Multi_Data_Generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93ab21dc-fd84-4bd7-8e46-9b4cc07e7f0e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: refl1d in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (0.8.16)\n",
      "Requirement already satisfied: bumps>=0.7.16 in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (from refl1d) (0.9.1)\n",
      "Requirement already satisfied: numpy in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from refl1d) (1.24.3)\n",
      "Requirement already satisfied: scipy in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from refl1d) (1.9.1)\n",
      "Requirement already satisfied: matplotlib in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from refl1d) (3.7.1)\n",
      "Requirement already satisfied: periodictable in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (from refl1d) (1.6.1)\n",
      "Requirement already satisfied: numba in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (from refl1d) (0.58.1)\n",
      "Requirement already satisfied: six in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from bumps>=0.7.16->refl1d) (1.16.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (2.8.2)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib->refl1d) (5.2.0)\n",
      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (from numba->refl1d) (0.41.1)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib->refl1d) (3.11.0)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install refl1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca6ca005-2302-43d9-aed3-bd71a75bda7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: seaborn in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (0.13.0)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from seaborn) (1.24.3)\n",
      "Requirement already satisfied: pandas>=1.2 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (5.2.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2022.7)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.3->seaborn) (3.11.0)\n",
      "Requirement already satisfied: six>=1.5 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1625ab69-1374-407b-934c-648229fadec7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: plotly in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (5.18.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /global/u1/q/qubri000/.local/perlmutter/pytorch2.0.1/lib/python3.9/site-packages (from plotly) (8.2.3)\n",
      "Requirement already satisfied: packaging in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from plotly) (23.0)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbc99e4d-23c9-437a-975e-179a1c2f1f32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: ipympl in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (0.9.3)\n",
      "Requirement already satisfied: ipython<9 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (8.12.0)\n",
      "Requirement already satisfied: numpy in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (1.24.3)\n",
      "Requirement already satisfied: ipython-genutils in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (0.2.0)\n",
      "Requirement already satisfied: pillow in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (9.4.0)\n",
      "Requirement already satisfied: traitlets<6 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (5.7.1)\n",
      "Requirement already satisfied: ipywidgets<9,>=7.6.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (7.6.5)\n",
      "Requirement already satisfied: matplotlib<4,>=3.4.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipympl) (3.7.1)\n",
      "Requirement already satisfied: backcall in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (0.2.0)\n",
      "Requirement already satisfied: decorator in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (5.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (0.18.1)\n",
      "Requirement already satisfied: matplotlib-inline in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (0.1.6)\n",
      "Requirement already satisfied: pickleshare in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (0.7.5)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (3.0.36)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (2.15.1)\n",
      "Requirement already satisfied: stack-data in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (0.2.0)\n",
      "Requirement already satisfied: typing-extensions in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (4.5.0)\n",
      "Requirement already satisfied: pexpect>4.3 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipython<9->ipympl) (4.8.0)\n",
      "Requirement already satisfied: ipykernel>=4.5.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipywidgets<9,>=7.6.0->ipympl) (6.19.2)\n",
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      "Requirement already satisfied: contourpy>=1.0.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib<4,>=3.4.0->ipympl) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib<4,>=3.4.0->ipympl) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib<4,>=3.4.0->ipympl) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib<4,>=3.4.0->ipympl) (1.4.4)\n",
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      "Requirement already satisfied: pyparsing>=2.3.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib<4,>=3.4.0->ipympl) (3.0.9)\n",
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      "Requirement already satisfied: importlib-resources>=3.2.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from matplotlib<4,>=3.4.0->ipympl) (5.2.0)\n",
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      "Requirement already satisfied: comm>=0.1.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets<9,>=7.6.0->ipympl) (0.1.2)\n",
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      "Requirement already satisfied: tornado>=6.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets<9,>=7.6.0->ipympl) (6.2)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from jedi>=0.16->ipython<9->ipympl) (0.8.3)\n",
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      "Requirement already satisfied: jupyter-core in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from nbformat>=4.2.0->ipywidgets<9,>=7.6.0->ipympl) (5.3.0)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from pexpect>4.3->ipython<9->ipympl) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython<9->ipympl) (0.2.5)\n",
      "Requirement already satisfied: six>=1.5 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib<4,>=3.4.0->ipympl) (1.16.0)\n",
      "Requirement already satisfied: notebook>=4.4.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from widgetsnbextension~=3.5.0->ipywidgets<9,>=7.6.0->ipympl) (6.5.4)\n",
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      "Requirement already satisfied: idna>=2.8 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<9,>=7.6.0->ipympl) (3.4)\n",
      "Requirement already satisfied: sniffio>=1.1 in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<9,>=7.6.0->ipympl) (1.2.0)\n",
      "Requirement already satisfied: pycparser in /global/common/software/nersc/pm-2022q4/sw/pytorch/2.0.1/lib/python3.9/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<9,>=7.6.0->ipympl) (2.21)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install ipympl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eeec45a9-314f-4cc9-a2a8-8bf8fa8d0ddf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#importing packages needed to generate data\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import math\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "import refl1d\n",
    "from refl1d.names import *\n",
    "\n",
    "#import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "%matplotlib inline\n",
    "import seaborn as sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5afae20-0fa4-4156-9cba-f17d2175f125",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#This block is the class that is used to generate the SLD and NR curves\n",
    "\n",
    "from scipy.interpolate import CubicSpline\n",
    "import random\n",
    "def calculate_reflectivity(q, model_description, q_resolution=0.0294855):\n",
    "    \"\"\"\n",
    "        Reflectivity calculation using refl1d\n",
    "    \"\"\"\n",
    "    zeros = np.zeros(len(q))\n",
    "    dq = q_resolution * q / 2.355\n",
    "\n",
    "    # The QProbe object represents the beam\n",
    "    probe = QProbe(q, dq, data=(zeros, zeros))\n",
    "\n",
    "    layers = model_description['layers']\n",
    "    sample = Slab(material=SLD(name=layers[0]['name'],\n",
    "                               rho=layers[0]['sld']), interface=layers[0]['roughness'])\n",
    "    # Add each layer\n",
    "    for l in layers[1:]:\n",
    "        sample = sample | Slab(material=SLD(name=l['name'],\n",
    "                               rho=l['sld'], irho=l['isld']),\n",
    "                               thickness=l['thickness'], interface=l['roughness'])\n",
    "\n",
    "    probe.background = Parameter(value=model_description['background'], name='background')\n",
    "    expt = Experiment(probe=probe, sample=sample)\n",
    "\n",
    "    q, r = expt.reflectivity()\n",
    "    z, sld, _ = expt.smooth_profile()\n",
    "    #this makes all SLD curves have 900 datapoints per curve\n",
    "    zNew = np.linspace(z[0], z[-1], num=900)\n",
    "    newCurve = CubicSpline(z, sld)\n",
    "    sldNew = []\n",
    "    for i in range(zNew.shape[0]):\n",
    "      sldNew.append(newCurve(zNew[i]))\n",
    "    return model_description['scale'] * r, zNew, sldNew\n",
    "class ReflectivityModels(object):\n",
    "        # Neutrons come in from the last item in the list\n",
    "\n",
    "    def __init__(self,first, second, q=None, name='polymer'):\n",
    "                #the roughness and thickness are varied using first and second variables input into the initialization function.\n",
    "\n",
    "        self.parameters = [\n",
    "                  dict(i=0, par='roughness', bounds=[0, 5]),\n",
    "                  # The following is the Si oxide layer\n",
    "                  dict(i=1, par='sld', bounds=[3.0, 3.9]),\n",
    "                  dict(i=1, par='thickness', bounds=[10, 25]),\n",
    "                  dict(i=1, par='roughness', bounds=[1, 10]),\n",
    "\n",
    "                  # The next two layers are the polymer\n",
    "                  dict(i=2, par='sld', bounds=[0, 6.4]),\n",
    "                  dict(i=2, par='thickness', bounds=[first*10, 300]),\n",
    "                  dict(i=2, par='roughness', bounds=[(first-1)*10, first*10]),\n",
    "                  dict(i=3, par='sld', bounds=[0, 6.4]),\n",
    "                  dict(i=3, par='thickness', bounds=[10*second, 300]),\n",
    "                  dict(i=3, par='roughness', bounds=[10*(second-1), 10*second]),\n",
    "                 ]\n",
    "        self.model_description = dict(layers=[\n",
    "                                dict(sld=2.07, isld=0, thickness=0, roughness=3, name='substrate'),\n",
    "                                dict(sld=3.3, isld=0, thickness=16.2139, roughness=7, name='siox'),\n",
    "                                dict(sld=1.82, isld=0, thickness=random.randint(first*10, 300), roughness=random.randint((first-1)*10, first*10), name='bottom'),\n",
    "                                dict(sld=3.83, isld=0, thickness=random.randint(second*10, 300), roughness=random.randint((second-1)*10, second*10), name='bulk'),\n",
    "                                # dict(sld=0.0, isld=0, thickness=1000, roughness=0, name='top_layer'),\n",
    "                                dict(sld=0.0, isld=0, thickness=0, roughness=0, name='air')\n",
    "                         ],\n",
    "                         scale=1,\n",
    "                         background=0,\n",
    "                        )\n",
    "        # The following are unmodified physical parameters and corresponding reflectivity data\n",
    "        self._pars_array = []\n",
    "        self._refl_array = []\n",
    "        # This creates the SLD curve list\n",
    "        self._smooth_array = []\n",
    "        # The following are the parameters, mapped between -1 and 1.\n",
    "        self._train_pars = []\n",
    "        self._train_data = None\n",
    "        self._config_name = name\n",
    "\n",
    "        if q is None:\n",
    "            self.q = np.logspace(np.log10(0.008101436040354381), np.log10(0.1975709062238298), num=308)\n",
    "        else:\n",
    "            self.q = q\n",
    "\n",
    "    @classmethod\n",
    "    def from_dict(cls, pars, q_array=None):\n",
    "        \"\"\"\n",
    "            Create ReflectivityModels object from a dict that\n",
    "            defines the reflectivity model parameters and how\n",
    "            the training set should be generated.\n",
    "        \"\"\"\n",
    "        m = cls(q_array, name=pars['name'])\n",
    "        m.model_description =  pars['model']\n",
    "        m.parameters = pars['parameters']\n",
    "        return m\n",
    "\n",
    "    def generate(self, n=100):\n",
    "        \"\"\"\n",
    "            Generate a random sample of models\n",
    "        \"\"\"\n",
    "        npars = len(self.parameters)\n",
    "        self._train_pars = np.random.uniform(low=-1, high=1, size=[n, npars])\n",
    "        # Compute model parameters and reflectivity using these values\n",
    "        self.compute_reflectivity()\n",
    "\n",
    "    def to_model_parameters(self, pars):\n",
    "        \"\"\"\n",
    "            Transform an array of parameters to a list of calculable models\n",
    "        \"\"\"\n",
    "        pars_array = np.zeros(pars.shape)\n",
    "\n",
    "        for i, par in enumerate(self.parameters):\n",
    "            a = (par['bounds'][1]-par['bounds'][0])/2.\n",
    "            b = (par['bounds'][1]+par['bounds'][0])/2.\n",
    "            pars_array.T[i] = pars.T[i] * a + b\n",
    "\n",
    "        return pars_array\n",
    "\n",
    "    def compute_reflectivity(self):\n",
    "        \"\"\"\n",
    "            Transform an array of parameters to a list of calculable models\n",
    "            and compute reflectivity\n",
    "        \"\"\"\n",
    "        print(\"Computing reflectivity\")\n",
    "        self._pars_array = self.to_model_parameters(self._train_pars)\n",
    "        # Compute reflectivity\n",
    "        for p in self._pars_array:\n",
    "            _desc = self.get_model_description(p)\n",
    "            r, z, sld = calculate_reflectivity(self.q, _desc)\n",
    "            self._refl_array.append(r)\n",
    "            self._smooth_array.append([z,sld])\n",
    "    def get_model_description(self, pars):\n",
    "        \"\"\"\n",
    "            Convert the parameter list to a model description that we can use\n",
    "            to compute R(q).\n",
    "        \"\"\"\n",
    "        for i, par in enumerate(self.parameters):\n",
    "            self.model_description['layers'][par['i']][par['par']] = pars[i]\n",
    "        return self.model_description\n",
    "\n",
    "    def get_preprocessed_data(self, errors=None):\n",
    "        \"\"\"\n",
    "            Pre-process data\n",
    "            If errors is provided, a random error will be added, taking the errors array\n",
    "            as a relative uncertainty.\n",
    "        \"\"\"\n",
    "        if errors is None:\n",
    "\n",
    "            self._train_data = np.log10(self._refl_array*self.q**2/self.q[0]**2)\n",
    "        else:\n",
    "            _data = self._refl_array * (1.0 + np.random.normal(size=len(errors)) * errors)\n",
    "            # Catch the few cases where we generated a negative intensity and take\n",
    "            # the absolute value\n",
    "            _data[_data<0] = np.fabs(_data[_data<0])\n",
    "            self._train_data = np.log10(_data*self.q**2/self.q[0]**2)\n",
    "\n",
    "        return self._train_pars, self._train_data\n",
    "\n",
    "    def save(self, output_dir=''):\n",
    "        \"\"\"\n",
    "            Save all data relevant to a training set\n",
    "            @param output_dir: directory used to store training sets\n",
    "        \"\"\"\n",
    "        # Save q values\n",
    "        np.save(os.path.join(output_dir, \"%s_q_values\" % self._config_name), self.q)\n",
    "\n",
    "        # Save training set\n",
    "        if self._train_data is not None:\n",
    "            np.save(os.path.join(output_dir, \"%s_data\" % self._config_name), self._train_data)\n",
    "            np.save(os.path.join(output_dir, \"%s_pars\" % self._config_name), self._train_pars)\n",
    "\n",
    "    def load(self, data_dir=''):\n",
    "        self.q = np.load(os.path.join(data_dir, \"%s_q_values.npy\" % self._config_name))\n",
    "        self._train_data = np.load(os.path.join(data_dir, \"%s_data.npy\" % self._config_name))\n",
    "        self._train_pars = np.load(os.path.join(data_dir, \"%s_pars.npy\" % self._config_name))\n",
    "        return self.q, self._train_data, self._train_pars\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ce650d-b97e-4a8f-a89c-324cbc8e1060",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "1 2\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "1 3\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "1 4\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "1 5\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "2 1\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "2 2\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "2 3\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "2 4\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "2 5\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "3 1\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "3 2\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "3 3\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "3 4\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "3 5\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "4 1\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "4 2\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "4 5\n",
      "Computing reflectivity\n",
      "(50000, 2, 308) (50000, 2, 900)\n",
      "5 1\n",
      "Computing reflectivity\n"
     ]
    }
   ],
   "source": [
    "# NOTE: Here I would pass the q-array of the time-resolved data when instantiating this object.\n",
    "\n",
    "count = 0\n",
    "# Generate 100 curves\n",
    "for first in range(1,6):\n",
    "    for second in range(1,6):\n",
    "        print(first, second)\n",
    "        m = ReflectivityModels(first, second)\n",
    "        #number of curves being generated\n",
    "        m.generate(50000)\n",
    "        pars, train_data = m.get_preprocessed_data()\n",
    "        min_x = float('inf')\n",
    "        max_x = -float('inf')\n",
    "        #moving all SLD curves along the x axis such that they all start at x=0\n",
    "        for index in range(len(m._smooth_array)):\n",
    "          min_x = min(m._smooth_array[index][0])\n",
    "          for i in range(len(m._smooth_array[index][0])):\n",
    "            m._smooth_array[index][0][i] -= min_x\n",
    "        #saving as npy files\n",
    "        settingUp = []\n",
    "        SLDSet = []\n",
    "        for i in range(len(m._smooth_array)):\n",
    "          settingUp.append(np.array([m.q,m._refl_array[i]]))\n",
    "          SLDSet.append(np.array(m._smooth_array[i]))\n",
    "        totalStack = np.stack(settingUp)\n",
    "        totalParams = np.stack(SLDSet)\n",
    "        print(totalStack.shape, totalParams.shape)\n",
    "        np.save(\"SLD_CurvesPoly\" + str(first) + str(second) + \".npy\", totalParams)\n",
    "        np.save(\"NR-SLD_CurvesPoly\" + str(first) + str(second) + \".npy\", totalStack)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c06c089-06a4-4cbc-93f3-8f1488365a12",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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