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654 lines
389 KiB
Plaintext
654 lines
389 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "709c49bd",
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"metadata": {},
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"source": [
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"# Showcasing of AMBER utilizing synthetic neutron scattering data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d8b17015",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from AMBER.background import background\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "63aaf7ad",
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"metadata": {},
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"source": [
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"## Generate signal data\n",
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"The data for this tutorial is generated using the expected neutron scattering signal in an inelastic exmperiment measuring MnF$_2$. The dispersion relation, i.e. the energy at a give (H,K,L) position is given by the analytical formula [Yamany et al. 2010](https://www.doi.org/10.1139/P10-081):\n",
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"\n",
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"$$\\omega(H,K,L) = \\sqrt{\\left(2S z_2 J_2+D+2 z_1 S J_1 \\sin{L \\pi}^2\\right)^2-\\left(2 S z_2 J_2 \\cos(H \\pi)\\cos(K \\pi) \\cos(L \\pi)\\right)^2}$$\n",
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"\n",
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"In the above $J_1$, $J_2$, and $D$ are the magnetic coupling strengths and single-ion anisotropy which determine the amplitude of the dispersion. The parameters $S = 5/2$, $z_1 = 2$, and $z_2 = 8$ corresponds the the size of the magnetic spin, and the number of nearest and next-nearest neighbours in the MnF$_2$ lattice. \n",
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"\n",
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"From the above equation, the imporant fact is that it depends on the 3D vector $Q = (H,K,L)$. For simplicity, we assume that $K$ is zero and thus reduce the data to 3D, i.e. $(H,L,\\omega)$\n",
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"\n",
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"Due to how neutron scattering experiments function, the above dispersion will not be infinitely thin but rather extended. Here, this is replicated by a simple gaussian smearing along $\\omega$\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4c67ae0c-684b-4d85-92ba-44907594cce6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define parameters, dispersion, and Smearing\n",
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"S = 5/2\n",
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"z1 = 2\n",
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"z2 = 8\n",
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"\n",
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"def SpinWave(Q,J1,J2,D):\n",
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" return np.sqrt(np.power(2*S*z2*J2+D+2*z1*S*J1*np.sin(Q[2]*np.pi)**2,2.0)-\n",
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" \n",
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" np.power(2*S*z2*J2*np.cos(Q[0]*np.pi)*np.cos(Q[1]*np.pi)*np.cos(Q[2]*np.pi),2.0))\n",
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"\n",
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"def Intensity(H,K,L,E):\n",
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" sigmaE = 0.25\n",
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" omega = SpinWave(np.array([H,K,L]), J1=0.0354,J2=0.1499,D=0.131 )\n",
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" I = np.exp(-np.power(omega-E,2.0)/(2*sigmaE**2))\n",
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" \n",
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" return I"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b8cbd985",
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"metadata": {},
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"source": [
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"## Define 3D grid domain\n",
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"\n",
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"The input to AMBER is a 3D cube of intensities which is define below"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b1e107a4-306c-49dd-95d9-732ba4284c1e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data will ge simulated along H and L but with K = 0\n",
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"\n",
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"h = np.linspace(-0.1,2.1,101)\n",
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"k = 0\n",
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"l = np.linspace(-0.1,2.1,101)\n",
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"\n",
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"# Choose a sufficient energy range\n",
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"e = np.linspace(0.5,8,61)\n",
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"\n",
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"# Generate the grid upon which the dispersion is calculated\n",
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"\n",
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"H,L,E = np.meshgrid(h,l,e)\n",
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"K = np.zeros_like(H)\n",
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"\n",
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"# Intensities are calculated with the smearing and scaled\n",
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"I = 30*Intensity(H,K,L,E)\n",
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"\n",
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"# Poisson noice is added to the intensity\n",
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"I = np.random.poisson(I).astype(float)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c087a9d-e273-4be6-8151-87169990074b",
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"metadata": {},
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"source": [
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"### Introduction of NaN-values\n",
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"\n",
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"Triple axis instruments cannot measure all lengths of $Q$. this we can mimic by exchaning the intensity at these points by NaNs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9180b624-f76b-4d62-b87c-bda5388ef3f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"QLength = np.linalg.norm([H,L],axis=0)\n",
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"\n",
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"I[QLength<0.35] = np.nan\n",
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"I[QLength>2.5] = np.nan\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1bfcdd4-2525-44f9-9f75-3b9e81c2491f",
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"metadata": {},
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"source": [
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"### Add background\n",
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"The main feature of AMBER is the background segmentation, which requires the data to have a background. As describe in the [article](???), background is assumed to:\n",
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"\n",
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"- Rotation independence of the background.\n",
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"- Smooth change of background along energy and $|\\vec{Q}|$.\n",
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"- The signal is sparse but continuous in energy and $|\\vec{Q}|$.\n",
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"\n",
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"In the following, a background is generated with a higher amplitude for low and high $|\\vec{Q}|$ mimicing instrumental background artefacts from triple axis neutron experiments - these corresponds to the direct beam contribution and to increased background at larger scattering angles. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "99d07fc8-250c-4870-bfa8-a68dd212ad95",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Background definition\n",
|
|
"def background_simulation(q,amplitude,gamma,mu,amplitude2,gamma2):\n",
|
|
" return amplitude*((gamma/(q**2+gamma**2))+amplitude2*np.exp(-np.power(q-mu,2.0)/(2*gamma2**2)))\n",
|
|
"\n",
|
|
"\n",
|
|
"Q = np.linalg.norm([H,K,L],axis=0)\n",
|
|
"\n",
|
|
"# Choose suitable valies\n",
|
|
"gamma = 0.5\n",
|
|
"mu = 3.0\n",
|
|
"amplitude = 20\n",
|
|
"amplitude2 = 1.0\n",
|
|
"gamma2 = 0.5\n",
|
|
"\n",
|
|
"# Generate an example of the background for visual inspection\n",
|
|
"q = np.linspace(0.25,Q.max(),201)\n",
|
|
"bg_test = background_simulation(q,amplitude,gamma,mu,amplitude2,gamma2)\n",
|
|
"\n",
|
|
"# display background amplitude\n",
|
|
"fig,ax = plt.subplots()\n",
|
|
"ax.plot(q,bg_test)\n",
|
|
"ax.set_xlabel('|Q|')\n",
|
|
"ax.set_ylabel('Background intensity')\n",
|
|
"\n",
|
|
"\n",
|
|
"## Add background to data\n",
|
|
"bg_tmp = background_simulation(Q,amplitude,gamma,mu,amplitude2,gamma2)\n",
|
|
"\n",
|
|
"I+=np.random.poisson(bg_tmp)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0d7f0e5e-6f8f-450d-b1ff-afce5699f64a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plot a constant energy plot\n",
|
|
"The now background affected data is plotted to show a \"before\" picture. This is done by choosing a specific energy ($\\omega \\sim 4.5$ meV) and plotting the intensity as a color map"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "456b2e68-9d32-4e78-9575-e6a2bcf7e9e5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, 'L [r.l.u.]')"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"<Figure size 640x480 with 1 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig,ax = plt.subplots()\n",
|
|
"\n",
|
|
"# Find the closest energy slice\n",
|
|
"energy = 4.5\n",
|
|
"EIdx = np.argmin(np.abs(E-energy))\n",
|
|
"ax.pcolormesh(H[:, :, EIdx], L[:, :, EIdx], I[:, :, EIdx], vmin=0, vmax = 50)\n",
|
|
"\n",
|
|
"ax.axis('equal')\n",
|
|
"ax.set_title('E = {:.2f} meV'.format(e[EIdx]))\n",
|
|
"ax.set_xlabel('H [r.l.u.]')\n",
|
|
"ax.set_ylabel('L [r.l.u.]')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "19859951-3686-46a3-b11e-0cc03b5e97fc",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plot a constant H map"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "5484ba62-d027-408b-9e34-e0f0e0ab4528",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0, 0.5, 'E [meV]')"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig,ax = plt.subplots()\n",
|
|
"\n",
|
|
"h_value = 0.25\n",
|
|
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"ax.pcolormesh(L[:, HIdx, :],E[:, HIdx, :],I[:, HIdx, :], vmin=0, vmax=50)\n",
|
|
"\n",
|
|
"ax.set_xlabel('L [r.l.u.]')\n",
|
|
"ax.set_ylabel('E [meV]')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2f7f37f2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Run denoising algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "3a58ab49",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Initialize the AMBER background object\n",
|
|
"\n",
|
|
"AMBER = background(dtype=np.float32)\n",
|
|
"\n",
|
|
"# Set the grid sizes\n",
|
|
"AMBER.set_gridcell_size(dqx = 0.022, dqy = 0.022, dE = 0.125)\n",
|
|
"\n",
|
|
"# Alternatively these can be set from the h, l, and e arrays like\n",
|
|
"# AMBER.set_volume_from_limits([h[0],l[0],e[0]],[h[-1],l[-1],e[-1]],)\n",
|
|
"\n",
|
|
"# Input the data\n",
|
|
"AMBER.set_binned_data(h, l, e, I)\n",
|
|
"\n",
|
|
"bins = int((q.max()-q.min())/0.022)\n",
|
|
"\n",
|
|
"# define maximum radius and number of bins\n",
|
|
"AMBER.set_radial_bins(q.max(),n_bins=bins)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cfb11870",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Set algorithm parameters ($\\lambda, \\beta, \\mu$)\n",
|
|
"\n",
|
|
"lambda and mu will be determined as described in the paper and we select beta using cross validation\n",
|
|
"\n",
|
|
"beta_ is selected using cross validation, i.e. mask out the top q quantile of intensity. The beta value for the lowest Root-Mean-Square-Error is then chosen"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "60e2fbf2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Test - ( 0.1 )\n",
|
|
"RMSE - ( 4.4478 0.1 55.79589160155072 ) : 3.4932678\n",
|
|
"Test - ( 1.0 )\n",
|
|
"RMSE - ( 4.4478 1.0 55.79589160155072 ) : 3.4923534\n",
|
|
"Test - ( 10.0 )\n",
|
|
"RMSE - ( 4.4478 10.0 55.79589160155072 ) : 3.4850137\n",
|
|
"Test - ( 100.0 )\n",
|
|
"RMSE - ( 4.4478 100.0 55.79589160155072 ) : 3.459206\n",
|
|
"Test - ( 200.0 )\n",
|
|
"RMSE - ( 4.4478 200.0 55.79589160155072 ) : 3.452546\n",
|
|
"Test - ( 300.0 )\n",
|
|
"RMSE - ( 4.4478 300.0 55.79589160155072 ) : 3.4521124\n",
|
|
"Test - ( 400.0 )\n",
|
|
"RMSE - ( 4.4478 400.0 55.79589160155072 ) : 3.454414\n",
|
|
"Test - ( 500.0 )\n",
|
|
"RMSE - ( 4.4478 500.0 55.79589160155072 ) : 3.4581234\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"lambda_tmp = AMBER.MAD_lambda()\n",
|
|
"mu_tmp = AMBER.mu_estimator()\n",
|
|
"\n",
|
|
"\n",
|
|
"beta_range_tmp = np.array([0.1,1.0,10.0,100.0,200.0,300.0,400.0,500.0])\n",
|
|
"rmse = AMBER.cross_validation(q=0.3,beta_range= beta_range_tmp, lambda_=lambda_tmp, mu_=mu_tmp,n_epochs=15,verbose=False)\n",
|
|
"\n",
|
|
"\n",
|
|
"beta_tmp = beta_range_tmp[np.argmin(rmse)]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e81bce2a",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Run the denoising algorithm using the parameters obtained using the heuristic"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "1f9a8679",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Iteration 1\n",
|
|
" Loss function: 24658333.70478791\n",
|
|
" Iteration 2\n",
|
|
" Loss function: 18359579.755618207\n",
|
|
" Iteration 3\n",
|
|
" Loss function: 15629052.90024966\n",
|
|
" Iteration 4\n",
|
|
" Loss function: 14376783.00374195\n",
|
|
" Iteration 5\n",
|
|
" Loss function: 13776632.868500363\n",
|
|
" Iteration 6\n",
|
|
" Loss function: 13504604.810678879\n",
|
|
" Iteration 7\n",
|
|
" Loss function: 13392707.644277826\n",
|
|
" Iteration 8\n",
|
|
" Loss function: 13349203.821932396\n",
|
|
" Iteration 9\n",
|
|
" Loss function: 13332601.151676292\n",
|
|
" Iteration 10\n",
|
|
" Loss function: 13326251.723918369\n",
|
|
" Iteration 11\n",
|
|
" Loss function: 13323807.99708973\n",
|
|
" Iteration 12\n",
|
|
" Loss function: 13322858.198862182\n",
|
|
" Iteration 13\n",
|
|
" Loss function: 13322487.582507188\n",
|
|
" Iteration 14\n",
|
|
" Loss function: 13322342.369417826\n",
|
|
" Iteration 15\n",
|
|
" Loss function: 13322285.33291491\n",
|
|
" Iteration 16\n",
|
|
" Loss function: 13322262.891915664\n",
|
|
" Iteration 17\n",
|
|
" Loss function: 13322254.058312038\n",
|
|
" Iteration 18\n",
|
|
" Loss function: 13322250.579985036\n",
|
|
" Iteration 19\n",
|
|
" Loss function: 13322249.209292663\n",
|
|
" Iteration 20\n",
|
|
" Loss function: 13322248.66973307\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# set number of epochs\n",
|
|
"n_epochs = 20\n",
|
|
"\n",
|
|
"AMBER.denoising(AMBER.Ygrid,lambda_tmp,beta_tmp,mu_tmp,n_epochs,verbose=True) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e909ad23",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Compute substracted signal"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "15d23f11",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# The subtracted signal is given by\n",
|
|
"\n",
|
|
"Y_sub = AMBER.Ygrid - AMBER.b_grid\n",
|
|
"Y_back = AMBER.b_grid\n",
|
|
"\n",
|
|
"# reshape the data to fit \n",
|
|
"Y_sub = Y_sub.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n",
|
|
"Y_back = Y_back.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n",
|
|
"\n",
|
|
"# reshape the observation\n",
|
|
"Y_obs = AMBER.Ygrid.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bf1032b5",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Display substracted signal"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "e9e74383",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
|
"text/plain": [
|
|
"<Figure size 2000x900 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# reshape the observation\n",
|
|
"Y_obs = AMBER.Ygrid.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n",
|
|
"\n",
|
|
"\n",
|
|
"fig0 = plt.figure(figsize=(20, 9))\n",
|
|
"\n",
|
|
"# Plot 1: Observations Y\n",
|
|
"ax0 = fig0.add_subplot(1, 2, 1)\n",
|
|
"\n",
|
|
"energy = 4.5\n",
|
|
"EIdx = np.argmin(np.abs(E-energy))\n",
|
|
"ax0.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_obs[:,:,EIdx],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax0.axis('equal')\n",
|
|
"ax0.set_xlabel('H [r.l.u.]')\n",
|
|
"ax0.set_ylabel('L [r.l.u.]')\n",
|
|
"ax0.set_title('Observations')\n",
|
|
"\n",
|
|
"# Plot 2: Subtracted Y\n",
|
|
"ax1 = fig0.add_subplot(1, 2, 2)\n",
|
|
"\n",
|
|
"energy = 4.5\n",
|
|
"EIdx = np.argmin(np.abs(E-energy))\n",
|
|
"p = ax1.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_sub[:,:,EIdx],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax1.axis('equal')\n",
|
|
"ax1.set_xlabel('H [r.l.u.]')\n",
|
|
"ax1.set_ylabel('L [r.l.u.]')\n",
|
|
"ax1.set_title('Subtracted signal')\n",
|
|
"\n",
|
|
"fig0.colorbar(p)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "da0f7346",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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"text/plain": [
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"<Figure size 2000x900 with 3 Axes>"
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]
|
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},
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
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"source": [
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"fig0,axx = plt.subplots(figsize=(20, 9),ncols=2,nrows=1)\n",
|
|
"\n",
|
|
"# Plot 1: Observations Y\n",
|
|
"\n",
|
|
"ax0 = axx[0]\n",
|
|
"\n",
|
|
"h_value = 0.25\n",
|
|
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"ax0.pcolormesh(L[:,HIdx,:],E[:,HIdx,:],Y_obs[:,HIdx,:],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax0.set_xlabel('H [r.l.u.]')\n",
|
|
"ax0.set_ylabel('L [r.l.u.]')\n",
|
|
"ax0.set_title('Observations')\n",
|
|
"\n",
|
|
"# Plot 2: Subtracted Y\n",
|
|
"ax1 = axx[1]\n",
|
|
"\n",
|
|
"\n",
|
|
"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"p = ax1.pcolormesh(L[:,HIdx,:],E[:,HIdx,:],Y_sub[:,HIdx,:],vmin=-2,vmax=50)\n",
|
|
"\n",
|
|
"ax1.set_xlabel('L [r.l.u.]')\n",
|
|
"ax1.set_ylabel('E [meV]')\n",
|
|
"ax1.set_title('Subtracted signal')\n",
|
|
"\n",
|
|
"\n",
|
|
"fig0.colorbar(p,ax =axx, shrink=0.2, location='bottom')\n",
|
|
"\n",
|
|
"#plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "71d4ed7e-dec7-4c84-842a-d03030e42db3",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Compare the signal with the subtracted signal in a 1D cut"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "12829c7d-fbfd-4e4e-b3a1-c759d83f834e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
|
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},
|
|
"metadata": {},
|
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"output_type": "display_data"
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}
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],
|
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"source": [
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"fig0 = plt.figure()\n",
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"\n",
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"# Plot 1: Observations Y\n",
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"ax0 = fig0.add_subplot(1, 1, 1)\n",
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"\n",
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"h_value = 0.25\n",
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"e_value = 5.5 # meV\n",
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"EIdx = np.argmin(np.abs(e-e_value))\n",
|
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"HIdx = np.argmin(np.abs(h-h_value))\n",
|
|
"ax0.scatter(L[:,HIdx,EIdx],Y_obs[:,HIdx,EIdx],label='Observations')\n",
|
|
"ax0.scatter(L[:,HIdx,EIdx],Y_sub[:,HIdx,EIdx],label='Subtracted Signal')\n",
|
|
"ax0.scatter(L[:,HIdx,EIdx],Y_back[:,HIdx,EIdx],label='Background')\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"ax0.set_xlabel('L [r.l.u.]')\n",
|
|
"ax0.set_ylabel('I [Arb. unit]')\n",
|
|
"ax0.legend()\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|