|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "id": "6b645603-12c0-4b3d-ab3c-529ad4f373e3", |
| 6 | + "metadata": {}, |
| 7 | + "source": [ |
| 8 | + "# ODIN in WFM mode\n", |
| 9 | + "\n", |
| 10 | + "This is a simulation of the ODIN chopper cascade in WFM mode.\n", |
| 11 | + "We also show how one can convert the neutron arrival times at the detector to wavelength." |
| 12 | + ] |
| 13 | + }, |
| 14 | + { |
| 15 | + "cell_type": "code", |
| 16 | + "execution_count": null, |
| 17 | + "id": "899a245a-0ae7-45dd-b684-3b9d1c034cc6", |
| 18 | + "metadata": {}, |
| 19 | + "outputs": [], |
| 20 | + "source": [ |
| 21 | + "import numpy as np\n", |
| 22 | + "import scipp as sc\n", |
| 23 | + "import plopp as pp\n", |
| 24 | + "import tof\n", |
| 25 | + "\n", |
| 26 | + "Hz = sc.Unit(\"Hz\")\n", |
| 27 | + "deg = sc.Unit(\"deg\")\n", |
| 28 | + "meter = sc.Unit(\"m\")" |
| 29 | + ] |
| 30 | + }, |
| 31 | + { |
| 32 | + "cell_type": "markdown", |
| 33 | + "id": "3890d24c-93ab-4faa-b653-c9208b5eba23", |
| 34 | + "metadata": {}, |
| 35 | + "source": [ |
| 36 | + "## Create a source pulse\n", |
| 37 | + "\n", |
| 38 | + "We first create a source with 4 pulses containing 800,000 neutrons each,\n", |
| 39 | + "and whose distribution follows the ESS time and wavelength profiles (both thermal and cold neutrons are included)." |
| 40 | + ] |
| 41 | + }, |
| 42 | + { |
| 43 | + "cell_type": "code", |
| 44 | + "execution_count": null, |
| 45 | + "id": "872800a5-4cf7-424e-8d2d-0db81292456c", |
| 46 | + "metadata": {}, |
| 47 | + "outputs": [], |
| 48 | + "source": [ |
| 49 | + "source = tof.Source(facility=\"ess\", neutrons=500_000, pulses=4)\n", |
| 50 | + "source.plot()" |
| 51 | + ] |
| 52 | + }, |
| 53 | + { |
| 54 | + "cell_type": "markdown", |
| 55 | + "id": "b5ffdab6-10cd-4719-8ff2-86e5e1d10571", |
| 56 | + "metadata": {}, |
| 57 | + "source": [ |
| 58 | + "## Component set-up\n", |
| 59 | + "\n", |
| 60 | + "### Choppers\n", |
| 61 | + "\n", |
| 62 | + "The ODIN chopper cascade consists of:\n", |
| 63 | + "\n", |
| 64 | + "- 2 WFM choppers\n", |
| 65 | + "- 5 frame-overlap choppers\n", |
| 66 | + "- 2 band-control choppers\n", |
| 67 | + "- 1 T0 chopper" |
| 68 | + ] |
| 69 | + }, |
| 70 | + { |
| 71 | + "cell_type": "code", |
| 72 | + "execution_count": null, |
| 73 | + "id": "34314bcc-978c-468c-b5a1-d84ce33e53d5", |
| 74 | + "metadata": {}, |
| 75 | + "outputs": [], |
| 76 | + "source": [ |
| 77 | + "parameters = {\n", |
| 78 | + " \"WFMC_1\": {\n", |
| 79 | + " \"frequency\": 56.0,\n", |
| 80 | + " \"phase\": 93.244,\n", |
| 81 | + " \"distance\": 6.85,\n", |
| 82 | + " \"open\": [-1.9419, 49.5756, 98.9315, 146.2165, 191.5176, 234.9179],\n", |
| 83 | + " \"close\": [1.9419, 55.7157, 107.2332, 156.5891, 203.8741, 249.1752]\n", |
| 84 | + " },\n", |
| 85 | + " \"WFMC_2\": {\n", |
| 86 | + " \"frequency\": 56.0,\n", |
| 87 | + " \"phase\": 97.128,\n", |
| 88 | + " \"distance\": 7.15,\n", |
| 89 | + " \"open\": [-1.9419, 51.8318, 103.3493, 152.7052, 199.9903, 245.2914],\n", |
| 90 | + " \"close\": [1.9419, 57.9719, 111.6510, 163.0778, 212.3468, 259.5486]\n", |
| 91 | + " },\n", |
| 92 | + " \"FOC_1\": {\n", |
| 93 | + " \"frequency\": 42.0,\n", |
| 94 | + " \"phase\": 81.303297,\n", |
| 95 | + " \"distance\": 8.4,\n", |
| 96 | + " \"open\": [-5.1362, 42.5536, 88.2425, 132.0144, 173.9497, 216.7867],\n", |
| 97 | + " \"close\": [5.1362, 54.2095, 101.2237, 146.2653, 189.417, 230.7582]\n", |
| 98 | + " },\n", |
| 99 | + " \"BP_1\": {\n", |
| 100 | + " \"frequency\": 7.0,\n", |
| 101 | + " \"phase\": 31.080,\n", |
| 102 | + " \"distance\": 8.45,\n", |
| 103 | + " \"open\": [-23.6029],\n", |
| 104 | + " \"close\": [23.6029]\n", |
| 105 | + " },\n", |
| 106 | + " \"FOC_2\": {\n", |
| 107 | + " \"frequency\": 42.0,\n", |
| 108 | + " \"phase\": 107.013442,\n", |
| 109 | + " \"distance\": 12.2,\n", |
| 110 | + " \"open\": [-16.3227, 53.7401, 120.8633, 185.1701, 246.7787, 307.0165],\n", |
| 111 | + " \"close\": [16.3227, 86.8303, 154.3794, 218.7551, 280.7508, 340.3188]\n", |
| 112 | + " },\n", |
| 113 | + " \"BP_2\": {\n", |
| 114 | + " \"frequency\": 7.0,\n", |
| 115 | + " \"phase\": 44.224,\n", |
| 116 | + " \"distance\": 12.25,\n", |
| 117 | + " \"open\": [-34.4663],\n", |
| 118 | + " \"close\": [34.4663]\n", |
| 119 | + " },\n", |
| 120 | + " \"T0_alpha\": {\n", |
| 121 | + " \"frequency\": 14.0,\n", |
| 122 | + " \"phase\": 179.672,\n", |
| 123 | + " \"distance\": 13.5,\n", |
| 124 | + " \"open\": [-167.8986],\n", |
| 125 | + " \"close\": [167.8986]\n", |
| 126 | + " },\n", |
| 127 | + " \"T0_beta\": {\n", |
| 128 | + " \"frequency\": 14.0,\n", |
| 129 | + " \"phase\": 179.672,\n", |
| 130 | + " \"distance\": 13.7,\n", |
| 131 | + " \"open\": [-167.8986],\n", |
| 132 | + " \"close\": [167.8986]\n", |
| 133 | + " },\n", |
| 134 | + " \"FOC_3\": {\n", |
| 135 | + " \"frequency\": 28.0,\n", |
| 136 | + " \"phase\": 92.993,\n", |
| 137 | + " \"distance\": 17.0,\n", |
| 138 | + " \"open\": [-20.302, 45.247, 108.0457, 168.2095, 225.8489, 282.2199],\n", |
| 139 | + " \"close\": [20.302, 85.357, 147.6824, 207.3927, 264.5977, 319.4024]\n", |
| 140 | + " },\n", |
| 141 | + " \"FOC_4\": {\n", |
| 142 | + " \"frequency\": 14.0,\n", |
| 143 | + " \"phase\": 61.584,\n", |
| 144 | + " \"distance\": 23.69,\n", |
| 145 | + " \"open\": [-16.7157, 29.1882, 73.1661, 115.2988, 155.6636, 195.5254],\n", |
| 146 | + " \"close\": [16.7157, 61.8217, 105.0352, 146.4355, 186.0987, 224.0978]\n", |
| 147 | + " },\n", |
| 148 | + " \"FOC_5\": {\n", |
| 149 | + " \"frequency\": 14.0,\n", |
| 150 | + " \"phase\": 82.581,\n", |
| 151 | + " \"distance\": 33.0,\n", |
| 152 | + " \"open\": [-25.8514, 38.3239, 99.8064, 160.1254, 217.4321, 272.5426],\n", |
| 153 | + " \"close\": [25.8514, 88.4621, 147.4729, 204.0245, 257.7603, 313.7139]\n", |
| 154 | + " },\n", |
| 155 | + "\n", |
| 156 | + "}\n", |
| 157 | + "\n", |
| 158 | + "choppers = [\n", |
| 159 | + " tof.Chopper(\n", |
| 160 | + " frequency=ch[\"frequency\"] * Hz,\n", |
| 161 | + " direction=tof.Clockwise,\n", |
| 162 | + " open=sc.array(dims=[\"cutout\"], values=ch[\"open\"], unit=\"deg\"),\n", |
| 163 | + " close=sc.array(dims=[\"cutout\"], values=ch[\"close\"], unit=\"deg\"),\n", |
| 164 | + " phase=ch[\"phase\"] * deg,\n", |
| 165 | + " distance=ch[\"distance\"] * meter,\n", |
| 166 | + " name=key,\n", |
| 167 | + " )\n", |
| 168 | + " for key, ch in parameters.items()\n", |
| 169 | + "]" |
| 170 | + ] |
| 171 | + }, |
| 172 | + { |
| 173 | + "cell_type": "markdown", |
| 174 | + "id": "bd6e1399-7f05-4d5c-83e3-7d249d9e0a61", |
| 175 | + "metadata": {}, |
| 176 | + "source": [ |
| 177 | + "### Detector\n", |
| 178 | + "\n", |
| 179 | + "ODIN has a single detector panel 60.5 meters from the source." |
| 180 | + ] |
| 181 | + }, |
| 182 | + { |
| 183 | + "cell_type": "code", |
| 184 | + "execution_count": null, |
| 185 | + "id": "a377e75c-51a5-4994-af9c-92139d27bee3", |
| 186 | + "metadata": {}, |
| 187 | + "outputs": [], |
| 188 | + "source": [ |
| 189 | + "detectors = [\n", |
| 190 | + " tof.Detector(distance=60.5 * meter, name=\"detector\"),\n", |
| 191 | + "]" |
| 192 | + ] |
| 193 | + }, |
| 194 | + { |
| 195 | + "cell_type": "markdown", |
| 196 | + "id": "4deb2f50-92b7-4838-b180-17e3ce43743d", |
| 197 | + "metadata": {}, |
| 198 | + "source": [ |
| 199 | + "## Run the simulation\n", |
| 200 | + "\n", |
| 201 | + "We propagate our pulse of neutrons through the chopper cascade and inspect the results." |
| 202 | + ] |
| 203 | + }, |
| 204 | + { |
| 205 | + "cell_type": "code", |
| 206 | + "execution_count": null, |
| 207 | + "id": "e5812eda-2e56-4f72-89bc-4e778046243f", |
| 208 | + "metadata": {}, |
| 209 | + "outputs": [], |
| 210 | + "source": [ |
| 211 | + "model = tof.Model(source=source, choppers=choppers, detectors=detectors)\n", |
| 212 | + "results = model.run()\n", |
| 213 | + "results.plot(blocked_rays=5000)" |
| 214 | + ] |
| 215 | + }, |
| 216 | + { |
| 217 | + "cell_type": "markdown", |
| 218 | + "id": "51e45cee-a630-4886-bc9f-a744a20cba28", |
| 219 | + "metadata": {}, |
| 220 | + "source": [ |
| 221 | + "We can see that the chopper cascade is implementing WFM and pulse-skipping at the same time!\n", |
| 222 | + "\n", |
| 223 | + "## Wavelength as a function of time-of-arrival\n", |
| 224 | + "\n", |
| 225 | + "### Plotting wavelength vs time-of-arrival\n", |
| 226 | + "\n", |
| 227 | + "Since we know the true wavelength of our neutrons,\n", |
| 228 | + "as well as the time at which the neutrons arrive at the detector\n", |
| 229 | + "(coordinate named `toa` in the detector reading),\n", |
| 230 | + "we can plot an image of the wavelengths as a function of time-of-arrival:" |
| 231 | + ] |
| 232 | + }, |
| 233 | + { |
| 234 | + "cell_type": "code", |
| 235 | + "execution_count": null, |
| 236 | + "id": "c3f48d70-7836-4e6a-aa9c-e278303af30c", |
| 237 | + "metadata": {}, |
| 238 | + "outputs": [], |
| 239 | + "source": [ |
| 240 | + "# Squeeze the pulse dimension since we only have one pulse\n", |
| 241 | + "events = results['detector'].data.flatten(to='event')\n", |
| 242 | + "# Remove the events that don't make it to the detector\n", |
| 243 | + "events = events[~events.masks['blocked_by_others']]\n", |
| 244 | + "# Histogram and plot\n", |
| 245 | + "events.hist(wavelength=500, toa=500).plot(norm='log', grid=True)" |
| 246 | + ] |
| 247 | + }, |
| 248 | + { |
| 249 | + "cell_type": "markdown", |
| 250 | + "id": "aadc3250-f49d-462f-8aac-2d3500596b6b", |
| 251 | + "metadata": {}, |
| 252 | + "source": [ |
| 253 | + "### Defining a conversion from `toa` to `wavelength`\n", |
| 254 | + "\n", |
| 255 | + "The image above shows that there is a pretty tight correlation between time-of-arrival and wavelength.\n", |
| 256 | + "\n", |
| 257 | + "We compute the mean wavelength inside a given `toa` bin to define a relation between `toa` and `wavelength`." |
| 258 | + ] |
| 259 | + }, |
| 260 | + { |
| 261 | + "cell_type": "code", |
| 262 | + "execution_count": null, |
| 263 | + "id": "9e8f056e-8c8e-4c1b-8dbf-5b350bba5c94", |
| 264 | + "metadata": {}, |
| 265 | + "outputs": [], |
| 266 | + "source": [ |
| 267 | + "binned = events.bin(toa=500)\n", |
| 268 | + "\n", |
| 269 | + "# Weighted mean of wavelength inside each bin\n", |
| 270 | + "mu = (\n", |
| 271 | + " binned.bins.data * binned.bins.coords['wavelength']\n", |
| 272 | + ").bins.sum() / binned.bins.sum()\n", |
| 273 | + "\n", |
| 274 | + "# Variance of wavelengths inside each bin\n", |
| 275 | + "var = (\n", |
| 276 | + " binned.bins.data * (binned.bins.coords['wavelength'] - mu) ** 2\n", |
| 277 | + ") / binned.bins.sum()" |
| 278 | + ] |
| 279 | + }, |
| 280 | + { |
| 281 | + "cell_type": "markdown", |
| 282 | + "id": "fb5cddc2-9ebf-427f-b38f-cf25b2c5cdfc", |
| 283 | + "metadata": {}, |
| 284 | + "source": [ |
| 285 | + "We can now overlay our mean wavelength function on the image above:" |
| 286 | + ] |
| 287 | + }, |
| 288 | + { |
| 289 | + "cell_type": "code", |
| 290 | + "execution_count": null, |
| 291 | + "id": "b0a83ab4-af22-40a4-851a-4a80ce358f45", |
| 292 | + "metadata": {}, |
| 293 | + "outputs": [], |
| 294 | + "source": [ |
| 295 | + "import matplotlib.pyplot as plt\n", |
| 296 | + "\n", |
| 297 | + "fig, ax = plt.subplots(2, 1)\n", |
| 298 | + "\n", |
| 299 | + "f = events.hist(wavelength=500, tof=500).plot(norm='log', cbar=False, ax=ax[0])\n", |
| 300 | + "mu.name = 'Wavelength'\n", |
| 301 | + "mu.plot(ax=ax[0], color='C1', grid=True)\n", |
| 302 | + "stddev = sc.sqrt(var.hist())\n", |
| 303 | + "stddev.name = 'Standard deviation'\n", |
| 304 | + "stddev.plot(ax=ax[1], grid=True)\n", |
| 305 | + "fig.set_size_inches(6, 8)\n", |
| 306 | + "fig.tight_layout()" |
| 307 | + ] |
| 308 | + }, |
| 309 | + { |
| 310 | + "cell_type": "markdown", |
| 311 | + "id": "d3675286-e36e-4d65-9d6c-012f5b4c39e5", |
| 312 | + "metadata": {}, |
| 313 | + "source": [ |
| 314 | + "## Computing wavelengths\n", |
| 315 | + "\n", |
| 316 | + "We set up an interpolator that will compute wavelengths given an array of `toas`." |
| 317 | + ] |
| 318 | + }, |
| 319 | + { |
| 320 | + "cell_type": "code", |
| 321 | + "execution_count": null, |
| 322 | + "id": "0f231769-8e79-4692-b4f0-a1e9e11782e1", |
| 323 | + "metadata": {}, |
| 324 | + "outputs": [], |
| 325 | + "source": [ |
| 326 | + "from scipp.scipy.interpolate import interp1d\n", |
| 327 | + "\n", |
| 328 | + "# Set up interpolator\n", |
| 329 | + "y = mu.copy()\n", |
| 330 | + "y.coords['toa'] = sc.midpoints(y.coords['toa'])\n", |
| 331 | + "f = interp1d(y, 'toa', bounds_error=False)\n", |
| 332 | + "\n", |
| 333 | + "# Compute wavelengths\n", |
| 334 | + "wavs = f(events.coords['toa'].rename_dims(event='toa'))\n", |
| 335 | + "wavelengths = sc.DataArray(\n", |
| 336 | + " data=sc.ones(sizes=wavs.sizes, unit='counts'), coords={'wavelength': wavs.data}\n", |
| 337 | + ").rename_dims(toa='event')\n", |
| 338 | + "wavelengths" |
| 339 | + ] |
| 340 | + }, |
| 341 | + { |
| 342 | + "cell_type": "markdown", |
| 343 | + "id": "a6512644-d9b7-47e1-861b-9dde24fa012a", |
| 344 | + "metadata": {}, |
| 345 | + "source": [ |
| 346 | + "We can now compare our computed wavelengths to the true wavelengths of the neutrons:" |
| 347 | + ] |
| 348 | + }, |
| 349 | + { |
| 350 | + "cell_type": "code", |
| 351 | + "execution_count": null, |
| 352 | + "id": "1f4aad58-a1b0-4c90-9215-ab2e52191bbb", |
| 353 | + "metadata": {}, |
| 354 | + "outputs": [], |
| 355 | + "source": [ |
| 356 | + "pp.plot(\n", |
| 357 | + " {\n", |
| 358 | + " 'wfm': wavelengths.hist(wavelength=300),\n", |
| 359 | + " 'original': events.hist(wavelength=300),\n", |
| 360 | + " }\n", |
| 361 | + ")" |
| 362 | + ] |
| 363 | + } |
| 364 | + ], |
| 365 | + "metadata": { |
| 366 | + "kernelspec": { |
| 367 | + "display_name": "Python 3 (ipykernel)", |
| 368 | + "language": "python", |
| 369 | + "name": "python3" |
| 370 | + }, |
| 371 | + "language_info": { |
| 372 | + "codemirror_mode": { |
| 373 | + "name": "ipython", |
| 374 | + "version": 3 |
| 375 | + }, |
| 376 | + "file_extension": ".py", |
| 377 | + "mimetype": "text/x-python", |
| 378 | + "name": "python", |
| 379 | + "nbconvert_exporter": "python", |
| 380 | + "pygments_lexer": "ipython3" |
| 381 | + } |
| 382 | + }, |
| 383 | + "nbformat": 4, |
| 384 | + "nbformat_minor": 5 |
| 385 | +} |
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