diff --git a/.gitignore b/.gitignore index ff5a50d..c756032 100644 --- a/.gitignore +++ b/.gitignore @@ -41,6 +41,13 @@ docs/_build # VSCode .vscode +# Large files +*.hdf +*normalized.tiff +*sample.tiff +*open_beam.tiff +ProofOfConcept/sample_data/*.tiff + # Misc ..* *.log diff --git a/ProofOfConcept/Bragg_Edge_Prototype+EssImaging.ipynb b/ProofOfConcept/Bragg_Edge_Prototype+EssImaging.ipynb index f33aa98..e4eda65 100644 --- a/ProofOfConcept/Bragg_Edge_Prototype+EssImaging.ipynb +++ b/ProofOfConcept/Bragg_Edge_Prototype+EssImaging.ipynb @@ -210,7 +210,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -380,7 +380,7 @@ "\n", "\n", "\n", - "
scipp.DataArray (66.41 MB)
    • wavelength: 250
    • y: 128
    • x: 128
    • Ltotal
      ()
      float64
      m
      34.8
      Values:
      array(34.8)
    • tof
      (wavelength [bin-edge])
      float64
      ms
      0.0, 0.16, ..., 39.84, 40.0
      Values:
      array([ 0.  ,  0.16,  0.32,  0.48,  0.64,  0.8 ,  0.96,  1.12,  1.28,\n",
+       "
      scipp.DataArray (66.41 MB)
        • wavelength: 250
        • y: 128
        • x: 128
        • Ltotal
          ()
          float64
          m
          34.8
          Values:
          array(34.8)
        • tof
          (wavelength [bin-edge])
          float64
          ms
          0.0, 0.16, ..., 39.84, 40.0
          Values:
          array([ 0.  ,  0.16,  0.32,  0.48,  0.64,  0.8 ,  0.96,  1.12,  1.28,\n",
        "        1.44,  1.6 ,  1.76,  1.92,  2.08,  2.24,  2.4 ,  2.56,  2.72,\n",
        "        2.88,  3.04,  3.2 ,  3.36,  3.52,  3.68,  3.84,  4.  ,  4.16,\n",
        "        4.32,  4.48,  4.64,  4.8 ,  4.96,  5.12,  5.28,  5.44,  5.6 ,\n",
@@ -407,7 +407,7 @@
        "       34.56, 34.72, 34.88, 35.04, 35.2 , 35.36, 35.52, 35.68, 35.84,\n",
        "       36.  , 36.16, 36.32, 36.48, 36.64, 36.8 , 36.96, 37.12, 37.28,\n",
        "       37.44, 37.6 , 37.76, 37.92, 38.08, 38.24, 38.4 , 38.56, 38.72,\n",
-       "       38.88, 39.04, 39.2 , 39.36, 39.52, 39.68, 39.84, 40.  ])
        • wavelength
          (wavelength [bin-edge])
          float64
          Å
          0.0, 0.018, ..., 4.529, 4.547
          Values:
          array([0.        , 0.01818866, 0.03637732, 0.05456599, 0.07275465,\n",
+       "       38.88, 39.04, 39.2 , 39.36, 39.52, 39.68, 39.84, 40.  ])
        • wavelength
          (wavelength [bin-edge])
          float64
          Å
          0.0, 0.018, ..., 4.529, 4.547
          Values:
          array([0.        , 0.01818866, 0.03637732, 0.05456599, 0.07275465,\n",
        "       0.09094331, 0.10913197, 0.12732063, 0.1455093 , 0.16369796,\n",
        "       0.18188662, 0.20007528, 0.21826395, 0.23645261, 0.25464127,\n",
        "       0.27282993, 0.29101859, 0.30920726, 0.32739592, 0.34558458,\n",
@@ -457,7 +457,7 @@
        "       4.27433559, 4.29252425, 4.31071292, 4.32890158, 4.34709024,\n",
        "       4.3652789 , 4.38346757, 4.40165623, 4.41984489, 4.43803355,\n",
        "       4.45622221, 4.47441088, 4.49259954, 4.5107882 , 4.52897686,\n",
-       "       4.54716552])
        • x
          (x [bin-edge])
          float64
          cm
          0.0, 0.078, ..., 9.922, 10.0
          Values:
          array([ 0.      ,  0.078125,  0.15625 ,  0.234375,  0.3125  ,  0.390625,\n",
+       "       4.54716552])
        • x
          (x [bin-edge])
          float64
          cm
          0.0, 0.078, ..., 9.922, 10.0
          Values:
          array([ 0.      ,  0.078125,  0.15625 ,  0.234375,  0.3125  ,  0.390625,\n",
        "        0.46875 ,  0.546875,  0.625   ,  0.703125,  0.78125 ,  0.859375,\n",
        "        0.9375  ,  1.015625,  1.09375 ,  1.171875,  1.25    ,  1.328125,\n",
        "        1.40625 ,  1.484375,  1.5625  ,  1.640625,  1.71875 ,  1.796875,\n",
@@ -478,7 +478,7 @@
        "        8.4375  ,  8.515625,  8.59375 ,  8.671875,  8.75    ,  8.828125,\n",
        "        8.90625 ,  8.984375,  9.0625  ,  9.140625,  9.21875 ,  9.296875,\n",
        "        9.375   ,  9.453125,  9.53125 ,  9.609375,  9.6875  ,  9.765625,\n",
-       "        9.84375 ,  9.921875, 10.      ])
        • y
          (y [bin-edge])
          float64
          cm
          0.0, 0.078, ..., 9.922, 10.0
          Values:
          array([ 0.      ,  0.078125,  0.15625 ,  0.234375,  0.3125  ,  0.390625,\n",
+       "        9.84375 ,  9.921875, 10.      ])
        • y
          (y [bin-edge])
          float64
          cm
          0.0, 0.078, ..., 9.922, 10.0
          Values:
          array([ 0.      ,  0.078125,  0.15625 ,  0.234375,  0.3125  ,  0.390625,\n",
        "        0.46875 ,  0.546875,  0.625   ,  0.703125,  0.78125 ,  0.859375,\n",
        "        0.9375  ,  1.015625,  1.09375 ,  1.171875,  1.25    ,  1.328125,\n",
        "        1.40625 ,  1.484375,  1.5625  ,  1.640625,  1.71875 ,  1.796875,\n",
@@ -499,7 +499,7 @@
        "        8.4375  ,  8.515625,  8.59375 ,  8.671875,  8.75    ,  8.828125,\n",
        "        8.90625 ,  8.984375,  9.0625  ,  9.140625,  9.21875 ,  9.296875,\n",
        "        9.375   ,  9.453125,  9.53125 ,  9.609375,  9.6875  ,  9.765625,\n",
-       "        9.84375 ,  9.921875, 10.      ])
        • (wavelength, y, x)
          float64
          𝟙
          0.0, 0.0, ..., 0.0, 0.0
          σ = 0.0, 0.0, ..., 0.0, 0.0
          Values:
          array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        9.84375 ,  9.921875, 10.      ])
        • (wavelength, y, x)
          float64
          𝟙
          0.0, 0.0, ..., 0.0, 0.0
          σ = 0.0, 0.0, ..., 0.0, 0.0
          Values:
          array([[[0., 0., 0., ..., 0., 0., 0.],\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
        "        ...,\n",
@@ -595,7 +595,7 @@
        "        ...,\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
        "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]]])
        • no-ob
          (wavelength, y, x)
          bool
          True, True, ..., True, True
          Values:
          array([[[ True,  True,  True, ...,  True,  True,  True],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]])
        • no-ob
          (wavelength, y, x)
          bool
          True, True, ..., True, True
          Values:
          array([[[ True,  True,  True, ...,  True,  True,  True],\n",
        "        [ True,  True,  True, ...,  True,  True,  True],\n",
        "        [ True,  True,  True, ...,  True,  True,  True],\n",
        "        ...,\n",
@@ -685,7 +685,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ca5965f126c4e83a4363d9443c8fbf0",
+       "model_id": "bae01f94901e48859099ee3fc6174b7b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -742,7 +742,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de39b352a52f426c81ff48112993ab80",
+       "model_id": "18274234663c43aaae2f6fab59e63def",
        "version_major": 2,
        "version_minor": 0
       },
@@ -784,6 +784,129 @@
     "normed_2d + normed_1d"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = normalized_img.data.values.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelengths = sc.midpoints(normalized_img.coords[\"wavelength\"]).values.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tifffile as tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tiff.imwrite(\"sample_data/iron.tiff\", data, imagej=True, metadata={\"wavelengths\": wavelengths.tolist()})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with tiff.TiffFile(\"sample_data/iron.tiff\") as f:\n",
+    "    read_data = f.asarray()\n",
+    "    read_wavelengths = f.imagej_metadata[\"wavelengths\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "read_wavelengths_values = json.loads(read_wavelengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "read_data = tiff.imread(\"sample_data/iron.tiff\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(250, 128, 128)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(read_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(250, 128, 128)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(image_stack)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1828,7 +1951,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/ProofOfConcept/Bragg_Fitting_STAP.ipynb b/ProofOfConcept/Bragg_Fitting_STAP.ipynb
new file mode 100644
index 0000000..b19e1db
--- /dev/null
+++ b/ProofOfConcept/Bragg_Fitting_STAP.ipynb
@@ -0,0 +1,1607 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load in open beam and sample images from SciTiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "DimensionError",
+     "evalue": "Invalid slice dimension: 'c': no coordinate for that dimension. Coordinates are (tof, source_position, sample_position, x, y, z, )",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mDimensionError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[62]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipp\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msc\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscitiff_helper\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_scitiff\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m sample_da = \u001b[43mload_scitiff\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msample_data/sample_5e11_mcstas.tiff\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      5\u001b[39m openbeam_da = load_scitiff(\u001b[33m\"\u001b[39m\u001b[33msample_data/open_beam_5e11_mcstas.tiff\u001b[39m\u001b[33m\"\u001b[39m, background=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\christianvedel\\EasyScienceSource\\imaging-lib\\ProofOfConcept\\scitiff_helper.py:51\u001b[39m, in \u001b[36mload_scitiff\u001b[39m\u001b[34m(filename, background, show)\u001b[39m\n\u001b[32m     43\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mess\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mimaging\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnormalize\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m apply_threshold_to_background_image\n\u001b[32m     45\u001b[39m da = (\n\u001b[32m     46\u001b[39m     scitiff.load_scitiff(filename)[\u001b[33m\"\u001b[39m\u001b[33mimage\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     47\u001b[39m     \u001b[38;5;66;03m#.astype(int)\u001b[39;00m\n\u001b[32m     48\u001b[39m     .astype(\u001b[38;5;28mfloat\u001b[39m)\n\u001b[32m     49\u001b[39m     .rename_dims({\u001b[33m\"\u001b[39m\u001b[33mt\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mtof\u001b[39m\u001b[33m\"\u001b[39m})\n\u001b[32m     50\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m variances = \u001b[43mda\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mc\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscalar\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvariances\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m.data.values\n\u001b[32m     53\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m background:\n\u001b[32m     54\u001b[39m     da = apply_threshold_to_background_image(\n\u001b[32m     55\u001b[39m         background=da, background_threshold=sc.scalar(\u001b[32m1.0\u001b[39m, unit=\u001b[33m\"\u001b[39m\u001b[33mcounts\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     56\u001b[39m     )\n",
+      "\u001b[31mDimensionError\u001b[39m: Invalid slice dimension: 'c': no coordinate for that dimension. Coordinates are (tof, source_position, sample_position, x, y, z, )"
+     ]
+    }
+   ],
+   "source": [
+    "import scipp as sc\n",
+    "from scitiff_helper import load_scitiff\n",
+    "\n",
+    "sample_da = load_scitiff(\"sample_data/sample_5e11_mcstas (2).tiff\")\n",
+    "openbeam_da = load_scitiff(\"sample_data/open_beam_5e11_mcstas (2).tiff\", background=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalize data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalize sample image by open beam and replace 0 counts in OB with 1 to avoid NaN's"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (76.53 MB)
            • tof: 500
            • y: 100
            • x: 100
            • c
              ()
              string
              data
              Values:
              'data'
            • pixel_position
              (x, y)
              vector3
              m
              [-0.12375    -0.12375    60.50008608], [-0.12375    -0.12375    60.50008608], ..., [ 0.12375     0.12375    60.50008608], [ 0.12375     0.12375    60.50008608]
              Values:
              array([[[-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608]],\n",
+       "\n",
+       "       [[ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608]]], shape=(100, 100, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (tof [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01529735, 0.01553592, 0.01577448, 0.01601304, 0.0162516 ,\n",
+       "       0.01649017, 0.01672873, 0.01696729, 0.01720586, 0.01744442,\n",
+       "       0.01768298, 0.01792155, 0.01816011, 0.01839867, 0.01863724,\n",
+       "       0.0188758 , 0.01911436, 0.01935293, 0.01959149, 0.01983005,\n",
+       "       0.02006862, 0.02030718, 0.02054574, 0.0207843 , 0.02102287,\n",
+       "       0.02126143, 0.02149999, 0.02173856, 0.02197712, 0.02221568,\n",
+       "       0.02245425, 0.02269281, 0.02293137, 0.02316994, 0.0234085 ,\n",
+       "       0.02364706, 0.02388563, 0.02412419, 0.02436275, 0.02460132,\n",
+       "       0.02483988, 0.02507844, 0.025317  , 0.02555557, 0.02579413,\n",
+       "       0.02603269, 0.02627126, 0.02650982, 0.02674838, 0.02698695,\n",
+       "       0.02722551, 0.02746407, 0.02770264, 0.0279412 , 0.02817976,\n",
+       "       0.02841833, 0.02865689, 0.02889545, 0.02913402, 0.02937258,\n",
+       "       0.02961114, 0.0298497 , 0.03008827, 0.03032683, 0.03056539,\n",
+       "       0.03080396, 0.03104252, 0.03128108, 0.03151965, 0.03175821,\n",
+       "       0.03199677, 0.03223534, 0.0324739 , 0.03271246, 0.03295103,\n",
+       "       0.03318959, 0.03342815, 0.03366672, 0.03390528, 0.03414384,\n",
+       "       0.0343824 , 0.03462097, 0.03485953, 0.03509809, 0.03533666,\n",
+       "       0.03557522, 0.03581378, 0.03605235, 0.03629091, 0.03652947,\n",
+       "       0.03676804, 0.0370066 , 0.03724516, 0.03748373, 0.03772229,\n",
+       "       0.03796085, 0.03819942, 0.03843798, 0.03867654, 0.0389151 ,\n",
+       "       0.03915367, 0.03939223, 0.03963079, 0.03986936, 0.04010792,\n",
+       "       0.04034648, 0.04058505, 0.04082361, 0.04106217, 0.04130074,\n",
+       "       0.0415393 , 0.04177786, 0.04201643, 0.04225499, 0.04249355,\n",
+       "       0.04273212, 0.04297068, 0.04320924, 0.0434478 , 0.04368637,\n",
+       "       0.04392493, 0.04416349, 0.04440206, 0.04464062, 0.04487918,\n",
+       "       0.04511775, 0.04535631, 0.04559487, 0.04583344, 0.046072  ,\n",
+       "       0.04631056, 0.04654913, 0.04678769, 0.04702625, 0.04726482,\n",
+       "       0.04750338, 0.04774194, 0.0479805 , 0.04821907, 0.04845763,\n",
+       "       0.04869619, 0.04893476, 0.04917332, 0.04941188, 0.04965045,\n",
+       "       0.04988901, 0.05012757, 0.05036614, 0.0506047 , 0.05084326,\n",
+       "       0.05108183, 0.05132039, 0.05155895, 0.05179752, 0.05203608,\n",
+       "       0.05227464, 0.0525132 , 0.05275177, 0.05299033, 0.05322889,\n",
+       "       0.05346746, 0.05370602, 0.05394458, 0.05418315, 0.05442171,\n",
+       "       0.05466027, 0.05489884, 0.0551374 , 0.05537596, 0.05561453,\n",
+       "       0.05585309, 0.05609165, 0.05633022, 0.05656878, 0.05680734,\n",
+       "       0.0570459 , 0.05728447, 0.05752303, 0.05776159, 0.05800016,\n",
+       "       0.05823872, 0.05847728, 0.05871585, 0.05895441, 0.05919297,\n",
+       "       0.05943154, 0.0596701 , 0.05990866, 0.06014723, 0.06038579,\n",
+       "       0.06062435, 0.06086292, 0.06110148, 0.06134004, 0.06157861,\n",
+       "       0.06181717, 0.06205573, 0.06229429, 0.06253286, 0.06277142,\n",
+       "       0.06300998, 0.06324855, 0.06348711, 0.06372567, 0.06396424,\n",
+       "       0.0642028 , 0.06444136, 0.06467993, 0.06491849, 0.06515705,\n",
+       "       0.06539562, 0.06563418, 0.06587274, 0.06611131, 0.06634987,\n",
+       "       0.06658843, 0.06682699, 0.06706556, 0.06730412, 0.06754268,\n",
+       "       0.06778125, 0.06801981, 0.06825837, 0.06849694, 0.0687355 ,\n",
+       "       0.06897406, 0.06921263, 0.06945119, 0.06968975, 0.06992832,\n",
+       "       0.07016688, 0.07040544, 0.07064401, 0.07088257, 0.07112113,\n",
+       "       0.07135969, 0.07159826, 0.07183682, 0.07207538, 0.07231395,\n",
+       "       0.07255251, 0.07279107, 0.07302964, 0.0732682 , 0.07350676,\n",
+       "       0.07374533, 0.07398389, 0.07422245, 0.07446102, 0.07469958,\n",
+       "       0.07493814, 0.07517671, 0.07541527, 0.07565383, 0.07589239,\n",
+       "       0.07613096, 0.07636952, 0.07660808, 0.07684665, 0.07708521,\n",
+       "       0.07732377, 0.07756234, 0.0778009 , 0.07803946, 0.07827803,\n",
+       "       0.07851659, 0.07875515, 0.07899372, 0.07923228, 0.07947084,\n",
+       "       0.07970941, 0.07994797, 0.08018653, 0.08042509, 0.08066366,\n",
+       "       0.08090222, 0.08114078, 0.08137935, 0.08161791, 0.08185647,\n",
+       "       0.08209504, 0.0823336 , 0.08257216, 0.08281073, 0.08304929,\n",
+       "       0.08328785, 0.08352642, 0.08376498, 0.08400354, 0.08424211,\n",
+       "       0.08448067, 0.08471923, 0.08495779, 0.08519636, 0.08543492,\n",
+       "       0.08567348, 0.08591205, 0.08615061, 0.08638917, 0.08662774,\n",
+       "       0.0868663 , 0.08710486, 0.08734343, 0.08758199, 0.08782055,\n",
+       "       0.08805912, 0.08829768, 0.08853624, 0.08877481, 0.08901337,\n",
+       "       0.08925193, 0.08949049, 0.08972906, 0.08996762, 0.09020618,\n",
+       "       0.09044475, 0.09068331, 0.09092187, 0.09116044, 0.091399  ,\n",
+       "       0.09163756, 0.09187613, 0.09211469, 0.09235325, 0.09259182,\n",
+       "       0.09283038, 0.09306894, 0.09330751, 0.09354607, 0.09378463,\n",
+       "       0.09402319, 0.09426176, 0.09450032, 0.09473888, 0.09497745,\n",
+       "       0.09521601, 0.09545457, 0.09569314, 0.0959317 , 0.09617026,\n",
+       "       0.09640883, 0.09664739, 0.09688595, 0.09712452, 0.09736308,\n",
+       "       0.09760164, 0.09784021, 0.09807877, 0.09831733, 0.09855589,\n",
+       "       0.09879446, 0.09903302, 0.09927158, 0.09951015, 0.09974871,\n",
+       "       0.09998727, 0.10022584, 0.1004644 , 0.10070296, 0.10094153,\n",
+       "       0.10118009, 0.10141865, 0.10165722, 0.10189578, 0.10213434,\n",
+       "       0.10237291, 0.10261147, 0.10285003, 0.10308859, 0.10332716,\n",
+       "       0.10356572, 0.10380428, 0.10404285, 0.10428141, 0.10451997,\n",
+       "       0.10475854, 0.1049971 , 0.10523566, 0.10547423, 0.10571279,\n",
+       "       0.10595135, 0.10618992, 0.10642848, 0.10666704, 0.10690561,\n",
+       "       0.10714417, 0.10738273, 0.10762129, 0.10785986, 0.10809842,\n",
+       "       0.10833698, 0.10857555, 0.10881411, 0.10905267, 0.10929124,\n",
+       "       0.1095298 , 0.10976836, 0.11000693, 0.11024549, 0.11048405,\n",
+       "       0.11072262, 0.11096118, 0.11119974, 0.11143831, 0.11167687,\n",
+       "       0.11191543, 0.11215399, 0.11239256, 0.11263112, 0.11286968,\n",
+       "       0.11310825, 0.11334681, 0.11358537, 0.11382394, 0.1140625 ,\n",
+       "       0.11430106, 0.11453963, 0.11477819, 0.11501675, 0.11525532,\n",
+       "       0.11549388, 0.11573244, 0.11597101, 0.11620957, 0.11644813,\n",
+       "       0.11668669, 0.11692526, 0.11716382, 0.11740238, 0.11764095,\n",
+       "       0.11787951, 0.11811807, 0.11835664, 0.1185952 , 0.11883376,\n",
+       "       0.11907233, 0.11931089, 0.11954945, 0.11978802, 0.12002658,\n",
+       "       0.12026514, 0.12050371, 0.12074227, 0.12098083, 0.1212194 ,\n",
+       "       0.12145796, 0.12169652, 0.12193508, 0.12217365, 0.12241221,\n",
+       "       0.12265077, 0.12288934, 0.1231279 , 0.12336646, 0.12360503,\n",
+       "       0.12384359, 0.12408215, 0.12432072, 0.12455928, 0.12479784,\n",
+       "       0.12503641, 0.12527497, 0.12551353, 0.1257521 , 0.12599066,\n",
+       "       0.12622922, 0.12646778, 0.12670635, 0.12694491, 0.12718347,\n",
+       "       0.12742204, 0.1276606 , 0.12789916, 0.12813773, 0.12837629,\n",
+       "       0.12861485, 0.12885342, 0.12909198, 0.12933054, 0.12956911,\n",
+       "       0.12980767, 0.13004623, 0.1302848 , 0.13052336, 0.13076192,\n",
+       "       0.13100048, 0.13123905, 0.13147761, 0.13171617, 0.13195474,\n",
+       "       0.1321933 , 0.13243186, 0.13267043, 0.13290899, 0.13314755,\n",
+       "       0.13338612, 0.13362468, 0.13386324, 0.13410181, 0.13434037,\n",
+       "       0.13457893])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • z
              ()
              float64
              m
              60.50008607945975
              Values:
              array(60.50008608)
            • (tof, y, x)
              float64
              𝟙
              1.910e-07, 3.223e-35, ..., 0.522, 0.750
              σ = 0.000, 5.677e-18, ..., 0.890, 0.708
              Values:
              array([[[1.90958104e-07, 3.22307206e-35, 0.00000000e+00, ...,\n",
+       "         5.59635353e+00, 2.61284236e-04, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.09200635e-10, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [8.89200878e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [6.38951498e-24, 0.00000000e+00, 2.61551941e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.01266697e-16],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.12041968e-22, ...,\n",
+       "         0.00000000e+00, 6.13249729e-11, 1.02997002e+02],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.03374390e+02, 0.00000000e+00, 4.54903145e+01]],\n",
+       "\n",
+       "       [[0.00000000e+00, 3.42579443e-38, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 2.91616002e-23],\n",
+       "        [0.00000000e+00, 4.05064732e-01, 4.17823106e-01, ...,\n",
+       "         2.99957027e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [5.61191034e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 2.78917694e+01, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 2.66153278e-21, ...,\n",
+       "         0.00000000e+00, 5.99764855e-07, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.09137054e+02, ...,\n",
+       "         3.35921720e-02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 1.03730469e+02, 4.62731272e-02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[9.36448723e-02, 1.72879195e+00, 0.00000000e+00, ...,\n",
+       "         1.02489926e-02, 0.00000000e+00, 6.52526694e-07],\n",
+       "        [9.08751540e-34, 7.90317112e-14, 4.33704071e-12, ...,\n",
+       "         1.28664146e-15, 8.67571615e-09, 7.71208794e-28],\n",
+       "        [9.23123240e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.33274040e-01, 4.96043101e-19, 7.74029315e-01],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.47227882e-26],\n",
+       "        [4.05311188e+02, 0.00000000e+00, 2.03482147e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [2.96646332e+02, 3.73204875e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[2.72658829e-01, 1.68612805e+00, 1.60494039e+00, ...,\n",
+       "         8.56200294e-01, 6.48226816e-01, 5.20221714e-01],\n",
+       "        [6.96467873e-01, 1.20298449e+00, 1.33739470e+00, ...,\n",
+       "         6.34719932e-01, 5.26167794e-01, 1.92603162e+00],\n",
+       "        [9.80406697e-01, 3.78364033e-01, 2.79610355e+00, ...,\n",
+       "         9.49193043e-01, 7.60097928e-01, 1.65405877e+00],\n",
+       "        ...,\n",
+       "        [1.50455437e+00, 1.88143149e+00, 1.15166644e+00, ...,\n",
+       "         6.77036114e-01, 1.57227120e+00, 3.56905104e-01],\n",
+       "        [2.03500019e+00, 1.36866861e+00, 6.03077076e-01, ...,\n",
+       "         1.47086450e+00, 1.88439441e+00, 1.45516913e+00],\n",
+       "        [1.16466336e+00, 9.41187024e-01, 1.48405279e+00, ...,\n",
+       "         1.42418543e+00, 4.21042768e-01, 2.46566176e+00]],\n",
+       "\n",
+       "       [[2.32255931e-01, 2.68303152e-01, 1.51824650e+00, ...,\n",
+       "         2.20600080e+00, 2.91543841e-01, 1.38574004e+00],\n",
+       "        [1.24602795e+00, 3.05399321e-01, 6.99547263e-01, ...,\n",
+       "         5.59010008e-01, 3.81593166e-01, 5.45219919e-01],\n",
+       "        [5.64873438e-01, 8.26161233e-01, 7.33745357e-01, ...,\n",
+       "         1.93860790e-01, 5.40371239e-01, 2.00541665e-01],\n",
+       "        ...,\n",
+       "        [9.56452096e-01, 2.54903312e-05, 3.38563037e-01, ...,\n",
+       "         7.03194050e-01, 2.82889847e-01, 7.26524070e-01],\n",
+       "        [9.79660344e-01, 7.99461926e-01, 4.25342931e-01, ...,\n",
+       "         2.15346833e+00, 4.56075881e-01, 8.52963423e-01],\n",
+       "        [6.81917182e-01, 3.41369875e-01, 3.08308752e-01, ...,\n",
+       "         1.99508320e-01, 1.11537314e+00, 6.62059051e-01]],\n",
+       "\n",
+       "       [[5.31180620e-01, 8.53313594e-01, 6.49021955e-01, ...,\n",
+       "         5.35556113e-01, 4.42021227e-01, 6.83188807e-01],\n",
+       "        [7.47625835e-01, 2.01160642e-01, 1.10232321e+00, ...,\n",
+       "         4.21086993e-01, 3.75775541e-01, 1.60313157e+00],\n",
+       "        [4.21645202e-01, 2.14089489e+00, 2.75984696e-01, ...,\n",
+       "         1.05419686e+00, 2.76401073e-01, 8.37948121e-01],\n",
+       "        ...,\n",
+       "        [2.01912797e+00, 1.62284045e+00, 4.51914116e-01, ...,\n",
+       "         1.22240813e+00, 1.23230254e+00, 1.92746162e+00],\n",
+       "        [1.49114273e+00, 5.14608100e-01, 9.50042427e-01, ...,\n",
+       "         8.83945625e-01, 9.55997804e-01, 1.65743995e+00],\n",
+       "        [5.69020134e-01, 1.56214983e+00, 1.74702907e+00, ...,\n",
+       "         2.30999351e+00, 5.21566677e-01, 7.49675023e-01]]],\n",
+       "      shape=(500, 100, 100))

              Variances (σ²):
              array([[[1.90958105e-07, 3.22307206e-35, 0.00000000e+00, ...,\n",
+       "         1.19090018e+01, 2.61284236e-04, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.09200635e-10, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [8.89200878e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [1.75736813e-26, 0.00000000e+00, 2.61551941e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.01266697e-16],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.12041968e-22, ...,\n",
+       "         0.00000000e+00, 6.13249729e-11, 1.02997002e+02],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.03374390e+02, 0.00000000e+00, 4.54903145e+01]],\n",
+       "\n",
+       "       [[0.00000000e+00, 3.42579443e-38, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 2.91616002e-23],\n",
+       "        [0.00000000e+00, 4.06113560e-01, 4.17823106e-01, ...,\n",
+       "         2.99957027e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [5.61191034e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 2.78917694e+01, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 2.66153278e-21, ...,\n",
+       "         0.00000000e+00, 6.37498396e-09, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.09137054e+02, ...,\n",
+       "         3.35921720e-02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 6.21553568e+03, 4.62731272e-02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[9.36493373e-02, 1.72879195e+00, 0.00000000e+00, ...,\n",
+       "         1.02489926e-02, 0.00000000e+00, 6.52526694e-07],\n",
+       "        [9.08751540e-34, 7.90317112e-14, 4.33704071e-12, ...,\n",
+       "         1.28664146e-15, 8.67571615e-09, 7.71208794e-28],\n",
+       "        [9.23123240e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.25173939e-03, 4.96043101e-19, 7.74029315e-01],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.47227882e-26],\n",
+       "        [4.05311188e+02, 0.00000000e+00, 2.03482147e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [2.96646332e+02, 3.73204875e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[8.29682656e-02, 2.78473323e+00, 2.87777576e+00, ...,\n",
+       "         6.76428034e-01, 4.17953055e-01, 4.25238732e-01],\n",
+       "        [7.99895235e-01, 1.48433450e+00, 2.39726540e+00, ...,\n",
+       "         7.03101873e-01, 4.89960461e-01, 3.45448874e+00],\n",
+       "        [8.75485090e-01, 1.48026792e-01, 8.02070034e+00, ...,\n",
+       "         7.39601277e-01, 5.70172328e-01, 2.64430534e+00],\n",
+       "        ...,\n",
+       "        [2.60919164e+00, 5.32892763e+00, 1.49731370e+00, ...,\n",
+       "         5.86360706e-01, 1.88604041e+00, 1.21746874e-01],\n",
+       "        [3.26486428e+00, 1.15037798e+00, 2.72973100e-01, ...,\n",
+       "         2.09087222e+00, 4.79857884e+00, 2.67460654e+00],\n",
+       "        [1.48225680e+00, 1.53944899e+00, 2.92003817e+00, ...,\n",
+       "         3.16127570e+00, 2.21262198e-01, 4.81238456e+00]],\n",
+       "\n",
+       "       [[1.30361714e-01, 8.89494491e-02, 2.86794784e+00, ...,\n",
+       "         7.05571159e+00, 3.69606155e-01, 2.71260801e+00],\n",
+       "        [2.25714898e+00, 1.44986459e-01, 3.27323073e-01, ...,\n",
+       "         2.91549791e-01, 1.97662072e-01, 3.29251327e-01],\n",
+       "        [8.16893698e-01, 6.09351074e-01, 6.02419517e-01, ...,\n",
+       "         1.21871167e-01, 7.76750107e-01, 6.66794103e-02],\n",
+       "        ...,\n",
+       "        [1.42528682e+00, 7.25699209e-06, 1.66105681e-01, ...,\n",
+       "         6.20117586e-01, 1.31758142e-01, 9.60537218e-01],\n",
+       "        [1.10386112e+00, 6.09685746e-01, 2.35107231e-01, ...,\n",
+       "         6.57646277e+00, 2.96955660e-01, 6.44656631e-01],\n",
+       "        [5.41076077e-01, 1.67816940e-01, 1.37460037e-01, ...,\n",
+       "         5.10837481e-02, 1.23573587e+00, 5.34181615e-01]],\n",
+       "\n",
+       "       [[7.61670952e-01, 8.32537391e-01, 4.63323136e-01, ...,\n",
+       "         3.04582364e-01, 2.62486671e-01, 5.55696964e-01],\n",
+       "        [7.52704902e-01, 1.27181150e-01, 1.49224920e+00, ...,\n",
+       "         4.26699645e-01, 2.78857975e-01, 2.87861542e+00],\n",
+       "        [2.66874936e-01, 5.51491639e+00, 2.71410076e-01, ...,\n",
+       "         1.16839121e+00, 3.01234375e-01, 8.39820953e-01],\n",
+       "        ...,\n",
+       "        [5.46750173e+00, 2.85808244e+00, 3.43620095e-01, ...,\n",
+       "         1.75200192e+00, 2.35520354e+00, 4.67660239e+00],\n",
+       "        [2.70148894e+00, 2.83603542e-01, 1.79938174e+00, ...,\n",
+       "         1.00855790e+00, 1.06009425e+00, 2.45994179e+00],\n",
+       "        [3.68192689e-01, 3.88096746e+00, 4.60334317e+00, ...,\n",
+       "         4.90351277e+00, 7.92428528e-01, 5.01761240e-01]]],\n",
+       "      shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[tof:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "  c                          string          ()  \"data\"\n",
+       "* pixel_position            vector3              [m]  (x, y)  [(-0.12375, -0.12375, 60.5001), (-0.12375, -0.12375, 60.5001), ..., (0.12375, 0.12375, 60.5001), (0.12375, 0.12375, 60.5001)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (tof [bin-edge])  [0.0152974, 0.0155359, ..., 0.13434, 0.134579]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "  z                         float64              [m]  ()  60.5001\n",
+       "Data:\n",
+       "                            float64  [dimensionless]  (tof, y, x)  [1.90958e-07, 3.22307e-35, ..., 0.521567, 0.749675]  [1.90958e-07, 3.22307e-35, ..., 0.792429, 0.501761]\n"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized = sample_da / openbeam_da\n",
+    "normalized"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cutting off a large spike at very small ToFs which are irrelevant to the spectral region of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_time_bound = 0.02 * sc.Unit('s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff_data = normalized['tof', lower_time_bound:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the normalized image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8861af5d4ca4e54ab3fa0403d264991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cutoff_data.mean('tof').plot(aspect='equal', title=\"Normalized Image\", grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Region Of Interest (ROI) selector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scitiff_helper import make_rectangle_tool\n",
+    "roi_container = set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the widget below, choose the empty white box at the bottom left and drag and drop to define your ROI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4eeb45770ff42a0ab7a5e2cbfc3555b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBar(children=(InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(…"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_rectangle_tool(cutoff_data, roi_container=roi_container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the defined ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROI=roi_container.pop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the ROI and its spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Slice the normalized image with the defined ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi = ROI.slice_dataarray(cutoff_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the sliced image to ensure it is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "399ac157f5bd4afb8d414bef26540cdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_roi.mean('tof').plot(\n",
+    "    aspect='equal', title='Normalized Region of interest', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the spectrum of the ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fddf152b082440e295f75aac0841ebfe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_roi.mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Full Spectrum of normalized ROI', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert time-of-flight to wavelength using scippneutron"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, define the conversion graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "total_straight_beam_length_no_scatter(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)->Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "wavelength_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "energy_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "position\n",
+       "\n",
+       "position\n",
+       "\n",
+       "\n",
+       "\n",
+       "position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)->wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)->energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scippneutron.conversion import graph\n",
+    "\n",
+    "# Prepare graph for transformation\n",
+    "PLAIN_GRAPH = {**graph.beamline.beamline(False), **graph.tof.kinematic(\"tof\")}\n",
+    "sc.show_graph(PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rename the \"sample_position\" to \"position\" in the meta-data, as this is was the conversion graph expects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi_renamed = normalized_roi.copy(deep=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi_renamed.coords['position'] = normalized_roi_renamed.coords['sample_position']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Optional) add the flight-path because it isn't present in the SciTiff (Can be used to overwrite the calculated lengths, if they're wrong)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#normalized_roi.coords['Ltotal'] = sc.scalar(60.5, unit='m')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Converting the spectrum to wavelength using the conversion graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelength_transformed = normalized_roi_renamed.transform_coords('wavelength', graph=PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the converted spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8c248eb7a9f4e6ab393bdc7236d0305",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wavelength_transformed.mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Full Spectrum of ROI in wavelength', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cut off lower and upper bounds of spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_bound = 1.4 * sc.Unit('Å')\n",
+    "upper_bound = 6.0 * sc.Unit('Å')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sliced_spectrum = wavelength_transformed.mean('x').mean('y')['wavelength', lower_bound:upper_bound]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8345f6674ad4f9ba2038b4f86f33c67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sliced_spectrum.plot(aspect='equal', title='Sliced Spectrum of ROI in wavelength', grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the transmission and wavelengths for fitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelengths = sc.midpoints(sliced_spectrum.coords['wavelength']).values\n",
+    "transmissions = sliced_spectrum.values\n",
+    "variances = sliced_spectrum.variances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Physical model with Ncrystal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NCrystal\n",
+    "import NCrystal.plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We initalize the model\n",
+    "mat = NCrystal.NCMATComposer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We set the model parameters to Iron (slightly off on purpose)\n",
+    "mat.set_cellsg_cubic(2.90)\n",
+    "mat.set_atompos([   ('Fe',0,0,0),\n",
+    "                    ('Fe',0.5, 0.5, 0.5),\n",
+    "                    ])\n",
+    "mat.set_dyninfo_debyetemp(\"Fe\", debye_temp=356)\n",
+    "mat.set_default_temperature(250)\n",
+    "mat.refine_crystal_structure(quiet=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------------------------------------------\n",
+      "------------------------------------   NCrystal Material Info   ------------------------------------\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Data source: \n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Density : 7.60478 g/cm3, 0.0820042 atoms/Aa^3\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mole): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mass): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atom data:\n",
+      "   Fe = Fe(cohSL=9.45fm cohXS=11.2221barn incXS=0.4barn absXS=2.56barn mass=55.8472u Z=26)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Averaged quantities:\n",
+      "   Atomic mass               : 55.8472u\n",
+      "   Absorption XS at 2200m/s  : 2.56 barn\n",
+      "   Free scattering XS        : 11.2134 barn\n",
+      "   Scattering length density : 7.7494 10^-6/Aa^2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Temperature : 250 kelvin\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "State of matter: Solid (crystalline)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Space group number      : 229\n",
+      "Lattice spacings   [Aa] : 2.9 2.9 2.9\n",
+      "Lattice angles    [deg] : 90 90 90\n",
+      "Unit cell volume [Aa^3] : 24.389\n",
+      "Atoms / unit cell       : 2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atoms in unit cell (total 2):\n",
+      "     2 Fe atoms [T_Debye=356K, MSD=0.00542405Aa^2]\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atomic coordinates:\n",
+      "     Fe            0            0            0\n",
+      "     Fe          1/2          1/2          1/2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Dynamic info for Fe (100%):\n",
+      "   type: S(alpha,beta) [from VDOSDebye]\n",
+      "   VDOS E_max: 30.6777 meV\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL info type: SymEqvGroup\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL planes (d_lower = 0.1 Aa, d_upper = inf Aa):\n",
+      "  H   K   L  d_hkl[Aa] Mult. FSquared[barn]\n",
+      "  1   1   0    2.05061   12     3.39475\n",
+      "  2   0   0       1.45    6     3.22621\n",
+      "  2   1   1    1.18392   24     3.06603\n",
+      "  2   2   0     1.0253   12     2.91381\n",
+      "  3   1   0   0.917061   24     2.76914\n",
+      "  2   2   2   0.837158    8     2.63166\n",
+      "  3   2   1   0.775058   48       2.501\n",
+      "  4   0   0      0.725    6     2.37683\n",
+      "  4   1   1   0.683537   24     2.25882\n",
+      "  3   3   0   0.683537   12     2.25882\n",
+      "  (some planes left out for brevity, increase verbosity to show all)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Absorption process (objects):\n",
+      "  AbsOOV(sigma_2200=2.56barn)\n",
+      "Scattering process (objects):\n",
+      "  ProcComposition(3 components, isotropic)\n",
+      "     |-- ElIncScatter(nelements=1;max_contrib=0.4barn)\n",
+      "     |-- PowderBragg(nplanes=231;2dmax=4.10122Aa;max_contrib=14.4027barn)\n",
+      "     \\-- SABScatter(nalpha=50;nbeta=100;Emax=0.5eV;T=250K;M=55.8472u;sigma_free=11.2134barn)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daa98a65e94448b397f8a8a4732a5789",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
          \n",
+       "                
          \n",
+       "                    Figure\n",
+       "                
          \n",
+       "                \n",
+       "            
          \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Information about the model can be viewed with this method\n",
+    "mat.inspect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NCMAT v7\n",
+      "# Autogenerated by NCMATComposer\n",
+      "#\n",
+      "# Fe (cubic, SG-229)\n",
+      "#\n",
+      "# Atoms per unit cell: 2xFe\n",
+      "#\n",
+      "# NOTICE: crystal structure was verified with spglib to be self-consistent.\n",
+      "#\n",
+      "@CELL\n",
+      "  cubic 2.9\n",
+      "@SPACEGROUP\n",
+      "  229\n",
+      "@ATOMPOSITIONS\n",
+      "  Fe 0 0 0\n",
+      "  Fe 1/2 1/2 1/2\n",
+      "@TEMPERATURE\n",
+      "  default 250\n",
+      "@DYNINFO\n",
+      "  element Fe\n",
+      "  fraction 1\n",
+      "  type vdosdebye\n",
+      "  debye_temp 356\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And a short summary can be viewed by printing\n",
+    "print(mat())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing Data sets and initial model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculating the simulated spectrum using this model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )\n",
+    "absorptions = scatter.absorption.xsect(wl=wavelengths)\n",
+    "scattered = scatter.scatter.xsect(wl=wavelengths)\n",
+    "# Using an arbitrary scale constant of 0.05 to scale cross sections and absorptions. Depends on width of material in a real model.\n",
+    "simulated = np.exp(-0.1*(absorptions+scattered))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89b4c3cf84c44e65b290f8d2511b9312",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
          \n",
+       "                
          \n",
+       "                    Figure\n",
+       "                
          \n",
+       "                \n",
+       "            
          \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='MCStas Data')\n",
+    "plt.plot(wavelengths, simulated, label=\"BCC Iron Model\")\n",
+    "plt.title(\"Pre-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bragg-edge fitting with Ncrystal and EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from easyscience.Objects.variable import Parameter\n",
+    "from easyscience.Objects.ObjectClasses import BaseObj\n",
+    "from easyscience.fitting import Fitter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up the fitting models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fitting parameters and their initial values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lattice_constant =  Parameter(name=\"Lattice constant\",          value=2.85, unit=\"angstrom\",    min=0,  max=4,    fixed=False)\n",
+    "debye_temperature = Parameter(name=\"Debye temperature\",         value=300,  unit=\"kelvin\",      min=250,  max=800,  fixed=False)\n",
+    "temperature =       Parameter(name=\"Temperature\",               value=300,  unit=\"kelvin\",      min=0,  max=600,  fixed=False)\n",
+    "intensity_scale =   Parameter(name=\"Intensity scale factor\",    value=0.05,                      fixed=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fit functions to go into EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iron_bragg(wavelength):\n",
+    "    # Initalize the NCrystal material composer\n",
+    "    material = NCrystal.NCMATComposer()\n",
+    "    # Set the material parameters with the fitting variables\n",
+    "    material.set_cellsg_cubic(lattice_constant.value)\n",
+    "    material.set_atompos([  ('Fe',0,0,0),\n",
+    "                            ('Fe',0.5, 0.5, 0.5),\n",
+    "                            ])\n",
+    "    material.set_dyninfo_debyetemp(\"Fe\", debye_temp=debye_temperature.value)\n",
+    "    material.set_default_temperature(temperature.value)\n",
+    "\n",
+    "    # Identify the space group from atomic positions\n",
+    "    material.refine_crystal_structure(quiet=True)\n",
+    "\n",
+    "    #print(\"lattice constant:\", lattice_constant.value)\n",
+    "    scatter = material.load()\n",
+    "\n",
+    "    # Create scattering spectrum\n",
+    "    spectrum = np.exp(-intensity_scale.value*(scatter.scatter.xsect(wl=wavelength) + scatter.absorption.xsect(wl=wavelength)))\n",
+    "    return spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience container for the fitting parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iron_spectrum = BaseObj(\n",
+    "                        name='Iron transmission spectrum', \n",
+    "                        lattice_constant=lattice_constant, \n",
+    "                        debye_temperature=debye_temperature,\n",
+    "                        temperature=temperature,\n",
+    "                        intensity_scale=intensity_scale,\n",
+    "                        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience fitting objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitter = Fitter(iron_spectrum, iron_bragg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EasyScience can fit data with a variety of different minimizers. To list the available methods, use the \"available_minimizers\" method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LMFit',\n",
+       " 'LMFit_leastsq',\n",
+       " 'LMFit_powell',\n",
+       " 'LMFit_cobyla',\n",
+       " 'LMFit_differential_evolution',\n",
+       " 'LMFit_scipy_least_squares',\n",
+       " 'Bumps',\n",
+       " 'Bumps_simplex',\n",
+       " 'Bumps_newton',\n",
+       " 'Bumps_lm',\n",
+       " 'DFO',\n",
+       " 'DFO_leastsq']"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitter.available_minimizers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the fits in this notebook, we will use \"LMFit_leastsq\" which is a standard leastsquares fitting method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minimizer should be set with enum LMFit_leastsq\n"
+     ]
+    }
+   ],
+   "source": [
+    "fitter.switch_minimizer(\"LMFit_leastsq\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing the actual Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for LMFit_leastsq\n",
+    "results = fitter.fit(x=wavelengths, y=transmissions, \n",
+    "                     #weights=1/np.sqrt(variances), \n",
+    "                     minimizer_kwargs={\"epsfcn\":0.005,\n",
+    "                                       \"ftol\":1.49012e-15,\n",
+    "                                       \"xtol\":1.49012e-15,\n",
+    "                                       },\n",
+    "                    #max_nfev=10000\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print the fitted values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lattice_constant)\n",
+    "print(debye_temperature)\n",
+    "print(temperature)\n",
+    "print(intensity_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing the fitted model and data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to calculate the spectra with the fitted model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulated = iron_bragg(wavelengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7137c8d65b9841c28485398b5b320ed1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
          \n",
+       "                
          \n",
+       "                    Figure\n",
+       "                
          \n",
+       "                \n",
+       "            
          \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='MCStas Data')\n",
+    "plt.plot(wavelengths, simulated, label=\"Iron Model fit to MCStas data\")\n",
+    "plt.xlabel(\"Wavelength (Å)\")\n",
+    "plt.ylabel(\"Transmission\")\n",
+    "plt.title(\"Post-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "easyimaging",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ProofOfConcept/IMAT_fitting.ipynb b/ProofOfConcept/IMAT_fitting.ipynb
new file mode 100644
index 0000000..9f0ccec
--- /dev/null
+++ b/ProofOfConcept/IMAT_fitting.ipynb
@@ -0,0 +1,9446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Timepix data reduction @ISIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scippnexus as snx\n",
+    "import scipp as sc\n",
+    "\n",
+    "\n",
+    "def _load_isis_nexus(filename) -> sc.DataArray:\n",
+    "    sample_events = snx.load(\n",
+    "        filename,\n",
+    "        root='entry/instrument/event_mode_detectors/timepix3/timepix3_events',\n",
+    "    )\n",
+    "    sample_events.bins.coords[\"event_time_zero\"] = sc.bins_like(\n",
+    "        sample_events, fill_value=sample_events.coords[\"event_time_zero\"]\n",
+    "    )\n",
+    "    da: sc.DataArray = sample_events.bins.concat().value\n",
+    "    da = da.drop_coords(\"event_time_zero\")\n",
+    "    da.coords[\"tof\"] = da.coords.pop(\"event_time_offset\")\n",
+    "    return da.astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (662.12 MB)
            • event: 43392376
            • event_id
              (event)
              int32
              537160, 119222, ..., 931270, 701989
              Values:
              array([537160, 119222, 699775, ..., 181694, 931270, 701989],\n",
+       "      shape=(43392376,), dtype=int32)
            • tof
              (event)
              int32
              ns
              696013, 711116, ..., 38740816, 38744577
              Values:
              array([  696013,   711116,   716629, ..., 38735874, 38740816, 38744577],\n",
+       "      shape=(43392376,), dtype=int32)
            • (event)
              float64
              counts
              1.0, 1.0, ..., 1.0, 1.0
              Values:
              array([1., 1., 1., ..., 1., 1., 1.], shape=(43392376,))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[event:43392376, ]\n",
+       "Coordinates:\n",
+       "* event_id                    int32          (event)  [537160, 119222, ..., 931270, 701989]\n",
+       "* tof                         int32             [ns]  (event)  [696013, 711116, ..., 38740816, 38744577]\n",
+       "Data:\n",
+       "                            float64         [counts]  (event)  [1, 1, ..., 1, 1]\n"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# From login server, /ess/data/ymir/2024/597001/raw/597001_00011386.hdf\n",
+    "sample_da = _load_isis_nexus(\"sample_data/597001_00011386.hdf\")\n",
+    "sample_da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (1.41 GB)
            • event: 94761986
            • event_id
              (event)
              int32
              743151, 498287, ..., 580152, 730514
              Values:
              array([743151, 498287, 500270, ..., 766673, 580152, 730514],\n",
+       "      shape=(94761986,), dtype=int32)
            • tof
              (event)
              int32
              ns
              256782, 269046, ..., 46554050, 46554679
              Values:
              array([  256782,   269046,   337136, ..., 46553075, 46554050, 46554679],\n",
+       "      shape=(94761986,), dtype=int32)
            • (event)
              float64
              counts
              1.0, 1.0, ..., 1.0, 1.0
              Values:
              array([1., 1., 1., ..., 1., 1., 1.], shape=(94761986,))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[event:94761986, ]\n",
+       "Coordinates:\n",
+       "* event_id                    int32          (event)  [743151, 498287, ..., 580152, 730514]\n",
+       "* tof                         int32             [ns]  (event)  [256782, 269046, ..., 46554050, 46554679]\n",
+       "Data:\n",
+       "                            float64         [counts]  (event)  [1, 1, ..., 1, 1]\n"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# From login server, /ess/data/ymir/2024/597001/raw/597001_00011385.hdf\n",
+    "open_beam_da = _load_isis_nexus(\"sample_data/597001_00011385.hdf\")  # Open Beam\n",
+    "open_beam_da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "DEFAULT_RESOLUTION = np.array((1024, 1024))\n",
+    "DEFAULT_TOF_BIN_EDGES = sc.linspace(\n",
+    "    dim='tof', start=100.0, stop=99999999.0, num=101, unit=\"ns\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def _reduce_isis_data(\n",
+    "    da: sc.DataArray,\n",
+    "    virtual_resolution: np.ndarray = DEFAULT_RESOLUTION,\n",
+    "    tof_bin_edges: sc.Variable = DEFAULT_TOF_BIN_EDGES,\n",
+    "    show_result: bool = False,\n",
+    "    background: bool = False,\n",
+    ") -> sc.DataArray:\n",
+    "    image_ids = sc.arange(\n",
+    "        dim='event_id', start=0, stop=virtual_resolution.prod(), unit=None\n",
+    "    )\n",
+    "    arbitrary_x_coord = sc.arange(dim='x', start=0, stop=virtual_resolution[0])\n",
+    "    arbitrary_y_coord = sc.arange(dim='y', start=0, stop=virtual_resolution[1])\n",
+    "    reduced = (\n",
+    "        da.bin(tof=tof_bin_edges)\n",
+    "        .group(image_ids)\n",
+    "        .hist()\n",
+    "        .fold(dim='event_id', dims=['x', 'y'], shape=virtual_resolution)\n",
+    "    )\n",
+    "    reduced.coords.update({\"x\": arbitrary_x_coord, \"y\": arbitrary_y_coord})\n",
+    "    reduced.coords.pop(\"event_id\")\n",
+    "    if show_result:\n",
+    "        display(reduced)\n",
+    "    if background:\n",
+    "        from ess.imaging.normalize import apply_threshold_to_background_image\n",
+    "\n",
+    "        reduced.name = \"OpenBeam\"\n",
+    "        reduced = apply_threshold_to_background_image(\n",
+    "            reduced, background_threshold=sc.scalar(1.0, unit=\"counts\")\n",
+    "        )\n",
+    "    else:\n",
+    "        from ess.imaging.normalize import apply_threshold_to_sample_images\n",
+    "\n",
+    "        reduced.name = \"Sample\"\n",
+    "        reduced = apply_threshold_to_sample_images(\n",
+    "            reduced, sample_threshold=sc.scalar(0.0, unit=\"counts\")\n",
+    "        )\n",
+    "    # We're adding variances after reduction since some zeros are removed\n",
+    "    reduced.data.variances = reduced.data.values\n",
+    "    return reduced\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (800.02 MB)
            • tof: 100
            • x: 1024
            • y: 1024
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              Values:
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              (y)
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              𝟙
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              Values:
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              float64
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+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[tof:100, x:1024, y:1024, ]\n",
+       "Coordinates:\n",
+       "* tof                       float64             [ns]  (tof [bin-edge])  [100, 1.0001e+06, ..., 9.9e+07, 1e+08]\n",
+       "* x                           int64  [dimensionless]  (x)  [0, 1, ..., 1022, 1023]\n",
+       "* y                           int64  [dimensionless]  (y)  [0, 1, ..., 1022, 1023]\n",
+       "Data:\n",
+       "                            float64         [counts]  (tof, x, y)  [0, 0, ..., 0, 0]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+       "\n",
+       "\n",
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+       "Show/Hide attributes\n",
+       "\n",
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          scipp.DataArray (800.02 MB)
            • tof: 100
            • x: 1024
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              Values:
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              (x)
              int64
              𝟙
              0, 1, ..., 1022, 1023
              Values:
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            • y
              (y)
              int64
              𝟙
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              Values:
              array([   0,    1,    2, ..., 1021, 1022, 1023], shape=(1024,))
            • (tof, x, y)
              float64
              counts
              0.0, 0.0, ..., 0.0, 0.0
              Values:
              array([[[0., 0., 0., ..., 0., 0., 0.],\n",
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+       "* y                           int64  [dimensionless]  (y)  [0, 1, ..., 1022, 1023]\n",
+       "Data:\n",
+       "                            float64         [counts]  (tof, x, y)  [0, 0, ..., 0, 0]\n"
+      ]
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+     "output_type": "display_data"
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+      "text/html": [
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            • tof: 100
            • x: 1024
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+       "       9.60000030e+07, 9.70000020e+07, 9.80000010e+07, 9.90000000e+07,\n",
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              (x)
              int64
              𝟙
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            • y
              (y)
              int64
              𝟙
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              Values:
              array([   0,    1,    2, ..., 1021, 1022, 1023], shape=(1024,))
            • (tof, x, y)
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              𝟙
              0.0, 0.0, ..., 0.0, 0.0
              σ = 0.0, 0.0, ..., 0.0, 0.0
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+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], shape=(100, 1024, 1024))

              Variances (σ²):
              array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]], shape=(100, 1024, 1024))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[tof:100, x:1024, y:1024, ]\n",
+       "Coordinates:\n",
+       "* tof                       float64             [ns]  (tof [bin-edge])  [100, 1.0001e+06, ..., 9.9e+07, 1e+08]\n",
+       "* x                           int64  [dimensionless]  (x)  [0, 1, ..., 1022, 1023]\n",
+       "* y                           int64  [dimensionless]  (y)  [0, 1, ..., 1022, 1023]\n",
+       "Data:\n",
+       "  Normalized                float64  [dimensionless]  (tof, x, y)  [0, 0, ..., 0, 0]  [0, 0, ..., 0, 0]\n"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_sample = _reduce_isis_data(sample_da, show_result=True)\n",
+    "reduced_ob = _reduce_isis_data(open_beam_da, show_result=True, background=True)\n",
+    "normalized = reduced_sample / reduced_ob\n",
+    "normalized.name = \"Normalized\"\n",
+    "normalized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-01T13:46:44.635441\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.10.1, https://matplotlib.org/\n",
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+       "   \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_sample.mean('tof').plot(aspect='equal', title=\"Sample Image\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-01T13:46:45.476258\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.10.1, https://matplotlib.org/\n",
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+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
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+   "source": [
+    "reduced_ob.mean('tof').plot(aspect='equal', title=\"Open Beam Image\")"
+   ]
+  },
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+   "execution_count": 10,
+   "metadata": {},
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+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized.mean('tof').plot(aspect='equal', title='Normalized Sample Image')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
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+       " \n",
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+       "    2025-04-01T13:46:46.541895\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.10.1, https://matplotlib.org/\n",
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+       "    \n",
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+       "    \n",
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+       "    \n",
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+       "    \n",
+       "    \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       " \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized['x', 400:600]['y', 400:600].mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Normalized Sample Image (Zoomed)', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scitiff\n",
+    "\n",
+    "scitiff.save_scitiff(\n",
+    "    reduced_sample.rename_dims({'tof': \"t\"}).astype('float32'), \"sample_data/sample.tiff\"\n",
+    ")\n",
+    "scitiff.save_scitiff(\n",
+    "    reduced_ob.rename_dims({'tof': \"t\"}).astype('float32'), \"sample_data/open_beam.tiff\"\n",
+    ")\n",
+    "scitiff.save_scitiff(\n",
+    "    normalized.rename_dims({'tof': \"t\"}).astype('float32'), \"sample_data/normalized.tiff\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-01T13:46:50.024802\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.10.1, https://matplotlib.org/\n",
+       "     \n",
+       "    \n",
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+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       " \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Loading\n",
+    "normalized_image_stack = scitiff.load_scitiff(\"sample_data/normalized.tiff\", squeeze=True)\n",
+    "normalized_image_da = normalized_image_stack['image']\n",
+    "normalized_image_da['x', 400:600]['y', 400:600].mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Normalized Sample Image (Loaded)', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "total_straight_beam_length_no_scatter(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)->Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "wavelength_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "energy_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "position\n",
+       "\n",
+       "position\n",
+       "\n",
+       "\n",
+       "\n",
+       "position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)->wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)->energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scippneutron.conversion import graph\n",
+    "\n",
+    "\n",
+    "PLAIN_GRAPH = {**graph.beamline.beamline(False), **graph.tof.kinematic(\"tof\")}\n",
+    "sc.show_graph(PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apparently it is about 56 meters?\n",
+    "normalized.coords['Ltotal'] = sc.scalar(56.0, unit='m')\n",
+    "wavelength_transformed = normalized.transform_coords('wavelength', graph=PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-01T13:46:50.655090\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.10.1, https://matplotlib.org/\n",
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+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       " \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wavelength_transformed['x', 400:600]['y', 400:600].mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Normalized Sample Image (Zoomed)', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_bound = 1.5 * sc.Unit('Å')\n",
+    "upper_bound = 5.0 * sc.Unit('Å')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "region = wavelength_transformed['x', 400:600]['y', 400:600]\n",
+    "spectrum = region.mean('x').mean('y')\n",
+    "sliced_spectrum = spectrum['wavelength', lower_bound:upper_bound]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelengths = sc.midpoints(sliced_spectrum.coords['wavelength']).values\n",
+    "transmissions = sliced_spectrum.values\n",
+    "transmissions_variances = sliced_spectrum.variances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Physical model with Ncrystal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NCrystal\n",
+    "import NCrystal.plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We initalize the model\n",
+    "mat = NCrystal.NCMATComposer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We set the model parameters to Iron (slightly off on purpose)\n",
+    "mat.set_cellsg_cubic(2.90)\n",
+    "mat.set_atompos([   ('Fe',0,0,0),\n",
+    "                    ('Fe',0.5, 0.5, 0.5),\n",
+    "                    ])\n",
+    "mat.set_dyninfo_debyetemp(\"Fe\", debye_temp=356)\n",
+    "mat.set_default_temperature(250)\n",
+    "mat.refine_crystal_structure(quiet=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------------------------------------------\n",
+      "------------------------------------   NCrystal Material Info   ------------------------------------\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Data source: \n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Density : 7.60478 g/cm3, 0.0820042 atoms/Aa^3\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mole): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mass): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atom data:\n",
+      "   Fe = Fe(cohSL=9.45fm cohXS=11.2221barn incXS=0.4barn absXS=2.56barn mass=55.8472u Z=26)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Averaged quantities:\n",
+      "   Atomic mass               : 55.8472u\n",
+      "   Absorption XS at 2200m/s  : 2.56 barn\n",
+      "   Free scattering XS        : 11.2134 barn\n",
+      "   Scattering length density : 7.7494 10^-6/Aa^2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Temperature : 250 kelvin\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "State of matter: Solid (crystalline)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Space group number      : 229\n",
+      "Lattice spacings   [Aa] : 2.9 2.9 2.9\n",
+      "Lattice angles    [deg] : 90 90 90\n",
+      "Unit cell volume [Aa^3] : 24.389\n",
+      "Atoms / unit cell       : 2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atoms in unit cell (total 2):\n",
+      "     2 Fe atoms [T_Debye=356K, MSD=0.00542405Aa^2]\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atomic coordinates:\n",
+      "     Fe            0            0            0\n",
+      "     Fe          1/2          1/2          1/2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Dynamic info for Fe (100%):\n",
+      "   type: S(alpha,beta) [from VDOSDebye]\n",
+      "   VDOS E_max: 30.6777 meV\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL info type: SymEqvGroup\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL planes (d_lower = 0.1 Aa, d_upper = inf Aa):\n",
+      "  H   K   L  d_hkl[Aa] Mult. FSquared[barn]\n",
+      "  1   1   0    2.05061   12     3.39475\n",
+      "  2   0   0       1.45    6     3.22621\n",
+      "  2   1   1    1.18392   24     3.06603\n",
+      "  2   2   0     1.0253   12     2.91381\n",
+      "  3   1   0   0.917061   24     2.76914\n",
+      "  2   2   2   0.837158    8     2.63166\n",
+      "  3   2   1   0.775058   48       2.501\n",
+      "  4   0   0      0.725    6     2.37683\n",
+      "  4   1   1   0.683537   24     2.25882\n",
+      "  3   3   0   0.683537   12     2.25882\n",
+      "  (some planes left out for brevity, increase verbosity to show all)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Absorption process (objects):\n",
+      "  AbsOOV(sigma_2200=2.56barn)\n",
+      "Scattering process (objects):\n",
+      "  ProcComposition(3 components, isotropic)\n",
+      "     |-- ElIncScatter(nelements=1;max_contrib=0.4barn)\n",
+      "     |-- PowderBragg(nplanes=231;2dmax=4.10122Aa;max_contrib=14.4027barn)\n",
+      "     \\-- SABScatter(nalpha=50;nbeta=100;Emax=0.5eV;T=250K;M=55.8472u;sigma_free=11.2134barn)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Information about the model can be viewed with this method\n",
+    "mat.inspect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NCMAT v7\n",
+      "# Autogenerated by NCMATComposer\n",
+      "#\n",
+      "# Fe (cubic, SG-229)\n",
+      "#\n",
+      "# Atoms per unit cell: 2xFe\n",
+      "#\n",
+      "# NOTICE: crystal structure was verified with spglib to be self-consistent.\n",
+      "#\n",
+      "@CELL\n",
+      "  cubic 2.9\n",
+      "@SPACEGROUP\n",
+      "  229\n",
+      "@ATOMPOSITIONS\n",
+      "  Fe 0 0 0\n",
+      "  Fe 1/2 1/2 1/2\n",
+      "@TEMPERATURE\n",
+      "  default 250\n",
+      "@DYNINFO\n",
+      "  element Fe\n",
+      "  fraction 1\n",
+      "  type vdosdebye\n",
+      "  debye_temp 356\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And a short summary can be viewed by printing\n",
+    "print(mat())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculating the simulated spectrum using this model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )\n",
+    "absorptions = scatter.absorption.xsect(wl=wavelengths)\n",
+    "scattered = scatter.scatter.xsect(wl=wavelengths)\n",
+    "# Using an arbitrary scale constant of 0.05 to scale cross sections and absorptions. Depends on width of material in a real model.\n",
+    "simulated = np.exp(-0.05*(absorptions+scattered))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading reference Iron slab data from J-PARC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('Iron_slab.dat', 'r') as file:\n",
+    "    slab_data = file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slab_wavelengths = np.asarray([float(slab_data[i].split(\"\\t\")[1]) for i in range(len(slab_data)-1)])\n",
+    "slab_transmissions = np.asarray([float(slab_data[i].split(\"\\t\")[2]) for i in range(len(slab_data)-1)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(4.99449)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slab_wavelengths[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing Data sets and initial model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='IMAT Iron')\n",
+    "#plt.plot(wavelengths, transmissions, label=\"Data\")\n",
+    "plt.plot(wavelengths, simulated, label=\"Iron Model\")\n",
+    "plt.plot(slab_wavelengths, slab_transmissions, label='Raden Iron Slab')\n",
+    "plt.title(\"Pre-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bragg-edge fitting with Ncrystal and EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from easyscience.Objects.variable import Parameter\n",
+    "from easyscience.Objects.ObjectClasses import BaseObj\n",
+    "from easyscience.fitting import Fitter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up the fitting models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fitting parameters and their initial values \\\n",
+    "We're defining 2 sets of parameters because we want to fit a model to both sets of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lattice_constant =  Parameter(name=\"Lattice constant\",          value=2.85, unit=\"angstrom\",    min=0,  max=4,    fixed=False)\n",
+    "debye_temperature = Parameter(name=\"Debye temperature\",         value=300,  unit=\"kelvin\",      min=100,  max=800,  fixed=False)\n",
+    "temperature =       Parameter(name=\"Temperature\",               value=300,  unit=\"kelvin\",      min=0,  max=600,  fixed=False)\n",
+    "intensity_scale =   Parameter(name=\"Intensity scale factor\",    value=0.05,                      fixed=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lattice_constant_slab =  Parameter(name=\"Lattice constant\",          value=2.85, unit=\"angstrom\",    min=0,  max=4,    fixed=False)\n",
+    "debye_temperature_slab = Parameter(name=\"Debye temperature\",         value=300,  unit=\"kelvin\",      min=100,  max=800,  fixed=False)\n",
+    "temperature_slab =       Parameter(name=\"Temperature\",               value=300,  unit=\"kelvin\",      min=0,  max=600,  fixed=False)\n",
+    "intensity_scale_slab =   Parameter(name=\"Intensity scale factor\",    value=0.05,                      fixed=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fit functions to go into EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iron_bragg(wavelength):\n",
+    "    # Initalize the NCrystal material composer\n",
+    "    material = NCrystal.NCMATComposer()\n",
+    "    # Set the material parameters with the fitting variables\n",
+    "    material.set_cellsg_cubic(lattice_constant.value)\n",
+    "    material.set_atompos([  ('Fe',0,0,0),\n",
+    "                            ('Fe',0.5, 0.5, 0.5),\n",
+    "                            ])\n",
+    "    material.set_dyninfo_debyetemp(\"Fe\", debye_temp=debye_temperature.value)\n",
+    "    material.set_default_temperature(temperature.value)\n",
+    "\n",
+    "    # Identify the space group from atomic positions\n",
+    "    material.refine_crystal_structure(quiet=True)\n",
+    "\n",
+    "    #print(\"lattice constant:\", lattice_constant.value)\n",
+    "    scatter = material.load()\n",
+    "\n",
+    "    # Create scattering spectrum\n",
+    "    spectrum = np.exp(-intensity_scale.value*(scatter.scatter.xsect(wl=wavelength) + scatter.absorption.xsect(wl=wavelength)))\n",
+    "    return spectrum\n",
+    "\n",
+    "def iron_bragg_slab(wavelength):\n",
+    "    # Initalize the NCrystal material composer\n",
+    "    material = NCrystal.NCMATComposer()\n",
+    "    # Set the material parameters with the fitting variables\n",
+    "    material.set_cellsg_cubic(lattice_constant_slab.value)\n",
+    "    material.set_atompos([  ('Fe',0,0,0),\n",
+    "                            ('Fe',0.5, 0.5, 0.5),\n",
+    "                            ])\n",
+    "    material.set_dyninfo_debyetemp(\"Fe\", debye_temp=debye_temperature_slab.value)\n",
+    "    material.set_default_temperature(temperature_slab.value)\n",
+    "\n",
+    "    # Identify the space group from atomic positions\n",
+    "    material.refine_crystal_structure(quiet=True)\n",
+    "\n",
+    "    #print(\"lattice constant:\", lattice_constant.value)\n",
+    "    scatter = material.load()\n",
+    "\n",
+    "    # Create scattering spectrum\n",
+    "    spectrum = np.exp(-intensity_scale_slab.value*(scatter.scatter.xsect(wl=wavelength) + scatter.absorption.xsect(wl=wavelength)))\n",
+    "    return spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience container for the fitting parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iron_spectrum = BaseObj(\n",
+    "                        name='Iron transmission spectrum', \n",
+    "                        lattice_constant=lattice_constant, \n",
+    "                        debye_temperature=debye_temperature,\n",
+    "                        temperature=temperature,\n",
+    "                        intensity_scale=intensity_scale,\n",
+    "                        )\n",
+    "iron_spectrum_slab = BaseObj(\n",
+    "                        name='Iron transmission spectrum slab', \n",
+    "                        lattice_constant_slab=lattice_constant_slab, \n",
+    "                        debye_temperature_slab=debye_temperature_slab,\n",
+    "                        temperature_slab=temperature_slab,\n",
+    "                        intensity_scale_slab=intensity_scale_slab,\n",
+    "                        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience fitting objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitter = Fitter(iron_spectrum, iron_bragg)\n",
+    "fitter_slab = Fitter(iron_spectrum_slab, iron_bragg_slab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EasyScience can fit data with a variety of different minimizers. To list the available methods, use the \"available_minimizers\" method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LMFit',\n",
+       " 'LMFit_leastsq',\n",
+       " 'LMFit_powell',\n",
+       " 'LMFit_cobyla',\n",
+       " 'LMFit_differential_evolution',\n",
+       " 'LMFit_scipy_least_squares',\n",
+       " 'Bumps',\n",
+       " 'Bumps_simplex',\n",
+       " 'Bumps_newton',\n",
+       " 'Bumps_lm',\n",
+       " 'DFO',\n",
+       " 'DFO_leastsq']"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitter.available_minimizers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the fits in this notebook, we will use \"LMFit_leastsq\" which is a standard leastsquares fitting method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minimizer should be set with enum LMFit_leastsq\n",
+      "minimizer should be set with enum LMFit_leastsq\n"
+     ]
+    }
+   ],
+   "source": [
+    "fitter.switch_minimizer(\"LMFit_leastsq\")\n",
+    "fitter_slab.switch_minimizer(\"LMFit_leastsq\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we fit the reduced Iron powder data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for LMFit_leastsq\n",
+    "results = fitter.fit(x=wavelengths, y=transmissions, \n",
+    "                     weights=1/np.sqrt(transmissions_variances), \n",
+    "                     minimizer_kwargs={\"epsfcn\":0.005,\n",
+    "                                       \"ftol\":1.49012e-15,\n",
+    "                                       \"xtol\":1.49012e-15,\n",
+    "                                       },\n",
+    "                    #max_nfev=10000\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print the fitted values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lattice_constant)\n",
+    "print(debye_temperature)\n",
+    "print(temperature)\n",
+    "print(intensity_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then fit the reference Iron slab data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for LMFit_leastsq\n",
+    "results_slab = fitter_slab.fit(x=slab_wavelengths, y=slab_transmissions, \n",
+    "                     #weights=1/np.sqrt(transmissions_variances), \n",
+    "                     minimizer_kwargs={\"epsfcn\":0.005,\n",
+    "                                       \"ftol\":1.49012e-15,\n",
+    "                                       \"xtol\":1.49012e-15,\n",
+    "                                       },\n",
+    "                    #max_nfev=10000\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lattice_constant_slab)\n",
+    "print(debye_temperature_slab)\n",
+    "print(temperature_slab)\n",
+    "print(intensity_scale_slab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing the fitted models and data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to calculate the spectras with the fitted models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulated = iron_bragg(wavelengths)\n",
+    "simulated_slab = iron_bragg_slab(slab_wavelengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "#plt.plot(wavelengths, transmissions, label='ISIS iMAT Iron')\n",
+    "#plt.plot(wavelengths, simulated, label=\"Iron Model fit to iMAT Iron\")\n",
+    "plt.plot(slab_wavelengths, slab_transmissions, label=\"Raden Iron Slab\")\n",
+    "plt.plot(slab_wavelengths, simulated_slab, label='Iron Model fit to Raden data')\n",
+    "plt.xlabel(\"Wavelength (Å)\")\n",
+    "plt.ylabel(\"Transmission\")\n",
+    "plt.title(\"Post-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "easyimaging",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ProofOfConcept/McStas_fitting.ipynb b/ProofOfConcept/McStas_fitting.ipynb
new file mode 100644
index 0000000..1c0509b
--- /dev/null
+++ b/ProofOfConcept/McStas_fitting.ipynb
@@ -0,0 +1,2553 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load in open beam and sample images from SciTiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          sample_data/sample_5e11_mcstas (2).tiff
          "
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (76.53 MB)
            • tof: 500
            • y: 100
            • x: 100
            • c
              ()
              string
              data
              Values:
              'data'
            • pixel_position
              (x, y)
              vector3
              m
              [-0.12375    -0.12375    60.50008608], [-0.12375    -0.12375    60.50008608], ..., [ 0.12375     0.12375    60.50008608], [ 0.12375     0.12375    60.50008608]
              Values:
              array([[[-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608]],\n",
+       "\n",
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+       "\n",
+       "       [[ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
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+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608]],\n",
+       "\n",
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+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        ...,\n",
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+       "\n",
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+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        ...,\n",
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            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
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            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (tof [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01529735, 0.01553592, 0.01577448, 0.01601304, 0.0162516 ,\n",
+       "       0.01649017, 0.01672873, 0.01696729, 0.01720586, 0.01744442,\n",
+       "       0.01768298, 0.01792155, 0.01816011, 0.01839867, 0.01863724,\n",
+       "       0.0188758 , 0.01911436, 0.01935293, 0.01959149, 0.01983005,\n",
+       "       0.02006862, 0.02030718, 0.02054574, 0.0207843 , 0.02102287,\n",
+       "       0.02126143, 0.02149999, 0.02173856, 0.02197712, 0.02221568,\n",
+       "       0.02245425, 0.02269281, 0.02293137, 0.02316994, 0.0234085 ,\n",
+       "       0.02364706, 0.02388563, 0.02412419, 0.02436275, 0.02460132,\n",
+       "       0.02483988, 0.02507844, 0.025317  , 0.02555557, 0.02579413,\n",
+       "       0.02603269, 0.02627126, 0.02650982, 0.02674838, 0.02698695,\n",
+       "       0.02722551, 0.02746407, 0.02770264, 0.0279412 , 0.02817976,\n",
+       "       0.02841833, 0.02865689, 0.02889545, 0.02913402, 0.02937258,\n",
+       "       0.02961114, 0.0298497 , 0.03008827, 0.03032683, 0.03056539,\n",
+       "       0.03080396, 0.03104252, 0.03128108, 0.03151965, 0.03175821,\n",
+       "       0.03199677, 0.03223534, 0.0324739 , 0.03271246, 0.03295103,\n",
+       "       0.03318959, 0.03342815, 0.03366672, 0.03390528, 0.03414384,\n",
+       "       0.0343824 , 0.03462097, 0.03485953, 0.03509809, 0.03533666,\n",
+       "       0.03557522, 0.03581378, 0.03605235, 0.03629091, 0.03652947,\n",
+       "       0.03676804, 0.0370066 , 0.03724516, 0.03748373, 0.03772229,\n",
+       "       0.03796085, 0.03819942, 0.03843798, 0.03867654, 0.0389151 ,\n",
+       "       0.03915367, 0.03939223, 0.03963079, 0.03986936, 0.04010792,\n",
+       "       0.04034648, 0.04058505, 0.04082361, 0.04106217, 0.04130074,\n",
+       "       0.0415393 , 0.04177786, 0.04201643, 0.04225499, 0.04249355,\n",
+       "       0.04273212, 0.04297068, 0.04320924, 0.0434478 , 0.04368637,\n",
+       "       0.04392493, 0.04416349, 0.04440206, 0.04464062, 0.04487918,\n",
+       "       0.04511775, 0.04535631, 0.04559487, 0.04583344, 0.046072  ,\n",
+       "       0.04631056, 0.04654913, 0.04678769, 0.04702625, 0.04726482,\n",
+       "       0.04750338, 0.04774194, 0.0479805 , 0.04821907, 0.04845763,\n",
+       "       0.04869619, 0.04893476, 0.04917332, 0.04941188, 0.04965045,\n",
+       "       0.04988901, 0.05012757, 0.05036614, 0.0506047 , 0.05084326,\n",
+       "       0.05108183, 0.05132039, 0.05155895, 0.05179752, 0.05203608,\n",
+       "       0.05227464, 0.0525132 , 0.05275177, 0.05299033, 0.05322889,\n",
+       "       0.05346746, 0.05370602, 0.05394458, 0.05418315, 0.05442171,\n",
+       "       0.05466027, 0.05489884, 0.0551374 , 0.05537596, 0.05561453,\n",
+       "       0.05585309, 0.05609165, 0.05633022, 0.05656878, 0.05680734,\n",
+       "       0.0570459 , 0.05728447, 0.05752303, 0.05776159, 0.05800016,\n",
+       "       0.05823872, 0.05847728, 0.05871585, 0.05895441, 0.05919297,\n",
+       "       0.05943154, 0.0596701 , 0.05990866, 0.06014723, 0.06038579,\n",
+       "       0.06062435, 0.06086292, 0.06110148, 0.06134004, 0.06157861,\n",
+       "       0.06181717, 0.06205573, 0.06229429, 0.06253286, 0.06277142,\n",
+       "       0.06300998, 0.06324855, 0.06348711, 0.06372567, 0.06396424,\n",
+       "       0.0642028 , 0.06444136, 0.06467993, 0.06491849, 0.06515705,\n",
+       "       0.06539562, 0.06563418, 0.06587274, 0.06611131, 0.06634987,\n",
+       "       0.06658843, 0.06682699, 0.06706556, 0.06730412, 0.06754268,\n",
+       "       0.06778125, 0.06801981, 0.06825837, 0.06849694, 0.0687355 ,\n",
+       "       0.06897406, 0.06921263, 0.06945119, 0.06968975, 0.06992832,\n",
+       "       0.07016688, 0.07040544, 0.07064401, 0.07088257, 0.07112113,\n",
+       "       0.07135969, 0.07159826, 0.07183682, 0.07207538, 0.07231395,\n",
+       "       0.07255251, 0.07279107, 0.07302964, 0.0732682 , 0.07350676,\n",
+       "       0.07374533, 0.07398389, 0.07422245, 0.07446102, 0.07469958,\n",
+       "       0.07493814, 0.07517671, 0.07541527, 0.07565383, 0.07589239,\n",
+       "       0.07613096, 0.07636952, 0.07660808, 0.07684665, 0.07708521,\n",
+       "       0.07732377, 0.07756234, 0.0778009 , 0.07803946, 0.07827803,\n",
+       "       0.07851659, 0.07875515, 0.07899372, 0.07923228, 0.07947084,\n",
+       "       0.07970941, 0.07994797, 0.08018653, 0.08042509, 0.08066366,\n",
+       "       0.08090222, 0.08114078, 0.08137935, 0.08161791, 0.08185647,\n",
+       "       0.08209504, 0.0823336 , 0.08257216, 0.08281073, 0.08304929,\n",
+       "       0.08328785, 0.08352642, 0.08376498, 0.08400354, 0.08424211,\n",
+       "       0.08448067, 0.08471923, 0.08495779, 0.08519636, 0.08543492,\n",
+       "       0.08567348, 0.08591205, 0.08615061, 0.08638917, 0.08662774,\n",
+       "       0.0868663 , 0.08710486, 0.08734343, 0.08758199, 0.08782055,\n",
+       "       0.08805912, 0.08829768, 0.08853624, 0.08877481, 0.08901337,\n",
+       "       0.08925193, 0.08949049, 0.08972906, 0.08996762, 0.09020618,\n",
+       "       0.09044475, 0.09068331, 0.09092187, 0.09116044, 0.091399  ,\n",
+       "       0.09163756, 0.09187613, 0.09211469, 0.09235325, 0.09259182,\n",
+       "       0.09283038, 0.09306894, 0.09330751, 0.09354607, 0.09378463,\n",
+       "       0.09402319, 0.09426176, 0.09450032, 0.09473888, 0.09497745,\n",
+       "       0.09521601, 0.09545457, 0.09569314, 0.0959317 , 0.09617026,\n",
+       "       0.09640883, 0.09664739, 0.09688595, 0.09712452, 0.09736308,\n",
+       "       0.09760164, 0.09784021, 0.09807877, 0.09831733, 0.09855589,\n",
+       "       0.09879446, 0.09903302, 0.09927158, 0.09951015, 0.09974871,\n",
+       "       0.09998727, 0.10022584, 0.1004644 , 0.10070296, 0.10094153,\n",
+       "       0.10118009, 0.10141865, 0.10165722, 0.10189578, 0.10213434,\n",
+       "       0.10237291, 0.10261147, 0.10285003, 0.10308859, 0.10332716,\n",
+       "       0.10356572, 0.10380428, 0.10404285, 0.10428141, 0.10451997,\n",
+       "       0.10475854, 0.1049971 , 0.10523566, 0.10547423, 0.10571279,\n",
+       "       0.10595135, 0.10618992, 0.10642848, 0.10666704, 0.10690561,\n",
+       "       0.10714417, 0.10738273, 0.10762129, 0.10785986, 0.10809842,\n",
+       "       0.10833698, 0.10857555, 0.10881411, 0.10905267, 0.10929124,\n",
+       "       0.1095298 , 0.10976836, 0.11000693, 0.11024549, 0.11048405,\n",
+       "       0.11072262, 0.11096118, 0.11119974, 0.11143831, 0.11167687,\n",
+       "       0.11191543, 0.11215399, 0.11239256, 0.11263112, 0.11286968,\n",
+       "       0.11310825, 0.11334681, 0.11358537, 0.11382394, 0.1140625 ,\n",
+       "       0.11430106, 0.11453963, 0.11477819, 0.11501675, 0.11525532,\n",
+       "       0.11549388, 0.11573244, 0.11597101, 0.11620957, 0.11644813,\n",
+       "       0.11668669, 0.11692526, 0.11716382, 0.11740238, 0.11764095,\n",
+       "       0.11787951, 0.11811807, 0.11835664, 0.1185952 , 0.11883376,\n",
+       "       0.11907233, 0.11931089, 0.11954945, 0.11978802, 0.12002658,\n",
+       "       0.12026514, 0.12050371, 0.12074227, 0.12098083, 0.1212194 ,\n",
+       "       0.12145796, 0.12169652, 0.12193508, 0.12217365, 0.12241221,\n",
+       "       0.12265077, 0.12288934, 0.1231279 , 0.12336646, 0.12360503,\n",
+       "       0.12384359, 0.12408215, 0.12432072, 0.12455928, 0.12479784,\n",
+       "       0.12503641, 0.12527497, 0.12551353, 0.1257521 , 0.12599066,\n",
+       "       0.12622922, 0.12646778, 0.12670635, 0.12694491, 0.12718347,\n",
+       "       0.12742204, 0.1276606 , 0.12789916, 0.12813773, 0.12837629,\n",
+       "       0.12861485, 0.12885342, 0.12909198, 0.12933054, 0.12956911,\n",
+       "       0.12980767, 0.13004623, 0.1302848 , 0.13052336, 0.13076192,\n",
+       "       0.13100048, 0.13123905, 0.13147761, 0.13171617, 0.13195474,\n",
+       "       0.1321933 , 0.13243186, 0.13267043, 0.13290899, 0.13314755,\n",
+       "       0.13338612, 0.13362468, 0.13386324, 0.13410181, 0.13434037,\n",
+       "       0.13457893])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • z
              ()
              float64
              m
              60.50008607945975
              Values:
              array(60.50008608)
            • (tof, y, x)
              float64
              counts
              1.910e-07, 3.223e-35, ..., 0.522, 1.960
              σ = 0.000, 5.677e-18, ..., 0.723, 1.400
              Values:
              array([[[1.90958104e-07, 3.22307206e-35, 0.00000000e+00, ...,\n",
+       "         5.59635353e+00, 2.61284236e-04, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.09200635e-10, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [8.89200878e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [2.32312746e-21, 0.00000000e+00, 2.61551941e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.01266697e-16],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.12041968e-22, ...,\n",
+       "         0.00000000e+00, 6.13249729e-11, 1.02997002e+02],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.03374390e+02, 0.00000000e+00, 4.54903145e+01]],\n",
+       "\n",
+       "       [[0.00000000e+00, 3.42579443e-38, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 2.91616002e-23],\n",
+       "        [0.00000000e+00, 4.05064732e-01, 4.17823106e-01, ...,\n",
+       "         2.99957027e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [5.61191034e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 2.78917694e+01, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 2.66153278e-21, ...,\n",
+       "         0.00000000e+00, 5.64265101e-05, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.09137054e+02, ...,\n",
+       "         3.35921720e-02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 1.81303909e+02, 4.62731272e-02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[9.36448723e-02, 1.72879195e+00, 0.00000000e+00, ...,\n",
+       "         1.02489926e-02, 0.00000000e+00, 6.52526694e-07],\n",
+       "        [9.08751540e-34, 7.90317112e-14, 4.33704071e-12, ...,\n",
+       "         1.28664146e-15, 8.67571615e-09, 7.71208794e-28],\n",
+       "        [9.23123240e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.98039818e+01, 4.96043101e-19, 7.74029315e-01],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.47227882e-26],\n",
+       "        [4.05311188e+02, 0.00000000e+00, 2.03482147e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [2.96646332e+02, 3.73204875e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[1.14035249e+00, 2.74235845e+00, 2.33162475e+00, ...,\n",
+       "         2.01165724e+00, 1.65707994e+00, 9.67500210e-01],\n",
+       "        [1.02876151e+00, 2.14782906e+00, 1.74395442e+00, ...,\n",
+       "         9.36675966e-01, 8.62362325e-01, 3.14211488e+00],\n",
+       "        [2.17429352e+00, 1.33304036e+00, 3.70026016e+00, ...,\n",
+       "         2.37446785e+00, 1.78348637e+00, 2.74600172e+00],\n",
+       "        ...,\n",
+       "        [2.17290258e+00, 1.91401482e+00, 1.90596783e+00, ...,\n",
+       "         1.31099558e+00, 3.37148070e+00, 1.41970193e+00],\n",
+       "        [3.84966111e+00, 3.85709524e+00, 2.13589644e+00, ...,\n",
+       "         2.55662346e+00, 1.88439441e+00, 1.94378603e+00],\n",
+       "        [1.98092365e+00, 9.41187024e-01, 1.87357461e+00, ...,\n",
+       "         1.55538011e+00, 1.13855064e+00, 2.46566176e+00]],\n",
+       "\n",
+       "       [[5.09899378e-01, 1.02643478e+00, 2.02400494e+00, ...,\n",
+       "         2.20600080e+00, 2.91543841e-01, 1.38574004e+00],\n",
+       "        [1.24602795e+00, 8.39753985e-01, 2.54091859e+00, ...,\n",
+       "         1.67099571e+00, 1.01778960e+00, 1.39510274e+00],\n",
+       "        [6.11245930e-01, 2.04550791e+00, 1.54944801e+00, ...,\n",
+       "         3.68156672e-01, 5.40371239e-01, 7.24093616e-01],\n",
+       "        ...,\n",
+       "        [1.25572169e+00, 8.95375852e-05, 9.23705280e-01, ...,\n",
+       "         1.35812724e+00, 7.79195845e-01, 9.48764563e-01],\n",
+       "        [1.72118402e+00, 1.88639307e+00, 1.09681106e+00, ...,\n",
+       "         2.22368407e+00, 1.01992118e+00, 2.09121752e+00],\n",
+       "        [1.44547153e+00, 9.31457698e-01, 9.04701889e-01, ...,\n",
+       "         9.34636056e-01, 2.12961793e+00, 1.36380088e+00]],\n",
+       "\n",
+       "       [[5.31180620e-01, 1.62092340e+00, 1.49920571e+00, ...,\n",
+       "         1.44600868e+00, 1.07337296e+00, 1.41376197e+00],\n",
+       "        [1.29775381e+00, 3.82176846e-01, 1.71189070e+00, ...,\n",
+       "         5.90530097e-01, 6.96661055e-01, 2.32407856e+00],\n",
+       "        [9.47060585e-01, 2.14089489e+00, 3.58087778e-01, ...,\n",
+       "         1.95387697e+00, 2.76401073e-01, 1.53667068e+00],\n",
+       "        ...,\n",
+       "        [2.25123215e+00, 2.41684484e+00, 8.62927258e-01, ...,\n",
+       "         1.89549088e+00, 1.43932641e+00, 1.92746162e+00],\n",
+       "        [2.05037761e+00, 1.41430104e+00, 9.50042427e-01, ...,\n",
+       "         1.45954883e+00, 1.68631101e+00, 1.65743995e+00],\n",
+       "        [1.37977612e+00, 1.61105323e+00, 1.74702907e+00, ...,\n",
+       "         2.30999351e+00, 5.22336721e-01, 1.95977569e+00]]],\n",
+       "      shape=(500, 100, 100))

              Variances (σ²):
              array([[[1.90958104e-07, 3.22307206e-35, 0.00000000e+00, ...,\n",
+       "         5.59635353e+00, 2.61284236e-04, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.09200635e-10, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [8.89200878e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [2.32312746e-21, 0.00000000e+00, 2.61551941e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.01266697e-16],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.12041968e-22, ...,\n",
+       "         0.00000000e+00, 6.13249729e-11, 1.02997002e+02],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.03374390e+02, 0.00000000e+00, 4.54903145e+01]],\n",
+       "\n",
+       "       [[0.00000000e+00, 3.42579443e-38, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 2.91616002e-23],\n",
+       "        [0.00000000e+00, 4.05064732e-01, 4.17823106e-01, ...,\n",
+       "         2.99957027e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [5.61191034e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 2.78917694e+01, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 2.66153278e-21, ...,\n",
+       "         0.00000000e+00, 5.64265101e-05, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.09137054e+02, ...,\n",
+       "         3.35921720e-02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 1.81303909e+02, 4.62731272e-02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[9.36448723e-02, 1.72879195e+00, 0.00000000e+00, ...,\n",
+       "         1.02489926e-02, 0.00000000e+00, 6.52526694e-07],\n",
+       "        [9.08751540e-34, 7.90317112e-14, 4.33704071e-12, ...,\n",
+       "         1.28664146e-15, 8.67571615e-09, 7.71208794e-28],\n",
+       "        [9.23123240e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.98039818e+01, 4.96043101e-19, 7.74029315e-01],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.47227882e-26],\n",
+       "        [4.05311188e+02, 0.00000000e+00, 2.03482147e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [2.96646332e+02, 3.73204875e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[1.14035249e+00, 2.74235845e+00, 2.33162475e+00, ...,\n",
+       "         2.01165724e+00, 1.65707994e+00, 9.67500210e-01],\n",
+       "        [1.02876151e+00, 2.14782906e+00, 1.74395442e+00, ...,\n",
+       "         9.36675966e-01, 8.62362325e-01, 3.14211488e+00],\n",
+       "        [2.17429352e+00, 1.33304036e+00, 3.70026016e+00, ...,\n",
+       "         2.37446785e+00, 1.78348637e+00, 2.74600172e+00],\n",
+       "        ...,\n",
+       "        [2.17290258e+00, 1.91401482e+00, 1.90596783e+00, ...,\n",
+       "         1.31099558e+00, 3.37148070e+00, 1.41970193e+00],\n",
+       "        [3.84966111e+00, 3.85709524e+00, 2.13589644e+00, ...,\n",
+       "         2.55662346e+00, 1.88439441e+00, 1.94378603e+00],\n",
+       "        [1.98092365e+00, 9.41187024e-01, 1.87357461e+00, ...,\n",
+       "         1.55538011e+00, 1.13855064e+00, 2.46566176e+00]],\n",
+       "\n",
+       "       [[5.09899378e-01, 1.02643478e+00, 2.02400494e+00, ...,\n",
+       "         2.20600080e+00, 2.91543841e-01, 1.38574004e+00],\n",
+       "        [1.24602795e+00, 8.39753985e-01, 2.54091859e+00, ...,\n",
+       "         1.67099571e+00, 1.01778960e+00, 1.39510274e+00],\n",
+       "        [6.11245930e-01, 2.04550791e+00, 1.54944801e+00, ...,\n",
+       "         3.68156672e-01, 5.40371239e-01, 7.24093616e-01],\n",
+       "        ...,\n",
+       "        [1.25572169e+00, 8.95375852e-05, 9.23705280e-01, ...,\n",
+       "         1.35812724e+00, 7.79195845e-01, 9.48764563e-01],\n",
+       "        [1.72118402e+00, 1.88639307e+00, 1.09681106e+00, ...,\n",
+       "         2.22368407e+00, 1.01992118e+00, 2.09121752e+00],\n",
+       "        [1.44547153e+00, 9.31457698e-01, 9.04701889e-01, ...,\n",
+       "         9.34636056e-01, 2.12961793e+00, 1.36380088e+00]],\n",
+       "\n",
+       "       [[5.31180620e-01, 1.62092340e+00, 1.49920571e+00, ...,\n",
+       "         1.44600868e+00, 1.07337296e+00, 1.41376197e+00],\n",
+       "        [1.29775381e+00, 3.82176846e-01, 1.71189070e+00, ...,\n",
+       "         5.90530097e-01, 6.96661055e-01, 2.32407856e+00],\n",
+       "        [9.47060585e-01, 2.14089489e+00, 3.58087778e-01, ...,\n",
+       "         1.95387697e+00, 2.76401073e-01, 1.53667068e+00],\n",
+       "        ...,\n",
+       "        [2.25123215e+00, 2.41684484e+00, 8.62927258e-01, ...,\n",
+       "         1.89549088e+00, 1.43932641e+00, 1.92746162e+00],\n",
+       "        [2.05037761e+00, 1.41430104e+00, 9.50042427e-01, ...,\n",
+       "         1.45954883e+00, 1.68631101e+00, 1.65743995e+00],\n",
+       "        [1.37977612e+00, 1.61105323e+00, 1.74702907e+00, ...,\n",
+       "         2.30999351e+00, 5.22336721e-01, 1.95977569e+00]]],\n",
+       "      shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[tof:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "  c                          string          ()  \"data\"\n",
+       "* pixel_position            vector3              [m]  (x, y)  [(-0.12375, -0.12375, 60.5001), (-0.12375, -0.12375, 60.5001), ..., (0.12375, 0.12375, 60.5001), (0.12375, 0.12375, 60.5001)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (tof [bin-edge])  [0.0152974, 0.0155359, ..., 0.13434, 0.134579]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "  z                         float64              [m]  ()  60.5001\n",
+       "Data:\n",
+       "                            float64         [counts]  (tof, y, x)  [1.90958e-07, 3.22307e-35, ..., 0.522337, 1.95978]  [1.90958e-07, 3.22307e-35, ..., 0.522337, 1.95978]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
          sample_data/open_beam_5e11_mcstas (2).tiff
          "
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (76.53 MB)
            • tof: 500
            • y: 100
            • x: 100
            • c
              ()
              string
              data
              Values:
              'data'
            • pixel_position
              (x, y)
              vector3
              m
              [-0.12375    -0.12375    60.50008608], [-0.12375    -0.12375    60.50008608], ..., [ 0.12375     0.12375    60.50008608], [ 0.12375     0.12375    60.50008608]
              Values:
              array([[[-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608]],\n",
+       "\n",
+       "       [[ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608]]], shape=(100, 100, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (tof [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01529735, 0.01553592, 0.01577448, 0.01601304, 0.0162516 ,\n",
+       "       0.01649017, 0.01672873, 0.01696729, 0.01720586, 0.01744442,\n",
+       "       0.01768298, 0.01792155, 0.01816011, 0.01839867, 0.01863724,\n",
+       "       0.0188758 , 0.01911436, 0.01935293, 0.01959149, 0.01983005,\n",
+       "       0.02006862, 0.02030718, 0.02054574, 0.0207843 , 0.02102287,\n",
+       "       0.02126143, 0.02149999, 0.02173856, 0.02197712, 0.02221568,\n",
+       "       0.02245425, 0.02269281, 0.02293137, 0.02316994, 0.0234085 ,\n",
+       "       0.02364706, 0.02388563, 0.02412419, 0.02436275, 0.02460132,\n",
+       "       0.02483988, 0.02507844, 0.025317  , 0.02555557, 0.02579413,\n",
+       "       0.02603269, 0.02627126, 0.02650982, 0.02674838, 0.02698695,\n",
+       "       0.02722551, 0.02746407, 0.02770264, 0.0279412 , 0.02817976,\n",
+       "       0.02841833, 0.02865689, 0.02889545, 0.02913402, 0.02937258,\n",
+       "       0.02961114, 0.0298497 , 0.03008827, 0.03032683, 0.03056539,\n",
+       "       0.03080396, 0.03104252, 0.03128108, 0.03151965, 0.03175821,\n",
+       "       0.03199677, 0.03223534, 0.0324739 , 0.03271246, 0.03295103,\n",
+       "       0.03318959, 0.03342815, 0.03366672, 0.03390528, 0.03414384,\n",
+       "       0.0343824 , 0.03462097, 0.03485953, 0.03509809, 0.03533666,\n",
+       "       0.03557522, 0.03581378, 0.03605235, 0.03629091, 0.03652947,\n",
+       "       0.03676804, 0.0370066 , 0.03724516, 0.03748373, 0.03772229,\n",
+       "       0.03796085, 0.03819942, 0.03843798, 0.03867654, 0.0389151 ,\n",
+       "       0.03915367, 0.03939223, 0.03963079, 0.03986936, 0.04010792,\n",
+       "       0.04034648, 0.04058505, 0.04082361, 0.04106217, 0.04130074,\n",
+       "       0.0415393 , 0.04177786, 0.04201643, 0.04225499, 0.04249355,\n",
+       "       0.04273212, 0.04297068, 0.04320924, 0.0434478 , 0.04368637,\n",
+       "       0.04392493, 0.04416349, 0.04440206, 0.04464062, 0.04487918,\n",
+       "       0.04511775, 0.04535631, 0.04559487, 0.04583344, 0.046072  ,\n",
+       "       0.04631056, 0.04654913, 0.04678769, 0.04702625, 0.04726482,\n",
+       "       0.04750338, 0.04774194, 0.0479805 , 0.04821907, 0.04845763,\n",
+       "       0.04869619, 0.04893476, 0.04917332, 0.04941188, 0.04965045,\n",
+       "       0.04988901, 0.05012757, 0.05036614, 0.0506047 , 0.05084326,\n",
+       "       0.05108183, 0.05132039, 0.05155895, 0.05179752, 0.05203608,\n",
+       "       0.05227464, 0.0525132 , 0.05275177, 0.05299033, 0.05322889,\n",
+       "       0.05346746, 0.05370602, 0.05394458, 0.05418315, 0.05442171,\n",
+       "       0.05466027, 0.05489884, 0.0551374 , 0.05537596, 0.05561453,\n",
+       "       0.05585309, 0.05609165, 0.05633022, 0.05656878, 0.05680734,\n",
+       "       0.0570459 , 0.05728447, 0.05752303, 0.05776159, 0.05800016,\n",
+       "       0.05823872, 0.05847728, 0.05871585, 0.05895441, 0.05919297,\n",
+       "       0.05943154, 0.0596701 , 0.05990866, 0.06014723, 0.06038579,\n",
+       "       0.06062435, 0.06086292, 0.06110148, 0.06134004, 0.06157861,\n",
+       "       0.06181717, 0.06205573, 0.06229429, 0.06253286, 0.06277142,\n",
+       "       0.06300998, 0.06324855, 0.06348711, 0.06372567, 0.06396424,\n",
+       "       0.0642028 , 0.06444136, 0.06467993, 0.06491849, 0.06515705,\n",
+       "       0.06539562, 0.06563418, 0.06587274, 0.06611131, 0.06634987,\n",
+       "       0.06658843, 0.06682699, 0.06706556, 0.06730412, 0.06754268,\n",
+       "       0.06778125, 0.06801981, 0.06825837, 0.06849694, 0.0687355 ,\n",
+       "       0.06897406, 0.06921263, 0.06945119, 0.06968975, 0.06992832,\n",
+       "       0.07016688, 0.07040544, 0.07064401, 0.07088257, 0.07112113,\n",
+       "       0.07135969, 0.07159826, 0.07183682, 0.07207538, 0.07231395,\n",
+       "       0.07255251, 0.07279107, 0.07302964, 0.0732682 , 0.07350676,\n",
+       "       0.07374533, 0.07398389, 0.07422245, 0.07446102, 0.07469958,\n",
+       "       0.07493814, 0.07517671, 0.07541527, 0.07565383, 0.07589239,\n",
+       "       0.07613096, 0.07636952, 0.07660808, 0.07684665, 0.07708521,\n",
+       "       0.07732377, 0.07756234, 0.0778009 , 0.07803946, 0.07827803,\n",
+       "       0.07851659, 0.07875515, 0.07899372, 0.07923228, 0.07947084,\n",
+       "       0.07970941, 0.07994797, 0.08018653, 0.08042509, 0.08066366,\n",
+       "       0.08090222, 0.08114078, 0.08137935, 0.08161791, 0.08185647,\n",
+       "       0.08209504, 0.0823336 , 0.08257216, 0.08281073, 0.08304929,\n",
+       "       0.08328785, 0.08352642, 0.08376498, 0.08400354, 0.08424211,\n",
+       "       0.08448067, 0.08471923, 0.08495779, 0.08519636, 0.08543492,\n",
+       "       0.08567348, 0.08591205, 0.08615061, 0.08638917, 0.08662774,\n",
+       "       0.0868663 , 0.08710486, 0.08734343, 0.08758199, 0.08782055,\n",
+       "       0.08805912, 0.08829768, 0.08853624, 0.08877481, 0.08901337,\n",
+       "       0.08925193, 0.08949049, 0.08972906, 0.08996762, 0.09020618,\n",
+       "       0.09044475, 0.09068331, 0.09092187, 0.09116044, 0.091399  ,\n",
+       "       0.09163756, 0.09187613, 0.09211469, 0.09235325, 0.09259182,\n",
+       "       0.09283038, 0.09306894, 0.09330751, 0.09354607, 0.09378463,\n",
+       "       0.09402319, 0.09426176, 0.09450032, 0.09473888, 0.09497745,\n",
+       "       0.09521601, 0.09545457, 0.09569314, 0.0959317 , 0.09617026,\n",
+       "       0.09640883, 0.09664739, 0.09688595, 0.09712452, 0.09736308,\n",
+       "       0.09760164, 0.09784021, 0.09807877, 0.09831733, 0.09855589,\n",
+       "       0.09879446, 0.09903302, 0.09927158, 0.09951015, 0.09974871,\n",
+       "       0.09998727, 0.10022584, 0.1004644 , 0.10070296, 0.10094153,\n",
+       "       0.10118009, 0.10141865, 0.10165722, 0.10189578, 0.10213434,\n",
+       "       0.10237291, 0.10261147, 0.10285003, 0.10308859, 0.10332716,\n",
+       "       0.10356572, 0.10380428, 0.10404285, 0.10428141, 0.10451997,\n",
+       "       0.10475854, 0.1049971 , 0.10523566, 0.10547423, 0.10571279,\n",
+       "       0.10595135, 0.10618992, 0.10642848, 0.10666704, 0.10690561,\n",
+       "       0.10714417, 0.10738273, 0.10762129, 0.10785986, 0.10809842,\n",
+       "       0.10833698, 0.10857555, 0.10881411, 0.10905267, 0.10929124,\n",
+       "       0.1095298 , 0.10976836, 0.11000693, 0.11024549, 0.11048405,\n",
+       "       0.11072262, 0.11096118, 0.11119974, 0.11143831, 0.11167687,\n",
+       "       0.11191543, 0.11215399, 0.11239256, 0.11263112, 0.11286968,\n",
+       "       0.11310825, 0.11334681, 0.11358537, 0.11382394, 0.1140625 ,\n",
+       "       0.11430106, 0.11453963, 0.11477819, 0.11501675, 0.11525532,\n",
+       "       0.11549388, 0.11573244, 0.11597101, 0.11620957, 0.11644813,\n",
+       "       0.11668669, 0.11692526, 0.11716382, 0.11740238, 0.11764095,\n",
+       "       0.11787951, 0.11811807, 0.11835664, 0.1185952 , 0.11883376,\n",
+       "       0.11907233, 0.11931089, 0.11954945, 0.11978802, 0.12002658,\n",
+       "       0.12026514, 0.12050371, 0.12074227, 0.12098083, 0.1212194 ,\n",
+       "       0.12145796, 0.12169652, 0.12193508, 0.12217365, 0.12241221,\n",
+       "       0.12265077, 0.12288934, 0.1231279 , 0.12336646, 0.12360503,\n",
+       "       0.12384359, 0.12408215, 0.12432072, 0.12455928, 0.12479784,\n",
+       "       0.12503641, 0.12527497, 0.12551353, 0.1257521 , 0.12599066,\n",
+       "       0.12622922, 0.12646778, 0.12670635, 0.12694491, 0.12718347,\n",
+       "       0.12742204, 0.1276606 , 0.12789916, 0.12813773, 0.12837629,\n",
+       "       0.12861485, 0.12885342, 0.12909198, 0.12933054, 0.12956911,\n",
+       "       0.12980767, 0.13004623, 0.1302848 , 0.13052336, 0.13076192,\n",
+       "       0.13100048, 0.13123905, 0.13147761, 0.13171617, 0.13195474,\n",
+       "       0.1321933 , 0.13243186, 0.13267043, 0.13290899, 0.13314755,\n",
+       "       0.13338612, 0.13362468, 0.13386324, 0.13410181, 0.13434037,\n",
+       "       0.13457893])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • z
              ()
              float64
              m
              60.50008607945975
              Values:
              array(60.50008608)
            • (tof, y, x)
              float64
              counts
              1.0, 1.0, ..., 1.001, 2.614
              σ = 0.080, 0.0, ..., 1.001, 1.617
              Values:
              array([[[  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,  47.69390106],\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        ...,\n",
+       "        [363.58432007,  60.70173645,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [183.09933472,   1.        ,   1.        , ...,  84.79289246,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ]],\n",
+       "\n",
+       "       [[  1.        ,   1.        ,   2.2572794 , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        ...,\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "          94.08105469,   1.        ],\n",
+       "        [218.49197388,  91.15695953,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.74783659,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ]],\n",
+       "\n",
+       "       [[  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,  21.01462173,   1.        , ..., 127.76381683,\n",
+       "           1.        ,   1.        ],\n",
+       "        ...,\n",
+       "        [  1.        ,   1.        ,   1.        , ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        ,   1.        , ..., 122.85718536,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   1.        , 462.99301147, ...,   1.        ,\n",
+       "          95.22931671,   1.        ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[  4.18234205,   1.6264236 ,   1.45277965, ...,   2.34951711,\n",
+       "           2.55632734,   1.85978436],\n",
+       "        [  1.47711265,   1.78541708,   1.30399382, ...,   1.47573113,\n",
+       "           1.63894928,   1.63139319],\n",
+       "        [  2.2177465 ,   3.5231688 ,   1.32336307, ...,   2.50156474,\n",
+       "           2.34639025,   1.66015971],\n",
+       "        ...,\n",
+       "        [  1.44421673,   1.01731837,   1.65496516, ...,   1.93637466,\n",
+       "           2.14433789,   3.97781348],\n",
+       "        [  1.89172518,   2.81813669,   3.54166412, ...,   1.73817742,\n",
+       "           1.        ,   1.33578014],\n",
+       "        [  1.70085514,   1.        ,   1.26247168, ...,   1.0921191 ,\n",
+       "           2.70412111,   1.        ]],\n",
+       "\n",
+       "       [[  2.19542027,   3.82565308,   1.33312011, ...,   1.        ,\n",
+       "           1.        ,   1.        ],\n",
+       "        [  1.        ,   2.74969172,   3.6322329 , ...,   2.98920536,\n",
+       "           2.66721129,   2.55878901],\n",
+       "        [  1.0820936 ,   2.47591853,   2.1116972 , ...,   1.89907753,\n",
+       "           1.        ,   3.61068916],\n",
+       "        ...,\n",
+       "        [  1.31289554,   3.51260972,   2.72831106, ...,   1.93136907,\n",
+       "           2.75441432,   1.30589557],\n",
+       "        [  1.75691915,   2.35957837,   2.57865119, ...,   1.03260589,\n",
+       "           2.23629713,   2.45170832],\n",
+       "        [  2.11971712,   2.72858787,   2.93440223, ...,   4.68469715,\n",
+       "           1.90933228,   2.05993843]],\n",
+       "\n",
+       "       [[  1.        ,   1.89956355,   2.30994606, ...,   2.7000134 ,\n",
+       "           2.42832899,   2.06935763],\n",
+       "        [  1.73583329,   1.89985895,   1.55298436, ...,   1.40239453,\n",
+       "           1.85392869,   1.44971168],\n",
+       "        [  2.24610782,   1.        ,   1.29749143, ...,   1.85342705,\n",
+       "           1.        ,   1.83384943],\n",
+       "        ...,\n",
+       "        [  1.11495268,   1.4892683 ,   1.90949392, ...,   1.55062032,\n",
+       "           1.1679976 ,   1.        ],\n",
+       "        [  1.37503779,   2.74830699,   1.        , ...,   1.6511749 ,\n",
+       "           1.7639277 ,   1.        ],\n",
+       "        [  2.42482829,   1.03130519,   1.        , ...,   1.        ,\n",
+       "           1.00147641,   2.61416698]]], shape=(500, 100, 100))

              Variances (σ²):
              array([[[6.35182485e-03, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.01558590e-01, 0.00000000e+00, 5.82696639e-22],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 4.76939011e+01],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.84587270e-01, ...,\n",
+       "         0.00000000e+00, 4.25331131e-10, 1.94688106e-03],\n",
+       "        ...,\n",
+       "        [3.63584320e+02, 6.07017365e+01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [1.83099335e+02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         8.47928925e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [1.06027517e-02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[0.00000000e+00, 0.00000000e+00, 2.25727940e+00, ...,\n",
+       "         0.00000000e+00, 4.40442376e-03, 3.53905198e-04],\n",
+       "        [0.00000000e+00, 6.39227591e-03, 0.00000000e+00, ...,\n",
+       "         1.11321840e-37, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.18332226e-21, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 9.40810547e+01, 0.00000000e+00],\n",
+       "        [2.18491974e+02, 9.11569595e+01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 5.78109520e-23],\n",
+       "        [2.66337842e-01, 1.74783659e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 7.09714935e-29, 0.00000000e+00]],\n",
+       "\n",
+       "       [[5.09160280e-04, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.14082263e-09, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 1.24966880e-19, 0.00000000e+00],\n",
+       "        [9.26308479e-24, 2.10146217e+01, 0.00000000e+00, ...,\n",
+       "         1.27763817e+02, 4.36634982e-05, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 1.02449901e-27, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.22857185e+02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.62993011e+02, ...,\n",
+       "         0.00000000e+00, 9.52293167e+01, 2.57639981e-19]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[4.18234205e+00, 1.62642360e+00, 1.45277965e+00, ...,\n",
+       "         2.34951711e+00, 2.55632734e+00, 1.85978436e+00],\n",
+       "        [1.47711265e+00, 1.78541708e+00, 1.30399382e+00, ...,\n",
+       "         1.47573113e+00, 1.63894928e+00, 1.63139319e+00],\n",
+       "        [2.21774650e+00, 3.52316880e+00, 1.32336307e+00, ...,\n",
+       "         2.50156474e+00, 2.34639025e+00, 1.66015971e+00],\n",
+       "        ...,\n",
+       "        [1.44421673e+00, 1.01731837e+00, 1.65496516e+00, ...,\n",
+       "         1.93637466e+00, 2.14433789e+00, 3.97781348e+00],\n",
+       "        [1.89172518e+00, 2.81813669e+00, 3.54166412e+00, ...,\n",
+       "         1.73817742e+00, 8.20679188e-01, 1.33578014e+00],\n",
+       "        [1.70085514e+00, 6.75366521e-01, 1.26247168e+00, ...,\n",
+       "         1.09211910e+00, 2.70412111e+00, 3.86006653e-01]],\n",
+       "\n",
+       "       [[2.19542027e+00, 3.82565308e+00, 1.33312011e+00, ...,\n",
+       "         9.96562421e-01, 9.18403804e-01, 6.90977931e-01],\n",
+       "        [6.51249766e-01, 2.74969172e+00, 3.63223290e+00, ...,\n",
+       "         2.98920536e+00, 2.66721129e+00, 2.55878901e+00],\n",
+       "        [1.08209360e+00, 2.47591853e+00, 2.11169720e+00, ...,\n",
+       "         1.89907753e+00, 8.09513688e-01, 3.61068916e+00],\n",
+       "        ...,\n",
+       "        [1.31289554e+00, 3.51260972e+00, 2.72831106e+00, ...,\n",
+       "         1.93136907e+00, 2.75441432e+00, 1.30589557e+00],\n",
+       "        [1.75691915e+00, 2.35957837e+00, 2.57865119e+00, ...,\n",
+       "         1.03260589e+00, 2.23629713e+00, 2.45170832e+00],\n",
+       "        [2.11971712e+00, 2.72858787e+00, 2.93440223e+00, ...,\n",
+       "         4.68469715e+00, 1.90933228e+00, 2.05993843e+00]],\n",
+       "\n",
+       "       [[8.16898823e-01, 1.89956355e+00, 2.30994606e+00, ...,\n",
+       "         2.70001340e+00, 2.42832899e+00, 2.06935763e+00],\n",
+       "        [1.73583329e+00, 1.89985895e+00, 1.55298436e+00, ...,\n",
+       "         1.40239453e+00, 1.85392869e+00, 1.44971168e+00],\n",
+       "        [2.24610782e+00, 7.36134470e-01, 1.29749143e+00, ...,\n",
+       "         1.85342705e+00, 3.25053632e-01, 1.83384943e+00],\n",
+       "        ...,\n",
+       "        [1.11495268e+00, 1.48926830e+00, 1.90949392e+00, ...,\n",
+       "         1.55062032e+00, 1.16799760e+00, 7.39989400e-01],\n",
+       "        [1.37503779e+00, 2.74830699e+00, 9.41012144e-01, ...,\n",
+       "         1.65117490e+00, 1.76392770e+00, 2.92126149e-01],\n",
+       "        [2.42482829e+00, 1.03130519e+00, 9.35848832e-01, ...,\n",
+       "         4.86035466e-01, 1.00147641e+00, 2.61416698e+00]]],\n",
+       "      shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[tof:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "  c                          string          ()  \"data\"\n",
+       "* pixel_position            vector3              [m]  (x, y)  [(-0.12375, -0.12375, 60.5001), (-0.12375, -0.12375, 60.5001), ..., (0.12375, 0.12375, 60.5001), (0.12375, 0.12375, 60.5001)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (tof [bin-edge])  [0.0152974, 0.0155359, ..., 0.13434, 0.134579]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "  z                         float64              [m]  ()  60.5001\n",
+       "Data:\n",
+       "                            float64         [counts]  (tof, y, x)  [1, 1, ..., 1.00148, 2.61417]  [0.00635182, 0, ..., 1.00148, 2.61417]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipp as sc\n",
+    "from scitiff_helper import load_scitiff\n",
+    "\n",
+    "sample_da = load_scitiff(\"sample_data/sample_5e11_mcstas (2).tiff\")\n",
+    "openbeam_da = load_scitiff(\"sample_data/open_beam_5e11_mcstas (2).tiff\", background=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalize data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalize sample image by open beam and replace 0 counts in OB with 1 to avoid NaN's"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (76.53 MB)
            • tof: 500
            • y: 100
            • x: 100
            • c
              ()
              string
              data
              Values:
              'data'
            • pixel_position
              (x, y)
              vector3
              m
              [-0.12375    -0.12375    60.50008608], [-0.12375    -0.12375    60.50008608], ..., [ 0.12375     0.12375    60.50008608], [ 0.12375     0.12375    60.50008608]
              Values:
              array([[[-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608],\n",
+       "        [-0.12375   , -0.12375   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608],\n",
+       "        [-0.12125   , -0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608],\n",
+       "        [-0.11875   , -0.11875   , 60.50008608]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608],\n",
+       "        [ 0.11875   ,  0.11875   , 60.50008608]],\n",
+       "\n",
+       "       [[ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608],\n",
+       "        [ 0.12125   ,  0.12125   , 60.50008608]],\n",
+       "\n",
+       "       [[ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        ...,\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608],\n",
+       "        [ 0.12375   ,  0.12375   , 60.50008608]]], shape=(100, 100, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (tof [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01529735, 0.01553592, 0.01577448, 0.01601304, 0.0162516 ,\n",
+       "       0.01649017, 0.01672873, 0.01696729, 0.01720586, 0.01744442,\n",
+       "       0.01768298, 0.01792155, 0.01816011, 0.01839867, 0.01863724,\n",
+       "       0.0188758 , 0.01911436, 0.01935293, 0.01959149, 0.01983005,\n",
+       "       0.02006862, 0.02030718, 0.02054574, 0.0207843 , 0.02102287,\n",
+       "       0.02126143, 0.02149999, 0.02173856, 0.02197712, 0.02221568,\n",
+       "       0.02245425, 0.02269281, 0.02293137, 0.02316994, 0.0234085 ,\n",
+       "       0.02364706, 0.02388563, 0.02412419, 0.02436275, 0.02460132,\n",
+       "       0.02483988, 0.02507844, 0.025317  , 0.02555557, 0.02579413,\n",
+       "       0.02603269, 0.02627126, 0.02650982, 0.02674838, 0.02698695,\n",
+       "       0.02722551, 0.02746407, 0.02770264, 0.0279412 , 0.02817976,\n",
+       "       0.02841833, 0.02865689, 0.02889545, 0.02913402, 0.02937258,\n",
+       "       0.02961114, 0.0298497 , 0.03008827, 0.03032683, 0.03056539,\n",
+       "       0.03080396, 0.03104252, 0.03128108, 0.03151965, 0.03175821,\n",
+       "       0.03199677, 0.03223534, 0.0324739 , 0.03271246, 0.03295103,\n",
+       "       0.03318959, 0.03342815, 0.03366672, 0.03390528, 0.03414384,\n",
+       "       0.0343824 , 0.03462097, 0.03485953, 0.03509809, 0.03533666,\n",
+       "       0.03557522, 0.03581378, 0.03605235, 0.03629091, 0.03652947,\n",
+       "       0.03676804, 0.0370066 , 0.03724516, 0.03748373, 0.03772229,\n",
+       "       0.03796085, 0.03819942, 0.03843798, 0.03867654, 0.0389151 ,\n",
+       "       0.03915367, 0.03939223, 0.03963079, 0.03986936, 0.04010792,\n",
+       "       0.04034648, 0.04058505, 0.04082361, 0.04106217, 0.04130074,\n",
+       "       0.0415393 , 0.04177786, 0.04201643, 0.04225499, 0.04249355,\n",
+       "       0.04273212, 0.04297068, 0.04320924, 0.0434478 , 0.04368637,\n",
+       "       0.04392493, 0.04416349, 0.04440206, 0.04464062, 0.04487918,\n",
+       "       0.04511775, 0.04535631, 0.04559487, 0.04583344, 0.046072  ,\n",
+       "       0.04631056, 0.04654913, 0.04678769, 0.04702625, 0.04726482,\n",
+       "       0.04750338, 0.04774194, 0.0479805 , 0.04821907, 0.04845763,\n",
+       "       0.04869619, 0.04893476, 0.04917332, 0.04941188, 0.04965045,\n",
+       "       0.04988901, 0.05012757, 0.05036614, 0.0506047 , 0.05084326,\n",
+       "       0.05108183, 0.05132039, 0.05155895, 0.05179752, 0.05203608,\n",
+       "       0.05227464, 0.0525132 , 0.05275177, 0.05299033, 0.05322889,\n",
+       "       0.05346746, 0.05370602, 0.05394458, 0.05418315, 0.05442171,\n",
+       "       0.05466027, 0.05489884, 0.0551374 , 0.05537596, 0.05561453,\n",
+       "       0.05585309, 0.05609165, 0.05633022, 0.05656878, 0.05680734,\n",
+       "       0.0570459 , 0.05728447, 0.05752303, 0.05776159, 0.05800016,\n",
+       "       0.05823872, 0.05847728, 0.05871585, 0.05895441, 0.05919297,\n",
+       "       0.05943154, 0.0596701 , 0.05990866, 0.06014723, 0.06038579,\n",
+       "       0.06062435, 0.06086292, 0.06110148, 0.06134004, 0.06157861,\n",
+       "       0.06181717, 0.06205573, 0.06229429, 0.06253286, 0.06277142,\n",
+       "       0.06300998, 0.06324855, 0.06348711, 0.06372567, 0.06396424,\n",
+       "       0.0642028 , 0.06444136, 0.06467993, 0.06491849, 0.06515705,\n",
+       "       0.06539562, 0.06563418, 0.06587274, 0.06611131, 0.06634987,\n",
+       "       0.06658843, 0.06682699, 0.06706556, 0.06730412, 0.06754268,\n",
+       "       0.06778125, 0.06801981, 0.06825837, 0.06849694, 0.0687355 ,\n",
+       "       0.06897406, 0.06921263, 0.06945119, 0.06968975, 0.06992832,\n",
+       "       0.07016688, 0.07040544, 0.07064401, 0.07088257, 0.07112113,\n",
+       "       0.07135969, 0.07159826, 0.07183682, 0.07207538, 0.07231395,\n",
+       "       0.07255251, 0.07279107, 0.07302964, 0.0732682 , 0.07350676,\n",
+       "       0.07374533, 0.07398389, 0.07422245, 0.07446102, 0.07469958,\n",
+       "       0.07493814, 0.07517671, 0.07541527, 0.07565383, 0.07589239,\n",
+       "       0.07613096, 0.07636952, 0.07660808, 0.07684665, 0.07708521,\n",
+       "       0.07732377, 0.07756234, 0.0778009 , 0.07803946, 0.07827803,\n",
+       "       0.07851659, 0.07875515, 0.07899372, 0.07923228, 0.07947084,\n",
+       "       0.07970941, 0.07994797, 0.08018653, 0.08042509, 0.08066366,\n",
+       "       0.08090222, 0.08114078, 0.08137935, 0.08161791, 0.08185647,\n",
+       "       0.08209504, 0.0823336 , 0.08257216, 0.08281073, 0.08304929,\n",
+       "       0.08328785, 0.08352642, 0.08376498, 0.08400354, 0.08424211,\n",
+       "       0.08448067, 0.08471923, 0.08495779, 0.08519636, 0.08543492,\n",
+       "       0.08567348, 0.08591205, 0.08615061, 0.08638917, 0.08662774,\n",
+       "       0.0868663 , 0.08710486, 0.08734343, 0.08758199, 0.08782055,\n",
+       "       0.08805912, 0.08829768, 0.08853624, 0.08877481, 0.08901337,\n",
+       "       0.08925193, 0.08949049, 0.08972906, 0.08996762, 0.09020618,\n",
+       "       0.09044475, 0.09068331, 0.09092187, 0.09116044, 0.091399  ,\n",
+       "       0.09163756, 0.09187613, 0.09211469, 0.09235325, 0.09259182,\n",
+       "       0.09283038, 0.09306894, 0.09330751, 0.09354607, 0.09378463,\n",
+       "       0.09402319, 0.09426176, 0.09450032, 0.09473888, 0.09497745,\n",
+       "       0.09521601, 0.09545457, 0.09569314, 0.0959317 , 0.09617026,\n",
+       "       0.09640883, 0.09664739, 0.09688595, 0.09712452, 0.09736308,\n",
+       "       0.09760164, 0.09784021, 0.09807877, 0.09831733, 0.09855589,\n",
+       "       0.09879446, 0.09903302, 0.09927158, 0.09951015, 0.09974871,\n",
+       "       0.09998727, 0.10022584, 0.1004644 , 0.10070296, 0.10094153,\n",
+       "       0.10118009, 0.10141865, 0.10165722, 0.10189578, 0.10213434,\n",
+       "       0.10237291, 0.10261147, 0.10285003, 0.10308859, 0.10332716,\n",
+       "       0.10356572, 0.10380428, 0.10404285, 0.10428141, 0.10451997,\n",
+       "       0.10475854, 0.1049971 , 0.10523566, 0.10547423, 0.10571279,\n",
+       "       0.10595135, 0.10618992, 0.10642848, 0.10666704, 0.10690561,\n",
+       "       0.10714417, 0.10738273, 0.10762129, 0.10785986, 0.10809842,\n",
+       "       0.10833698, 0.10857555, 0.10881411, 0.10905267, 0.10929124,\n",
+       "       0.1095298 , 0.10976836, 0.11000693, 0.11024549, 0.11048405,\n",
+       "       0.11072262, 0.11096118, 0.11119974, 0.11143831, 0.11167687,\n",
+       "       0.11191543, 0.11215399, 0.11239256, 0.11263112, 0.11286968,\n",
+       "       0.11310825, 0.11334681, 0.11358537, 0.11382394, 0.1140625 ,\n",
+       "       0.11430106, 0.11453963, 0.11477819, 0.11501675, 0.11525532,\n",
+       "       0.11549388, 0.11573244, 0.11597101, 0.11620957, 0.11644813,\n",
+       "       0.11668669, 0.11692526, 0.11716382, 0.11740238, 0.11764095,\n",
+       "       0.11787951, 0.11811807, 0.11835664, 0.1185952 , 0.11883376,\n",
+       "       0.11907233, 0.11931089, 0.11954945, 0.11978802, 0.12002658,\n",
+       "       0.12026514, 0.12050371, 0.12074227, 0.12098083, 0.1212194 ,\n",
+       "       0.12145796, 0.12169652, 0.12193508, 0.12217365, 0.12241221,\n",
+       "       0.12265077, 0.12288934, 0.1231279 , 0.12336646, 0.12360503,\n",
+       "       0.12384359, 0.12408215, 0.12432072, 0.12455928, 0.12479784,\n",
+       "       0.12503641, 0.12527497, 0.12551353, 0.1257521 , 0.12599066,\n",
+       "       0.12622922, 0.12646778, 0.12670635, 0.12694491, 0.12718347,\n",
+       "       0.12742204, 0.1276606 , 0.12789916, 0.12813773, 0.12837629,\n",
+       "       0.12861485, 0.12885342, 0.12909198, 0.12933054, 0.12956911,\n",
+       "       0.12980767, 0.13004623, 0.1302848 , 0.13052336, 0.13076192,\n",
+       "       0.13100048, 0.13123905, 0.13147761, 0.13171617, 0.13195474,\n",
+       "       0.1321933 , 0.13243186, 0.13267043, 0.13290899, 0.13314755,\n",
+       "       0.13338612, 0.13362468, 0.13386324, 0.13410181, 0.13434037,\n",
+       "       0.13457893])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • z
              ()
              float64
              m
              60.50008607945975
              Values:
              array(60.50008608)
            • (tof, y, x)
              float64
              𝟙
              1.910e-07, 3.223e-35, ..., 0.522, 0.750
              σ = 0.000, 5.677e-18, ..., 0.890, 0.708
              Values:
              array([[[1.90958104e-07, 3.22307206e-35, 0.00000000e+00, ...,\n",
+       "         5.59635353e+00, 2.61284236e-04, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.09200635e-10, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [8.89200878e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [6.38951498e-24, 0.00000000e+00, 2.61551941e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.01266697e-16],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.12041968e-22, ...,\n",
+       "         0.00000000e+00, 6.13249729e-11, 1.02997002e+02],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.03374390e+02, 0.00000000e+00, 4.54903145e+01]],\n",
+       "\n",
+       "       [[0.00000000e+00, 3.42579443e-38, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 2.91616002e-23],\n",
+       "        [0.00000000e+00, 4.05064732e-01, 4.17823106e-01, ...,\n",
+       "         2.99957027e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [5.61191034e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 2.78917694e+01, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 2.66153278e-21, ...,\n",
+       "         0.00000000e+00, 5.99764855e-07, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.09137054e+02, ...,\n",
+       "         3.35921720e-02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 1.03730469e+02, 4.62731272e-02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[9.36448723e-02, 1.72879195e+00, 0.00000000e+00, ...,\n",
+       "         1.02489926e-02, 0.00000000e+00, 6.52526694e-07],\n",
+       "        [9.08751540e-34, 7.90317112e-14, 4.33704071e-12, ...,\n",
+       "         1.28664146e-15, 8.67571615e-09, 7.71208794e-28],\n",
+       "        [9.23123240e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.33274040e-01, 4.96043101e-19, 7.74029315e-01],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.47227882e-26],\n",
+       "        [4.05311188e+02, 0.00000000e+00, 2.03482147e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [2.96646332e+02, 3.73204875e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[2.72658829e-01, 1.68612805e+00, 1.60494039e+00, ...,\n",
+       "         8.56200294e-01, 6.48226816e-01, 5.20221714e-01],\n",
+       "        [6.96467873e-01, 1.20298449e+00, 1.33739470e+00, ...,\n",
+       "         6.34719932e-01, 5.26167794e-01, 1.92603162e+00],\n",
+       "        [9.80406697e-01, 3.78364033e-01, 2.79610355e+00, ...,\n",
+       "         9.49193043e-01, 7.60097928e-01, 1.65405877e+00],\n",
+       "        ...,\n",
+       "        [1.50455437e+00, 1.88143149e+00, 1.15166644e+00, ...,\n",
+       "         6.77036114e-01, 1.57227120e+00, 3.56905104e-01],\n",
+       "        [2.03500019e+00, 1.36866861e+00, 6.03077076e-01, ...,\n",
+       "         1.47086450e+00, 1.88439441e+00, 1.45516913e+00],\n",
+       "        [1.16466336e+00, 9.41187024e-01, 1.48405279e+00, ...,\n",
+       "         1.42418543e+00, 4.21042768e-01, 2.46566176e+00]],\n",
+       "\n",
+       "       [[2.32255931e-01, 2.68303152e-01, 1.51824650e+00, ...,\n",
+       "         2.20600080e+00, 2.91543841e-01, 1.38574004e+00],\n",
+       "        [1.24602795e+00, 3.05399321e-01, 6.99547263e-01, ...,\n",
+       "         5.59010008e-01, 3.81593166e-01, 5.45219919e-01],\n",
+       "        [5.64873438e-01, 8.26161233e-01, 7.33745357e-01, ...,\n",
+       "         1.93860790e-01, 5.40371239e-01, 2.00541665e-01],\n",
+       "        ...,\n",
+       "        [9.56452096e-01, 2.54903312e-05, 3.38563037e-01, ...,\n",
+       "         7.03194050e-01, 2.82889847e-01, 7.26524070e-01],\n",
+       "        [9.79660344e-01, 7.99461926e-01, 4.25342931e-01, ...,\n",
+       "         2.15346833e+00, 4.56075881e-01, 8.52963423e-01],\n",
+       "        [6.81917182e-01, 3.41369875e-01, 3.08308752e-01, ...,\n",
+       "         1.99508320e-01, 1.11537314e+00, 6.62059051e-01]],\n",
+       "\n",
+       "       [[5.31180620e-01, 8.53313594e-01, 6.49021955e-01, ...,\n",
+       "         5.35556113e-01, 4.42021227e-01, 6.83188807e-01],\n",
+       "        [7.47625835e-01, 2.01160642e-01, 1.10232321e+00, ...,\n",
+       "         4.21086993e-01, 3.75775541e-01, 1.60313157e+00],\n",
+       "        [4.21645202e-01, 2.14089489e+00, 2.75984696e-01, ...,\n",
+       "         1.05419686e+00, 2.76401073e-01, 8.37948121e-01],\n",
+       "        ...,\n",
+       "        [2.01912797e+00, 1.62284045e+00, 4.51914116e-01, ...,\n",
+       "         1.22240813e+00, 1.23230254e+00, 1.92746162e+00],\n",
+       "        [1.49114273e+00, 5.14608100e-01, 9.50042427e-01, ...,\n",
+       "         8.83945625e-01, 9.55997804e-01, 1.65743995e+00],\n",
+       "        [5.69020134e-01, 1.56214983e+00, 1.74702907e+00, ...,\n",
+       "         2.30999351e+00, 5.21566677e-01, 7.49675023e-01]]],\n",
+       "      shape=(500, 100, 100))

              Variances (σ²):
              array([[[1.90958105e-07, 3.22307206e-35, 0.00000000e+00, ...,\n",
+       "         1.19090018e+01, 2.61284236e-04, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         1.09200635e-10, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [8.89200878e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [1.75736813e-26, 0.00000000e+00, 2.61551941e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.01266697e-16],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 4.12041968e-22, ...,\n",
+       "         0.00000000e+00, 6.13249729e-11, 1.02997002e+02],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         3.03374390e+02, 0.00000000e+00, 4.54903145e+01]],\n",
+       "\n",
+       "       [[0.00000000e+00, 3.42579443e-38, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 2.91616002e-23],\n",
+       "        [0.00000000e+00, 4.06113560e-01, 4.17823106e-01, ...,\n",
+       "         2.99957027e+01, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [5.61191034e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 2.78917694e+01, 0.00000000e+00],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 2.66153278e-21, ...,\n",
+       "         0.00000000e+00, 6.37498396e-09, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 0.00000000e+00, 1.09137054e+02, ...,\n",
+       "         3.35921720e-02, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [0.00000000e+00, 6.21553568e+03, 4.62731272e-02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       [[9.36493373e-02, 1.72879195e+00, 0.00000000e+00, ...,\n",
+       "         1.02489926e-02, 0.00000000e+00, 6.52526694e-07],\n",
+       "        [9.08751540e-34, 7.90317112e-14, 4.33704071e-12, ...,\n",
+       "         1.28664146e-15, 8.67571615e-09, 7.71208794e-28],\n",
+       "        [9.23123240e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         2.25173939e-03, 4.96043101e-19, 7.74029315e-01],\n",
+       "        ...,\n",
+       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 3.47227882e-26],\n",
+       "        [4.05311188e+02, 0.00000000e+00, 2.03482147e+02, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
+       "        [2.96646332e+02, 3.73204875e+00, 0.00000000e+00, ...,\n",
+       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[8.29682656e-02, 2.78473323e+00, 2.87777576e+00, ...,\n",
+       "         6.76428034e-01, 4.17953055e-01, 4.25238732e-01],\n",
+       "        [7.99895235e-01, 1.48433450e+00, 2.39726540e+00, ...,\n",
+       "         7.03101873e-01, 4.89960461e-01, 3.45448874e+00],\n",
+       "        [8.75485090e-01, 1.48026792e-01, 8.02070034e+00, ...,\n",
+       "         7.39601277e-01, 5.70172328e-01, 2.64430534e+00],\n",
+       "        ...,\n",
+       "        [2.60919164e+00, 5.32892763e+00, 1.49731370e+00, ...,\n",
+       "         5.86360706e-01, 1.88604041e+00, 1.21746874e-01],\n",
+       "        [3.26486428e+00, 1.15037798e+00, 2.72973100e-01, ...,\n",
+       "         2.09087222e+00, 4.79857884e+00, 2.67460654e+00],\n",
+       "        [1.48225680e+00, 1.53944899e+00, 2.92003817e+00, ...,\n",
+       "         3.16127570e+00, 2.21262198e-01, 4.81238456e+00]],\n",
+       "\n",
+       "       [[1.30361714e-01, 8.89494491e-02, 2.86794784e+00, ...,\n",
+       "         7.05571159e+00, 3.69606155e-01, 2.71260801e+00],\n",
+       "        [2.25714898e+00, 1.44986459e-01, 3.27323073e-01, ...,\n",
+       "         2.91549791e-01, 1.97662072e-01, 3.29251327e-01],\n",
+       "        [8.16893698e-01, 6.09351074e-01, 6.02419517e-01, ...,\n",
+       "         1.21871167e-01, 7.76750107e-01, 6.66794103e-02],\n",
+       "        ...,\n",
+       "        [1.42528682e+00, 7.25699209e-06, 1.66105681e-01, ...,\n",
+       "         6.20117586e-01, 1.31758142e-01, 9.60537218e-01],\n",
+       "        [1.10386112e+00, 6.09685746e-01, 2.35107231e-01, ...,\n",
+       "         6.57646277e+00, 2.96955660e-01, 6.44656631e-01],\n",
+       "        [5.41076077e-01, 1.67816940e-01, 1.37460037e-01, ...,\n",
+       "         5.10837481e-02, 1.23573587e+00, 5.34181615e-01]],\n",
+       "\n",
+       "       [[7.61670952e-01, 8.32537391e-01, 4.63323136e-01, ...,\n",
+       "         3.04582364e-01, 2.62486671e-01, 5.55696964e-01],\n",
+       "        [7.52704902e-01, 1.27181150e-01, 1.49224920e+00, ...,\n",
+       "         4.26699645e-01, 2.78857975e-01, 2.87861542e+00],\n",
+       "        [2.66874936e-01, 5.51491639e+00, 2.71410076e-01, ...,\n",
+       "         1.16839121e+00, 3.01234375e-01, 8.39820953e-01],\n",
+       "        ...,\n",
+       "        [5.46750173e+00, 2.85808244e+00, 3.43620095e-01, ...,\n",
+       "         1.75200192e+00, 2.35520354e+00, 4.67660239e+00],\n",
+       "        [2.70148894e+00, 2.83603542e-01, 1.79938174e+00, ...,\n",
+       "         1.00855790e+00, 1.06009425e+00, 2.45994179e+00],\n",
+       "        [3.68192689e-01, 3.88096746e+00, 4.60334317e+00, ...,\n",
+       "         4.90351277e+00, 7.92428528e-01, 5.01761240e-01]]],\n",
+       "      shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[tof:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "  c                          string          ()  \"data\"\n",
+       "* pixel_position            vector3              [m]  (x, y)  [(-0.12375, -0.12375, 60.5001), (-0.12375, -0.12375, 60.5001), ..., (0.12375, 0.12375, 60.5001), (0.12375, 0.12375, 60.5001)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (tof [bin-edge])  [0.0152974, 0.0155359, ..., 0.13434, 0.134579]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "  z                         float64              [m]  ()  60.5001\n",
+       "Data:\n",
+       "                            float64  [dimensionless]  (tof, y, x)  [1.90958e-07, 3.22307e-35, ..., 0.521567, 0.749675]  [1.90958e-07, 3.22307e-35, ..., 0.792429, 0.501761]\n"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized = sample_da / openbeam_da\n",
+    "normalized"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cutting off a large spike at very small ToFs which are irrelevant to the spectral region of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_time_bound = 0.02 * sc.Unit('s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff_data = normalized['tof', lower_time_bound:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the normalized image with more tick labels to better identify sample region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c1bc221235b49c4bbfdcf7c37c3dd20",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cutoff_data.mean('tof').plot(aspect='equal', title=\"Normalized Image\", grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Region Of Interest (ROI) selector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scitiff_helper import make_rectangle_tool\n",
+    "roi_container = set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the widget below, choose the empty white box at the bottom left and drag and drop to define your ROI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62f076e7be7b4b1e9d40e185fc1aa094",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBar(children=(InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(…"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_rectangle_tool(cutoff_data, roi_container=roi_container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the defined ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROI=roi_container.pop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the ROI and its spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Slice the normalized image with the defined ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi = ROI.slice_dataarray(cutoff_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the sliced image to ensure it is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8923d9717bd54ae5b10755d2f582d58c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_roi.mean('tof').plot(\n",
+    "    aspect='equal', title='Normalized Region of interest', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the spectrum of the ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e0118e6b91448c39459a4edd8342d5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_roi.mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Full Spectrum of normalized ROI', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert time-of-flight to wavelength using scippneutron"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, define the conversion graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "total_straight_beam_length_no_scatter(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)->Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "wavelength_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "energy_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "position\n",
+       "\n",
+       "position\n",
+       "\n",
+       "\n",
+       "\n",
+       "position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)->wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)->energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scippneutron.conversion import graph\n",
+    "\n",
+    "# Prepare graph for transformation\n",
+    "PLAIN_GRAPH = {**graph.beamline.beamline(False), **graph.tof.kinematic(\"tof\")}\n",
+    "sc.show_graph(PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rename the \"sample_position\" to \"position\" in the meta-data, as this is was the conversion graph expects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi_renamed = normalized_roi.copy(deep=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi_renamed.coords['position'] = normalized_roi_renamed.coords['sample_position']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, add the flight-path because it isn't present in the SciTiff (Can be used to overwrite the calculated lengths, if they're wrong)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#normalized_roi.coords['Ltotal'] = sc.scalar(60.5, unit='m')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Converting the spectrum to wavelength using the conversion graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelength_transformed = normalized_roi_renamed.transform_coords('wavelength', graph=PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the converted spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d5d89286ab045c586fe89d87449899e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wavelength_transformed.mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Full Spectrum of ROI in wavelength', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cut off lower and upper bounds of spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_bound = 1.4 * sc.Unit('Å')\n",
+    "upper_bound = 6.0 * sc.Unit('Å')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sliced_spectrum = wavelength_transformed.mean('x').mean('y')['wavelength', lower_bound:upper_bound]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53d97dd6b52a4f1893609a5c89a1c363",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sliced_spectrum.plot(aspect='equal', title='Sliced Spectrum of ROI in wavelength', grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the transmission and wavelengths for fitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelengths = sc.midpoints(sliced_spectrum.coords['wavelength']).values\n",
+    "transmissions = sliced_spectrum.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Physical model with Ncrystal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NCrystal\n",
+    "import NCrystal.plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We initalize the model\n",
+    "mat = NCrystal.NCMATComposer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We set the model parameters to Iron (slightly off on purpose)\n",
+    "mat.set_cellsg_cubic(2.90)\n",
+    "mat.set_atompos([   ('Fe',0,0,0),\n",
+    "                    ('Fe',0.5, 0.5, 0.5),\n",
+    "                    ])\n",
+    "mat.set_dyninfo_debyetemp(\"Fe\", debye_temp=356)\n",
+    "mat.set_default_temperature(250)\n",
+    "mat.refine_crystal_structure(quiet=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------------------------------------------\n",
+      "------------------------------------   NCrystal Material Info   ------------------------------------\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Data source: \n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Density : 7.60478 g/cm3, 0.0820042 atoms/Aa^3\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mole): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mass): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atom data:\n",
+      "   Fe = Fe(cohSL=9.45fm cohXS=11.2221barn incXS=0.4barn absXS=2.56barn mass=55.8472u Z=26)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Averaged quantities:\n",
+      "   Atomic mass               : 55.8472u\n",
+      "   Absorption XS at 2200m/s  : 2.56 barn\n",
+      "   Free scattering XS        : 11.2134 barn\n",
+      "   Scattering length density : 7.7494 10^-6/Aa^2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Temperature : 250 kelvin\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "State of matter: Solid (crystalline)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Space group number      : 229\n",
+      "Lattice spacings   [Aa] : 2.9 2.9 2.9\n",
+      "Lattice angles    [deg] : 90 90 90\n",
+      "Unit cell volume [Aa^3] : 24.389\n",
+      "Atoms / unit cell       : 2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atoms in unit cell (total 2):\n",
+      "     2 Fe atoms [T_Debye=356K, MSD=0.00542405Aa^2]\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atomic coordinates:\n",
+      "     Fe            0            0            0\n",
+      "     Fe          1/2          1/2          1/2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Dynamic info for Fe (100%):\n",
+      "   type: S(alpha,beta) [from VDOSDebye]\n",
+      "   VDOS E_max: 30.6777 meV\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL info type: SymEqvGroup\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL planes (d_lower = 0.1 Aa, d_upper = inf Aa):\n",
+      "  H   K   L  d_hkl[Aa] Mult. FSquared[barn]\n",
+      "  1   1   0    2.05061   12     3.39475\n",
+      "  2   0   0       1.45    6     3.22621\n",
+      "  2   1   1    1.18392   24     3.06603\n",
+      "  2   2   0     1.0253   12     2.91381\n",
+      "  3   1   0   0.917061   24     2.76914\n",
+      "  2   2   2   0.837158    8     2.63166\n",
+      "  3   2   1   0.775058   48       2.501\n",
+      "  4   0   0      0.725    6     2.37683\n",
+      "  4   1   1   0.683537   24     2.25882\n",
+      "  3   3   0   0.683537   12     2.25882\n",
+      "  (some planes left out for brevity, increase verbosity to show all)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Absorption process (objects):\n",
+      "  AbsOOV(sigma_2200=2.56barn)\n",
+      "Scattering process (objects):\n",
+      "  ProcComposition(3 components, isotropic)\n",
+      "     |-- ElIncScatter(nelements=1;max_contrib=0.4barn)\n",
+      "     |-- PowderBragg(nplanes=231;2dmax=4.10122Aa;max_contrib=14.4027barn)\n",
+      "     \\-- SABScatter(nalpha=50;nbeta=100;Emax=0.5eV;T=250K;M=55.8472u;sigma_free=11.2134barn)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfadc55b41554dadaed31fdd50381f0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
          \n",
+       "                
          \n",
+       "                    Figure\n",
+       "                
          \n",
+       "                \n",
+       "            
          \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Information about the model can be viewed with this method\n",
+    "mat.inspect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NCMAT v7\n",
+      "# Autogenerated by NCMATComposer\n",
+      "#\n",
+      "# Fe (cubic, SG-229)\n",
+      "#\n",
+      "# Atoms per unit cell: 2xFe\n",
+      "#\n",
+      "# NOTICE: crystal structure was verified with spglib to be self-consistent.\n",
+      "#\n",
+      "@CELL\n",
+      "  cubic 2.9\n",
+      "@SPACEGROUP\n",
+      "  229\n",
+      "@ATOMPOSITIONS\n",
+      "  Fe 0 0 0\n",
+      "  Fe 1/2 1/2 1/2\n",
+      "@TEMPERATURE\n",
+      "  default 250\n",
+      "@DYNINFO\n",
+      "  element Fe\n",
+      "  fraction 1\n",
+      "  type vdosdebye\n",
+      "  debye_temp 356\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And a short summary can be viewed by printing\n",
+    "print(mat())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing Data sets and initial model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculating the simulated spectrum using this model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )\n",
+    "absorptions = scatter.absorption.xsect(wl=wavelengths)\n",
+    "scattered = scatter.scatter.xsect(wl=wavelengths)\n",
+    "# Using an arbitrary scale constant of 0.05 to scale cross sections and absorptions. Depends on width of material in a real model.\n",
+    "simulated = np.exp(-0.1*(absorptions+scattered))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading reference Iron slab data from J-PARC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('Iron_slab.dat', 'r') as file:\n",
+    "    slab_data = file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slab_wavelengths = np.asarray([float(slab_data[i].split(\"\\t\")[1]) for i in range(len(slab_data)-1)])\n",
+    "slab_transmissions = np.asarray([float(slab_data[i].split(\"\\t\")[2]) for i in range(len(slab_data)-1)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(4.99449)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slab_wavelengths[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing Data sets and initial model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfdafc478f1148e2a6ecd204131f7019",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
          \n",
+       "                
          \n",
+       "                    Figure\n",
+       "                
          \n",
+       "                \n",
+       "            
          \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='MCStas Data')\n",
+    "#plt.plot(wavelengths, transmissions, label=\"Data\")\n",
+    "plt.plot(wavelengths, simulated, label=\"BCC Iron Model\")\n",
+    "plt.plot(slab_wavelengths, slab_transmissions, label='Raden Iron Slab')\n",
+    "plt.title(\"Pre-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bragg-edge fitting with Ncrystal and EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from easyscience.Objects.variable import Parameter\n",
+    "from easyscience.Objects.ObjectClasses import BaseObj\n",
+    "from easyscience.fitting import Fitter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up the fitting models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fitting parameters and their initial values \\\n",
+    "We're defining 2 sets of parameters because we want to fit a model to both sets of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lattice_constant =  Parameter(name=\"Lattice constant\",          value=2.85, unit=\"angstrom\",    min=0,  max=4,    fixed=False)\n",
+    "debye_temperature = Parameter(name=\"Debye temperature\",         value=300,  unit=\"kelvin\",      min=250,  max=800,  fixed=False)\n",
+    "temperature =       Parameter(name=\"Temperature\",               value=300,  unit=\"kelvin\",      min=0,  max=600,  fixed=False)\n",
+    "intensity_scale =   Parameter(name=\"Intensity scale factor\",    value=0.05,                      fixed=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lattice_constant_slab =  Parameter(name=\"Lattice constant\",          value=2.85, unit=\"angstrom\",    min=0,  max=4,    fixed=False)\n",
+    "debye_temperature_slab = Parameter(name=\"Debye temperature\",         value=300,  unit=\"kelvin\",      min=250,  max=800,  fixed=False)\n",
+    "temperature_slab =       Parameter(name=\"Temperature\",               value=300,  unit=\"kelvin\",      min=0,  max=600,  fixed=False)\n",
+    "intensity_scale_slab =   Parameter(name=\"Intensity scale factor\",    value=0.05,                      fixed=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fit functions to go into EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iron_bragg(wavelength):\n",
+    "    # Initalize the NCrystal material composer\n",
+    "    material = NCrystal.NCMATComposer()\n",
+    "    # Set the material parameters with the fitting variables\n",
+    "    material.set_cellsg_cubic(lattice_constant.value)\n",
+    "    material.set_atompos([  ('Fe',0,0,0),\n",
+    "                            ('Fe',0.5, 0.5, 0.5),\n",
+    "                            ])\n",
+    "    material.set_dyninfo_debyetemp(\"Fe\", debye_temp=debye_temperature.value)\n",
+    "    material.set_default_temperature(temperature.value)\n",
+    "\n",
+    "    # Identify the space group from atomic positions\n",
+    "    material.refine_crystal_structure(quiet=True)\n",
+    "\n",
+    "    #print(\"lattice constant:\", lattice_constant.value)\n",
+    "    scatter = material.load()\n",
+    "\n",
+    "    # Create scattering spectrum\n",
+    "    spectrum = np.exp(-intensity_scale.value*(scatter.scatter.xsect(wl=wavelength) + scatter.absorption.xsect(wl=wavelength)))\n",
+    "    return spectrum\n",
+    "\n",
+    "def iron_bragg_slab(wavelength):\n",
+    "    # Initalize the NCrystal material composer\n",
+    "    material = NCrystal.NCMATComposer()\n",
+    "    # Set the material parameters with the fitting variables\n",
+    "    material.set_cellsg_cubic(lattice_constant_slab.value)\n",
+    "    material.set_atompos([  ('Fe',0,0,0),\n",
+    "                            ('Fe',0.5, 0.5, 0.5),\n",
+    "                            ])\n",
+    "    material.set_dyninfo_debyetemp(\"Fe\", debye_temp=debye_temperature_slab.value)\n",
+    "    material.set_default_temperature(temperature_slab.value)\n",
+    "\n",
+    "    # Identify the space group from atomic positions\n",
+    "    material.refine_crystal_structure(quiet=True)\n",
+    "\n",
+    "    #print(\"lattice constant:\", lattice_constant.value)\n",
+    "    scatter = material.load()\n",
+    "\n",
+    "    # Create scattering spectrum\n",
+    "    spectrum = np.exp(-intensity_scale_slab.value*(scatter.scatter.xsect(wl=wavelength) + scatter.absorption.xsect(wl=wavelength)))\n",
+    "    return spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience container for the fitting parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iron_spectrum = BaseObj(\n",
+    "                        name='Iron transmission spectrum', \n",
+    "                        lattice_constant=lattice_constant, \n",
+    "                        debye_temperature=debye_temperature,\n",
+    "                        temperature=temperature,\n",
+    "                        intensity_scale=intensity_scale,\n",
+    "                        )\n",
+    "iron_spectrum_slab = BaseObj(\n",
+    "                        name='Iron transmission spectrum slab', \n",
+    "                        lattice_constant_slab=lattice_constant_slab, \n",
+    "                        debye_temperature_slab=debye_temperature_slab,\n",
+    "                        temperature_slab=temperature_slab,\n",
+    "                        intensity_scale_slab=intensity_scale_slab,\n",
+    "                        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience fitting objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitter = Fitter(iron_spectrum, iron_bragg)\n",
+    "fitter_slab = Fitter(iron_spectrum_slab, iron_bragg_slab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EasyScience can fit data with a variety of different minimizers. To list the available methods, use the \"available_minimizers\" method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LMFit',\n",
+       " 'LMFit_leastsq',\n",
+       " 'LMFit_powell',\n",
+       " 'LMFit_cobyla',\n",
+       " 'LMFit_differential_evolution',\n",
+       " 'LMFit_scipy_least_squares',\n",
+       " 'Bumps',\n",
+       " 'Bumps_simplex',\n",
+       " 'Bumps_newton',\n",
+       " 'Bumps_lm',\n",
+       " 'DFO',\n",
+       " 'DFO_leastsq']"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitter.available_minimizers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the fits in this notebook, we will use \"LMFit_leastsq\" which is a standard leastsquares fitting method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minimizer should be set with enum LMFit_leastsq\n",
+      "minimizer should be set with enum LMFit_leastsq\n"
+     ]
+    }
+   ],
+   "source": [
+    "fitter.switch_minimizer(\"LMFit_leastsq\")\n",
+    "fitter_slab.switch_minimizer(\"LMFit_leastsq\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we fit the reduced Iron powder data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for LMFit_leastsq\n",
+    "results = fitter.fit(x=wavelengths, y=transmissions, \n",
+    "                     #weights=1/np.sqrt(transmissions_variances), \n",
+    "                     minimizer_kwargs={\"epsfcn\":0.005,\n",
+    "                                       \"ftol\":1.49012e-15,\n",
+    "                                       \"xtol\":1.49012e-15,\n",
+    "                                       },\n",
+    "                    #max_nfev=10000\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print the fitted values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lattice_constant)\n",
+    "print(debye_temperature)\n",
+    "print(temperature)\n",
+    "print(intensity_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then fit the reference Iron slab data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for LMFit_leastsq\n",
+    "results_slab = fitter_slab.fit(x=slab_wavelengths, y=slab_transmissions, \n",
+    "                     #weights=1/np.sqrt(transmissions_variances), \n",
+    "                     minimizer_kwargs={\"epsfcn\":0.005,\n",
+    "                                       \"ftol\":1.49012e-15,\n",
+    "                                       \"xtol\":1.49012e-15,\n",
+    "                                       },\n",
+    "                    #max_nfev=10000\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lattice_constant_slab)\n",
+    "print(debye_temperature_slab)\n",
+    "print(temperature_slab)\n",
+    "print(intensity_scale_slab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing the fitted models and data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to calculate the spectras with the fitted models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulated = iron_bragg(wavelengths)\n",
+    "simulated_slab = iron_bragg_slab(slab_wavelengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a507335aa8104fd8a08d579a01c60128",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
          \n",
+       "                
          \n",
+       "                    Figure\n",
+       "                
          \n",
+       "                \n",
+       "            
          \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='MCStas Data')\n",
+    "plt.plot(wavelengths, simulated, label=\"Iron Model fit to MCStas data\")\n",
+    "#plt.plot(slab_wavelengths, slab_transmissions, label=\"Raden Iron Slab\")\n",
+    "#plt.plot(slab_wavelengths, simulated_slab, label='BCC Iron Model fit to Raden data')\n",
+    "plt.xlabel(\"Wavelength (Å)\")\n",
+    "plt.ylabel(\"Transmission\")\n",
+    "plt.title(\"Post-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "easyimaging",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ProofOfConcept/McStas_fitting_old.ipynb b/ProofOfConcept/McStas_fitting_old.ipynb
new file mode 100644
index 0000000..902834e
--- /dev/null
+++ b/ProofOfConcept/McStas_fitting_old.ipynb
@@ -0,0 +1,1837 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load in open beam and sample images from SciTiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (38.15 MB)
            • t: 500
            • y: 100
            • x: 100
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (t [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01534551, 0.01558398, 0.01582245, 0.01606092, 0.01629939,\n",
+       "       0.01653787, 0.01677634, 0.01701481, 0.01725328, 0.01749175,\n",
+       "       0.01773022, 0.01796869, 0.01820716, 0.01844564, 0.01868411,\n",
+       "       0.01892258, 0.01916105, 0.01939952, 0.01963799, 0.01987646,\n",
+       "       0.02011493, 0.02035341, 0.02059188, 0.02083035, 0.02106882,\n",
+       "       0.02130729, 0.02154576, 0.02178423, 0.02202271, 0.02226118,\n",
+       "       0.02249965, 0.02273812, 0.02297659, 0.02321506, 0.02345353,\n",
+       "       0.023692  , 0.02393048, 0.02416895, 0.02440742, 0.02464589,\n",
+       "       0.02488436, 0.02512283, 0.0253613 , 0.02559977, 0.02583825,\n",
+       "       0.02607672, 0.02631519, 0.02655366, 0.02679213, 0.0270306 ,\n",
+       "       0.02726907, 0.02750754, 0.02774602, 0.02798449, 0.02822296,\n",
+       "       0.02846143, 0.0286999 , 0.02893837, 0.02917684, 0.02941531,\n",
+       "       0.02965379, 0.02989226, 0.03013073, 0.0303692 , 0.03060767,\n",
+       "       0.03084614, 0.03108461, 0.03132309, 0.03156156, 0.03180003,\n",
+       "       0.0320385 , 0.03227697, 0.03251544, 0.03275391, 0.03299238,\n",
+       "       0.03323086, 0.03346933, 0.0337078 , 0.03394627, 0.03418474,\n",
+       "       0.03442321, 0.03466168, 0.03490015, 0.03513863, 0.0353771 ,\n",
+       "       0.03561557, 0.03585404, 0.03609251, 0.03633098, 0.03656945,\n",
+       "       0.03680792, 0.0370464 , 0.03728487, 0.03752334, 0.03776181,\n",
+       "       0.03800028, 0.03823875, 0.03847722, 0.03871569, 0.03895417,\n",
+       "       0.03919264, 0.03943111, 0.03966958, 0.03990805, 0.04014652,\n",
+       "       0.04038499, 0.04062347, 0.04086194, 0.04110041, 0.04133888,\n",
+       "       0.04157735, 0.04181582, 0.04205429, 0.04229276, 0.04253124,\n",
+       "       0.04276971, 0.04300818, 0.04324665, 0.04348512, 0.04372359,\n",
+       "       0.04396206, 0.04420053, 0.04443901, 0.04467748, 0.04491595,\n",
+       "       0.04515442, 0.04539289, 0.04563136, 0.04586983, 0.0461083 ,\n",
+       "       0.04634678, 0.04658525, 0.04682372, 0.04706219, 0.04730066,\n",
+       "       0.04753913, 0.0477776 , 0.04801608, 0.04825455, 0.04849302,\n",
+       "       0.04873149, 0.04896996, 0.04920843, 0.0494469 , 0.04968537,\n",
+       "       0.04992385, 0.05016232, 0.05040079, 0.05063926, 0.05087773,\n",
+       "       0.0511162 , 0.05135467, 0.05159314, 0.05183162, 0.05207009,\n",
+       "       0.05230856, 0.05254703, 0.0527855 , 0.05302397, 0.05326244,\n",
+       "       0.05350091, 0.05373939, 0.05397786, 0.05421633, 0.0544548 ,\n",
+       "       0.05469327, 0.05493174, 0.05517021, 0.05540868, 0.05564716,\n",
+       "       0.05588563, 0.0561241 , 0.05636257, 0.05660104, 0.05683951,\n",
+       "       0.05707798, 0.05731646, 0.05755493, 0.0577934 , 0.05803187,\n",
+       "       0.05827034, 0.05850881, 0.05874728, 0.05898575, 0.05922423,\n",
+       "       0.0594627 , 0.05970117, 0.05993964, 0.06017811, 0.06041658,\n",
+       "       0.06065505, 0.06089352, 0.061132  , 0.06137047, 0.06160894,\n",
+       "       0.06184741, 0.06208588, 0.06232435, 0.06256282, 0.06280129,\n",
+       "       0.06303977, 0.06327824, 0.06351671, 0.06375518, 0.06399365,\n",
+       "       0.06423212, 0.06447059, 0.06470906, 0.06494754, 0.06518601,\n",
+       "       0.06542448, 0.06566295, 0.06590142, 0.06613989, 0.06637836,\n",
+       "       0.06661684, 0.06685531, 0.06709378, 0.06733225, 0.06757072,\n",
+       "       0.06780919, 0.06804766, 0.06828613, 0.06852461, 0.06876308,\n",
+       "       0.06900155, 0.06924002, 0.06947849, 0.06971696, 0.06995543,\n",
+       "       0.0701939 , 0.07043238, 0.07067085, 0.07090932, 0.07114779,\n",
+       "       0.07138626, 0.07162473, 0.0718632 , 0.07210167, 0.07234015,\n",
+       "       0.07257862, 0.07281709, 0.07305556, 0.07329403, 0.0735325 ,\n",
+       "       0.07377097, 0.07400944, 0.07424792, 0.07448639, 0.07472486,\n",
+       "       0.07496333, 0.0752018 , 0.07544027, 0.07567874, 0.07591722,\n",
+       "       0.07615569, 0.07639416, 0.07663263, 0.0768711 , 0.07710957,\n",
+       "       0.07734804, 0.07758651, 0.07782499, 0.07806346, 0.07830193,\n",
+       "       0.0785404 , 0.07877887, 0.07901734, 0.07925581, 0.07949428,\n",
+       "       0.07973276, 0.07997123, 0.0802097 , 0.08044817, 0.08068664,\n",
+       "       0.08092511, 0.08116358, 0.08140205, 0.08164053, 0.081879  ,\n",
+       "       0.08211747, 0.08235594, 0.08259441, 0.08283288, 0.08307135,\n",
+       "       0.08330982, 0.0835483 , 0.08378677, 0.08402524, 0.08426371,\n",
+       "       0.08450218, 0.08474065, 0.08497912, 0.0852176 , 0.08545607,\n",
+       "       0.08569454, 0.08593301, 0.08617148, 0.08640995, 0.08664842,\n",
+       "       0.08688689, 0.08712537, 0.08736384, 0.08760231, 0.08784078,\n",
+       "       0.08807925, 0.08831772, 0.08855619, 0.08879466, 0.08903314,\n",
+       "       0.08927161, 0.08951008, 0.08974855, 0.08998702, 0.09022549,\n",
+       "       0.09046396, 0.09070243, 0.09094091, 0.09117938, 0.09141785,\n",
+       "       0.09165632, 0.09189479, 0.09213326, 0.09237173, 0.0926102 ,\n",
+       "       0.09284868, 0.09308715, 0.09332562, 0.09356409, 0.09380256,\n",
+       "       0.09404103, 0.0942795 , 0.09451798, 0.09475645, 0.09499492,\n",
+       "       0.09523339, 0.09547186, 0.09571033, 0.0959488 , 0.09618727,\n",
+       "       0.09642575, 0.09666422, 0.09690269, 0.09714116, 0.09737963,\n",
+       "       0.0976181 , 0.09785657, 0.09809504, 0.09833352, 0.09857199,\n",
+       "       0.09881046, 0.09904893, 0.0992874 , 0.09952587, 0.09976434,\n",
+       "       0.10000281, 0.10024129, 0.10047976, 0.10071823, 0.1009567 ,\n",
+       "       0.10119517, 0.10143364, 0.10167211, 0.10191058, 0.10214906,\n",
+       "       0.10238753, 0.102626  , 0.10286447, 0.10310294, 0.10334141,\n",
+       "       0.10357988, 0.10381836, 0.10405683, 0.1042953 , 0.10453377,\n",
+       "       0.10477224, 0.10501071, 0.10524918, 0.10548765, 0.10572613,\n",
+       "       0.1059646 , 0.10620307, 0.10644154, 0.10668001, 0.10691848,\n",
+       "       0.10715695, 0.10739542, 0.1076339 , 0.10787237, 0.10811084,\n",
+       "       0.10834931, 0.10858778, 0.10882625, 0.10906472, 0.10930319,\n",
+       "       0.10954167, 0.10978014, 0.11001861, 0.11025708, 0.11049555,\n",
+       "       0.11073402, 0.11097249, 0.11121097, 0.11144944, 0.11168791,\n",
+       "       0.11192638, 0.11216485, 0.11240332, 0.11264179, 0.11288026,\n",
+       "       0.11311874, 0.11335721, 0.11359568, 0.11383415, 0.11407262,\n",
+       "       0.11431109, 0.11454956, 0.11478803, 0.11502651, 0.11526498,\n",
+       "       0.11550345, 0.11574192, 0.11598039, 0.11621886, 0.11645733,\n",
+       "       0.1166958 , 0.11693428, 0.11717275, 0.11741122, 0.11764969,\n",
+       "       0.11788816, 0.11812663, 0.1183651 , 0.11860357, 0.11884205,\n",
+       "       0.11908052, 0.11931899, 0.11955746, 0.11979593, 0.1200344 ,\n",
+       "       0.12027287, 0.12051135, 0.12074982, 0.12098829, 0.12122676,\n",
+       "       0.12146523, 0.1217037 , 0.12194217, 0.12218064, 0.12241912,\n",
+       "       0.12265759, 0.12289606, 0.12313453, 0.123373  , 0.12361147,\n",
+       "       0.12384994, 0.12408841, 0.12432689, 0.12456536, 0.12480383,\n",
+       "       0.1250423 , 0.12528077, 0.12551924, 0.12575771, 0.12599618,\n",
+       "       0.12623466, 0.12647313, 0.1267116 , 0.12695007, 0.12718854,\n",
+       "       0.12742701, 0.12766548, 0.12790395, 0.12814243, 0.1283809 ,\n",
+       "       0.12861937, 0.12885784, 0.12909631, 0.12933478, 0.12957325,\n",
+       "       0.12981173, 0.1300502 , 0.13028867, 0.13052714, 0.13076561,\n",
+       "       0.13100408, 0.13124255, 0.13148102, 0.1317195 , 0.13195797,\n",
+       "       0.13219644, 0.13243491, 0.13267338, 0.13291185, 0.13315032,\n",
+       "       0.13338879, 0.13362727, 0.13386574, 0.13410421, 0.13434268,\n",
+       "       0.13458115])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.247, -0.242, ..., 0.242, 0.247
              Values:
              array([-0.2475, -0.2425, -0.2375, -0.2325, -0.2275, -0.2225, -0.2175,\n",
+       "       -0.2125, -0.2075, -0.2025, -0.1975, -0.1925, -0.1875, -0.1825,\n",
+       "       -0.1775, -0.1725, -0.1675, -0.1625, -0.1575, -0.1525, -0.1475,\n",
+       "       -0.1425, -0.1375, -0.1325, -0.1275, -0.1225, -0.1175, -0.1125,\n",
+       "       -0.1075, -0.1025, -0.0975, -0.0925, -0.0875, -0.0825, -0.0775,\n",
+       "       -0.0725, -0.0675, -0.0625, -0.0575, -0.0525, -0.0475, -0.0425,\n",
+       "       -0.0375, -0.0325, -0.0275, -0.0225, -0.0175, -0.0125, -0.0075,\n",
+       "       -0.0025,  0.0025,  0.0075,  0.0125,  0.0175,  0.0225,  0.0275,\n",
+       "        0.0325,  0.0375,  0.0425,  0.0475,  0.0525,  0.0575,  0.0625,\n",
+       "        0.0675,  0.0725,  0.0775,  0.0825,  0.0875,  0.0925,  0.0975,\n",
+       "        0.1025,  0.1075,  0.1125,  0.1175,  0.1225,  0.1275,  0.1325,\n",
+       "        0.1375,  0.1425,  0.1475,  0.1525,  0.1575,  0.1625,  0.1675,\n",
+       "        0.1725,  0.1775,  0.1825,  0.1875,  0.1925,  0.1975,  0.2025,\n",
+       "        0.2075,  0.2125,  0.2175,  0.2225,  0.2275,  0.2325,  0.2375,\n",
+       "        0.2425,  0.2475])
            • z
              ()
              float64
              m
              60.5
              Values:
              array(60.5)
            • (t, y, x)
              int64
              counts
              0, 0, ..., 0, 3
              Values:
              array([[[ 0,  0,  0, ...,  2,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 2,  0,  0, ...,  0,  0,  0],\n",
+       "        ...,\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0, 10],\n",
+       "        [ 0,  0,  0, ..., 17,  0,  6]],\n",
+       "\n",
+       "       [[ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  5,  0,  0],\n",
+       "        [ 2,  0,  0, ...,  5,  5,  0],\n",
+       "        ...,\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [20,  0, 10, ...,  0,  0,  0],\n",
+       "        [ 0, 13,  0, ...,  0,  0,  0]],\n",
+       "\n",
+       "       [[ 0,  1,  0, ...,  0,  0,  0],\n",
+       "        [11,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        ...,\n",
+       "        [18,  0, 16, ...,  0,  0,  0],\n",
+       "        [ 0,  0, 14, ...,  0, 18,  0],\n",
+       "        [17,  1,  0, ...,  0,  0,  0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 1,  5,  4, ...,  3,  3,  1],\n",
+       "        [ 1,  5,  3, ...,  2,  2,  6],\n",
+       "        [ 4,  3,  6, ...,  4,  5,  5],\n",
+       "        ...,\n",
+       "        [ 4,  3,  3, ...,  2,  5,  2],\n",
+       "        [ 7,  7,  3, ...,  5,  4,  3],\n",
+       "        [ 3,  2,  4, ...,  3,  1,  3]],\n",
+       "\n",
+       "       [[ 2,  3,  4, ...,  5,  1,  3],\n",
+       "        [ 2,  1,  4, ...,  3,  2,  2],\n",
+       "        [ 1,  5,  2, ...,  0,  1,  1],\n",
+       "        ...,\n",
+       "        [ 2,  0,  2, ...,  2,  1,  2],\n",
+       "        [ 3,  3,  2, ...,  4,  3,  4],\n",
+       "        [ 4,  2,  2, ...,  2,  5,  3]],\n",
+       "\n",
+       "       [[ 0,  1,  2, ...,  1,  1,  1],\n",
+       "        [ 2,  0,  3, ...,  0,  0,  3],\n",
+       "        [ 1,  2,  0, ...,  3,  0,  2],\n",
+       "        ...,\n",
+       "        [ 3,  3,  1, ...,  3,  2,  3],\n",
+       "        [ 3,  2,  1, ...,  2,  1,  3],\n",
+       "        [ 1,  2,  2, ...,  2,  0,  3]]], shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[t:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (t [bin-edge])  [0.0153455, 0.015584, ..., 0.134343, 0.134581]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.2475, -0.2425, ..., 0.2425, 0.2475]\n",
+       "  z                         float64              [m]  ()  60.5\n",
+       "Data:\n",
+       "                              int64         [counts]  (t, y, x)  [0, 0, ..., 0, 3]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (38.15 MB)
            • t: 500
            • y: 100
            • x: 100
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (t [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01534551, 0.01558398, 0.01582245, 0.01606092, 0.01629939,\n",
+       "       0.01653787, 0.01677634, 0.01701481, 0.01725328, 0.01749175,\n",
+       "       0.01773022, 0.01796869, 0.01820716, 0.01844564, 0.01868411,\n",
+       "       0.01892258, 0.01916105, 0.01939952, 0.01963799, 0.01987646,\n",
+       "       0.02011493, 0.02035341, 0.02059188, 0.02083035, 0.02106882,\n",
+       "       0.02130729, 0.02154576, 0.02178423, 0.02202271, 0.02226118,\n",
+       "       0.02249965, 0.02273812, 0.02297659, 0.02321506, 0.02345353,\n",
+       "       0.023692  , 0.02393048, 0.02416895, 0.02440742, 0.02464589,\n",
+       "       0.02488436, 0.02512283, 0.0253613 , 0.02559977, 0.02583825,\n",
+       "       0.02607672, 0.02631519, 0.02655366, 0.02679213, 0.0270306 ,\n",
+       "       0.02726907, 0.02750754, 0.02774602, 0.02798449, 0.02822296,\n",
+       "       0.02846143, 0.0286999 , 0.02893837, 0.02917684, 0.02941531,\n",
+       "       0.02965379, 0.02989226, 0.03013073, 0.0303692 , 0.03060767,\n",
+       "       0.03084614, 0.03108461, 0.03132309, 0.03156156, 0.03180003,\n",
+       "       0.0320385 , 0.03227697, 0.03251544, 0.03275391, 0.03299238,\n",
+       "       0.03323086, 0.03346933, 0.0337078 , 0.03394627, 0.03418474,\n",
+       "       0.03442321, 0.03466168, 0.03490015, 0.03513863, 0.0353771 ,\n",
+       "       0.03561557, 0.03585404, 0.03609251, 0.03633098, 0.03656945,\n",
+       "       0.03680792, 0.0370464 , 0.03728487, 0.03752334, 0.03776181,\n",
+       "       0.03800028, 0.03823875, 0.03847722, 0.03871569, 0.03895417,\n",
+       "       0.03919264, 0.03943111, 0.03966958, 0.03990805, 0.04014652,\n",
+       "       0.04038499, 0.04062347, 0.04086194, 0.04110041, 0.04133888,\n",
+       "       0.04157735, 0.04181582, 0.04205429, 0.04229276, 0.04253124,\n",
+       "       0.04276971, 0.04300818, 0.04324665, 0.04348512, 0.04372359,\n",
+       "       0.04396206, 0.04420053, 0.04443901, 0.04467748, 0.04491595,\n",
+       "       0.04515442, 0.04539289, 0.04563136, 0.04586983, 0.0461083 ,\n",
+       "       0.04634678, 0.04658525, 0.04682372, 0.04706219, 0.04730066,\n",
+       "       0.04753913, 0.0477776 , 0.04801608, 0.04825455, 0.04849302,\n",
+       "       0.04873149, 0.04896996, 0.04920843, 0.0494469 , 0.04968537,\n",
+       "       0.04992385, 0.05016232, 0.05040079, 0.05063926, 0.05087773,\n",
+       "       0.0511162 , 0.05135467, 0.05159314, 0.05183162, 0.05207009,\n",
+       "       0.05230856, 0.05254703, 0.0527855 , 0.05302397, 0.05326244,\n",
+       "       0.05350091, 0.05373939, 0.05397786, 0.05421633, 0.0544548 ,\n",
+       "       0.05469327, 0.05493174, 0.05517021, 0.05540868, 0.05564716,\n",
+       "       0.05588563, 0.0561241 , 0.05636257, 0.05660104, 0.05683951,\n",
+       "       0.05707798, 0.05731646, 0.05755493, 0.0577934 , 0.05803187,\n",
+       "       0.05827034, 0.05850881, 0.05874728, 0.05898575, 0.05922423,\n",
+       "       0.0594627 , 0.05970117, 0.05993964, 0.06017811, 0.06041658,\n",
+       "       0.06065505, 0.06089352, 0.061132  , 0.06137047, 0.06160894,\n",
+       "       0.06184741, 0.06208588, 0.06232435, 0.06256282, 0.06280129,\n",
+       "       0.06303977, 0.06327824, 0.06351671, 0.06375518, 0.06399365,\n",
+       "       0.06423212, 0.06447059, 0.06470906, 0.06494754, 0.06518601,\n",
+       "       0.06542448, 0.06566295, 0.06590142, 0.06613989, 0.06637836,\n",
+       "       0.06661684, 0.06685531, 0.06709378, 0.06733225, 0.06757072,\n",
+       "       0.06780919, 0.06804766, 0.06828613, 0.06852461, 0.06876308,\n",
+       "       0.06900155, 0.06924002, 0.06947849, 0.06971696, 0.06995543,\n",
+       "       0.0701939 , 0.07043238, 0.07067085, 0.07090932, 0.07114779,\n",
+       "       0.07138626, 0.07162473, 0.0718632 , 0.07210167, 0.07234015,\n",
+       "       0.07257862, 0.07281709, 0.07305556, 0.07329403, 0.0735325 ,\n",
+       "       0.07377097, 0.07400944, 0.07424792, 0.07448639, 0.07472486,\n",
+       "       0.07496333, 0.0752018 , 0.07544027, 0.07567874, 0.07591722,\n",
+       "       0.07615569, 0.07639416, 0.07663263, 0.0768711 , 0.07710957,\n",
+       "       0.07734804, 0.07758651, 0.07782499, 0.07806346, 0.07830193,\n",
+       "       0.0785404 , 0.07877887, 0.07901734, 0.07925581, 0.07949428,\n",
+       "       0.07973276, 0.07997123, 0.0802097 , 0.08044817, 0.08068664,\n",
+       "       0.08092511, 0.08116358, 0.08140205, 0.08164053, 0.081879  ,\n",
+       "       0.08211747, 0.08235594, 0.08259441, 0.08283288, 0.08307135,\n",
+       "       0.08330982, 0.0835483 , 0.08378677, 0.08402524, 0.08426371,\n",
+       "       0.08450218, 0.08474065, 0.08497912, 0.0852176 , 0.08545607,\n",
+       "       0.08569454, 0.08593301, 0.08617148, 0.08640995, 0.08664842,\n",
+       "       0.08688689, 0.08712537, 0.08736384, 0.08760231, 0.08784078,\n",
+       "       0.08807925, 0.08831772, 0.08855619, 0.08879466, 0.08903314,\n",
+       "       0.08927161, 0.08951008, 0.08974855, 0.08998702, 0.09022549,\n",
+       "       0.09046396, 0.09070243, 0.09094091, 0.09117938, 0.09141785,\n",
+       "       0.09165632, 0.09189479, 0.09213326, 0.09237173, 0.0926102 ,\n",
+       "       0.09284868, 0.09308715, 0.09332562, 0.09356409, 0.09380256,\n",
+       "       0.09404103, 0.0942795 , 0.09451798, 0.09475645, 0.09499492,\n",
+       "       0.09523339, 0.09547186, 0.09571033, 0.0959488 , 0.09618727,\n",
+       "       0.09642575, 0.09666422, 0.09690269, 0.09714116, 0.09737963,\n",
+       "       0.0976181 , 0.09785657, 0.09809504, 0.09833352, 0.09857199,\n",
+       "       0.09881046, 0.09904893, 0.0992874 , 0.09952587, 0.09976434,\n",
+       "       0.10000281, 0.10024129, 0.10047976, 0.10071823, 0.1009567 ,\n",
+       "       0.10119517, 0.10143364, 0.10167211, 0.10191058, 0.10214906,\n",
+       "       0.10238753, 0.102626  , 0.10286447, 0.10310294, 0.10334141,\n",
+       "       0.10357988, 0.10381836, 0.10405683, 0.1042953 , 0.10453377,\n",
+       "       0.10477224, 0.10501071, 0.10524918, 0.10548765, 0.10572613,\n",
+       "       0.1059646 , 0.10620307, 0.10644154, 0.10668001, 0.10691848,\n",
+       "       0.10715695, 0.10739542, 0.1076339 , 0.10787237, 0.10811084,\n",
+       "       0.10834931, 0.10858778, 0.10882625, 0.10906472, 0.10930319,\n",
+       "       0.10954167, 0.10978014, 0.11001861, 0.11025708, 0.11049555,\n",
+       "       0.11073402, 0.11097249, 0.11121097, 0.11144944, 0.11168791,\n",
+       "       0.11192638, 0.11216485, 0.11240332, 0.11264179, 0.11288026,\n",
+       "       0.11311874, 0.11335721, 0.11359568, 0.11383415, 0.11407262,\n",
+       "       0.11431109, 0.11454956, 0.11478803, 0.11502651, 0.11526498,\n",
+       "       0.11550345, 0.11574192, 0.11598039, 0.11621886, 0.11645733,\n",
+       "       0.1166958 , 0.11693428, 0.11717275, 0.11741122, 0.11764969,\n",
+       "       0.11788816, 0.11812663, 0.1183651 , 0.11860357, 0.11884205,\n",
+       "       0.11908052, 0.11931899, 0.11955746, 0.11979593, 0.1200344 ,\n",
+       "       0.12027287, 0.12051135, 0.12074982, 0.12098829, 0.12122676,\n",
+       "       0.12146523, 0.1217037 , 0.12194217, 0.12218064, 0.12241912,\n",
+       "       0.12265759, 0.12289606, 0.12313453, 0.123373  , 0.12361147,\n",
+       "       0.12384994, 0.12408841, 0.12432689, 0.12456536, 0.12480383,\n",
+       "       0.1250423 , 0.12528077, 0.12551924, 0.12575771, 0.12599618,\n",
+       "       0.12623466, 0.12647313, 0.1267116 , 0.12695007, 0.12718854,\n",
+       "       0.12742701, 0.12766548, 0.12790395, 0.12814243, 0.1283809 ,\n",
+       "       0.12861937, 0.12885784, 0.12909631, 0.12933478, 0.12957325,\n",
+       "       0.12981173, 0.1300502 , 0.13028867, 0.13052714, 0.13076561,\n",
+       "       0.13100408, 0.13124255, 0.13148102, 0.1317195 , 0.13195797,\n",
+       "       0.13219644, 0.13243491, 0.13267338, 0.13291185, 0.13315032,\n",
+       "       0.13338879, 0.13362727, 0.13386574, 0.13410421, 0.13434268,\n",
+       "       0.13458115])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.247, -0.242, ..., 0.242, 0.247
              Values:
              array([-0.2475, -0.2425, -0.2375, -0.2325, -0.2275, -0.2225, -0.2175,\n",
+       "       -0.2125, -0.2075, -0.2025, -0.1975, -0.1925, -0.1875, -0.1825,\n",
+       "       -0.1775, -0.1725, -0.1675, -0.1625, -0.1575, -0.1525, -0.1475,\n",
+       "       -0.1425, -0.1375, -0.1325, -0.1275, -0.1225, -0.1175, -0.1125,\n",
+       "       -0.1075, -0.1025, -0.0975, -0.0925, -0.0875, -0.0825, -0.0775,\n",
+       "       -0.0725, -0.0675, -0.0625, -0.0575, -0.0525, -0.0475, -0.0425,\n",
+       "       -0.0375, -0.0325, -0.0275, -0.0225, -0.0175, -0.0125, -0.0075,\n",
+       "       -0.0025,  0.0025,  0.0075,  0.0125,  0.0175,  0.0225,  0.0275,\n",
+       "        0.0325,  0.0375,  0.0425,  0.0475,  0.0525,  0.0575,  0.0625,\n",
+       "        0.0675,  0.0725,  0.0775,  0.0825,  0.0875,  0.0925,  0.0975,\n",
+       "        0.1025,  0.1075,  0.1125,  0.1175,  0.1225,  0.1275,  0.1325,\n",
+       "        0.1375,  0.1425,  0.1475,  0.1525,  0.1575,  0.1625,  0.1675,\n",
+       "        0.1725,  0.1775,  0.1825,  0.1875,  0.1925,  0.1975,  0.2025,\n",
+       "        0.2075,  0.2125,  0.2175,  0.2225,  0.2275,  0.2325,  0.2375,\n",
+       "        0.2425,  0.2475])
            • z
              ()
              float64
              m
              60.5
              Values:
              array(60.5)
            • (t, y, x)
              int64
              counts
              0, 0, ..., 1, 4
              Values:
              array([[[ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  6],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        ...,\n",
+       "        [14,  7,  0, ...,  0,  0,  0],\n",
+       "        [13,  0,  0, ...,  9,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0]],\n",
+       "\n",
+       "       [[ 0,  0,  1, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        ...,\n",
+       "        [ 0,  0,  0, ...,  0,  9,  0],\n",
+       "        [14,  9,  0, ..., 11,  0,  0],\n",
+       "        [ 0,  1,  0, ...,  0,  0,  0]],\n",
+       "\n",
+       "       [[ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  3,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  4,  0, ..., 11,  0,  0],\n",
+       "        ...,\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0,  0, ...,  0,  0,  0],\n",
+       "        [ 0,  0, 21, ...,  0,  9,  0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 7,  3,  2, ...,  4,  5,  3],\n",
+       "        [ 2,  3,  2, ...,  2,  2,  2],\n",
+       "        [ 3,  5,  2, ...,  4,  4,  3],\n",
+       "        ...,\n",
+       "        [ 3,  2,  3, ...,  3,  4,  6],\n",
+       "        [ 4,  5,  6, ...,  3,  2,  2],\n",
+       "        [ 3,  1,  2, ...,  3,  5,  0]],\n",
+       "\n",
+       "       [[ 4,  5,  2, ...,  1,  1,  0],\n",
+       "        [ 1,  4,  6, ...,  5,  4,  4],\n",
+       "        [ 1,  4,  3, ...,  3,  1,  5],\n",
+       "        ...,\n",
+       "        [ 1,  5,  4, ...,  3,  3,  2],\n",
+       "        [ 3,  3,  4, ...,  1,  3,  3],\n",
+       "        [ 4,  4,  4, ...,  6,  3,  3]],\n",
+       "\n",
+       "       [[ 1,  3,  4, ...,  5,  4,  4],\n",
+       "        [ 2,  3,  2, ...,  2,  3,  2],\n",
+       "        [ 4,  1,  2, ...,  3,  0,  3],\n",
+       "        ...,\n",
+       "        [ 2,  2,  2, ...,  2,  2,  1],\n",
+       "        [ 2,  5,  1, ...,  2,  2,  1],\n",
+       "        [ 4,  2,  2, ...,  1,  1,  4]]], shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[t:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (t [bin-edge])  [0.0153455, 0.015584, ..., 0.134343, 0.134581]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.2475, -0.2425, ..., 0.2425, 0.2475]\n",
+       "  z                         float64              [m]  ()  60.5\n",
+       "Data:\n",
+       "                              int64         [counts]  (t, y, x)  [0, 0, ..., 1, 4]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipp as sc\n",
+    "import scitiff\n",
+    "\n",
+    "sample_da = scitiff.load_scitiff(\"sample_data/sample_5e11_mcstas.tiff\")['image'].astype(int)\n",
+    "display(sample_da)\n",
+    "openbeam_da = scitiff.load_scitiff(\"sample_data/open_beam_5e11_mcstas.tiff\")['image'].astype(int)\n",
+    "display(openbeam_da)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalize data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalize sample image by open beam and replace 0 counts in OB with 1 to avoid NaN's"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (38.15 MB)
            • t: 500
            • y: 100
            • x: 100
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (t [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01534551, 0.01558398, 0.01582245, 0.01606092, 0.01629939,\n",
+       "       0.01653787, 0.01677634, 0.01701481, 0.01725328, 0.01749175,\n",
+       "       0.01773022, 0.01796869, 0.01820716, 0.01844564, 0.01868411,\n",
+       "       0.01892258, 0.01916105, 0.01939952, 0.01963799, 0.01987646,\n",
+       "       0.02011493, 0.02035341, 0.02059188, 0.02083035, 0.02106882,\n",
+       "       0.02130729, 0.02154576, 0.02178423, 0.02202271, 0.02226118,\n",
+       "       0.02249965, 0.02273812, 0.02297659, 0.02321506, 0.02345353,\n",
+       "       0.023692  , 0.02393048, 0.02416895, 0.02440742, 0.02464589,\n",
+       "       0.02488436, 0.02512283, 0.0253613 , 0.02559977, 0.02583825,\n",
+       "       0.02607672, 0.02631519, 0.02655366, 0.02679213, 0.0270306 ,\n",
+       "       0.02726907, 0.02750754, 0.02774602, 0.02798449, 0.02822296,\n",
+       "       0.02846143, 0.0286999 , 0.02893837, 0.02917684, 0.02941531,\n",
+       "       0.02965379, 0.02989226, 0.03013073, 0.0303692 , 0.03060767,\n",
+       "       0.03084614, 0.03108461, 0.03132309, 0.03156156, 0.03180003,\n",
+       "       0.0320385 , 0.03227697, 0.03251544, 0.03275391, 0.03299238,\n",
+       "       0.03323086, 0.03346933, 0.0337078 , 0.03394627, 0.03418474,\n",
+       "       0.03442321, 0.03466168, 0.03490015, 0.03513863, 0.0353771 ,\n",
+       "       0.03561557, 0.03585404, 0.03609251, 0.03633098, 0.03656945,\n",
+       "       0.03680792, 0.0370464 , 0.03728487, 0.03752334, 0.03776181,\n",
+       "       0.03800028, 0.03823875, 0.03847722, 0.03871569, 0.03895417,\n",
+       "       0.03919264, 0.03943111, 0.03966958, 0.03990805, 0.04014652,\n",
+       "       0.04038499, 0.04062347, 0.04086194, 0.04110041, 0.04133888,\n",
+       "       0.04157735, 0.04181582, 0.04205429, 0.04229276, 0.04253124,\n",
+       "       0.04276971, 0.04300818, 0.04324665, 0.04348512, 0.04372359,\n",
+       "       0.04396206, 0.04420053, 0.04443901, 0.04467748, 0.04491595,\n",
+       "       0.04515442, 0.04539289, 0.04563136, 0.04586983, 0.0461083 ,\n",
+       "       0.04634678, 0.04658525, 0.04682372, 0.04706219, 0.04730066,\n",
+       "       0.04753913, 0.0477776 , 0.04801608, 0.04825455, 0.04849302,\n",
+       "       0.04873149, 0.04896996, 0.04920843, 0.0494469 , 0.04968537,\n",
+       "       0.04992385, 0.05016232, 0.05040079, 0.05063926, 0.05087773,\n",
+       "       0.0511162 , 0.05135467, 0.05159314, 0.05183162, 0.05207009,\n",
+       "       0.05230856, 0.05254703, 0.0527855 , 0.05302397, 0.05326244,\n",
+       "       0.05350091, 0.05373939, 0.05397786, 0.05421633, 0.0544548 ,\n",
+       "       0.05469327, 0.05493174, 0.05517021, 0.05540868, 0.05564716,\n",
+       "       0.05588563, 0.0561241 , 0.05636257, 0.05660104, 0.05683951,\n",
+       "       0.05707798, 0.05731646, 0.05755493, 0.0577934 , 0.05803187,\n",
+       "       0.05827034, 0.05850881, 0.05874728, 0.05898575, 0.05922423,\n",
+       "       0.0594627 , 0.05970117, 0.05993964, 0.06017811, 0.06041658,\n",
+       "       0.06065505, 0.06089352, 0.061132  , 0.06137047, 0.06160894,\n",
+       "       0.06184741, 0.06208588, 0.06232435, 0.06256282, 0.06280129,\n",
+       "       0.06303977, 0.06327824, 0.06351671, 0.06375518, 0.06399365,\n",
+       "       0.06423212, 0.06447059, 0.06470906, 0.06494754, 0.06518601,\n",
+       "       0.06542448, 0.06566295, 0.06590142, 0.06613989, 0.06637836,\n",
+       "       0.06661684, 0.06685531, 0.06709378, 0.06733225, 0.06757072,\n",
+       "       0.06780919, 0.06804766, 0.06828613, 0.06852461, 0.06876308,\n",
+       "       0.06900155, 0.06924002, 0.06947849, 0.06971696, 0.06995543,\n",
+       "       0.0701939 , 0.07043238, 0.07067085, 0.07090932, 0.07114779,\n",
+       "       0.07138626, 0.07162473, 0.0718632 , 0.07210167, 0.07234015,\n",
+       "       0.07257862, 0.07281709, 0.07305556, 0.07329403, 0.0735325 ,\n",
+       "       0.07377097, 0.07400944, 0.07424792, 0.07448639, 0.07472486,\n",
+       "       0.07496333, 0.0752018 , 0.07544027, 0.07567874, 0.07591722,\n",
+       "       0.07615569, 0.07639416, 0.07663263, 0.0768711 , 0.07710957,\n",
+       "       0.07734804, 0.07758651, 0.07782499, 0.07806346, 0.07830193,\n",
+       "       0.0785404 , 0.07877887, 0.07901734, 0.07925581, 0.07949428,\n",
+       "       0.07973276, 0.07997123, 0.0802097 , 0.08044817, 0.08068664,\n",
+       "       0.08092511, 0.08116358, 0.08140205, 0.08164053, 0.081879  ,\n",
+       "       0.08211747, 0.08235594, 0.08259441, 0.08283288, 0.08307135,\n",
+       "       0.08330982, 0.0835483 , 0.08378677, 0.08402524, 0.08426371,\n",
+       "       0.08450218, 0.08474065, 0.08497912, 0.0852176 , 0.08545607,\n",
+       "       0.08569454, 0.08593301, 0.08617148, 0.08640995, 0.08664842,\n",
+       "       0.08688689, 0.08712537, 0.08736384, 0.08760231, 0.08784078,\n",
+       "       0.08807925, 0.08831772, 0.08855619, 0.08879466, 0.08903314,\n",
+       "       0.08927161, 0.08951008, 0.08974855, 0.08998702, 0.09022549,\n",
+       "       0.09046396, 0.09070243, 0.09094091, 0.09117938, 0.09141785,\n",
+       "       0.09165632, 0.09189479, 0.09213326, 0.09237173, 0.0926102 ,\n",
+       "       0.09284868, 0.09308715, 0.09332562, 0.09356409, 0.09380256,\n",
+       "       0.09404103, 0.0942795 , 0.09451798, 0.09475645, 0.09499492,\n",
+       "       0.09523339, 0.09547186, 0.09571033, 0.0959488 , 0.09618727,\n",
+       "       0.09642575, 0.09666422, 0.09690269, 0.09714116, 0.09737963,\n",
+       "       0.0976181 , 0.09785657, 0.09809504, 0.09833352, 0.09857199,\n",
+       "       0.09881046, 0.09904893, 0.0992874 , 0.09952587, 0.09976434,\n",
+       "       0.10000281, 0.10024129, 0.10047976, 0.10071823, 0.1009567 ,\n",
+       "       0.10119517, 0.10143364, 0.10167211, 0.10191058, 0.10214906,\n",
+       "       0.10238753, 0.102626  , 0.10286447, 0.10310294, 0.10334141,\n",
+       "       0.10357988, 0.10381836, 0.10405683, 0.1042953 , 0.10453377,\n",
+       "       0.10477224, 0.10501071, 0.10524918, 0.10548765, 0.10572613,\n",
+       "       0.1059646 , 0.10620307, 0.10644154, 0.10668001, 0.10691848,\n",
+       "       0.10715695, 0.10739542, 0.1076339 , 0.10787237, 0.10811084,\n",
+       "       0.10834931, 0.10858778, 0.10882625, 0.10906472, 0.10930319,\n",
+       "       0.10954167, 0.10978014, 0.11001861, 0.11025708, 0.11049555,\n",
+       "       0.11073402, 0.11097249, 0.11121097, 0.11144944, 0.11168791,\n",
+       "       0.11192638, 0.11216485, 0.11240332, 0.11264179, 0.11288026,\n",
+       "       0.11311874, 0.11335721, 0.11359568, 0.11383415, 0.11407262,\n",
+       "       0.11431109, 0.11454956, 0.11478803, 0.11502651, 0.11526498,\n",
+       "       0.11550345, 0.11574192, 0.11598039, 0.11621886, 0.11645733,\n",
+       "       0.1166958 , 0.11693428, 0.11717275, 0.11741122, 0.11764969,\n",
+       "       0.11788816, 0.11812663, 0.1183651 , 0.11860357, 0.11884205,\n",
+       "       0.11908052, 0.11931899, 0.11955746, 0.11979593, 0.1200344 ,\n",
+       "       0.12027287, 0.12051135, 0.12074982, 0.12098829, 0.12122676,\n",
+       "       0.12146523, 0.1217037 , 0.12194217, 0.12218064, 0.12241912,\n",
+       "       0.12265759, 0.12289606, 0.12313453, 0.123373  , 0.12361147,\n",
+       "       0.12384994, 0.12408841, 0.12432689, 0.12456536, 0.12480383,\n",
+       "       0.1250423 , 0.12528077, 0.12551924, 0.12575771, 0.12599618,\n",
+       "       0.12623466, 0.12647313, 0.1267116 , 0.12695007, 0.12718854,\n",
+       "       0.12742701, 0.12766548, 0.12790395, 0.12814243, 0.1283809 ,\n",
+       "       0.12861937, 0.12885784, 0.12909631, 0.12933478, 0.12957325,\n",
+       "       0.12981173, 0.1300502 , 0.13028867, 0.13052714, 0.13076561,\n",
+       "       0.13100408, 0.13124255, 0.13148102, 0.1317195 , 0.13195797,\n",
+       "       0.13219644, 0.13243491, 0.13267338, 0.13291185, 0.13315032,\n",
+       "       0.13338879, 0.13362727, 0.13386574, 0.13410421, 0.13434268,\n",
+       "       0.13458115])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.247, -0.242, ..., 0.242, 0.247
              Values:
              array([-0.2475, -0.2425, -0.2375, -0.2325, -0.2275, -0.2225, -0.2175,\n",
+       "       -0.2125, -0.2075, -0.2025, -0.1975, -0.1925, -0.1875, -0.1825,\n",
+       "       -0.1775, -0.1725, -0.1675, -0.1625, -0.1575, -0.1525, -0.1475,\n",
+       "       -0.1425, -0.1375, -0.1325, -0.1275, -0.1225, -0.1175, -0.1125,\n",
+       "       -0.1075, -0.1025, -0.0975, -0.0925, -0.0875, -0.0825, -0.0775,\n",
+       "       -0.0725, -0.0675, -0.0625, -0.0575, -0.0525, -0.0475, -0.0425,\n",
+       "       -0.0375, -0.0325, -0.0275, -0.0225, -0.0175, -0.0125, -0.0075,\n",
+       "       -0.0025,  0.0025,  0.0075,  0.0125,  0.0175,  0.0225,  0.0275,\n",
+       "        0.0325,  0.0375,  0.0425,  0.0475,  0.0525,  0.0575,  0.0625,\n",
+       "        0.0675,  0.0725,  0.0775,  0.0825,  0.0875,  0.0925,  0.0975,\n",
+       "        0.1025,  0.1075,  0.1125,  0.1175,  0.1225,  0.1275,  0.1325,\n",
+       "        0.1375,  0.1425,  0.1475,  0.1525,  0.1575,  0.1625,  0.1675,\n",
+       "        0.1725,  0.1775,  0.1825,  0.1875,  0.1925,  0.1975,  0.2025,\n",
+       "        0.2075,  0.2125,  0.2175,  0.2225,  0.2275,  0.2325,  0.2375,\n",
+       "        0.2425,  0.2475])
            • z
              ()
              float64
              m
              60.5
              Values:
              array(60.5)
            • (t, y, x)
              float64
              𝟙
              0.0, 0.0, ..., 0.0, 0.75
              Values:
              array([[[ 0.        ,  0.        ,  0.        , ...,  2.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 2.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        ...,\n",
+       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        , 10.        ],\n",
+       "        [ 0.        ,  0.        ,  0.        , ..., 17.        ,\n",
+       "          0.        ,  6.        ]],\n",
+       "\n",
+       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 0.        ,  0.        ,  0.        , ...,  5.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 2.        ,  0.        ,  0.        , ...,  5.        ,\n",
+       "          5.        ,  0.        ],\n",
+       "        ...,\n",
+       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 1.42857143,  0.        , 10.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 0.        , 13.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ]],\n",
+       "\n",
+       "       [[ 0.        ,  1.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [11.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        ...,\n",
+       "        [18.        ,  0.        , 16.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ],\n",
+       "        [ 0.        ,  0.        , 14.        , ...,  0.        ,\n",
+       "         18.        ,  0.        ],\n",
+       "        [17.        ,  1.        ,  0.        , ...,  0.        ,\n",
+       "          0.        ,  0.        ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[ 0.14285714,  1.66666667,  2.        , ...,  0.75      ,\n",
+       "          0.6       ,  0.33333333],\n",
+       "        [ 0.5       ,  1.66666667,  1.5       , ...,  1.        ,\n",
+       "          1.        ,  3.        ],\n",
+       "        [ 1.33333333,  0.6       ,  3.        , ...,  1.        ,\n",
+       "          1.25      ,  1.66666667],\n",
+       "        ...,\n",
+       "        [ 1.33333333,  1.5       ,  1.        , ...,  0.66666667,\n",
+       "          1.25      ,  0.33333333],\n",
+       "        [ 1.75      ,  1.4       ,  0.5       , ...,  1.66666667,\n",
+       "          2.        ,  1.5       ],\n",
+       "        [ 1.        ,  2.        ,  2.        , ...,  1.        ,\n",
+       "          0.2       ,  3.        ]],\n",
+       "\n",
+       "       [[ 0.5       ,  0.6       ,  2.        , ...,  5.        ,\n",
+       "          1.        ,  3.        ],\n",
+       "        [ 2.        ,  0.25      ,  0.66666667, ...,  0.6       ,\n",
+       "          0.5       ,  0.5       ],\n",
+       "        [ 1.        ,  1.25      ,  0.66666667, ...,  0.        ,\n",
+       "          1.        ,  0.2       ],\n",
+       "        ...,\n",
+       "        [ 2.        ,  0.        ,  0.5       , ...,  0.66666667,\n",
+       "          0.33333333,  1.        ],\n",
+       "        [ 1.        ,  1.        ,  0.5       , ...,  4.        ,\n",
+       "          1.        ,  1.33333333],\n",
+       "        [ 1.        ,  0.5       ,  0.5       , ...,  0.33333333,\n",
+       "          1.66666667,  1.        ]],\n",
+       "\n",
+       "       [[ 0.        ,  0.33333333,  0.5       , ...,  0.2       ,\n",
+       "          0.25      ,  0.25      ],\n",
+       "        [ 1.        ,  0.        ,  1.5       , ...,  0.        ,\n",
+       "          0.        ,  1.5       ],\n",
+       "        [ 0.25      ,  2.        ,  0.        , ...,  1.        ,\n",
+       "          0.        ,  0.66666667],\n",
+       "        ...,\n",
+       "        [ 1.5       ,  1.5       ,  0.5       , ...,  1.5       ,\n",
+       "          1.        ,  3.        ],\n",
+       "        [ 1.5       ,  0.4       ,  1.        , ...,  1.        ,\n",
+       "          0.5       ,  3.        ],\n",
+       "        [ 0.25      ,  1.        ,  1.        , ...,  2.        ,\n",
+       "          0.        ,  0.75      ]]], shape=(500, 100, 100))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[t:500, y:100, x:100, ]\n",
+       "Coordinates:\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (t [bin-edge])  [0.0153455, 0.015584, ..., 0.134343, 0.134581]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.2475, -0.2425, ..., 0.2425, 0.2475]\n",
+       "  z                         float64              [m]  ()  60.5\n",
+       "Data:\n",
+       "                            float64  [dimensionless]  (t, y, x)  [0, 0, ..., 0, 0.75]\n"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from ess.imaging.normalize import (\n",
+    "    apply_threshold_to_background_image,\n",
+    "    apply_threshold_to_sample_images,\n",
+    ")\n",
+    "\n",
+    "sample_threshold = apply_threshold_to_sample_images(samples=sample_da, sample_threshold=sc.scalar(0, unit=\"counts\"))\n",
+    "openbeam_threshold = apply_threshold_to_background_image(background=openbeam_da, background_threshold=sc.scalar(1, unit=\"counts\"))\n",
+    "normalized = sample_threshold / openbeam_threshold\n",
+    "normalized"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the normalized image with more tick labels to better identify sample region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[,\n",
+       " ,\n",
+       " ,\n",
+       " ,\n",
+       " ,\n",
+       " ,\n",
+       " ,\n",
+       " ,\n",
+       " ]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = normalized.mean('t').plot(aspect='auto', title=\"Normalized Image\", grid=True)\n",
+    "fig.ax.set_xticks(np.linspace(0.1,-0.1,9))\n",
+    "fig.ax.set_yticks(np.linspace(0.2,-0.2,9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef00b0ddc0ea4e77bc7594b04a7fa10a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Region Of Interest (ROI) selector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scitiff_helper import make_rectangle_tool\n",
+    "roi_container = set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rename \"t\" dimension to \"tof\" because Scipp workflow requires it to be called \"tof\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_renamed = normalized.rename_dims({'t': \"tof\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the widget below, choose the empty white box at the bottom left and drag and drop to define your ROI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69b2669971db480bb7460462a34a4d5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBar(children=(InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(…"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_rectangle_tool(normalized_renamed, roi_container=roi_container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the defined ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROI=roi_container.pop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the ROI and its spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Slice the normalized image with the defined ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi = ROI.slice_dataarray(normalized_renamed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the sliced image to ensure it is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee23401cc4b54d42a4cbe9d29713e842",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_roi.mean('tof').plot(\n",
+    "    aspect='auto', title='Normalized Region of interest', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the spectrum of the ROI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d75aa181934f4e7ab3cb42bac06f983f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_roi.mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Full Spectrum of normalized ROI', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert time-of-flight to wavelength using scippneutron"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, define the conversion graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "total_straight_beam_length_no_scatter(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)->Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "wavelength_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "energy_from_tof(...)\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "source_position\n",
+       "\n",
+       "\n",
+       "\n",
+       "source_position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "position\n",
+       "\n",
+       "position\n",
+       "\n",
+       "\n",
+       "\n",
+       "position->Compute (Ltotal) = total_straight_beam_length_no_scatter(source_position, position)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (wavelength) = wavelength_from_tof(tof, Ltotal)->wavelength\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "tof\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (wavelength) = wavelength_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "tof->Compute (energy) = energy_from_tof(tof, Ltotal)\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "Compute (energy) = energy_from_tof(tof, Ltotal)->energy\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scippneutron.conversion import graph\n",
+    "\n",
+    "PLAIN_GRAPH = {**graph.beamline.beamline(False), **graph.tof.kinematic(\"tof\")}\n",
+    "sc.show_graph(PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, add the flight-path because it isn't present in the SciTiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalized_roi_renamed = normalized_roi.copy(deep=False)\n",
+    "normalized_roi_renamed.coords['Ltotal'] = sc.scalar(60.5, unit='m')\n",
+    "wavelength_transformed = normalized_roi_renamed.transform_coords('wavelength', graph=PLAIN_GRAPH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7aac92663ff14fc5bc86570f319ad9ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wavelength_transformed.mean('x').mean('y').plot(\n",
+    "    aspect='equal', title='Full Spectrum of ROI in wavelength', grid=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cut off lower and upper bounds of spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_bound = 1.5 * sc.Unit('Å')\n",
+    "upper_bound = 5.0 * sc.Unit('Å')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sliced_spectrum = wavelength_transformed.mean('x').mean('y')['wavelength', lower_bound:upper_bound]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34e35c9b07934ab0a85cf476922070a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sliced_spectrum.plot(aspect='equal', title='Sliced Spectrum of ROI in wavelength', grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the transmission and wavelengths for fitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wavelengths = sc.midpoints(sliced_spectrum.coords['wavelength']).values\n",
+    "transmissions = sliced_spectrum.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Physical model with Ncrystal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NCrystal\n",
+    "import NCrystal.plot\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We initalize the model\n",
+    "mat = NCrystal.NCMATComposer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We set the model parameters to Iron (slightly off on purpose)\n",
+    "mat.set_cellsg_cubic(2.90)\n",
+    "mat.set_atompos([   ('Fe',0,0,0),\n",
+    "                    ('Fe',0.5, 0.5, 0.5),\n",
+    "                    ])\n",
+    "mat.set_dyninfo_debyetemp(\"Fe\", debye_temp=356)\n",
+    "mat.set_default_temperature(250)\n",
+    "mat.refine_crystal_structure(quiet=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------------------------------------------\n",
+      "------------------------------------   NCrystal Material Info   ------------------------------------\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Data source: \n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Density : 7.60478 g/cm3, 0.0820042 atoms/Aa^3\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mole): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Composition (by mass): 100% Fe\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atom data:\n",
+      "   Fe = Fe(cohSL=9.45fm cohXS=11.2221barn incXS=0.4barn absXS=2.56barn mass=55.8472u Z=26)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Averaged quantities:\n",
+      "   Atomic mass               : 55.8472u\n",
+      "   Absorption XS at 2200m/s  : 2.56 barn\n",
+      "   Free scattering XS        : 11.2134 barn\n",
+      "   Scattering length density : 7.7494 10^-6/Aa^2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Temperature : 250 kelvin\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "State of matter: Solid (crystalline)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Space group number      : 229\n",
+      "Lattice spacings   [Aa] : 2.9 2.9 2.9\n",
+      "Lattice angles    [deg] : 90 90 90\n",
+      "Unit cell volume [Aa^3] : 24.389\n",
+      "Atoms / unit cell       : 2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atoms in unit cell (total 2):\n",
+      "     2 Fe atoms [T_Debye=356K, MSD=0.00542405Aa^2]\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Atomic coordinates:\n",
+      "     Fe            0            0            0\n",
+      "     Fe          1/2          1/2          1/2\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Dynamic info for Fe (100%):\n",
+      "   type: S(alpha,beta) [from VDOSDebye]\n",
+      "   VDOS E_max: 30.6777 meV\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL info type: SymEqvGroup\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "HKL planes (d_lower = 0.1 Aa, d_upper = inf Aa):\n",
+      "  H   K   L  d_hkl[Aa] Mult. FSquared[barn]\n",
+      "  1   1   0    2.05061   12     3.39475\n",
+      "  2   0   0       1.45    6     3.22621\n",
+      "  2   1   1    1.18392   24     3.06603\n",
+      "  2   2   0     1.0253   12     2.91381\n",
+      "  3   1   0   0.917061   24     2.76914\n",
+      "  2   2   2   0.837158    8     2.63166\n",
+      "  3   2   1   0.775058   48       2.501\n",
+      "  4   0   0      0.725    6     2.37683\n",
+      "  4   1   1   0.683537   24     2.25882\n",
+      "  3   3   0   0.683537   12     2.25882\n",
+      "  (some planes left out for brevity, increase verbosity to show all)\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Absorption process (objects):\n",
+      "  AbsOOV(sigma_2200=2.56barn)\n",
+      "Scattering process (objects):\n",
+      "  ProcComposition(3 components, isotropic)\n",
+      "     |-- ElIncScatter(nelements=1;max_contrib=0.4barn)\n",
+      "     |-- PowderBragg(nplanes=231;2dmax=4.10122Aa;max_contrib=14.4027barn)\n",
+      "     \\-- SABScatter(nalpha=50;nbeta=100;Emax=0.5eV;T=250K;M=55.8472u;sigma_free=11.2134barn)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Information about the model can be viewed with this method\n",
+    "mat.inspect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NCMAT v7\n",
+      "# Autogenerated by NCMATComposer\n",
+      "#\n",
+      "# Fe (cubic, SG-229)\n",
+      "#\n",
+      "# Atoms per unit cell: 2xFe\n",
+      "#\n",
+      "# NOTICE: crystal structure was verified with spglib to be self-consistent.\n",
+      "#\n",
+      "@CELL\n",
+      "  cubic 2.9\n",
+      "@SPACEGROUP\n",
+      "  229\n",
+      "@ATOMPOSITIONS\n",
+      "  Fe 0 0 0\n",
+      "  Fe 1/2 1/2 1/2\n",
+      "@TEMPERATURE\n",
+      "  default 250\n",
+      "@DYNINFO\n",
+      "  element Fe\n",
+      "  fraction 1\n",
+      "  type vdosdebye\n",
+      "  debye_temp 356\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And a short summary can be viewed by printing\n",
+    "print(mat())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing Data sets and initial model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculating the simulated spectrum using this model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter = mat.load(#cfg_params=\"comp=inelas\"\n",
+    "                    )\n",
+    "absorptions = scatter.absorption.xsect(wl=wavelengths)\n",
+    "scattered = scatter.scatter.xsect(wl=wavelengths)\n",
+    "# Using an arbitrary scale constant of 0.05 to scale cross sections and absorptions. Depends on width of material in a real model.\n",
+    "simulated = np.exp(-0.1*(absorptions+scattered))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='McStas Data')\n",
+    "#plt.plot(wavelengths, transmissions, label=\"Data\")\n",
+    "plt.plot(wavelengths, simulated, label=\"BCC Iron Model\")\n",
+    "plt.title(\"Pre-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bragg-edge fitting with Ncrystal and EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from easyscience.Objects.variable import Parameter\n",
+    "from easyscience.Objects.ObjectClasses import BaseObj\n",
+    "from easyscience.fitting import Fitter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the fitting models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fitting parameters and their initial values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lattice_constant =  Parameter(name=\"Lattice constant\",          value=2.85, unit=\"angstrom\",    min=0,  max=4,    fixed=False)\n",
+    "debye_temperature = Parameter(name=\"Debye temperature\",         value=300,  unit=\"kelvin\",      min=250,  max=800,  fixed=False)\n",
+    "temperature =       Parameter(name=\"Temperature\",               value=300,  unit=\"kelvin\",      min=0,  max=600,  fixed=False)\n",
+    "intensity_scale =   Parameter(name=\"Intensity scale factor\",    value=0.15,                      fixed=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the fit functions to go into EasyScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iron_bragg(wavelength):\n",
+    "    # Initalize the NCrystal material composer\n",
+    "    material = NCrystal.NCMATComposer()\n",
+    "    # Set the material parameters with the fitting variables\n",
+    "    material.set_cellsg_cubic(lattice_constant.value)\n",
+    "    material.set_atompos([  ('Fe',0,0,0),\n",
+    "                            ('Fe',0.5, 0.5, 0.5),\n",
+    "                            ])\n",
+    "    material.set_dyninfo_debyetemp(\"Fe\", debye_temp=debye_temperature.value)\n",
+    "    material.set_default_temperature(temperature.value)\n",
+    "\n",
+    "    # Identify the space group from atomic positions\n",
+    "    material.refine_crystal_structure(quiet=True)\n",
+    "\n",
+    "    #print(\"lattice constant:\", lattice_constant.value)\n",
+    "    scatter = material.load()\n",
+    "\n",
+    "    # Create scattering spectrum\n",
+    "    spectrum = np.exp(-intensity_scale.value*(scatter.scatter.xsect(wl=wavelength) + scatter.absorption.xsect(wl=wavelength)))\n",
+    "    return spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience container for the fitting parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iron_spectrum = BaseObj(\n",
+    "                        name='Iron transmission spectrum', \n",
+    "                        lattice_constant=lattice_constant, \n",
+    "                        debye_temperature=debye_temperature,\n",
+    "                        temperature=temperature,\n",
+    "                        intensity_scale=intensity_scale,\n",
+    "                        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make the EasyScience fitting objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitter = Fitter(iron_spectrum, iron_bragg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EasyScience can fit data with a variety of different minimizers. To list the available methods, use the \"available_minimizers\" method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LMFit',\n",
+       " 'LMFit_leastsq',\n",
+       " 'LMFit_powell',\n",
+       " 'LMFit_cobyla',\n",
+       " 'LMFit_differential_evolution',\n",
+       " 'LMFit_scipy_least_squares',\n",
+       " 'Bumps',\n",
+       " 'Bumps_simplex',\n",
+       " 'Bumps_newton',\n",
+       " 'Bumps_lm',\n",
+       " 'DFO',\n",
+       " 'DFO_leastsq']"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitter.available_minimizers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the fits in this notebook, we will use \"LMFit_leastsq\" which is a standard leastsquares fitting method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minimizer should be set with enum LMFit_leastsq\n"
+     ]
+    }
+   ],
+   "source": [
+    "fitter.switch_minimizer(\"LMFit_leastsq\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing the actual Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for LMFit_leastsq\n",
+    "results = fitter.fit(x=wavelengths, y=transmissions, \n",
+    "                     #weights=1/np.sqrt(transmissions_variances), \n",
+    "                     minimizer_kwargs={\"epsfcn\":0.005,\n",
+    "                                       \"ftol\":1.49012e-15,\n",
+    "                                       \"xtol\":1.49012e-15,\n",
+    "                                       },\n",
+    "                    #max_nfev=10000\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print the fitted values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lattice_constant)\n",
+    "print(debye_temperature)\n",
+    "print(temperature)\n",
+    "print(intensity_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing the fitted model and data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we need to calculate the spectras with the fitted models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulated = iron_bragg(wavelengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(wavelengths, transmissions, label='MCStas Data')\n",
+    "plt.plot(wavelengths, simulated, label=\"BCC Iron Model fit to MCStas data\")\n",
+    "plt.title(\"Post-fitting comparison\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "STAP25",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ProofOfConcept/mcstas_to_scitiff_wfm.ipynb b/ProofOfConcept/mcstas_to_scitiff_wfm.ipynb
new file mode 100644
index 0000000..f095f9a
--- /dev/null
+++ b/ProofOfConcept/mcstas_to_scitiff_wfm.ipynb
@@ -0,0 +1,14973 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Bragg-edge imaging with ODIN\n",
+    "\n",
+    "This notebook illustrates how to convert recorded events on the ODIN detector to a single wavelength spectrum,\n",
+    "revealing a Bragg edge in the data.\n",
+    "WFM mode was used in the chopper cascade."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## Loading dataset\n",
+    "\n",
+    "> Loader is not part of ``essimaging`` since McStas dataset format is not stabilized yet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipp as sc\n",
+    "import sciline as sl\n",
+    "import pathlib\n",
+    "\n",
+    "from odin_mcstas_helper import (\n",
+    "    McStasManualResolution,\n",
+    "    load_choppers,\n",
+    "    PLAIN_GRAPH,\n",
+    "    load_mcstas_data,\n",
+    ")\n",
+    "\n",
+    "example_resolution = McStasManualResolution((100, 100))\n",
+    "# Small resolution for faster testing and documentation build.\n",
+    "\n",
+    "# sample_file_path = pathlib.Path(\"mcstas_data/sample/\")\n",
+    "sample_file_path = pathlib.Path(\"mcstas_data/sample_5e11/\")\n",
+    "\n",
+    "# openbeam_file_path = pathlib.Path(\"mcstas_data/open_beam/\")\n",
+    "openbeam_file_path = pathlib.Path(\"mcstas_data/open_beam_5e11/\")\n",
+    "\n",
+    "file_dir_path = sample_file_path\n",
+    "# file_dir_path = openbeam_file_path\n",
+    "\n",
+    "TOF_BIN_EDGES = sc.linspace(\n",
+    "    dim='tof', start=0.015345509212344533, stop=0.134581150782376, num=501, unit=\"s\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "673089f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (11.23 GB)
            • pixel_id: 10000
            • Ltotal
              (pixel_id)
              float64
              m
              60.501, 60.501, ..., 60.501, 60.501
              Values:
              array([60.50063281, 60.50062775, 60.50062279, ..., 60.50062279,\n",
+       "       60.50062775, 60.50063281], shape=(10000,))
            • pixel_id
              (pixel_id)
              int64
              𝟙
              0, 1, ..., 9998, 9999
              Values:
              array([   0,    1,    2, ..., 9997, 9998, 9999], shape=(10000,))
            • position
              (pixel_id)
              vector3
              m
              [-0.12375 -0.2475  60.5    ], [-0.12125 -0.2475  60.5    ], ..., [ 0.12125  0.2475  60.5    ], [ 0.12375  0.2475  60.5    ]
              Values:
              array([[-0.12375, -0.2475 , 60.5    ],\n",
+       "       [-0.12125, -0.2475 , 60.5    ],\n",
+       "       [-0.11875, -0.2475 , 60.5    ],\n",
+       "       ...,\n",
+       "       [ 0.11875,  0.2475 , 60.5    ],\n",
+       "       [ 0.12125,  0.2475 , 60.5    ],\n",
+       "       [ 0.12375,  0.2475 , 60.5    ]], shape=(10000, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • (pixel_id)
              float64
              counts
              binned data [len=8194, len=8223, ..., len=8605, len=8495]
              dim='events',\n",
+       "content=DataArray(\n",
+       "          dims=(events: 137029864),\n",
+       "          data=float64[counts],\n",
+       "          coords={'position':vector3[m], 'x':float64[m], 'y':float64[m],\n",
+       "                  'sim_wavelength':float64[Å], 'event_time_zero':datetime64[ns],\n",
+       "                  'event_time_offset':float64[s], 'Ltotal':float64[m]})
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[pixel_id:10000, ]\n",
+       "Coordinates:\n",
+       "* Ltotal                    float64              [m]  (pixel_id)  [60.5006, 60.5006, ..., 60.5006, 60.5006]\n",
+       "* pixel_id                    int64  [dimensionless]  (pixel_id)  [0, 1, ..., 9998, 9999]\n",
+       "  position                  vector3              [m]  (pixel_id)  [(-0.12375, -0.2475, 60.5), (-0.12125, -0.2475, 60.5), ..., (0.12125, 0.2475, 60.5), (0.12375, 0.2475, 60.5)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "  source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "Data:\n",
+       "                          DataArrayView          (pixel_id)  binned data: dim='events', content=DataArray(\n",
+       "          dims=(events: 137029864),\n",
+       "          data=float64[counts],\n",
+       "          coords={'position':vector3[m], 'x':float64[m], 'y':float64[m],\n",
+       "                  'sim_wavelength':float64[Å], 'event_time_zero':datetime64[ns],\n",
+       "                  'event_time_offset':float64[s], 'Ltotal':float64[m]})\n"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da = load_mcstas_data(file_dir_path, resolution=example_resolution, graph=PLAIN_GRAPH)\n",
+    "da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       "\n",
+       "\n",
+       " \n",
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+       "    2025-04-03T13:07:06.368702\n",
+       "    image/svg+xml\n",
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+       "      Matplotlib v3.9.2, https://matplotlib.org/\n",
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+       "  \n",
+       " \n",
+       " \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Visualize\n",
+    "fig_raw = (\n",
+    "    da.bins.concat()\n",
+    "    .hist(event_time_offset=300)\n",
+    "    .plot(title='Before frame unwrapping', grid=True)\n",
+    ")\n",
+    "fig_raw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28afb5c49e2a43cab69e1779780b47a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Figure(children=(HBar(children=(HTML(value=''),)), HBox(children=(VBar(children=(Toolbar(children=(ButtonTool(…"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plopp as pp\n",
+    "\n",
+    "pp.scatter3d(da.hist(), pos='position', cbar=True, pixel_size=0.005)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Choppers\n",
+    "\n",
+    "To accurately compute the wavelengths of the neutrons from their time-of-arrival,\n",
+    "we need the parameters of the choppers in the beamline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b9bc0e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "component_text_file_path = pathlib.Path(\n",
+    "    \"mcstas_data/instrument_code/component_parameters.txt\"\n",
+    ")\n",
+    "disk_choppers = load_choppers(\n",
+    "    file_dir_path / pathlib.Path(\"mccode.h5\"),\n",
+    "    component_text_file_path,\n",
+    "    chopper_position_offset=sc.vector(value=[-0.026, 0, -0.025], unit='m'),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b432c836",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "begin5\n",
+       "\n",
+       "end5\n",
+       "\n",
+       "begin0\n",
+       "\n",
+       "end0\n",
+       "\n",
+       "begin1\n",
+       "\n",
+       "end1\n",
+       "\n",
+       "begin2\n",
+       "\n",
+       "end2\n",
+       "\n",
+       "begin3\n",
+       "\n",
+       "end3\n",
+       "\n",
+       "begin4\n",
+       "\n",
+       "end4\n",
+       "\n",
+       "TDC\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beam position\n",
+       "\n",
+       "\n"
+      ],
+      "text/html": [
+       "
          \n",
+       "    \n",
+       "    
          \n",
+       "        
          \n",
+       "            
          • axle_position
            scipp
            Variable
            ()
            vector3
            m
            [0.    0.    6.925]
          • frequency
            scipp
            Variable
            ()
            float64
            Hz
            -56.0
          • beam_position
            scipp
            Variable
            ()
            float64
            deg
            0.0
          • phase
            scipp
            Variable
            ()
            float64
            deg
            -91.93
          • slit_begin
            scipp
            Variable
            (cutouts: 6)
            float64
            deg
            -2.85, 45.800, ..., 174.420, -147.940
          • slit_end
            scipp
            Variable
            (cutouts: 6)
            float64
            deg
            2.85, 54.800, ..., 191.920, -127.940
          • slit_height
            NoneType
            ()
            None
          • radius
            NoneType
            ()
            None
          \n",
+       "        
          \n",
+       "        
          \n",
+       "            
          \n",
+       "            \n",
+       "\n",
+       "\n",
+       "begin5\n",
+       "\n",
+       "end5\n",
+       "\n",
+       "begin0\n",
+       "\n",
+       "end0\n",
+       "\n",
+       "begin1\n",
+       "\n",
+       "end1\n",
+       "\n",
+       "begin2\n",
+       "\n",
+       "end2\n",
+       "\n",
+       "begin3\n",
+       "\n",
+       "end3\n",
+       "\n",
+       "begin4\n",
+       "\n",
+       "end4\n",
+       "\n",
+       "TDC\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beam position\n",
+       "\n",
+       "\n",
+       "\n",
+       "            
          \n",
+       "        
          \n",
+       "    
          \n",
+       "
          \n"
+      ],
+      "text/plain": [
+       "DiskChopper(axle_position= ()    vector3              [m]  (0, 0, 6.925), frequency= ()    float64             [Hz]  -56, beam_position= ()    float64            [deg]  0, phase= ()    float64            [deg]  -91.93, slit_begin= (cutouts: 6)    float64            [deg]  [-2.85, 45.8, ..., 174.42, -147.94], slit_end= (cutouts: 6)    float64            [deg]  [2.85, 54.8, ..., 191.92, -127.94], slit_height=None, radius=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "disk_choppers['wfmc_1']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "### Check that the chopper settings make sense with a quick `tof` run\n",
+    "\n",
+    "As useful sanity check is to run a basic simulation,\n",
+    "propagating neutrons through the chopper cascade,\n",
+    "using the [Tof](https://tof.readthedocs.io) package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Plot(ax=, fig=
          )"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ess.reduce.time_of_flight.fakes import FakeBeamline\n",
+    "\n",
+    "Ltotal = da.coords['Ltotal'].mean()\n",
+    "ess_beamline = FakeBeamline(\n",
+    "    choppers=disk_choppers,\n",
+    "    monitors={\"detector\": Ltotal},\n",
+    "    run_length=sc.scalar(1 / 14, unit=\"s\") * 8,\n",
+    "    events_per_pulse=100_000,\n",
+    ")\n",
+    "\n",
+    "ess_beamline.model_result.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "We observe that the WFM choppers make 6 distinct frames at the detector,\n",
+    "and that the other choppers skip every other pulse to maximize wavelength coverage.\n",
+    "\n",
+    "We can now compare the counts on the detector to our raw data,\n",
+    "to make sure they broadly resemble each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-03T13:07:29.416340\n",
+       "    image/svg+xml\n",
+       "    \n",
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+       "      Matplotlib v3.9.2, https://matplotlib.org/\n",
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+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "     \n",
+       "    \n",
+       "   \n",
+       "   \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       " \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "TiledFigure(nrows=1, ncols=2)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = ess_beamline.get_monitor(\"detector\")[0]\n",
+    "\n",
+    "# Visualize\n",
+    "fig_raw + raw_data.hist(event_time_offset=300).squeeze().plot(\n",
+    "    title='Tof simulation (no sample)'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "## Use WFM workflow\n",
+    "\n",
+    "We now set up the workflow which will convert the raw neutron arrival times to a real time-of-flight,\n",
+    "and thus a wavelength.\n",
+    "\n",
+    "### Setting up the workflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "DistanceResolution\n",
+       "\n",
+       "DistanceResolution\n",
+       "\n",
+       "\n",
+       "\n",
+       "TimeOfFlightLookupTable\n",
+       "\n",
+       "TimeOfFlightLookupTable\n",
+       "via:\n",
+       "compute_tof_lookup_table\n",
+       "\n",
+       "\n",
+       "\n",
+       "DistanceResolution->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "LookupTableRelativeErrorThreshold\n",
+       "\n",
+       "LookupTableRelativeErrorThreshold\n",
+       "\n",
+       "\n",
+       "\n",
+       "LookupTableRelativeErrorThreshold->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "Ltotal\n",
+       "\n",
+       "\n",
+       "\n",
+       "TofData\n",
+       "\n",
+       "TofData\n",
+       "via:\n",
+       "time_of_flight_data\n",
+       "\n",
+       "\n",
+       "\n",
+       "Ltotal->TofData\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "LtotalRange\n",
+       "\n",
+       "LtotalRange\n",
+       "\n",
+       "\n",
+       "\n",
+       "LtotalRange->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulsePeriod\n",
+       "\n",
+       "PulsePeriod\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulsePeriod->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulsePeriod->TofData\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulseStride\n",
+       "\n",
+       "PulseStride\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulseStride->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulseStride->TofData\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulseStrideOffset\n",
+       "\n",
+       "PulseStrideOffset\n",
+       "\n",
+       "\n",
+       "\n",
+       "PulseStrideOffset->TofData\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "RawData\n",
+       "\n",
+       "RawData\n",
+       "\n",
+       "\n",
+       "\n",
+       "RawData->TofData\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "SimulationResults\n",
+       "\n",
+       "SimulationResults\n",
+       "\n",
+       "\n",
+       "\n",
+       "SimulationResults->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "TimeOfFlightLookupTable->TofData\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "TimeResolution\n",
+       "\n",
+       "TimeResolution\n",
+       "\n",
+       "\n",
+       "\n",
+       "TimeResolution->TimeOfFlightLookupTable\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from ess.reduce import time_of_flight\n",
+    "\n",
+    "workflow = sl.Pipeline(\n",
+    "    time_of_flight.providers(), params=time_of_flight.default_parameters()\n",
+    ")\n",
+    "workflow[time_of_flight.RawData] = da\n",
+    "workflow[time_of_flight.Ltotal] = da.coords['Ltotal']\n",
+    "workflow[time_of_flight.LtotalRange] = (\n",
+    "    sc.scalar(55.0, unit=\"m\"),\n",
+    "    sc.scalar(65.0, unit=\"m\"),\n",
+    ")\n",
+    "workflow[time_of_flight.PulseStride] = 2  # Need for pulse-skipping\n",
+    "workflow[time_of_flight.SimulationResults] = time_of_flight.simulate_beamline(\n",
+    "    choppers=disk_choppers, neutrons=2_000_000\n",
+    ")\n",
+    "\n",
+    "workflow.visualize(time_of_flight.TofData)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "### Inspect the lookup table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-03T13:07:41.585141\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.9.2, https://matplotlib.org/\n",
+       "     \n",
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+       "      \n",
+       "     \n",
+       "    \n",
+       "   \n",
+       "   \n",
+       "   \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "TiledFigure(nrows=1, ncols=2)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = workflow.compute(time_of_flight.TimeOfFlightLookupTable).squeeze()\n",
+    "\n",
+    "table['pulse', 0].plot(title='Pulse 0') + table['pulse', 1].plot(title='Pulse 1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "### Compute neutron time-of-flight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (12.25 GB)
            • pixel_id: 10000
            • Ltotal
              (pixel_id)
              float64
              m
              60.501, 60.501, ..., 60.501, 60.501
              Values:
              array([60.50063281, 60.50062775, 60.50062279, ..., 60.50062279,\n",
+       "       60.50062775, 60.50063281], shape=(10000,))
            • pixel_id
              (pixel_id)
              int64
              𝟙
              0, 1, ..., 9998, 9999
              Values:
              array([   0,    1,    2, ..., 9997, 9998, 9999], shape=(10000,))
            • position
              (pixel_id)
              vector3
              m
              [-0.12375 -0.2475  60.5    ], [-0.12125 -0.2475  60.5    ], ..., [ 0.12125  0.2475  60.5    ], [ 0.12375  0.2475  60.5    ]
              Values:
              array([[-0.12375, -0.2475 , 60.5    ],\n",
+       "       [-0.12125, -0.2475 , 60.5    ],\n",
+       "       [-0.11875, -0.2475 , 60.5    ],\n",
+       "       ...,\n",
+       "       [ 0.11875,  0.2475 , 60.5    ],\n",
+       "       [ 0.12125,  0.2475 , 60.5    ],\n",
+       "       [ 0.12375,  0.2475 , 60.5    ]], shape=(10000, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • (pixel_id)
              float64
              counts
              binned data [len=8194, len=8223, ..., len=8605, len=8495]
              dim='events',\n",
+       "content=DataArray(\n",
+       "          dims=(events: 137029864),\n",
+       "          data=float64[counts],\n",
+       "          coords={'position':vector3[m], 'x':float64[m], 'y':float64[m],\n",
+       "                  'sim_wavelength':float64[Å], 'event_time_zero':datetime64[ns],\n",
+       "                  'event_time_offset':float64[s], 'Ltotal':float64[m], 'tof':float64[s]})
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[pixel_id:10000, ]\n",
+       "Coordinates:\n",
+       "* Ltotal                    float64              [m]  (pixel_id)  [60.5006, 60.5006, ..., 60.5006, 60.5006]\n",
+       "* pixel_id                    int64  [dimensionless]  (pixel_id)  [0, 1, ..., 9998, 9999]\n",
+       "  position                  vector3              [m]  (pixel_id)  [(-0.12375, -0.2475, 60.5), (-0.12125, -0.2475, 60.5), ..., (0.12125, 0.2475, 60.5), (0.12375, 0.2475, 60.5)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "  source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "Data:\n",
+       "                          DataArrayView          (pixel_id)  binned data: dim='events', content=DataArray(\n",
+       "          dims=(events: 137029864),\n",
+       "          data=float64[counts],\n",
+       "          coords={'position':vector3[m], 'x':float64[m], 'y':float64[m],\n",
+       "                  'sim_wavelength':float64[Å], 'event_time_zero':datetime64[ns],\n",
+       "                  'event_time_offset':float64[s], 'Ltotal':float64[m], 'tof':float64[s]})\n"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tofs = workflow.compute(time_of_flight.TofData)\n",
+    "tofs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (13.27 GB)
            • pixel_id: 10000
            • Ltotal
              (pixel_id)
              float64
              m
              60.501, 60.501, ..., 60.501, 60.501
              Values:
              array([60.50063281, 60.50062775, 60.50062279, ..., 60.50062279,\n",
+       "       60.50062775, 60.50063281], shape=(10000,))
            • pixel_id
              (pixel_id)
              int64
              𝟙
              0, 1, ..., 9998, 9999
              Values:
              array([   0,    1,    2, ..., 9997, 9998, 9999], shape=(10000,))
            • position
              (pixel_id)
              vector3
              m
              [-0.12375 -0.2475  60.5    ], [-0.12125 -0.2475  60.5    ], ..., [ 0.12125  0.2475  60.5    ], [ 0.12375  0.2475  60.5    ]
              Values:
              array([[-0.12375, -0.2475 , 60.5    ],\n",
+       "       [-0.12125, -0.2475 , 60.5    ],\n",
+       "       [-0.11875, -0.2475 , 60.5    ],\n",
+       "       ...,\n",
+       "       [ 0.11875,  0.2475 , 60.5    ],\n",
+       "       [ 0.12125,  0.2475 , 60.5    ],\n",
+       "       [ 0.12375,  0.2475 , 60.5    ]], shape=(10000, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • (pixel_id)
              float64
              counts
              binned data [len=8194, len=8223, ..., len=8605, len=8495]
              dim='events',\n",
+       "content=DataArray(\n",
+       "          dims=(events: 137029864),\n",
+       "          data=float64[counts],\n",
+       "          coords={'position':vector3[m], 'x':float64[m], 'y':float64[m],\n",
+       "                  'sim_wavelength':float64[Å], 'event_time_zero':datetime64[ns],\n",
+       "                  'event_time_offset':float64[s], 'Ltotal':float64[m], 'tof':float64[s],\n",
+       "                  'wavelength':float64[Å]})
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[pixel_id:10000, ]\n",
+       "Coordinates:\n",
+       "  Ltotal                    float64              [m]  (pixel_id)  [60.5006, 60.5006, ..., 60.5006, 60.5006]\n",
+       "* pixel_id                    int64  [dimensionless]  (pixel_id)  [0, 1, ..., 9998, 9999]\n",
+       "  position                  vector3              [m]  (pixel_id)  [(-0.12375, -0.2475, 60.5), (-0.12125, -0.2475, 60.5), ..., (0.12125, 0.2475, 60.5), (0.12375, 0.2475, 60.5)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "  source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "Data:\n",
+       "                          DataArrayView          (pixel_id)  binned data: dim='events', content=DataArray(\n",
+       "          dims=(events: 137029864),\n",
+       "          data=float64[counts],\n",
+       "          coords={'position':vector3[m], 'x':float64[m], 'y':float64[m],\n",
+       "                  'sim_wavelength':float64[Å], 'event_time_zero':datetime64[ns],\n",
+       "                  'event_time_offset':float64[s], 'Ltotal':float64[m], 'tof':float64[s],\n",
+       "                  'wavelength':float64[Å]})\n"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wavs = tofs.transform_coords('wavelength', graph=PLAIN_GRAPH)\n",
+    "wavs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "We can now compare our computed wavelengths to the true wavelengths of the neutrons in the McStas simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
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+       "    2025-04-03T13:09:46.977005\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.9.2, https://matplotlib.org/\n",
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+       "  \n",
+       " \n",
+       " \n",
+       "  \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 2 artists)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wavelength_bins = sc.linspace(\n",
+    "    'sim_wavelength', start=0.5, stop=9.1, num=201, unit='angstrom'\n",
+    ")\n",
+    "true_wavs = da.hist(sim_wavelength=wavelength_bins).rename(sim_wavelength='wavelength')\n",
+    "\n",
+    "pp.plot(\n",
+    "    {\n",
+    "        'true': true_wavs.sum('pixel_id'),\n",
+    "        'wfm': wavs.bins.concat().hist(\n",
+    "            wavelength=wavelength_bins.rename(sim_wavelength='wavelength')\n",
+    "        ),\n",
+    "    },\n",
+    "    title=\"ODIN McStas simulation \\nTrue Wavelength vs. WFM Stitched Wavelength\\n\",\n",
+    "    grid=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23",
+   "metadata": {},
+   "source": [
+    "## Region of interest\n",
+    "\n",
+    "Looking at the counts on the 2d detector panel,\n",
+    "we see that there is a central rectangular darker region,\n",
+    "surrounded by brighter edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "145b9593",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (30.90 MB)
            • pixel_id: 10000
            • wavelength: 200
            • Ltotal
              (pixel_id)
              float64
              m
              60.501, 60.501, ..., 60.501, 60.501
              Values:
              array([60.50063281, 60.50062775, 60.50062279, ..., 60.50062279,\n",
+       "       60.50062775, 60.50063281], shape=(10000,))
            • pixel_id
              (pixel_id)
              int64
              𝟙
              0, 1, ..., 9998, 9999
              Values:
              array([   0,    1,    2, ..., 9997, 9998, 9999], shape=(10000,))
            • position
              (pixel_id)
              vector3
              m
              [-0.12375 -0.2475  60.5    ], [-0.12125 -0.2475  60.5    ], ..., [ 0.12125  0.2475  60.5    ], [ 0.12375  0.2475  60.5    ]
              Values:
              array([[-0.12375, -0.2475 , 60.5    ],\n",
+       "       [-0.12125, -0.2475 , 60.5    ],\n",
+       "       [-0.11875, -0.2475 , 60.5    ],\n",
+       "       ...,\n",
+       "       [ 0.11875,  0.2475 , 60.5    ],\n",
+       "       [ 0.12125,  0.2475 , 60.5    ],\n",
+       "       [ 0.12375,  0.2475 , 60.5    ]], shape=(10000, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • wavelength
              (wavelength [bin-edge])
              float64
              Å
              0.5, 0.543, ..., 9.057, 9.1
              Values:
              array([0.5  , 0.543, 0.586, 0.629, 0.672, 0.715, 0.758, 0.801, 0.844,\n",
+       "       0.887, 0.93 , 0.973, 1.016, 1.059, 1.102, 1.145, 1.188, 1.231,\n",
+       "       1.274, 1.317, 1.36 , 1.403, 1.446, 1.489, 1.532, 1.575, 1.618,\n",
+       "       1.661, 1.704, 1.747, 1.79 , 1.833, 1.876, 1.919, 1.962, 2.005,\n",
+       "       2.048, 2.091, 2.134, 2.177, 2.22 , 2.263, 2.306, 2.349, 2.392,\n",
+       "       2.435, 2.478, 2.521, 2.564, 2.607, 2.65 , 2.693, 2.736, 2.779,\n",
+       "       2.822, 2.865, 2.908, 2.951, 2.994, 3.037, 3.08 , 3.123, 3.166,\n",
+       "       3.209, 3.252, 3.295, 3.338, 3.381, 3.424, 3.467, 3.51 , 3.553,\n",
+       "       3.596, 3.639, 3.682, 3.725, 3.768, 3.811, 3.854, 3.897, 3.94 ,\n",
+       "       3.983, 4.026, 4.069, 4.112, 4.155, 4.198, 4.241, 4.284, 4.327,\n",
+       "       4.37 , 4.413, 4.456, 4.499, 4.542, 4.585, 4.628, 4.671, 4.714,\n",
+       "       4.757, 4.8  , 4.843, 4.886, 4.929, 4.972, 5.015, 5.058, 5.101,\n",
+       "       5.144, 5.187, 5.23 , 5.273, 5.316, 5.359, 5.402, 5.445, 5.488,\n",
+       "       5.531, 5.574, 5.617, 5.66 , 5.703, 5.746, 5.789, 5.832, 5.875,\n",
+       "       5.918, 5.961, 6.004, 6.047, 6.09 , 6.133, 6.176, 6.219, 6.262,\n",
+       "       6.305, 6.348, 6.391, 6.434, 6.477, 6.52 , 6.563, 6.606, 6.649,\n",
+       "       6.692, 6.735, 6.778, 6.821, 6.864, 6.907, 6.95 , 6.993, 7.036,\n",
+       "       7.079, 7.122, 7.165, 7.208, 7.251, 7.294, 7.337, 7.38 , 7.423,\n",
+       "       7.466, 7.509, 7.552, 7.595, 7.638, 7.681, 7.724, 7.767, 7.81 ,\n",
+       "       7.853, 7.896, 7.939, 7.982, 8.025, 8.068, 8.111, 8.154, 8.197,\n",
+       "       8.24 , 8.283, 8.326, 8.369, 8.412, 8.455, 8.498, 8.541, 8.584,\n",
+       "       8.627, 8.67 , 8.713, 8.756, 8.799, 8.842, 8.885, 8.928, 8.971,\n",
+       "       9.014, 9.057, 9.1  ])
            • (pixel_id, wavelength)
              float64
              counts
              0.0, 0.0, ..., 0.0, 0.0
              σ = 0.0, 0.0, ..., 0.0, 0.0
              Values:
              array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]], shape=(10000, 200))

              Variances (σ²):
              array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]], shape=(10000, 200))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[pixel_id:10000, wavelength:200, ]\n",
+       "Coordinates:\n",
+       "* Ltotal                    float64              [m]  (pixel_id)  [60.5006, 60.5006, ..., 60.5006, 60.5006]\n",
+       "* pixel_id                    int64  [dimensionless]  (pixel_id)  [0, 1, ..., 9998, 9999]\n",
+       "  position                  vector3              [m]  (pixel_id)  [(-0.12375, -0.2475, 60.5), (-0.12125, -0.2475, 60.5), ..., (0.12125, 0.2475, 60.5), (0.12375, 0.2475, 60.5)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "  source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* wavelength                float64             [Å]  (wavelength [bin-edge])  [0.5, 0.543, ..., 9.057, 9.1]\n",
+       "Data:\n",
+       "                            float64         [counts]  (pixel_id, wavelength)  [0, 0, ..., 0, 0]  [0, 0, ..., 0, 0]\n"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_wavs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-03T13:09:52.721319\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.9.2, https://matplotlib.org/\n",
+       "     \n",
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+       "       \n",
+       "       \n",
+       "       \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "     \n",
+       "    \n",
+       "   \n",
+       "   \n",
+       "   \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "folded = wavs.fold(\n",
+    "    dim='pixel_id', sizes={'y': example_resolution[1], 'x': example_resolution[0]}\n",
+    ").hist()\n",
+    "folded.plot(aspect='equal', title='2D Image')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "The dark region is where the beam was absorbed by the sample,\n",
+    "and this is the region of interest.\n",
+    "The brighter edges need to be discarded.\n",
+    "\n",
+    "We crop the data using simple array slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       " \n",
+       "  \n",
+       "   \n",
+       "    \n",
+       "    2025-04-03T13:09:55.316517\n",
+       "    image/svg+xml\n",
+       "    \n",
+       "     \n",
+       "      Matplotlib v3.9.2, https://matplotlib.org/\n",
+       "     \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
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+       "      \n",
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+       "      \n",
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+       "      \n",
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+       "      \n",
+       "      \n",
+       "     \n",
+       "    \n",
+       "   \n",
+       "   \n",
+       "   \n",
+       "    \n",
+       "   \n",
+       "  \n",
+       " \n",
+       "\n"
+      ],
+      "text/plain": [
+       "Figure(600x400, 1 artists)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sel = slice(\n",
+    "    int(0.2 * example_resolution[0]),\n",
+    "    int(example_resolution[0] - 0.2 * example_resolution[0]),\n",
+    "    1,\n",
+    ")\n",
+    "sample_cropped = folded['y', sel]['x', sel]\n",
+    "sample_cropped.plot(aspect='equal', title='Cropped Sample')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4055ff2a",
+   "metadata": {},
+   "source": [
+    "## Save images as Scitiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c601f4c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (76.68 MB)
            • pixel_id: 10000
            • tof: 500
            • Ltotal
              (pixel_id)
              float64
              m
              60.501, 60.501, ..., 60.501, 60.501
              Values:
              array([60.50063281, 60.50062775, 60.50062279, ..., 60.50062279,\n",
+       "       60.50062775, 60.50063281], shape=(10000,))
            • pixel_id
              (pixel_id)
              int64
              𝟙
              0, 1, ..., 9998, 9999
              Values:
              array([   0,    1,    2, ..., 9997, 9998, 9999], shape=(10000,))
            • position
              (pixel_id)
              vector3
              m
              [-0.12375 -0.2475  60.5    ], [-0.12125 -0.2475  60.5    ], ..., [ 0.12125  0.2475  60.5    ], [ 0.12375  0.2475  60.5    ]
              Values:
              array([[-0.12375, -0.2475 , 60.5    ],\n",
+       "       [-0.12125, -0.2475 , 60.5    ],\n",
+       "       [-0.11875, -0.2475 , 60.5    ],\n",
+       "       ...,\n",
+       "       [ 0.11875,  0.2475 , 60.5    ],\n",
+       "       [ 0.12125,  0.2475 , 60.5    ],\n",
+       "       [ 0.12375,  0.2475 , 60.5    ]], shape=(10000, 3))
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (tof [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01534551, 0.01558398, 0.01582245, 0.01606092, 0.01629939,\n",
+       "       0.01653787, 0.01677634, 0.01701481, 0.01725328, 0.01749175,\n",
+       "       0.01773022, 0.01796869, 0.01820716, 0.01844564, 0.01868411,\n",
+       "       0.01892258, 0.01916105, 0.01939952, 0.01963799, 0.01987646,\n",
+       "       0.02011493, 0.02035341, 0.02059188, 0.02083035, 0.02106882,\n",
+       "       0.02130729, 0.02154576, 0.02178423, 0.02202271, 0.02226118,\n",
+       "       0.02249965, 0.02273812, 0.02297659, 0.02321506, 0.02345353,\n",
+       "       0.023692  , 0.02393048, 0.02416895, 0.02440742, 0.02464589,\n",
+       "       0.02488436, 0.02512283, 0.0253613 , 0.02559977, 0.02583825,\n",
+       "       0.02607672, 0.02631519, 0.02655366, 0.02679213, 0.0270306 ,\n",
+       "       0.02726907, 0.02750754, 0.02774602, 0.02798449, 0.02822296,\n",
+       "       0.02846143, 0.0286999 , 0.02893837, 0.02917684, 0.02941531,\n",
+       "       0.02965379, 0.02989226, 0.03013073, 0.0303692 , 0.03060767,\n",
+       "       0.03084614, 0.03108461, 0.03132309, 0.03156156, 0.03180003,\n",
+       "       0.0320385 , 0.03227697, 0.03251544, 0.03275391, 0.03299238,\n",
+       "       0.03323086, 0.03346933, 0.0337078 , 0.03394627, 0.03418474,\n",
+       "       0.03442321, 0.03466168, 0.03490015, 0.03513863, 0.0353771 ,\n",
+       "       0.03561557, 0.03585404, 0.03609251, 0.03633098, 0.03656945,\n",
+       "       0.03680792, 0.0370464 , 0.03728487, 0.03752334, 0.03776181,\n",
+       "       0.03800028, 0.03823875, 0.03847722, 0.03871569, 0.03895417,\n",
+       "       0.03919264, 0.03943111, 0.03966958, 0.03990805, 0.04014652,\n",
+       "       0.04038499, 0.04062347, 0.04086194, 0.04110041, 0.04133888,\n",
+       "       0.04157735, 0.04181582, 0.04205429, 0.04229276, 0.04253124,\n",
+       "       0.04276971, 0.04300818, 0.04324665, 0.04348512, 0.04372359,\n",
+       "       0.04396206, 0.04420053, 0.04443901, 0.04467748, 0.04491595,\n",
+       "       0.04515442, 0.04539289, 0.04563136, 0.04586983, 0.0461083 ,\n",
+       "       0.04634678, 0.04658525, 0.04682372, 0.04706219, 0.04730066,\n",
+       "       0.04753913, 0.0477776 , 0.04801608, 0.04825455, 0.04849302,\n",
+       "       0.04873149, 0.04896996, 0.04920843, 0.0494469 , 0.04968537,\n",
+       "       0.04992385, 0.05016232, 0.05040079, 0.05063926, 0.05087773,\n",
+       "       0.0511162 , 0.05135467, 0.05159314, 0.05183162, 0.05207009,\n",
+       "       0.05230856, 0.05254703, 0.0527855 , 0.05302397, 0.05326244,\n",
+       "       0.05350091, 0.05373939, 0.05397786, 0.05421633, 0.0544548 ,\n",
+       "       0.05469327, 0.05493174, 0.05517021, 0.05540868, 0.05564716,\n",
+       "       0.05588563, 0.0561241 , 0.05636257, 0.05660104, 0.05683951,\n",
+       "       0.05707798, 0.05731646, 0.05755493, 0.0577934 , 0.05803187,\n",
+       "       0.05827034, 0.05850881, 0.05874728, 0.05898575, 0.05922423,\n",
+       "       0.0594627 , 0.05970117, 0.05993964, 0.06017811, 0.06041658,\n",
+       "       0.06065505, 0.06089352, 0.061132  , 0.06137047, 0.06160894,\n",
+       "       0.06184741, 0.06208588, 0.06232435, 0.06256282, 0.06280129,\n",
+       "       0.06303977, 0.06327824, 0.06351671, 0.06375518, 0.06399365,\n",
+       "       0.06423212, 0.06447059, 0.06470906, 0.06494754, 0.06518601,\n",
+       "       0.06542448, 0.06566295, 0.06590142, 0.06613989, 0.06637836,\n",
+       "       0.06661684, 0.06685531, 0.06709378, 0.06733225, 0.06757072,\n",
+       "       0.06780919, 0.06804766, 0.06828613, 0.06852461, 0.06876308,\n",
+       "       0.06900155, 0.06924002, 0.06947849, 0.06971696, 0.06995543,\n",
+       "       0.0701939 , 0.07043238, 0.07067085, 0.07090932, 0.07114779,\n",
+       "       0.07138626, 0.07162473, 0.0718632 , 0.07210167, 0.07234015,\n",
+       "       0.07257862, 0.07281709, 0.07305556, 0.07329403, 0.0735325 ,\n",
+       "       0.07377097, 0.07400944, 0.07424792, 0.07448639, 0.07472486,\n",
+       "       0.07496333, 0.0752018 , 0.07544027, 0.07567874, 0.07591722,\n",
+       "       0.07615569, 0.07639416, 0.07663263, 0.0768711 , 0.07710957,\n",
+       "       0.07734804, 0.07758651, 0.07782499, 0.07806346, 0.07830193,\n",
+       "       0.0785404 , 0.07877887, 0.07901734, 0.07925581, 0.07949428,\n",
+       "       0.07973276, 0.07997123, 0.0802097 , 0.08044817, 0.08068664,\n",
+       "       0.08092511, 0.08116358, 0.08140205, 0.08164053, 0.081879  ,\n",
+       "       0.08211747, 0.08235594, 0.08259441, 0.08283288, 0.08307135,\n",
+       "       0.08330982, 0.0835483 , 0.08378677, 0.08402524, 0.08426371,\n",
+       "       0.08450218, 0.08474065, 0.08497912, 0.0852176 , 0.08545607,\n",
+       "       0.08569454, 0.08593301, 0.08617148, 0.08640995, 0.08664842,\n",
+       "       0.08688689, 0.08712537, 0.08736384, 0.08760231, 0.08784078,\n",
+       "       0.08807925, 0.08831772, 0.08855619, 0.08879466, 0.08903314,\n",
+       "       0.08927161, 0.08951008, 0.08974855, 0.08998702, 0.09022549,\n",
+       "       0.09046396, 0.09070243, 0.09094091, 0.09117938, 0.09141785,\n",
+       "       0.09165632, 0.09189479, 0.09213326, 0.09237173, 0.0926102 ,\n",
+       "       0.09284868, 0.09308715, 0.09332562, 0.09356409, 0.09380256,\n",
+       "       0.09404103, 0.0942795 , 0.09451798, 0.09475645, 0.09499492,\n",
+       "       0.09523339, 0.09547186, 0.09571033, 0.0959488 , 0.09618727,\n",
+       "       0.09642575, 0.09666422, 0.09690269, 0.09714116, 0.09737963,\n",
+       "       0.0976181 , 0.09785657, 0.09809504, 0.09833352, 0.09857199,\n",
+       "       0.09881046, 0.09904893, 0.0992874 , 0.09952587, 0.09976434,\n",
+       "       0.10000281, 0.10024129, 0.10047976, 0.10071823, 0.1009567 ,\n",
+       "       0.10119517, 0.10143364, 0.10167211, 0.10191058, 0.10214906,\n",
+       "       0.10238753, 0.102626  , 0.10286447, 0.10310294, 0.10334141,\n",
+       "       0.10357988, 0.10381836, 0.10405683, 0.1042953 , 0.10453377,\n",
+       "       0.10477224, 0.10501071, 0.10524918, 0.10548765, 0.10572613,\n",
+       "       0.1059646 , 0.10620307, 0.10644154, 0.10668001, 0.10691848,\n",
+       "       0.10715695, 0.10739542, 0.1076339 , 0.10787237, 0.10811084,\n",
+       "       0.10834931, 0.10858778, 0.10882625, 0.10906472, 0.10930319,\n",
+       "       0.10954167, 0.10978014, 0.11001861, 0.11025708, 0.11049555,\n",
+       "       0.11073402, 0.11097249, 0.11121097, 0.11144944, 0.11168791,\n",
+       "       0.11192638, 0.11216485, 0.11240332, 0.11264179, 0.11288026,\n",
+       "       0.11311874, 0.11335721, 0.11359568, 0.11383415, 0.11407262,\n",
+       "       0.11431109, 0.11454956, 0.11478803, 0.11502651, 0.11526498,\n",
+       "       0.11550345, 0.11574192, 0.11598039, 0.11621886, 0.11645733,\n",
+       "       0.1166958 , 0.11693428, 0.11717275, 0.11741122, 0.11764969,\n",
+       "       0.11788816, 0.11812663, 0.1183651 , 0.11860357, 0.11884205,\n",
+       "       0.11908052, 0.11931899, 0.11955746, 0.11979593, 0.1200344 ,\n",
+       "       0.12027287, 0.12051135, 0.12074982, 0.12098829, 0.12122676,\n",
+       "       0.12146523, 0.1217037 , 0.12194217, 0.12218064, 0.12241912,\n",
+       "       0.12265759, 0.12289606, 0.12313453, 0.123373  , 0.12361147,\n",
+       "       0.12384994, 0.12408841, 0.12432689, 0.12456536, 0.12480383,\n",
+       "       0.1250423 , 0.12528077, 0.12551924, 0.12575771, 0.12599618,\n",
+       "       0.12623466, 0.12647313, 0.1267116 , 0.12695007, 0.12718854,\n",
+       "       0.12742701, 0.12766548, 0.12790395, 0.12814243, 0.1283809 ,\n",
+       "       0.12861937, 0.12885784, 0.12909631, 0.12933478, 0.12957325,\n",
+       "       0.12981173, 0.1300502 , 0.13028867, 0.13052714, 0.13076561,\n",
+       "       0.13100408, 0.13124255, 0.13148102, 0.1317195 , 0.13195797,\n",
+       "       0.13219644, 0.13243491, 0.13267338, 0.13291185, 0.13315032,\n",
+       "       0.13338879, 0.13362727, 0.13386574, 0.13410421, 0.13434268,\n",
+       "       0.13458115])
            • (pixel_id, tof)
              float64
              counts
              0.000, 0.0, ..., 4.190, 1.764
              σ = 0.000, 0.0, ..., 1.518, 1.010
              Values:
              array([[4.36987526e-04, 0.00000000e+00, 3.06014494e-01, ...,\n",
+       "        1.86547540e+00, 2.00784524e+00, 3.85417970e-01],\n",
+       "       [0.00000000e+00, 0.00000000e+00, 1.13499283e+01, ...,\n",
+       "        2.05149850e+00, 1.68715121e+00, 2.26415811e+00],\n",
+       "       [2.98194716e+00, 2.38750848e+00, 9.60613733e-01, ...,\n",
+       "        2.41826139e+00, 5.81257823e-01, 1.81725616e+00],\n",
+       "       ...,\n",
+       "       [1.73570357e-08, 0.00000000e+00, 1.86340517e-13, ...,\n",
+       "        1.80057407e+00, 2.81489591e+00, 2.49389703e+00],\n",
+       "       [1.01487443e+01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "        4.96652521e+00, 2.93262175e+00, 3.07474394e+00],\n",
+       "       [6.74465079e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "        4.01588742e+00, 4.19028297e+00, 1.76396272e+00]],\n",
+       "      shape=(10000, 500))

              Variances (σ²):
              array([[1.90958098e-07, 0.00000000e+00, 9.36448706e-02, ...,\n",
+       "        1.14035254e+00, 8.97487895e-01, 1.43592106e-01],\n",
+       "       [0.00000000e+00, 0.00000000e+00, 1.28820871e+02, ...,\n",
+       "        1.02907723e+00, 9.27991177e-01, 1.29765684e+00],\n",
+       "       [8.89200885e+00, 5.61225491e+00, 9.22778743e-01, ...,\n",
+       "        1.45904568e+00, 2.11791267e-01, 9.26256123e-01],\n",
+       "       ...,\n",
+       "       [3.01266689e-16, 0.00000000e+00, 3.47227883e-26, ...,\n",
+       "        8.39114758e-01, 1.43921850e+00, 1.34635220e+00],\n",
+       "       [1.02997005e+02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "        2.55435858e+00, 1.42273447e+00, 1.65734644e+00],\n",
+       "       [4.54903143e+01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "        2.41094230e+00, 2.30415211e+00, 1.01942443e+00]],\n",
+       "      shape=(10000, 500))
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[pixel_id:10000, tof:500, ]\n",
+       "Coordinates:\n",
+       "* Ltotal                    float64              [m]  (pixel_id)  [60.5006, 60.5006, ..., 60.5006, 60.5006]\n",
+       "* pixel_id                    int64  [dimensionless]  (pixel_id)  [0, 1, ..., 9998, 9999]\n",
+       "  position                  vector3              [m]  (pixel_id)  [(-0.12375, -0.2475, 60.5), (-0.12125, -0.2475, 60.5), ..., (0.12125, 0.2475, 60.5), (0.12375, 0.2475, 60.5)]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "  source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (tof [bin-edge])  [0.0153455, 0.015584, ..., 0.134343, 0.134581]\n",
+       "Data:\n",
+       "                            float64         [counts]  (pixel_id, tof)  [0.000436988, 0, ..., 4.19028, 1.76396]  [1.90958e-07, 0, ..., 2.30415, 1.01942]\n"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img = tofs.bin(tof=TOF_BIN_EDGES).hist()\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bb12d955",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.Variable (234.68 KB)
            • (x: 100, y: 100, z: 1)
              vector3
              m
              [-0.12375 -0.2475  60.5    ], [-0.12125 -0.2475  60.5    ], ..., [ 0.12125  0.2475  60.5    ], [ 0.12375  0.2475  60.5    ]
              Values:
              array([[[[-0.12375, -0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.12125, -0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.11875, -0.2475 , 60.5    ]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.11875, -0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12125, -0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12375, -0.2475 , 60.5    ]]],\n",
+       "\n",
+       "\n",
+       "       [[[-0.12375, -0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.12125, -0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.11875, -0.2425 , 60.5    ]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.11875, -0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12125, -0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12375, -0.2425 , 60.5    ]]],\n",
+       "\n",
+       "\n",
+       "       [[[-0.12375, -0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.12125, -0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.11875, -0.2375 , 60.5    ]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.11875, -0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12125, -0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12375, -0.2375 , 60.5    ]]],\n",
+       "\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "\n",
+       "       [[[-0.12375,  0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.12125,  0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.11875,  0.2375 , 60.5    ]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.11875,  0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12125,  0.2375 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12375,  0.2375 , 60.5    ]]],\n",
+       "\n",
+       "\n",
+       "       [[[-0.12375,  0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.12125,  0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.11875,  0.2425 , 60.5    ]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.11875,  0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12125,  0.2425 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12375,  0.2425 , 60.5    ]]],\n",
+       "\n",
+       "\n",
+       "       [[[-0.12375,  0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.12125,  0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[-0.11875,  0.2475 , 60.5    ]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.11875,  0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12125,  0.2475 , 60.5    ]],\n",
+       "\n",
+       "        [[ 0.12375,  0.2475 , 60.5    ]]]], shape=(100, 100, 1, 3))
          "
+      ],
+      "text/plain": [
+       " (x: 100, y: 100, z: 1)    vector3              [m]  [(-0.12375, -0.2475, 60.5), (-0.12125, -0.2475, 60.5), ..., (0.12125, 0.2475, 60.5), (0.12375, 0.2475, 60.5)]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
          \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide data repr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "Show/Hide attributes\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
          scipp.DataArray (38.15 MB)
            • c: 2
            • x: 100
            • y: 100
            • z: 1
            • t: 500
            • c
              (c)
              string
              data, variances
              Values:
              ["data", "variances"]
            • sample_position
              ()
              vector3
              m
              [2.60e-02 0.00e+00 6.05e+01]
              Values:
              array([2.60e-02, 0.00e+00, 6.05e+01])
            • source_position
              ()
              vector3
              m
              [0. 0. 0.]
              Values:
              array([0., 0., 0.])
            • tof
              (t [bin-edge])
              float64
              s
              0.015, 0.016, ..., 0.134, 0.135
              Values:
              array([0.01534551, 0.01558398, 0.01582245, 0.01606092, 0.01629939,\n",
+       "       0.01653787, 0.01677634, 0.01701481, 0.01725328, 0.01749175,\n",
+       "       0.01773022, 0.01796869, 0.01820716, 0.01844564, 0.01868411,\n",
+       "       0.01892258, 0.01916105, 0.01939952, 0.01963799, 0.01987646,\n",
+       "       0.02011493, 0.02035341, 0.02059188, 0.02083035, 0.02106882,\n",
+       "       0.02130729, 0.02154576, 0.02178423, 0.02202271, 0.02226118,\n",
+       "       0.02249965, 0.02273812, 0.02297659, 0.02321506, 0.02345353,\n",
+       "       0.023692  , 0.02393048, 0.02416895, 0.02440742, 0.02464589,\n",
+       "       0.02488436, 0.02512283, 0.0253613 , 0.02559977, 0.02583825,\n",
+       "       0.02607672, 0.02631519, 0.02655366, 0.02679213, 0.0270306 ,\n",
+       "       0.02726907, 0.02750754, 0.02774602, 0.02798449, 0.02822296,\n",
+       "       0.02846143, 0.0286999 , 0.02893837, 0.02917684, 0.02941531,\n",
+       "       0.02965379, 0.02989226, 0.03013073, 0.0303692 , 0.03060767,\n",
+       "       0.03084614, 0.03108461, 0.03132309, 0.03156156, 0.03180003,\n",
+       "       0.0320385 , 0.03227697, 0.03251544, 0.03275391, 0.03299238,\n",
+       "       0.03323086, 0.03346933, 0.0337078 , 0.03394627, 0.03418474,\n",
+       "       0.03442321, 0.03466168, 0.03490015, 0.03513863, 0.0353771 ,\n",
+       "       0.03561557, 0.03585404, 0.03609251, 0.03633098, 0.03656945,\n",
+       "       0.03680792, 0.0370464 , 0.03728487, 0.03752334, 0.03776181,\n",
+       "       0.03800028, 0.03823875, 0.03847722, 0.03871569, 0.03895417,\n",
+       "       0.03919264, 0.03943111, 0.03966958, 0.03990805, 0.04014652,\n",
+       "       0.04038499, 0.04062347, 0.04086194, 0.04110041, 0.04133888,\n",
+       "       0.04157735, 0.04181582, 0.04205429, 0.04229276, 0.04253124,\n",
+       "       0.04276971, 0.04300818, 0.04324665, 0.04348512, 0.04372359,\n",
+       "       0.04396206, 0.04420053, 0.04443901, 0.04467748, 0.04491595,\n",
+       "       0.04515442, 0.04539289, 0.04563136, 0.04586983, 0.0461083 ,\n",
+       "       0.04634678, 0.04658525, 0.04682372, 0.04706219, 0.04730066,\n",
+       "       0.04753913, 0.0477776 , 0.04801608, 0.04825455, 0.04849302,\n",
+       "       0.04873149, 0.04896996, 0.04920843, 0.0494469 , 0.04968537,\n",
+       "       0.04992385, 0.05016232, 0.05040079, 0.05063926, 0.05087773,\n",
+       "       0.0511162 , 0.05135467, 0.05159314, 0.05183162, 0.05207009,\n",
+       "       0.05230856, 0.05254703, 0.0527855 , 0.05302397, 0.05326244,\n",
+       "       0.05350091, 0.05373939, 0.05397786, 0.05421633, 0.0544548 ,\n",
+       "       0.05469327, 0.05493174, 0.05517021, 0.05540868, 0.05564716,\n",
+       "       0.05588563, 0.0561241 , 0.05636257, 0.05660104, 0.05683951,\n",
+       "       0.05707798, 0.05731646, 0.05755493, 0.0577934 , 0.05803187,\n",
+       "       0.05827034, 0.05850881, 0.05874728, 0.05898575, 0.05922423,\n",
+       "       0.0594627 , 0.05970117, 0.05993964, 0.06017811, 0.06041658,\n",
+       "       0.06065505, 0.06089352, 0.061132  , 0.06137047, 0.06160894,\n",
+       "       0.06184741, 0.06208588, 0.06232435, 0.06256282, 0.06280129,\n",
+       "       0.06303977, 0.06327824, 0.06351671, 0.06375518, 0.06399365,\n",
+       "       0.06423212, 0.06447059, 0.06470906, 0.06494754, 0.06518601,\n",
+       "       0.06542448, 0.06566295, 0.06590142, 0.06613989, 0.06637836,\n",
+       "       0.06661684, 0.06685531, 0.06709378, 0.06733225, 0.06757072,\n",
+       "       0.06780919, 0.06804766, 0.06828613, 0.06852461, 0.06876308,\n",
+       "       0.06900155, 0.06924002, 0.06947849, 0.06971696, 0.06995543,\n",
+       "       0.0701939 , 0.07043238, 0.07067085, 0.07090932, 0.07114779,\n",
+       "       0.07138626, 0.07162473, 0.0718632 , 0.07210167, 0.07234015,\n",
+       "       0.07257862, 0.07281709, 0.07305556, 0.07329403, 0.0735325 ,\n",
+       "       0.07377097, 0.07400944, 0.07424792, 0.07448639, 0.07472486,\n",
+       "       0.07496333, 0.0752018 , 0.07544027, 0.07567874, 0.07591722,\n",
+       "       0.07615569, 0.07639416, 0.07663263, 0.0768711 , 0.07710957,\n",
+       "       0.07734804, 0.07758651, 0.07782499, 0.07806346, 0.07830193,\n",
+       "       0.0785404 , 0.07877887, 0.07901734, 0.07925581, 0.07949428,\n",
+       "       0.07973276, 0.07997123, 0.0802097 , 0.08044817, 0.08068664,\n",
+       "       0.08092511, 0.08116358, 0.08140205, 0.08164053, 0.081879  ,\n",
+       "       0.08211747, 0.08235594, 0.08259441, 0.08283288, 0.08307135,\n",
+       "       0.08330982, 0.0835483 , 0.08378677, 0.08402524, 0.08426371,\n",
+       "       0.08450218, 0.08474065, 0.08497912, 0.0852176 , 0.08545607,\n",
+       "       0.08569454, 0.08593301, 0.08617148, 0.08640995, 0.08664842,\n",
+       "       0.08688689, 0.08712537, 0.08736384, 0.08760231, 0.08784078,\n",
+       "       0.08807925, 0.08831772, 0.08855619, 0.08879466, 0.08903314,\n",
+       "       0.08927161, 0.08951008, 0.08974855, 0.08998702, 0.09022549,\n",
+       "       0.09046396, 0.09070243, 0.09094091, 0.09117938, 0.09141785,\n",
+       "       0.09165632, 0.09189479, 0.09213326, 0.09237173, 0.0926102 ,\n",
+       "       0.09284868, 0.09308715, 0.09332562, 0.09356409, 0.09380256,\n",
+       "       0.09404103, 0.0942795 , 0.09451798, 0.09475645, 0.09499492,\n",
+       "       0.09523339, 0.09547186, 0.09571033, 0.0959488 , 0.09618727,\n",
+       "       0.09642575, 0.09666422, 0.09690269, 0.09714116, 0.09737963,\n",
+       "       0.0976181 , 0.09785657, 0.09809504, 0.09833352, 0.09857199,\n",
+       "       0.09881046, 0.09904893, 0.0992874 , 0.09952587, 0.09976434,\n",
+       "       0.10000281, 0.10024129, 0.10047976, 0.10071823, 0.1009567 ,\n",
+       "       0.10119517, 0.10143364, 0.10167211, 0.10191058, 0.10214906,\n",
+       "       0.10238753, 0.102626  , 0.10286447, 0.10310294, 0.10334141,\n",
+       "       0.10357988, 0.10381836, 0.10405683, 0.1042953 , 0.10453377,\n",
+       "       0.10477224, 0.10501071, 0.10524918, 0.10548765, 0.10572613,\n",
+       "       0.1059646 , 0.10620307, 0.10644154, 0.10668001, 0.10691848,\n",
+       "       0.10715695, 0.10739542, 0.1076339 , 0.10787237, 0.10811084,\n",
+       "       0.10834931, 0.10858778, 0.10882625, 0.10906472, 0.10930319,\n",
+       "       0.10954167, 0.10978014, 0.11001861, 0.11025708, 0.11049555,\n",
+       "       0.11073402, 0.11097249, 0.11121097, 0.11144944, 0.11168791,\n",
+       "       0.11192638, 0.11216485, 0.11240332, 0.11264179, 0.11288026,\n",
+       "       0.11311874, 0.11335721, 0.11359568, 0.11383415, 0.11407262,\n",
+       "       0.11431109, 0.11454956, 0.11478803, 0.11502651, 0.11526498,\n",
+       "       0.11550345, 0.11574192, 0.11598039, 0.11621886, 0.11645733,\n",
+       "       0.1166958 , 0.11693428, 0.11717275, 0.11741122, 0.11764969,\n",
+       "       0.11788816, 0.11812663, 0.1183651 , 0.11860357, 0.11884205,\n",
+       "       0.11908052, 0.11931899, 0.11955746, 0.11979593, 0.1200344 ,\n",
+       "       0.12027287, 0.12051135, 0.12074982, 0.12098829, 0.12122676,\n",
+       "       0.12146523, 0.1217037 , 0.12194217, 0.12218064, 0.12241912,\n",
+       "       0.12265759, 0.12289606, 0.12313453, 0.123373  , 0.12361147,\n",
+       "       0.12384994, 0.12408841, 0.12432689, 0.12456536, 0.12480383,\n",
+       "       0.1250423 , 0.12528077, 0.12551924, 0.12575771, 0.12599618,\n",
+       "       0.12623466, 0.12647313, 0.1267116 , 0.12695007, 0.12718854,\n",
+       "       0.12742701, 0.12766548, 0.12790395, 0.12814243, 0.1283809 ,\n",
+       "       0.12861937, 0.12885784, 0.12909631, 0.12933478, 0.12957325,\n",
+       "       0.12981173, 0.1300502 , 0.13028867, 0.13052714, 0.13076561,\n",
+       "       0.13100408, 0.13124255, 0.13148102, 0.1317195 , 0.13195797,\n",
+       "       0.13219644, 0.13243491, 0.13267338, 0.13291185, 0.13315032,\n",
+       "       0.13338879, 0.13362727, 0.13386574, 0.13410421, 0.13434268,\n",
+       "       0.13458115])
            • x
              (x)
              float64
              m
              -0.124, -0.121, ..., 0.121, 0.124
              Values:
              array([-0.12375, -0.12125, -0.11875, -0.11625, -0.11375, -0.11125,\n",
+       "       -0.10875, -0.10625, -0.10375, -0.10125, -0.09875, -0.09625,\n",
+       "       -0.09375, -0.09125, -0.08875, -0.08625, -0.08375, -0.08125,\n",
+       "       -0.07875, -0.07625, -0.07375, -0.07125, -0.06875, -0.06625,\n",
+       "       -0.06375, -0.06125, -0.05875, -0.05625, -0.05375, -0.05125,\n",
+       "       -0.04875, -0.04625, -0.04375, -0.04125, -0.03875, -0.03625,\n",
+       "       -0.03375, -0.03125, -0.02875, -0.02625, -0.02375, -0.02125,\n",
+       "       -0.01875, -0.01625, -0.01375, -0.01125, -0.00875, -0.00625,\n",
+       "       -0.00375, -0.00125,  0.00125,  0.00375,  0.00625,  0.00875,\n",
+       "        0.01125,  0.01375,  0.01625,  0.01875,  0.02125,  0.02375,\n",
+       "        0.02625,  0.02875,  0.03125,  0.03375,  0.03625,  0.03875,\n",
+       "        0.04125,  0.04375,  0.04625,  0.04875,  0.05125,  0.05375,\n",
+       "        0.05625,  0.05875,  0.06125,  0.06375,  0.06625,  0.06875,\n",
+       "        0.07125,  0.07375,  0.07625,  0.07875,  0.08125,  0.08375,\n",
+       "        0.08625,  0.08875,  0.09125,  0.09375,  0.09625,  0.09875,\n",
+       "        0.10125,  0.10375,  0.10625,  0.10875,  0.11125,  0.11375,\n",
+       "        0.11625,  0.11875,  0.12125,  0.12375])
            • y
              (y)
              float64
              m
              -0.247, -0.242, ..., 0.242, 0.247
              Values:
              array([-0.2475, -0.2425, -0.2375, -0.2325, -0.2275, -0.2225, -0.2175,\n",
+       "       -0.2125, -0.2075, -0.2025, -0.1975, -0.1925, -0.1875, -0.1825,\n",
+       "       -0.1775, -0.1725, -0.1675, -0.1625, -0.1575, -0.1525, -0.1475,\n",
+       "       -0.1425, -0.1375, -0.1325, -0.1275, -0.1225, -0.1175, -0.1125,\n",
+       "       -0.1075, -0.1025, -0.0975, -0.0925, -0.0875, -0.0825, -0.0775,\n",
+       "       -0.0725, -0.0675, -0.0625, -0.0575, -0.0525, -0.0475, -0.0425,\n",
+       "       -0.0375, -0.0325, -0.0275, -0.0225, -0.0175, -0.0125, -0.0075,\n",
+       "       -0.0025,  0.0025,  0.0075,  0.0125,  0.0175,  0.0225,  0.0275,\n",
+       "        0.0325,  0.0375,  0.0425,  0.0475,  0.0525,  0.0575,  0.0625,\n",
+       "        0.0675,  0.0725,  0.0775,  0.0825,  0.0875,  0.0925,  0.0975,\n",
+       "        0.1025,  0.1075,  0.1125,  0.1175,  0.1225,  0.1275,  0.1325,\n",
+       "        0.1375,  0.1425,  0.1475,  0.1525,  0.1575,  0.1625,  0.1675,\n",
+       "        0.1725,  0.1775,  0.1825,  0.1875,  0.1925,  0.1975,  0.2025,\n",
+       "        0.2075,  0.2125,  0.2175,  0.2225,  0.2275,  0.2325,  0.2375,\n",
+       "        0.2425,  0.2475])
            • z
              (z)
              float64
              m
              60.500
              Values:
              array([60.5002152])
            • (c, x, y, z, t)
              float32
              counts
              1.909581e-07, 0.0, ..., 2.304152, 1.0194244
              Values:
              array([[[[[1.90958104e-07, 0.00000000e+00, 9.36448723e-02, ...,\n",
+       "           1.14035249e+00, 8.97487879e-01, 1.43592104e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 1.28820877e+02, ...,\n",
+       "           1.02907729e+00, 9.27991152e-01, 1.29765689e+00]],\n",
+       "\n",
+       "         [[8.89200878e+00, 5.61225510e+00, 9.22778726e-01, ...,\n",
+       "           1.45904565e+00, 2.11791262e-01, 9.26256120e-01]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[2.32312746e-21, 0.00000000e+00, 3.36233582e+02, ...,\n",
+       "           8.87060285e-01, 1.43583798e+00, 2.07095361e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.05311188e+02, 0.00000000e+00, ...,\n",
+       "           3.81416392e+00, 1.93374383e+00, 1.42187238e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 2.96646332e+02, ...,\n",
+       "           2.40981841e+00, 1.27733397e+00, 7.96573043e-01]]],\n",
+       "\n",
+       "\n",
+       "        [[[3.22307206e-35, 3.42579443e-38, 1.72879195e+00, ...,\n",
+       "           3.18478918e+00, 1.23632216e+00, 9.68605280e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.05064732e-01, 7.90317112e-14, ...,\n",
+       "           2.03952980e+00, 4.89318281e-01, 3.56527835e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "           1.45515895e+00, 2.32485628e+00, 1.29108512e+00]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.51747906e+00, 6.35530412e-01, 1.78131449e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           4.50277901e+00, 1.12163591e+00, 9.15096223e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 1.81303909e+02, 3.73204875e+00, ...,\n",
+       "           1.38945556e+00, 1.25217187e+00, 8.42039764e-01]]],\n",
+       "\n",
+       "\n",
+       "        [[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           3.03744292e+00, 1.69370270e+00, 1.32360685e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.17823106e-01, 0.00000000e+00, ...,\n",
+       "           2.09293628e+00, 2.02386475e+00, 1.49039662e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           3.08005953e+00, 9.83998716e-01, 3.58726472e-01]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 2.66153278e-21, 2.56299377e+02, ...,\n",
+       "           6.92302346e-01, 1.34959555e+00, 4.37098801e-01]],\n",
+       "\n",
+       "         [[4.12041968e-22, 1.09137054e+02, 2.03482147e+02, ...,\n",
+       "           2.12783647e+00, 1.40891480e+00, 2.48633400e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.62731272e-02, 0.00000000e+00, ...,\n",
+       "           2.00853443e+00, 1.57524645e+00, 6.04599774e-01]]],\n",
+       "\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "\n",
+       "        [[[5.59635353e+00, 0.00000000e+00, 1.02489926e-02, ...,\n",
+       "           2.01132488e+00, 2.81510448e+00, 4.49818581e-01]],\n",
+       "\n",
+       "         [[1.09200635e-10, 2.99957027e+01, 0.00000000e+00, ...,\n",
+       "           1.27903652e+00, 1.44172740e+00, 7.66069061e-05]],\n",
+       "\n",
+       "         [[0.00000000e+00, 2.98039818e+01, 0.00000000e+00, ...,\n",
+       "           2.35189128e+00, 7.52224088e-01, 1.20181143e+00]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           2.34773707e+00, 6.45009398e-01, 1.57186723e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 3.35921720e-02, 0.00000000e+00, ...,\n",
+       "           2.59450912e+00, 2.18579865e+00, 1.45943356e+00]],\n",
+       "\n",
+       "         [[3.03374390e+02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.26739216e+00, 1.84410930e+00, 1.59677708e+00]]],\n",
+       "\n",
+       "\n",
+       "        [[[2.61284236e-04, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.65714300e+00, 1.02839196e+00, 3.36461872e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 8.67571615e-09, ...,\n",
+       "           1.25123167e+00, 1.24353695e+00, 8.20444077e-02]],\n",
+       "\n",
+       "         [[0.00000000e+00, 2.78917694e+01, 4.24154364e-13, ...,\n",
+       "           1.44367111e+00, 8.16694200e-01, 7.81571143e-05]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 5.64265101e-05, 0.00000000e+00, ...,\n",
+       "           3.74670649e+00, 3.11956555e-01, 1.12755370e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 3.38899323e+02, ...,\n",
+       "           2.12907672e+00, 1.93196785e+00, 5.08492708e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.41123080e+00, 1.83106911e+00, 2.14733064e-01]]],\n",
+       "\n",
+       "\n",
+       "        [[[0.00000000e+00, 2.91616002e-23, 6.52526694e-07, ...,\n",
+       "           7.39546716e-01, 1.83966124e+00, 6.17341340e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 7.71208794e-28, ...,\n",
+       "           2.02406144e+00, 2.76503587e+00, 9.54026580e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 7.74029315e-01, ...,\n",
+       "           2.74610829e+00, 1.03851902e+00, 1.22213876e+00]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[3.01266697e-16, 0.00000000e+00, 3.47227882e-26, ...,\n",
+       "           8.39114785e-01, 1.43921852e+00, 1.34635222e+00]],\n",
+       "\n",
+       "         [[1.02997002e+02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           2.55435848e+00, 1.42273450e+00, 1.65734649e+00]],\n",
+       "\n",
+       "         [[4.54903145e+01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           2.41094232e+00, 2.30415201e+00, 1.01942444e+00]]]],\n",
+       "\n",
+       "\n",
+       "\n",
+       "       [[[[1.90958104e-07, 0.00000000e+00, 9.36448723e-02, ...,\n",
+       "           1.14035249e+00, 8.97487879e-01, 1.43592104e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 1.28820877e+02, ...,\n",
+       "           1.02907729e+00, 9.27991152e-01, 1.29765689e+00]],\n",
+       "\n",
+       "         [[8.89200878e+00, 5.61225510e+00, 9.22778726e-01, ...,\n",
+       "           1.45904565e+00, 2.11791262e-01, 9.26256120e-01]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[2.32312746e-21, 0.00000000e+00, 3.36233582e+02, ...,\n",
+       "           8.87060285e-01, 1.43583798e+00, 2.07095361e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.05311188e+02, 0.00000000e+00, ...,\n",
+       "           3.81416392e+00, 1.93374383e+00, 1.42187238e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 2.96646332e+02, ...,\n",
+       "           2.40981841e+00, 1.27733397e+00, 7.96573043e-01]]],\n",
+       "\n",
+       "\n",
+       "        [[[3.22307206e-35, 3.42579443e-38, 1.72879195e+00, ...,\n",
+       "           3.18478918e+00, 1.23632216e+00, 9.68605280e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.05064732e-01, 7.90317112e-14, ...,\n",
+       "           2.03952980e+00, 4.89318281e-01, 3.56527835e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 5.80689609e-01, 0.00000000e+00, ...,\n",
+       "           1.45515895e+00, 2.32485628e+00, 1.29108512e+00]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.51747906e+00, 6.35530412e-01, 1.78131449e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           4.50277901e+00, 1.12163591e+00, 9.15096223e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 1.81303909e+02, 3.73204875e+00, ...,\n",
+       "           1.38945556e+00, 1.25217187e+00, 8.42039764e-01]]],\n",
+       "\n",
+       "\n",
+       "        [[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           3.03744292e+00, 1.69370270e+00, 1.32360685e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.17823106e-01, 0.00000000e+00, ...,\n",
+       "           2.09293628e+00, 2.02386475e+00, 1.49039662e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           3.08005953e+00, 9.83998716e-01, 3.58726472e-01]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 2.66153278e-21, 2.56299377e+02, ...,\n",
+       "           6.92302346e-01, 1.34959555e+00, 4.37098801e-01]],\n",
+       "\n",
+       "         [[4.12041968e-22, 1.09137054e+02, 2.03482147e+02, ...,\n",
+       "           2.12783647e+00, 1.40891480e+00, 2.48633400e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 4.62731272e-02, 0.00000000e+00, ...,\n",
+       "           2.00853443e+00, 1.57524645e+00, 6.04599774e-01]]],\n",
+       "\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "\n",
+       "        [[[5.59635353e+00, 0.00000000e+00, 1.02489926e-02, ...,\n",
+       "           2.01132488e+00, 2.81510448e+00, 4.49818581e-01]],\n",
+       "\n",
+       "         [[1.09200635e-10, 2.99957027e+01, 0.00000000e+00, ...,\n",
+       "           1.27903652e+00, 1.44172740e+00, 7.66069061e-05]],\n",
+       "\n",
+       "         [[0.00000000e+00, 2.98039818e+01, 0.00000000e+00, ...,\n",
+       "           2.35189128e+00, 7.52224088e-01, 1.20181143e+00]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           2.34773707e+00, 6.45009398e-01, 1.57186723e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 3.35921720e-02, 0.00000000e+00, ...,\n",
+       "           2.59450912e+00, 2.18579865e+00, 1.45943356e+00]],\n",
+       "\n",
+       "         [[3.03374390e+02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.26739216e+00, 1.84410930e+00, 1.59677708e+00]]],\n",
+       "\n",
+       "\n",
+       "        [[[2.61284236e-04, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.65714300e+00, 1.02839196e+00, 3.36461872e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 8.67571615e-09, ...,\n",
+       "           1.25123167e+00, 1.24353695e+00, 8.20444077e-02]],\n",
+       "\n",
+       "         [[0.00000000e+00, 2.78917694e+01, 4.24154364e-13, ...,\n",
+       "           1.44367111e+00, 8.16694200e-01, 7.81571143e-05]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[0.00000000e+00, 5.64265101e-05, 0.00000000e+00, ...,\n",
+       "           3.74670649e+00, 3.11956555e-01, 1.12755370e+00]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 3.38899323e+02, ...,\n",
+       "           2.12907672e+00, 1.93196785e+00, 5.08492708e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           1.41123080e+00, 1.83106911e+00, 2.14733064e-01]]],\n",
+       "\n",
+       "\n",
+       "        [[[0.00000000e+00, 2.91616002e-23, 6.52526694e-07, ...,\n",
+       "           7.39546716e-01, 1.83966124e+00, 6.17341340e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 7.71208794e-28, ...,\n",
+       "           2.02406144e+00, 2.76503587e+00, 9.54026580e-01]],\n",
+       "\n",
+       "         [[0.00000000e+00, 0.00000000e+00, 7.74029315e-01, ...,\n",
+       "           2.74610829e+00, 1.03851902e+00, 1.22213876e+00]],\n",
+       "\n",
+       "         ...,\n",
+       "\n",
+       "         [[3.01266697e-16, 0.00000000e+00, 3.47227882e-26, ...,\n",
+       "           8.39114785e-01, 1.43921852e+00, 1.34635222e+00]],\n",
+       "\n",
+       "         [[1.02997002e+02, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           2.55435848e+00, 1.42273450e+00, 1.65734649e+00]],\n",
+       "\n",
+       "         [[4.54903145e+01, 0.00000000e+00, 0.00000000e+00, ...,\n",
+       "           2.41094232e+00, 2.30415201e+00, 1.01942444e+00]]]]],\n",
+       "      shape=(2, 100, 100, 1, 500), dtype=float32)
          "
+      ],
+      "text/plain": [
+       "\n",
+       "Dimensions: Sizes[c:2, x:100, y:100, z:1, t:500, ]\n",
+       "Coordinates:\n",
+       "* c                          string          (c)  [\"data\", \"variances\"]\n",
+       "* sample_position           vector3              [m]  ()  (0.026, 0, 60.5)\n",
+       "* source_position           vector3              [m]  ()  (0, 0, 0)\n",
+       "* tof                       float64              [s]  (t [bin-edge])  [0.0153455, 0.015584, ..., 0.134343, 0.134581]\n",
+       "* x                         float64              [m]  (x)  [-0.12375, -0.12125, ..., 0.12125, 0.12375]\n",
+       "* y                         float64              [m]  (y)  [-0.2475, -0.2425, ..., 0.2425, 0.2475]\n",
+       "* z                         float64              [m]  (z)  [60.5002]\n",
+       "Data:\n",
+       "                            float32         [counts]  (c, x, y, z, t)  [1.90958e-07, 0, ..., 2.30415, 1.01942]\n"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from odin_mcstas_helper import as_scitiff\n",
+    "\n",
+    "tof_scitiff_hist = as_scitiff(\n",
+    "    img, x_width=example_resolution[0], y_height=example_resolution[1]\n",
+    ")\n",
+    "tof_scitiff_hist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "280d65bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scitiff\n",
+    "\n",
+    "output_file_path = file_dir_path.stem + \"_mcstas.tiff\"\n",
+    "scitiff.save_scitiff(tof_scitiff_hist, output_file_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "easyimaging",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ProofOfConcept/odin_mcstas_helper.py b/ProofOfConcept/odin_mcstas_helper.py
new file mode 100644
index 0000000..e8cb845
--- /dev/null
+++ b/ProofOfConcept/odin_mcstas_helper.py
@@ -0,0 +1,581 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright (c) 2025 Scipp contributors (https://github.com/scipp)
+# Temporary helper for mcstas loader.
+# Many of these functions will end up in ess.imaging package or mcstox package.
+# This will be moved to the odin package in the future.
+import pathlib
+from types import MappingProxyType
+from typing import NewType, cast
+
+import h5py
+import mcstastox
+import scipp as sc
+import scipp.constants as scc
+import scippnexus as snx
+from scippneutron.chopper import DiskChopper
+from scippneutron.conversion import graph
+
+from ess.reduce.nexus.types import FilePath
+
+# Prepare graph for transformation
+PLAIN_GRAPH = {**graph.beamline.beamline(False), **graph.tof.kinematic("tof")}
+
+_DataPath = NewType('_DataPath', str)
+_DefaultDataPath = _DataPath(
+    "entry1/data/transmission_event_signal_dat_list_p_t_x_y_z_vx_vy_vz/events"
+)
+_FileLock = NewType('_FileLock', bool)
+"""Lock the file to prevent concurrent access."""
+_DefaultFileLock = _FileLock(True)
+OdinSimulationRawData = NewType('OdinSimulationRawData', sc.DataArray)
+ProbabilityToCountsScaleFactor = NewType('ProbabilityToCountsScaleFactor', sc.Variable)
+"""Translate the probability to counts."""
+DefaultProbabilityToCountsScaleFactor = ProbabilityToCountsScaleFactor(
+    sc.scalar(1_000, unit='dimensionless')
+)
+DetectorStartX = NewType('DetectorStartX', sc.Variable)
+"""Start of the detector in x direction."""
+DefaultDetectorStartX = DetectorStartX(sc.scalar(-0.03, unit='m'))
+DetectorStartY = NewType('DetectorStartY', sc.Variable)
+"""Start of the detector in y direction."""
+DefaultDetectorStartY = DetectorStartY(sc.scalar(-0.03, unit='m'))
+
+DetectorEndX = NewType('DetectorEndX', sc.Variable)
+"""End of the detector in x direction."""
+DefaultDetectorEndX = DetectorEndX(sc.scalar(0.03, unit='m'))
+DetectorEndY = NewType('DetectorEndY', sc.Variable)
+"""End of the detector in y direction."""
+DefaultDetectorEndY = DetectorEndY(sc.scalar(0.03, unit='m'))
+
+McStasManualResolution = NewType('McStasManualResolution', tuple)
+"""Manual resolution for McStas data (how many pixels per axis x, y)"""
+DefaultMcStasManualResolution = McStasManualResolution((1024, 1024))
+
+
+def extract_component_from_vector(var: sc.Variable, component: str) -> sc.Variable:
+    component_to_index = {'x': 0, 'y': 1, 'z': 2}
+    return sc.array(
+        values=var.values[:, component_to_index[component]],
+        unit=var.unit,
+        dims=component,
+    )
+
+
+def _nth_col_or_row_lookup(
+    start: sc.Variable, stop: sc.Variable, resolution: int, dim: str
+) -> sc.Lookup:
+    """Lookup the nth column or row."""
+    position = sc.linspace(
+        dim, start=start, stop=stop, num=resolution + 1, unit=start.unit
+    )
+    nth_col_or_row = sc.arange(dim=dim, start=0, stop=resolution, unit='dimensionless')
+    hist = sc.DataArray(data=nth_col_or_row, coords={dim: position})
+    return sc.lookup(hist, dim)
+
+
+def _position_to_pixel_id(
+    *,
+    x_pos: sc.Variable,
+    y_pos: sc.Variable,
+    detector_start_x: DetectorStartX = DefaultDetectorStartX,
+    detector_start_y: DetectorStartY = DefaultDetectorStartY,
+    detector_end_x: DetectorEndX = DefaultDetectorEndX,
+    detector_end_y: DetectorEndY = DefaultDetectorEndY,
+    resolution: McStasManualResolution = DefaultMcStasManualResolution,
+) -> sc.Variable:
+    """Hardcode pixel ids from positions."""
+    x_position_lookup = _nth_col_or_row_lookup(
+        detector_start_x, detector_end_x, resolution[0], 'x'
+    )
+    y_position_lookup = _nth_col_or_row_lookup(
+        detector_start_y, detector_end_y, resolution[1], 'y'
+    )
+    n_cols = x_position_lookup[x_pos]
+    n_rows = y_position_lookup[y_pos]
+    return n_rows * resolution[0] + n_cols
+
+
+McStasVelocities = NewType('McStasVelocities', sc.DataGroup)
+
+
+def load_velocities(
+    file_path: FilePath,
+    _data_path: _DataPath = _DefaultDataPath,
+    _file_lock: _FileLock = _DefaultFileLock,
+) -> McStasVelocities:
+    with snx.File(file_path, "r", locking=_file_lock) as f:
+        data = f[_data_path][()].rename_dims({'dim_0': 'event'})
+        velocities = data['dim_1', 5:8]
+        vx = cast(sc.Variable, velocities['dim_1', 0].copy())
+        vy = cast(sc.Variable, velocities['dim_1', 1].copy())
+        vz = cast(sc.Variable, velocities['dim_1', 2].copy())
+        for v_component in (vx, vy, vz):
+            v_component.unit = 'm/s'
+        # Add special tags if you want to use them as coordinates
+        # for example, da.coords['vx_MC'] = vx
+        # to distinguish them from the measurement
+        return McStasVelocities(sc.DataGroup(vx=vx, vy=vy, vz=vz))
+
+
+LoadTrueVelocities = NewType('LoadTrueVelocities', bool)
+DefaultLoadTrueVelocities = LoadTrueVelocities(True)
+
+
+def load_odin_simulation_data(
+    file_path: FilePath,
+    _data_path: _DataPath = _DefaultDataPath,
+    _file_lock: _FileLock = _DefaultFileLock,
+    detector_start_x: DetectorStartX = DefaultDetectorStartX,
+    detector_start_y: DetectorStartY = DefaultDetectorStartY,
+    detector_end_x: DetectorEndX = DefaultDetectorEndX,
+    detector_end_y: DetectorEndY = DefaultDetectorEndY,
+    resolution: McStasManualResolution = DefaultMcStasManualResolution,
+    probability_scale_factor: ProbabilityToCountsScaleFactor = DefaultProbabilityToCountsScaleFactor,  # noqa: E501
+    load_true_velocities: LoadTrueVelocities = DefaultLoadTrueVelocities,
+) -> OdinSimulationRawData:
+    with snx.File(file_path, "r", locking=_file_lock) as f:
+        # The name p_t_x_y_z_vx_vy_vz represents
+        # probability, time of arrival, position(x, y, z) and velocity(vx, vy, vz).
+        # The name also represents the order of each field in the table.
+        # For example, probability is the first field, so data['dim_1', 0] is the probability.  # noqa: E501
+        data = f[_data_path][()].rename_dims({'dim_0': 'event'})
+        probabilities = cast(sc.Variable, data['dim_1', 0].copy())
+        probabilities.unit = 'dimensionless'
+        time_of_arrival = cast(sc.Variable, data['dim_1', 1].copy())
+        time_of_arrival.unit = 's'  # Hardcoded unit from the data.
+        positions = data['dim_1', 2:5]
+        counts = (probabilities / probabilities.max()) * probability_scale_factor
+        counts.unit = 'counts'
+        counts.variances = counts.values**2
+        # Units are hardcoded from the data.
+        x_pos = cast(sc.Variable, positions['dim_1', 0].copy())
+        x_pos.unit = 'm'
+        y_pos = cast(sc.Variable, positions['dim_1', 1].copy())
+        y_pos.unit = 'm'
+        pixel_id = _position_to_pixel_id(
+            x_pos=x_pos,
+            y_pos=y_pos,
+            detector_start_x=detector_start_x,
+            detector_start_y=detector_start_y,
+            detector_end_x=detector_end_x,
+            detector_end_y=detector_end_y,
+            resolution=resolution,
+        )
+        da = sc.DataArray(
+            data=counts.copy(),
+            coords={
+                'time_of_arrival': time_of_arrival.to(unit='us'),
+                'sample_position': sc.vector([0.0, 0.0, 60.5], unit='m'),
+                # Hardcoded from the data.
+                'source_position': sc.vector([0.0, 0.0, 0.0], unit="m"),
+                # Hardcoded from the data.
+                'pixel_id': pixel_id,
+            },
+        )
+        if load_true_velocities:
+            velocities = load_velocities(file_path, _data_path, _file_lock)
+            speeds = sc.norm(
+                sc.vectors(
+                    dims=['event'],
+                    values=sc.transpose(
+                        sc.concat(list(velocities.values()), 'speed')
+                    ).values,
+                    unit='m/s',
+                )
+            )
+            da.coords['sim_wavelength'] = (scc.h / scc.neutron_mass / speeds).to(
+                unit='angstrom'
+            )
+
+        return OdinSimulationRawData(da.to(dtype=float))
+
+
+def pixel_ids_to_x(
+    *,
+    pixel_id: sc.Variable,
+    resolution: McStasManualResolution = DefaultMcStasManualResolution,
+    detector_start_x: DetectorStartX = DefaultDetectorStartX,
+    detector_end_x: DetectorEndX = DefaultDetectorEndX,
+) -> sc.Variable:
+    n_col = pixel_id % resolution[0]
+    x_interval = (detector_end_x - detector_start_x) / resolution[0]
+    return (
+        detector_start_x + n_col * x_interval
+    ) + x_interval / 2  # Center of the pixel|
+
+
+def pixel_ids_to_y(
+    *,
+    pixel_id: sc.Variable,
+    resolution: McStasManualResolution = DefaultMcStasManualResolution,
+    detector_start_y: DetectorStartY = DefaultDetectorStartY,
+    detector_end_y: DetectorEndY = DefaultDetectorEndY,
+) -> sc.Variable:
+    n_row = pixel_id // resolution[0]
+    y_interval = (detector_end_y - detector_start_y) / resolution[1]
+    return (
+        detector_start_y + n_row * y_interval
+    ) + y_interval / 2  # Center of the pixel
+
+
+def pixel_ids_to_position(
+    *, x: sc.Variable, y: sc.Variable, z: sc.Variable
+) -> sc.Variable:
+    z = sc.zeros_like(x) + z
+    var = (
+        sc.concat([x, y, z], 'event')
+        .fold('event', dims=['pos', 'event'], shape=[3, len(x)])
+        .transpose(dims=['event', 'pos'])
+        .values
+    )
+    return sc.vectors(dims=['event'], values=var, unit='m')
+
+
+# Choppers
+Hz = sc.Unit("Hz")
+deg = sc.Unit("deg")
+meter = sc.Unit("m")
+
+parameters = {
+    "WFMC_1": {
+        "frequency": 56.0,
+        "phase": 93.244,
+        "distance": 6.85,
+        "open": [-1.9419, 49.5756, 98.9315, 146.2165, 191.5176, 234.9179],
+        "close": [1.9419, 55.7157, 107.2332, 156.5891, 203.8741, 249.1752],
+    },
+    "WFMC_2": {
+        "frequency": 56.0,
+        "phase": 97.128,
+        "distance": 7.15,
+        "open": [-1.9419, 51.8318, 103.3493, 152.7052, 199.9903, 245.2914],
+        "close": [1.9419, 57.9719, 111.6510, 163.0778, 212.3468, 259.5486],
+    },
+    "FOC_1": {
+        "frequency": 42.0,
+        "phase": 81.303297,
+        "distance": 8.4,
+        "open": [-5.1362, 42.5536, 88.2425, 132.0144, 173.9497, 216.7867],
+        "close": [5.1362, 54.2095, 101.2237, 146.2653, 189.417, 230.7582],
+    },
+    "BP_1": {
+        "frequency": 7.0,
+        "phase": 31.080,
+        "distance": 8.45,
+        "open": [-23.6029],
+        "close": [23.6029],
+    },
+    "FOC_2": {
+        "frequency": 42.0,
+        "phase": 107.013442,
+        "distance": 12.2,
+        "open": [-16.3227, 53.7401, 120.8633, 185.1701, 246.7787, 307.0165],
+        "close": [16.3227, 86.8303, 154.3794, 218.7551, 280.7508, 340.3188],
+    },
+    "BP_2": {
+        "frequency": 7.0,
+        "phase": 44.224,
+        "distance": 12.25,
+        "open": [-34.4663],
+        "close": [34.4663],
+    },
+    "T0_alpha": {
+        "frequency": 14.0,
+        "phase": 179.672,
+        "distance": 13.5,
+        "open": [-167.8986],
+        "close": [167.8986],
+    },
+    "T0_beta": {
+        "frequency": 14.0,
+        "phase": 179.672,
+        "distance": 13.7,
+        "open": [-167.8986],
+        "close": [167.8986],
+    },
+    "FOC_3": {
+        "frequency": 28.0,
+        "phase": 92.993,
+        "distance": 17.0,
+        "open": [-20.302, 45.247, 108.0457, 168.2095, 225.8489, 282.2199],
+        "close": [20.302, 85.357, 147.6824, 207.3927, 264.5977, 319.4024],
+    },
+    "FOC_4": {
+        "frequency": 14.0,
+        "phase": 61.584,
+        "distance": 23.69,
+        "open": [-16.7157, 29.1882, 73.1661, 115.2988, 155.6636, 195.5254],
+        "close": [16.7157, 61.8217, 105.0352, 146.4355, 186.0987, 224.0978],
+    },
+    "FOC_5": {
+        "frequency": 14.0,
+        "phase": 82.581,
+        "distance": 33.0,
+        "open": [-25.8514, 38.3239, 99.8064, 160.1254, 217.4321, 272.5426],
+        "close": [25.8514, 88.4621, 147.4729, 204.0245, 257.7603, 313.7139],
+    },
+}
+
+DISK_CHOPPERS = MappingProxyType(
+    {
+        key: DiskChopper(
+            frequency=-ch["frequency"] * Hz,
+            beam_position=sc.scalar(0.0, unit="deg"),
+            phase=-ch["phase"] * deg,
+            axle_position=sc.vector(value=[0, 0, ch["distance"]], unit="m"),
+            slit_begin=sc.array(dims=["cutout"], values=ch["open"], unit="deg"),
+            slit_end=sc.array(dims=["cutout"], values=ch["close"], unit="deg"),
+        )
+        for key, ch in parameters.items()
+    }
+)
+"""Hard-coded DISK_CHOPPERS dictionary for ESS ODIN choppers."""
+
+
+def _retrieve_attribute_value(var: h5py.Dataset, param_vars: dict) -> float:
+    import ast
+
+    value: str = var.attrs['value'].decode('utf-8')
+    if (values := value.split('+')) and len(values) > 1:
+        return float(ast.literal_eval(values[-1]))
+    elif (values := value.split(' ')) and len(values) > 1:
+        return float(ast.literal_eval(values[-1]))
+    elif (values := value.split('.')) and len(values) > 2:
+        var_name = values[-1]
+        return param_vars[var_name]
+    else:
+        return float(value)
+
+
+def _merge_names(names: list[str]) -> str:
+    real_name = '_'.join(names[0].split('_')[1:])
+    return real_name if len(names) == 1 else real_name[:-1]
+
+
+def _merge_chopper_components(
+    components: list[dict], position_offset: sc.Variable | None = None
+) -> tuple[str, DiskChopper]:
+    main_phase = components[0]['phase']
+    frequency = components[0]['frequency']
+    position = components[0]['position']
+    # radius = components[0]['radius']
+    position_offset = (
+        sc.vector(value=[-0.026, 0, -0.025], unit='m')
+        if position_offset is None
+        else position_offset
+    )
+    openings = []
+    closings = []
+    for component in components:
+        phase_diff = component['phase'] - main_phase
+        theta_0 = component['theta_0']
+        openings.append(phase_diff - theta_0 / 2)
+        closings.append(phase_diff + theta_0 / 2)
+    return (
+        _merge_names([comp['name'] for comp in components]),
+        DiskChopper(
+            frequency=-sc.scalar(frequency, unit='Hz'),
+            beam_position=sc.scalar(0.0, unit="deg"),
+            phase=-sc.scalar(main_phase, unit='deg'),
+            axle_position=position + position_offset,
+            slit_begin=sc.array(dims=['cutouts'], values=openings, unit='deg'),
+            slit_end=sc.array(dims=['cutouts'], values=closings, unit='deg'),
+        ),
+    )
+
+
+def _load_parameters_from_text_file(file_path: pathlib.Path) -> dict:
+    components = file_path.read_text().split("COMPONENT")
+    choppers = {}
+    for component in components:
+        lines = component.strip().split("\n")
+        if len(lines) < 2:
+            continue
+        if lines[0].endswith("DiskChopper"):
+            name, _ = lines[0].strip().split(" = ")
+            params = {
+                comps[0]: comps[1]
+                for ln in lines[1:-1]
+                if (comps := ln.strip().split('='))
+            }
+            choppers[name] = {
+                'phase': float(params['phase']),
+            }
+    return choppers
+
+
+def load_choppers(
+    nexus_file_path: pathlib.Path,
+    component_text_file_path: pathlib.Path,
+    chopper_position_offset: sc.Variable | None = None,
+) -> dict:
+    from functools import partial
+
+    position_offset = (
+        sc.vector(value=[-0.026, 0, -0.025], unit='m')
+        if chopper_position_offset is None
+        else chopper_position_offset
+    )
+    path = "entry1/instrument/components"
+    param_var_path = "entry1/simulation/Param"
+    extra_params = _load_parameters_from_text_file(component_text_file_path)
+    with h5py.File(nexus_file_path, "r") as f:
+        # Retrieve parameter variables
+        param_var_ds = f[param_var_path]
+        param_vars = {
+            var_name: float(param_var_ds[var_name][()][0].decode('utf-8'))
+            for var_name in param_var_ds.keys()
+        }
+        data = f[path]
+        diskchopper_components = {}
+        value_retriever = partial(_retrieve_attribute_value, param_vars=param_vars)
+        for component_key in data.keys():
+            component_type = data[component_key]["Component_type"][()]
+            if component_type == b"DiskChopper":
+                real_key = '_'.join(component_key.split("_")[1:])
+                component_dict = {
+                    'name': component_key,
+                    'frequency': value_retriever(
+                        data[component_key]["parameters"]['nu']
+                    ),
+                    'phase': extra_params[real_key]['phase'],
+                    'position': sc.vector(
+                        value=data[component_key]["Position"][()], unit="m"
+                    ),
+                    'radius': value_retriever(
+                        data[component_key]["parameters"]['radius']
+                    ),
+                    'theta_0': value_retriever(
+                        data[component_key]["parameters"]['theta_0']
+                    ),
+                }
+                diskchopper_components.setdefault(
+                    tuple(data[component_key]["Position"][()].tolist()), []
+                ).append(component_dict)
+    return {
+        parsed[0]: parsed[1]
+        for components in diskchopper_components.values()
+        if len(parsed := _merge_chopper_components(components, position_offset)) > 0
+    }
+
+
+def to_scipp(
+    file_path: pathlib.Path,
+    virtual_resolution: McStasManualResolution = DefaultMcStasManualResolution,
+) -> sc.DataArray:
+    with mcstastox.Read(file_path) as loaded_data:
+        output = loaded_data.export_scipp_simple(
+            source_name="Source", sample_name="sample_position"
+        )
+        raw_event_data = loaded_data.get_event_data(
+            variables=["id", "t", "L"], component_name="detector"
+        )
+        detector_pos, _ = loaded_data.get_component_placement("detector")
+
+    # Just want the x/y detector position to center the detector, ignore z
+    detector_pos[2] = 0
+    output.coords["position"] = output.coords["position"] - sc.vector(
+        value=detector_pos, unit="m"
+    )
+    output.coords["x"] = sc.array(
+        dims=["events"], values=output.coords["position"].fields.x.values, unit="m"
+    )
+    output.coords["y"] = sc.array(
+        dims=["events"], values=output.coords["position"].fields.y.values, unit="m"
+    )
+
+    # Get ground truth simulated wavelength
+    output.coords["sim_wavelength"] = sc.array(
+        dims=["events"], values=raw_event_data["L"], unit="Å"
+    )
+    output.coords['time_of_arrival'] = output.coords.pop("t")
+    width_or_height = sc.scalar(0.25, unit='m')
+    output.coords['pixel_id'] = _position_to_pixel_id(
+        x_pos=output.coords['x'],
+        y_pos=output.coords['y'],
+        detector_start_x=-width_or_height / 2,
+        detector_start_y=-width_or_height / 2,
+        detector_end_x=width_or_height / 2,
+        detector_end_y=width_or_height / 2,
+        resolution=virtual_resolution,
+    )
+    output.variances = output.data.values**2
+    return output
+
+
+def to_nexus(
+    da, *, resolution: McStasManualResolution, graph: dict = PLAIN_GRAPH
+) -> sc.DataArray:
+    # Copy data and add new coordinates
+    out: sc.DataArray = da.copy(deep=False)
+    # Event time zero/offset
+    period = (1.0 / sc.scalar(14.0, unit='Hz')).to(unit='ns')
+    start = sc.datetime("2024-01-01T12:00:00.000000000")
+    out.coords['event_time_zero'] = (
+        period * (da.coords['time_of_arrival'].to(unit='ns', copy=False) // period)
+    ).to(dtype=int) + start
+    out.coords['event_time_offset'] = out.coords.pop('time_of_arrival') % period.to(
+        unit=da.coords['time_of_arrival'].unit
+    )
+    # Group by pixel_id to have pixel positions on the top level
+    pixel_id = sc.arange(dim='pixel_id', start=0, stop=resolution[0] * resolution[1])
+    out = out.group(pixel_id)
+    width_or_height = sc.scalar(0.25, unit='m')
+    x = pixel_ids_to_x(
+        pixel_id=pixel_id,
+        resolution=resolution,
+        detector_start_x=-width_or_height / 2,
+        detector_end_x=width_or_height / 2,
+    )
+    y = pixel_ids_to_y(
+        pixel_id=pixel_id,
+        resolution=resolution,
+        detector_start_y=-width_or_height,
+        detector_end_y=width_or_height,
+    )
+    z = sc.zeros_like(x) + sc.scalar(60.5, unit='m')
+    var = (
+        sc.concat([x, y, z], 'pixel_id')
+        .fold('pixel_id', dims=['pos', 'pixel_id'], shape=[3, len(x)])
+        .transpose(dims=['pixel_id', 'pos'])
+        .values
+    )
+    out.coords['position'] = sc.vectors(dims=['pixel_id'], values=var, unit='m')
+    return out.transform_coords("Ltotal", graph=graph, keep_intermediate=True)
+
+
+def load_mcstas_data(
+    file_path: pathlib.Path,
+    show: bool = True,
+    resolution: McStasManualResolution = DefaultMcStasManualResolution,
+    graph: dict = PLAIN_GRAPH,
+) -> sc.DataArray:
+    """Load mcstas data from a file."""
+    return to_nexus(
+        to_scipp(file_path, virtual_resolution=resolution),
+        resolution=resolution,
+        graph=graph,
+    )
+
+
+def as_scitiff(da: sc.DataArray, x_width: int, y_height: int) -> sc.DataArray:
+    da = da.fold(dim="pixel_id", dims=["x", "y", "z"], shape=[x_width, y_height, 1])
+    copied = da.copy(deep=False).drop_coords(["position", "pixel_id"])
+    display(positions := da.coords['position'])
+    copied.coords['x'] = sc.array(
+        dims=['x'],
+        values=positions.flatten(dims=['x', 'y', 'z'], to='x').values[:, 0][:x_width],
+        unit='m',
+    )
+    copied.coords['y'] = sc.array(
+        dims=['y'],
+        values=positions.flatten(dims=['x', 'y', 'z'], to='y').values[:, 1][::y_height],
+        unit='m',
+    )
+    copied.coords['z'] = copied.coords.pop('Ltotal').mean().broadcast(dims=['z'], shape=[1])
+    variances = copied.copy(deep=False)
+    variances.data.values = copied.data.variances
+    concated = sc.concat([copied, variances], dim='c').rename_dims({'tof': 't'})
+    concated.coords['c'] = sc.array(dims=['c'], values=['data', 'variances'])
+    concated.variances = None
+    return concated.astype('float32')
diff --git a/ProofOfConcept/sample_data/iron.tiff b/ProofOfConcept/sample_data/iron.tiff
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diff --git a/ProofOfConcept/sample_data/sample_5e11_mcstas.tiff b/ProofOfConcept/sample_data/sample_5e11_mcstas.tiff
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diff --git a/ProofOfConcept/scitiff_helper.py b/ProofOfConcept/scitiff_helper.py
new file mode 100644
index 0000000..69fda5a
--- /dev/null
+++ b/ProofOfConcept/scitiff_helper.py
@@ -0,0 +1,153 @@
+import scipp as sc
+import scitiff
+from dataclasses import dataclass
+
+
+class _FileNameLog:
+    def __init__(self, filename: str) -> None:
+        self._filename = filename
+
+    def _repr_html_(self) -> str:
+        return f"
          {self._filename}
          "
+
+
+def _transform_position(da: sc.DataArray) -> sc.DataArray:
+    x = da.coords["x"]
+    y = da.coords["y"]
+    z = da.coords["z"]
+    z = z.broadcast(dims=["z"], shape=[1 if z.shape == () else z.shape[0]])
+    length = x.shape[0] * y.shape[0] * z.shape[0]
+    new_da = da.copy()
+
+    def _wrap_pos(var: sc.Variable) -> sc.Variable:
+        return (
+            var.broadcast(
+                dims=[var.dim, "position"], shape=[var.shape[0], length // var.shape[0]]
+            ).flatten(dims=[var.dim, "position"], to="position")
+            # .rename_dims({var.dim: 'element'})
+        )
+
+    xyz = sc.concat([_wrap_pos(x), _wrap_pos(y), _wrap_pos(z)], "pos").transpose(
+        ["position", "pos"]
+    )
+    position = (
+        sc.vectors(dims=["position"], values=xyz.values, unit=xyz.unit)
+        .fold(dim="position", sizes={"x": x.shape[0], "y": y.shape[0], "z": z.shape[0]})
+        .squeeze()
+    )
+    new_da.coords["pixel_position"] = position
+    return new_da
+
+
+def load_scitiff(filename, background: bool = False, show: bool = True) -> sc.DataArray:
+    from ess.imaging.normalize import apply_threshold_to_background_image
+
+    da = (
+        scitiff.load_scitiff(filename)["image"]
+        #.astype(int)
+        .astype(float)
+        .rename_dims({"t": "tof"})
+    )
+    variances = da["c", sc.scalar("variances")].data.values
+
+    if background:
+        da = apply_threshold_to_background_image(
+            background=da, background_threshold=sc.scalar(1.0, unit="counts")
+        )
+    da = _transform_position(da)["c", 0].copy(deep=True)
+    da.variances = variances
+
+    if show:
+        display(_FileNameLog(filename))
+        display(da)
+
+    return da
+
+
+@dataclass
+class ROI:
+    x_b: sc.Variable
+    x_t: sc.Variable
+    y_b: sc.Variable
+    y_t: sc.Variable
+
+    def slice_dataarray(self, da: sc.DataArray) -> sc.Variable:
+        """
+        Convert the ROI to a variable slice
+        """
+        x = da.coords["x"]
+        y = da.coords["y"]
+        return da[x.dim, self.x_b : self.x_t][y.dim, self.y_b : self.y_t]
+
+    def __hash__(self) -> int:
+        return hash((self.x_b.value, self.x_t.value, self.y_b.value, self.y_t.value))
+
+
+def make_rectangle_tool(normalized: sc.DataArray, roi_container: set):
+    import plopp as pp
+    from plopp.widgets.drawing import DrawingTool
+    from functools import partial
+    from mpltoolbox import Rectangles
+
+    def vertical_sum(da, rect_info):
+        """
+        Function that slices the data according to the
+        rectangle size/position, and sums along the
+        vertical dimension.
+        """
+        x = rect_info["x"]
+        y = rect_info["y"]
+        b = min(y["bottom"], y["top"])
+        t = max(y["bottom"], y["top"])
+        l = min(x["left"], x["right"])
+        r = max(x["left"], x["right"])
+        roi_container.add((ROI(x_b=l, x_t=r, y_b=b, y_t=t)))
+        return normalized[y["dim"], b:t][x["dim"], l:r].mean("x").mean("y")
+
+    def _get_rect_info(artist, figure):
+        """
+        Convert the raw rectangle info to a dict containing the dimensions of
+        each axis, and values with units.
+        """
+        return lambda: {
+            "x": {
+                "dim": figure.canvas.dims["x"],
+                "left": sc.scalar(artist.xy[0], unit=figure.canvas.units["x"]),
+                "right": sc.scalar(
+                    artist.xy[0] + artist.width, unit=figure.canvas.units["x"]
+                ),
+            },
+            "y": {
+                "dim": figure.canvas.dims["y"],
+                "bottom": sc.scalar(artist.xy[1], unit=figure.canvas.units["y"]),
+                "top": sc.scalar(
+                    artist.xy[1] + artist.height, unit=figure.canvas.units["y"]
+                ),
+            },
+        }
+
+    RectangleTool = partial(
+        DrawingTool,
+        tool=Rectangles,
+        get_artist_info=_get_rect_info,
+        icon="vector-square",
+    )
+
+    from plopp.widgets import HBar
+
+    data_node = pp.Node(normalized.mean("tof"))
+
+    f2d = pp.imagefigure(
+        data_node,
+        norm="log",
+        title="Select ROIs here:\nAverage along TOF",
+        aspect="equal",
+    )
+    f1d = pp.linefigure(title="Distribution in ROIs")
+
+    r = RectangleTool(
+        figure=f2d, input_node=data_node, func=vertical_sum, destination=f1d
+    )
+    f2d.toolbar["roi"] = r
+
+    return HBar([f2d, f1d])
diff --git a/ProofOfConcept/test.tif b/ProofOfConcept/test.tif
new file mode 100644
index 0000000..e7cd1eb
Binary files /dev/null and b/ProofOfConcept/test.tif differ
diff --git a/pyproject.toml b/pyproject.toml
index c7e1d37..a180bd4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -86,6 +86,7 @@ repo = "easyimaging"
 line-length = 127
 exclude = [
     "docs",
+    "ProofOfConcept*",
 ]
 
 [tool.ruff.format]