Rework diffusion models

docs/docs/tutorials/delta_lorentz.ipynb

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Delta Lorentz\n",
+    "Model of Delta function and Lorentzian with intensities given by the Debye-Waller factor:\n",
+    "\n",
+    "$ I\n",
+    "= K \\exp \\left( \\frac{-\\langle u^2 \\rangle Q^2}{3} \\right)[A_0 \\delta(E) + A_1 L(E, \\Gamma)]\n",
+    "$,\n",
+    "\n",
+    "where $K$ is the scale factor, $\\langle u^2 \\rangle$ is the mean square displacement, $Q$ is\n",
+    "the scattering vector, $A_0$ and $A_1$ are the relative amplitudes of the delta function and\n",
+    "Lorentzian, respectively, with the constraint that $A_0+A_1=1$, and $L(E, \\Gamma)$ is the\n",
+    "Lorentzian function with width $\\Gamma$. $A_0$, $A_1$ and the width of the Lorentzian can be\n",
+    "the same at all $Q$ or be allowed to vary with $Q$.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from easydynamics.sample_model.diffusion_model.delta_lorentz import DeltaLorentz\n",
+    "\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d6e1984",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.linspace(0.5, 2, 7)\n",
+    "energy = np.linspace(-2, 2, 501)\n",
+    "scale = 1.0\n",
+    "mean_u_squared = 0.5\n",
+    "A_0 = 0.2\n",
+    "lorentzian_width = 0.2\n",
+    "\n",
+    "diffusion_model = DeltaLorentz(\n",
+    "    scale=scale,\n",
+    "    mean_u_squared=mean_u_squared,\n",
+    "    A_0=A_0,\n",
+    "    lorentzian_width=lorentzian_width,\n",
+    "    allow_Q_variation={'A_0': True},\n",
+    "    Q=Q,\n",
+    "    lorentzian_name='Lorentzian',\n",
+    "    delta_name='Delta function',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "389aa3cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "component_collections = diffusion_model.get_component_collections()\n",
+    "cmap = plt.cm.jet\n",
+    "nQ = len(component_collections)\n",
+    "plt.figure()\n",
+    "for Q_index in range(len(component_collections)):\n",
+    "    color = cmap(Q_index / (nQ - 1))\n",
+    "    y = component_collections[Q_index].evaluate(energy)\n",
+    "    plt.plot(energy, y, label=f'Q={Q[Q_index]} Å^-1', color=color)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "plt.xlabel('Energy (meV)')\n",
+    "plt.ylabel('Intensity (arb. units)')\n",
+    "plt.title('Delta-Lorentz Model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf360b45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diffusion_model.get_all_variables()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "533dc719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diffusion_model.get_free_parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "742005ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "component_collections[1].get_all_parameters()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/tutorials/diffusion_model.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Diffusion Model\n",
-    "We support several standard models of diffusion. Here we show an example of Browniand Translational Diffusion, where the scattering is a Lorentzian with width ($\\Gamma$) given by $\\Gamma = D Q^2$, where $D$ is the diffusion coefficient (in m$^2$/s) and $Q$ is the momentum transfer."
+    "We support several standard models of diffusion. Here we show an example of Brownian Translational Diffusion, where the scattering is a Lorentzian with width ($\\Gamma$) given by $\\Gamma = D Q^2$, where $D$ is the diffusion coefficient (in m$^2$/s) and $Q$ is the momentum transfer."
    ]
   },
   {
@@ -41,12 +41,10 @@
     "diffusion_coefficient = 2.4e-9  # m^2/s\n",
     "\n",
     "diffusion_model = BrownianTranslationalDiffusion(\n",
-    "    display_name='DiffusionModel',\n",
-    "    scale=scale,\n",
-    "    diffusion_coefficient=diffusion_coefficient,\n",
+    "    display_name='DiffusionModel', scale=scale, diffusion_coefficient=diffusion_coefficient, Q=Q\n",
     ")\n",
     "\n",
-    "component_collections = diffusion_model.create_component_collections(Q)\n",
+    "component_collections = diffusion_model.get_component_collections()\n",
     "\n",
     "\n",
     "cmap = plt.cm.jet\n",
@@ -87,7 +85,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "easydynamics_newbase",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -101,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.14.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/index.md

@@ -50,6 +50,9 @@ tutorials.
   balancing.
 - [Diffusion model](diffusion_model.ipynb) – Learn how to create and use
   a model of diffusion.
+- [DeltaLorentz](delta_lorentz.ipynb) – Learn how to create and use a
+  model with a Delta function and a Lorentzian that have a shared
+  Debye-Waller-like Q-dependence.
 - [Sample model](sample_model.ipynb) – Learn how to create a model of
   the scattering from your sample including model components and
   diffusion models.

docs/docs/tutorials/tutorial0_basics.ipynb

@@ -452,7 +452,7 @@
    "id": "dc33728c",
    "metadata": {},
    "source": [
-    "To see the parameters we can use the `get_all_parameters()` method:"
+    "To see the parameters we can use the `get_all_parameters()` method. We can also see only the parameters that can be fitted:"
    ]
   },
   {
@@ -464,6 +464,16 @@
    "source": [
     "parameter_analysis.get_all_parameters()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "068f755c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.get_fittable_parameters()"
+   ]
   }
  ],
  "metadata": {

docs/docs/tutorials/tutorial0_more_advanced.ipynb

@@ -33,7 +33,7 @@
    "id": "4c8e97b7",
    "metadata": {},
    "source": [
-    "As before, we createa an `Experiment` to hold the data, and rebin it."
+    "As before, we create an `Experiment` to hold the data, and rebin it."
    ]
   },
   {

docs/docs/tutorials/tutorial1_brownian.ipynb

@@ -688,7 +688,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "diffusion_model.get_all_parameters()"
+    "diffusion_model.get_global_variables()"
    ]
   },
   {
@@ -706,7 +706,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "parameter_analysis.get_all_parameters()"
+    "parameter_analysis.get_all_variables()"
    ]
   }
  ],