Osmary_Resputtering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "import scipy as sp\n",
    "import math\n",
    "import random\n",
    "from scipy import optimize, signal\n",
    "from lmfit import Model, models\n",
    "from numpy import exp, linspace, random\n",
    "from lmfit.models import GaussianModel,ConstantModel\n",
    "from matplotlib.widgets import LassoSelector\n",
    "from matplotlib.path import Path\n",
    "from uncertainties import ufloat\n",
    "from uncertainties.umath import * , sqrt\n",
    "from scipy.optimize import curve_fit\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from numpy import diff\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)\n",
    "\n",
    "tiemposMaxAr = {}\n",
    "intensidadesMaxAr = {}\n",
    "maximosMaxAr = {}\n",
    "for i in range(1,41):\n",
    "    if i in [13,18,33,37,38,39]:\n",
    "        pass\n",
    "    elif i < 10:\n",
    "        tiemposMaxAr[i] = np.genfromtxt(f\"./datosdepositos/2AZ2/2AZ200{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidadesMaxAr[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ2/2AZ200{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "    else:\n",
    "        tiemposMaxAr[i] = np.genfromtxt(f\"./datosdepositos/2AZ2/2AZ20{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidadesMaxAr[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ2/2AZ20{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "        \n",
    "tiemposMaxMs = {}\n",
    "intensidadesMaxMs = {}\n",
    "maximosMaxMs = {}\n",
    "for i in range(1,40):\n",
    "    if i in [15,21,33]:\n",
    "        pass\n",
    "    elif i < 10:\n",
    "        tiemposMaxMs[i] = np.genfromtxt(f\"./datosdepositos/2AZ3/2AZ300{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidadesMaxMs[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ3/2AZ300{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "    else:\n",
    "        tiemposMaxMs[i] = np.genfromtxt(f\"./datosdepositos/2AZ3/2AZ30{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidadesMaxMs[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ3/2AZ30{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "        \n",
    "tiemposMaxVac = {}\n",
    "intensidadesMaxVac = {}\n",
    "maximosMaxVac = {}\n",
    "for i in range(0,32):\n",
    "    if i < 10:\n",
    "        tiemposMaxVac[i] = np.genfromtxt(f\"./txtconvertidos/310119MaxE00{i}_Ch1.txt\", skip_header=1635, usecols=0)*1e6\n",
    "        intensidadesMaxVac[i] = abs(np.genfromtxt(f\"./txtconvertidos/310119MaxE00{i}_Ch1.txt\", skip_header=1635,usecols=1))\n",
    "    else:\n",
    "        tiemposMaxVac[i] = np.genfromtxt(f\"./txtconvertidos/310119MaxE0{i}_Ch1.txt\", skip_header=1635, usecols=0)*1e6\n",
    "        intensidadesMaxVac[i] = abs(np.genfromtxt(f\"./txtconvertidos/310119MaxE0{i}_Ch1.txt\", skip_header=1635,usecols=1))    \n",
    "tiempos50Ar = {}\n",
    "intensidades50Ar = {}\n",
    "maximos50Ar = {}\n",
    "for i in range(0,40):\n",
    "    if i in [32]:\n",
    "        pass\n",
    "    elif i < 10:\n",
    "        tiempos50Ar[i] = np.genfromtxt(f\"./datosdepositos/2AZ4/2AZ400{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades50Ar[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ4/2AZ400{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "    else:\n",
    "        tiempos50Ar[i] = np.genfromtxt(f\"./datosdepositos/2AZ4/2AZ40{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades50Ar[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ4/2AZ40{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "        \n",
    "tiempos50Ms = {}\n",
    "intensidades50Ms = {}\n",
    "maximos50Ms = {}\n",
    "for i in range(1,34):\n",
    "    if i in [15,21]:\n",
    "        pass\n",
    "    elif i < 10:\n",
    "        tiempos50Ms[i] = np.genfromtxt(f\"./datosdepositos/2AZ5/2AZ500{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades50Ms[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ5/2AZ500{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "    else:\n",
    "        tiempos50Ms[i] = np.genfromtxt(f\"./datosdepositos/2AZ5/2AZ50{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades50Ms[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ5/2AZ50{i}_Ch1.txt\", skip_header=2635,usecols=1))   \n",
    "        \n",
    "tiempos50Vac = {}\n",
    "intensidades50Vac = {}\n",
    "maximos50Vac = {}\n",
    "for i in range(65,97):\n",
    "    if i < 10:\n",
    "        tiempos50Vac[i] = np.genfromtxt(f\"./txtconvertidos/310119MaxE00{i}_Ch1.txt\", skip_header=1635, usecols=0)*1e6\n",
    "        intensidades50Vac[i] = abs(np.genfromtxt(f\"./txtconvertidos/310119MaxE00{i}_Ch1.txt\", skip_header=1635,usecols=1))\n",
    "    else:\n",
    "        tiempos50Vac[i] = np.genfromtxt(f\"./txtconvertidos/310119MaxE0{i}_Ch1.txt\", skip_header=1635, usecols=0)*1e6\n",
    "        intensidades50Vac[i] = abs(np.genfromtxt(f\"./txtconvertidos/310119MaxE0{i}_Ch1.txt\", skip_header=1635,usecols=1))   \n",
    "tiempos150Ar = {}\n",
    "intensidades150Ar = {}\n",
    "maximos150Ar = {}\n",
    "for i in range(1,42):\n",
    "    if i in [13,18,33]:\n",
    "        pass\n",
    "    elif i < 10:\n",
    "        tiempos150Ar[i] = np.genfromtxt(f\"./datosdepositos/2AZ6/2AZ600{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades150Ar[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ6/2AZ600{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "    else:\n",
    "        tiempos150Ar[i] = np.genfromtxt(f\"./datosdepositos/2AZ6/2AZ60{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades150Ar[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ6/2AZ60{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "        \n",
    "tiempos150Ms = {}\n",
    "intensidades150Ms = {}\n",
    "maximos150Ms = {}\n",
    "for i in range(1,41):\n",
    "    if i in [15,21,33]:\n",
    "        pass\n",
    "    elif i < 10:\n",
    "        tiempos150Ms[i] = np.genfromtxt(f\"./datosdepositos/2AZ7/2AZ700{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades150Ms[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ7/2AZ700{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "    else:\n",
    "        tiempos150Ms[i] = np.genfromtxt(f\"./datosdepositos/2AZ7/2AZ70{i}_Ch1.txt\", skip_header=2635, usecols=0)*1e6\n",
    "        intensidades150Ms[i] = abs(np.genfromtxt(f\"./datosdepositos/2AZ7/2AZ70{i}_Ch1.txt\", skip_header=2635,usecols=1))\n",
    "        \n",
    "tiempos150Vac = {}\n",
    "intensidades150Vac = {}\n",
    "maximos150Vac = {}\n",
    "for i in range(32,64):\n",
    "    if i < 10:\n",
    "        tiempos150Vac[i] = np.genfromtxt(f\"./txtconvertidos/310119MaxE00{i}_Ch1.txt\", skip_header=1635, usecols=0)*1e6\n",
    "        intensidades150Vac[i] = abs(np.genfromtxt(f\"./txtconvertidos/310119MaxE00{i}_Ch1.txt\", skip_header=1635,usecols=1))\n",
    "    else:\n",
    "        tiempos150Vac[i] = np.genfromtxt(f\"./txtconvertidos/310119MaxE0{i}_Ch1.txt\", skip_header=1635, usecols=0)*1e6\n",
    "        intensidades150Vac[i] = abs(np.genfromtxt(f\"./txtconvertidos/310119MaxE0{i}_Ch1.txt\", skip_header=1635,usecols=1))                  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1) 21 J/cm$^2$, PLD+Ar\n",
    "\n",
    "Se usa la fuerte aproximación de que no ocurren colisiones del oro con el argon entre el blanco y los sustratos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_picos = {}\n",
    "\n",
    "t_picos[5] = tiemposMaxAr[3][np.where(intensidadesMaxAr[3] == np.amax(intensidadesMaxAr[3]))[0][0]]\n",
    "t_picos[10] = tiempos150Ar[38][np.where(intensidades150Ar[38] == np.amax(intensidades150Ar[38]))[0][0]]\n",
    "plt.errorbar(t_picos.values(), [5,10], fmt='-o', yerr=[0.5,0.5], xerr=[0.025, 0.025])\n",
    "plt.ylabel('x   (mm)', fontsize=20)\n",
    "plt.xlabel('t  ($\\mu$s)', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28.91, 18.51)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vel_ion_Ar = (ufloat(10, 0.5)-ufloat(5,.5))/(ufloat(0.553, 0.025)-ufloat(0.3422,0.025))\n",
    "vel_ion_Ar_baj = 18.51 \n",
    "vel_ion_Ar_alt = 28.91\n",
    "vel_ion_Ar\n",
    "#t_golpe_Ar = ufloat(52,0.5)/vel_ion_Ar\n",
    "(23.71+5.2,23.71-5.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass_gold = 3.27e-25\n",
    "\n",
    "def KtoVel(K):\n",
    "    K_joules = K*(1.60218e-19)\n",
    "    return (1/1000)*sqrt((2*K_joules)/(mass_gold))\n",
    "\n",
    "def xResputtAr(t, v=vel_ion_Ar, v_sputt=ufloat(1.5,0.5)):\n",
    "    t_golpe_Ar = ufloat(52,0.5)/v\n",
    "    if t <= t_golpe_Ar:\n",
    "        pos = vel_ion_Ar*t\n",
    "    else:\n",
    "        pos = 52 - KtoVel(v_sputt)*(t-t_golpe_Ar)\n",
    "    return pos\n",
    "\n",
    "def xReboundAr(t, v=vel_ion_Ar):\n",
    "    t_golpe_Ar = ufloat(52,0.5)/v\n",
    "    if t <= t_golpe_Ar:\n",
    "        pos = v*t\n",
    "    else:\n",
    "        pos = 52 - v*(t-t_golpe_Ar)\n",
    "    return pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "observed_peaks_Ar = [ufloat(0.82,0.1), ufloat(1.4,0.2), ufloat(2, 0.2), ufloat(2.8,0.25), ufloat(3.1,0.26), ufloat(3.3,0.6),ufloat(4.1, 0.8)]\n",
    "ob_val = [x.nominal_value for x in observed_peaks_Ar]\n",
    "ob_err = [x.std_dev for x in observed_peaks_Ar]\n",
    "x_v = [5,10,15,20,25,30,45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_sputt_Ar_uf = [xResputtAr(t) for t in tiemposMaxAr[3]*ufloat(1, 0.00019)]\n",
    "pos_sputt_Ar_val = [x.nominal_value for x in pos_sputt_Ar_uf]\n",
    "pos_sputt_Ar_err = [x.std_dev for x in pos_sputt_Ar_uf]\n",
    "\n",
    "pos_sputt_Ar_uf_baj = [xResputtAr(t,v=vel_ion_Ar_baj,v_sputt=KtoVel(1)) for t in tiemposMaxAr[3]*ufloat(1, 0.00019)]\n",
    "pos_sputt_Ar_val_baj = [x.nominal_value for x in pos_sputt_Ar_uf_baj]\n",
    "pos_sputt_Ar_err_baj = [x.std_dev for x in pos_sputt_Ar_uf_baj]\n",
    "\n",
    "pos_sputt_Ar_uf_alt = [xResputtAr(t,v=vel_ion_Ar_alt,v_sputt=KtoVel(2)) for t in tiemposMaxAr[3]*ufloat(1, 0.00019)]\n",
    "pos_sputt_Ar_val_alt = [x.nominal_value for x in pos_sputt_Ar_uf_alt]\n",
    "pos_sputt_Ar_err_alt = [x.std_dev for x in pos_sputt_Ar_uf_alt]\n",
    "\n",
    "pos_rebound_Ar_uf = [xReboundAr(t) for t in tiemposMaxAr[3]*ufloat(1, 0.00019)]\n",
    "pos_rebound_Ar_val = [x.nominal_value for x in pos_rebound_Ar_uf]\n",
    "pos_rebound_Ar_err = [x.std_dev for x in pos_rebound_Ar_uf]\n",
    "\n",
    "pos_rebound_Ar_uf_baj = [xReboundAr(t, v=vel_ion_Ar_baj) for t in tiemposMaxAr[3]*ufloat(1, 0.00019)]\n",
    "pos_rebound_Ar_val_baj = [x.nominal_value for x in pos_rebound_Ar_uf_baj]\n",
    "pos_rebound_Ar_err_baj = [x.std_dev for x in pos_rebound_Ar_uf_baj]\n",
    "\n",
    "pos_rebound_Ar_uf_alt = np.array([xReboundAr(t, v=vel_ion_Ar_alt) for t in tiemposMaxAr[3]*ufloat(1, 0.00019)])\n",
    "pos_rebound_Ar_val_alt = [x.nominal_value for x in pos_rebound_Ar_uf_alt]\n",
    "pos_rebound_Ar_err_alt = [x.std_dev for x in pos_rebound_Ar_uf_alt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PLD + Ar, $21$ J/cm$^2$')"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt_Ar2 = np.array(pos_sputt_Ar_val_baj[16500:])\n",
    "alt_Ar2[0:5000] = 52\n",
    "plt.plot(tiemposMaxAr[3], pos_sputt_Ar_val, color='blue', linewidth=1, label='Resputtering, K entre 1 a 2 eV')\n",
    "#plt.plot(tiemposMaxAr[3][16500:], pos_sputt_Ar_val_alt[16500:], color='black', linewidth=1, label='\"Resputtering\" con K=1.5eV')\n",
    "#plt.plot(tiemposMaxAr[3][21500:], pos_sputt_Ar_val_baj[21500:], color='blue', linewidth=2, label='\"Resputtering\" con K=1.5eV')\n",
    "plt.fill_between(tiemposMaxAr[3][16500:],pos_sputt_Ar_val_alt[16500:], alt_Ar2,color='blue', alpha=0.2)\n",
    "\n",
    "alt_Ar = np.zeros(21500)\n",
    "alt_Ar[:] = 52\n",
    "alt_Ar[0:16500] = pos_rebound_Ar_val_alt[0:16500]\n",
    "plt.plot(tiemposMaxAr[3][0:18500], pos_rebound_Ar_val[0:18500], color='lightgreen', linewidth=2, label='Trayecto de Au II y Au I rápidas')\n",
    "plt.fill_between(tiemposMaxAr[3][0:21500], alt_Ar, pos_rebound_Ar_val_baj[0:21500],color='lightgreen', alpha=0.2)\n",
    "\n",
    "alt_Ar1 = np.array(pos_rebound_Ar_val_baj[16500:])\n",
    "alt_Ar1[0:5000] = 52\n",
    "plt.plot(tiemposMaxAr[3][18500:], pos_rebound_Ar_val[18500:], color='purple', linewidth=2, label='Rebound elástico')\n",
    "plt.fill_between(tiemposMaxAr[3][16500:], pos_rebound_Ar_val_alt[16500:], alt_Ar1,color='purple', alpha=0.2)\n",
    "\n",
    "plt.errorbar(ob_val,x_v, xerr=ob_err, yerr=0.5, fmt='s', color= 'orange', ecolor='orange', label = 'Máximos de Au I Observados')\n",
    "plt.arrow(1, 35, 0.23, 5, head_width=0.1, head_length=0.5, linewidth=4, color='black')\n",
    "plt.arrow(2.9, 43, 0.23, -5,head_width=0.1, head_length=0.5, linewidth=4, color='black')\n",
    "plt.arrow(6, 45, 1, -1.5,head_width=.7, head_length=.6, linewidth=4, color='black')\n",
    "plt.legend(loc=4)\n",
    "plt.xlabel('$t$ ($\\mu$s)', fontsize=15)\n",
    "plt.ylabel('$x$ (mm)', fontsize=15)\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(4.0, 60)\n",
    "plt.yticks([5,10,15,20,25,30,35,40,45,50])\n",
    "plt.axhline(52, linestyle='--', color='red', linewidth=3)\n",
    "plt.text(7,53, 'Sustratos', fontsize=15)\n",
    "plt.title('PLD + Ar, $21$ J/cm$^2$', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1) 21 J/cm$^2$, PLD+Ar+MS\n",
    "\n",
    "Se usa la fuerte aproximación de que no ocurren colisiones del oro con el argon entre el blanco y los sustratos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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