{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d09c917d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Math, display\n",
    "import numpy as np\n",
    "import sympy as sp\n",
    "from scipy import constants as const\n",
    "from tqdm import tqdm\n",
    "\n",
    "#add relative path to backend\n",
    "import sys\n",
    "sys.path.append('../../clean_diag/backend')\n",
    "\n",
    "import trap_units as si\n",
    "from twod_trap import PancakeTrap\n",
    "\n",
    "from spilling_helpers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b021bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "333.017 Hz\n",
      "2.01106353355856 uK\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{\\omega_{t r}}{\\omega_{t ax}} \\approx 9.02$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "initial_power = 91 * si.uW \n",
    "initial_grad = 0 * si.G/si.cm\n",
    "\n",
    "trap: PancakeTrap = PancakeTrap(\n",
    "    power=0,  # Set pancake laser power to 0, no 2D trap\n",
    "    grad_z= initial_grad,\n",
    "    grad_r=0,\n",
    "    power_tweezer= initial_power,\n",
    "    waist_tweezer= 2.16/2 * si.um,\n",
    "\n",
    "    m= 161 * const.value(\"atomic mass constant\"),\n",
    "    mu_b= 9.93 * const.value(\"Bohr magneton\" ),\n",
    "    a=180*(4 * np.pi * const.epsilon_0 * const.value(\"Bohr radius\")**3)/(2 * const.epsilon_0 * const.c),\n",
    "\n",
    "    wvl = 532 * si.nm,\n",
    ")\n",
    "axial_width = trap.get_tweezer_rayleigh()\n",
    "zr = float(trap.subs(trap.get_tweezer_rayleigh()))\n",
    "\n",
    "x, y, z = trap.x, trap.y, trap.z\n",
    "\n",
    "print(f\"{float(trap.subs(trap.get_omega_ax_tweezer()))/2/np.pi:.3f} Hz\")\n",
    "print(f\"{trap.subs(trap.a *2*trap.power_tweezer/(np.pi*trap.waist_tweezer**2)/ const.k) /si.uK} uK\")\n",
    "\n",
    "aspect_ratio = trap.get_omega_r_tweezer() / trap.get_omega_ax_tweezer()\n",
    "_aspect_ratio_latex = sp.latex(trap.omega_r_tweezer / trap.omega_ax_tweezer)\n",
    "display(Math(f\"{_aspect_ratio_latex} \\\\approx {trap.subs(aspect_ratio).evalf():.2f}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b3485046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.887882840502134"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zr/si.um"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ebf9e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91666666666669"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "powers, atom_number = sweep_power_old(trap, 0*si.G/si.cm, 0.15*si.uW, 100, -0.*zr, 1.1*zr,max_spill_steps=250, max_atoms=10)\n",
    "calculate_stepsize(powers,atom_number,plot=True)/si.uW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "293d79ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.12170219141973"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(powers[0])/si.uW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "275775bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111.37170219141973"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(powers[-1])/si.uW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "165109df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(powers/powers[0], atom_number, linewidth=2, color=\"tab:orange\")\n",
    "plt.axhline(0, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(1, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(2, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(3, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(4, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(5, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(6, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(7, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(8, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(9, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(10, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "\n",
    "#plt.xlim(1,1.23)\n",
    "#plt.ylim(0,10.5)\n",
    "\n",
    "plt.xlabel(r\"tweezer power in units of $P_0(B'=0) = 91 \\text{µW}$\")\n",
    "plt.ylabel(\"mean atom number\")\n",
    "#plt.title(\"Controll over particle number with Dysprosium\")\n",
    "\n",
    "plt.savefig(\"stepplot_0G.png\", dpi = 300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa99a1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025777777777777785"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gradients, atom_number = sweep_gradient_old(trap, 91*si.uW, 0.004*si.G/si.cm, 50, -0.0*zr, 1.1*zr,max_spill_steps=200)\n",
    "calculate_stepsize(gradients,atom_number,plot=True)/si.G*si.cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bcbea262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(gradients/si.G*si.cm, atom_number, linewidth=2)\n",
    "plt.axhline(0, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(1, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(2, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(3, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(4, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(5, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(6, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(7, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(8, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(9, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "plt.axhline(10, color=\"grey\", linestyle=\"dashed\",alpha=0.5)\n",
    "\n",
    "#plt.xlim(1,1.23)\n",
    "#plt.ylim(0,10.5)\n",
    "\n",
    "plt.xlabel(r\"magnetic gradient [G/cm]\")\n",
    "plt.ylabel(\"mean atom number\")\n",
    "#plt.title(\"Controll over particle number with Dysprosium\")\n",
    "\n",
    "plt.savefig(\"stepplot_grad.png\", dpi = 300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd9e561",
   "metadata": {},
   "source": [
    "# Sensibilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6c6c27c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1064 = np.load(\"data/sweeps_1064.npz\")\n",
    "data_532 = np.load(\"data/sweeps_532.npz\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b94596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "\n",
    "# Left plot\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.plot(data_1064[\"gradient\"]/si.G*si.cm, data_1064[\"gradient_rel\"], \"o\", label=\"1064nm: gradient\")\n",
    "ax1.plot(data_1064[\"gradient\"]/si.G*si.cm, data_1064[\"power_rel\"], \"o\", label=\"1064nm: power\")\n",
    "\n",
    "#ax1.set_title(r\"relative precision (1064nm, a=184, $\\eta$=9)\")\n",
    "ax1.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "ax1.set_ylabel(r\"$\\delta B'_{10\\%}/B'$ [G/cm]\")\n",
    "\n",
    "new_tick_locations = data_1064[\"power\"][::3]/si.uW\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "ax2.set_xticks(new_tick_locations)\n",
    "ax2.set_xlabel(r\"$P_0(B')$ [uW]\")\n",
    "\n",
    "ax1.legend()\n",
    "ax1.grid()\n",
    "\n",
    "# Right plot (duplicate or variant)\n",
    "ax4 = ax3.twiny()\n",
    "\n",
    "ax3.set_yscale(\"log\")\n",
    "ax3.plot(data_532[\"gradient\"]/si.G*si.cm, data_532[\"gradient_rel\"], \"o\", label=\"532nm: gradient\")\n",
    "ax3.plot(data_532[\"gradient\"]/si.G*si.cm, data_532[\"power_rel\"], \"o\", label=\"532nm: power\")\n",
    "\n",
    "#ax3.set_title(r\"relative precision (second condition)\")\n",
    "ax3.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "\n",
    "new_tick_locations = data_532[\"power\"][::3]/si.uW\n",
    "ax4.set_xlim(ax3.get_xlim())\n",
    "ax4.set_xticks(new_tick_locations)\n",
    "ax4.set_xlabel(r\"$P_0(B')$ [uW]\")\n",
    "\n",
    "ax3.legend()\n",
    "ax3.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"rel_err.png\", dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "424ae8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(15, 5), sharey=True)\n",
    "\n",
    "# Left plot\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "ax1.set_yscale(\"linear\")\n",
    "ax1.plot(data_1064[\"gradient\"]/si.G*si.cm, data_1064[\"gradient_step\"]/si.G*si.cm,\"o\", label=\"1064nm: gradient\")\n",
    "# ax1.plot(data[\"gradient\"]/si.G*si.cm, data[\"power_step\"]/si.uW, \"o\", label=\"power\")\n",
    "\n",
    "#ax1.set_title(\"absolute precision (1064nm, a=184, η=9)\")\n",
    "ax1.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "ax1.set_ylabel(r\"$\\delta B'_{10\\%}$ [G/cm]\")\n",
    "\n",
    "new_tick_locations = data_1064[\"power\"][::3]/si.uW\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "ax2.set_xticks(new_tick_locations)\n",
    "ax2.set_xlabel(r\"$P_0(B')$ [uW]\")\n",
    "\n",
    "ax1.legend()\n",
    "ax1.grid()\n",
    "\n",
    "# Right plot (could use different or same data)\n",
    "ax4 = ax3.twiny()\n",
    "\n",
    "ax3.set_yscale(\"linear\")\n",
    "ax3.plot(data_532[\"gradient\"]/si.G*si.cm, data_532[\"gradient_step\"]/si.G*si.cm,\"o\", label=\"532nm: gradient\")\n",
    "# ax3.plot(data[\"gradient\"]/si.G*si.cm, data[\"power_step\"]/si.uW, \"o\", label=\"power\")\n",
    "\n",
    "#ax3.set_title(\"absolute precision (532nm, a=180, η=9)\")\n",
    "ax3.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "\n",
    "new_tick_locations = data_532[\"power\"][::3]/si.uW\n",
    "ax4.set_xlim(ax3.get_xlim())\n",
    "ax4.set_xticks(new_tick_locations)\n",
    "ax4.set_xlabel(r\"$P_0(B')$ [uW]\")\n",
    "\n",
    "ax3.legend()\n",
    "ax3.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"abs_gradient.png\", dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef01140f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(15, 5), sharey=True)\n",
    "\n",
    "# Left plot\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "ax1.set_yscale(\"linear\")\n",
    "ax1.plot(data_1064[\"gradient\"]/si.G*si.cm, data_1064[\"power_step\"]/si.uW,\"o\", color=\"tab:orange\", label=\"1064nm: power\")\n",
    "# ax1.plot(data[\"gradient\"]/si.G*si.cm, data[\"power_step\"]/si.uW, \"o\", label=\"power\")\n",
    "\n",
    "#ax1.set_title(\"absolute precision (1064nm, a=184, η=9)\")\n",
    "ax1.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "ax1.set_ylabel(r\"$\\delta P_{10\\%}$ [µW]\")\n",
    "\n",
    "new_tick_locations = data_1064[\"power\"][::3]/si.uW\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "ax2.set_xticks(new_tick_locations)\n",
    "ax2.set_xlabel(r\"$P_0(B')$ [µW]\")\n",
    "\n",
    "ax1.legend()\n",
    "ax1.grid()\n",
    "\n",
    "# Right plot (could use different or same data)\n",
    "ax4 = ax3.twiny()\n",
    "\n",
    "ax3.set_yscale(\"linear\")\n",
    "ax3.plot(data_532[\"gradient\"]/si.G*si.cm, data_532[\"power_step\"]/si.uW,\"o\", color=\"tab:orange\", label=\"532nm: power\")\n",
    "# ax3.plot(data[\"gradient\"]/si.G*si.cm, data[\"power_step\"]/si.uW, \"o\", label=\"power\")\n",
    "\n",
    "#ax3.set_title(\"absolute precision (532nm, a=180, η=9)\")\n",
    "ax3.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "\n",
    "new_tick_locations = data_532[\"power\"][::3]/si.uW\n",
    "ax4.set_xlim(ax3.get_xlim())\n",
    "ax4.set_xticks(new_tick_locations)\n",
    "ax4.set_xlabel(r\"$P_0(B')$ [µW]\")\n",
    "\n",
    "ax3.legend()\n",
    "ax3.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"abs_power.png\", dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b4f014b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 10), sharex=True, sharey='row')\n",
    "\n",
    "# -------------------------------\n",
    "# Top-left (1064 nm: δB' precision)\n",
    "ax1     = axs[0, 0]\n",
    "ax1_top = ax1.twiny()\n",
    "\n",
    "ax1.set_yscale(\"linear\")\n",
    "ax1.plot(data_1064[\"gradient\"]/si.G*si.cm,\n",
    "         data_1064[\"gradient_step\"]/si.G*si.cm,\n",
    "         \"o\", label=\"1064 nm: gradient\")\n",
    "\n",
    "ax1.set_ylabel(r\"$\\delta B'_{10\\%}$ [G/cm]\")\n",
    "#ax1.set_title(\"Absolute δB' precision (1064 nm)\")\n",
    "ax1.grid()\n",
    "ax1.legend(loc=\"best\")\n",
    "\n",
    "ticks_1064 = data_1064[\"power\"][::3]/si.uW\n",
    "ax1_top.set_xlim(ax1.get_xlim())\n",
    "ax1_top.set_xticks(ticks_1064)\n",
    "ax1_top.set_xlabel(r\"$P_0(B')$ [µW]\")\n",
    "\n",
    "\n",
    "# -------------------------------\n",
    "# Top-right (532 nm: δB' precision)\n",
    "ax2     = axs[0, 1]\n",
    "ax2_top = ax2.twiny()\n",
    "\n",
    "ax2.set_yscale(\"linear\")\n",
    "ax2.plot(data_532[\"gradient\"]/si.G*si.cm,\n",
    "         data_532[\"gradient_step\"]/si.G*si.cm,\n",
    "         \"o\", label=\"532 nm: gradient\")\n",
    "\n",
    "#ax2.set_title(\"Absolute δB' precision (532 nm)\")\n",
    "ax2.grid()\n",
    "ax2.legend(loc=\"best\")\n",
    "\n",
    "ticks_532 = data_532[\"power\"][::3]/si.uW\n",
    "ax2_top.set_xlim(ax2.get_xlim())\n",
    "ax2_top.set_xticks(ticks_532)\n",
    "ax2_top.set_xlabel(r\"$P_0(B')$ [µW]\")\n",
    "\n",
    "\n",
    "# -------------------------------\n",
    "# Bottom-left (1064 nm: δP precision)\n",
    "ax3     = axs[1, 0]\n",
    "ax3_top = ax3.twiny()\n",
    "\n",
    "ax3.set_yscale(\"linear\")\n",
    "ax3.plot(data_1064[\"gradient\"]/si.G*si.cm,\n",
    "         data_1064[\"power_step\"]/si.uW,\n",
    "         \"o\", color=\"tab:orange\", label=\"1064 nm: power\")\n",
    "\n",
    "ax3.set_ylabel(r\"$\\delta P_{10\\%}$ [µW]\")\n",
    "ax3.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "#ax3.set_title(\"Absolute δP precision (1064 nm)\")\n",
    "ax3.grid()\n",
    "ax3.legend(loc=\"best\")\n",
    "\n",
    "ax3_top.set_xlim(ax3.get_xlim())\n",
    "ax3_top.set_xticks(ticks_1064)\n",
    "ax3_top.set_xlabel(r\"$P_0(B')$ [µW]\")\n",
    "\n",
    "\n",
    "# -------------------------------\n",
    "# Bottom-right (532 nm: δP precision)\n",
    "ax4     = axs[1, 1]\n",
    "ax4_top = ax4.twiny()\n",
    "\n",
    "ax4.set_yscale(\"linear\")\n",
    "ax4.plot(data_532[\"gradient\"]/si.G*si.cm,\n",
    "         data_532[\"power_step\"]/si.uW,\n",
    "         \"o\", color=\"tab:orange\", label=\"532 nm: power\")\n",
    "\n",
    "ax4.set_xlabel(r\"gradient $B'$ [G/cm]\")\n",
    "#ax4.set_title(\"Absolute δP precision (532 nm)\")\n",
    "ax4.grid()\n",
    "ax4.legend(loc=\"best\")\n",
    "\n",
    "ax4_top.set_xlim(ax4.get_xlim())\n",
    "ax4_top.set_xticks(ticks_532)\n",
    "ax4_top.set_xlabel(r\"$P_0(B')$ [µW]\")\n",
    "\n",
    "\n",
    "# -------------------------------\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"abs_precision_2x2_shared.png\", dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb31e0af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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