{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6abc3695-d89b-4529-90f1-1e3f2b27c2c2",
   "metadata": {},
   "source": [
    "# Time dependence - function time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb553f3d-eef0-42eb-ab80-ad1b48d83428",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nexus as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5eb070eb-a0c6-40e1-8c73-9c59a4e2732f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define a phase jump\n",
    "class PhaseJump(nx.FunctionTime):\n",
    "\n",
    "    def __init__(self, t0):\n",
    "        super().__init__(\"phase jump\")\n",
    "        self.t0 = t0\n",
    "\n",
    "    def Function(self, time):\n",
    "        phase = 0\n",
    "        if time > self.t0:\n",
    "            phase = np.pi\n",
    "        return phase\n",
    "\n",
    "#create a FunctionTime object with a phase jump at 100 ns\n",
    "func_phase = PhaseJump(100)\n",
    "\n",
    "# create a time array\n",
    "times = np.linspace(0, 200, 1001)\n",
    "\n",
    "# calculate phase by call the function\n",
    "phase = [func_phase.Function(t) for t in times]\n",
    "\n",
    "plt.plot(times, phase)\n",
    "plt.xlabel(\"time (ns)\")\n",
    "plt.ylabel(\"phase (rad)\")\n",
    "plt.savefig(\"phase_jump.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ccf3aaf-b5d9-4ca6-bc97-6fe1f809163c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DisplacementJump(nx.FunctionTime):\n",
    "    def __init__(self, t0):\n",
    "        super().__init__(\"displacement jump\")\n",
    "        self.t0 = t0\n",
    "        self.k = nx.lib.moessbauer.Fe57.kvector\n",
    "        self.wavelength = nx.lib.moessbauer.Fe57.wavelength\n",
    "\n",
    "    def Function(self, time):\n",
    "        x = 0\n",
    "        if time > self.t0:\n",
    "            x = 1/2 * self.wavelength\n",
    "        phase = self.k * x\n",
    "        return phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f72412a-0639-4f38-a03d-f0723bff6504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# phase jump at 100 ns\n",
    "func = DisplacementJump(100)\n",
    "\n",
    "# create a time array\n",
    "times = np.linspace(0, 200, 1001)\n",
    "\n",
    "# calculate phase by call the function\n",
    "phase = [func_phase.Function(t) for t in times]\n",
    "\n",
    "plt.plot(times, phase)\n",
    "plt.xlabel(\"time (ns)\")\n",
    "plt.ylabel(\"phase (rad)\")\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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}