 0.33  │ 4.17 ± 0.36  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 4.48 ± 0.37  │ 4.63 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 4.78 ± 0.39  │ 7.46 ± 0.60  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 6.06 ± 0.49  │ 6.49 ± 0.52  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 6.26 ± 0.48  │ 3.75 ± 0.31  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 4.15 ± 0.34  │ 2.19 ± 0.22  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 5.05 ± 0.40  │ 2.88 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 3.45 ± 0.29  │ 2.94 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 5.09 ± 0.40  │ 4.48 ± 0.35  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 6.23 ± 0.50  │ 4.35 ± 0.36  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 2.53 ± 0.23  │ 5.59 ± 0.43  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 2.43 ± 0.22  │ 6.86 ± 0.55  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 3.69 ± 0.30  │ 5.46 ± 0.43  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 3.68 ± 0.31  │ 2.70 ± 0.24  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 5.96 ± 0.47  │ 3.00 ± 0.27  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 5.46 ± 0.43  │ 5.17 ± 0.41  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 6.62 ± 0.52  │ 4.24 ± 0.34  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 5.32 ± 0.43  │ 5.54 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 4.28 ± 0.36  │ 8.53 ± 0.66  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 5.97 ± 0.48  │ 7.53 ± 0.58  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 7.17 ± 0.56  │ 8.87 ± 0.69  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 5.17 ± 0.42  │ 12.02 ± 0.92 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 6.53 ± 0.53  │ 9.95 ± 0.77  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 6.01 ± 0.50  │ 10.43 ± 0.81 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 7.28 ± 0.59  │ 11.55 ± 0.91 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 11.97 ± 0.95 │ 9.76 ± 0.78  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 11.42 ± 0.90 │ 9.85 ± 0.78  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 13.0 ± 1.0   │ 16.1 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 15.5 ± 1.2   │ 16.2 ± 1.3   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 28.1 ± 2.1   │ 45.6 ± 3.5   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:31:52,363 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:31:52,364 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:31:52,366 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:31:52,366 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:31:52,368 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:20<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 91 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:33:36,028 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.26 │ 0.00 ± 0.27 │ -0.000 ± 0.065 │ 0.267 ± 0.020 │ 0.277 ± 0.021 │ 0.0655 ± 0.0048 │\n",
+      "└──────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 2.91 ± 0.29  │ 4.62 ± 0.38  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 2.96 ± 0.28  │ 5.65 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 6.34 ± 0.49  │ 5.15 ± 0.43  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 6.48 ± 0.51  │ 7.13 ± 0.56  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 5.02 ± 0.41  │ 2.92 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 6.16 ± 0.48  │ 3.43 ± 0.28  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 2.50 ± 0.24  │ 5.28 ± 0.42  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 5.92 ± 0.46  │ 3.40 ± 0.28  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 3.90 ± 0.32  │ 2.65 ± 0.23  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 3.83 ± 0.31  │ 3.11 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 3.20 ± 0.27  │ 2.94 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 3.57 ± 0.29  │ 4.68 ± 0.36  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 3.79 ± 0.31  │ 3.85 ± 0.31  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 2.20 ± 0.21  │ 4.81 ± 0.38  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 4.83 ± 0.38  │ 3.43 ± 0.29  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 5.23 ± 0.43  │ 4.71 ± 0.38  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 6.10 ± 0.49  │ 6.24 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 5.14 ± 0.41  │ 3.44 ± 0.30  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 4.56 ± 0.36  │ 6.61 ± 0.51  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 4.49 ± 0.37  │ 8.78 ± 0.70  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 6.63 ± 0.54  │ 5.14 ± 0.43  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 7.12 ± 0.57  │ 9.77 ± 0.76  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 4.64 ± 0.39  │ 9.62 ± 0.74  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 9.07 ± 0.71  │ 8.81 ± 0.69  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 7.14 ± 0.57  │ 12.52 ± 0.95 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 5.19 ± 0.42  │ 11.66 ± 0.90 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 6.18 ± 0.50  │ 9.75 ± 0.77  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 12.23 ± 0.95 │ 5.69 ± 0.48  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 11.26 ± 0.86 │ 17.5 ± 1.4   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 27.1 ± 2.1   │ 21.1 ± 1.6   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:33:36,043 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:33:36,044 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:33:36,045 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:33:36,046 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:33:36,048 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:24<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 94 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:35:22,537 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.26 │ -0.00 ± 0.26 │ 0.000 ± 0.064 │ 0.264 ± 0.020 │ 0.264 ± 0.020 │ 0.0646 ± 0.0049 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 2.34 ± 0.26  │ 3.96 ± 0.33  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 3.93 ± 0.33  │ 5.10 ± 0.40  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 5.71 ± 0.44  │ 3.59 ± 0.29  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 5.92 ± 0.48  │ 3.35 ± 0.28  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 5.09 ± 0.42  │ 2.78 ± 0.23  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 4.38 ± 0.34  │ 1.98 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 4.76 ± 0.38  │ 2.15 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 7.11 ± 0.56  │ 3.37 ± 0.27  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 4.94 ± 0.39  │ 1.93 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 5.32 ± 0.40  │ 3.07 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 4.31 ± 0.34  │ 1.40 ± 0.14  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 1.58 ± 0.16  │ 2.65 ± 0.21  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 2.50 ± 0.22  │ 4.22 ± 0.33  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 5.13 ± 0.38  │ 3.86 ± 0.29  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 3.64 ± 0.29  │ 4.10 ± 0.33  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 2.50 ± 0.21  │ 4.15 ± 0.32  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 4.44 ± 0.35  │ 2.61 ± 0.22  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 4.27 ± 0.34  │ 4.97 ± 0.39  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 5.65 ± 0.44  │ 4.70 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 6.37 ± 0.50  │ 5.21 ± 0.41  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 4.85 ± 0.39  │ 4.99 ± 0.40  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 6.40 ± 0.49  │ 6.83 ± 0.55  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 4.09 ± 0.34  │ 6.84 ± 0.55  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 3.97 ± 0.34  │ 9.09 ± 0.74  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 5.22 ± 0.43  │ 11.74 ± 0.91 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 9.62 ± 0.75  │ 10.42 ± 0.80 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 7.19 ± 0.57  │ 10.29 ± 0.80 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 11.35 ± 0.88 │ 11.72 ± 0.90 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 11.26 ± 0.88 │ 12.30 ± 0.94 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 16.5 ± 1.3   │ 35.0 ± 2.7   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:35:22,554 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:35:22,556 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:35:22,557 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:35:22,558 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:35:22,560 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:26<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:37:10,134 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.26 │ -0.00 ± 0.26 │ 0.000 ± 0.067 │ 0.267 ± 0.020 │ 0.263 ± 0.020 │ 0.0679 ± 0.0050 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]     │\n",
+      "╞════╪══════════════╪═════════════╡\n",
+      "│ 0  │ 1.93 ± 0.22  │ 4.11 ± 0.33 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 1  │ 1.93 ± 0.21  │ 4.09 ± 0.34 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 2  │ 3.32 ± 0.26  │ 3.58 ± 0.27 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 3  │ 4.63 ± 0.36  │ 4.97 ± 0.39 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 4  │ 3.59 ± 0.29  │ 1.15 ± 0.14 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 5  │ 3.38 ± 0.27  │ 2.52 ± 0.21 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 6  │ 3.17 ± 0.27  │ 2.17 ± 0.18 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 7  │ 6.15 ± 0.48  │ 1.53 ± 0.14 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 8  │ 3.22 ± 0.26  │ 2.31 ± 0.19 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 9  │ 3.39 ± 0.27  │ 2.20 ± 0.18 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 10 │ 1.71 ± 0.16  │ 1.60 ± 0.14 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 11 │ 2.30 ± 0.19  │ 1.47 ± 0.13 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 12 │ 3.06 ± 0.24  │ 2.94 ± 0.23 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 13 │ 3.54 ± 0.28  │ 2.93 ± 0.23 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 14 │ 2.92 ± 0.23  │ 3.47 ± 0.27 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 15 │ 4.48 ± 0.35  │ 2.69 ± 0.22 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 16 │ 2.54 ± 0.21  │ 5.13 ± 0.40 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 17 │ 4.21 ± 0.35  │ 4.63 ± 0.36 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 18 │ 4.35 ± 0.33  │ 5.47 ± 0.41 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 19 │ 3.53 ± 0.30  │ 2.54 ± 0.22 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 20 │ 4.15 ± 0.33  │ 7.84 ± 0.58 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 21 │ 5.25 ± 0.41  │ 6.89 ± 0.54 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 22 │ 6.55 ± 0.50  │ 5.28 ± 0.40 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 23 │ 5.71 ± 0.46  │ 6.38 ± 0.50 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 24 │ 4.41 ± 0.38  │ 5.69 ± 0.45 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 25 │ 5.09 ± 0.41  │ 7.27 ± 0.56 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 26 │ 8.14 ± 0.63  │ 9.80 ± 0.75 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 27 │ 7.64 ± 0.60  │ 6.46 ± 0.52 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 28 │ 12.73 ± 1.00 │ 17.3 ± 1.3  │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 29 │ 19.5 ± 1.5   │ 19.1 ± 1.4  │\n",
+      "└────┴──────────────┴─────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "nld_errors = []\n",
+    "gsf_errors = []\n",
+    "for extractor in extractors:\n",
+    "    nld_err, gsf_err = error_estimator.evaluate(extractor.nld, extractor.gsf)\n",
+    "    nld_errors.append(nld_err.copy())\n",
+    "    gsf_errors.append(gsf_err.copy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "490ba702-a5a8-4717-a49b-2b4cf1bff5df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ff5556759514faa9bb3dc58ac2e0c92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "955289786d5d41409c72be764dbca56b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84b7609888cf45308cb7d6f841292873",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def make_vec(nlds, counts, point):\n",
+    "    return om.Vector(E=np.array(counts)/1.0e6, values=np.array([nld.values[nld.index(point)] for nld in nlds]),\n",
+    "                     std=np.array([nld.std[nld.index(point)] for nld in nlds]))\n",
+    "\n",
+    "extractors[-1].plot()\n",
+    "\n",
+    "err = nld_errors[0].copy()\n",
+    "err.values *= 100.\n",
+    "fig, ax = err.plot(label=\"N=25000\")\n",
+    "for i in range(1, len(counts), 4):\n",
+    "    err = nld_errors[i].copy()\n",
+    "    err.values *= 100.\n",
+    "    err.plot(ax=ax, label=f\"N={counts[i]}\")\n",
+    "\n",
+    "err = nld_errors[-1].copy()\n",
+    "err.values *= 100.\n",
+    "err.plot(ax=ax, label=f\"N={counts[-1]}\")\n",
+    "    \n",
+    "ax.set_ylim(0, 100)\n",
+    "ax.set_ylabel(\"Relative error [%]\")\n",
+    "ax.legend(loc='best')\n",
+    "\n",
+    "nld_0MeV = make_vec(nld_errors, counts, 0)\n",
+    "nld_15MeV = make_vec(nld_errors, counts, 1.5)\n",
+    "nld_3MeV = make_vec(nld_errors, counts, 3)\n",
+    "nld_5MeV = make_vec(nld_errors, counts, 5)\n",
+    "\n",
+    "nld_0MeV = make_vec(nld_errors, counts, 0)\n",
+    "nld_0MeV.values *= 100.\n",
+    "nld_0MeV.std *= 100.\n",
+    "# nld_0MeV.save(\"nld_0MeV.csv\", sep=\"\\t\")\n",
+    "nld_2MeV = make_vec(nld_errors, counts, 2)\n",
+    "nld_2MeV.values *= 100.\n",
+    "nld_2MeV.std *= 100.\n",
+    "# nld_2MeV.save(\"nld_2MeV.csv\", sep=\"\\t\")\n",
+    "nld_4MeV = make_vec(nld_errors, counts, 4)\n",
+    "nld_4MeV.values *= 100.\n",
+    "nld_4MeV.std *= 100.\n",
+    "# nld_4MeV.save(\"nld_4MeV.csv\", sep=\"\\t\")\n",
+    "\n",
+    "nld_1MeV = make_vec(nld_errors, counts, 0)\n",
+    "_, ax = nld_0MeV.plot(label=r\"$E_x = 0$ MeV\")\n",
+    "nld_2MeV.plot(ax=ax, label=r\"$E_x = 2$ MeV\")\n",
+    "nld_4MeV.plot(ax=ax, label=r\"$E_x = 4$ MeV\")\n",
+    "ax.set_xlabel(\"Counts [$10^6$]\")\n",
+    "c = np.linspace(min(counts), max(counts), 1001)\n",
+    "ax.plot(c/1e6, (np.sqrt(counts[0])*20.)*1./np.sqrt(c))\n",
+    "ax.legend(loc='best')\n",
+    "ax.set_ylabel(\"Relative uncertanty [%]\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71efe2f7-63a5-4c3e-a0c5-723e9b0859c2",
+   "metadata": {},
+   "source": [
+    "### Investigate dependence on ensemble members"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a48c4fb1-2d00-4073-badf-eb0dc295dae8",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbc78f1db92f418fb5b0904c0e5e3de6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7200d306e31848d8a08f00af43322938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec438f2c9e1c46e5963c74b1dffaea60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b0af3ce436c47fda08c3d12ca39782c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b7a6fb2f7dc47bdb18dfda1deb5effa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87af5993ba9649bb92d8fa3baa49b991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ddb2869ba4e403e88fcb73bb303eae7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "224a99f2d404474491fef468341c9fe2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "427d8779ac074fb0bef8023d0bb849ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6387cc4c83374ef1afd35c802f8f385e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/6 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "586e24f4d44a4d288e88aac9091e848f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/6 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3217749e99e24c55968f4aaf23f1021b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/6 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2aa3a14e215245fd946df63543ef117c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "173d12fdd22e4c85ad2dc9a1e32542d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8befdd71e4834d6e93c9fc0d410dcaf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "228bc371ca6946439da1ea249c83e9b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5117f0931bd644a7beca3409deda7fcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71c3bb0c41cb4afd8e77ce5812260234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "500d0be3632c4b13b739df46071769f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/9 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4d934f158d448f9ada52ffe0f184174",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/9 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b15df1845ec946cf9b2ec50afd604ee5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/9 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c151481b72b4bae898b66bcbbc8475f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42f6bd53108e4a838c772ef4eec38b48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0f7c2b81bdf4405b8f4f7bf777c814d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7565ccfc296341108a0aa5bea73b867e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/11 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6cea2b88d904e4c947c60aed0a2f86a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/11 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8574e1093c594c4cba5f1eb225252c5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/11 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3325671cd5fe470788d127cdcbd0cfd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/12 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "171c81af2c2c4cd58b5249c76af77f3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/12 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afbe6e637e6743549f8b79c98df11a28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/12 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24501b5b04ea425d9b2fa225167fee7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64e02a5ba0e046b69f1a0435619bc132",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e3db738fdb54e9f8b7d56df3290a88b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #12 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #12 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cea7d801a014315af131207a937b494",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a29a28d3740148d6943e424be306bb71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35ec1d1c5eae46278e0918ef241daabc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "nld #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #12 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "nld #13 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for nld in extractor.nld:\n",
+      "#     nld.cut_nan()\n",
+      "\n",
+      "gsf #0 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #1 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #2 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #3 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #4 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #5 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #6 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #7 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #8 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #9 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #10 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #11 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #12 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n",
+      "gsf #13 contains nan's.\n",
+      "Consider removing them e.g. with:\n",
+      "# for gsf in extractor.gsf:\n",
+      "#     gsf.cut_nan()\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ensemble_full = om.Ensemble(raw=raw_orig, path=\"saved_run/ensemble/full\") # Note raw will be changed later manually.\n",
+    "ensemble_full.unfolder = unfolder\n",
+    "ensemble_full.first_generation_method = firstgen\n",
+    "\n",
+    "extractors_ens = []\n",
+    "for n in range(3, 15):\n",
+    "    ensemble_full.generate(n, regenerate=True)\n",
+    "    ensemble_full.rebin(E_rebinned, member=\"firstgen\")\n",
+    "    extractors_ens.append(om.Extractor())\n",
+    "    extractors_ens[-1].trapezoid = trapezoid_cut\n",
+    "    extractors_ens[-1].extract_from(ensemble_full, regenerate=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4256da72-829b-4e50-9c66-ad211e8b6165",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:43:04,405 - ompy.error_finder - DEBUG - Processing an ensemble with 3 members\n",
+      "2021-08-12 10:43:04,406 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:43:04,407 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:43:04,408 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:43:04,409 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:10<00:00 Sampling 4 chains, 500 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 82 seconds.\n",
+      "There were 310 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The acceptance probability does not match the target. It is 0.5323431089331814, but should be close to 0.8. Try to increase the number of tuning steps.\n",
+      "There were 29 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "There were 117 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The acceptance probability does not match the target. It is 0.6415360461552452, but should be close to 0.8. Try to increase the number of tuning steps.\n",
+      "There were 44 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
+      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:44:36,679 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬─────────────┬─────────────┬───────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D         │ σ_F         │ σ_α           │\n",
+      "╞═════════════╪═════════════╪════════════════╪═════════════╪═════════════╪═══════════════╡\n",
+      "│ 0.00 ± 0.31 │ 0.00 ± 0.32 │ -0.000 ± 0.069 │ 0.39 ± 0.15 │ 0.41 ± 0.16 │ 0.088 ± 0.037 │\n",
+      "└─────────────┴─────────────┴────────────────┴─────────────┴─────────────┴───────────────┘\n",
+      "┌────┬───────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]       │ σ_f [%]     │\n",
+      "╞════╪═══════════════╪═════════════╡\n",
+      "│ 0  │ 2.32 ± 0.94   │ 3.2 ± 1.3   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 1  │ 0.046 ± 0.039 │ 1.38 ± 0.58 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 2  │ 0.049 ± 0.044 │ 2.5 ± 1.1   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 3  │ 3.3 ± 1.2     │ 3.3 ± 1.3   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 4  │ 2.8 ± 1.0     │ 0.85 ± 0.49 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 5  │ 3.9 ± 1.6     │ 2.9 ± 1.5   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 6  │ 4.4 ± 1.7     │ 0.25 ± 0.32 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 7  │ 3.5 ± 1.4     │ 1.31 ± 0.55 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 8  │ 4.5 ± 1.8     │ 2.03 ± 0.91 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 9  │ 3.8 ± 1.5     │ 1.16 ± 0.46 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 10 │ 1.24 ± 0.53   │ 2.21 ± 0.95 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 11 │ 2.05 ± 0.82   │ 2.5 ± 1.2   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 12 │ 2.39 ± 1.00   │ 6.9 ± 2.7   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 13 │ 3.9 ± 1.5     │ 0.10 ± 0.11 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 14 │ 1.54 ± 0.65   │ 2.20 ± 0.84 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 15 │ 4.2 ± 1.6     │ 1.81 ± 0.74 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 16 │ 6.4 ± 3.2     │ 4.3 ± 2.5   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 17 │ 6.6 ± 2.7     │ 1.24 ± 0.80 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 18 │ 3.2 ± 1.3     │ 8.3 ± 3.6   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 19 │ 1.29 ± 0.73   │ 7.5 ± 3.1   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 20 │ 1.30 ± 0.72   │ 0.27 ± 0.19 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 21 │ 1.47 ± 0.82   │ 7.8 ± 2.9   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 22 │ 11.4 ± 4.5    │ 0.35 ± 0.31 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 23 │ 4.7 ± 2.1     │ 5.6 ± 2.2   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 24 │ 3.5 ± 1.5     │ 2.5 ± 1.0   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 25 │ 3.5 ± 1.6     │ 2.8 ± 1.3   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 26 │ 10.3 ± 4.1    │ 7.3 ± 3.2   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 27 │ 4.3 ± 1.8     │ 5.6 ± 2.6   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 28 │ 12.2 ± 4.7    │ 34 ± 16     │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 29 │ 16.5 ± 6.8    │ 31 ± 11     │\n",
+      "└────┴───────────────┴─────────────┘\n",
+      "2021-08-12 10:44:36,687 - ompy.error_finder - DEBUG - Processing an ensemble with 4 members\n",
+      "2021-08-12 10:44:36,688 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:44:36,689 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:44:36,690 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:44:36,690 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
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+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:01<00:00 Sampling 4 chains, 19 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n",
+      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "There were 13 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
+      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:45:57,231 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬─────────────┬────────────────┬───────────────┬─────────────┬───────────────┐\n",
+      "│ D            │ F           │ α              │ σ_D           │ σ_F         │ σ_α           │\n",
+      "╞══════════════╪═════════════╪════════════════╪═══════════════╪═════════════╪═══════════════╡\n",
+      "│ -0.00 ± 0.31 │ 0.00 ± 0.43 │ -0.000 ± 0.071 │ 0.346 ± 0.082 │ 0.48 ± 0.12 │ 0.080 ± 0.019 │\n",
+      "└──────────────┴─────────────┴────────────────┴───────────────┴─────────────┴───────────────┘\n",
+      "┌────┬─────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]     │ σ_f [%]     │\n",
+      "╞════╪═════════════╪═════════════╡\n",
+      "│ 0  │ 0.51 ± 0.32 │ 1.96 ± 0.49 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 1  │ 0.62 ± 0.31 │ 0.22 ± 0.15 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 2  │ 1.21 ± 0.31 │ 3.18 ± 0.76 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 3  │ 3.35 ± 0.85 │ 2.98 ± 0.73 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 4  │ 2.11 ± 0.51 │ 0.28 ± 0.15 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 5  │ 2.79 ± 0.66 │ 2.94 ± 0.69 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 6  │ 2.23 ± 0.57 │ 1.65 ± 0.41 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 7  │ 3.87 ± 0.92 │ 1.49 ± 0.37 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 8  │ 4.07 ± 0.98 │ 2.19 ± 0.54 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 9  │ 2.14 ± 0.51 │ 1.48 ± 0.38 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 10 │ 0.78 ± 0.23 │ 1.48 ± 0.38 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 11 │ 2.45 ± 0.58 │ 0.52 ± 0.25 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 12 │ 3.08 ± 0.73 │ 5.7 ± 1.3   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 13 │ 1.33 ± 0.34 │ 1.77 ± 0.46 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 14 │ 0.26 ± 0.23 │ 5.0 ± 1.2   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 15 │ 4.3 ± 1.1   │ 3.24 ± 0.83 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 16 │ 4.9 ± 1.2   │ 4.6 ± 1.1   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 17 │ 4.5 ± 1.1   │ 3.19 ± 0.77 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 18 │ 6.8 ± 1.7   │ 8.4 ± 2.0   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 19 │ 3.62 ± 0.88 │ 2.85 ± 0.69 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 20 │ 3.19 ± 0.81 │ 6.1 ± 1.5   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 21 │ 3.69 ± 0.89 │ 6.9 ± 1.7   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 22 │ 7.8 ± 1.8   │ 1.65 ± 0.54 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 23 │ 4.5 ± 1.1   │ 9.4 ± 2.3   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 24 │ 2.90 ± 0.75 │ 9.2 ± 2.3   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 25 │ 6.6 ± 1.6   │ 5.2 ± 1.3   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 26 │ 7.8 ± 1.8   │ 6.0 ± 1.5   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 27 │ 5.2 ± 1.3   │ 7.3 ± 1.8   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 28 │ 10.7 ± 2.7  │ 26.9 ± 6.3  │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 29 │ 25.5 ± 6.1  │ 24.6 ± 5.9  │\n",
+      "└────┴─────────────┴─────────────┘\n",
+      "2021-08-12 10:45:57,237 - ompy.error_finder - DEBUG - Processing an ensemble with 5 members\n",
+      "2021-08-12 10:45:57,238 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:45:57,239 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:45:57,240 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:45:57,241 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
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+       "                background-size: auto;\n",
+       "            }\n",
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+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:29<00:00 Sampling 4 chains, 57 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 98 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "There were 8 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The acceptance probability does not match the target. It is 0.6820218655545547, but should be close to 0.8. Try to increase the number of tuning steps.\n",
+      "There were 48 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The acceptance probability does not match the target. It is 0.5621465385353126, but should be close to 0.8. Try to increase the number of tuning steps.\n",
+      "The acceptance probability does not match the target. It is 0.9501354874730317, but should be close to 0.8. Try to increase the number of tuning steps.\n",
+      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
+      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:47:45,945 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬───────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α           │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═══════════════╡\n",
+      "│ 0.00 ± 0.28 │ 0.00 ± 0.34 │ -0.000 ± 0.064 │ 0.309 ± 0.058 │ 0.368 ± 0.063 │ 0.068 ± 0.012 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴───────────────┘\n",
+      "┌────┬───────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]       │ σ_f [%]     │\n",
+      "╞════╪═══════════════╪═════════════╡\n",
+      "│ 0  │ 2.22 ± 0.57   │ 1.41 ± 0.55 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 1  │ 0.41 ± 0.51   │ 3.44 ± 0.71 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 2  │ 2.12 ± 0.49   │ 4.29 ± 0.82 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 3  │ 2.80 ± 0.58   │ 3.72 ± 0.66 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 4  │ 2.74 ± 0.55   │ 1.67 ± 0.46 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 5  │ 2.85 ± 0.55   │ 1.91 ± 0.35 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 6  │ 3.10 ± 0.54   │ 2.01 ± 0.40 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 7  │ 4.17 ± 0.72   │ 0.96 ± 0.32 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 8  │ 3.54 ± 0.62   │ 1.21 ± 0.24 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 9  │ 2.09 ± 0.37   │ 3.27 ± 0.65 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 10 │ 2.74 ± 0.47   │ 1.02 ± 0.23 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 11 │ 2.08 ± 0.35   │ 0.89 ± 0.24 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 12 │ 2.15 ± 0.37   │ 5.19 ± 0.90 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 13 │ 2.73 ± 0.47   │ 2.41 ± 0.42 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 14 │ 0.128 ± 0.079 │ 3.60 ± 0.64 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 15 │ 3.33 ± 0.56   │ 2.12 ± 0.43 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 16 │ 3.74 ± 0.66   │ 4.41 ± 0.81 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 17 │ 4.43 ± 0.77   │ 3.90 ± 0.72 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 18 │ 5.13 ± 0.94   │ 6.1 ± 1.1   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 19 │ 2.85 ± 0.49   │ 2.83 ± 0.57 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 20 │ 2.94 ± 0.50   │ 6.0 ± 1.0   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 21 │ 5.77 ± 0.98   │ 6.0 ± 1.1   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 22 │ 7.5 ± 1.3     │ 1.78 ± 0.47 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 23 │ 3.26 ± 0.56   │ 8.1 ± 1.4   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 24 │ 2.88 ± 0.53   │ 7.6 ± 1.3   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 25 │ 6.2 ± 1.1     │ 5.22 ± 0.88 │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 26 │ 9.2 ± 1.6     │ 7.0 ± 1.3   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 27 │ 2.37 ± 0.43   │ 6.8 ± 1.2   │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 28 │ 9.5 ± 1.7     │ 24.6 ± 4.2  │\n",
+      "├────┼───────────────┼─────────────┤\n",
+      "│ 29 │ 27.3 ± 4.6    │ 27.3 ± 4.9  │\n",
+      "└────┴───────────────┴─────────────┘\n",
+      "2021-08-12 10:47:45,954 - ompy.error_finder - DEBUG - Processing an ensemble with 6 members\n",
+      "2021-08-12 10:47:45,956 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:47:45,957 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:47:45,957 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:47:45,959 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:08<00:00 Sampling 4 chains, 949 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 78 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "There were 944 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The acceptance probability does not match the target. It is 2.8572558743334045e-23, but should be close to 0.8. Try to increase the number of tuning steps.\n",
+      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
+      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:49:15,688 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬───────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α           │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═══════════════╡\n",
+      "│ 0.00 ± 0.27 │ 0.00 ± 0.31 │ -0.000 ± 0.081 │ 0.273 ± 0.037 │ 0.316 ± 0.045 │ 0.087 ± 0.011 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴───────────────┘\n",
+      "┌────┬─────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]     │ σ_f [%]     │\n",
+      "╞════╪═════════════╪═════════════╡\n",
+      "│ 0  │ 2.86 ± 0.50 │ 1.47 ± 0.20 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 1  │ 0.26 ± 0.17 │ 3.49 ± 0.50 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 2  │ 1.55 ± 0.19 │ 3.99 ± 0.51 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 3  │ 3.58 ± 0.43 │ 3.63 ± 0.43 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 4  │ 2.85 ± 0.38 │ 1.86 ± 0.38 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 5  │ 2.97 ± 0.38 │ 1.92 ± 0.26 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 6  │ 3.97 ± 0.55 │ 2.70 ± 0.35 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 7  │ 7.31 ± 0.90 │ 1.02 ± 0.28 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 8  │ 3.50 ± 0.43 │ 2.14 ± 0.28 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 9  │ 3.21 ± 0.39 │ 2.73 ± 0.39 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 10 │ 2.68 ± 0.35 │ 1.81 ± 0.24 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 11 │ 2.38 ± 0.28 │ 0.83 ± 0.22 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 12 │ 1.83 ± 0.23 │ 3.85 ± 0.46 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 13 │ 3.64 ± 0.44 │ 2.70 ± 0.34 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 14 │ 0.15 ± 0.13 │ 3.51 ± 0.60 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 15 │ 4.97 ± 0.55 │ 3.19 ± 0.53 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 16 │ 3.25 ± 0.52 │ 4.47 ± 0.63 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 17 │ 4.39 ± 0.67 │ 5.29 ± 0.74 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 18 │ 5.15 ± 0.68 │ 6.11 ± 0.73 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 19 │ 4.01 ± 0.50 │ 3.28 ± 0.49 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 20 │ 3.50 ± 0.41 │ 8.11 ± 1.00 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 21 │ 5.02 ± 0.62 │ 5.32 ± 0.62 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 22 │ 8.3 ± 1.1   │ 3.60 ± 0.51 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 23 │ 5.03 ± 0.57 │ 6.85 ± 0.91 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 24 │ 4.46 ± 0.54 │ 5.80 ± 0.73 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 25 │ 5.80 ± 0.70 │ 5.00 ± 0.61 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 26 │ 10.2 ± 1.3  │ 6.1 ± 1.0   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 27 │ 7.81 ± 0.96 │ 6.4 ± 1.0   │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 28 │ 9.7 ± 1.2   │ 24.0 ± 2.9  │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 29 │ 25.3 ± 3.4  │ 22.9 ± 2.9  │\n",
+      "└────┴─────────────┴─────────────┘\n",
+      "2021-08-12 10:49:15,696 - ompy.error_finder - DEBUG - Processing an ensemble with 7 members\n",
+      "2021-08-12 10:49:15,697 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:49:15,698 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:49:15,699 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:49:15,700 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "                border: none;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:43<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+       "<IPython.core.display.HTML object>"
+      ]
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 54 seconds.\n",
+      "The number of effective samples is smaller than 25% for some parameters.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:50:20,251 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.25 │ -0.00 ± 0.29 │ 0.000 ± 0.077 │ 0.260 ± 0.031 │ 0.297 ± 0.033 │ 0.0791 ± 0.0088 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬─────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]     │ σ_f [%]     │\n",
+      "╞════╪═════════════╪═════════════╡\n",
+      "│ 0  │ 1.31 ± 0.30 │ 3.14 ± 0.39 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 1  │ 2.23 ± 0.32 │ 3.56 ± 0.42 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 2  │ 3.26 ± 0.39 │ 2.96 ± 0.36 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 3  │ 5.00 ± 0.59 │ 5.07 ± 0.58 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 4  │ 2.79 ± 0.34 │ 0.70 ± 0.20 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 5  │ 3.25 ± 0.41 │ 1.99 ± 0.23 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 6  │ 3.31 ± 0.40 │ 1.83 ± 0.22 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 7  │ 6.36 ± 0.75 │ 1.68 ± 0.24 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 8  │ 4.11 ± 0.48 │ 2.46 ± 0.30 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 9  │ 3.18 ± 0.38 │ 1.71 ± 0.23 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 10 │ 2.07 ± 0.27 │ 1.82 ± 0.23 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 11 │ 2.40 ± 0.29 │ 1.53 ± 0.22 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 12 │ 2.60 ± 0.31 │ 3.57 ± 0.42 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 13 │ 3.72 ± 0.43 │ 3.44 ± 0.40 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 14 │ 1.51 ± 0.21 │ 3.60 ± 0.44 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 15 │ 4.27 ± 0.51 │ 3.19 ± 0.40 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 16 │ 2.78 ± 0.33 │ 5.57 ± 0.65 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 17 │ 4.15 ± 0.49 │ 5.26 ± 0.62 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 18 │ 3.74 ± 0.44 │ 5.50 ± 0.62 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 19 │ 4.04 ± 0.49 │ 3.14 ± 0.40 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 20 │ 3.20 ± 0.41 │ 6.71 ± 0.76 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 21 │ 5.92 ± 0.70 │ 6.44 ± 0.75 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 22 │ 6.83 ± 0.80 │ 4.82 ± 0.57 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 23 │ 3.13 ± 0.41 │ 7.13 ± 0.84 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 24 │ 3.49 ± 0.47 │ 6.02 ± 0.71 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 25 │ 6.08 ± 0.72 │ 6.51 ± 0.78 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 26 │ 10.4 ± 1.2  │ 10.6 ± 1.2  │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 27 │ 4.78 ± 0.60 │ 7.69 ± 0.92 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 28 │ 8.12 ± 0.98 │ 20.8 ± 2.4  │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 29 │ 23.2 ± 2.6  │ 24.1 ± 2.7  │\n",
+      "└────┴─────────────┴─────────────┘\n",
+      "2021-08-12 10:50:20,264 - ompy.error_finder - DEBUG - Processing an ensemble with 8 members\n",
+      "2021-08-12 10:50:20,265 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:50:20,266 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:50:20,266 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:50:20,267 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "                border: none;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:54<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 64 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:51:35,371 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.23 │ 0.00 ± 0.28 │ -0.000 ± 0.073 │ 0.238 ± 0.024 │ 0.287 ± 0.028 │ 0.0747 ± 0.0072 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬─────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]     │ σ_f [%]     │\n",
+      "╞════╪═════════════╪═════════════╡\n",
+      "│ 0  │ 2.19 ± 0.29 │ 2.51 ± 0.27 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 1  │ 2.23 ± 0.28 │ 4.92 ± 0.48 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 2  │ 3.14 ± 0.33 │ 3.64 ± 0.37 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 3  │ 5.16 ± 0.53 │ 4.67 ± 0.46 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 4  │ 3.37 ± 0.34 │ 1.17 ± 0.17 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 5  │ 3.00 ± 0.31 │ 1.90 ± 0.21 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 6  │ 3.37 ± 0.34 │ 2.22 ± 0.24 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 7  │ 6.16 ± 0.62 │ 1.76 ± 0.20 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 8  │ 3.70 ± 0.37 │ 2.21 ± 0.24 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 9  │ 2.97 ± 0.31 │ 2.01 ± 0.22 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 10 │ 2.14 ± 0.23 │ 1.85 ± 0.20 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 11 │ 2.34 ± 0.24 │ 1.38 ± 0.17 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 12 │ 3.12 ± 0.32 │ 3.30 ± 0.34 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 13 │ 3.47 ± 0.36 │ 3.38 ± 0.34 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 14 │ 1.79 ± 0.20 │ 3.94 ± 0.39 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 15 │ 4.88 ± 0.50 │ 3.26 ± 0.34 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 16 │ 2.60 ± 0.27 │ 5.05 ± 0.50 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 17 │ 4.16 ± 0.42 │ 4.72 ± 0.47 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 18 │ 4.13 ± 0.40 │ 5.84 ± 0.59 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 19 │ 3.89 ± 0.38 │ 2.70 ± 0.30 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 20 │ 3.14 ± 0.33 │ 6.45 ± 0.64 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 21 │ 5.42 ± 0.54 │ 6.28 ± 0.62 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 22 │ 6.46 ± 0.64 │ 5.07 ± 0.53 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 23 │ 3.85 ± 0.41 │ 6.82 ± 0.66 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 24 │ 3.98 ± 0.43 │ 6.91 ± 0.71 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 25 │ 6.04 ± 0.60 │ 7.67 ± 0.77 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 26 │ 8.89 ± 0.90 │ 9.52 ± 0.95 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 27 │ 8.94 ± 0.85 │ 7.29 ± 0.72 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 28 │ 8.15 ± 0.85 │ 19.6 ± 2.0  │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 29 │ 21.9 ± 2.1  │ 22.0 ± 2.1  │\n",
+      "└────┴─────────────┴─────────────┘\n",
+      "2021-08-12 10:51:35,386 - ompy.error_finder - DEBUG - Processing an ensemble with 9 members\n",
+      "2021-08-12 10:51:35,387 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:51:35,388 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:51:35,389 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:51:35,390 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "                border: none;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:08<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 78 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:53:05,209 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.22 │ -0.00 ± 0.27 │ 0.000 ± 0.069 │ 0.227 ± 0.019 │ 0.278 ± 0.024 │ 0.0698 ± 0.0063 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬─────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]     │ σ_f [%]     │\n",
+      "╞════╪═════════════╪═════════════╡\n",
+      "│ 0  │ 2.15 ± 0.25 │ 3.32 ± 0.31 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 1  │ 1.91 ± 0.23 │ 4.46 ± 0.39 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 2  │ 3.08 ± 0.29 │ 3.70 ± 0.33 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 3  │ 4.82 ± 0.43 │ 4.33 ± 0.38 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 4  │ 3.33 ± 0.31 │ 1.39 ± 0.17 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 5  │ 3.14 ± 0.29 │ 2.61 ± 0.24 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 6  │ 3.05 ± 0.28 │ 2.41 ± 0.22 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 7  │ 6.01 ± 0.53 │ 1.42 ± 0.15 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 8  │ 3.22 ± 0.29 │ 2.39 ± 0.23 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 9  │ 3.48 ± 0.31 │ 2.32 ± 0.21 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 10 │ 1.80 ± 0.18 │ 1.59 ± 0.16 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 11 │ 2.49 ± 0.23 │ 1.35 ± 0.14 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 12 │ 3.27 ± 0.30 │ 3.13 ± 0.28 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 13 │ 3.60 ± 0.32 │ 2.84 ± 0.26 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 14 │ 2.56 ± 0.24 │ 3.93 ± 0.35 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 15 │ 4.73 ± 0.41 │ 3.05 ± 0.28 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 16 │ 2.66 ± 0.25 │ 5.07 ± 0.45 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 17 │ 4.63 ± 0.40 │ 4.57 ± 0.41 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 18 │ 4.66 ± 0.41 │ 5.73 ± 0.50 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 19 │ 3.82 ± 0.34 │ 2.59 ± 0.25 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 20 │ 4.09 ± 0.38 │ 8.48 ± 0.73 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 21 │ 4.87 ± 0.44 │ 7.38 ± 0.66 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 22 │ 6.41 ± 0.56 │ 5.42 ± 0.48 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 23 │ 5.39 ± 0.47 │ 6.26 ± 0.53 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 24 │ 4.77 ± 0.46 │ 6.78 ± 0.61 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 25 │ 5.50 ± 0.50 │ 7.09 ± 0.63 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 26 │ 8.50 ± 0.74 │ 9.23 ± 0.80 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 27 │ 8.36 ± 0.74 │ 6.47 ± 0.58 │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 28 │ 12.9 ± 1.1  │ 18.4 ± 1.6  │\n",
+      "├────┼─────────────┼─────────────┤\n",
+      "│ 29 │ 20.6 ± 1.8  │ 20.3 ± 1.8  │\n",
+      "└────┴─────────────┴─────────────┘\n",
+      "2021-08-12 10:53:05,230 - ompy.error_finder - DEBUG - Processing an ensemble with 10 members\n",
+      "2021-08-12 10:53:05,231 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:53:05,232 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:53:05,233 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:53:05,236 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:09<00:00 Sampling 4 chains, 0 divergences]\n",
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+      ],
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 80 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:54:36,888 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.26 │ -0.00 ± 0.26 │ 0.000 ± 0.067 │ 0.267 ± 0.020 │ 0.263 ± 0.020 │ 0.0679 ± 0.0050 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬─────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]     │\n",
+      "╞════╪══════════════╪═════════════╡\n",
+      "│ 0  │ 1.93 ± 0.22  │ 4.11 ± 0.33 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 1  │ 1.93 ± 0.21  │ 4.09 ± 0.34 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 2  │ 3.32 ± 0.26  │ 3.58 ± 0.27 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 3  │ 4.63 ± 0.36  │ 4.97 ± 0.39 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 4  │ 3.59 ± 0.29  │ 1.15 ± 0.14 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 5  │ 3.38 ± 0.27  │ 2.52 ± 0.21 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 6  │ 3.17 ± 0.27  │ 2.17 ± 0.18 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 7  │ 6.15 ± 0.48  │ 1.53 ± 0.14 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 8  │ 3.22 ± 0.26  │ 2.31 ± 0.19 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 9  │ 3.39 ± 0.27  │ 2.20 ± 0.18 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 10 │ 1.71 ± 0.16  │ 1.60 ± 0.14 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 11 │ 2.30 ± 0.19  │ 1.47 ± 0.13 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 12 │ 3.06 ± 0.24  │ 2.94 ± 0.23 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 13 │ 3.54 ± 0.28  │ 2.93 ± 0.23 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 14 │ 2.92 ± 0.23  │ 3.47 ± 0.27 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 15 │ 4.48 ± 0.35  │ 2.69 ± 0.22 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 16 │ 2.54 ± 0.21  │ 5.13 ± 0.40 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 17 │ 4.21 ± 0.35  │ 4.63 ± 0.36 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 18 │ 4.35 ± 0.33  │ 5.47 ± 0.41 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 19 │ 3.53 ± 0.30  │ 2.54 ± 0.22 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 20 │ 4.15 ± 0.33  │ 7.84 ± 0.58 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 21 │ 5.25 ± 0.41  │ 6.89 ± 0.54 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 22 │ 6.55 ± 0.50  │ 5.28 ± 0.40 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 23 │ 5.71 ± 0.46  │ 6.38 ± 0.50 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 24 │ 4.41 ± 0.38  │ 5.69 ± 0.45 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 25 │ 5.09 ± 0.41  │ 7.27 ± 0.56 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 26 │ 8.14 ± 0.63  │ 9.80 ± 0.75 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 27 │ 7.64 ± 0.60  │ 6.46 ± 0.52 │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 28 │ 12.73 ± 1.00 │ 17.3 ± 1.3  │\n",
+      "├────┼──────────────┼─────────────┤\n",
+      "│ 29 │ 19.5 ± 1.5   │ 19.1 ± 1.4  │\n",
+      "└────┴──────────────┴─────────────┘\n",
+      "2021-08-12 10:54:36,906 - ompy.error_finder - DEBUG - Processing an ensemble with 11 members\n",
+      "2021-08-12 10:54:36,907 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:54:36,908 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:54:36,909 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:54:36,911 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:16<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
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+       "<IPython.core.display.HTML object>"
+      ]
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+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 87 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:56:14,609 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.24 │ 0.00 ± 0.27 │ -0.000 ± 0.066 │ 0.245 ± 0.017 │ 0.270 ± 0.018 │ 0.0665 ± 0.0047 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 2.01 ± 0.20  │ 4.70 ± 0.33  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 1.64 ± 0.19  │ 4.03 ± 0.29  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 3.42 ± 0.25  │ 3.26 ± 0.24  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 4.48 ± 0.31  │ 5.77 ± 0.40  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 3.33 ± 0.24  │ 1.33 ± 0.12  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 3.08 ± 0.22  │ 2.44 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 3.03 ± 0.22  │ 2.92 ± 0.21  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 6.22 ± 0.43  │ 1.30 ± 0.11  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 3.22 ± 0.23  │ 2.67 ± 0.19  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 3.41 ± 0.25  │ 2.15 ± 0.15  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 1.72 ± 0.15  │ 1.43 ± 0.12  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 3.37 ± 0.24  │ 2.18 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 3.01 ± 0.22  │ 3.06 ± 0.22  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 3.75 ± 0.27  │ 2.65 ± 0.19  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 2.82 ± 0.21  │ 3.67 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 4.66 ± 0.33  │ 2.53 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 2.45 ± 0.19  │ 4.70 ± 0.33  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 4.45 ± 0.31  │ 4.26 ± 0.30  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 4.44 ± 0.32  │ 5.23 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 3.76 ± 0.28  │ 2.48 ± 0.19  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 3.91 ± 0.29  │ 7.96 ± 0.57  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 5.03 ± 0.36  │ 6.64 ± 0.47  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 6.22 ± 0.44  │ 5.38 ± 0.39  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 5.38 ± 0.39  │ 6.27 ± 0.45  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 4.73 ± 0.36  │ 5.66 ± 0.40  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 4.78 ± 0.35  │ 6.91 ± 0.49  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 7.80 ± 0.56  │ 10.34 ± 0.71 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 7.34 ± 0.53  │ 6.59 ± 0.47  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 12.21 ± 0.83 │ 16.8 ± 1.2   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 21.5 ± 1.5   │ 19.0 ± 1.3   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:56:14,625 - ompy.error_finder - DEBUG - Processing an ensemble with 12 members\n",
+      "2021-08-12 10:56:14,626 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:56:14,627 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:56:14,628 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:56:14,630 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
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+       "                border: none;\n",
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+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:29<00:00 Sampling 4 chains, 0 divergences]\n",
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+       "    "
+      ],
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+     "output_type": "display_data"
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 99 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 10:58:05,160 - ompy.error_finder - INFO - Inference results:\n",
+      "┌──────────────┬──────────────┬───────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D            │ F            │ α             │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞══════════════╪══════════════╪═══════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ -0.00 ± 0.24 │ -0.00 ± 0.26 │ 0.000 ± 0.062 │ 0.244 ± 0.016 │ 0.263 ± 0.016 │ 0.0627 ± 0.0038 │\n",
+      "└──────────────┴──────────────┴───────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 1.65 ± 0.17  │ 4.53 ± 0.30  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 2.11 ± 0.18  │ 3.82 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 3.94 ± 0.26  │ 4.11 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 4.39 ± 0.29  │ 5.47 ± 0.35  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 3.22 ± 0.22  │ 1.87 ± 0.14  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 3.59 ± 0.24  │ 2.60 ± 0.17  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 3.34 ± 0.22  │ 2.84 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 5.92 ± 0.37  │ 1.60 ± 0.12  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 3.19 ± 0.21  │ 2.45 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 3.48 ± 0.22  │ 2.15 ± 0.15  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 1.55 ± 0.13  │ 1.53 ± 0.11  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 3.39 ± 0.22  │ 2.35 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 3.00 ± 0.20  │ 3.18 ± 0.21  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 3.75 ± 0.24  │ 2.56 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 2.57 ± 0.18  │ 3.54 ± 0.23  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 4.47 ± 0.29  │ 2.76 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 2.76 ± 0.19  │ 4.23 ± 0.27  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 4.33 ± 0.27  │ 4.05 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 4.69 ± 0.29  │ 4.99 ± 0.32  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 3.83 ± 0.25  │ 2.64 ± 0.18  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 4.04 ± 0.27  │ 7.92 ± 0.51  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 4.78 ± 0.31  │ 6.34 ± 0.41  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 6.11 ± 0.40  │ 5.30 ± 0.34  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 5.33 ± 0.35  │ 5.92 ± 0.38  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 5.37 ± 0.36  │ 6.16 ± 0.41  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 5.02 ± 0.34  │ 7.48 ± 0.46  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 7.38 ± 0.46  │ 10.57 ± 0.69 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 7.28 ± 0.46  │ 6.96 ± 0.45  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 11.52 ± 0.74 │ 16.1 ± 1.0   │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 21.5 ± 1.4   │ 20.5 ± 1.3   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 10:58:05,180 - ompy.error_finder - DEBUG - Processing an ensemble with 13 members\n",
+      "2021-08-12 10:58:05,181 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 10:58:05,183 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 10:58:05,183 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 10:58:05,186 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "                background: #F44336;\n",
+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 02:04<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 134 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 11:00:31,261 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬──────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F            │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪══════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.25 │ -0.00 ± 0.26 │ -0.000 ± 0.062 │ 0.250 ± 0.014 │ 0.258 ± 0.015 │ 0.0623 ± 0.0035 │\n",
+      "└─────────────┴──────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬──────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]      │\n",
+      "╞════╪══════════════╪══════════════╡\n",
+      "│ 0  │ 1.56 ± 0.15  │ 4.46 ± 0.26  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 1  │ 2.14 ± 0.16  │ 3.91 ± 0.24  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 2  │ 3.82 ± 0.24  │ 3.97 ± 0.24  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 3  │ 4.52 ± 0.28  │ 5.29 ± 0.31  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 4  │ 5.44 ± 0.33  │ 1.88 ± 0.13  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 5  │ 3.49 ± 0.21  │ 2.48 ± 0.15  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 6  │ 3.51 ± 0.22  │ 2.65 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 7  │ 6.12 ± 0.35  │ 1.49 ± 0.10  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 8  │ 3.40 ± 0.20  │ 2.44 ± 0.15  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 9  │ 3.38 ± 0.21  │ 2.12 ± 0.13  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 10 │ 1.79 ± 0.12  │ 1.58 ± 0.11  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 11 │ 3.32 ± 0.20  │ 2.20 ± 0.14  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 12 │ 3.19 ± 0.19  │ 3.23 ± 0.19  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 13 │ 3.49 ± 0.21  │ 2.48 ± 0.15  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 14 │ 2.46 ± 0.16  │ 3.34 ± 0.21  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 15 │ 4.87 ± 0.28  │ 2.71 ± 0.17  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 16 │ 2.65 ± 0.17  │ 4.38 ± 0.25  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 17 │ 4.20 ± 0.25  │ 4.02 ± 0.24  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 18 │ 4.65 ± 0.28  │ 4.98 ± 0.30  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 19 │ 3.68 ± 0.23  │ 2.55 ± 0.16  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 20 │ 3.89 ± 0.24  │ 7.59 ± 0.44  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 21 │ 4.73 ± 0.29  │ 6.12 ± 0.36  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 22 │ 6.04 ± 0.37  │ 5.16 ± 0.31  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 23 │ 6.70 ± 0.40  │ 6.31 ± 0.37  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 24 │ 5.36 ± 0.34  │ 5.87 ± 0.35  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 25 │ 4.91 ± 0.30  │ 7.11 ± 0.42  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 26 │ 7.15 ± 0.43  │ 10.28 ± 0.58 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 27 │ 7.13 ± 0.42  │ 7.75 ± 0.45  │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 28 │ 11.68 ± 0.69 │ 15.85 ± 0.90 │\n",
+      "├────┼──────────────┼──────────────┤\n",
+      "│ 29 │ 21.1 ± 1.2   │ 19.9 ± 1.1   │\n",
+      "└────┴──────────────┴──────────────┘\n",
+      "2021-08-12 11:00:31,269 - ompy.error_finder - DEBUG - Processing an ensemble with 14 members\n",
+      "2021-08-12 11:00:31,271 - ompy.error_finder - DEBUG - Before removing nan: 45 NLD values and 31 GSF values\n",
+      "2021-08-12 11:00:31,273 - ompy.error_finder - WARNING - NLDs and/or γSFs contains nan's. They will be removed\n",
+      "2021-08-12 11:00:31,274 - ompy.error_finder - DEBUG - After removing nan: 30 NLD values and 30 GSF values\n",
+      "2021-08-12 11:00:31,278 - ompy.error_finder - INFO - Starting pyMC3 inference - logarithmic model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [σ_f, σ_ρ, α, F, D, σ_α, σ_F, σ_D]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
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+       "            }\n",
+       "        </style>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 02:43<00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 172 seconds.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-08-12 11:03:35,101 - ompy.error_finder - INFO - Inference results:\n",
+      "┌─────────────┬─────────────┬────────────────┬───────────────┬───────────────┬─────────────────┐\n",
+      "│ D           │ F           │ α              │ σ_D           │ σ_F           │ σ_α             │\n",
+      "╞═════════════╪═════════════╪════════════════╪═══════════════╪═══════════════╪═════════════════╡\n",
+      "│ 0.00 ± 0.28 │ 0.00 ± 0.27 │ -0.000 ± 0.067 │ 0.277 ± 0.015 │ 0.277 ± 0.014 │ 0.0674 ± 0.0035 │\n",
+      "└─────────────┴─────────────┴────────────────┴───────────────┴───────────────┴─────────────────┘\n",
+      "┌────┬──────────────┬───────────────┐\n",
+      "│    │ σ_ρ [%]      │ σ_f [%]       │\n",
+      "╞════╪══════════════╪═══════════════╡\n",
+      "│ 0  │ 1.57 ± 0.14  │ 4.22 ± 0.23   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 1  │ 2.08 ± 0.15  │ 3.81 ± 0.21   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 2  │ 4.02 ± 0.23  │ 3.80 ± 0.21   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 3  │ 4.37 ± 0.24  │ 5.04 ± 0.27   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 4  │ 5.32 ± 0.29  │ 1.83 ± 0.12   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 5  │ 3.60 ± 0.20  │ 2.43 ± 0.14   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 6  │ 4.06 ± 0.22  │ 2.56 ± 0.15   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 7  │ 5.69 ± 0.31  │ 1.469 ± 0.096 │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 8  │ 3.92 ± 0.21  │ 2.43 ± 0.14   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 9  │ 3.67 ± 0.20  │ 2.37 ± 0.14   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 10 │ 1.82 ± 0.11  │ 2.19 ± 0.12   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 11 │ 3.17 ± 0.17  │ 2.16 ± 0.13   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 12 │ 3.24 ± 0.18  │ 3.28 ± 0.18   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 13 │ 3.40 ± 0.19  │ 2.78 ± 0.15   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 14 │ 2.30 ± 0.14  │ 3.21 ± 0.18   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 15 │ 4.86 ± 0.26  │ 2.55 ± 0.14   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 16 │ 3.77 ± 0.21  │ 4.17 ± 0.22   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 17 │ 4.54 ± 0.25  │ 3.87 ± 0.21   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 18 │ 4.51 ± 0.26  │ 4.93 ± 0.27   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 19 │ 4.26 ± 0.24  │ 2.83 ± 0.16   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 20 │ 3.71 ± 0.22  │ 8.13 ± 0.45   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 21 │ 4.55 ± 0.26  │ 5.84 ± 0.31   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 22 │ 5.87 ± 0.32  │ 5.00 ± 0.27   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 23 │ 6.56 ± 0.36  │ 6.63 ± 0.36   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 24 │ 5.31 ± 0.30  │ 5.61 ± 0.32   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 25 │ 4.73 ± 0.27  │ 6.70 ± 0.37   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 26 │ 7.32 ± 0.40  │ 10.69 ± 0.59  │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 27 │ 6.94 ± 0.38  │ 7.61 ± 0.41   │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 28 │ 11.93 ± 0.65 │ 15.57 ± 0.85  │\n",
+      "├────┼──────────────┼───────────────┤\n",
+      "│ 29 │ 20.0 ± 1.1   │ 22.8 ± 1.2    │\n",
+      "└────┴──────────────┴───────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "nld_ens_errors = []\n",
+    "gsf_ens_errors = []\n",
+    "for extractor in extractors_ens:\n",
+    "    nld_err, gsf_err = error_estimator.evaluate(extractor.nld, extractor.gsf)\n",
+    "    nld_ens_errors.append(nld_err.copy())\n",
+    "    gsf_ens_errors.append(gsf_err.copy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "995621af-f7b1-4d40-a0a0-b1ec9c0b989a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15b6a08502454707898db3bc6a1dd72f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def make_vec(nlds, point, counts=np.array(range(3,15))):\n",
+    "    return om.Vector(E=np.array(counts),\n",
+    "                     values=np.array([nld.std[nld.index(point)] for nld in nlds])/np.array([nld.values[nld.index(point)] for nld in nlds]))\n",
+    "\n",
+    "nld_0MeV = make_vec(nld_ens_errors, 1)\n",
+    "nld_0MeV.values *= 100.\n",
+    "#nld_0MeV.save(\"nld_ens1MeV.csv\", sep=\"\\t\")\n",
+    "nld_2MeV = make_vec(nld_ens_errors, 3)\n",
+    "nld_2MeV.values *= 100.\n",
+    "#nld_2MeV.save(\"nld_ens3MeV.csv\", sep=\"\\t\")\n",
+    "nld_4MeV = make_vec(nld_ens_errors, 5)\n",
+    "nld_4MeV.values *= 100.\n",
+    "#nld_4MeV.save(\"nld_ens5MeV.csv\", sep=\"\\t\")\n",
+    "\n",
+    "_, ax = nld_0MeV.plot(label=r\"$E_x = 1$ MeV\", linestyle='')\n",
+    "nld_2MeV.plot(ax=ax, label=r\"$E_x = 3$ MeV\", linestyle='')\n",
+    "nld_4MeV.plot(ax=ax, label=r\"$E_x = 5$ MeV\", linestyle='')\n",
+    "ax.plot(np.linspace(3, 14, 1001), 10.*np.sqrt(8.*7.)/np.sqrt(np.linspace(3, 14, 1001)*(np.linspace(3, 14, 1001)-1)))\n",
+    "ax.set_xlabel(\"Ensemble members\")\n",
+    "ax.set_ylabel(\"Relative error [%]\")\n",
+    "ax.legend(loc='best');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "754fd57a-b53d-48c7-aea6-8601fb0e6e9f",
+   "metadata": {},
+   "source": [
+    "### Checking if the uncertanties makes sense\n",
+    "The estimated uncertanties are for NLDs and $\\gamma$SFs that are taken from a stocastic input. The relative error of the total number of counts ($\\sqrt{N}/N$) found in the first generation matrix should give the lower limit for the estimated uncertanties.\n",
+    "\n",
+    "For the NLD we are interested in the total number of counts along the diagonals $P(E_f+E_\\gamma, E_\\gamma)$, $E_\\gamma \\in [E_\\gamma^{min}, E_\\gamma^{max}]$. For the $\\gamma$SF we are interested in the horizonal lines, $P(E_x, E_\\gamma)$, $E_x \\in [E_x^{min}, E_x^{max}]$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "9b5af5ed-2211-4c19-8c93-e67da5b20c42",
+   "metadata": {},
+   "outputs": [
+    {
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+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
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+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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+     },
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+    },
+    {
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+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for ensemble, gsf_err in zip(ensembles, gsf_errors):\n",
+    "    \n",
+    "    matrix = ensemble.get_raw(0).copy()\n",
+    "    for n in range(1, len(ensemble.raw_ensemble)):\n",
+    "        matrix.values += ensemble.get_raw(n).values\n",
+    "    matrix.values *= 1./len(ensemble.raw_ensemble)\n",
+    "    mat = matrix.copy()\n",
+    "    mat.rebin('both', E_rebinned, inplace=True)\n",
+    "    trapezoid_cut.act_on(mat)\n",
+    "    \n",
+    "    vals = np.zeros(len(fg_matrix.Eg))\n",
+    "    for i in mat.range_Eg:\n",
+    "        for j in mat.range_Ex:\n",
+    "            vals[i] += mat.values[j,i]\n",
+    "    gsf_counts = om.Vector(E=mat.Eg.copy(), values=vals, units='keV')\n",
+    "    gsf_counts.to_MeV()\n",
+    "    gsf_counts.values = 100./np.sqrt(gsf_counts.values)\n",
+    "    \n",
+    "    fig, ax = gsf_counts.plot()\n",
+    "    er = gsf_err.copy()\n",
+    "    er.values *= 100.\n",
+    "    er.plot(ax=ax)\n",
+    "    ax.set_ylabel(\"Relative error [%]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "f2204f56-aae6-4766-b5d4-4206881e1683",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.3833305269815916\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b07468db1d5a4abdafe6b02f37907c50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now similar but for NLD\n",
+    "# First we will add together all the first generation matrices in the ensemble and take the average for each bin.\n",
+    "fg_matrix = ensemble_full.get_firstgen(0)\n",
+    "for i in range(1, len(ensemble_full.raw_ensemble)):\n",
+    "    fg_matrix.values += ensemble_full.get_firstgen(i).values\n",
+    "\n",
+    "fg_matrix.values *= 1./len(ensemble_full.raw_ensemble) # There are 15 ensemble members\n",
+    "\n",
+    "# Absolute worst would be the statistics from the raw matrix?\n",
+    "#fg_matrix = raw_orig.copy()\n",
+    "fg_matrix.rebin('both', E_rebinned, inplace=True)\n",
+    "\n",
+    "\n",
+    "# Next we cut\n",
+    "trapezoid_cut.act_on(fg_matrix)\n",
+    "\n",
+    "Ef = nld_ens_errors[-1].E*1000.\n",
+    "Ef0 = Ef[0]\n",
+    "values = np.zeros(len(Ef))\n",
+    "for i, ef in enumerate(Ef):\n",
+    "    for j in fg_matrix.range_Eg:\n",
+    "        if ef + fg_matrix.Eg[j] < min(fg_matrix.Ex) or ef + fg_matrix.Eg[j] > max(fg_matrix.Ex):\n",
+    "            continue\n",
+    "        index_Ex = fg_matrix.index_Ex(ef + fg_matrix.Eg[j])\n",
+    "        values[i] += fg_matrix.values[index_Ex, j]\n",
+    "        #print(i, \" \", index_Ex, \" \", j, \" \", fg_matrix.Ex[index_Ex], \" \", fg_matrix.Eg[j], \" \", fg_matrix.Ex[index_Ex]-fg_matrix.Eg[j])\n",
+    "print(100./np.sqrt(values[-1]))\n",
+    "nld_counts = om.Vector(E=Ef, values=values, units='keV')\n",
+    "nld_counts.to_MeV()\n",
+    "nld_counts.values = 100./np.sqrt(nld_counts.values)\n",
+    "fig, ax = nld_counts.plot()\n",
+    "er = nld_ens_errors[-1].copy()\n",
+    "er.values *= 100.\n",
+    "er.plot(ax=ax)\n",
+    "ax.set_ylabel(\"Relative error [%]\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "db6d7c7b-b2e2-463f-a343-cc7e71096237",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d2887183f554fa099a03119213770c7",
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+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
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+      ]
+     },
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+    {
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+    },
+    {
+     "data": {
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+      },
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+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for ensemble, nld_err in zip(ensembles, nld_errors):\n",
+    "    \n",
+    "    matrix = ensemble.get_raw(0).copy()\n",
+    "    for n in range(1, len(ensemble.raw_ensemble)):\n",
+    "        matrix.values += ensemble.get_raw(n).values\n",
+    "    matrix.values *= 1./len(ensemble.raw_ensemble)\n",
+    "    mat = matrix.copy()\n",
+    "    mat.rebin('both', E_rebinned, inplace=True)\n",
+    "    trapezoid_cut.act_on(mat)\n",
+    "        \n",
+    "    Ef = nld_err.E*1e3\n",
+    "    vals = np.zeros(len(Ef))\n",
+    "    for i, ef in enumerate(Ef):\n",
+    "        for j in fg_matrix.range_Eg:\n",
+    "            if ef + fg_matrix.Eg[j] < min(fg_matrix.Ex) or ef + fg_matrix.Eg[j] > max(fg_matrix.Ex):\n",
+    "                continue\n",
+    "            index_Ex = fg_matrix.index_Ex(ef + fg_matrix.Eg[j])\n",
+    "            vals[i] += fg_matrix.values[index_Ex, j]\n",
+    "    \n",
+    "    nld_counts = om.Vector(E=Ef, values=vals, units='keV')\n",
+    "    nld_counts.to_MeV()\n",
+    "    nld_counts.values = 100./np.sqrt(nld_counts.values)\n",
+    "    \n",
+    "    #fig, ax = nld_counts.plot()\n",
+    "    er = nld_err.copy()\n",
+    "    er.values *= 100.\n",
+    "    nld_counts.values *= er.values[15]/nld_counts.values[15]\n",
+    "    fig, ax = nld_counts.plot()\n",
+    "    er.plot(ax=ax)\n",
+    "    ax.set_ylabel(\"Relative error [%]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10dd58b-0ebd-4613-b42e-850de5baff16",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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+   "pygments_lexer": "ipython3",
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+ "nbformat_minor": 5
+}
diff --git a/ompy/__init__.py b/ompy/__init__.py
index 1b288017..c0b06e00 100644
--- a/ompy/__init__.py
+++ b/ompy/__init__.py
@@ -44,8 +44,15 @@
     from .decomposition import nld_T_product, index
     from .normalizer_nld import (NormalizerNLD, load_levels_discrete,
                                  load_levels_smooth)
+    from .normalizer_nld_v2 import NormalizerNLD as NormalizerNLDv2
+    from .normalizer_ct import NormalizerNLD as NormalizerNLDct
+    from .normalizer_simultan_ct import NormalizerSimultan as NormalizerSimultanCT
+    from .normalizer_simultan_v2 import NormalizerSimultan as NormalizerSimultanV2
+    from .normalizer_simultan_spincut import NormalizerSimultan as NormalizerSimultanSC
     from .normalizer_gsf import NormalizerGSF
     from .normalizer_simultan import NormalizerSimultan
-    from .ensembleNormalizer import EnsembleNormalizer
+    from .ensemble_normalizer import EnsembleNormalizer
     from .models import NormalizationParameters, ResultsNormalized
     from .introspection import logging, hooks
+    from .dist import FermiDirac
+    from .error_finder import ErrorFinder
diff --git a/ompy/dist/__init__.py b/ompy/dist/__init__.py
new file mode 100644
index 00000000..a3b93d28
--- /dev/null
+++ b/ompy/dist/__init__.py
@@ -0,0 +1,7 @@
+import pymc as pm
+from packaging import version
+
+if version.parse(pm.__version__) < version.parse("4.0.0"):
+    from .fermi_dirac import FermiDirac
+else:
+    from .fermi_dirac_pymc4 import FermiDirac
diff --git a/ompy/dist/fermi_dirac.py b/ompy/dist/fermi_dirac.py
new file mode 100644
index 00000000..70cb3e4e
--- /dev/null
+++ b/ompy/dist/fermi_dirac.py
@@ -0,0 +1,164 @@
+import numpy as np
+
+import theano.tensor as tt
+from pymc3.distributions.continuous import (
+    PositiveContinuous,
+    assert_negative_support,
+    draw_values,
+    generate_samples)
+from pymc3.distributions.dist_math import bound
+from pymc3.theanof import floatX
+
+
+class FermiDirac(PositiveContinuous):
+    """
+    Fermi-Dirac distribution.
+
+    The pdf of this distribution is
+
+    .. math::
+
+        f(x \mid \lambda \mu) =
+            \frac{\lambda}{\lambda\mu + \ln(1 + e^{-\lambda\mu})}
+            \frac{1}{e^{\lambda(x - \mu)} + 1}
+
+    .. plot::
+
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        x = np.linspace(0, 5., 1000)
+        lam = [1.0, 10., 25.]
+        mu = [0.5, 1.0, 2.0]
+
+        def pdf(x, lam, mu):
+            return (lam/np.log(1 + np.exp(lam*mu))*(1/(np.exp(lam*(x-mu)) + 1))
+
+        for l in lam:
+            for m in mu:
+                pdf = pdf(x, l, m)
+                plt.plot(x, pdf, label=f"$\lambda = {l}$, $\mu = {m}$")
+        plt.xlabel("x", fontsize=12)
+        plt.ylabel("f(x)", fontsize=12)
+        plt.legend(loc=1)
+        plt.show()
+
+    ========  ============================
+    Support   :math:`x \in [0, \infty)`
+    ========  ============================
+
+
+    Parameters
+    ----------
+    lam: float
+        Rate of decay at mu (lam > 0)
+    mu: float
+        Decay position
+
+    Examples
+    --------
+    .. code-block:: python
+
+        with pm.Model():
+            x = ompy.FermiDirac('x', lam=10.0, mu=1.2)
+
+    """
+
+    def __init__(self, lam, mu, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.lam = tt.as_tensor_variable(floatX(lam))
+        self.mu = tt.as_tensor_variable(floatX(mu))
+        assert_negative_support(lam, "lam", "FermiDirac")
+
+        self.median = self._ppf(0.5, self.lam, self.mu)
+
+    def _ppf(self, q, lam, mu):
+        """
+        Calculate the CDF for the Fermi-Dirac distribution.
+        """
+        return mu - np.log((1 + np.exp(lam*mu))**(1-q) - 1)/lam
+
+    def _random(self, lam, mu, size=None):
+        """
+        Draw a random number from the Fermi-Dirac distribution.
+        """
+        v = np.random.uniform(size=size)
+        return self._ppf(v, lam, mu)
+
+    def random(self, point=None, size=None):
+        """
+        Draw random values from Fermi-Dirac distribution.
+
+        Parameters
+        ----------
+        point: dict, optional
+            Dict of variable values on which random values are to be
+            conditioned (uses default point if not specified).
+        size: int, optional
+            Desired size of random sample (returns one sample if not
+            specified).
+
+        Returns
+        -------
+        array
+        """
+        lam, mu = draw_values([self.lam, self.mu], point=point, size=size)
+        return generate_samples(self._random, lam, mu, dist_shape=self.shape,
+                                size=size)
+
+    def logp(self, value):
+        """
+        Calculate log-probability of Fermi-Dirac distribution at specified
+        value.
+
+        Parameters
+        ----------
+        value: numeric
+            Value(s) for which log-probability is calculated. If the log
+            probabilities for multiple values are desired the values must be
+            provided in a numpy array or theano tensor
+
+        Returns
+        -------
+        TensorVariable
+        """
+
+        # The formula is
+        # p(x) = lam/ln(1 + exp(lam*mu)) * 1/(exp(lam*(x-mu)) + 1)
+        # ln(p(x)) = ln(lam/ln(1 + exp(lam*mu))) - ln(exp(lam*(x-mu)) + 1)
+
+        lam = self.lam
+        mu = self.mu
+
+        N = lam/tt.log(1 + tt.exp(lam*mu))
+        logp = tt.log(N) - tt.log(tt.exp(lam*(value - mu)) + 1)
+        return bound(logp, value >= 0, lam > 0)
+
+    def logcdf(self, value):
+        """
+        Compute the log of cumulative distribution function for the Fermi-Dirac
+        distribution at the specified value.
+
+        Parameters
+        ----------
+        value: numeric or np.ndarray or theano.tensor
+            Value(s) for which log CDF is calculated. If the log CDF for
+            multiple values are desired the values must be provided in a numpy
+            array or theano tensor.
+
+        Returns
+        -------
+        TensorVariable
+        """
+
+        # The formula is CDF
+        # P(x) = 1 - ln(1 + exp(-lam*(x-mu)))/ln(1 + exp(lam*mu))
+        # ln(P(x)) = ln(1 - ln(1 + exp(-lam*(x-mu)))/ln(1 + exp(lam*mu)))
+
+        lam = self.lam
+        mu = self.mu
+
+        logcdf = tt.log(1 - tt.log(1 + tt.exp(-lam*(value - mu))) /
+                        tt.log(1 + tt.exp(lam*mu)))
+        return bound(logcdf, value >= 0, lam > 0)
diff --git a/ompy/dist/fermi_dirac_pymc4.py b/ompy/dist/fermi_dirac_pymc4.py
new file mode 100644
index 00000000..a13b8d73
--- /dev/null
+++ b/ompy/dist/fermi_dirac_pymc4.py
@@ -0,0 +1,128 @@
+import pymc as pm
+import numpy as np
+from typing import List, Tuple
+
+import pytensor.tensor as at
+from pytensor.tensor.random.op import RandomVariable
+
+from pymc.distributions.continuous import (PositiveContinuous,
+                                            assert_negative_support)
+#from pymc.distributions.dist_math import bound
+
+"""
+TODO:
+    - Add tests according to pymc3 standard, 
+        see https://github.com/pymc-devs/pymc3/blob/main/docs/source/developer_guide_implementing_distribution.md  # noqa
+"""
+
+
+class FermiDiracRV(RandomVariable):
+    name: str = "fermidirac"
+
+    # Minimum dim. is a scalar (0 = scalar, 1 = vector, etc.)
+    ndim_supp: int = 0
+
+    # Number of parameters for the RV
+    ndims_params: List[int] = [0, 0]
+
+    # Datatype, floatX is continious
+    dtype: str = "floatX"
+
+    # Print name
+    _print_name: Tuple[str, str] = ("FermiDirac", "\\operatorname{FermiDirac}")
+
+
+    @classmethod
+    def rng_fn(cls,
+               rng: np.random.RandomState,
+               lam: np.ndarray,
+               mu: np.ndarray, size: Tuple[int, ...]
+               ) -> np.ndarray:
+        q = rng.uniform(size=size)
+        return mu - np.log((1 + np.exp(lam*mu))**(1-q) - 1)/lam
+
+
+fermidirac = FermiDiracRV()
+
+
+class FermiDirac(PositiveContinuous):
+    """
+    Fermi-Dirac distribution.
+
+    The pdf of this distribution is
+
+    .. math::
+
+        f(x \mid \lambda \mu) =
+            \frac{\lambda}{\lambda\mu - \ln(1 + e^{-\lambda\mu})}
+            \frac{1}{e^{\lambda(x - \mu)} + 1}
+
+    .. plot::
+
+
+    ========  ============================
+    Support   :math:`x \in [0, \infty)`
+    Mean
+    Variance
+    ========  ============================
+
+
+    Parameters
+    ----------
+    lam: float
+        Rate of decay at mu (lam > 0)
+    mu: float
+        Decay position
+    """
+    rv_op = fermidirac
+
+    @classmethod
+    def dist(cls, lam, mu, *args, **kwargs):
+        lam = at.as_tensor_variable(floatX(lam))
+        mu = at.as_tensor_variable(floatX(mu))
+
+        assert_negative_support(lam, "lam", "FermiDirac")
+
+        return super().dist([lam, mu], *args, **kwargs)
+
+    def logp(value, lam, mu):
+        """
+        Calculate log-probability of Fermi-Dirac distribution at spesified
+        value.
+
+        Parameters
+        ----------
+        value: numeric
+            Value(s) for which log-probability is calculated. If the log
+            probabilities for multiple values are desired the values must be
+            provided in a numpy array or Aesara tensor
+
+        Returns
+        -------
+        TensorVariable
+        """
+
+        N = lam/at.log(1 + at.exp(lam*mu))
+        logp = at.log(N) - at.log(at.exp(lam*(value - mu)) + 1)
+        return bound(logp, value >= 0, lam > 0)
+
+    def logcdf(value, lam, mu):
+        """
+        Compute the log of cumulative distribution function for the Fermi-Dirac
+        distribution at the specified value.
+
+        Parameters
+        ----------
+        value: numeric or np.ndarray or aesara.tensor
+            Value(s) for which log CDF is calculated. If the log CDF for
+            multiple values are desired the values must be provided in a numpy
+            array or Aesara tensor.
+
+        Returns
+        -------
+        TensorVariable
+        """
+
+        logcdf = at.log(1 - at.log(1 + at.exp(-lam*(value - mu))) /
+                        at.log(1 + at.exp(lam*mu)))
+        return bound(logcdf, value >= 0, lam > 0)
diff --git a/ompy/ensemble.py b/ompy/ensemble.py
index d3a3776c..9c17e390 100644
--- a/ompy/ensemble.py
+++ b/ompy/ensemble.py
@@ -126,6 +126,11 @@ def __init__(self, raw: Optional[Matrix] = None,
 
         self.raw.state = "raw"
 
+    def __len__(self) -> int:
+        """ Returns the number of ensemble members
+        """
+        return self.size
+
     def load(self, path: Optional[Union[str, Path]] = None):
         """ Loads a saved ensamble. Alternative to `regenerate`.
 
@@ -417,7 +422,7 @@ def rebin(self, out_array: np.ndarray, member: str) -> None:
         self.std_firstgen = firstgen_std
 
     def generate_gaussian(self, state: str,
-                          rstate: Optional[np.random.Generator] = np.random.default_rng) -> np.ndarray: # noqa
+                          rstate: Optional[np.random.Generator] = np.random.default_rng) -> np.ndarray:  # noqa
         """Generates an array with Gaussian perturbations of a matrix.
         Note that entries are truncated at 0 (only positive).
 
diff --git a/ompy/ensembleNormalizer.py b/ompy/ensemble_normalizer.py
similarity index 98%
rename from ompy/ensembleNormalizer.py
rename to ompy/ensemble_normalizer.py
index 2b0937cc..f0b5a616 100644
--- a/ompy/ensembleNormalizer.py
+++ b/ompy/ensemble_normalizer.py
@@ -268,17 +268,22 @@ def plot(self, ax: Tuple[Any, Any] = None,
             else:
                 fig, ax_stats = plt.subplots(2, 1)  # dummy
             Emin = samples["nld"].iloc[0].E[-1]
-            x = np.linspace(Emin, normalizer_nld.norm_pars.Sn[0], num=20)
+            if self.normalizer_simultan is None:
+                Emin = self.normalizer_nld.limit_high[0]
+            elif self.normalizer_simultan is not None:
+                Emin = self.normalizer_simultan.normalizer_nld.limit_high[0]
+            x = np.linspace(Emin, normalizer_nld.norm_pars.Sn[0], num=101)
             stats_nld_model = \
                 self.plot_nld_ext_stats(ax_stats[0], x=x, samples=samples,
                                         normalizer_nld=normalizer_nld,
                                         percentiles=percentiles,
                                         color=colors[2],
                                         label="model")
+            stats_nld_model['x'] = x
 
             E = samples["gsf"].iloc[0].E
-            xlow = np.linspace(0.001, E[0], num=20)
-            xhigh = np.linspace(E[-1], normalizer_gsf.norm_pars.Sn[0], num=20)
+            xlow = np.linspace(0.001, E[0], num=101)
+            xhigh = np.linspace(E[-1], normalizer_gsf.norm_pars.Sn[0], num=101)
             stats_gsf_model = \
                 self.plot_gsf_ext_stats(ax_stats[1], xlow=xlow, xhigh=xhigh,
                                         samples=samples,
@@ -525,6 +530,8 @@ def stats_from_df(df: pd.DataFrame,
         np.fromiter(map(fmap, indexed, repeat(out)), dtype=float)
         stats = pd.DataFrame(out[:, :])
         stats = pd.DataFrame({'median': stats.median(),
+                              'mean': stats.mean(),
+                              'std': stats.std(),
                               'low': stats.quantile(percentiles[0], axis=0),
                               'high': stats.quantile(percentiles[1], axis=0)})
         return stats
diff --git a/ompy/error_finder.py b/ompy/error_finder.py
new file mode 100644
index 00000000..8162d9a4
--- /dev/null
+++ b/ompy/error_finder.py
@@ -0,0 +1,338 @@
+import logging
+import numpy as np
+import pymc as pm
+import termtables as tt
+
+from numpy import ndarray
+from typing import Optional, Union, Any, Tuple, List, Dict
+
+
+from .vector import Vector
+from .matrix import Matrix
+from .dist import FermiDirac
+
+
+class ErrorFinder:
+    """ Find the relative errors from an ensemble of NLDs and γSFs.
+
+    This class uses pyMC3 to calculate the relative errors of the data points
+    in the extracted NLDs and γSFs. The class implements two different models,
+    one logarithmic and one linear. The logarithmic model will usually be more
+    stable and should be used if the linear doesn't converge.
+
+    Attributes:
+        algorithm (string): Indicate what algorithm to use Currently only 'log'
+            is implemented. Default is 'log'
+        seed (int): Seed used in the pyMC3 sampling.
+        options (dict): The pymc3.sample options to use.
+        testvals (dict): A dictionary with testvalues to feed to pymc3 when
+            declaring the prior distributions. This can probably be left to
+            the default, but could be changed if needed.
+        trace: The trace of the inference data. None if the class hasn't
+            ran.
+    TODO:
+        - Trace should always be saved. Calculations can take hours!
+        - Refactor the linear model (maybe remove?)
+        - Refactor how data are conditioned
+    """
+
+    LOG = logging.getLogger(__name__)  # overwrite parent variable
+    logging.captureWarnings(True)
+
+    def __init__(self, algorithm: Optional[str] = 'log',
+                 options: Optional[Dict[str, Any]] = None,
+                 seed: Optional[int] = 7392):
+        """ Initialize ErrorFinder. See attributes above for arguments.
+
+        Raises:
+            NotImplementedError if algorithm isn't recognized.
+
+        """
+
+        if not (algorithm.lower() == 'log'):
+            raise NotImplementedError(
+                f"Algorithm '{algorithm}' not implemented")
+
+        self.algorithm = algorithm.lower()
+        self.options = {} if options is None else options
+        self.options.setdefault("return_inferencedata", False)
+        self.seed = seed
+        self.testvals = {'σ_D': 0.25, 'σ_F': 0.25, 'σ_α': 0.05,
+                         'σ_A': 0.25, 'σ_B': 0.25, 'A': 1.0, 'B': 1.0}
+        self.prior_parameters = {'σ_ρ': {'lam': 10., 'mu': 1.},
+                                 'σ_f': {'lam': 10., 'mu': 1.}}
+
+    def __call__(self, *args, **kwargs) -> Union[Tuple[Vector, Vector], Any]:
+        """ Wrapper for evaluate """
+        return self.evaluate(*args, **kwargs)
+
+    def evaluate(self, nlds: List[Vector],
+                 gsfs: List[Vector],
+                 median: Optional[bool] = False,
+                 full: Optional[bool] = False
+                 ) -> Union[Tuple[Vector, Vector], Any]:
+        """ Evaluate the model and find the relative errors
+        of the NLDs and γSFs passed to the function.
+
+        Args:
+            nlds (List[Vector]): List of the NLDs in an ensemble
+            gsfs (List[Vector]): List of the γSFs in an ensemble
+            median (bool, optional): If the mean of the relative error should
+                be used rather than the mean. Default is False.
+            full (bool, optional): If the sample trace should
+                be returned or not.
+        Returns:
+            The relative error of the nuclear level density and
+            gamma strength function.
+            Optionally returns the full sample trace if `trace` is true.
+
+        Raises:
+            ValueError: If length of `nlds` doesn't match the length of `gsfs`.
+            pyMC3 may raise errors. Please report if such errors occurs.
+        """
+        algo = None
+        if self.algorithm == 'log':
+            algo = self.logarithm
+        else:
+            raise NotImplementedError(
+                f"Algorithm '{algorithm}' not implemented")
+
+        return algo(nlds, gsfs, median=median, full=full)
+
+    def logarithm(self, nlds: List[Vector], gsfs: List[Vector],
+                  median: Optional[bool] = False, full: Optional[bool] = False
+                  ) -> Union[Tuple[Vector, Vector], Any]:
+        """Use Bayesian inference to estimate the relative errors of the NLD
+        and GSF from an ensemble. See Ingeberg et al., NIM (TBA)
+
+        Args:
+            nlds (List[Vector]): Ensemble of NLDs
+            gsfs (List[Vector]): Ensemble of GSFs
+            median (bool, optional): If the resulting relative errors should be
+                the mean or the median.
+            full (bool, optional): If the trace from the pyMC3 sampling should
+                be returned. Useful for debugging.
+        """
+
+        assert len(nlds) == len(gsfs), \
+            "Number of ensemble members of the NLD and GSF doesn't match."
+        N = len(nlds)
+
+        E_nld, q_nld = format_data(nlds)
+        E_gsf, q_gsf = format_data(gsfs)
+
+        M_nld, M_gsf = len(E_nld), len(E_gsf)
+        coef_mask_nld, values_mask_nld = format_mask(N, M_nld)
+        coef_mask_gsf, values_mask_gsf = format_mask(N, M_gsf)
+
+        self.LOG.info("Starting pyMC3 inference - logarithmic model")
+        self.LOG.debug(f"Inference with an ensemble with N={N} members with "
+                       f"{M_nld} NLD bins and {M_gsf} GSF bins.")
+
+        with pm.Model() as model:
+
+            σ_D = pm.HalfFlat("σ_D", testval=self.testvals['σ_D'])
+            σ_F = pm.HalfFlat("σ_F", testval=self.testvals['σ_F'])
+            σ_α = pm.HalfFlat("σ_α", testval=self.testvals['σ_α'])
+
+            D = pm.Normal("D", mu=0, sigma=σ_D, shape=[N, N-1])[:, coef_mask_nld]  # noqa
+            F = pm.Normal("F", mu=0, sigma=σ_F, shape=[N, N-1])[:, coef_mask_gsf]  # noqa
+            α = pm.Normal("α", mu=0, sigma=σ_α, shape=[N, N-1])
+
+            σ_ρ = FermiDirac("σ_ρ", lam=self.prior_parameters['σ_ρ']['lam'],
+                             mu=self.prior_parameters['σ_ρ']['mu'],
+                             shape=M_nld)[values_mask_nld]
+
+            σ_f = FermiDirac("σ_f", lam=self.prior_parameters['σ_f']['lam'],
+                             mu=self.prior_parameters['σ_f']['mu'],
+                             shape=M_gsf)[values_mask_gsf]
+
+            μ_ρ = D + α[:, coef_mask_nld] * E_nld[values_mask_nld]
+            μ_f = F + α[:, coef_mask_gsf] * E_gsf[values_mask_gsf]
+
+            q_ρ = pm.Normal("q_ρ", mu=μ_ρ, sigma=np.sqrt(2)*σ_ρ,
+                            observed=np.log(q_nld))
+            q_f = pm.Normal("q_f", mu=μ_f, sigma=np.sqrt(2)*σ_f,
+                            observed=np.log(q_gsf))
+
+            # Perform the sampling
+            trace = pm.sample(random_seed=self.seed, **self.options)
+        self.trace = trace
+        self.display_results(trace)
+
+        mid = np.median if median else np.mean
+        nld_rel_err = Vector(E=E_nld, values=mid(trace['σ_ρ'], axis=0),
+                             std=np.std(trace['σ_ρ'], axis=0), units='MeV')
+        gsf_rel_err = Vector(E=E_gsf, values=mid(trace['σ_f'], axis=0),
+                             std=np.std(trace['σ_f'], axis=0), units='MeV')
+        if full:
+            return nld_rel_err, gsf_rel_err, trace
+        else:
+            return nld_rel_err, gsf_rel_err
+
+    def display_results(self, trace: Any) -> None:
+        """ Print the results from the pyMC3 inference to the log.
+        """
+
+        def line(idx) -> list:
+            mean = trace[idx].mean()
+            sigma = trace[idx].std()
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            return fmts % (mean, sigma)
+        header = []
+        values = []
+        try:
+            for idx in ['A', 'B', 'α', 'σ_A', 'σ_B', 'σ_α']:
+                values.append(line(idx))
+            header = ['A', 'B', 'α', 'σ_A', 'σ_B', 'σ_α']
+        except KeyError:
+            for idx in ['D', 'F', 'α', 'σ_D', 'σ_F', 'σ_α']:
+                values.append(line(idx))
+            header = ['D', 'F', 'α', 'σ_D', 'σ_F', 'σ_α']
+
+        errs = []
+        _, M_nld = trace['σ_ρ'].shape
+        _, M_gsf = trace['σ_f'].shape
+        errs = [[f'{m}', '', ''] for m in range(max([M_nld, M_gsf]))]
+        for m in range(M_nld):
+            mean = trace['σ_ρ'][:, m].mean() * 100.
+            sigma = trace['σ_ρ'][:, m].std() * 100.
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            errs[m][1] = fmts % (mean, sigma)
+        for m in range(M_gsf):
+            mean = trace['σ_f'][:, m].mean() * 100.
+            sigma = trace['σ_f'][:, m].std() * 100.
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            errs[m][2] = fmts % (mean, sigma)
+
+        self.LOG.info("Inference results:\n%s\n%s",
+                      tt.to_string([values], header=header),
+                      tt.to_string(errs, header=['', 'σ_ρ [%]', 'σ_f [%]']))
+
+
+def all_equal(iterator):
+    """ Check if all elements in an iterator are equal.
+    """
+
+    iterator = iter(iterator)
+    try:
+        first = next(iterator)
+    except StopIteration:
+        return True
+    return all(first == x for x in iterator)
+
+
+def remove_nans(vecs: List[Vector]) -> List[Vector]:
+    """ Remove all points that are nan's for each member of the ensemble"""
+
+    vecs_no_nan = [vec.copy() for vec in vecs]
+    for vec in vecs_no_nan:
+        vec.cut_nan(inplace=True)
+
+    return vecs_no_nan
+
+
+def keep_only(vecs: List[Vector]) -> List[Vector]:
+    """ Deletes all the points that are not shared by all vectors in the list
+    and returns the list with only the points shared among all vectors.
+
+    Args:
+        vecs (List): List of similar vectors
+    Returns: List of vectors with only the points that share x-value among all
+        of the input vectors.
+    """
+    vecs = remove_nans(vecs)
+    E = [vec.E.copy() for vec in vecs]
+    energies = {}
+    for vec in vecs:
+        for E in vec.E:
+            if E not in energies:
+                energies[E] = [False] * len(vecs)
+
+    # Next we will add if the point is present or not
+    for n, vec in enumerate(vecs):
+        for E in vec.E:
+            energies[E][n] = True
+
+    keep_energy = []
+    for key in energies:
+        if np.all(energies[key]):
+            keep_energy.append(key)
+
+    vec_all_common = [vec.copy() for vec in vecs]
+    for vec in vec_all_common:
+        E = []
+        values = []
+        for e, value in zip(vec.E, vec.values):
+            if e in keep_energy:
+                E.append(e)
+                values.append(value)
+        vec.E = np.array(E)
+        vec.values = np.array(values)
+
+    return vec_all_common
+
+
+def format_data(vecs: List[Vector]) -> Tuple[ndarray, ndarray]:
+    """Find and build required variables for pymc3 model.
+    This function takes an ensemble (NLDs or GSFs) and finds the energy
+    bins that are not nan and are shared among all members. It then
+    builds a tensor:
+
+        q_{r,i,j} = \ln(\frac{v_{r,j}}{v_{i,j}})
+
+    where i and r ≠ i are the ensemble member index and j is the
+    bin index.
+
+    Args:
+        vecs (List[Vector]): List of vectors. Typically an ensemble
+            of NLDs or GSFs.
+    Returns:
+        E (array): energy bins shared among all of the input vector
+        q (array): Observation tensor
+    """
+    # Remove all bins that contains nan's
+    N = len(vecs)
+    vecs = keep_only(vecs)
+
+    E = vecs[0].E.copy()
+    if vecs[0].units == 'keV':
+        E /= 1.0e3
+    M = len(E)
+
+    # Masking to get proper broadcasting of shapes
+    mask = np.tile(np.arange(M, dtype=int), (N-1, 1))
+
+    # Next we will create the observation tensor
+    q = []
+    for r in range(N):
+        not_r = []
+        for i in range(N):
+            if i == r:
+                continue
+            not_r.append(vecs[i].values.copy())
+        q.append(vecs[r].values[mask]/not_r)
+    q = np.array(q)
+    return E, q
+
+
+def format_mask(N: int, M: int) -> Tuple[ndarray, ndarray]:
+    """Setup masking arrays for correct broadcasting.
+
+    Args:
+        N (int): Number of ensemble members
+        M (int): Number of bins
+    Returns:
+        coef_mask (array): Masking broadcasting the coefficients.
+        values_mask (array): Masking broadcasting the values.
+    """
+
+    coef_mask = np.repeat(np.arange(N-1), M).reshape(N-1, M)
+    values_mask = np.array([np.tile(np.arange(M, dtype=int), (N-1, 1))] * N)
+    return coef_mask, values_mask
diff --git a/ompy/extractor.py b/ompy/extractor.py
index c421b762..fb31313a 100644
--- a/ompy/extractor.py
+++ b/ompy/extractor.py
@@ -14,6 +14,7 @@
 from .vector import Vector
 from .decomposition import chisquare_diagonal, nld_T_product
 from .action import Action
+from .error_finder import ErrorFinder
 
 if 'JPY_PARENT_PID' in os.environ:
     from tqdm import tqdm_notebook as tqdm
@@ -47,6 +48,8 @@ class Extractor:
         trapezoid (Action[Matrix]): The Action cutting the matrices of the
             Ensemble into the desired shape where from the nld and gsf will be
             extracted from.
+        error_estimator (ErrorFinder): The algorithm used to estimate the
+            relative errors of the extracted NLD and γSF.
         path (path): The path to save and/or load nld and gsf to/from.
         extend_diagonal_by_resolution (bool, optional): If `True`,
             the fit will be extended beyond Ex=Eg by the (FWHM) of the
@@ -58,6 +61,11 @@ class Extractor:
         seed (int): Random seed for reproducibility of results.
         resolution_Ex (float or np.ndarray, optional): Resolution (FWHM) along
             Ex axis (particle detector resolution). Defaults to 150 keV
+        rel_err_missing (float): Relative error used for points that cannot be
+            estimated by error_estimator object.
+        suppress_warning (bool): Suppress warnings. The warnings are usually
+            expected. Set this attribute to `True` in order to avoid enourmous
+            amounts of warnings in your notebooks.
 
 
     TODO:
@@ -67,11 +75,13 @@ class Extractor:
     """
     def __init__(self, ensemble: Optional[Ensemble] = None,
                  trapezoid: Optional[Action] = None,
+                 error_estimator: Optional[ErrorFinder] = None,
                  path: Optional[Union[str, Path]] =
                  'saved_run/extractor'):
         """
         ensemble (Ensemble, optional): see above
         trapezoid (Action[Matrix], optional): see above
+        error_estimator (ErrorFinder, optional): see above
         path (Path or str, optional): see above
         """
         self.ensemble = ensemble
@@ -81,6 +91,7 @@ def __init__(self, ensemble: Optional[Ensemble] = None,
         self.nld: List[Vector] = []
         self.gsf: List[Vector] = []
         self.trapezoid = trapezoid
+        self.error_estimator = error_estimator
 
         if path is None:
             self.path = None
@@ -96,28 +107,47 @@ def __init__(self, ensemble: Optional[Ensemble] = None,
         self.extend_fit_by_resolution: bool = False
         self.resolution_Ex = 150  # keV
 
+        self.rel_err_missing = 0.3
+        self.suppress_warning = False
+
     def __call__(self, ensemble: Optional[Ensemble] = None,
                  trapezoid: Optional[Action] = None):
         return self.extract_from(ensemble, trapezoid)
 
     def extract_from(self, ensemble: Optional[Ensemble] = None,
                      trapezoid: Optional[Action] = None,
-                     regenerate: Optional[bool] = None):
+                     error_estimator: Optional[ErrorFinder] = None,
+                     regenerate: Optional[bool] = None,
+                     return_trace: Optional[bool] = False) -> Union[None, Any]:
         """Decompose each first generation matrix in an Ensemble
 
         If `regenerate` is `True` it saves the extracted nld and gsf to file,
         or loads them if already generated. Exposes the vectors in the
         attributes self.nld and self.gsf.
 
+        If the error_estimator attribute or argument is set then relative
+        errors will be estimated using the provided algorithm. Points in the
+        nld and gsf that the algorithm are unable to estimate will be set to
+        30%. If error_estimator isn't set (is None), then the std attribute
+        of nld and gsf will not be set.
+
         Args:
             ensemble (Ensemble, optional): The ensemble to extract nld and gsf
                 from. Can be provided in when initializing instead.
             trapezoid (Action, optional): An Action describing the cut to apply
                 to the matrices to obtain the desired region for extracting nld
                 and gsf.
+            error_estimator (ErrorFinder, optional): Object responsible for
+                estimating the relative errors of the extracted nld and gsf.
             regenerate (bool, optional): Whether to regenerate all nld and gsf
                 even if they are found on disk.
-
+            return_trace (bool, optional): If the trace from the ErrorFinder
+                algorithm should be returned or not. If `error_estimator`
+                argument and `error_estimator` attribute are both None this
+                will have no affect.
+        Returns: Trace from inference sampling of the relative errors if
+            `return_trace` argument is true and either the `error_estimator`
+            argument or attribute are set.
         Raises:
             ValueError: If no Ensemble instance is provided here or earlier.
         """
@@ -129,6 +159,8 @@ def extract_from(self, ensemble: Optional[Ensemble] = None,
             self.trapezoid = trapezoid
         elif self.trapezoid is None:
             raise ValueError("A 'trapezoid' cut must be given'")
+        if error_estimator is not None:
+            self.error_estimator = error_estimator
         if regenerate is None:
             regenerate = self.regenerate
         self.path = Path(self.path)
@@ -153,7 +185,30 @@ def extract_from(self, ensemble: Optional[Ensemble] = None,
         self.nld = nlds
         self.gsf = gsfs
 
+        trace = None
+
+        if self.error_estimator is not None and regenerate:
+            LOG.debug("Estimating relative errors")
+
+            # We allways get the trace. If the user doesn't want it, we do
+            # not give it. Doesn't matter really!
+            nld_rel_err, gsf_rel_err, trace = self.error_estimator(nlds, gsfs,
+                                                                   full=True)
+            nld_rel_err.to_keV()
+            gsf_rel_err.to_keV()
+            for i, (nld, gsf) in enumerate(zip(self.nld, self.gsf)):
+                nld.std = self.rel_err_missing * nld.values
+                gsf.std = self.rel_err_missing * gsf.values
+                idx_nld = nld.indices(nld_rel_err.E)
+                idx_gsf = gsf.indices(gsf_rel_err.E)
+                nld.std[idx_nld] = nld_rel_err.values * nld.values[idx_nld]
+                gsf.std[idx_gsf] = gsf_rel_err.values * gsf.values[idx_gsf]
+                nld.save(self.path / f'nld_{i}.npy')  # Overwrite with errors!
+                gsf.save(self.path / f'gsf_{i}.npy')  # Overwrite with errors!
+
         self.check_unconstrained_results()
+        if return_trace:
+            return trace  # If error_estimator is not set, then allways None.
 
     def step(self, num: int) -> Tuple[Vector, Vector]:
         """ Wrapper around _extract in order to be consistent with other classes
@@ -298,7 +353,8 @@ def errfun(x: np.ndarray) -> float:
 
         # Convert transmission coefficient to the more useful
         # gamma strength function
-        gsf = T/(2*np.pi*matrix.Eg**3)
+        Eg = matrix.Eg / 1e3  # Ensure we devide by MeV and not keV
+        gsf = T/(2*np.pi*Eg**3)
 
         if product:
             nld_0 = np.where(np.isnan(nld), np.zeros_like(nld), nld)
@@ -536,18 +592,20 @@ def check_unconstrained_results(self) -> bool:
         for i, vec in enumerate(self.nld):
             if np.isnan(vec.values).any():
                 contains_nan = True
-                LOG.warning(f"nld #{i} contains nan's.\n"
-                            "Consider removing them e.g. with:\n"
-                            "# for nld in extractor.nld:\n"
-                            "#     nld.cut_nan()\n")
+                if not self.suppress_warning:
+                    LOG.warning(f"nld #{i} contains nan's.\n"
+                                "Consider removing them e.g. with:\n"
+                                "# for nld in extractor.nld:\n"
+                                "#     nld.cut_nan()\n")
 
         for i, vec in enumerate(self.nld):
             if np.isnan(vec.values).any():
                 contains_nan = True
-                LOG.warning(f"gsf #{i} contains nan's.\n"
-                            "Consider removing them e.g. with:\n"
-                            "# for gsf in extractor.gsf:\n"
-                            "#     gsf.cut_nan()\n")
+                if not self.suppress_warning:
+                    LOG.warning(f"gsf #{i} contains nan's.\n"
+                                "Consider removing them e.g. with:\n"
+                                "# for gsf in extractor.gsf:\n"
+                                "#     gsf.cut_nan()\n")
 
         return contains_nan
 
diff --git a/ompy/library.py b/ompy/library.py
index c9751654..9278563d 100644
--- a/ompy/library.py
+++ b/ompy/library.py
@@ -294,9 +294,9 @@ def interpolate_matrix_2D(matrix_in, E0_array_in, E1_array_in,
 def log_interp1d(xx, yy, **kwargs):
     """ Interpolate a 1-D function.logarithmically """
     logy = np.log(yy)
-    lin_interp = interp1d(xx, logy, kind='linear', **kwargs)
-    log_interp = lambda zz: np.exp(lin_interp(zz))  # noqa
-    return log_interp
+    kwargs.setdefault('kind', 'linear')
+    lin_interp = interp1d(xx, logy, **kwargs)
+    return lambda zz: np.exp(lin_interp(zz))
 
 
 def call_model(fun,pars,pars_req):
diff --git a/ompy/matrix.py b/ompy/matrix.py
index 57aa1faf..b0fa4816 100644
--- a/ompy/matrix.py
+++ b/ompy/matrix.py
@@ -85,7 +85,8 @@ def __init__(self,
                  std: Optional[np.ndarray] = None,
                  path: Optional[Union[str, Path]] = None,
                  shape: Optional[Tuple[int, int]] = None,
-                 state: Union[str, MatrixState] = None):
+                 state: Union[str, MatrixState] = None,
+                 **kwargs):
         """
         There is the option to initialize it in an empty state.
         In that case, all class variables will be None.
@@ -132,7 +133,7 @@ def __init__(self,
         self.std = std
 
         if path is not None:
-            self.load(path)
+            self.load(path, **kwargs)
         self.verify_integrity()
 
         self.state = state
@@ -680,11 +681,18 @@ def trapezoid(self, Ex_min: float, Ex_max: float,
         mask[Eg >= Ex + dEg] = True
         matrix[mask] = 0
 
+        matrix.std = self.std
+        if self.std is not None:
+            mat = Matrix(Ex=self.Ex, Eg=self.Eg, values=self.std)
+            mat.trapezoid(Ex_min, Ex_max, Eg_min, Eg_max, inplace=True)
+            matrix.std = mat.values
+
         if inplace:
             self.values = matrix.values
             self.Ex = matrix.Ex
             self.Eg = matrix.Eg
             self.state = matrix.state
+            self.std = matrix.std
         else:
             return matrix
 
diff --git a/ompy/models.py b/ompy/models.py
index 2bcd9dd0..16159fca 100644
--- a/ompy/models.py
+++ b/ompy/models.py
@@ -443,6 +443,9 @@ class NormalizationParameters(Model):
     #: Parameters necessary for the spin cut model
     _spincutPars: Dict[str, Any] = field(default=None,
             metadata='parameters necessary for the spin cut model')  # noqa
+    #: Optional, user given NLD at Sn
+    rhoSn: Optional[Tuple[float, float]] = field(default=None,
+            metadata='Level density at the neutron separation energy')  # noqa
 
     #: Optional Parameters (do not check in `is_changed`).
     #: Defaults to ["A", "Z", "exclude_check_change"]
diff --git a/ompy/normalizer_ct.py b/ompy/normalizer_ct.py
new file mode 100644
index 00000000..5085c197
--- /dev/null
+++ b/ompy/normalizer_ct.py
@@ -0,0 +1,729 @@
+import numpy as np
+import copy
+import logging
+import termtables as tt
+import json
+import pymultinest
+import matplotlib.pyplot as plt
+import warnings
+from contextlib import redirect_stdout
+from numpy import ndarray
+from scipy.optimize import differential_evolution, curve_fit
+from typing import Optional, Tuple, Any, Union, Callable, Dict
+from pathlib import Path
+
+from .stats import truncnorm_ppf
+from .vector import Vector
+from .library import self_if_none
+from .spinfunctions import SpinFunctions
+from .filehandling import load_discrete
+from .models import ResultsNormalized, NormalizationParameters
+from .abstract_normalizer import AbstractNormalizer
+
+TupleDict = Dict[str, Tuple[float, float]]
+
+
+class NormalizerNLD(AbstractNormalizer):
+    """ Normalizes NLD to empirical data
+
+    Normalizes nld/gsf according to::
+
+        nld' = nld * A * np.exp(alpha * Ex), and
+
+    This is the transformation eq (3), Schiller2000
+
+    Takes empirical data in form of an array of discrete levels,
+    neutron separation energy Sn, a model to estimate what the
+    NLD is at Sn, and several parameters for the model as well
+    as bounds on normalization parameters.
+
+    As a consequence of a complex problem, this class has a complex
+    interface. Much of the book-keeping associated with normalization
+    has been automated, but there is still a lot of settings and
+    parameters for the user to take care of. Some default values
+    has been seen, but the user must be _EXTREMELY_ careful when
+    evaluating the output.
+
+    Attributes:
+        discrete (Vector): The discrete NLD at lower energies. [MeV]
+        nld (Vector): The NLD to normalize. Gets converted to [MeV] from [keV].
+        norm_pars (NormalizationParameters): Normalization parameters like
+            experimental D₀, and spin(-cut) model
+        bounds (Dict[str, Tuple[float, float]): The bounds on each of
+            the parameters. Its keys are 'A', 'alpha', 'T', and 'D0'. The
+            values are on the form (min, max).
+        model (Callable[..., ndarray]): The model to use at high energies
+            to estimate the NLD. Defaults to constant temperature model.
+        de_kwargs(dict): Additional keywords to differential evolution.
+             Defaults to `{"seed": 65424}`. Note that `bounds` has been
+             taken out as a separate attribute, but is a keyword of de.
+        multinest_path (Path): Where to save the multinest output.
+            defaults to 'multinest'.
+        multinest_kwargs (dict): Additional keywords to multinest. Defaults to
+            `{"seed": 65498, "resume": False}`
+        res (ResultsNormalized): Results of the normalization
+        smooth_levels_fwhm (float): FWHM with which the discrete levels shall
+            be smoothed when loading from file. Defaults to 0.1 MeV.
+        path (Path): The path save the results.
+
+    """
+    LOG = logging.getLogger(__name__)  # overwrite parent variable
+    logging.captureWarnings(True)
+
+    def __init__(self, *,
+                 nld: Optional[Vector] = None,
+                 discrete: Optional[Union[str, Vector]] = None,
+                 path: Optional[Union[str, Path]] = 'saved_run/normalizers',
+                 regenerate: bool = False,
+                 norm_pars: Optional[NormalizationParameters] = None) -> None:
+        """ Normalizes nld ang gSF.
+
+        Note:
+            The prefered syntax is `Normalizer(nld=...)`
+            If neither is given, the nld (and other parameters) can be
+            explicity
+            be set later by::
+
+                `normalizer.normalize(..., nld=...)`
+
+            or::
+
+                `normalizer.nld = ...`
+
+            In the later case you *might* have to send in a copy if it's a
+            mutable to ensure it is not changed.
+
+        Args:
+            extractor: see above
+            nld: see above
+            discrete: see above
+            path: see above
+            norm_pars: see above
+        TODO:
+            - parameter to limit the number of multinest samples to store. Note
+              that the samples should be shuffled to retain some "random"
+              samples from the pdf (not the importance weighted)
+
+        """
+        super().__init__(regenerate)
+
+        # Create the private variables
+        self._discrete = None
+        self._discrete_path = None
+        self._D0 = None
+        self._smooth_levels_fwhm = None
+        self.norm_pars = norm_pars
+        self.bounds = {'A': [0.1, 1e3], 'alpha': [1e-1, 20]}  # D0 bounds set later
+        self.model: Optional[Callable[..., ndarray]] = self.const_temperature
+        # self.curried_model = lambda *arg: None
+        self.de_kwargs = {"seed": 65424}
+        self.multinest_path = Path('multinest')
+        self.multinest_kwargs: dict = {"seed": 65498, "resume": False}
+
+        # Handle the method parameters
+        self.smooth_levels_fwhm = 0.1
+        self.nld = None if nld is None else nld.copy()
+        self.discrete = discrete
+
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.limit_low = None
+        self.limit_high = None
+        self.std_fake = None  # See `normalize`
+
+        if path is None:
+            self.path = None
+        else:
+            self.path = Path(path)
+            self.path.mkdir(exist_ok=True, parents=True)
+
+    def __call__(self, *args, **kwargs) -> None:
+        """ Wrapper around normalize """
+        self.normalize(*args, **kwargs)
+
+    def normalize(self, *, limit_low: Optional[Tuple[float, float]] = None,
+                  limit_high: Optional[Tuple[float, float]] = None,
+                  nld: Optional[Vector] = None,
+                  discrete: Optional[Vector] = None,
+                  bounds: Optional[TupleDict] = None,
+                  norm_pars: Optional[NormalizationParameters] = None,
+                  num: int = 0) -> None:
+        """ Normalize NLD to a low and high energy region
+
+        Args:
+            limit_low: The limits (start, stop) where to normalize
+               to discrete levels.
+            limit_high: The limits (start, stop) where to normalize to
+                a theoretical model and neutron separation energy at high
+                energies.
+            nld: The nuclear level density vector to normalize.
+            discrete: The discrete level density at low energies to
+                normalize to.
+            bounds: The bounds of the parameters
+            norm_pars (NormalizationParameters): Normalization parameters like
+            experimental D₀, and spin(-cut) model
+            num (optional): Loop number, defauts to 0
+            regenerate: Whether to use already generated files (False) or
+                generate them all anew (True).
+
+        """
+        if not self.regenerate:
+            try:
+                self.load()
+                return
+            except FileNotFoundError:
+                pass
+
+        # Update internal state
+        self.limit_low = self.self_if_none(limit_low)
+        self.limit_high = self.self_if_none(limit_high)
+        limit_low = self.limit_low
+        limit_high = self.limit_high
+
+        discrete = self.self_if_none(discrete)
+        discrete.to_MeV()
+        nld = self.self_if_none(nld)
+
+        self.norm_pars = self.self_if_none(norm_pars)
+        self.norm_pars.is_changed(include=["D0", "Sn", "spincutModel",
+                                           "spincutPars"])  # check that set
+
+        self.bounds = self.self_if_none(bounds)
+
+        # ensure that it's updated if running again
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.LOG.info(f"\n\n---------\nNormalizing nld #{num}")
+        nld = nld.copy()
+        self.LOG.debug("Setting NLD, convert to MeV")
+        nld.to_MeV()
+        self.LOG.debug("Setting NLD, removing nan")
+        nld.cut_nan()
+
+        # Need to give some sort of standard deviation for sensible results
+        # Otherwise deviations at higher level density will have an
+        # uncreasonably high weight.
+        if self.std_fake is None:
+            self.std_fake = False
+        if self.std_fake or nld.std is None:
+            self.std_fake = True
+            nld.std = nld.values * 0.3  # x% is an arb. choice
+        self.nld = nld
+
+        # Use DE to get an inital guess before optimizing
+        args, guess = self.initial_guess(limit_low, limit_high)
+        # Optimize using multinest
+        popt, samples = self.optimize(num, args, guess)
+
+        transformed = nld.transform(popt['A'][0], popt['alpha'][0],
+                                    inplace=False)
+        if self.std_fake:
+            nld.std = None
+            transformed.std = None
+
+        self.res.nld = transformed
+        self.res.pars = popt
+        self.res.samples = samples
+        ext_model = lambda E: self.const_temperature(E, T=popt['T'][0],
+                                                     Eshift=popt['Eshift'][0])
+        self.res.nld_model = ext_model
+
+        self.save()  # save instance
+
+    def initial_guess(self, limit_low: Optional[Tuple[float, float]] = None,
+                      limit_high: Optional[Tuple[float, float]] = None
+                      ) -> Tuple[Tuple[float, float, float, float],
+                                 Dict[str, float]]:
+        """ Find an inital guess for the constant, α, T and D₀
+
+        Uses differential evolution to perform the guessing.
+
+        Args:
+            limit_low: The limits (start, stop) where to normalize
+               to discrete levels.
+            limit_high: The limits (start, stop) where to normalize to
+                a theoretical model and neutron separation energy at high
+                energies.
+
+        Returns:
+           The arguments used for chi^2 minimization and the
+           minimizer.
+        """
+        limit_low = self.self_if_none(limit_low)
+        limit_high = self.self_if_none(limit_high)
+
+        bounds = list(self.bounds.values())
+        spinParsstring = json.dumps(self.norm_pars.spincutPars, indent=4,
+                                    sort_keys=True)
+
+        self.LOG.debug("Using bounds %s", bounds)
+        self.LOG.debug("Using spincutModel %s", self.norm_pars.spincutModel)
+        self.LOG.debug("Using spincutPars %s", spinParsstring)
+
+        nld_low = self.nld.cut(*limit_low, inplace=False)
+        discrete = self.discrete.cut(*limit_low, inplace=False)
+        nld_high = self.nld.cut(*limit_high, inplace=False)
+
+        # Artificially increase the nld rel error at low
+        #nld_low.std = nld_low.values * 0.3
+        #nld_low.std *= np.sqrt(2)  # Maybe I'm wrong, it should be twice as high?
+        # Instead we will be looking at the cumulative NLD
+
+        nldSn = self.nldSn_from_D0(**self.norm_pars.asdict())[1]
+        rel_uncertainty = self.norm_pars.D0[1]/self.norm_pars.D0[0]
+        nldSn = np.array([nldSn, nldSn * rel_uncertainty])
+
+        # Then...
+        self.coefs = np.polyfit(nld_high.E, np.log(nld_high.values), 1)
+        self.LOG.debug(f"Found best polynomial: a={self.coefs[1]}, b={self.coefs[0]}")
+
+        # We fit the model using curve_fit
+        self.ct_model = lambda E, A, alpha: A*np.exp(self.coefs[1]+(self.coefs[0]+alpha)*E)
+
+        def neglnlike(*args, **kwargs):
+            return - self.lnlike(*args, **kwargs)
+        args = (nld_low, nld_high, discrete, self.ct_model, self.norm_pars.Sn[0],
+                nldSn)
+        res = differential_evolution(neglnlike, bounds=bounds, args=args,
+                                     **self.de_kwargs)
+
+        self.LOG.info("DE results:\n%s", tt.to_string([res.x.tolist()],
+                      header=['A', 'α [MeV⁻¹]']))#, 'T [MeV]', 'Eshift [MeV]']))
+
+        #p0 = dict(zip(["A", "alpha", "T", "Eshift"], (res.x).T))
+        p0 = dict(zip(["A", "alpha"], (res.x).T))
+        for key, res in p0.items():
+            if res in self.bounds[key]:
+                self.LOG.warning(f"DE result for {key} is at edge its bound:"
+                                 f"{self.bounds[key]}. This will probably lead"
+                                 f"to wrong estimations in multinest, too.")
+
+        return args, p0
+
+    def optimize(self, num: int, args,
+                 guess: Dict[str, float]) -> Tuple[Dict[str, float], Dict[str, float]]:
+        """Find parameters given model constraints and an initial guess
+
+        Employs Multinest
+
+        Args:
+            num (int): Loop number
+            args_nld (Iterable): Additional arguments for the nld lnlike
+            guess (Dict[str, float]): The initial guess of the parameters
+
+        Returns:
+            Tuple:
+            - popt (Dict[str, Tuple[float, float]]): Median and 1sigma of the
+                parameters
+            - samples (Dict[str, List[float]]): Multinest samples.
+                Note: They are still importance weighted, not random draws
+                from the posterior.
+
+        Raises:
+            ValueError: Invalid parameters for automatic prior
+
+        Note:
+            You might want to adjust the priors for your specific case! Here
+            we just propose a general solution that might often work out of
+            the box.
+        """
+        if guess['alpha'] < 0:
+            raise NotImplementedError("Prior selection not implemented for "
+                                      "α < 0")
+        alpha_exponent = np.log10(guess['alpha'])
+
+        #if guess['T'] < 0:
+        #    raise ValueError("Prior selection not implemented for T < 0; "
+        #                     "negative temperature is unphysical")
+        #T_exponent = np.log10(guess['T'])
+
+        A = guess['A']
+
+        # truncations from absolute values
+        lower_A, upper_A = 0., np.inf
+        mu_A, sigma_A = A, 10*A
+        a_A = (lower_A - mu_A) / sigma_A
+        b_A = (upper_A - mu_A) / sigma_A
+
+        lower_Eshift, upper_Eshift = -5., 5
+        mu_Eshift, sigma_Eshift = 0, 5
+        a_Eshift = (lower_Eshift - mu_Eshift) / sigma_Eshift
+        b_Eshift = (upper_Eshift - mu_Eshift) / sigma_Eshift
+
+        def prior(cube, ndim, nparams):
+            # NOTE: You may want to adjust this for your case!
+            # truncated normal prior
+            cube[0] = truncnorm_ppf(cube[0], a_A, b_A)*sigma_A+mu_A
+            # log-uniform prior
+            # if alpha = 1e2, it's between 1e1 and 1e3
+            cube[1] = 10**(cube[1]*2 + (alpha_exponent-1))
+            # log-uniform prior
+            # if T = 1e2, it's between 1e1 and 1e3
+            # cube[2] = 10**(cube[2]*2 + (T_exponent-1))
+            # truncated normal prior
+            # cube[3] = truncnorm_ppf(cube[3], a_Eshift,
+            #                        b_Eshift)*sigma_Eshift + mu_Eshift
+
+            # if np.isinf(cube[3]):
+            #    self.LOG.debug("Encountered inf in cube[3]:\n%s", cube[3])
+
+        def loglike(cube, ndim, nparams):
+            return self.lnlike(cube, *args)
+
+        self.multinest_path.mkdir(exist_ok=True)
+        path = self.multinest_path / f"nld_norm_{num}_"
+        assert len(str(path)) < 60, "Total path length too long for multinest"
+
+        self.LOG.info("Starting multinest")
+        self.LOG.debug("with following keywords %s:", self.multinest_kwargs)
+        #  Hack where stdout from Multinest is redirected as info messages
+        self.LOG.write = lambda msg: (self.LOG.info(msg) if msg != '\n'
+                                      else None)
+        with redirect_stdout(self.LOG):
+            pymultinest.run(loglike, prior, len(guess),
+                            outputfiles_basename=str(path),
+                            **self.multinest_kwargs)
+
+        # Save parameters for analyzer
+        names = list(guess.keys())
+        json.dump(names, open(str(path) + 'params.json', 'w'))
+        analyzer = pymultinest.Analyzer(len(guess),
+                                        outputfiles_basename=str(path))
+
+        stats = analyzer.get_stats()
+
+        samples = analyzer.get_equal_weighted_posterior()[:, :-1]
+        samples = dict(zip(names, samples.T))
+
+        T = self.coefs[0] + samples['alpha']
+        Eshift = -T*(np.log(T*samples['A']) + self.coefs[1])
+        samples['T'] = T
+        samples['Eshift'] = Eshift
+
+        names += ['T', 'Eshift']
+        stats['marginals'].append({'1sigma': (np.quantile(T, 0.16),
+                                              np.quantile(T, 0.84)),
+                                   'median': np.quantile(T, 0.5)})
+        stats['marginals'].append({'1sigma': (np.quantile(Eshift, 0.16),
+                                              np.quantile(Eshift, 0.84)),
+                                   'median': np.quantile(Eshift, 0.5)})
+
+        # Format the output
+        popt = dict()
+        vals = []
+        for name, m in zip(names, stats['marginals']):
+            lo, hi = m['1sigma']
+            med = m['median']
+            sigma = (hi - lo) / 2
+            popt[name] = (med, sigma)
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            vals.append(fmts % (med, sigma))
+
+        self.LOG.info("Multinest results:\n%s", tt.to_string([vals],
+                      header=['A', 'α [MeV⁻¹]', 'T [MeV]', 'Eshift [MeV]']))
+
+        return popt, samples
+
+    def plot(self, *, ax: Any = None,
+             add_label: bool = True,
+             results: Optional[ResultsNormalized] = None,
+             add_figlegend: bool = True,
+             plot_fitregion: bool = True,
+             reset_color_cycle: bool = True,
+             **kwargs) -> Tuple[Any, Any]:
+        """Plot the NLD, discrete levels and result of normalization
+
+        Args:
+            ax (optional): The matplotlib axis to plot onto. Creates axis
+                is not provided
+            add_label (bool, optional): Defaults to `True`.
+            add_figlegend (bool, optional):Defaults to `True`.
+            results (ResultsNormalized, optional): If provided, nld and model
+                are taken from here instead.
+            plot_fitregion (Optional[bool], optional): Defaults to `True`.
+            reset_color_cycle (Optional[bool], optional): Defaults to `True`
+            **kwargs: Description
+
+        Returns:
+            fig, ax
+        """
+        if ax is None:
+            fig, ax = plt.subplots()
+        else:
+            fig = ax.figure
+
+        if reset_color_cycle:
+            ax.set_prop_cycle(None)
+
+        res = self.res if results is None else results
+        pars = res.pars
+        nld = res.nld
+
+        labelNld = '_exp.'
+        labelNldSn = None
+        labelModel = "_model"
+        labelDiscrete = "_known levels"
+        if add_label:
+            labelNld = 'exp.'
+            labelNldSn = r'$\rho(S_n)$'
+            labelModel = 'model'
+            labelDiscrete = "known levels"
+        nld.plot(ax=ax, label=labelNld, **kwargs)
+
+        self.discrete.plot(ax=ax, kind='step', c='k', label=labelDiscrete)
+
+        nldSn = self.nldSn_from_D0(**self.norm_pars.asdict())[1]
+        rel_uncertainty = self.norm_pars.D0[1]/self.norm_pars.D0[0]
+        nldSn = np.array([nldSn, nldSn * rel_uncertainty])
+
+        x = np.linspace(self.limit_high[0], self.norm_pars.Sn[0])
+        model = Vector(E=x, values=self.model(E=x,
+                                              T=pars['T'][0],
+                                              Eshift=pars['Eshift'][0]))
+
+        ax.errorbar(self.norm_pars.Sn[0], nldSn[0], yerr=nldSn[1],
+                    label=labelNldSn, fmt="ks", markerfacecolor='none')
+        # workaround for enseble Normalizer; always keep these label
+        for i in range(3):
+            ax.lines[-(i+1)]._label = "_nld(Sn)"
+
+        ax.plot(model.E, model.values, "--", label=labelModel, markersize=0,
+                c='g', **kwargs)
+
+        if plot_fitregion:
+            ax.axvspan(self.limit_low[0], self.limit_low[1], color='grey',
+                       alpha=0.1, label="fit limits")
+            ax.axvspan(self.limit_high[0], self.limit_high[1], color='grey',
+                       alpha=0.1)
+
+        ax.set_yscale('log')
+        ax.set_ylabel(r"Level density $\rho(E_x)~[\mathrm{MeV}^{-1}]$")
+        ax.set_xlabel(r"Excitation energy $E_x~[\mathrm{MeV}]$")
+        ax.set_ylim(bottom=0.5/(nld.E[1]-nld.E[0]))
+
+        if fig is not None and add_figlegend:
+            fig.legend(loc=9, ncol=3, frameon=False)
+
+        return fig, ax
+
+    @staticmethod
+    def lnlike(x: Tuple[float, float, float, float], nld_low: Vector,
+               nld_high: Vector, discrete: Vector,
+               model: Callable[..., ndarray],
+               Sn, nldSn) -> float:
+        """ Compute log likelihood of the normalization fitting
+
+        This is the result up a, which is irrelevant for the maximization
+
+        Args:
+            x: The arguments ordered as A, alpha, T and Eshift
+            nld_low: The lower region where discrete levels will be
+                fitted.
+            nld_high: The upper region to fit to model.
+            discrete: The discrete levels to be used in fitting the
+                lower region.
+            model: The model to use when fitting the upper region.
+                Must support the keyword arguments
+                ``model(E=..., T=..., Eshift=...) -> ndarray``
+
+        Returns:
+            lnlike: log likelihood
+        """
+        #A, alpha, T, Eshift = x[:4]  # slicing needed for multinest?
+        A, alpha = x[:2]  # slicing needed for multinest?
+        transformed_low = nld_low.transform(A, alpha, inplace=False)
+        transformed_high = nld_high.transform(A, alpha, inplace=False)
+
+        err_low = transformed_low.error(discrete)
+        expected = Vector(E=transformed_high.E,
+                          values=model(E=transformed_high.E,
+                                       A=A, alpha=alpha))
+        err_high = transformed_high.error(expected)
+
+        nldSn_model = model(E=Sn, A=A, alpha=alpha)
+        err_nldSn = ((nldSn[0] - nldSn_model)/nldSn[1])**2
+
+        ln_stds = (np.log(transformed_low.std).sum()
+                   + np.log(transformed_high.std).sum())
+
+        return -0.5*(err_low + err_high + err_nldSn + ln_stds)
+
+    @staticmethod
+    def const_temperature(E: ndarray, T: float, Eshift: float) -> ndarray:
+        """ Constant Temperature NLD"""
+        ct = np.exp((E - Eshift) / T) / T
+        return ct
+
+    @staticmethod
+    def nldSn_from_D0(D0: Union[float, Tuple[float, float]],
+                      Sn: Union[float, Tuple[float, float]], Jtarget: float,
+                      spincutModel: str, spincutPars: Dict[str, Any],
+                      **kwargs) -> Tuple[float, float]:
+        """Calculate nld(Sn) from D0
+
+
+        1/D0 = nld(Sn) * ( g(Jtarget+1/2, pi_target)
+                         + g(Jtarget1/2, pi_target) )
+        Here we assume equal parity, g(J,pi) = g(J)/2 and
+        nld(Sn) = 1/D0 * 2/(g(Jtarget+1/2) + g(Jtarget-1/2))
+        For the case Jtarget = 0, the g(Jtarget-1/2) = 0
+
+        Parameters:
+            D0 (float or [float, float]):
+                Average resonance spacing from s waves [eV]. If a tuple,
+                it is assumed that it is of the form `[value, uncertainty]`.
+            Sn (float or [float, float]):
+                Separation energy [MeV]. If a tuple, it is assumed that it is of
+                the form `[value, uncertainty]`.
+            Jtarget (float):
+                Target spin
+            spincutModel (str):
+                Model to for the spincut
+            spincutPars Dict[str, Any]:
+                Additional parameters necessary for the spin cut model
+            **kwargs: Description
+
+
+        Returns:
+            nld: Ex=Sn and nld at Sn [MeV, 1/MeV]
+        """
+
+        D0 = np.atleast_1d(D0)[0]
+        Sn = np.atleast_1d(Sn)[0]
+
+        def g(J):
+            return SpinFunctions(Ex=Sn, J=J,
+                                 model=spincutModel,
+                                 pars=spincutPars).distribution()
+
+        if Jtarget == 0:
+            summe = 1 / 2 * g(Jtarget + 1 / 2)
+        else:
+            summe = 1 / 2 * (g(Jtarget - 1 / 2) + g(Jtarget + 1 / 2))
+
+        nld = 1 / (summe * D0 * 1e-6)
+        return [Sn, nld]
+
+    @staticmethod
+    def D0_from_nldSn(nld_model: Callable[..., Any],
+                      Sn: Union[float, Tuple[float, float]], Jtarget: float,
+                      spincutModel: str, spincutPars: Dict[str, Any],
+                      **kwargs) -> Tuple[float, float]:
+        """Calculate D0 from nld(Sn), assuming equiparity.
+
+        This is the inverse of `nldSn_from_D0`
+
+        Parameters:
+            nld_model (Callable[..., Any]): Model for nld above data of the
+                from `y = nld_model(E)` in 1/MeV.
+            Sn (float or [float, float]):
+                Separation energy [MeV]. If a tuple, it is assumed that it is of
+                the form `[value, uncertainty]`.
+            Jtarget (float):
+                Target spin
+            spincutModel (str):
+                Model to for the spincut
+            spincutPars Dict[str, Any]:
+                Additional parameters necessary for the spin cut model
+            **kwargs: Description
+
+
+        Returns:
+            D0: D0 in eV
+        """
+
+        Sn = np.atleast_1d(Sn)[0]
+        nld = nld_model(Sn)
+
+        def g(J):
+            return SpinFunctions(Ex=Sn, J=J,
+                                 model=spincutModel,
+                                 pars=spincutPars).distribution()
+
+        if Jtarget == 0:
+            summe = 1 / 2 * g(Jtarget + 1 / 2)
+        else:
+            summe = 1 / 2 * (g(Jtarget - 1 / 2) + g(Jtarget + 1 / 2))
+
+        D0 = 1 / (summe * nld * 1e-6)
+        return D0
+
+    @property
+    def discrete(self) -> Optional[Vector]:
+        return self._discrete
+
+    @discrete.setter
+    def discrete(self, value: Optional[Union[Path, str, Vector]]) -> None:
+        if value is None:
+            self._discretes = None
+            self.LOG.debug("Set `discrete` to None")
+        elif isinstance(value, (str, Path)):
+            if self.nld is None:
+                raise ValueError(f"`nld` must be set before loading levels")
+            nld = self.nld.copy()
+            nld.to_MeV()
+            self.LOG.debug("Set `discrete` levels from file with FWHM %s",
+                           self.smooth_levels_fwhm)
+            self._discrete = load_levels_smooth(value, nld.E,
+                                                self.smooth_levels_fwhm)
+            self._discrete.units = "MeV"
+            self._discrete_path = value
+
+        elif isinstance(value, Vector):
+            if self.nld is not None and np.any(self.nld.E != value.E):
+                raise ValueError("`nld` and `discrete` must"
+                                 " have same energy binning")
+            self._discrete = value
+            self.LOG.debug("Set `discrete` by Vector")
+        else:
+            raise ValueError(f"Value {value} is not supported"
+                             " for discrete levels")
+
+    @property
+    def smooth_levels_fwhm(self) -> Optional[float]:
+        return self._smooth_levels_fwhm
+
+    @smooth_levels_fwhm.setter
+    def smooth_levels_fwhm(self, value: float) -> None:
+        self._smooth_levels_fwhm = value
+        if self._discrete_path is not None:
+            self.discrete = self._discrete_path
+
+    def self_if_none(self, *args, **kwargs):
+        """ wrapper for lib.self_if_none """
+        return self_if_none(self, *args, **kwargs)
+
+
+def load_levels_discrete(path: Union[str, Path], energy: ndarray) -> Vector:
+    """ Load discrete levels without smoothing
+
+    Assumes linear equdistant binning
+
+    Args:
+        path: The file to load
+        energy: The binning to use
+    Returns:
+        A vector describing the levels
+    """
+    histogram, _ = load_discrete(path, energy, 0.1)
+    return Vector(values=histogram, E=energy, units='MeV')
+
+
+def load_levels_smooth(path: Union[str, Path], energy: ndarray,
+                       resolution: float = 0.1) -> Vector:
+    """ Load discrete levels with smoothing
+
+    Assumes linear equdistant binning
+
+    Args:
+        path: The file to load
+        energy: The binning to use in MeV
+        resolution: The resolution (FWHM) of the smoothing to use in MeV
+    Returns:
+        A vector describing the smoothed levels
+    """
+    histogram, smoothed = load_discrete(path, energy, resolution)
+    return Vector(values=smoothed if resolution > 0 else histogram, E=energy,
+                  units='MeV')
diff --git a/ompy/normalizer_gsf.py b/ompy/normalizer_gsf.py
index 9d177d3d..9ac895cb 100644
--- a/ompy/normalizer_gsf.py
+++ b/ompy/normalizer_gsf.py
@@ -167,6 +167,10 @@ def normalize(self, *, gsf: Optional[Vector] = None,
         else:
             self.nld = self.self_if_none(nld)
 
+        self.LOG.debug("Setting NLD, convert to MeV and removing nan")
+        self.nld.to_MeV()
+        self.nld.cut_nan()
+
         alpha = self.self_if_none(alpha, nonable=True)
         if alpha is None:
             self.LOG.debug("Setting alpha from from normalizer_nld")
@@ -190,20 +194,29 @@ def normalize(self, *, gsf: Optional[Vector] = None,
         else:
             self.res = ResultsNormalized(name="Results NLD and GSF, stepwise")
 
+        self.LOG.debug("Setting GSF, convert to MeV and removing nan")
+        self._gsf = gsf.copy()
+        self._gsf.to_MeV()
+        self._gsf.cut_nan()
+        self._gsf.transform(alpha=alpha, inplace=True)
+
         self.LOG.info(f"Normalizing #{num}")
-        self._gsf = gsf.copy()  # make a copy as it will be transformed
-        gsf.to_MeV()
-        gsf = gsf.transform(alpha=alpha, inplace=False)
-        self._gsf = gsf
 
-        self.model_low.autorange(gsf)
-        self.model_high.autorange(gsf)
-        self._gsf_low, self._gsf_high = self.extrapolate(gsf)
+        self.model_low.autorange(self._gsf)
+        self.model_high.autorange(self._gsf)
+        self._gsf_low, self._gsf_high = self.extrapolate(self._gsf)
 
         # experimental Gg and calc. both in meV
         B_norm = self.norm_pars.Gg[0] / self.Gg_before_norm()
-        # propagate uncertainty of D0
-        B_norm_unc = B_norm * self.norm_pars.D0[1] / self.norm_pars.D0[0]
+        # propagate uncertainty of D0 and Gg0
+        B_norm_unc = B_norm * np.sqrt(
+            (self.norm_pars.D0[1] / self.norm_pars.D0[0])**2
+            + (self.norm_pars.Gg[1] / self.norm_pars.Gg[0])**2)
+
+        num_units = max(0, int(-np.floor(np.log10(B_norm_unc))) + 1)
+        num_units = f"%.{num_units}f"
+        self.LOG.info(f"Normalizing coeficient B = {num_units} ± {num_units}",
+                      B_norm, B_norm_unc)
 
         # apply transformation and export results
         self._gsf.transform(B_norm)
diff --git a/ompy/normalizer_nld.py b/ompy/normalizer_nld.py
index e2317b67..d0232636 100644
--- a/ompy/normalizer_nld.py
+++ b/ompy/normalizer_nld.py
@@ -198,6 +198,8 @@ def normalize(self, *, limit_low: Optional[Tuple[float, float]] = None,
         nld = nld.copy()
         self.LOG.debug("Setting NLD, convert to MeV")
         nld.to_MeV()
+        self.LOG.debug("Setting NLD, removing nan")
+        nld.cut_nan()
 
         # Need to give some sort of standard deviation for sensible results
         # Otherwise deviations at higher level density will have an
@@ -263,6 +265,11 @@ def initial_guess(self, limit_low: Optional[Tuple[float, float]] = None,
         discrete = self.discrete.cut(*limit_low, inplace=False)
         nld_high = self.nld.cut(*limit_high, inplace=False)
 
+        # Artificially increase the nld rel error at low
+        #nld_low.std = nld_low.values * 0.3
+        #nld_low.std *= np.sqrt(2)  # Maybe I'm wrong, it should be twice as high?
+        # Instead we will be looking at the cumulative NLD
+
         nldSn = self.nldSn_from_D0(**self.norm_pars.asdict())[1]
         rel_uncertainty = self.norm_pars.D0[1]/self.norm_pars.D0[0]
         nldSn = np.array([nldSn, nldSn * rel_uncertainty])
@@ -481,6 +488,32 @@ def plot(self, *, ax: Any = None,
 
         return fig, ax
 
+    def lnlike_v2(self, x: Tuple[float, float, float, float], nld_low: Vector,
+                  nld_high: Vector, discrete: Vector,
+                  model: Callable[..., ndarray],
+                  Sn, nldSn) -> float:
+        """ Compute log likelihood of the ...
+        """
+        A, alpha, T, Eshift = x[:4]  # slicing needed for multinest?
+        transformed_low = nld_low.transform(A, alpha, inplace=False)
+        transformed_high = nld_high.transform(A, alpha, inplace=False)
+
+        err_low = transformed_low.error(discrete)
+        expected = Vector(E=transformed_high.E,
+                          values=model(E=transformed_high.E,
+                                       T=T, Eshift=Eshift))
+        err_high = transformed_high.error(expected)
+
+        # calculate the D0-equivalent of T and Eshift used
+        D0 = self.D0_from_nldSn(lambda E: model(E, T=T, Eshift=Eshift), **self.norm_pars.asdict())
+
+        err_nldSn = ((D0 - self.norm_pars.D0[0])/self.norm_pars.D0[1])**2
+
+        ln_stds = (np.log(transformed_low.std).sum()
+                   + np.log(transformed_high.std).sum())
+
+        return -0.5*(err_low + err_high + err_nldSn + ln_stds)
+
     @staticmethod
     def lnlike(x: Tuple[float, float, float, float], nld_low: Vector,
                nld_high: Vector, discrete: Vector,
diff --git a/ompy/normalizer_nld_v2.py b/ompy/normalizer_nld_v2.py
new file mode 100644
index 00000000..e126c532
--- /dev/null
+++ b/ompy/normalizer_nld_v2.py
@@ -0,0 +1,731 @@
+import numpy as np
+import copy
+import logging
+import termtables as tt
+import json
+import pymultinest
+import matplotlib.pyplot as plt
+import warnings
+from contextlib import redirect_stdout
+from numpy import ndarray
+from scipy.optimize import differential_evolution
+from typing import Optional, Tuple, Any, Union, Callable, Dict
+from pathlib import Path
+
+from .stats import truncnorm_ppf, normal_ppf
+from .vector import Vector
+from .library import self_if_none
+from .spinfunctions import SpinFunctions
+from .filehandling import load_discrete
+from .models import ResultsNormalized, NormalizationParameters
+from .abstract_normalizer import AbstractNormalizer
+
+TupleDict = Dict[str, Tuple[float, float]]
+
+
+class NormalizerNLD(AbstractNormalizer):
+    """ Normalizes NLD to empirical data
+
+    Normalizes nld/gsf according to::
+
+        nld' = nld * A * np.exp(alpha * Ex), and
+
+    This is the transformation eq (3), Schiller2000
+
+    Takes empirical data in form of an array of discrete levels,
+    neutron separation energy Sn, a model to estimate what the
+    NLD is at Sn, and several parameters for the model as well
+    as bounds on normalization parameters.
+
+    As a consequence of a complex problem, this class has a complex
+    interface. Much of the book-keeping associated with normalization
+    has been automated, but there is still a lot of settings and
+    parameters for the user to take care of. Some default values
+    has been seen, but the user must be _EXTREMELY_ careful when
+    evaluating the output.
+
+    Attributes:
+        discrete (Vector): The discrete NLD at lower energies. [MeV]
+        nld (Vector): The NLD to normalize. Gets converted to [MeV] from [keV].
+        norm_pars (NormalizationParameters): Normalization parameters like
+            experimental D₀, and spin(-cut) model
+        bounds (Dict[str, Tuple[float, float]): The bounds on each of
+            the parameters. Its keys are 'A', 'alpha', 'T', and 'D0'. The
+            values are on the form (min, max).
+        model (Callable[..., ndarray]): The model to use at high energies
+            to estimate the NLD. Defaults to constant temperature model.
+        de_kwargs(dict): Additional keywords to differential evolution.
+             Defaults to `{"seed": 65424}`. Note that `bounds` has been
+             taken out as a separate attribute, but is a keyword of de.
+        multinest_path (Path): Where to save the multinest output.
+            defaults to 'multinest'.
+        multinest_kwargs (dict): Additional keywords to multinest. Defaults to
+            `{"seed": 65498, "resume": False}`
+        res (ResultsNormalized): Results of the normalization
+        smooth_levels_fwhm (float): FWHM with which the discrete levels shall
+            be smoothed when loading from file. Defaults to 0.1 MeV.
+        path (Path): The path save the results.
+
+    """
+    LOG = logging.getLogger(__name__)  # overwrite parent variable
+    logging.captureWarnings(True)
+
+    def __init__(self, *,
+                 nld: Optional[Vector] = None,
+                 discrete: Optional[Union[str, Vector]] = None,
+                 path: Optional[Union[str, Path]] = 'saved_run/normalizers',
+                 regenerate: bool = False,
+                 norm_pars: Optional[NormalizationParameters] = None) -> None:
+        """ Normalizes nld ang gSF.
+
+        Note:
+            The prefered syntax is `Normalizer(nld=...)`
+            If neither is given, the nld (and other parameters) can be
+            explicity
+            be set later by::
+
+                `normalizer.normalize(..., nld=...)`
+
+            or::
+
+                `normalizer.nld = ...`
+
+            In the later case you *might* have to send in a copy if it's a
+            mutable to ensure it is not changed.
+
+        Args:
+            extractor: see above
+            nld: see above
+            discrete: see above
+            path: see above
+            norm_pars: see above
+        TODO:
+            - parameter to limit the number of multinest samples to store. Note
+              that the samples should be shuffled to retain some "random"
+              samples from the pdf (not the importance weighted)
+
+        """
+        super().__init__(regenerate)
+
+        # Create the private variables
+        self._discrete = None
+        self._discrete_path = None
+        self._D0 = None
+        self._smooth_levels_fwhm = None
+        self.norm_pars = norm_pars
+        self.bounds = {'A': [0.1, 1e3], 'alpha': [1e-1, 20], 'T': [0.1, 1],
+                       'Eshift': [-5, 5]}  # D0 bounds set later
+        self.model: Optional[Callable[..., ndarray]] = self.const_temperature
+        # self.curried_model = lambda *arg: None
+        self.de_kwargs = {"seed": 65424}
+        self.multinest_path = Path('multinest')
+        self.multinest_kwargs: dict = {"seed": 65498, "resume": False}
+
+        # Handle the method parameters
+        self.smooth_levels_fwhm = 0.3
+        self.nld = None if nld is None else nld.copy()
+        self.discrete = discrete
+
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.limit_low = None
+        self.limit_high = None
+        self.std_fake = None  # See `normalize`
+
+        if path is None:
+            self.path = None
+        else:
+            self.path = Path(path)
+            self.path.mkdir(exist_ok=True, parents=True)
+
+    def __call__(self, *args, **kwargs) -> None:
+        """ Wrapper around normalize """
+        self.normalize(*args, **kwargs)
+
+    def normalize(self, *, limit_low: Optional[Tuple[float, float]] = None,
+                  limit_high: Optional[Tuple[float, float]] = None,
+                  nld: Optional[Vector] = None,
+                  discrete: Optional[Vector] = None,
+                  bounds: Optional[TupleDict] = None,
+                  norm_pars: Optional[NormalizationParameters] = None,
+                  num: int = 0) -> None:
+        """ Normalize NLD to a low and high energy region
+
+        Args:
+            limit_low: The limits (start, stop) where to normalize
+               to discrete levels.
+            limit_high: The limits (start, stop) where to normalize to
+                a theoretical model and neutron separation energy at high
+                energies.
+            nld: The nuclear level density vector to normalize.
+            discrete: The discrete level density at low energies to
+                normalize to.
+            bounds: The bounds of the parameters
+            norm_pars (NormalizationParameters): Normalization parameters like
+            experimental D₀, and spin(-cut) model
+            num (optional): Loop number, defauts to 0
+            regenerate: Whether to use already generated files (False) or
+                generate them all anew (True).
+
+        """
+        if not self.regenerate:
+            try:
+                self.load()
+                return
+            except FileNotFoundError:
+                pass
+
+        # Update internal state
+        self.limit_low = self.self_if_none(limit_low)
+        self.limit_high = self.self_if_none(limit_high)
+        limit_low = self.limit_low
+        limit_high = self.limit_high
+
+        discrete = self.self_if_none(discrete)
+        discrete.to_MeV()
+        nld = self.self_if_none(nld)
+
+        self.norm_pars = self.self_if_none(norm_pars)
+        self.norm_pars.is_changed(include=["D0", "Sn", "spincutModel",
+                                           "spincutPars"])  # check that set
+
+        self.bounds = self.self_if_none(bounds)
+
+        # ensure that it's updated if running again
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.LOG.info(f"\n\n---------\nNormalizing nld #{num}")
+        nld = nld.copy()
+        self.LOG.debug("Setting NLD, convert to MeV")
+        nld.to_MeV()
+        self.LOG.debug("Setting NLD, removing nan")
+        nld.cut_nan()
+
+        # Need to give some sort of standard deviation for sensible results
+        # Otherwise deviations at higher level density will have an
+        # uncreasonably high weight.
+        if self.std_fake is None:
+            self.std_fake = False
+        if self.std_fake or nld.std is None:
+            self.std_fake = True
+            nld.std = nld.values * 0.3  # x% is an arb. choice
+        self.nld = nld
+
+        # Use DE to get an inital guess before optimizing
+        args, guess = self.initial_guess(limit_low, limit_high)
+        # Optimize using multinest
+        popt, samples = self.optimize(num, args, guess)
+
+        transformed = nld.transform(popt['A'][0], popt['alpha'][0],
+                                    inplace=False)
+        if self.std_fake:
+            nld.std = None
+            transformed.std = None
+
+        self.res.nld = transformed
+        self.res.pars = popt
+        self.res.samples = samples
+        ext_model = lambda E: self.model(E, T=popt['T'][0],
+                                         Eshift=popt['Eshift'][0])
+        self.res.nld_model = ext_model
+
+        self.save()  # save instance
+
+    def initial_guess(self, limit_low: Optional[Tuple[float, float]] = None,
+                      limit_high: Optional[Tuple[float, float]] = None
+                      ) -> Tuple[Tuple[float, float, float, float],
+                                 Dict[str, float]]:
+        """ Find an inital guess for the constant, α, T and D₀
+
+        Uses differential evolution to perform the guessing.
+
+        Args:
+            limit_low: The limits (start, stop) where to normalize
+               to discrete levels.
+            limit_high: The limits (start, stop) where to normalize to
+                a theoretical model and neutron separation energy at high
+                energies.
+
+        Returns:
+           The arguments used for chi^2 minimization and the
+           minimizer.
+        """
+        limit_low = self.self_if_none(limit_low)
+        limit_high = self.self_if_none(limit_high)
+
+        bounds = list(self.bounds.values())
+        spinParsstring = json.dumps(self.norm_pars.spincutPars, indent=4,
+                                    sort_keys=True)
+
+        self.LOG.debug("Using bounds %s", bounds)
+        self.LOG.debug("Using spincutModel %s", self.norm_pars.spincutModel)
+        self.LOG.debug("Using spincutPars %s", spinParsstring)
+
+        nld_low = self.nld.cut(*limit_low, inplace=False)
+        discrete = self.discrete.cut(*limit_low, inplace=False)
+        nld_high = self.nld.cut(*limit_high, inplace=False)
+
+        # Artificially increase the nld rel error at low
+        nld_low.std = nld_low.values * 0.3
+        # Instead we will be looking at the cumulative NLD
+
+        nldSn = self.nldSn_from_D0(**self.norm_pars.asdict())[1]
+        rel_uncertainty = self.norm_pars.D0[1]/self.norm_pars.D0[0]
+        nldSn = np.array([nldSn, nldSn * rel_uncertainty])
+
+        if self.norm_pars.rhoSn is not None:
+            nldSn = self.norm_pars.rhoSn
+
+        def neglnlike(x, *args, **kwargs):
+            # We have a requirement that T, Eshift should fit NLDSn,
+            # therefore we will add a really lot to ensure that this
+            # is the case in the DE
+            Eshift = self.norm_pars.Sn[0] - x[2] * np.log(x[2]*nldSn[0])
+            EshiftSigma = nldSn[1]*x[2]/nldSn[0]
+            EshiftError = ((Eshift - x[3])/EshiftSigma)**2
+            return EshiftError - self.lnlike(x, *args, **kwargs)
+        args = (nld_low, nld_high, discrete, self.model, self.norm_pars.Sn[0],
+                nldSn)
+        res = differential_evolution(neglnlike, bounds=bounds, args=args,
+                                     **self.de_kwargs)
+
+        self.LOG.info("DE results:\n%s", tt.to_string([res.x.tolist()],
+                      header=['A', 'α [MeV⁻¹]', 'T [MeV]', 'Eshift [MeV]']))
+
+        p0 = dict(zip(["A", "alpha", "T", "Eshift"], (res.x).T))
+        for key, res in p0.items():
+            if res in self.bounds[key]:
+                self.LOG.warning(f"DE result for {key} is at edge its bound:"
+                                 f"{self.bounds[key]}. This will probably lead"
+                                 f"to wrong estimations in multinest, too.")
+
+        return args, p0
+
+    def optimize(self, num: int, args,
+                 guess: Dict[str, float]) -> Tuple[Dict[str, float], Dict[str, float]]:
+        """Find parameters given model constraints and an initial guess
+
+        Employs Multinest
+
+        Args:
+            num (int): Loop number
+            args_nld (Iterable): Additional arguments for the nld lnlike
+            guess (Dict[str, float]): The initial guess of the parameters
+
+        Returns:
+            Tuple:
+            - popt (Dict[str, Tuple[float, float]]): Median and 1sigma of the
+                parameters
+            - samples (Dict[str, List[float]]): Multinest samples.
+                Note: They are still importance weighted, not random draws
+                from the posterior.
+
+        Raises:
+            ValueError: Invalid parameters for automatic prior
+
+        Note:
+            You might want to adjust the priors for your specific case! Here
+            we just propose a general solution that might often work out of
+            the box.
+        """
+        if guess['alpha'] < 0:
+            raise NotImplementedError("Prior selection not implemented for "
+                                      "α < 0")
+        alpha_exponent = np.log10(guess['alpha'])
+
+        if guess['T'] < 0:
+            raise ValueError("Prior selection not implemented for T < 0; "
+                             "negative temperature is unphysical")
+        T_exponent = np.log10(guess['T'])
+
+        A = guess['A']
+
+        # truncations from absolute values
+        lower_A, upper_A = 0., np.inf
+        mu_A, sigma_A = A, 10*A
+        a_A = (lower_A - mu_A) / sigma_A
+        b_A = (upper_A - mu_A) / sigma_A
+
+        lower_Eshift, upper_Eshift = -5., 5
+        mu_Eshift, sigma_Eshift = 0, 5
+        a_Eshift = (lower_Eshift - mu_Eshift) / sigma_Eshift
+        b_Eshift = (upper_Eshift - mu_Eshift) / sigma_Eshift
+
+        mu_rhoSn, sigma_rhoSn = self.norm_pars.rhoSn
+        Sn = self.norm_pars.Sn[0]
+
+        def prior(cube, ndim, nparams):
+            # NOTE: You may want to adjust this for your case!
+            # truncated normal prior
+            cube[0] = truncnorm_ppf(cube[0], a_A, b_A)*sigma_A+mu_A
+            # log-uniform prior
+            # if alpha = 1e2, it's between 1e1 and 1e3
+            cube[1] = 10**(cube[1]*2 + (alpha_exponent-1))
+            # log-uniform prior
+            # if T = 1e2, it's between 1e1 and 1e3
+            cube[2] = 10**(cube[2]*2 + (T_exponent-1))
+            # truncated normal prior
+            rhoSn = normal_ppf(cube[3])*sigma_rhoSn + mu_rhoSn
+            cube[3] = Sn - cube[2]*np.log(cube[2] * rhoSn)
+
+            #cube[3] = truncnorm_ppf(cube[3], a_Eshift,
+                                    #b_Eshift)*sigma_Eshift + mu_Eshift
+
+            if np.isinf(cube[3]):
+                self.LOG.debug("Encountered inf in cube[3]:\n%s", cube[3])
+
+        def loglike(cube, ndim, nparams):
+            return self.lnlike(cube, *args)
+
+        self.multinest_path.mkdir(exist_ok=True)
+        path = self.multinest_path / f"nld_norm_{num}_"
+        assert len(str(path)) < 60, "Total path length too long for multinest"
+
+        self.LOG.info("Starting multinest")
+        self.LOG.debug("with following keywords %s:", self.multinest_kwargs)
+        #  Hack where stdout from Multinest is redirected as info messages
+        self.LOG.write = lambda msg: (self.LOG.info(msg) if msg != '\n'
+                                      else None)
+        with redirect_stdout(self.LOG):
+            pymultinest.run(loglike, prior, len(guess),
+                            outputfiles_basename=str(path),
+                            **self.multinest_kwargs)
+
+        # Save parameters for analyzer
+        names = list(guess.keys())
+        json.dump(names, open(str(path) + 'params.json', 'w'))
+        analyzer = pymultinest.Analyzer(len(guess),
+                                        outputfiles_basename=str(path))
+
+        stats = analyzer.get_stats()
+
+        samples = analyzer.get_equal_weighted_posterior()[:, :-1]
+        samples = dict(zip(names, samples.T))
+
+        # Format the output
+        popt = dict()
+        vals = []
+        for name, m in zip(names, stats['marginals']):
+            lo, hi = m['1sigma']
+            med = m['median']
+            sigma = (hi - lo) / 2
+            popt[name] = (med, sigma)
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            vals.append(fmts % (med, sigma))
+
+        self.LOG.info("Multinest results:\n%s", tt.to_string([vals],
+                      header=['A', 'α [MeV⁻¹]', 'T [MeV]', 'Eshift [MeV]']))
+
+        return popt, samples
+
+    def plot(self, *, ax: Any = None,
+             add_label: bool = True,
+             results: Optional[ResultsNormalized] = None,
+             add_figlegend: bool = True,
+             plot_fitregion: bool = True,
+             reset_color_cycle: bool = True,
+             **kwargs) -> Tuple[Any, Any]:
+        """Plot the NLD, discrete levels and result of normalization
+
+        Args:
+            ax (optional): The matplotlib axis to plot onto. Creates axis
+                is not provided
+            add_label (bool, optional): Defaults to `True`.
+            add_figlegend (bool, optional):Defaults to `True`.
+            results (ResultsNormalized, optional): If provided, nld and model
+                are taken from here instead.
+            plot_fitregion (Optional[bool], optional): Defaults to `True`.
+            reset_color_cycle (Optional[bool], optional): Defaults to `True`
+            **kwargs: Description
+
+        Returns:
+            fig, ax
+        """
+        if ax is None:
+            fig, ax = plt.subplots()
+        else:
+            fig = ax.figure
+
+        if reset_color_cycle:
+            ax.set_prop_cycle(None)
+
+        res = self.res if results is None else results
+        pars = res.pars
+        nld = res.nld
+
+        labelNld = '_exp.'
+        labelNldSn = None
+        labelModel = "_model"
+        labelDiscrete = "_known levels"
+        if add_label:
+            labelNld = 'exp.'
+            labelNldSn = r'$\rho(S_n)$'
+            labelModel = 'model'
+            labelDiscrete = "known levels"
+        nld.plot(ax=ax, label=labelNld, **kwargs)
+
+        self.discrete.plot(ax=ax, kind='step', c='k', label=labelDiscrete)
+
+        nldSn = self.nldSn_from_D0(**self.norm_pars.asdict())[1]
+        rel_uncertainty = self.norm_pars.D0[1]/self.norm_pars.D0[0]
+        nldSn = np.array([nldSn, nldSn * rel_uncertainty])
+
+        x = np.linspace(self.limit_high[0], self.norm_pars.Sn[0])
+        model = Vector(E=x, values=self.model(E=x,
+                                              T=pars['T'][0],
+                                              Eshift=pars['Eshift'][0]))
+
+        ax.errorbar(self.norm_pars.Sn[0], nldSn[0], yerr=nldSn[1],
+                    label=labelNldSn, fmt="ks", markerfacecolor='none')
+        # workaround for enseble Normalizer; always keep these label
+        for i in range(3):
+            ax.lines[-(i+1)]._label = "_nld(Sn)"
+
+        ax.plot(model.E, model.values, "--", label=labelModel, markersize=0,
+                c='g', **kwargs)
+
+        if plot_fitregion:
+            ax.axvspan(self.limit_low[0], self.limit_low[1], color='grey',
+                       alpha=0.1, label="fit limits")
+            ax.axvspan(self.limit_high[0], self.limit_high[1], color='grey',
+                       alpha=0.1)
+
+        ax.set_yscale('log')
+        ax.set_ylabel(r"Level density $\rho(E_x)~[\mathrm{MeV}^{-1}]$")
+        ax.set_xlabel(r"Excitation energy $E_x~[\mathrm{MeV}]$")
+        ax.set_ylim(bottom=0.5/(nld.E[1]-nld.E[0]))
+
+        if fig is not None and add_figlegend:
+            fig.legend(loc=9, ncol=3, frameon=False)
+
+        return fig, ax
+
+    @staticmethod
+    def lnlike(x: Tuple[float, float, float, float], nld_low: Vector,
+               nld_high: Vector, discrete: Vector,
+               model: Callable[..., ndarray],
+               Sn, nldSn) -> float:
+        """ Compute log likelihood of the normalization fitting
+
+        This is the result up a, which is irrelevant for the maximization
+
+        Args:
+            x: The arguments ordered as A, alpha, T and Eshift
+            nld_low: The lower region where discrete levels will be
+                fitted.
+            nld_high: The upper region to fit to model.
+            discrete: The discrete levels to be used in fitting the
+                lower region.
+            model: The model to use when fitting the upper region.
+                Must support the keyword arguments
+                ``model(E=..., T=..., Eshift=...) -> ndarray``
+        Returns:
+            lnlike: log likelihood
+        """
+        A, alpha, T, Eshift = x[:4]  # slicing needed for multinest?
+        transformed_low = nld_low.transform(A, alpha, inplace=False)
+        transformed_high = nld_high.transform(A, alpha, inplace=False)
+
+        err_low = transformed_low.error(discrete)
+        expected = Vector(E=transformed_high.E,
+                          values=model(E=transformed_high.E,
+                                       T=T, Eshift=Eshift))
+        err_high = transformed_high.error(expected)
+
+        # To ensure that the nldSn is weighted about as
+        # much as the exp data we will multiply with
+        # the number of points used to fit the `high`
+        # part of the NLD. This is reasonable because
+        # we trust this measurement equally as much
+        # as the measured NLD. This is meant to remove
+        # some bias introduced by having many points and only a single
+        # point at Sn.
+        nldSn_factor = 0  # len(transformed_high.E)
+
+        nldSn_model = model(E=Sn, T=T, Eshift=Eshift)
+        err_nldSn = nldSn_factor*((nldSn[0] - nldSn_model)/nldSn[1])**2
+
+        ln_stds = (np.log(transformed_low.std).sum()
+                   + np.log(transformed_high.std).sum())
+
+        return -0.5*(err_low + err_high + err_nldSn + ln_stds)
+
+    @staticmethod
+    def const_temperature(E: ndarray, T: float, Eshift: float) -> ndarray:
+        """ Constant Temperature NLD"""
+        ct = np.exp((E - Eshift) / T) / T
+        return ct
+
+    @staticmethod
+    def nldSn_from_D0(D0: Union[float, Tuple[float, float]],
+                      Sn: Union[float, Tuple[float, float]], Jtarget: float,
+                      spincutModel: str, spincutPars: Dict[str, Any],
+                      **kwargs) -> Tuple[float, float]:
+        """Calculate nld(Sn) from D0
+
+
+        1/D0 = nld(Sn) * ( g(Jtarget+1/2, pi_target)
+                         + g(Jtarget1/2, pi_target) )
+        Here we assume equal parity, g(J,pi) = g(J)/2 and
+        nld(Sn) = 1/D0 * 2/(g(Jtarget+1/2) + g(Jtarget-1/2))
+        For the case Jtarget = 0, the g(Jtarget-1/2) = 0
+
+        Parameters:
+            D0 (float or [float, float]):
+                Average resonance spacing from s waves [eV]. If a tuple,
+                it is assumed that it is of the form `[value, uncertainty]`.
+            Sn (float or [float, float]):
+                Separation energy [MeV]. If a tuple, it is assumed that it is of
+                the form `[value, uncertainty]`.
+            Jtarget (float):
+                Target spin
+            spincutModel (str):
+                Model to for the spincut
+            spincutPars Dict[str, Any]:
+                Additional parameters necessary for the spin cut model
+            **kwargs: Description
+
+
+        Returns:
+            nld: Ex=Sn and nld at Sn [MeV, 1/MeV]
+        """
+
+        D0 = np.atleast_1d(D0)[0]
+        Sn = np.atleast_1d(Sn)[0]
+
+        def g(J):
+            return SpinFunctions(Ex=Sn, J=J,
+                                 model=spincutModel,
+                                 pars=spincutPars).distribution()
+
+        if Jtarget == 0:
+            summe = 1 / 2 * g(Jtarget + 1 / 2)
+        else:
+            summe = 1 / 2 * (g(Jtarget - 1 / 2) + g(Jtarget + 1 / 2))
+
+        nld = 1 / (summe * D0 * 1e-6)
+        return [Sn, nld]
+
+    @staticmethod
+    def D0_from_nldSn(nld_model: Callable[..., Any],
+                      Sn: Union[float, Tuple[float, float]], Jtarget: float,
+                      spincutModel: str, spincutPars: Dict[str, Any],
+                      **kwargs) -> Tuple[float, float]:
+        """Calculate D0 from nld(Sn), assuming equiparity.
+
+        This is the inverse of `nldSn_from_D0`
+
+        Parameters:
+            nld_model (Callable[..., Any]): Model for nld above data of the
+                from `y = nld_model(E)` in 1/MeV.
+            Sn (float or [float, float]):
+                Separation energy [MeV]. If a tuple, it is assumed that it is of
+                the form `[value, uncertainty]`.
+            Jtarget (float):
+                Target spin
+            spincutModel (str):
+                Model to for the spincut
+            spincutPars Dict[str, Any]:
+                Additional parameters necessary for the spin cut model
+            **kwargs: Description
+
+
+        Returns:
+            D0: D0 in eV
+        """
+
+        Sn = np.atleast_1d(Sn)[0]
+        nld = nld_model(Sn)
+
+        def g(J):
+            return SpinFunctions(Ex=Sn, J=J,
+                                 model=spincutModel,
+                                 pars=spincutPars).distribution()
+
+        if Jtarget == 0:
+            summe = 1 / 2 * g(Jtarget + 1 / 2)
+        else:
+            summe = 1 / 2 * (g(Jtarget - 1 / 2) + g(Jtarget + 1 / 2))
+
+        D0 = 1 / (summe * nld * 1e-6)
+        return D0
+
+    @property
+    def discrete(self) -> Optional[Vector]:
+        return self._discrete
+
+    @discrete.setter
+    def discrete(self, value: Optional[Union[Path, str, Vector]]) -> None:
+        if value is None:
+            self._discretes = None
+            self.LOG.debug("Set `discrete` to None")
+        elif isinstance(value, (str, Path)):
+            if self.nld is None:
+                raise ValueError(f"`nld` must be set before loading levels")
+            nld = self.nld.copy()
+            nld.to_MeV()
+            self.LOG.debug("Set `discrete` levels from file with FWHM %s",
+                           self.smooth_levels_fwhm)
+            self._discrete = load_levels_smooth(value, nld.E,
+                                                self.smooth_levels_fwhm)
+            self._discrete.units = "MeV"
+            self._discrete_path = value
+
+        elif isinstance(value, Vector):
+            if self.nld is not None and np.any(self.nld.E != value.E):
+                raise ValueError("`nld` and `discrete` must"
+                                 " have same energy binning")
+            self._discrete = value
+            self.LOG.debug("Set `discrete` by Vector")
+        else:
+            raise ValueError(f"Value {value} is not supported"
+                             " for discrete levels")
+
+    @property
+    def smooth_levels_fwhm(self) -> Optional[float]:
+        return self._smooth_levels_fwhm
+
+    @smooth_levels_fwhm.setter
+    def smooth_levels_fwhm(self, value: float) -> None:
+        self._smooth_levels_fwhm = value
+        if self._discrete_path is not None:
+            self.discrete = self._discrete_path
+
+    def self_if_none(self, *args, **kwargs):
+        """ wrapper for lib.self_if_none """
+        return self_if_none(self, *args, **kwargs)
+
+
+def load_levels_discrete(path: Union[str, Path], energy: ndarray) -> Vector:
+    """ Load discrete levels without smoothing
+
+    Assumes linear equdistant binning
+
+    Args:
+        path: The file to load
+        energy: The binning to use
+    Returns:
+        A vector describing the levels
+    """
+    histogram, _ = load_discrete(path, energy, 0.1)
+    return Vector(values=histogram, E=energy, units='MeV')
+
+
+def load_levels_smooth(path: Union[str, Path], energy: ndarray,
+                       resolution: float = 0.1) -> Vector:
+    """ Load discrete levels with smoothing
+
+    Assumes linear equdistant binning
+
+    Args:
+        path: The file to load
+        energy: The binning to use in MeV
+        resolution: The resolution (FWHM) of the smoothing to use in MeV
+    Returns:
+        A vector describing the smoothed levels
+    """
+    histogram, smoothed = load_discrete(path, energy, resolution)
+    return Vector(values=smoothed if resolution > 0 else histogram, E=energy,
+                  units='MeV')
diff --git a/ompy/normalizer_simultan.py b/ompy/normalizer_simultan.py
index d27ba5fa..a8e559ae 100644
--- a/ompy/normalizer_simultan.py
+++ b/ompy/normalizer_simultan.py
@@ -6,6 +6,7 @@
 from numpy import ndarray
 from pathlib import Path
 from typing import Optional, Union, Tuple, Any, Callable, Dict, Iterable, List
+from scipy.optimize import differential_evolution
 import pymultinest
 import matplotlib.pyplot as plt
 from contextlib import redirect_stdout
@@ -122,19 +123,25 @@ def normalize(self, *, num: int = 0,
 
         # reset internal state
         self.res = ResultsNormalized(name="Results NLD")
-
+        norm_pars_org = copy.deepcopy(self.normalizer_nld.norm_pars)
         self.normalizer_nld = copy.deepcopy(self.self_if_none(normalizer_nld))
         self.normalizer_gsf = copy.deepcopy(self.self_if_none(normalizer_gsf))
         for norm in [self.normalizer_nld, self.normalizer_gsf]:
             norm._save_instance = False
             norm.regenerate = True
 
+        self.LOG.debug("Setting NLD and GSF, convert to MeV and removing nan")
         gsf = self.self_if_none(gsf)
+        gsf = gsf.copy()
+        gsf.to_MeV()
+        gsf.cut_nan()
+
         nld = self.self_if_none(nld)
         nld = nld.copy()
-        gsf = gsf.copy()
         nld.to_MeV()
-        gsf.to_MeV()
+        nld.cut_nan()
+
+        self.LOG.info(f"Normalizing #{num}")
 
         # Need to give some sort of standard deviation for sensible results
         # Otherwise deviations at higher level density will have an
@@ -153,7 +160,7 @@ def normalize(self, *, num: int = 0,
         self.normalizer_gsf.gsf_in = gsf  # update before initial guess
 
         # Use DE to get an inital guess before optimizing
-        args_nld, guess = self.initial_guess()
+        args_nld, guess = self.initial_guess(num)
         # Optimize using multinest
         popt, samples = self.optimize(num, args_nld, guess)
 
@@ -181,9 +188,11 @@ def normalize(self, *, num: int = 0,
         for model in [self.res.gsf_model_low, self.res.gsf_model_high]:
             model.shift_after = model.shift
 
+        self.normalizer_nld.norm_pars = copy.deepcopy(norm_pars_org)
+        self.normalizer_gsf.norm_pars = copy.deepcopy(norm_pars_org)
         self.save()  # save instance
 
-    def initial_guess(self) -> None:
+    def initial_guess(self, num: int) -> None:
         """ Find an inital guess for normalization parameters
 
         Uses guess of normalizer_nld and corresponding normalization of gsf
@@ -202,15 +211,25 @@ def initial_guess(self) -> None:
         nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
         nld_model = lambda E: normalizer_nld.model(E, T=T, Eshift=Eshift)  # noqa
 
-        normalizer_gsf.normalize(nld=nld, nld_model=nld_model, alpha=alpha)
+        normalizer_gsf.normalize(nld=nld, nld_model=nld_model, alpha=alpha, num=num)
         guess["B"] = normalizer_gsf.res.pars["B"][0]
 
+        # If D0 is not consistent with model prediction we should do a
+        # MLE from the simultan likelihood.
+        bounds = list(normalizer_nld.bounds.values())
+        bounds.append([0, 4*guess["B"]])
+
+        def neglnlike(x, *args):
+            return -self.lnlike(x, args)
+        res = differential_evolution(neglnlike, bounds=bounds, args=args_nld,
+                                     **normalizer_nld.de_kwargs)
+
         guess_print = copy.deepcopy(guess)
-        self.LOG.info("DE results/initial guess:\n%s",
-                      tt.to_string([list(guess_print.values())],
+        self.LOG.info("DE results/initial guess (# %d):\n%s", num,
+                      tt.to_string([res.x.tolist()],
                                    header=['A', 'α [MeV⁻¹]', 'T [MeV]',
                                            'Eshift [MeV]', 'B']))
-
+        guess = dict(zip(["A", "alpha", "T", "Eshift", "B"], (res.x).T))
         return args_nld, guess
 
     def optimize(self, num: int,
@@ -300,10 +319,10 @@ def loglike(cube, ndim, nparams):
         path = self.multinest_path / f"sim_norm_{num}_"
         assert len(str(path)) < 60, "Total path length too long for multinest"
 
-        self.LOG.info("Starting multinest: ")
+        self.LOG.info("Starting multinest (# %d): ", num)
         self.LOG.debug("with following keywords %s:", self.multinest_kwargs)
         #  Hack where stdout from Multinest is redirected as info messages
-        self.LOG.write = lambda msg: (self.LOG.info(msg) if msg != '\n'
+        self.LOG.write = lambda msg: (self.LOG.info("# %d: %s", num, msg) if msg != '\n'  # noqa
                                       else None)
 
         with redirect_stdout(self.LOG):
@@ -335,9 +354,9 @@ def loglike(cube, ndim, nparams):
             fmts = '\t'.join([fmt + " ± " + fmt])
             vals.append(fmts % (med, sigma))
 
-        self.LOG.info("Multinest results:\n%s", tt.to_string([vals],
-                      header=['A', 'α [MeV⁻¹]', 'T [MeV]',
-                              'Eshift [MeV]', 'B']))
+        self.LOG.info("Multinest results (# %d):\n%s", num,
+                      tt.to_string([vals], header=['A', 'α [MeV⁻¹]', 'T [MeV]',
+                                                   'Eshift [MeV]', 'B']))
 
         # reset state
         self.normalizer_gsf.norm_pars = norm_pars_org
diff --git a/ompy/normalizer_simultan_ct.py b/ompy/normalizer_simultan_ct.py
new file mode 100644
index 00000000..9a755693
--- /dev/null
+++ b/ompy/normalizer_simultan_ct.py
@@ -0,0 +1,443 @@
+import logging
+import numpy as np
+import copy
+import json
+import termtables as tt
+from numpy import ndarray
+from pathlib import Path
+from typing import Optional, Union, Tuple, Any, Callable, Dict, Iterable, List
+import pymultinest
+import matplotlib.pyplot as plt
+from contextlib import redirect_stdout
+
+from .abstract_normalizer import AbstractNormalizer
+from .extractor import Extractor
+from .library import log_interp1d, self_if_none
+from .models import Model, ResultsNormalized, ExtrapolationModelLow,\
+                    ExtrapolationModelHigh, NormalizationParameters
+from .normalizer_nld import NormalizerNLD
+from .normalizer_gsf import NormalizerGSF
+from .spinfunctions import SpinFunctions
+from .vector import Vector
+from .stats import truncnorm_ppf
+
+
+class NormalizerSimultan(AbstractNormalizer):
+
+    """ Simultaneous normalization of nld and gsf. Composed of Normalizer and NormalizerGSF as input, so read more on the normalization there
+
+    Attributes:
+        extractor (Extractor): Extractor instance
+        gsf (Optional[Vector], optional): gsf to normalize
+        multinest_path (Path, optional): Default path where multinest
+            saves files
+        multinest_kwargs (dict): Additional keywords to multinest. Defaults to
+            `{"seed": 65498, "resume": False}`
+        nld (Optional[Vector], optional): nld to normalize
+        normalizer_nld (NormalizerNLD): `NormalizerNLD` instance to get the normalization paramters
+        normalizer_gsf (NormalizerGSF): `NormalizerGSF` instance to get the normalization paramters
+        res (ResultsNormalized): Results
+        std_fake_gsf (bool): Whether the std. deviation is faked
+            (see `normalize`)
+        std_fake_nld (bool): Whether the std. deviation is faked
+            (see `normalize`)
+        path (Path): The path save the results.
+
+
+    TODO:
+        Work with more general models, too, not just CT for nld
+    """
+    LOG = logging.getLogger(__name__)
+    logging.captureWarnings(True)
+
+    def __init__(self, *,
+                 gsf: Optional[Vector] = None,
+                 nld: Optional[Vector] = None,
+                 normalizer_nld: Optional[NormalizerNLD] = None,
+                 normalizer_gsf: Optional[NormalizerGSF] = None,
+                 path: Optional[Union[str, Path]] = 'saved_run/normalizers',
+                 regenerate: bool = False):
+        """
+        TODO:
+            - currently have to set arguments here, an cannot set them in
+              "normalize"
+
+        Args:
+            gsf (optional): see above
+            nld (optional): see above
+            normalizer_nld (optional): see above
+            normalizer_gsf (optional): see above
+
+        """
+        super().__init__(regenerate)
+        if normalizer_nld is None:
+            self.normalizer_nld = None
+        else:
+            self.normalizer_nld = copy.deepcopy(normalizer_nld)
+
+        if normalizer_gsf is None:
+            self.normalizer_gsf = None
+        else:
+            self.normalizer_gsf = copy.deepcopy(normalizer_gsf)
+
+        self.gsf = None if gsf is None else gsf.copy()
+        self.nld = None if nld is None else nld.copy()
+
+        self.std_fake_nld: Optional[bool] = None  # See `normalize`
+        self.std_fake_gsf: Optional[bool] = None  # See `normalize`
+
+        self.res: Optional[ResultsNormalized] = None
+
+        self.multinest_path: Optional[Path] = Path('multinest')
+        self.multinest_kwargs: dict = {"seed": 65498, "resume": False}
+
+        if path is None:
+            self.path = None
+        else:
+            self.path = Path(path)
+            self.path.mkdir(exist_ok=True, parents=True)
+
+    def normalize(self, *, num: int = 0,
+                  gsf: Optional[Vector] = None,
+                  nld: Optional[Vector] = None,
+                  normalizer_nld: Optional[NormalizerNLD] = None,
+                  normalizer_gsf: Optional[NormalizerGSF] = None) -> None:
+        """Perform normalization and saves results to `self.res`
+
+        Args:
+            num (int, optional): Loop number
+            gsf (Optional[Vector], optional): gsf before normalization
+            nld (Optional[Vector], optional): nld before normalization
+            normalizer_nld (Optional[NormalizerNLD], optional): NormalizerNLD
+                instance
+            normalizer_gsf (Optional[NormalizerGSF], optional): NormalizerGSF
+                instance
+        """
+        if not self.regenerate:
+            try:
+                self.load()
+                return
+            except FileNotFoundError:
+                pass
+
+        # reset internal state
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.normalizer_nld = copy.deepcopy(self.self_if_none(normalizer_nld))
+        self.normalizer_gsf = copy.deepcopy(self.self_if_none(normalizer_gsf))
+        for norm in [self.normalizer_nld, self.normalizer_gsf]:
+            norm._save_instance = False
+            norm.regenerate = True
+
+        self.LOG.debug("Setting NLD and GSF, convert to MeV and removing nan")
+        gsf = self.self_if_none(gsf)
+        gsf = gsf.copy()
+        gsf.to_MeV()
+        gsf.cut_nan()
+
+        nld = self.self_if_none(nld)
+        nld = nld.copy()
+        nld.to_MeV()
+        nld.cut_nan()
+
+        # Need to give some sort of standard deviation for sensible results
+        # Otherwise deviations at higher level density will have an
+        # uncreasonably high weight.
+        if self.std_fake_nld is None:
+            self.std_fake_nld = False
+        if self.std_fake_nld or nld.std is None:
+            self.std_fake_nld = True
+            nld.std = nld.values * 0.3  # x% is an arb. choice
+        if self.std_fake_gsf or gsf.std is None:
+            self.std_fake_gsf = True
+            gsf.std = gsf.values * 0.3  # x% is an arb. choice
+
+        # update
+        self.normalizer_nld.nld = nld  # update before initial guess
+        self.normalizer_gsf.gsf_in = gsf  # update before initial guess
+
+        # Use DE to get an inital guess before optimizing
+        args_nld, guess = self.initial_guess()
+        # Optimize using multinest
+        popt, samples = self.optimize(num, args_nld, guess)
+
+        self.res.pars = popt
+        self.res.samples = samples
+
+        # reset
+        if self.std_fake_nld is True:
+            self.std_fake_nld = None
+            nld.std = None
+        if self.std_fake_gsf is True:
+            self.std_fake_gsf = None
+            gsf.std = None
+
+        self.res.nld = nld.transform(self.res.pars["A"][0],
+                                     self.res.pars["alpha"][0], inplace=False)
+        self.res.gsf = gsf.transform(self.res.pars["B"][0],
+                                     self.res.pars["alpha"][0], inplace=False)
+
+        self.normalizer_gsf.model_low.autorange(self.res.gsf)
+        self.normalizer_gsf.model_high.autorange(self.res.gsf)
+        self.normalizer_gsf.extrapolate(self.res.gsf)
+        self.res.gsf_model_low = self.normalizer_gsf.model_low
+        self.res.gsf_model_high = self.normalizer_gsf.model_high
+        for model in [self.res.gsf_model_low, self.res.gsf_model_high]:
+            model.shift_after = model.shift
+
+        self.save()  # save instance
+
+    def initial_guess(self) -> None:
+        """ Find an inital guess for normalization parameters
+
+        Uses guess of normalizer_nld and corresponding normalization of gsf
+
+        Returns:
+           The arguments used for chi^2 minimization and the
+           minimizer.
+        """
+        normalizer_nld = self.normalizer_nld
+        normalizer_gsf = self.normalizer_gsf
+
+        args_nld, guess = normalizer_nld.initial_guess()
+        #[A, alpha, T, Eshift] = [guess["A"], guess["alpha"],
+        #                         guess["T"], guess["Eshift"]]
+        [A, alpha] = [guess["A"], guess["alpha"]]
+
+        nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
+        nld_model = lambda E: normalizer_nld.ct_model(E, A=A, alpha=alpha)  # noqa
+
+        normalizer_gsf.normalize(nld=nld, nld_model=nld_model, alpha=alpha)
+        guess["B"] = normalizer_gsf.res.pars["B"][0]
+
+        guess_print = copy.deepcopy(guess)
+        self.LOG.info("DE results/initial guess:\n%s",
+                      tt.to_string([list(guess_print.values())],
+                                   header=['A', 'α [MeV⁻¹]', 'B']))
+
+        return args_nld, guess
+
+    def optimize(self, num: int,
+                 args_nld: Iterable,
+                 guess: Dict[str, float]) -> Tuple[Dict[str, Tuple[float, float]], Dict[str, List[float]]]:  # noqa
+        """Find parameters given model constraints and an initial guess
+
+        Employs Multinest.
+
+        Args:
+            num (int): Loop number
+            args_nld (Iterable): Additional arguments for the nld lnlike
+            guess (Dict[str, float]): The initial guess of the parameters
+
+        Returns:
+            Tuple:
+            - popt (Dict[str, Tuple[float, float]]): Median and 1sigma of the
+                parameters
+            - samples (Dict[str, List[float]]): Multinest samplesø.
+                Note: They are still importance weighted, not random draws
+                from the posterior.
+
+        Raises:
+            ValueError: Invalid parameters for automatix prior
+
+        Note:
+            You might want to adjust the priors for your specific case! Here
+            we just propose a general solution that might often work out of
+            the box.
+        """
+        if guess['alpha'] < 0:
+            raise NotImplementedError("Prior selection not implemented for "
+                                      "α < 0")
+        alpha_exponent = np.log10(guess['alpha'])
+
+        #if guess['T'] < 0:
+        #    raise ValueError("Prior selection not implemented for T < 0; "
+        #                     "negative temperature is unphysical")
+        #T_exponent = np.log10(guess['T'])
+
+        A = guess['A']
+        B = guess["B"]
+
+        # truncations from absolute values
+        lower_A, upper_A = 0., np.inf
+        mu_A, sigma_A = A, 10*A
+        a_A = (lower_A - mu_A) / sigma_A
+        b_A = (upper_A - mu_A) / sigma_A
+
+        lower_Eshift, upper_Eshift = -5., 5
+        mu_Eshift, sigma_Eshift = 0, 5
+        a_Eshift = (lower_Eshift - mu_Eshift) / sigma_Eshift
+        b_Eshift = (upper_Eshift - mu_Eshift) / sigma_Eshift
+
+        lower_B, upper_B = 0., np.inf
+        mu_B, sigma_B = B, 10*B
+        a_B = (lower_B - mu_B) / sigma_B
+        b_B = (upper_B - mu_B) / sigma_B
+
+        def prior(cube, ndim, nparams):
+            # NOTE: You may want to adjust this for your case!
+            # truncated normal prior
+            cube[0] = truncnorm_ppf(cube[0], a_A, b_A)*sigma_A + mu_A
+
+            # log-uniform prior
+            # if alpha = 1e2, it's between 1e1 and 1e3
+            cube[1] = 10**(cube[1]*2 + (alpha_exponent-1))
+            # log-uniform prior
+            # if T = 1e2, it's between 1e1 and 1e3
+            #cube[2] = 10**(cube[2]*2 + (T_exponent-1))
+            # truncated normal prior
+            #cube[3] = truncnorm_ppf(cube[3], a_Eshift,
+            #                        b_Eshift)*sigma_Eshift + mu_Eshift
+            # truncated normal prior
+            cube[2] = truncnorm_ppf(cube[2], a_B, b_B)*sigma_B + mu_B
+
+            #if np.isinf(cube[3]):
+            #    self.LOG.debug("Encountered inf in cube[3]:\n%s", cube[3])
+
+        def loglike(cube, ndim, nparams):
+            return self.lnlike(cube, args_nld=args_nld)
+
+        # parameters are changed in the lnlike
+        norm_pars_org = copy.deepcopy(self.normalizer_gsf.norm_pars)
+
+        self.multinest_path.mkdir(exist_ok=True)
+        path = self.multinest_path / f"sim_norm_{num}_"
+        assert len(str(path)) < 60, "Total path length too long for multinest"
+
+        self.LOG.info("Starting multinest: ")
+        self.LOG.debug("with following keywords %s:", self.multinest_kwargs)
+        #  Hack where stdout from Multinest is redirected as info messages
+        self.LOG.write = lambda msg: (self.LOG.info(msg) if msg != '\n'
+                                      else None)
+
+        with redirect_stdout(self.LOG):
+            pymultinest.run(loglike, prior, len(guess),
+                            outputfiles_basename=str(path),
+                            **self.multinest_kwargs)
+
+        # Save parameters for analyzer
+        names = list(guess.keys())
+        json.dump(names, open(str(path) + 'params.json', 'w'))
+        analyzer = pymultinest.Analyzer(len(guess),
+                                        outputfiles_basename=str(path))
+
+        stats = analyzer.get_stats()
+
+        samples = analyzer.get_equal_weighted_posterior()[:, :-1]
+        samples = dict(zip(names, samples.T))
+
+        T = self.normalizer_nld.coefs[0] + samples['alpha']
+        Eshift = -T*(np.log(T*samples['A']) + self.normalizer_nld.coefs[1])
+        samples['T'] = T
+        samples['Eshift'] = Eshift
+
+        names = ['A', 'alpha', 'T', 'Eshift', 'B']
+        Bstats = copy.deepcopy(stats['marginals'][-1])
+        del stats['marginals'][-1]
+        stats['marginals'].append({'1sigma': (np.quantile(T, 0.16),
+                                              np.quantile(T, 0.84)),
+                                   'median': np.quantile(T, 0.5)})
+        stats['marginals'].append({'1sigma': (np.quantile(Eshift, 0.16),
+                                              np.quantile(Eshift, 0.84)),
+                                   'median': np.quantile(Eshift, 0.5)})
+        stats['marginals'].append(Bstats)
+
+        # Format the output
+        popt = dict()
+        vals = []
+        for name, m in zip(names, stats['marginals']):
+            lo, hi = m['1sigma']
+            med = m['median']
+            sigma = (hi - lo) / 2
+            popt[name] = (med, sigma)
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            vals.append(fmts % (med, sigma))
+
+        self.LOG.info("Multinest results:\n%s", tt.to_string([vals],
+                      header=['A', 'α [MeV⁻¹]', 'T [MeV]',
+                              'Eshift [MeV]', 'B']))
+
+        # reset state
+        self.normalizer_gsf.norm_pars = norm_pars_org
+
+        return popt, samples
+
+    def lnlike(self, x: Tuple[float, float, float, float, float],
+               args_nld: Iterable) -> float:
+        """Compute log likelihood  of the normalization fitting
+
+        This is the result up to the constant, which is irrelevant for the
+        maximization
+
+        Args:
+            x (Tuple[float, float, float, float, float]): The arguments
+                ordered as A, alpha, T and Eshift, B
+            args_nld (TYPE): Additional arguments for the nld lnlike
+
+        Returns:
+            lnlike: log likelihood
+        """
+        A, alpha, B = x[:3]  # slicing needed for multinest?
+
+        normalizer_gsf = self.normalizer_gsf
+        normalizer_nld = self.normalizer_nld
+
+        err_nld = normalizer_nld.lnlike(x[:2], *args_nld)
+
+        nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
+        nld_model = lambda E: normalizer_nld.ct_model(E, A=A, alpha=alpha)  # noqa
+
+        normalizer_gsf.nld_model = nld_model
+        normalizer_gsf.nld = nld
+        # calculate the D0-equivalent of T and Eshift used
+        D0 = normalizer_nld.D0_from_nldSn(nld_model,
+                                          **normalizer_nld.norm_pars.asdict())
+        normalizer_gsf.norm_pars.D0 = [D0, np.nan]  # dummy uncertainty
+        normalizer_gsf._gsf = normalizer_gsf.gsf_in.transform(B, alpha,
+                                                              inplace=False)
+        normalizer_gsf._gsf_low, normalizer_gsf._gsf_high = \
+            normalizer_gsf.extrapolate()
+        err_gsf = normalizer_gsf.lnlike()
+        return err_nld + err_gsf
+
+    def plot(self, ax: Optional[Any] = None, add_label: bool = True,
+             add_figlegend: bool = True,
+             **kwargs) -> Tuple[Any, Any]:
+        """Plots nld and gsf
+
+        Args:
+            ax (optional): The matplotlib axis to plot onto. Creates axis
+                is not provided
+            add_label (bool, optional):Defaults to `True`.
+            add_figlegend (bool, optional): Defaults to `True`.
+            results Optional[ResultsNormalized]: If provided, gsf and model
+                are taken from here instead.
+            **kwargs: kwargs for plot
+
+        Returns:
+            fig, ax
+        """
+        if ax is None:
+            fig, ax = plt.subplots(1, 2, constrained_layout=True)
+        else:
+            fig = ax[0].figure
+
+        self.normalizer_nld.plot(ax=ax[0], add_label=True, results=self.res,
+                                 add_figlegend=False, **kwargs)
+        self.normalizer_gsf.plot(ax=ax[1], add_label=False, results=self.res,
+                                 add_figlegend=False, **kwargs)
+
+        ax[0].set_title("Level density")
+        ax[1].set_title(r"$\gamma$SF")
+
+        if add_figlegend:
+            fig.legend(loc=9, ncol=4, frameon=True)
+            fig.subplots_adjust(left=0.1, right=0.9, top=0.8, bottom=0.1)
+
+        return fig, ax
+
+    def self_if_none(self, *args, **kwargs):
+        """ wrapper for lib.self_if_none """
+        return self_if_none(self, *args, **kwargs)
diff --git a/ompy/normalizer_simultan_spincut.py b/ompy/normalizer_simultan_spincut.py
new file mode 100644
index 00000000..2f8e0ce8
--- /dev/null
+++ b/ompy/normalizer_simultan_spincut.py
@@ -0,0 +1,442 @@
+import logging
+import numpy as np
+import copy
+import json
+import termtables as tt
+from numpy import ndarray
+from pathlib import Path
+from typing import Optional, Union, Tuple, Any, Callable, Dict, Iterable, List
+import pymultinest
+import matplotlib.pyplot as plt
+from contextlib import redirect_stdout
+
+from .abstract_normalizer import AbstractNormalizer
+from .extractor import Extractor
+from .library import log_interp1d, self_if_none
+from .models import Model, ResultsNormalized, ExtrapolationModelLow,\
+                    ExtrapolationModelHigh, NormalizationParameters
+from .normalizer_nld import NormalizerNLD
+from .normalizer_gsf import NormalizerGSF
+from .spinfunctions import SpinFunctions
+from .vector import Vector
+from .stats import truncnorm_ppf, normal_ppf
+
+
+class NormalizerSimultan(AbstractNormalizer):
+
+    """ Simultaneous normalization of nld and gsf. Composed of Normalizer and NormalizerGSF as input, so read more on the normalization there
+
+    Attributes:
+        extractor (Extractor): Extractor instance
+        gsf (Optional[Vector], optional): gsf to normalize
+        multinest_path (Path, optional): Default path where multinest
+            saves files
+        multinest_kwargs (dict): Additional keywords to multinest. Defaults to
+            `{"seed": 65498, "resume": False}`
+        nld (Optional[Vector], optional): nld to normalize
+        normalizer_nld (NormalizerNLD): `NormalizerNLD` instance to get the normalization paramters
+        normalizer_gsf (NormalizerGSF): `NormalizerGSF` instance to get the normalization paramters
+        res (ResultsNormalized): Results
+        std_fake_gsf (bool): Whether the std. deviation is faked
+            (see `normalize`)
+        std_fake_nld (bool): Whether the std. deviation is faked
+            (see `normalize`)
+        path (Path): The path save the results.
+
+
+    TODO:
+        Work with more general models, too, not just CT for nld
+    """
+    LOG = logging.getLogger(__name__)
+    logging.captureWarnings(True)
+
+    def __init__(self, *,
+                 gsf: Optional[Vector] = None,
+                 nld: Optional[Vector] = None,
+                 normalizer_nld: Optional[NormalizerNLD] = None,
+                 normalizer_gsf: Optional[NormalizerGSF] = None,
+                 path: Optional[Union[str, Path]] = 'saved_run/normalizers',
+                 regenerate: bool = False):
+        """
+        TODO:
+            - currently have to set arguments here, an cannot set them in
+              "normalize"
+
+        Args:
+            gsf (optional): see above
+            nld (optional): see above
+            normalizer_nld (optional): see above
+            normalizer_gsf (optional): see above
+
+        """
+        super().__init__(regenerate)
+        if normalizer_nld is None:
+            self.normalizer_nld = None
+        else:
+            self.normalizer_nld = copy.deepcopy(normalizer_nld)
+
+        if normalizer_gsf is None:
+            self.normalizer_gsf = None
+        else:
+            self.normalizer_gsf = copy.deepcopy(normalizer_gsf)
+
+        self.gsf = None if gsf is None else gsf.copy()
+        self.nld = None if nld is None else nld.copy()
+
+        self.std_fake_nld: Optional[bool] = None  # See `normalize`
+        self.std_fake_gsf: Optional[bool] = None  # See `normalize`
+
+        self.res: Optional[ResultsNormalized] = None
+
+        self.multinest_path: Optional[Path] = Path('multinest')
+        self.multinest_kwargs: dict = {"seed": 65498, "resume": False}
+
+        if path is None:
+            self.path = None
+        else:
+            self.path = Path(path)
+            self.path.mkdir(exist_ok=True, parents=True)
+
+    def normalize(self, *, num: int = 0,
+                  gsf: Optional[Vector] = None,
+                  nld: Optional[Vector] = None,
+                  normalizer_nld: Optional[NormalizerNLD] = None,
+                  normalizer_gsf: Optional[NormalizerGSF] = None) -> None:
+        """Perform normalization and saves results to `self.res`
+
+        Args:
+            num (int, optional): Loop number
+            gsf (Optional[Vector], optional): gsf before normalization
+            nld (Optional[Vector], optional): nld before normalization
+            normalizer_nld (Optional[NormalizerNLD], optional): NormalizerNLD
+                instance
+            normalizer_gsf (Optional[NormalizerGSF], optional): NormalizerGSF
+                instance
+        """
+        if not self.regenerate:
+            try:
+                self.load()
+                return
+            except FileNotFoundError:
+                pass
+
+        # reset internal state
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.normalizer_nld = copy.deepcopy(self.self_if_none(normalizer_nld))
+        self.normalizer_gsf = copy.deepcopy(self.self_if_none(normalizer_gsf))
+        for norm in [self.normalizer_nld, self.normalizer_gsf]:
+            norm._save_instance = False
+            norm.regenerate = True
+
+        self.LOG.debug("Setting NLD and GSF, convert to MeV and removing nan")
+        gsf = self.self_if_none(gsf)
+        gsf = gsf.copy()
+        gsf.to_MeV()
+        gsf.cut_nan()
+
+        nld = self.self_if_none(nld)
+        nld = nld.copy()
+        nld.to_MeV()
+        nld.cut_nan()
+
+        # Need to give some sort of standard deviation for sensible results
+        # Otherwise deviations at higher level density will have an
+        # uncreasonably high weight.
+        if self.std_fake_nld is None:
+            self.std_fake_nld = False
+        if self.std_fake_nld or nld.std is None:
+            self.std_fake_nld = True
+            nld.std = nld.values * 0.3  # x% is an arb. choice
+        if self.std_fake_gsf or gsf.std is None:
+            self.std_fake_gsf = True
+            gsf.std = gsf.values * 0.3  # x% is an arb. choice
+
+        # update
+        self.normalizer_nld.nld = nld  # update before initial guess
+        self.normalizer_gsf.gsf_in = gsf  # update before initial guess
+
+        # Use DE to get an inital guess before optimizing
+        args_nld, guess = self.initial_guess()
+        # Optimize using multinest
+        popt, samples = self.optimize(num, args_nld, guess)
+
+        self.res.pars = popt
+        self.res.samples = samples
+
+        # reset
+        if self.std_fake_nld is True:
+            self.std_fake_nld = None
+            nld.std = None
+        if self.std_fake_gsf is True:
+            self.std_fake_gsf = None
+            gsf.std = None
+
+        self.res.nld = nld.transform(self.res.pars["A"][0],
+                                     self.res.pars["alpha"][0], inplace=False)
+        self.res.gsf = gsf.transform(self.res.pars["B"][0],
+                                     self.res.pars["alpha"][0], inplace=False)
+
+        self.normalizer_gsf.model_low.autorange(self.res.gsf)
+        self.normalizer_gsf.model_high.autorange(self.res.gsf)
+        self.normalizer_gsf.extrapolate(self.res.gsf)
+        self.res.gsf_model_low = self.normalizer_gsf.model_low
+        self.res.gsf_model_high = self.normalizer_gsf.model_high
+        for model in [self.res.gsf_model_low, self.res.gsf_model_high]:
+            model.shift_after = model.shift
+
+        self.save()  # save instance
+
+    def initial_guess(self) -> None:
+        """ Find an inital guess for normalization parameters
+
+        Uses guess of normalizer_nld and corresponding normalization of gsf
+
+        Returns:
+           The arguments used for chi^2 minimization and the
+           minimizer.
+        """
+        normalizer_nld = self.normalizer_nld
+        normalizer_gsf = self.normalizer_gsf
+
+        args_nld, guess = normalizer_nld.initial_guess()
+        [A, alpha, T, Eshift] = [guess["A"], guess["alpha"],
+                                 guess["T"], guess["Eshift"]]
+
+        nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
+        nld_model = lambda E: normalizer_nld.model(E, T=T, Eshift=Eshift)  # noqa
+
+        normalizer_gsf.normalize(nld=nld, nld_model=nld_model, alpha=alpha)
+        guess["B"] = normalizer_gsf.res.pars["B"][0]
+
+        guess_print = copy.deepcopy(guess)
+        self.LOG.info("DE results/initial guess:\n%s",
+                      tt.to_string([list(guess_print.values())],
+                                   header=['A', 'α [MeV⁻¹]', 'T [MeV]',
+                                           'Eshift [MeV]', 'B']))
+
+        return args_nld, guess
+
+    def optimize(self, num: int,
+                 args_nld: Iterable,
+                 guess: Dict[str, float]) -> Tuple[Dict[str, Tuple[float, float]], Dict[str, List[float]]]:  # noqa
+        """Find parameters given model constraints and an initial guess
+
+        Employs Multinest.
+
+        Args:
+            num (int): Loop number
+            args_nld (Iterable): Additional arguments for the nld lnlike
+            guess (Dict[str, float]): The initial guess of the parameters
+
+        Returns:
+            Tuple:
+            - popt (Dict[str, Tuple[float, float]]): Median and 1sigma of the
+                parameters
+            - samples (Dict[str, List[float]]): Multinest samplesø.
+                Note: They are still importance weighted, not random draws
+                from the posterior.
+
+        Raises:
+            ValueError: Invalid parameters for automatix prior
+
+        Note:
+            You might want to adjust the priors for your specific case! Here
+            we just propose a general solution that might often work out of
+            the box.
+        """
+        if guess['alpha'] < 0:
+            raise NotImplementedError("Prior selection not implemented for "
+                                      "α < 0")
+        alpha_exponent = np.log10(guess['alpha'])
+
+        if guess['T'] < 0:
+            raise ValueError("Prior selection not implemented for T < 0; "
+                             "negative temperature is unphysical")
+        T_exponent = np.log10(guess['T'])
+
+        A = guess['A']
+        B = guess["B"]
+
+        # truncations from absolute values
+        lower_A, upper_A = 0., np.inf
+        mu_A, sigma_A = A, 10*A
+        a_A = (lower_A - mu_A) / sigma_A
+        b_A = (upper_A - mu_A) / sigma_A
+
+        lower_Eshift, upper_Eshift = -5., 5
+        mu_Eshift, sigma_Eshift = 0, 5
+        a_Eshift = (lower_Eshift - mu_Eshift) / sigma_Eshift
+        b_Eshift = (upper_Eshift - mu_Eshift) / sigma_Eshift
+
+        lower_B, upper_B = 0., np.inf
+        mu_B, sigma_B = B, 10*B
+        a_B = (lower_B - mu_B) / sigma_B
+        b_B = (upper_B - mu_B) / sigma_B
+
+        mu_sigmaSn, sigma_sigmaSn = self.normalizer_nld.norm_pars.spincutPars['sigmaSn']
+        mu_sigmaD, sigma_sigmaD = self.normalizer_nld.norm_pars.spincutPars['sigmaD']
+
+        def prior(cube, ndim, nparams):
+            # NOTE: You may want to adjust this for your case!
+            # truncated normal prior
+            cube[0] = truncnorm_ppf(cube[0], a_A, b_A)*sigma_A + mu_A
+
+            # log-uniform prior
+            # if alpha = 1e2, it's between 1e1 and 1e3
+            cube[1] = 10**(cube[1]*2 + (alpha_exponent-1))
+            # log-uniform prior
+            # if T = 1e2, it's between 1e1 and 1e3
+            cube[2] = 10**(cube[2]*2 + (T_exponent-1))
+            # truncated normal prior
+            cube[3] = truncnorm_ppf(cube[3], a_Eshift,
+                                    b_Eshift)*sigma_Eshift + mu_Eshift
+            # truncated normal prior
+            cube[4] = truncnorm_ppf(cube[4], a_B, b_B)*sigma_B + mu_B
+
+            # sigmas
+            cube[5] = normal_ppf(cube[5])*sigma_sigmaD + mu_sigmaD
+            cube[6] = normal_ppf(cube[6])*sigma_sigmaSn + mu_sigmaSn
+            
+
+            if np.isinf(cube[3]):
+                self.LOG.debug("Encountered inf in cube[3]:\n%s", cube[3])
+
+        def loglike(cube, ndim, nparams):
+            return self.lnlike(cube, args_nld=args_nld)
+
+        # parameters are changed in the lnlike
+        norm_pars_org = copy.deepcopy(self.normalizer_gsf.norm_pars)
+
+        self.multinest_path.mkdir(exist_ok=True)
+        path = self.multinest_path / f"sim_norm_{num}_"
+        assert len(str(path)) < 60, "Total path length too long for multinest"
+
+        self.LOG.info("Starting multinest: ")
+        self.LOG.debug("with following keywords %s:", self.multinest_kwargs)
+        #  Hack where stdout from Multinest is redirected as info messages
+        self.LOG.write = lambda msg: (self.LOG.info(msg) if msg != '\n'
+                                      else None)
+
+        with redirect_stdout(self.LOG):
+            pymultinest.run(loglike, prior, len(guess)+2,
+                            outputfiles_basename=str(path),
+                            **self.multinest_kwargs)
+
+        # Save parameters for analyzer
+        names = list(guess.keys()) + ['sigmaD', 'sigmaSn']
+        json.dump(names, open(str(path) + 'params.json', 'w'))
+        analyzer = pymultinest.Analyzer(len(guess),
+                                        outputfiles_basename=str(path))
+
+        stats = analyzer.get_stats()
+
+        samples = analyzer.get_equal_weighted_posterior()[:, :-1]
+        samples = dict(zip(names, samples.T))
+
+        # Format the output
+        popt = dict()
+        vals = []
+        for name, m in zip(names, stats['marginals']):
+            lo, hi = m['1sigma']
+            med = m['median']
+            sigma = (hi - lo) / 2
+            popt[name] = (med, sigma)
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            vals.append(fmts % (med, sigma))
+
+        self.LOG.info("Multinest results:\n%s", tt.to_string([vals],
+                      header=['A', 'α [MeV⁻¹]', 'T [MeV]',
+                              'Eshift [MeV]', 'B', 'σD', 'σSn']))
+
+        # reset state
+        self.normalizer_gsf.norm_pars = norm_pars_org
+
+        return popt, samples
+
+    def lnlike(self, x: Tuple[float, float, float, float, float],
+               args_nld: Iterable) -> float:
+        """Compute log likelihood  of the normalization fitting
+
+        This is the result up to the constant, which is irrelevant for the
+        maximization
+
+        Args:
+            x (Tuple[float, float, float, float, float]): The arguments
+                ordered as A, alpha, T and Eshift, B
+            args_nld (TYPE): Additional arguments for the nld lnlike
+
+        Returns:
+            lnlike: log likelihood
+        """
+        A, alpha, T, Eshift, B, sigmaD, sigmaSn = x[:7]  # slicing needed for multinest?
+
+        normalizer_gsf = self.normalizer_gsf
+        normalizer_nld = self.normalizer_nld
+
+        err_nld = normalizer_nld.lnlike_v2(x[:4], *args_nld)
+
+        nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
+        nld_model = lambda E: normalizer_nld.model(E, T=T, Eshift=Eshift)  # noqa
+
+        normalizer_gsf.nld_model = nld_model
+        normalizer_gsf.nld = nld
+
+        # Change to the new sigmaD and sigmaSn
+        normalizer_nld.norm_pars.spincutPars['sigma2_disc'][1] = sigmaD**2
+        normalizer_nld.norm_pars.spincutPars['sigma2_Sn'][1] = sigmaSn**2
+        normalizer_gsf.norm_pars.spincutPars['sigma2_disc'][1] = sigmaD**2
+        normalizer_gsf.norm_pars.spincutPars['sigma2_Sn'][1] = sigmaSn**2
+
+        # calculate the D0-equivalent of T and Eshift used
+        D0 = normalizer_nld.D0_from_nldSn(nld_model,
+                                          **normalizer_gsf.norm_pars.asdict())
+        normalizer_gsf.norm_pars.D0 = [D0, np.nan]  # dummy uncertainty
+        normalizer_gsf._gsf = normalizer_gsf.gsf_in.transform(B, alpha,
+                                                              inplace=False)
+        normalizer_gsf._gsf_low, normalizer_gsf._gsf_high = \
+            normalizer_gsf.extrapolate()
+        err_gsf = normalizer_gsf.lnlike()
+        return err_nld + err_gsf
+
+    def plot(self, ax: Optional[Any] = None, add_label: bool = True,
+             add_figlegend: bool = True,
+             **kwargs) -> Tuple[Any, Any]:
+        """Plots nld and gsf
+
+        Args:
+            ax (optional): The matplotlib axis to plot onto. Creates axis
+                is not provided
+            add_label (bool, optional):Defaults to `True`.
+            add_figlegend (bool, optional): Defaults to `True`.
+            results Optional[ResultsNormalized]: If provided, gsf and model
+                are taken from here instead.
+            **kwargs: kwargs for plot
+
+        Returns:
+            fig, ax
+        """
+        if ax is None:
+            fig, ax = plt.subplots(1, 2, constrained_layout=True)
+        else:
+            fig = ax[0].figure
+
+        self.normalizer_nld.plot(ax=ax[0], add_label=True, results=self.res,
+                                 add_figlegend=False, **kwargs)
+        self.normalizer_gsf.plot(ax=ax[1], add_label=False, results=self.res,
+                                 add_figlegend=False, **kwargs)
+
+        ax[0].set_title("Level density")
+        ax[1].set_title(r"$\gamma$SF")
+
+        if add_figlegend:
+            fig.legend(loc=9, ncol=4, frameon=True)
+            fig.subplots_adjust(left=0.1, right=0.9, top=0.8, bottom=0.1)
+
+        return fig, ax
+
+    def self_if_none(self, *args, **kwargs):
+        """ wrapper for lib.self_if_none """
+        return self_if_none(self, *args, **kwargs)
diff --git a/ompy/normalizer_simultan_v2.py b/ompy/normalizer_simultan_v2.py
new file mode 100644
index 00000000..dcf4750e
--- /dev/null
+++ b/ompy/normalizer_simultan_v2.py
@@ -0,0 +1,438 @@
+import logging
+import numpy as np
+import copy
+import json
+import termtables as tt
+from numpy import ndarray
+from pathlib import Path
+from typing import Optional, Union, Tuple, Any, Callable, Dict, Iterable, List
+import pymultinest
+import matplotlib.pyplot as plt
+from contextlib import redirect_stdout
+
+from .abstract_normalizer import AbstractNormalizer
+from .extractor import Extractor
+from .library import log_interp1d, self_if_none
+from .models import Model, ResultsNormalized, ExtrapolationModelLow,\
+                    ExtrapolationModelHigh, NormalizationParameters
+from .normalizer_nld import NormalizerNLD
+from .normalizer_gsf import NormalizerGSF
+from .spinfunctions import SpinFunctions
+from .vector import Vector
+from .stats import truncnorm_ppf, normal_ppf
+
+
+class NormalizerSimultan(AbstractNormalizer):
+
+    """ Simultaneous normalization of nld and gsf. Composed of Normalizer and NormalizerGSF as input, so read more on the normalization there
+
+    Attributes:
+        extractor (Extractor): Extractor instance
+        gsf (Optional[Vector], optional): gsf to normalize
+        multinest_path (Path, optional): Default path where multinest
+            saves files
+        multinest_kwargs (dict): Additional keywords to multinest. Defaults to
+            `{"seed": 65498, "resume": False}`
+        nld (Optional[Vector], optional): nld to normalize
+        normalizer_nld (NormalizerNLD): `NormalizerNLD` instance to get the normalization paramters
+        normalizer_gsf (NormalizerGSF): `NormalizerGSF` instance to get the normalization paramters
+        res (ResultsNormalized): Results
+        std_fake_gsf (bool): Whether the std. deviation is faked
+            (see `normalize`)
+        std_fake_nld (bool): Whether the std. deviation is faked
+            (see `normalize`)
+        path (Path): The path save the results.
+
+
+    TODO:
+        Work with more general models, too, not just CT for nld
+    """
+    LOG = logging.getLogger(__name__)
+    logging.captureWarnings(True)
+
+    def __init__(self, *,
+                 gsf: Optional[Vector] = None,
+                 nld: Optional[Vector] = None,
+                 normalizer_nld: Optional[NormalizerNLD] = None,
+                 normalizer_gsf: Optional[NormalizerGSF] = None,
+                 path: Optional[Union[str, Path]] = 'saved_run/normalizers',
+                 regenerate: bool = False):
+        """
+        TODO:
+            - currently have to set arguments here, an cannot set them in
+              "normalize"
+
+        Args:
+            gsf (optional): see above
+            nld (optional): see above
+            normalizer_nld (optional): see above
+            normalizer_gsf (optional): see above
+
+        """
+        super().__init__(regenerate)
+        if normalizer_nld is None:
+            self.normalizer_nld = None
+        else:
+            self.normalizer_nld = copy.deepcopy(normalizer_nld)
+
+        if normalizer_gsf is None:
+            self.normalizer_gsf = None
+        else:
+            self.normalizer_gsf = copy.deepcopy(normalizer_gsf)
+
+        self.gsf = None if gsf is None else gsf.copy()
+        self.nld = None if nld is None else nld.copy()
+
+        self.std_fake_nld: Optional[bool] = None  # See `normalize`
+        self.std_fake_gsf: Optional[bool] = None  # See `normalize`
+
+        self.res: Optional[ResultsNormalized] = None
+
+        self.multinest_path: Optional[Path] = Path('multinest')
+        self.multinest_kwargs: dict = {"seed": 65498, "resume": False}
+
+        if path is None:
+            self.path = None
+        else:
+            self.path = Path(path)
+            self.path.mkdir(exist_ok=True, parents=True)
+
+    def normalize(self, *, num: int = 0,
+                  gsf: Optional[Vector] = None,
+                  nld: Optional[Vector] = None,
+                  normalizer_nld: Optional[NormalizerNLD] = None,
+                  normalizer_gsf: Optional[NormalizerGSF] = None) -> None:
+        """Perform normalization and saves results to `self.res`
+
+        Args:
+            num (int, optional): Loop number
+            gsf (Optional[Vector], optional): gsf before normalization
+            nld (Optional[Vector], optional): nld before normalization
+            normalizer_nld (Optional[NormalizerNLD], optional): NormalizerNLD
+                instance
+            normalizer_gsf (Optional[NormalizerGSF], optional): NormalizerGSF
+                instance
+        """
+        if not self.regenerate:
+            try:
+                self.load()
+                return
+            except FileNotFoundError:
+                pass
+
+        # reset internal state
+        self.res = ResultsNormalized(name="Results NLD")
+
+        self.normalizer_nld = copy.deepcopy(self.self_if_none(normalizer_nld))
+        self.normalizer_gsf = copy.deepcopy(self.self_if_none(normalizer_gsf))
+        for norm in [self.normalizer_nld, self.normalizer_gsf]:
+            norm._save_instance = False
+            norm.regenerate = True
+
+        self.LOG.debug("Setting NLD and GSF, convert to MeV and removing nan")
+        gsf = self.self_if_none(gsf)
+        gsf = gsf.copy()
+        gsf.to_MeV()
+        gsf.cut_nan()
+
+        nld = self.self_if_none(nld)
+        nld = nld.copy()
+        nld.to_MeV()
+        nld.cut_nan()
+
+        # Need to give some sort of standard deviation for sensible results
+        # Otherwise deviations at higher level density will have an
+        # uncreasonably high weight.
+        if self.std_fake_nld is None:
+            self.std_fake_nld = False
+        if self.std_fake_nld or nld.std is None:
+            self.std_fake_nld = True
+            nld.std = nld.values * 0.3  # x% is an arb. choice
+        if self.std_fake_gsf or gsf.std is None:
+            self.std_fake_gsf = True
+            gsf.std = gsf.values * 0.3  # x% is an arb. choice
+
+        # update
+        self.normalizer_nld.nld = nld  # update before initial guess
+        self.normalizer_gsf.gsf_in = gsf  # update before initial guess
+
+        # Use DE to get an inital guess before optimizing
+        args_nld, guess = self.initial_guess()
+        # Optimize using multinest
+        popt, samples = self.optimize(num, args_nld, guess)
+
+        self.res.pars = popt
+        self.res.samples = samples
+
+        # reset
+        if self.std_fake_nld is True:
+            self.std_fake_nld = None
+            nld.std = None
+        if self.std_fake_gsf is True:
+            self.std_fake_gsf = None
+            gsf.std = None
+
+        self.res.nld = nld.transform(self.res.pars["A"][0],
+                                     self.res.pars["alpha"][0], inplace=False)
+        self.res.gsf = gsf.transform(self.res.pars["B"][0],
+                                     self.res.pars["alpha"][0], inplace=False)
+
+        self.normalizer_gsf.model_low.autorange(self.res.gsf)
+        self.normalizer_gsf.model_high.autorange(self.res.gsf)
+        self.normalizer_gsf.extrapolate(self.res.gsf)
+        self.res.gsf_model_low = self.normalizer_gsf.model_low
+        self.res.gsf_model_high = self.normalizer_gsf.model_high
+        for model in [self.res.gsf_model_low, self.res.gsf_model_high]:
+            model.shift_after = model.shift
+
+        self.save()  # save instance
+
+    def initial_guess(self) -> None:
+        """ Find an inital guess for normalization parameters
+
+        Uses guess of normalizer_nld and corresponding normalization of gsf
+
+        Returns:
+           The arguments used for chi^2 minimization and the
+           minimizer.
+        """
+        normalizer_nld = self.normalizer_nld
+        normalizer_gsf = self.normalizer_gsf
+
+        args_nld, guess = normalizer_nld.initial_guess()
+        [A, alpha, T, Eshift] = [guess["A"], guess["alpha"],
+                                 guess["T"], guess["Eshift"]]
+
+        nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
+        nld_model = lambda E: normalizer_nld.model(E, T=T, Eshift=Eshift)  # noqa
+
+        normalizer_gsf.normalize(nld=nld, nld_model=nld_model, alpha=alpha)
+        guess["B"] = normalizer_gsf.res.pars["B"][0]
+
+        guess_print = copy.deepcopy(guess)
+        self.LOG.info("DE results/initial guess:\n%s",
+                      tt.to_string([list(guess_print.values())],
+                                   header=['A', 'α [MeV⁻¹]', 'T [MeV]',
+                                           'Eshift [MeV]', 'B']))
+
+        return args_nld, guess
+
+    def optimize(self, num: int,
+                 args_nld: Iterable,
+                 guess: Dict[str, float]) -> Tuple[Dict[str, Tuple[float, float]], Dict[str, List[float]]]:  # noqa
+        """Find parameters given model constraints and an initial guess
+
+        Employs Multinest.
+
+        Args:
+            num (int): Loop number
+            args_nld (Iterable): Additional arguments for the nld lnlike
+            guess (Dict[str, float]): The initial guess of the parameters
+
+        Returns:
+            Tuple:
+            - popt (Dict[str, Tuple[float, float]]): Median and 1sigma of the
+                parameters
+            - samples (Dict[str, List[float]]): Multinest samplesø.
+                Note: They are still importance weighted, not random draws
+                from the posterior.
+
+        Raises:
+            ValueError: Invalid parameters for automatix prior
+
+        Note:
+            You might want to adjust the priors for your specific case! Here
+            we just propose a general solution that might often work out of
+            the box.
+        """
+        if guess['alpha'] < 0:
+            raise NotImplementedError("Prior selection not implemented for "
+                                      "α < 0")
+        alpha_exponent = np.log10(guess['alpha'])
+
+        if guess['T'] < 0:
+            raise ValueError("Prior selection not implemented for T < 0; "
+                             "negative temperature is unphysical")
+        T_exponent = np.log10(guess['T'])
+
+        A = guess['A']
+        B = guess["B"]
+
+        # truncations from absolute values
+        lower_A, upper_A = 0., np.inf
+        mu_A, sigma_A = A, 10*A
+        a_A = (lower_A - mu_A) / sigma_A
+        b_A = (upper_A - mu_A) / sigma_A
+
+        lower_Eshift, upper_Eshift = -5., 5
+        mu_Eshift, sigma_Eshift = 0, 5
+        a_Eshift = (lower_Eshift - mu_Eshift) / sigma_Eshift
+        b_Eshift = (upper_Eshift - mu_Eshift) / sigma_Eshift
+
+        lower_B, upper_B = 0., np.inf
+        mu_B, sigma_B = B, 10*B
+        a_B = (lower_B - mu_B) / sigma_B
+        b_B = (upper_B - mu_B) / sigma_B
+
+        mu_rhoSn, sigma_rhoSn = self.normalizer_nld.norm_pars.rhoSn
+        Sn = self.normalizer_nld.norm_pars.Sn[0]
+
+        def prior(cube, ndim, nparams):
+            # NOTE: You may want to adjust this for your case!
+            # truncated normal prior
+            cube[0] = truncnorm_ppf(cube[0], a_A, b_A)*sigma_A + mu_A
+
+            # log-uniform prior
+            # if alpha = 1e2, it's between 1e1 and 1e3
+            cube[1] = 10**(cube[1]*2 + (alpha_exponent-1))
+            # log-uniform prior
+            # if T = 1e2, it's between 1e1 and 1e3
+            cube[2] = 10**(cube[2]*2 + (T_exponent-1))
+            # truncated normal prior
+            cube[3] = truncnorm_ppf(cube[3], a_Eshift,
+                                    b_Eshift)*sigma_Eshift + mu_Eshift
+            #rhoSn = normal_ppf(cube[3])*sigma_rhoSn + mu_rhoSn
+            #cube[3] = Sn - cube[2]*np.log(cube[2]*rhoSn)
+
+            # truncated normal prior
+            cube[4] = truncnorm_ppf(cube[4], a_B, b_B)*sigma_B + mu_B
+
+            if np.isinf(cube[3]):
+                self.LOG.debug("Encountered inf in cube[3]:\n%s", cube[3])
+
+        def loglike(cube, ndim, nparams):
+            return self.lnlike(cube, args_nld=args_nld)
+
+        # parameters are changed in the lnlike
+        norm_pars_org = copy.deepcopy(self.normalizer_gsf.norm_pars)
+
+        self.multinest_path.mkdir(exist_ok=True)
+        path = self.multinest_path / f"sim_norm_{num}_"
+        assert len(str(path)) < 60, "Total path length too long for multinest"
+
+        self.LOG.info("Starting multinest: ")
+        self.LOG.debug("with following keywords %s:", self.multinest_kwargs)
+        #  Hack where stdout from Multinest is redirected as info messages
+        self.LOG.write = lambda msg: (self.LOG.info(msg) if msg != '\n'
+                                      else None)
+
+        with redirect_stdout(self.LOG):
+            pymultinest.run(loglike, prior, len(guess),
+                            outputfiles_basename=str(path),
+                            **self.multinest_kwargs)
+
+        # Save parameters for analyzer
+        names = list(guess.keys())
+        json.dump(names, open(str(path) + 'params.json', 'w'))
+        analyzer = pymultinest.Analyzer(len(guess),
+                                        outputfiles_basename=str(path))
+
+        stats = analyzer.get_stats()
+
+        samples = analyzer.get_equal_weighted_posterior()[:, :-1]
+        samples = dict(zip(names, samples.T))
+
+        # Format the output
+        popt = dict()
+        vals = []
+        for name, m in zip(names, stats['marginals']):
+            lo, hi = m['1sigma']
+            med = m['median']
+            sigma = (hi - lo) / 2
+            popt[name] = (med, sigma)
+            i = max(0, int(-np.floor(np.log10(sigma))) + 1)
+            fmt = '%%.%df' % i
+            fmts = '\t'.join([fmt + " ± " + fmt])
+            vals.append(fmts % (med, sigma))
+
+        self.LOG.info("Multinest results:\n%s", tt.to_string([vals],
+                      header=['A', 'α [MeV⁻¹]', 'T [MeV]',
+                              'Eshift [MeV]', 'B']))
+
+        # reset state
+        self.normalizer_gsf.norm_pars = norm_pars_org
+
+        return popt, samples
+
+    def lnlike(self, x: Tuple[float, float, float, float, float],
+               args_nld: Iterable) -> float:
+        """Compute log likelihood  of the normalization fitting
+
+        This is the result up to the constant, which is irrelevant for the
+        maximization
+
+        Args:
+            x (Tuple[float, float, float, float, float]): The arguments
+                ordered as A, alpha, T and Eshift, B
+            args_nld (TYPE): Additional arguments for the nld lnlike
+
+        Returns:
+            lnlike: log likelihood
+        """
+        A, alpha, T, Eshift, B = x[:5]  # slicing needed for multinest?
+
+        normalizer_gsf = self.normalizer_gsf
+        normalizer_nld = self.normalizer_nld
+
+        err_nld = normalizer_nld.lnlike(x[:4], *args_nld)
+
+        nld = normalizer_nld.nld.transform(A, alpha, inplace=False)
+        nld_model = lambda E: normalizer_nld.model(E, T=T, Eshift=Eshift)*np.exp(-alpha)/A  # noqa
+
+        normalizer_gsf.nld_model = nld_model
+        normalizer_gsf.nld = normalizer_nld.nld.copy()  # We do not need to transform.
+        # calculate the D0-equivalent of T and Eshift used
+        D0 = normalizer_nld.D0_from_nldSn(nld_model,
+                                          **normalizer_nld.norm_pars.asdict())
+        normalizer_gsf.norm_pars.D0 = [D0, np.nan]  # dummy uncertainty
+        #normalizer_gsf._gsf = normalizer_gsf.gsf_in.transform(B, alpha,
+        #                                                      inplace=False)
+        #normalizer_gsf._gsf_low, normalizer_gsf._gsf_high = \
+        #    normalizer_gsf.extrapolate()
+        gam_theo = A*B*np.exp(alpha*self.normalizer_nld.norm_pars.Sn[0])*normalizer_gsf.Gg_standard()
+
+        gsf = normalizer_gsf.gsf_in.transform(B, alpha, inplace=False)
+        ln_gsf = -0.5*np.log(gsf.std).sum()
+        err_gsf = -0.5*(gam_theo - normalizer_gsf.norm_pars.Gg[0])/normalizer_gsf.norm_pars.Gg[1]
+
+        return err_nld + err_gsf + ln_gsf
+
+    def plot(self, ax: Optional[Any] = None, add_label: bool = True,
+             add_figlegend: bool = True,
+             **kwargs) -> Tuple[Any, Any]:
+        """Plots nld and gsf
+
+        Args:
+            ax (optional): The matplotlib axis to plot onto. Creates axis
+                is not provided
+            add_label (bool, optional):Defaults to `True`.
+            add_figlegend (bool, optional): Defaults to `True`.
+            results Optional[ResultsNormalized]: If provided, gsf and model
+                are taken from here instead.
+            **kwargs: kwargs for plot
+
+        Returns:
+            fig, ax
+        """
+        if ax is None:
+            fig, ax = plt.subplots(1, 2, constrained_layout=True)
+        else:
+            fig = ax[0].figure
+
+        self.normalizer_nld.plot(ax=ax[0], add_label=True, results=self.res,
+                                 add_figlegend=False, **kwargs)
+        self.normalizer_gsf.plot(ax=ax[1], add_label=False, results=self.res,
+                                 add_figlegend=False, **kwargs)
+
+        ax[0].set_title("Level density")
+        ax[1].set_title(r"$\gamma$SF")
+
+        if add_figlegend:
+            fig.legend(loc=9, ncol=4, frameon=True)
+            fig.subplots_adjust(left=0.1, right=0.9, top=0.8, bottom=0.1)
+
+        return fig, ax
+
+    def self_if_none(self, *args, **kwargs):
+        """ wrapper for lib.self_if_none """
+        return self_if_none(self, *args, **kwargs)
diff --git a/ompy/response.py b/ompy/response.py
index 10c554bf..c1a75832 100644
--- a/ompy/response.py
+++ b/ompy/response.py
@@ -21,6 +21,11 @@
 from .matrix import Matrix
 from .vector import Vector
 
+if 'JPY_PARENT_PID' in os.environ:
+    from tqdm import tqdm_notebook as tqdm
+else:
+    from tqdm import tqdm
+
 LOG = logging.getLogger(__name__)
 logging.captureWarnings(True)
 
@@ -325,7 +330,7 @@ def interpolate(self,
         R = np.zeros((N_out, N_out))
         # Loop over rows of the response matrix
         # TODO for speedup: Change this to a cython
-        for j, E in enumerate(Eout):
+        for j, E in enumerate(tqdm(Eout)):
             oneSigma = fwhm_abs_array[j] / 2.35
             Egmax = E + 6 * oneSigma
             i_Egmax = min(index(Eout, Egmax), N_out-1)
diff --git a/ompy/spinfunctions.py b/ompy/spinfunctions.py
index ad2c0b0d..89168196 100644
--- a/ompy/spinfunctions.py
+++ b/ompy/spinfunctions.py
@@ -22,11 +22,16 @@ def __init__(self, Ex: Union[float, Sequence], J: Union[float, Sequence],
         self.model = model
         self.pars = pars
 
-    def get_sigma2(self):
+    def get_sigma2(self, Ex: Optional[Union[float, Sequence]] = None,
+                   **kwargs):
         """ Get the square of the spin cut for a specified model """
         model = self.model
         pars = self.pars
 
+        pars['Ex'] = Ex
+        for key, value in kwargs.items():
+            pars[key] = value
+
         if model == "const":
             pars_req = {"sigma"}
             return call_model(self.gconst, pars, pars_req)
@@ -49,7 +54,10 @@ def get_sigma2(self):
             raise TypeError(
                 "\nError: Spincut model not supported; check spelling\n")
 
-    def distribution(self) -> Tuple[float, np.ndarray]:
+    def distribution(self, Ex: Optional[Union[float, Sequence]] = None,
+                     J: Optional[Union[float, Sequence]] = None,
+                     **kwargs
+                     ) -> Tuple[float, np.ndarray]:
         """Get spin distribution
 
         Note: Assuming equal parity
@@ -59,17 +67,19 @@ def distribution(self) -> Tuple[float, np.ndarray]:
                 depends on input Ex and J and is squeezed if only one of them
                 is an array. If both are arrays: `spinDist[Ex,J]`
         """
-        sigma2 = self.get_sigma2()
+        sigma2 = self.get_sigma2(Ex, **kwargs)
         sigma2 = sigma2[np.newaxis]  # ability to transpose "1D" array
 
-        spinDist = ((2. * self.J + 1.) / (2. * sigma2.T)
-                    * np.exp(-np.power(self.J + 0.5, 2.) / (2. * sigma2.T)))
+        J = self.J if J is None else J
+
+        spinDist = ((2. * J + 1.) / (2. * sigma2.T)
+                    * np.exp(-np.power(J + 0.5, 2.) / (2. * sigma2.T)))
         return np.squeeze(spinDist)  # return 1D if Ex or J is single entry
 
     # different spin cut models
 
     def gconst(self, sigma: float,
-              Ex: Optional[Union[float, Sequence]] = None) -> Union[float, Sequence] : # noqa
+              Ex: Optional[Union[float, Sequence]] = None) -> Union[float, Sequence]:  # noqa
         """
         Constant spin-cutoff parameter
 
@@ -83,7 +93,7 @@ def gconst(self, sigma: float,
         return np.full_like(Ex, sigma**2)
 
     def gEB05(self, mass: int, NLDa: float, Eshift: float,
-              Ex: Optional[Union[float, Sequence]] = None) -> Union[float, Sequence] : # noqa
+              Ex: Optional[Union[float, Sequence]] = None) -> Union[float, Sequence]: # noqa
         """
         Von Egidy & B PRC72,044311(2005), Eq. (4)
         The rigid moment of inertia formula (RMI)
diff --git a/ompy/vector.py b/ompy/vector.py
index 20975a64..53814e49 100644
--- a/ompy/vector.py
+++ b/ompy/vector.py
@@ -562,6 +562,11 @@ def index(self, E: float) -> int:
         """ Returns the closest index corresponding to the E value """
         return index(self.E, E)
 
+    def indices(self, E: Iterable[float]) -> ndarray:
+        """ Returns the closest indices correspoinding to the E values """
+        indices = [self.index(e) for e in E]
+        return np.array(indices)
+
     def __matmul__(self, other: Vector) -> Vector:
         result = self.copy()
         if isinstance(other, Vector):
diff --git a/release_note.md b/release_note.md
index f39db715..b1e45b75 100644
--- a/release_note.md
+++ b/release_note.md
@@ -6,6 +6,11 @@ Added:
 - `fill` attribute for `Matrix`, to easily fill counts in a given bin containing (Ex, Eg).
 - When saving and loading `Vector` from `csv` files one can now pass keyword arguments to the pandas `read_csv()` and `to_csv()` functions.
 - Added a keyword (`units`) to select the energy units when saving a `Vector` to file.
+- Added class `ErrorFinder`. This class uses pyMC3 to estimate the relative uncertanties of the NLD and gSF in an ensemble of NLDs and gSFs.
+- Added attribute `error_finder` to the `Extractor` class. If set the `Extractor` class will use the `error_finder` object to estimate the relative uncertanties of the extracted NLDs and gSFs. Points that the `ErrorFinder` are unable to estimate will be set to the largest value of those that was successfully estimated.
+- Added dependency `pyMC3>=3.11.2,<4.0`.
+- Added `indices()` to the `Vector` class get the index of several x-values. Similar to `Matrix.indices_Eg()` and `Matrix.indices_Ex()`
+- Added a notebook `uncertanty_by_counts` exploring the new algorithm used to estimate uncertanties before normalization.
 
 Changed:
 - Fixed a bug where the `std` attribute of `Vector` was not saved to file.
diff --git a/setup.py b/setup.py
index e99ba10a..db92d882 100755
--- a/setup.py
+++ b/setup.py
@@ -130,6 +130,7 @@ def write_version_py(filename='ompy/version_setup.py'):
 fname = "ompy/decomposition.c"  # otherwise it may not recompile
 if os.path.exists(fname):
     os.remove(fname)
+openmp = False
 
 extra_compile_args = ["-O3", "-ffast-math", "-march=native"]
 extra_link_args = []
@@ -171,7 +172,8 @@ def write_version_py(filename='ompy/version_setup.py'):
  "uncertainties>=3.0.3",
  "tqdm",
  "pathos",
- "pybind11>=2.6.0"
+ "pybind11>=2.6.0",
+ "pymc"
 ]
 
 setup(name='OMpy',
diff --git a/tests/test_dist.py b/tests/test_dist.py
new file mode 100644
index 00000000..a4f9a76c
--- /dev/null
+++ b/tests/test_dist.py
@@ -0,0 +1,67 @@
+import pytest
+import ompy as om
+import numpy as np
+import pymc3 as pm
+from numpy.testing import assert_equal, assert_allclose
+
+import matplotlib.pyplot as plt
+
+
+@pytest.mark.parametrize(
+    "lam,mu",
+    [(10., 1.),
+     (5., 1.),
+     (100., 2.),
+     (50., 0.5)])
+def test_fermi_dirac(lam, mu):
+
+    prob = om.FermiDirac.dist(lam=lam, mu=mu)
+
+    samples = prob.random(size=1000000)
+
+    hist, bins = np.histogram(samples, bins=100, density=True)
+
+    x = []
+    for n in range(len(bins) - 1):
+        x.append(0.5*(bins[n+1] + bins[n]))
+    x = np.array(x)
+    y = (lam/np.log(1 + np.exp(lam*mu)))/(np.exp(lam*(x - mu)) + 1)
+
+    # We use a fairly large tolerance since error should be stocastic
+    assert_allclose(hist, y, atol=0.1)
+
+
+@pytest.mark.parametrize(
+    "lam,mu",
+    [(10., 1.),
+     (5., 1.),
+     (100., 2.),
+     (50., 0.5)])
+def test_fermi_dirac_logp(lam, mu):
+
+    lam = 10.
+    mu = 1.
+
+    prob = om.FermiDirac.dist(lam=lam, mu=mu)
+
+    x = np.linspace(0, 3, 1001)
+    y = lam/np.log(1 + np.exp(lam*mu)) * 1/(np.exp(lam*(x - mu)) + 1)
+
+    y_r = np.exp(prob.logp(x).eval())
+    assert_allclose(y, y_r)
+
+
+@pytest.mark.parametrize(
+    "lam,mu",
+    [(10., 1.),
+     (5., 1.),
+     (100., 2.),
+     (50., 0.5)])
+def test_fermi_dirac_logcdf(lam, mu):
+
+    prob = om.FermiDirac.dist(lam=lam, mu=mu)
+    x = np.linspace(0, 3, 1001)
+
+    y = 1 - np.log(1 + np.exp(-lam*(x - mu)))/np.log(1 + np.exp(lam*mu))
+    y_r = np.exp(prob.logcdf(x).eval())
+    assert_allclose(y, y_r)
diff --git a/tests/test_error_finder.py b/tests/test_error_finder.py
new file mode 100644
index 00000000..056c7446
--- /dev/null
+++ b/tests/test_error_finder.py
@@ -0,0 +1,246 @@
+import pytest
+import ompy as om
+import numpy as np
+import pymc3 as pm
+from scipy.interpolate import interp1d
+from numpy.testing import assert_equal, assert_allclose
+
+def generate_data(resp_path):
+
+    Ex_min = 4400 # keV
+    Ex_max = 7700 # keV
+    Eg_min = 1300 # keV
+    Eg_max = Ex_max + 200 # keV
+    ensemble_size = 10
+    regenerate = False
+
+    raw = om.example_raw('Dy164')
+    raw.cut_diagonal(E1=(800, 0), E2=(7500, 7300))
+    raw.cut('Ex', 0, 8400)
+
+    try:
+        trapezoid_cut = om.Action('matrix')
+        trapezoid_cut.trapezoid(Ex_min=Ex_min, Ex_max=Ex_max, Eg_min=Eg_min, Eg_max=Eg_max, inplace=True)
+        ensemble = om.Ensemble(raw=raw)
+        ensemble.unfolder = om.Unfolder(response=raw)
+        ensemble.first_generation_method = om.FirstGeneration()
+        ensemble.generate(ensemble_size, regenerate=regenerate)
+        extractor = om.Extractor(ensemble=ensemble)
+        extractor.trapezoid = trapezoid_cut
+        extractor.extract_from(regenerate=regenerate)
+        return extractor.nld, extractor.gsf
+    except:
+        pass
+
+    Eg = raw.Eg
+    fwhm_abs = 90.44  # (90/1330 = 6.8%)
+    response = om.Response(resp_path)
+    R_ompy_unf, R_tab_unf = response.interpolate(Eg, fwhm_abs=fwhm_abs/10, return_table=True)
+
+    fthreshold = interp1d([30., 80., 122., 183., 244., 294., 344., 562., 779., 1000.],
+                          [0.0, 0.0, 0.0, 0.06, 0.44, 0.60, 0.87, 0.99, 1.00, 1.00],
+                          fill_value="extrapolate")
+
+    def apply_detector_threshold(response, table, fthreshold):
+        thres = fthreshold(response.Eg)
+        response.values = response.values * thres
+        # renormalize
+        response.values = om.div0(response.values, response.values.sum(axis=1)[:, np.newaxis])
+        table["eff_tot"] *= thres
+    apply_detector_threshold(R_ompy_unf, R_tab_unf, fthreshold)
+
+    unfolder = om.Unfolder(response=R_ompy_unf)
+    firstgen = om.FirstGeneration()
+    unfolder.use_compton_subtraction = True  # default
+    unfolder.response_tab = R_tab_unf
+
+    unfolder.FWHM_tweak_multiplier = {"fe": 1., "se": 1.1,
+                                      "de": 1.3, "511": 0.9}
+
+    trapezoid_cut = om.Action('matrix')
+    trapezoid_cut.trapezoid(Ex_min=Ex_min, Ex_max=Ex_max, Eg_min=Eg_min, Eg_max=Eg_max, inplace=True)
+    E_rebinned = np.arange(100., 8500, 200)
+    ensemble = om.Ensemble(raw=raw)
+
+    ensemble.unfolder = unfolder
+    ensemble.first_generation_method = firstgen
+    ensemble.generate(ensemble_size, regenerate=regenerate)
+    ensemble.rebin(E_rebinned, member="firstgen")
+
+    extractor = om.Extractor(ensemble=ensemble)
+    extractor.trapezoid = trapezoid_cut
+    extractor.suppress_warning = True
+    extractor.extract_from(regenerate=regenerate)
+
+    return extractor.nld, extractor.gsf
+
+
+def keep_only(self, vecs):
+    """ Takes a list of vectors and returns a list of vectors
+    where only the points shared between all vectors are returned.
+    """
+
+    E = [vec.E.copy() for vec in vecs]
+    energies = {}
+    for vec in vecs:
+        for E in vec.E:
+            if E not in energies:
+                energies[E] = [False] * len(vecs)
+
+    # Next we will add if the point is present or not
+    for n, vec in enumerate(vecs):
+        for E in vec.E:
+            energies[E][n] = True
+
+    keep_energy = []
+    for key in energies:
+        if np.all(energies[key]):
+            keep_energy.append(key)
+
+    vec_all_common = [vec.copy() for vec in vecs]
+    for vec in vec_all_common:
+        E = []
+        values = []
+        for e, value in zip(vec.E, vec.values):
+            if e in keep_energy:
+                E.append(e)
+                values.append(value)
+        vec.E = np.array(E)
+        vec.values = np.array(values)
+
+    return vec_all_common
+
+
+def condition_data(_nlds, _gsfs):
+    """ Ensures that data are copied and that all values are in correct
+    units. It also checks that all lengths are correct.
+    Args:
+        nlds (List[Vector]): List of the NLDs in an ensemble
+        gsfs (List[Vector]): List of the γSFs in an ensemble
+    Returns:
+        Tuple of nld energy points, gsf energy points and the observed
+        matrix for the NLD and γSF.
+    Raises:
+        IndexError: If the length of members of the ensemble have an
+            equal number of points in the NLD and γSF.
+    Warnings:
+        Will raise a warning if there are members in the ensemble that
+        contains one or more nan's. This is mostly to inform the user
+        and shouldn't be an issue later on.
+    TODO:
+        - Mitigation when cut_nan() results in different length vectors
+          of different members of the ensemble. (e.g. len(nld[0]) = 10,
+          len(nld[1]) = 9).
+    """
+
+    # Ensure that the same number of NLDs and GSFs are provided
+    assert len(_nlds) == len(_gsfs), \
+        "Number of nlds and gsfs is different" 
+
+    N = len(_nlds)
+
+    print(f"Processing an ensemble with {N} members")
+
+    # Make copy to ensure that we don't overwrite anything
+    nlds = [nld.copy() for nld in _nlds]
+    gsfs = [gsf.copy() for gsf in _gsfs]
+
+    print(f"Before removing nan: {len(nlds[0])} NLD values and "
+          f"{len(gsfs[0])} GSF values")
+
+    # Ensure that we have in MeV and that there isn't any nan's.
+    is_nan = False
+    for nld, gsf in zip(nlds, gsfs):
+        nld.to_MeV()
+        gsf.to_MeV()
+        is_nan = np.isnan(nld.values).any() or np.isnan(gsf.values).any()
+        nld.cut_nan()
+        gsf.cut_nan()
+    if is_nan:
+        print(f"NLDs and/or γSFs contains nan's."
+              " They will be removed")
+
+    # Next we will ensure that all members have all energies
+    if (not om.error_finder.all_equal([len(nld) for nld in nlds]) or
+       not om.error_finder.all_equal([len(gsf) for gsf in gsfs])):
+        print("Some members of the ensemble have different lengths. "
+              "Consider re-binning or changing limits.")
+        nlds = keep_only(nlds)
+        gsfs = keep_only(gsfs)
+
+    print(f"After removing nan: {len(nlds[0])} NLD values and "
+          f"{len(gsfs[0])} GSF values")
+
+    # Next we can extract the important parts
+    E_nld = nlds[0].E.copy()
+    E_gsf = gsfs[0].E.copy()
+
+    M_nld = len(E_nld)
+    M_gsf = len(E_gsf)
+
+    nld_obs = []
+    gsf_obs = []
+
+    idx_nld = np.tile(np.arange(M_nld, dtype=int), (N-1, 1))
+    idx_gsf = np.tile(np.arange(M_gsf, dtype=int), (N-1, 1))
+
+    for n, (nld, gsf) in enumerate(zip(nlds, gsfs)):
+
+        # Make a copy of the values arrays
+        nld_array = [nld.values.copy() for nld in nlds]
+        gsf_array = [gsf.values.copy() for gsf in gsfs]
+
+        del nld_array[n]
+        del gsf_array[n]
+
+        nld_array = np.array(nld_array)
+        gsf_array = np.array(gsf_array)
+
+        # Set the observed data
+        nld_obs.append(nld.values[idx_nld]/nld_array)
+        gsf_obs.append(gsf.values[idx_gsf]/gsf_array)
+
+    nld_obs = np.array(nld_obs)
+    gsf_obs = np.array(gsf_obs)
+
+    idx_coef_nld = np.repeat(np.arange(N-1), M_nld).reshape(N-1, M_nld)
+    idx_coef_gsf = np.repeat(np.arange(N-1), M_gsf).reshape(N-1, M_gsf)
+
+    idx_vals_nld = np.array([idx_nld] * N)
+    idx_vals_gsf = np.array([idx_gsf] * N)
+
+    return E_nld, E_gsf, nld_obs, gsf_obs, \
+        idx_coef_nld, idx_coef_gsf, idx_vals_nld, idx_vals_gsf
+
+
+def test_error_finder():
+    nlds, gsfs = None, None
+    try:
+        nlds, gsfs = generate_data("../OCL_response_functions/nai2012_for_opt13")
+    except:
+        nlds, gsfs = generate_data("OCL_response_functions/nai2012_for_opt13")
+
+    E_nld, E_gsf, nld_obs, gsf_obs, \
+        idx_coef_nld, idx_coef_gsf, idx_vals_nld, idx_vals_gsf = condition_data(nlds, gsfs)
+
+    N = len(nlds)
+    e_nld, q_nld = om.error_finder.format_data(nlds)
+    e_gsf, q_gsf = om.error_finder.format_data(gsfs)
+
+    M_nld = len(e_nld)
+    M_gsf = len(e_gsf)
+
+    c_mask_nld, v_mask_nld = om.error_finder.format_mask(N, M_nld)
+    c_mask_gsf, v_mask_gsf = om.error_finder.format_mask(N, M_gsf)
+
+    assert_equal(e_nld, E_nld)
+    assert_equal(e_gsf, E_gsf)
+
+    assert_equal(q_nld, nld_obs)
+    assert_equal(q_gsf, gsf_obs)
+
+    assert_equal(c_mask_nld, idx_coef_nld)
+    assert_equal(v_mask_nld, idx_vals_nld)
+
+    assert_equal(c_mask_gsf, idx_coef_gsf)
+    assert_equal(v_mask_gsf, idx_vals_gsf)