zeldovich-PLT f_NL validation notebook

check_bispectrum_pylians.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import os\n",
    "os.environ['ABACUS'] = '/mnt/home/lgarrison/abacus/'\n",
    "import sys\n",
    "sys.path.insert(0, '/mnt/home/lgarrison/abacus/')\n",
    "from Abacus import ReadAbacus\n",
    "\n",
    "from abacusnbody.analysis import tsc\n",
    "from abacusnbody.analysis import power_spectrum\n",
    "import pynbody\n",
    "\n",
    "import Pk_library as PKL\n",
    "\n",
    "from scipy.fft import rfftn, irfftn, fftfreq, rfftfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "box = 2000.\n",
    "Omega_M = 0.3175\n",
    "InitialRedshift = 99.\n",
    "growth = 1 / (1 + InitialRedshift)\n",
    "H0 = 100.\n",
    "c = 299792.458\n",
    "f_NL = 100.\n",
    "\n",
    "read_abacus_kwargs = dict(\n",
    "    pattern=\"ic_*\",\n",
    "    format='rvzel',\n",
    "    add_grid=True,\n",
    "    boxsize=box,\n",
    "    dtype='f4',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans = np.loadtxt('/mnt/ceph/users/lgarrison/zeldovich-fnl/quijote_png_LH_0/Tk_0.txt')\n",
    "from scipy.interpolate import CubicSpline\n",
    "loginterp = CubicSpline(*np.log(trans).T)\n",
    "trans_interp = lambda k: np.exp(loginterp(np.log(k)))\n",
    "\n",
    "invM = lambda k: 3 * Omega_M * H0**2 / (2 * growth * c**2 * k**2 * trans_interp(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pos(posdir, gadget=False):\n",
    "    if gadget:\n",
    "        pos = pynbody.load(posdir)\n",
    "        pos['pos'] /= 1e3\n",
    "    else:\n",
    "        pos = ReadAbacus.from_dir(\n",
    "            posdir,\n",
    "            **read_abacus_kwargs,\n",
    "            )\n",
    "    return pos\n",
    "\n",
    "def do_pk(posdir, gadget=False, nmesh=256, nthread=24):\n",
    "    pos = get_pos(posdir, gadget=gadget)\n",
    "    pk = power_spectrum.calc_power(\n",
    "        pos['pos'],\n",
    "        nmesh=nmesh,\n",
    "        Lbox=box,\n",
    "        nthread=nthread,\n",
    "        compensated=True,\n",
    "        interlaced=False,\n",
    "        )\n",
    "    return pk\n",
    "\n",
    "def get_kgrid(N):\n",
    "    return 2 * np.pi * np.stack(\n",
    "        np.meshgrid(\n",
    "            fftfreq(N, d=box/N), fftfreq(N, d=box/N), rfftfreq(N, d=box/N),\n",
    "            indexing='ij',\n",
    "            )\n",
    "        )\n",
    "\n",
    "def make_bardeen(delta):\n",
    "    field_fft = rfftn(delta, overwrite_x=False, workers=24)\n",
    "    # field_fft *= 1 / delta.size\n",
    "\n",
    "    kgrid = get_kgrid(delta.shape[0])\n",
    "    kmag = (kgrid**2).sum(axis=0)**0.5\n",
    "\n",
    "    with np.errstate(divide='ignore', invalid='ignore'):\n",
    "        field_fft *= invM(kmag)\n",
    "    field_fft[0,0,0] = 0.\n",
    "\n",
    "    delta = irfftn(field_fft, overwrite_x=True, workers=24)\n",
    "\n",
    "    return delta\n",
    "\n",
    "\n",
    "def do_bispectrum(posdir, gadget=False, nmesh=256, nthread=24, squeeze_kF=3, bardeen=True, fast=False):\n",
    "    pos = get_pos(posdir, gadget=gadget)\n",
    "    \n",
    "    delta = tsc.tsc_parallel(pos['pos'], nmesh, box, nthread=nthread)\n",
    "    delta /= np.mean(delta, dtype=np.float64)\n",
    "    delta -= 1.\n",
    "\n",
    "    if bardeen:\n",
    "        delta = make_bardeen(delta)\n",
    "    \n",
    "    # pk = power_spectrum.calc_power(pos['pos'], **bk_kwargs)[1:]\n",
    "    # theta = np.linspace(0, np.pi, 2)\n",
    "    # theta = np.array([np.pi/3])\n",
    "    kF = 2*np.pi/box\n",
    "    ksamp = np.arange(squeeze_kF, 33 if fast else 65, 1)*kF\n",
    "    # ksamp = [0.05, 0.1]\n",
    "    res = []\n",
    "    for k in ksamp:\n",
    "        if fast:\n",
    "            theta = np.array([2/3*np.pi])  # equilateral\n",
    "        else:\n",
    "            theta = np.array([2/3*np.pi, np.pi - 2*np.arcsin(squeeze_kF*kF/2/k)])  # equilateral, squeezed\n",
    "        bk1 = PKL.Bk(delta, box, k, k, theta, 'TSC', threads=nthread)\n",
    "        bk1 = vars(bk1)\n",
    "        \n",
    "        if not fast:\n",
    "            theta = np.array([np.pi])  # folded\n",
    "            bk2 = PKL.Bk(delta, box, k, k/2, theta, 'TSC', threads=nthread)\n",
    "            bk2 = vars(bk2)\n",
    "\n",
    "            # [k, k, k_eq, k_squeeze, k, k/2, k_folded]\n",
    "            res += [{k: np.concatenate((bk1[k], bk2[k])) for k in bk1}]\n",
    "        else:\n",
    "            res += [bk1]\n",
    "    res = {k: np.vstack([r[k] for r in res]).T for k in res[0].keys()}\n",
    "    res['ksamp'] = ksamp\n",
    "    res['squeeze_kF'] = squeeze_kF\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.34\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.60\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.34\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.57\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.48\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.51\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.73\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.75\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.50\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.73\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.75\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.53\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.81\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.49\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.82\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.76\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n"
     ]
    }
   ],
   "source": [
    "bk_gadget = do_bispectrum('/mnt/home/lgarrison/ceph/zeldovich-fnl/quijote_png_LH_0_fixed/2LPTNGLC/ics', gadget=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.57\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.34\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.34\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.58\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.73\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.76\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.80\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.54\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.73\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.75\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.77\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.75\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.78\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.73\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.50\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.76\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n"
     ]
    }
   ],
   "source": [
    "bk_gadget_fnl0 = do_bispectrum('/mnt/home/lgarrison/ceph/zeldovich-fnl/quijote_png_LH_0_fixed/2LPTNGLC_fnl0/ics', gadget=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.60\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.33\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.75\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.49\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.73\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.75\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.45\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.46\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.47\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.74\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n"
     ]
    }
   ],
   "source": [
    "bk_abacus = do_bispectrum('/mnt/home/lgarrison/ceph/zeldovich-fnl/quijote_png_LH_0_fixed/ic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.58\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.33\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.32\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.57\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.55\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.76\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.62\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.35\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.32\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.36\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.34\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.65\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.64\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.43\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.49\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.76\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.44\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.72\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.71\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.48\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.67\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.70\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.49\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.40\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.68\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.38\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.63\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.42\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.59\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.41\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.61\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.37\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.69\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.66\n",
      "\n",
      "Computing bispectrum of the field...\n",
      "Time to compute bispectrum = 1.39\n"
     ]
    }
   ],
   "source": [
    "bk_abacus_fnl0 = do_bispectrum('/mnt/home/lgarrison/ceph/zeldovich-fnl/quijote_png_LH_0_fixed/ic_fnl0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pk_abacus = do_pk('/mnt/home/lgarrison/ceph/zeldovich-fnl/quijote_png_LH_0_fixed/ic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_bk_fast(bk_gadget, f_NL=-50., bardeen=True):\n",
    "    fig, ax = plt.subplots(dpi=100)\n",
    "\n",
    "    t = 0\n",
    "    ax.plot(bk_gadget['ksamp'], bk_gadget['ksamp']**1 * (bk_gadget['B'][t] - bk_gadget_fnl0['B'][t])/f_NL, label='gadget')\n",
    "    ax.plot(bk_gadget['ksamp'], bk_gadget['ksamp']**1 * primordial_deriv(bk_gadget, t=t, bardeen=bardeen), label='theory')\n",
    "    # ax.plot(bk_gadget['ksamp'], (bk_gadget['B'][t] - bk_gadget_fnl0['B'][t])/f_NL/primordial_deriv(bk_gadget, t=t, bardeen=bardeen), label='gadget')\n",
    "    ax.set_yscale('log')\n",
    "\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(r'$k$ [$h$/Mpc]')\n",
    "    ax.set_ylabel(r'$k\\frac{d}{df_{NL}} B(k,k,k)$')\n",
    "\n",
    "plot_bk_fast(bk_gadget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_bk(bk_gadget, bk_gadget_fnl0, bk_abacus, bk_abacus_fnl0, f_NL=-50., bardeen=True, fn=None):\n",
    "    fig, axes = plt.subplots(3,1, sharex=True, gridspec_kw=dict(hspace=0), figsize=(6,8), dpi=100)\n",
    "\n",
    "    tilt = 3\n",
    "    for t,ax in enumerate(axes):\n",
    "        ax.plot(bk_gadget['ksamp'], bk_gadget['ksamp']**tilt * (bk_gadget['B'][t] - bk_gadget_fnl0['B'][t])/f_NL, label='gadget')\n",
    "        ax.plot(bk_abacus['ksamp'], bk_abacus['ksamp']**tilt * (bk_abacus['B'][t] - bk_abacus_fnl0['B'][t])/f_NL, label='abacus')\n",
    "        if t == 2:\n",
    "            ax.plot(bk_abacus['ksamp'], bk_abacus['ksamp']**tilt * primordial_deriv(bk_abacus, i=5, t=4, bardeen=bardeen), label='theory')\n",
    "        else:\n",
    "            ax.plot(bk_abacus['ksamp'], bk_abacus['ksamp']**tilt * primordial_deriv(bk_abacus, t=t, bardeen=bardeen), label='theory')\n",
    "\n",
    "        ax.set_yscale('log')\n",
    "\n",
    "    axes[0].legend()\n",
    "    axes[-1].set_xlabel(r'$k$ [$h$/Mpc]')\n",
    "    axes[0].set_ylabel(r'$' + f'k^{tilt}' + r'\\frac{d}{df_{NL}} B' + ('_\\phi' if bardeen else '') + r'(k,k,k)$')\n",
    "    axes[1].set_ylabel(r'$' + f'k^{tilt}' + r'\\frac{d}{df_{NL}} B' + ('_\\phi' if bardeen else '') + r'(k,k,' + f'{sqf if (sqf:=bk_abacus[\"squeeze_kF\"]) > 1 else \"\"}' + r'k_F)$')\n",
    "    axes[2].set_ylabel(r'$' + f'k^{tilt}' + r'\\frac{d}{df_{NL}} B' + ('_\\phi' if bardeen else '') + r'(k,k/2,k/2)$')\n",
    "\n",
    "    if fn:\n",
    "        fig.savefig(fn, bbox_inches='tight')\n",
    "\n",
    "def primordial_deriv_bardeen(bk, *, i=1, t=0):\n",
    "    # for when the delta field is already the bardeen potential\n",
    "    # bk['k']: [k, k, k_eq, k_squeeze, k, k/2, k_folded]\n",
    "    return 2 * (bk['Pk'][0] * bk['Pk'][i] + \n",
    "                bk['Pk'][0] * bk['Pk'][2+t] +\n",
    "                bk['Pk'][i] * bk['Pk'][2+t])\n",
    "\n",
    "def primordial_deriv(bk, *, i=1, t=0, bardeen=False):\n",
    "    if bardeen:\n",
    "        return primordial_deriv_bardeen(bk, i=i, t=t)\n",
    "    # bk['k']: [k, k, k_eq, k_squeeze, k, k/2, k_folded]\n",
    "    return 2 * (bk['Pk'][0] * bk['Pk'][i] * invM(bk['k'][0])**2 * invM(bk['k'][i])**2 + \n",
    "                bk['Pk'][0] * bk['Pk'][2+t] * invM(bk['k'][0])**2 * invM(bk['k'][2+t])**2 +\n",
    "                bk['Pk'][i] * bk['Pk'][2+t] * invM(bk['k'][i])**2 * invM(bk['k'][2+t])**2 ) / \\\n",
    "                    (invM(bk['k'][0]) * invM(bk['k'][i]) * invM(bk['k'][2+t]))\n",
    "\n",
    "plot_bk(bk_gadget, bk_gadget_fnl0, bk_abacus, bk_abacus_fnl0, f_NL=f_NL, bardeen=True,\n",
    "        # fn='bispectrum_validation.png',\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 2\n",
    "plt.figure(dpi=100)\n",
    "plt.semilogy(bk_abacus['ksamp'], bk_abacus['B'][t], label='abacus')\n",
    "plt.semilogy(bk_gadget['ksamp'], bk_gadget['B'][t], label='gadget')\n",
    "plt.semilogy(bk_abacus['ksamp'], bk_abacus_fnl0['B'][t], label='abacus fnl0')\n",
    "plt.semilogy(bk_gadget['ksamp'], bk_gadget_fnl0['B'][t], label='gadget fnl0')\n",
    "plt.legend()\n",
    "plt.yscale('symlog', linthresh=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00314159, 0.00628319, 0.00942478, 0.01256637, 0.01570796,\n",
       "        0.01884956, 0.02199115, 0.02513274, 0.02827433, 0.03141593,\n",
       "        0.03455752, 0.03769911, 0.0408407 , 0.0439823 , 0.04712389,\n",
       "        0.05026548, 0.05340708, 0.05654867, 0.05969026, 0.06283185,\n",
       "        0.06597345, 0.06911504, 0.07225663, 0.07539822, 0.07853982,\n",
       "        0.08168141, 0.084823  , 0.08796459, 0.09110619, 0.09424778,\n",
       "        0.09738937, 0.10053096, 0.10367256, 0.10681415, 0.10995574,\n",
       "        0.11309734, 0.11623893, 0.11938052, 0.12252211, 0.12566371,\n",
       "        0.1288053 , 0.13194689, 0.13508848, 0.13823008, 0.14137167,\n",
       "        0.14451326, 0.14765485, 0.15079645, 0.15393804, 0.15707963,\n",
       "        0.16022123, 0.16336282, 0.16650441, 0.169646  , 0.1727876 ,\n",
       "        0.17592919, 0.17907078, 0.18221237, 0.18535397, 0.18849556,\n",
       "        0.19163715, 0.19477874, 0.19792034, 0.20106193],\n",
       "       [0.00314159, 0.00628319, 0.00942478, 0.01256637, 0.01570796,\n",
       "        0.01884956, 0.02199115, 0.02513274, 0.02827433, 0.03141593,\n",
       "        0.03455752, 0.03769911, 0.0408407 , 0.0439823 , 0.04712389,\n",
       "        0.05026548, 0.05340708, 0.05654867, 0.05969026, 0.06283185,\n",
       "        0.06597345, 0.06911504, 0.07225663, 0.07539822, 0.07853982,\n",
       "        0.08168141, 0.084823  , 0.08796459, 0.09110619, 0.09424778,\n",
       "        0.09738937, 0.10053096, 0.10367256, 0.10681415, 0.10995574,\n",
       "        0.11309734, 0.11623893, 0.11938052, 0.12252211, 0.12566371,\n",
       "        0.1288053 , 0.13194689, 0.13508848, 0.13823008, 0.14137167,\n",
       "        0.14451326, 0.14765485, 0.15079645, 0.15393804, 0.15707963,\n",
       "        0.16022123, 0.16336282, 0.16650441, 0.169646  , 0.1727876 ,\n",
       "        0.17592919, 0.17907078, 0.18221237, 0.18535397, 0.18849556,\n",
       "        0.19163715, 0.19477874, 0.19792034, 0.20106193],\n",
       "       [0.00314159, 0.00628319, 0.00942478, 0.01256637, 0.01570796,\n",
       "        0.01884956, 0.02199115, 0.02513274, 0.02827433, 0.03141593,\n",
       "        0.03455752, 0.03769911, 0.0408407 , 0.0439823 , 0.04712389,\n",
       "        0.05026548, 0.05340708, 0.05654867, 0.05969026, 0.06283185,\n",
       "        0.06597345, 0.06911504, 0.07225663, 0.07539822, 0.07853982,\n",
       "        0.08168141, 0.084823  , 0.08796459, 0.09110619, 0.09424778,\n",
       "        0.09738937, 0.10053096, 0.10367256, 0.10681415, 0.10995574,\n",
       "        0.11309734, 0.11623893, 0.11938052, 0.12252211, 0.12566371,\n",
       "        0.1288053 , 0.13194689, 0.13508848, 0.13823008, 0.14137167,\n",
       "        0.14451326, 0.14765485, 0.15079645, 0.15393804, 0.15707963,\n",
       "        0.16022123, 0.16336282, 0.16650441, 0.169646  , 0.1727876 ,\n",
       "        0.17592919, 0.17907078, 0.18221237, 0.18535397, 0.18849556,\n",
       "        0.19163715, 0.19477874, 0.19792034, 0.20106193],\n",
       "       [0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319, 0.00628319,\n",
       "        0.00628319, 0.00628319, 0.00628319, 0.00628319],\n",
       "       [0.00314159, 0.00628319, 0.00942478, 0.01256637, 0.01570796,\n",
       "        0.01884956, 0.02199115, 0.02513274, 0.02827433, 0.03141593,\n",
       "        0.03455752, 0.03769911, 0.0408407 , 0.0439823 , 0.04712389,\n",
       "        0.05026548, 0.05340708, 0.05654867, 0.05969026, 0.06283185,\n",
       "        0.06597345, 0.06911504, 0.07225663, 0.07539822, 0.07853982,\n",
       "        0.08168141, 0.084823  , 0.08796459, 0.09110619, 0.09424778,\n",
       "        0.09738937, 0.10053096, 0.10367256, 0.10681415, 0.10995574,\n",
       "        0.11309734, 0.11623893, 0.11938052, 0.12252211, 0.12566371,\n",
       "        0.1288053 , 0.13194689, 0.13508848, 0.13823008, 0.14137167,\n",
       "        0.14451326, 0.14765485, 0.15079645, 0.15393804, 0.15707963,\n",
       "        0.16022123, 0.16336282, 0.16650441, 0.169646  , 0.1727876 ,\n",
       "        0.17592919, 0.17907078, 0.18221237, 0.18535397, 0.18849556,\n",
       "        0.19163715, 0.19477874, 0.19792034, 0.20106193],\n",
       "       [0.0015708 , 0.00314159, 0.00471239, 0.00628319, 0.00785398,\n",
       "        0.00942478, 0.01099557, 0.01256637, 0.01413717, 0.01570796,\n",
       "        0.01727876, 0.01884956, 0.02042035, 0.02199115, 0.02356194,\n",
       "        0.02513274, 0.02670354, 0.02827433, 0.02984513, 0.03141593,\n",
       "        0.03298672, 0.03455752, 0.03612832, 0.03769911, 0.03926991,\n",
       "        0.0408407 , 0.0424115 , 0.0439823 , 0.04555309, 0.04712389,\n",
       "        0.04869469, 0.05026548, 0.05183628, 0.05340708, 0.05497787,\n",
       "        0.05654867, 0.05811946, 0.05969026, 0.06126106, 0.06283185,\n",
       "        0.06440265, 0.06597345, 0.06754424, 0.06911504, 0.07068583,\n",
       "        0.07225663, 0.07382743, 0.07539822, 0.07696902, 0.07853982,\n",
       "        0.08011061, 0.08168141, 0.08325221, 0.084823  , 0.0863938 ,\n",
       "        0.08796459, 0.08953539, 0.09110619, 0.09267698, 0.09424778,\n",
       "        0.09581858, 0.09738937, 0.09896017, 0.10053096],\n",
       "       [0.0015708 , 0.00314159, 0.00471239, 0.00628319, 0.00785398,\n",
       "        0.00942478, 0.01099557, 0.01256637, 0.01413717, 0.01570796,\n",
       "        0.01727876, 0.01884956, 0.02042035, 0.02199115, 0.02356194,\n",
       "        0.02513274, 0.02670354, 0.02827433, 0.02984513, 0.03141593,\n",
       "        0.03298672, 0.03455752, 0.03612832, 0.03769911, 0.03926991,\n",
       "        0.0408407 , 0.0424115 , 0.0439823 , 0.04555309, 0.04712389,\n",
       "        0.04869469, 0.05026548, 0.05183628, 0.05340708, 0.05497787,\n",
       "        0.05654867, 0.05811946, 0.05969026, 0.06126106, 0.06283185,\n",
       "        0.06440265, 0.06597345, 0.06754424, 0.06911504, 0.07068583,\n",
       "        0.07225663, 0.07382743, 0.07539822, 0.07696902, 0.07853982,\n",
       "        0.08011061, 0.08168141, 0.08325221, 0.084823  , 0.0863938 ,\n",
       "        0.08796459, 0.08953539, 0.09110619, 0.09267698, 0.09424778,\n",
       "        0.09581858, 0.09738937, 0.09896017, 0.10053096]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bk_gadget['k'][]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e24b597b0>"
      ]
     },
     "execution_count": 167,
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(bk_gadget['ksamp'], np.abs((bk_gadget['B'][t] - bk_gadget_fnl0['B'][t])/bk_gadget['B'][t]), label='gadget')\n",
    "ax.plot(bk_abacus['ksamp'], np.abs((bk_abacus['B'][t] - bk_abacus_fnl0['B'][t])/bk_abacus['B'][t]), label='abacus')\n",
    "ax.set_yscale('log')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nc23HxMQwe/Zs9uzZ49r38MMPM3v2bHJyckhOTubRRx91XV9dXU15eTkXXHBBr9f4jW98g2effZZZs2bx/e9/nw0bNvT6XsES9v+yr127lt/+9rds2bKFiooKXnrpJa688kqvcx588EF++9vfUlFRwdSpU1m5ciULFixwHa+vr+e8887jwIED/Pa3vw376O/BZkZBGpoGb7RPY0HsJpU6WvDd0G5ycp96rdoBXOfa3d5h5+a/bWbHiQYqG9r4v2tCjOgIgiBEO7GJKgIyWM8Ogcsvv5yCggIeffRR8vPzcTgcTJs2DZvNFvA6zZmaev7557nzzjv5/e9/z7x580hJSeG3v/0tH3/8MQAJCQkB72NyWho8gwgdHR1e5yxZsoSjR4/y2muv8dZbb3HBBRewbNkyfve734X0XkMh7JGXlpYWZs6cyQMPPODz+HPPPccdd9zB3XffzdatW1mwYAFLlizxUpHp6els376d0tJSnnnmGaqqqsK9zEElNT6WCbnJ7n4vxz4Ga1NoN2lyTqU+ddRr989f2c2OEw0AbDl6qq9LFQRBiD40TaVuBuMrBL9LbW0te/bs4cc//jEXXHABU6ZM4dSp7v+uf/TRR67tzs5OtmzZwuTJkwFYt24d8+fP5/bbb6e4uJjx48d7GX5TUlIYM2YMb7/te1hwTk4OABUVFa59vvrK5OTkcNNNN/H000+zcuVKHnnkkaDfZ28Ie+RlyZIlLFmyxO/xFStWcPPNN3PLLbcAsHLlSt544w0eeugh7rvvPq9z8/LymDFjBmvXruXaa6/1eT+r1YrVanV939jYGIZ30f8UF2TwXFUerTHpJHbWw6kjMGx68DdoqlSv9W7x8sLmY/xzYxmapvxoh0620NDWQVqC/9JtQRAEITLJyMggKyuLRx55hOHDh1NWVsYPf/jDbuetWrWKCRMmMGXKFP7whz9w6tQpvva1rwEwfvx4nnrqKd544w0KCwv5+9//zqZNm1zpJ4Cf/exnLF26lNzcXJYsWUJTUxPr16/nW9/6FgkJCZx11lncf//9jBkzhpqaGn784x97Pf+ee+6hpKSEqVOnYrVaefXVV5kyxbsDfbgZUM+LzWZjy5YtLF682Gv/4sWLXTmyqqoqlwBpbGxk7dq1TJo0ye8977vvPtLS0lxfBQUF/fcGwsisUekAVGuZakdjhf+Tu2JtAluz2nZGXnaVN/Djl3cCcOeFExmVqUKTnx6vD8dyBUEQhAHGZDLx7LPPsmXLFqZNm8add97Jb3/7227n3X///fzmN79h5syZrFu3jv/85z8uu8XSpUu5+uqrue666zjzzDOpra3l9ttv97r+xhtvZOXKlTz44INMnTqVyy67jAMHDriOP/7443R0dDB79my+853vdDP2WiwW7rrrLmbMmMG5556L2Wzm2Wef7YefiJsBLVOpqanBbreTl+fdgTYvL4/KShVJOH78ODfffDO6rqPrOt/85jeZMWOG33veddddLF++3PV9Y2PjkBAwxU7xctSWxhgNaAoh/2pEXQDa62k4Vcs3nv4Ua6eDRZNy+Oai8Rw62UxZXStby+pZMCEn6Fu/uauS/PQEpo1IC349giAIQr9w4YUXsnv3bq99nv4TY/v666/3eX1cXBxPPPFEt74rXTMdX//61/n617/u8x5Tpkzhww8/9LuGH//4x92iMf3NoNTYal1yfrquu/aVlJSENKchLi6OuLg4Vq1axapVq7Db7eFcar8xITeFJIuZE/Z09VsIJfLS5H3uH15YQ1ldCiMzEvjDdbMwmTRmFaTzn23lbDtWH/Rtd55o4La/b2FUZiJrv78o+PUIgiAIwgAyoGmj7OxszGazK8piUF1d3S0aEyrLli1j9+7dbNq0qU/3GSjMJo2ZBelU6c60UW8jL0D5kb1YYkw8/OUS0hPVNOtZBekAbDtWH3Sp+boDNQCU1bVi7RwaIlAQBEE4/RhQ8WKxWCgpKWHNmjVe+9esWcP8+fMHcikRQfGodCoxxEtl4JM9afQWOgXaSX7x2aleqZ6i/FQsZhN1LTbK6oLrJLnhUI1ru7KhPfj1CIIgCMIAEnbx0tzczLZt21ypn9LSUrZt2+YqhV6+fDmPPfYYjz/+OHv27OHOO++krKyMpUuX9um5q1atoqioiDlz5vT1LQwYswoyqNIz1DchpY28hc6iYe1cN2eU1764GDNF+apDYTCpI1ung81H3CV40uBOEARBiFTCLl42b95McXExxcXFgBIrxcXF3HPPPQBcd911rFy5knvvvZdZs2axdu1aVq9ezejRo/v03KGWNgKV2ql0po0cjaGkjZTQOehQ7a3nZTb7vT/A1rL6Hm+5/Xg9bR3uVFF5vUReBEEQhMgk7IbdhQsX9uixuP3227uVap2O5KTEEZ81AprB1FYLnVaIievxOr2pAg3Y7JjIeFM55oYyn+cVj0rnyQ2wNYjIy4eHar2+L5fIiyAIUUTEjJk5zQnX7yHiZhv1lqGYNgKYPWkcVt3ZRK4puNSR7ZSK0uyJLVI7Th1VXem6UFygUlJ7yht7NOAa4iUvVYknES+CIEQDsbHq39fW1kGaIi14YfwejN9Lb4maccTLli1j2bJlrqmUQ4Xzp+RRuSmD0Vo1joZyTBljAl+g65hb1LiE2NFnwpEHoaMFWusgyXvydkFmAplJFupabOwub6R4VIbPW7Z32NlSpvwuVxWP5OH3D4nnRRCEqMBsNpOenk51dTUAiYmJ3dp1CP2Pruu0trZSXV1Neno6ZrO5T/eLGvEyVJk9JpNPtSxGU03Z0UOMGdND1VXbKWJ0NZBrxrSZUDMMmiuh/kg38aJpGsUF6by9t5qtZfV+xcsnZaewdTrITYnjnPHZPPz+IYm8CIIQNQwbNgzAJWCEwSM9Pd31++gLIl4GGUuMCT1lGDTv4UjpQcacF/j8moqjZAN1ejLnTBkB20cr8XLqKIwo6Xb+LKd4CVRxZKSM5o/LYkSGmjBa0dDu1TxQEARhqKJpGsOHDyc3N7fbRGRh4IiNje1zxMUgasTLUOuw60lq7ihohlOVR3s8d8fefSwCGmOyGZNkgfTRaip1ve9rjRlKwYiXeeOyGJ4WD0CrzU5DW4er6Z0gCMJQx2w2h+3DUxhcosawOxRLpQ1GjBoHQExLBdWNgUuUjxw5CICWqsqkyXCWmNf7rjiaWZCOpqmuubXN1m7HW6ydLmEzf1w28bFmspOVYBHfiyAIghCJRI14GcokZ6tBknnaKd7d5z8na+2001B1DIDUHOfwyXRnc7pTviMvqfGxjMtJBnxHXzYfPUWnQ2dEegIFzknU+ekqdSS9XgRBEIRIRMRLJOCMogyjjnf2+hcvm0pPkeFQKZ70PEO8GJEX/yknzzlHtNXDc1+GPa8C3ikjg/w0Q7xI5EUQBEGIPKJGvAzVPi8ApAwHIE+r54MDJ/32ZHl3XzXDNFXSrKWqa7zSRg6Hz+uKnb6XrWX1sOMF2PMKvH0vAB865xnN9xQv6SJeBEEQhMglasTLUPa8GOIlTuvAYqtnU+kpn6e9u7eaPKd4Ma4hdSRoZrDboLnK53VG5GX7sXr08m1qZ80+muoq2HGiAegSeUlXpl3xvAiCIAiRSNSIlyFNjAUSswEYpp3ymTo6UtPC4ZoWD/HirJM3x0DaCLXtJ3U0KS+FhFgzTdZObMe3uvYf3vwWDh0Ks5MY7kwVgUReBEEQhMhGxEukkGqkjup8mnbf21eNhoNcrV7tMCIv4Pa9+DHtxphNTB+ZRhw2Ymv3ufa3HfoAgLPGeje3E8OuIAiCEMmIeIkUnGJkhOkUpTUtHD7pPSn6nX0nyaIJMw5Ag6Rc98EgTLvFBelM1I5j0jtd+zJrtgDeKSNwp42qm9rpsPv20QiCIAjCYBE14mVIG3bBJV7OyFDRDs/UUautk48O17pTRsm5Kl1kkBE48gLK9zLdVKq+yZ4EwNjOQyTSzrwukZfspDgsZhMOHap66DsjCIIgCANN1IiXIW3YBVe59NSUFgCv1NGHh2qxdTooch5z+V0Mgom8jMpgmqbES8eES2hLzCdGc3B55jFyUuK8zjWZNIY7oy+SOhIEQRAijagRL0MeZ+RlVIyq/tlYWkezVaV4jCjM2XmdXue6MBrVBRAvw9LimRWrjh+JncC+uOkALEkt9Xm+9HoRBEEQIhURL5GCM/KSaK1mTFYiHXadDw6cRNd13tt3EoCZaU4h0VW8GGmjhhNg78QnnTYm6GqEwGZbAe+2qZEEMzp3+TzdMO1KubQgCIIQaYh4iRQMQdJYzqLJyoz7zt5q9lc1c6K+jbgYEwXOqEw38ZI8DMxxoNuh8bjv+5/cSywdNOqJ/OtwLK82FAKQUb8DOrvPPBrhShuJeBEEQRAiCxEvkYIxaLGtjgsmpAHw7r6TvL1XNZ6bNy6LmFZnE7qunheTCdKd4wL8DGikYhsAOx1j2FxWzyE9n3otDa2zHYzGdR5IrxdBEAQhUhHxEikkZKjoCTAny0aSxczJJitPrD8CwPmTc6GpQp3bNfICPfZ6oWI7ALsY69yhUZk2S22Wbeh2uvR6EQRBECKVqBEvQ75UWtNcjeriWqs4Z4LquHuySaV0Fk3KhUZDvAzrfn1Ppl2neKlLneLaZRo9X20c/bDb6RJ5EQRBECKVqBEvQ75UGiDFmTpqKleRFifjc5MpSIuFlpPO83xEXgL1erF3QuVOAGJHFgNg0iB/1vnqeNlH4PAeBmk0qmuydtLY3tHLNyQIgiAI4SdqxEtUYEyKbqxQkRYniyblQHM1oIMpBhKzul+b7jFduis1+6GzDSzJjJmoSqRnFqSTPOoMsCSDtQGqd3tdkmiJIT0xFoAKSR0JgiAIEYSIl0jCiKg0VZCbGs/cwkw0DZZMHw5NlepY8jBl0O1KRoBGdU6zLsNmcMWskdx96RR+87kZqktvwVx1zFfqSHq9CIIgCBGIiJdIwqg4aiwH4OEvl/DKN8/hjFEZHmZdH34XcEdemiqgo0ukxOl3IX8WMWYTt547lol5KWrfKKfvJYBpV3q9CIIgCJGEiJdIwiPyApCZZGHaiDSvfX7FS2IWxCap7YZj3scM8TJ8ZvfrRs9Tr0c/BF33OiS9XgRBEIRIRMRLJNEl8uKFkTbyZdYFVa3kK3XksEPFp2rbl3gZUQKmWGiuhFPeowKk4kgQBEGIRES8RBKuyEtltyiIS7yk+hEv4LvXS+0h6GiBmATIntj9mtgEGHGG2u7ie5FeL4IgCEIkIuIlkjDEi90KrXXexwI1qDPwFXlxmXWng8ns+7pRRurI2/cSLs9Lh93BwermPt1DEARBEAyiRrwM+SZ1ADEWSFTN6WjqkjpypY38eF7A3ajOM/LiYdb1y2jfpt0RTvFS2diO3aF3vSooHA6drz25iQtXvM+GQzW9uocgCIIgeBI14iUqmtSBV68XL4KJvPjq9RLIrGtQcCagQd1haKpy7c5JiSPGpGF36FQ39S519Pj6UtYdUKJlY2ldD2cLgiAIQs9EjXiJGjy67LroaIc25wd/oMhL17SRwxGceElIh7xpatsj+mI2aeSl9r7iaG9lI//3v32u7w+dbAn5HoIgCILQFREvkYavyEuzM2UUEw/x6f6vNdJGrbVgbVbVQ9ZGNfAxZ3Lg53qWTHswopemXWunnTue3YbN7iAvVQ2cFN+LIAiCEA5EvEQaroojj8iLp99F0/xfG5/mFjf1R91m3bypYI4N/FzDtFvW1bTbu8jL79/cz97KJrKSLDx9bgO/jnmUxJPbcfTSOyMIgiAIBiJeIo0UH5GXYPwuBhkevpdgzLoGhmm3cie01bt296bXy4eHanl03WFA559TNzH+rZv5Ysy7/Cvmbtr/+RVVvi0IgiAIvUTES6RhNKpr8hQvQVQaGXj2egnG72KQMgwyCgEdjm107XaXSweXNmpo6+C7z2/DrHfyXP5zTPz0N2jo7DFPxKFrJB74L6yaC68u9zIHC4IgCEKwiHiJNFyRF8+0UW8iL0ehfJvaDka8AIw+W716pI5GhBh5+el/dtLcUMuzib/nzLr/Ahpccj9/LnyIS233UZZ1Djg6YfNf4U+z4J1fQXtjcOsTBEEQBES8RB5G5KWtzj1gsTeRl9J10F6vWv/nFgX3bB+mXVfaqKFn8fLf7eVs3r6NFy0/Y7Zju5q1dP0/4axvMC43hb36KB4acT/c9BqMmA0drbD2/5SIMaJEgiAIgtADIl4ijYQMVR0E7oiLK/KS3/P1hnip2qFec6dATFxwzy44S72WbwV7B+A27Na3dtBi7fR7aXl9G8+/9C9ettzDRNMJtdavvQ6TlgAwLicZgEMnm2HMOXDLW/D5v0PWeFUdtf6Pwa1REARBOO2JOPFy7NgxFi5cSFFRETNmzOCFF14Y7CUNLJrmLpd2iZcQIi9G2sggGLOuQeZYiEtT4wmq96hHxseSEh8DQEWA6MsL//gLf9V/TrbWiD5sBtz6tle6yhAvh086y6U1DYqugCseUN8felcNkRQEQRCEHog48RITE8PKlSvZvXs3b731FnfeeSctLadZc7OULtOle5oo7YnR68UgWL8LgMkE+c7zy7e6do/owbS783g9n6/+I3FaBy1jFqN99XV3+svJ2JwkAGqabdS32twHRs5Rgqmtzu3REQRBEIQARJx4GT58OLNmzQIgNzeXzMxM6upOs7byrshLpWo2Z3UaWlPyer42NgGSPc4bXhzas/Od5xs9YnD7Xir8mHZffPcjhmt12DGT9MW/QVxyt3OS4mLIT1MpqEMnPZrVmWNg7Hlq++Bboa1VEARBOC0Ju3hZu3Ytl19+Ofn5+Wiaxssvv9ztnAcffJDCwkLi4+MpKSlh3bp1Pu+1efNmHA4HBQUF4V5mZJPikTZqdpYTW1IgLiW4643oi2aGvCDNugaGePGIvAxP89+orry+jdp96wGwZReBJdHvrcflOn0v1V0iaeMvUK+H3g5trYIgCMJpSdjFS0tLCzNnzuSBBx7wefy5557jjjvu4O6772br1q0sWLCAJUuWUFZW5nVebW0tX/nKV3jkkUfCvcTIJ9UjbWSkjoLxuxgYpt2cySoSEwqGeKncCZ1WtStA2uiJ9aXM4CAACYVnBry1l2nX64BTvBzfBG2nQluvIAiCcNoRdvGyZMkSfvnLX3L11Vf7PL5ixQpuvvlmbrnlFqZMmcLKlSspKCjgoYcecp1jtVq56qqruOuuu5g/f37A51mtVhobG72+hjyekZdQzLoGWePU64gQU0aghE9CBjg6oHq3uo2fXi+N7R38c+Mxik1KvDByTsBbG5GXbjOO0guU0NIdcPi90NcsCIIgnFYMqOfFZrOxZcsWFi9e7LV/8eLFbNigGqPpus5NN93E+eefzw033NDjPe+77z7S0tJcX1GRYvKMvITSoM5g9tdg/rdhwf8L/dmaBsNnqW1n6shfr5dnN5ZhtbYz3XRE7RgxO+CtxzlNu90iL+COvhyU1JEgCIIQmAEVLzU1NdjtdvLyvI2neXl5VFaqCMP69et57rnnePnll5k1axazZs1ix44dfu9511130dDQ4Po6duxYv76HASHFw7DrEi8hRF5ShsHiX0BmYe+e38X3YvR6qahvdw1WtHU6ePyDI0zWyojDpgZCGhEfP4x3po3K6lqxdnYpix7vIV50Gd4oCIIg+CdmMB6qdZmMrOu6a98555yDw+EI+l5xcXHExcWxatUqVq1ahd0eBb1CDPFit0LVLu99A0EX8ZKXGo9JA5vdQU2LldyUeF7bUU5lYztXJx4BBzByduCJ10BOShwpcTE0WTs5WtvKxDwPA/LosyEmQU3Trt4TutFYEARBOG0Y0MhLdnY2ZrPZFWUxqK6u7haNCZVly5axe/duNm3a1Kf7RAQxFkjMVttG75NQIi99xRAv1Xugo51Ys4m8VKPiqB1d13lkbSkAV+Q4I0M9pIxAiVa/vpfYeBjjnK0kVUeCIAhCAAZUvFgsFkpKSlizZo3X/jVr1vRozD3tMHq9WBvU60BGXtJGKvHk6HRFfvI9TLvrD9ayp6KRRIuZCTbVibcns66Bq+Koq3gBGH+heg2m30vDCXhwHrz1s6CeKwiCIEQPYRcvzc3NbNu2jW3btgFQWlrKtm3bXKXQy5cv57HHHuPxxx9nz5493HnnnZSVlbF06dI+PXfVqlUUFRUxZ05wH6IRT9c5RqkDKF40zSN19AngLV4eWXcYgBtnpWI+pbYZcUZQtx6XG8C0a4iXoxvA1kNX5ffuU9VQ258N6rmCIAhC9BB2z8vmzZtZtGiR6/vly5cDcOONN/Lkk09y3XXXUVtby7333ktFRQXTpk1j9erVjB492t8tg2LZsmUsW7aMxsZG0tLS+nSviKCrWEkewLQRKPFycI0rbWWYdt/dV836g7WYNPjamFr4FDVcMTEzqNuOd/V68SFOssZD2ihoKIMj62Hi4u7nANQchG3PqO3mKjUTyWQO5d0JgiAIQ5iwi5eFCxei91Atcvvtt3P77beH+9HRhWfkJSFDeUIGEmOgo9O0a/R6WX+wFoAl04aT0/CGOicIv4uBq8vuyWYcDh2TycPkq2mq6mjLEyp15E+8vPdr0J3GbN0BLScH1hMkCIIgDCoRN9uot0Rd2sgz8jKQfhcDI210cg/YWhme5t2p95YFhaojLqhKoyAZlZlIjEmj1WanstHHoEdXybQf30vlTtj5L7Ud41yT0YW4rzgccGANfPwI2DvCc09BEAQh7ESNeImqaiPwjrwMRlQhZbga8Kg7oHKHK20EMHdMJsUF6XBii9oRgniJNZsYnaXmH/n0vRSeC6YYqDsEdaXdj7/7K/U69WrInaK2myq7nxcKtlbY9Fd48Ez4xzXw+vek068gCEIEEzXiJerwFCyDEXnxNO1WbHOljQBuPXcs1B6C9nqIiYe8aSHdery/cmmA+DQocM5I6loyfWwT7FsNmgkW/ch7jEJvaKyAt34OfyiC15ZDzX73MWMgpiAIghBxiHiJVFIHOfICXs3q0hMt3HxOIdfNLuCCybnulNHwWWCODem2fgc0uk44X712HRXwzi/U68wvQvYE988l1MjLyf3w79tg5XT4YIUaBpk+Gi65HyZeos5pj4IZWYIgCFHKoHTY7Q+iqsMuKJOuOU512R2MyAt067T7k8s8ut6e2KxeQ0gZGbh7vfgphx5/oRIqpWuh06aa9h1+H0rfB1MsLPyBOq+3kZd/XAP1R9X2qPkw73aYdKmqWDq5T+1vbwjxXQmCIAgDRdREXqLO86JpbtPuYEVejAGNJ/eBtUuUxIi8jCgJ+baeFUc+GTYDknLA1gzHPlazjoyoy+yvQvootd2byEtHm1u4fPV/8LXXYcrl7lLreGeZvVUiL4IgCJFK1IiXqOSMryg/yeizB+f5KXlO47AOlZ+693e0uWcuBdlZ1xNjunR1k5XGdh9VPSaTR+roLdj/hhJLMQnek7J7E3kxKpNiE2HUWd2Px6eqV4m8CIIgRCwiXiKZBd+Fb6wPugFcv9AldQRAxXY1OiA5T40SCJGU+FjyUuMAP2MCwHtUwDu/VNtnfl0JKoPUXoiXhuPOa0f4HiQZJ+JFEAQh0oka8RJ1fV4iBZd42ebe5+rvMqfHSdL+GBeo0y44Iy8aVO2Eqh1KVJz9He9zjMhLay10WoN7sCFe/Imu+HT1KmkjQRCEiCVqxEvUeV4iBV+Rl+NOs24v/C4GPVYcJWXD8Jnu7+d/q3sEKiEDzBa1HWxpc+MJ9Zo2wvdxSRsJgiBEPFEjXoR+whgTUHvAXT58vPeVRgYBe724TnKmjhKz4KxvdD+uaaGbdp2Rl38fgoY2H34bw7ArpdKCIAgRi4gXITBJ2WpYIiivS2MFNB5XjeKMqEwv6DHyAjDnZhh3AVzxAMSl+D4nRNOuvf4YAB/WJvKz/+7qfoJ4XgRBECIeES9Cz3gOaTT6u+RM8S8ogmBcrqo4KqttpcPu8H1Saj7c8G+YfKn/GxmRl8bgxIutTomXCj2Ll7ae4NVPu8xF8iyV7mHAqCAIgjA4RI14EcNuP+IpXsKQMgIYlhpPksVMp0PnaG1r729kzIAKJvKi68Q0Kc/LSS0LgLtf2kllg8eASMPz4uhUJeGCIAhCxBE14kUMu/2Ip2m3F8MYfaFpmqtZXUDfS0+E4nlpbyDWroTSRWeVMH1EGg1tHXzvxe3oRpTFkqxSYs7zBUEQhMgjasSL0I8YnXZPlXqXSfeRoHwvPRGK58VZaXRKT2bqmOH84bqZxMWYWHeghr9/5Oy6q2lu34uUSwuCIEQkIl6EnknMhIwxaruzHSwpkD2xz7c1Ou32TbwEH3npOKX8LuV6FtNGpDE+N4W7lkwG4Ner97gjQFIuLQiCENGIeBGCw7OyaESxexZQH+ixUV0wuCIvPYuX6mMHAThpymZkRgIAX5k3hgUTsmnvcLD8+W3KPCzl0oIgCBGNiBchODzFSxhSRuDu9XKoutntOQkVI/JibQBbYBHUUHkEgI6kfDRnZ2CTSeO318wkLSGWT4838Od3DkKcIV7qe7cmQRAEoV8R8SIEh1fkpW9mXYNRWYmYTRrN1k6qm4Js79+V+FRlsoUeoy+2ujIAYjIKvPYPS4vnV1dNA2DVuwep1+PVAfG8CIIgRCRRI16kVLqfGT4TTLGgmftcaWQQF2NmVGYiEGBAYzC4fC+BTbsxzaqnS+qwwm7HLpuRz5Wz8rE7dD4qt6udkjYSBEGISKJGvEipdD8TnwbX/xO+8A9Izg3bbQ3fy8GwVBz5j7x02h2k2tT8o+EF43ye8/PPTiMvNY6Kdue8JDHsCoIgRCRRI16EAWDCRTBpSVhvOWW46tK7ZneQgxV9EUTk5VB1E8OoBWBYwXif56QlxHLW2CwaUdEgSRsJgiBEJiJehEHl87MLMGmw7kANeyt7KRaCKJc+cPgwFs2OAw1TWr7f80akJ9CkO8WLRF4EQRAiEhEvwqBSkJnIJdOU+HhsXWnvbhJEo7oKZ5l0c2wWmGP9njcyI9EdeRHPiyAIQkQi4kUYdG5dMBaA/2w7QVVjew9n+8AQLwGGMzYaZdLJIwLeamSGR+RF0kaCIAgRiYgXYdApHpXB7NEZdNh1/rbhSOg36CHy4nDodDjLpC2ZBT7PMRiRkUCTM/KiS9pIEAQhIhHxIkQEtzijL//4uIwWa2doF3t6Xnw0uyutbSHLUQNAUs6YgLcakZ5AozPyoreJeBEEQYhERLwIEcFFRXmMyUqkoa2DFzYfC+1iQ7x0tvk02e480UC+psSLKX1kwFvFx5qJSUwHJPIiCIIQqUSNeJEmdUMbs0nj5nNU87jH1x/B7ghhXEBsAsSnq20fFUe7yhvJ1+rUN6mBPS8AKelZak0dzeCwB78OQRAEYUCIGvEiTeqGPteUFJCeGEtZXStv7up50KIXAXwvnpEX0gJHXgDSM7Lc31ibQluHDzrtDh545wAfHqrt870EQRCEKBIvwtAnwWLmhrNGA/DIusOhXZzqW7zous7eE7Xk4EwBBSFehmWlYdWd5dRhSB29t+8kv3tzP1967CP+/tHRPt9PEAThdEfEixBR3DBvNBazia1l9Ww5Whf8hX4iL8fq2ki0nsSk6ehmCyRm93irkekJYe2ya4w+cOjwk5d38uvVe3CEkhYTBEEQvBDxIkQUuSnxXFmsOuA+sjaE6IufLrs7yxvIR6WMtNQRYOr5j/zIjERXxVE4Ii9HaloAGJ+r5jg9svYw3/rnVto7xE8jCILQG0S8CBGHUTb95u4q1wd/j/iJvOw80cBwzek1CSJlBN69XsLRZfdIrXoPty8cxx+um0msWeO1HRV86bGPqWux9fn+giAIpxsiXoSIY2JeCgsn5aDr8Pj6IEcG+I28hFZpBN69XtqaTwX3/AAcqWkFYEx2ElcVj+Spr51JanwMW46e4nMPbQheoHli74QNf4aXb4eOtj6vURAEYSgh4kWISIyRAc9vPsapYKITrsiLW7zous6uECuNAJLiYrCakwBoOFUT/KJ90GazU+kceVCYpe45b1wW//rGfEakJ1Ba08JVD67n48MhVCJV7oTHzoc3fwzb/gG7/9unNQqCIAw1RLwIEcn8cVkUDU+lvcPBMxvLer7AM23kcABQ0dBObYuNfJMz8pIWXOQFwBGXBkBLQwimYR8YKaO0hFgykiyu/RPyUnhp2XxmjEzjVGsH1z3yEbc9tZnd5QHSVJ02ePc+eOQ8qNju3n9wTZ/WKAiCMNQQ8SJEJJqmcf1cNYdoY2kQAiI5F9DA0QmtKoqx84Qy246JcaZ+0gLPNfLEFJ8KQFtT38TLUad4GZOV2O1Ybko8z952FteWjMSkKY/PpX9ax+3/2MK+yi79Zcq3wiML4f371XucfBlc84Q6dvBtaaYnCMJpRUSKl6uuuoqMjAyuueaawV6KMIjkpycABGdqNcdCUo7adpp2dzqjGMONtFGQnheA2KQMADpa6oO+xhelHn4XXyRaYvjttTN5885zuXxmPpoGq3dUcskf1/LNZz7hUPlJeOtn8OgFUL0LErOUaLnuaZhyOcSlQlsdnPikT+v0pKXdFlqHY0EQhAEmIsXLt7/9bZ566qnBXoYwyGQ60yxBV+R0Me3uOtFAIu0k2p1RjBDSRvEpSrw4+lgqbZhxx2T5Fi8G43NT+PP1xfzvO+dy6fRh6Dr879NjND18MXzwB9Dt2KZcDcs2wrSrQdOUYBu7UN0gTKmjI+tfxH7faD54dHlY7icIgtAfRKR4WbRoESkpKYO9DGGQCV28eJdL7yz3KJOOS4X4tKCfnZymRgSYgm1Sp+vKSNvpvdZSI22U3T1t5ItJw1J48EslrP72An4xYhOzTIdo0BO51bacyduu4dq/7+eh9w6xv6oJXddhwkXqwgNhEC+H32fkW98gVWulsPw1rJ2SihIEITIJu3hZu3Ytl19+Ofn5+Wiaxssvv9ztnAcffJDCwkLi4+MpKSlh3bp14V6GEAUY4qWtw06bLYgP0lR3xVF1UztVjVZGGOIlhJQRQKpzOGNMR5CzjXb+Cx4+G977tddut+clcOSlK0XpnVzf+jQAHxd+k2O5i3DosOnIKX7zv70s/sNazvnNu/z5mBqnQPlWaOlDZdTxzfDP64nRlfgapVWxcffB3t9PEAShHwm7eGlpaWHmzJk88MADPo8/99xz3HHHHdx9991s3bqVBQsWsGTJEsrKgqgoEU4rkuNiiDVrANS1hlIuXc6uEypiMiNVteYPJWUEkJmlxggkOlpotXX2fEHVTvV6fLNrV6utk6pGKwCFfjwvfnnvfmg7BblFLL7hB/zvjnP54AeL+MVnp7JwUg6WGBMn6tv4/YfNnIgbD+jKuNsbqnbD05+DjhY2OKZx1JELwMGta3t3P0EQhH4m7OJlyZIl/PKXv+Tqq6/2eXzFihXcfPPN3HLLLUyZMoWVK1dSUFDAQw891KvnWa1WGhsbvb6E6EDTNHfqqDkY8eL2vBiVRtOSDL9LcD1eDIy0UarWyolTQTSBa65Wr7WHXLuM5nTpibGkJ1p8XeWbk/tg02Nq++JfgzkGUGMLbpg3hie/Opdt91zEb6+Zgdmk8Z+WInVub3wvdYfh71dCez3N2bO4xbacrfoEANqPbpYZTIIgRCQD6nmx2Wxs2bKFxYsXe+1fvHgxGzZs6NU977vvPtLS0lxfBQXBl8MKkU9mUhwAtS3Wnk/28LzsLFfipdBSr/alhiZeiFOl0qm0cjwU8dJUDjYlWoyU0egQU0a88SPQ7TDpUhi3yOcpiZYYrp1dwI8/M4X37DMBsO17K7SS6cZyeOqz0FwFuVN5duIKWomnLWcWABM69rH1WH1oaxcEQRgABlS81NTUYLfbycvL89qfl5dHZaW7M+rFF1/Mtddey+rVqxk5ciSbNm3ye8+77rqLhoYG19exY8f6bf3CwJOZFAvAqaDSRp6RFxWBy8Porhta2sgw98ZpHZTXBVFx1Fzl3j6lRhoYZt1CHz1e/HJgDRx8C0yxsPiXPZ5+0/wxjC85n0Y9AYvtFMd2fRDcc1pq4akrob4MMsfCDS+x/oQSPslj5wIw03SIN3dVBLiJIAjC4DAo1Uaapnl9r+u617433niDkydP0trayvHjx5kzZ47fe8XFxZGamur1JUQPrshLUGkjFXnRm6uprFdelxSrUxSHmDYizl3tVlNT3fP5zR7nOFNHrjLpYP0u9g4VdQE48+uQNa7HSzRN42dXzmJ3fAkAb//3H9T3JPTaG+Hpq6FmnzIyf+U/OJJy2XxUNfMrnDYPhxZDjtbItp27VFWTIAhCBDGg4iU7Oxuz2ewVZQGorq7uFo0JlVWrVlFUVBRQ6AhDjyyn5yWoyEtiNphi0NDJpoG0+BjMjeXqWIjVRpjM2Iz5RnU9VPE4HNBy0v193WHA7XkJ2qy76a9Qs1+9j/O+H/RSLTEmpi38HACzrJv55jNb6bQ7/F+w5h6o2KYa3t3wMqSPYl9VE03tnSRZzEwuyEHPVT6azPodHDrZHPRaBEEQBoIBFS8Wi4WSkhLWrPE2Fq5Zs4b58+f36d7Lli1j9+7dAVNMwtAjIzGEXi8mEySr1NEwrY6xyVbodPpVQhUvgN2ioniN9T2MCGirUx4Vgzpn5CUUz0trHbx3n9o+/+6QetIAJE+9BIAZ2mF2HzzML1/b4/vEqt3wyd/U9uefgpyJAGw+ot7jGaMziDGbMI9UkZyZpkO8savK560EQRAGi7CLl+bmZrZt28a2bdsAKC0tZdu2ba5S6OXLl/PYY4/x+OOPs2fPHu68807KyspYunRpn54rkZfoJDNZiZeg0kbg8r3kaaeYGO/0qiTlQGx8yM/Wgp1v1NwlrVRXSou1k+omZ5l0MOLl3V9Dez3kTYMzbgx5raTmQ940TJrOAtOnPLnhCP/0NdDyzR+D7oCiz8KYc1y7Nx1RKaPZozPVjhFO8aId5s3dIl4EQYgswi5eNm/eTHFxMcXFxYASK8XFxdxzzz0AXHfddaxcuZJ7772XWbNmsXbtWlavXs3o0aP79FyJvEQnIaWNwCVecrV6j0qj0KMuAObEdAAcbQ20dwSo4nGZdZ2+rdpDrqhLRmIsaYmxgR9UvQc2P662L/41mMy9Wi/jLwTgGyOUYfgnL+9kx3EPs/GBt+DQ28oMfOHPXLt1XWeTM/IyZ4wai8CIMwCYbjrMjmN1VDa0925NTvZWNrJGRJAgCGEi7OJl4cKF6Lre7evJJ590nXP77bdz5MgRrFYrW7Zs4dxzzw33MoQowUgb1YY4IiBPO8UokzNiEqpZ10mMU7ykaK2U1wcolzb8LsOmqdemcsoqVWffHs26uu4ujZ58GYw9r1drBVyjAia1bOTiKTl0OnSe2KCEDPZOFXUBZQbOHOu67ER9GxUN7cSYNGaNSlc7cyZDbBLJWjtjtXLW7Pb2qYXKsn98wq1PbebNXX27jyAIAkTobKPeIGmj6CQruXfDGfM4xTBjNEAvxYuRNuqx14sRecmZDPHpANSf2AcEMRag7jAcegdMMXDRvb1ap4uCMyEuFa21ljumqsjP6zsqabZ2wtan4OQeSMiAc/+f12WbnSmjqSPSSLSohniYzDBc9Y+ZZTrUp9RRe4edQyfVen77xj6ZWC0IQp+JGvEiaaPoxOiw29DWEbiCxiA1H1CRlyy704vSy7SRYZpN0Vo5ESjyYoiX5DxXRMNWfQAIQrxUbFOvw2cGVRodEI8p05ObPmJsdhJtHXbe/OQAvPMrdc7Cu5SA8WCjM2U0d4z3fiN1NFM7xIeHamlo6+jVssrqWl3bB6qbeWnriV7dJ6zsfwOOBNkTRxCEiCNqxIsQnaQnKL+IrkN9MB+eHobdNJtTVPQy8uLdZbfV/3nNzrRRUo5LgJhPqXLpHqdJV+1Sr3nTerfGrjhTR9rBt/hciXrf2gd/gNYayBoPs7/W7RKj0mj2mEzvA07T7lzLETodOu/tC6LfjQ9Knf1uDP6wZv/gTqxuqYVnvwh/v0ptC4Iw5BDxIkQ0MWYT6U7Da1CpIw/PS2JbH8WLM/LS43wjH5GX5BbV6bnHHi+VzoGOw6b3bo1dcZp2ObGFz02JZ6R2kkub/632XfQLFZ3xoL7Vxv4q1cdl9mjfkZfx+hEsdPBmL0umjWZ9FxXlkZcax4n6Np75eBAHsdYfAUcn2G2w69+hXVv2Mbx7n2ooKAjCoBE14kU8L9GLazhjEOJFd/Z5ydCaiW11mkN7nTZSkZeUnjwvhmE3OQcyVeRlmF2lRnrs8WJMo86b2rs1dsVZMg06w6o38H8ZLxOndVCWNhsmLel2uuF3GZeTRFZynPfB9NGQmIVZ72SKdpT39lX7r7pyOOCFm+CxC2HzE2B1N7YzKq+mDEvhOxeovjIPvHNQeXEGg0aPkQfb/xn8dfZO9R7fvx8+fS7syxIEIXiiRryI5yV6yQyhUV2Dnki7rqILmm4HzeyeeRQqnpGXYD0vzrTRaK2KzCQLaQkByqRb66DR6f8Il3gBd/Rlw5+Y3/ouDl3jnrbr8eWT3XTUKJHO7H5Q0yBfRV8WJJbRYrPz4SE/aZZDb8Oul+D4Jnj1Dvj9ZHjtu1C129VpeEx2EtfOHklhdhK1LTb+uq60r++0dzR5iJcTW6DmQHDXHXhDDd4E2Ptat8OnWmz8Z9sJ2myDmBIThNOEqBEvQvRiRF6CKZeuabFRpXukP1Lze983Jc5p2KWVysZ2bJ0+DMMOO7Q6P9A90kbDtTomZvbw18vwu6SNCrmjbkCcvhcqdwDwX87jvcbhfFTaXXgYkZdufhcDp+/lwtTjALzpr2R64yPqdfTZKvpka4JNj8FD8/hBxR181vQBY9JjiDWb+O5iFX15dN3h4KvIwkljF8Pw9meDu27zE+7tQ++AzdvLc//re/nOs9u44a8f09jeJa3kcMD+N7tdIwhC7xDxIkQ8hng5FcQH3ckmG5V4fBD31u8CrrRRmtaKrkNFg4/oS0uN6lirmdSsoMRM2mPUdSXJ9YHvb4iXYWEy6xo4S6YBiE1kT9F3APjXFu8P7fYOO58eV2uc24N4mWhX0Yk1u6u6lzrXHVbTsAGu+DN8czN85T8w5Qp0zcwsfQ9/tDzIzP9cBE1VXDptOFPzU2m2dvLguwf7/n5DxUgbOUvB+fQ5JS4CceqImvYN6vfc2Q6H3nUd1nWd9/YrQ/Pmo6f48mMfew/I/PABeOZaePMnYXoTgnB6EzXiRTwv0UsonpeaZivVerp7R2/9LuCRNlKixadp10gZJWa7IjzVseqZRfE9DHSsUpGRsFUaGZhjYeLFavvs77D4rFkAvL6zghYPn8n2Y/V02HVyU+IoyEzwfS+naTex8RDD423UNNvYduyU9zmb/groKl2VNU7NmBq7EK77O4e+/DG/77iGk6RjbiiD9+/HZNL4/iWTAXjqo6OBU3L9gZH6mX2ziq41HIOj6wNfs+VvgA5jF8GM69Q+j9TR4ZoWqhqtWMwmMpMsfHq8gS888hE1zVZVKrf17+rE3f9R0TpBEPpE1IgX8bxELyGljZqt3mmjtD6IF2f0IplWQPdt2m1xlg8n57p2HdXVhPRCUw/dZCvDbNb1ZMn/wRf+Ced+nzNGZVCYnUSrzc7qHW6/h2skQGEmmqb5vk9SNqSPAuBLBep8r6ojWytsfVptz7m12+UH21L4s/1qVqTdpXZs+RvUHOTcCdmcNTYTW6eDP761v49vNkSMSeOZhTD1SrUdKHXUaXOLj9lfg0mXqu39rysTL7DhoBKqJaMzePa2s8hJiWNvZRPX/eVDavZ/rKaFgypZP745zG9IEE4/oka8CNFLKGmj7uKloPcPdkZeTDhIop3jviIEzd3Fyx6r2h7WUe7/3vZOOLlXbYerTNqTxEyYfCmYTGiaxjXOni8vbjnuOsUYxjina4l0V5ymXcP38tYeD/Gy819qoGT6aLfXxgOj0qhl2Jkw8RI1BuHtn6Np7ujLi1uOc7C6qVdvM2R03Z02Sh0BM69X27v/o4SYL/a+qirKkoepiq1R81Sjv7ZTcOwjANYfVH6is8dnMTEvhee/Po/8tHgOnWzh3Rf+7H2/fav7450JwmmFiBch4gkp8tLU1bDbh8hLbIJq249RLu3jw80lXlS0pam9g93WbPXotgC9TOoOKd9EbCJkFPZ+jUFyVfEINA0+Lq2jrLYVu0Pnk6M9mHUNnL6XsTY18uBwTYtKP+m626g752afxmijx8uY7CS44KfKG7Tnv3BsE2eMyuCiojwcOvz+zQGKvlgbocNpmk0ZDqPOUsLL1uRfVGxxGnXP+IpKyZljYKKz7Hzva9gdOh8eVuJl/nj1uy/MTuK5r8+jMCOOhR1rAWie9Dl1zb7X++WtCcLphIgXIeLJSlL9R+parD2eW9NspZowpY00zd1l11+jOkO8JOUAcLS2lSO6Ks2OqQ9QCuysBCK3SHlE+pn89ATOcX6w/uuT4+ytbKTJ2klyXAxThqcGvtgpXixV28hLjUPXYW9lkyqLrvwUYuKh+AaflxqRl8LsRMgrgplfVAfe+inoOt+7eBKaBq/vrGTniQaf9wgrRsooPg0siep3PPMLap+vni81B6B0rRJdZ3zFvX+yM3W09zV2n2igoa2DlLgYZoxwV40VZCbyr4ut5GiN1OipXHXoMnRTDNTsg9pD/fQGBeH0IGrEixh2o5eMJNUr5VRLB7oeeKhfTbOVynCljcA938hfo7oW78hLaU0LpU7xQlOF/9LY/qo0CoCROvrXJ8fZWKr8K2eMzsBs8uN3MRg+U314N57grBxVAry7otEddZl2jUpT+cDo8eJq1rfoLiV2jq6HA28yMS+Fz0wf7lpXv2OIF8+InGHAPfQONHXxKW15Ur1OWAzpHn+Wxp2v3kf9UfZ8+iEAZ47NJMbs/U9q5kHVwXd93LkcaI5jd6wzRbj/f+F4N4Jw2hI14kUMu9GLEXmx2R09dmWtabZxQs/BmjJKeUkSevBz9ITRZVdTvV66DYd0NahTPpejtS00kEyrKUXtr/MTfXF11h048bK4aBgpcTEcP9XGXz9Q6+rR7wIQl6wmZgPnJamxB8fKjsCul9Xxud2NugBtNjuVje0AFBriJW0knPl1tf3Wz8Bh58pZSki8vqMSR39PnDYa1DnHSACqQmrkXFXyvuNF9/6Odtj2D7XddSaUJUlVHgHm/SoNNG9ctvc51iZXRdKZVy5D0+CFZqd4kdSRIPSJqBEvQvSSYDGTEKv8FIHKpXVd52STlQ5iqP7KOrj1PZUW6AvOyEumqQ27Q3d9GLvoYtgtdUYaGpNUhQ51ftID4R7IGAQJFjOfmaE+tI0o0pzCHvwuBk7T7jRN9WUZffQFcHTAyDmQP8vnJUfrVNQpLSGWDKdvCYBz7oT4dKjeDdufZcHEbFLiYqhsbGdr1zLscOOKvAz33u9KHXlUHe3+jzLlphW4uxZ7Mvkz6qVeeVrOHp/lfXzPK9DZBlnjGTZlHudPyuUth/o5cnSD6rAsCEKvEPEiDAmC6fXS2N6JzRkZyUlPUcbKvuL0vIxMVOmSbr4Xl+dFiRfD49GZ5jTh1h3ufk+vsQBFfV9jCBipI4BYs8asgvTgLnT2exnZsgczdi5odvY4mXub30u8zLqeJGTAgu+q7Xd/RZxu48IilXZ77dMeysv7iq+0EcDUq8BsUb13DD/S5sfVa8mNvrs0T7wEHY2pWilTkxqZlJfifdwQQjO+AJrGV+aP4bieywEKVNWV0fROEISQEfEiDAmCES81zcrQmxwXQ3xsL0cCdMUZeRkRr57r5Xuxd0Cb83/PTs+L8YFtyR2v9vsyZhopo/QwjwUIgpLRGYzJSgRg+oi04H9OTtNuQs12LovdwjCtDntCFhR91u8lRhTKeJ4Xc2+D1JFKxG18hEudvpfXd1b0b+rIV9oIlGdn4iVqe/uzKjJ27CNVbebHjExyDidSVZfem7J2e/fKaSxXRl+AGdcCsGB8NoXZSbzR6Yy+SMm0IPQaES/CkCAo8dKkxEtOSpzfc0LGKS5yLeq5Xt1gjWnSphhIyKCxvcNVzp0yYpI65ivy4koZ9UN/lx7QNI0b548BcEU7giJvKpjj0Nob+IHleQAOj7oGYvz/rF2RF1+TtWPj4fy71fa637NgZAzJcTFUNLSz9Vh98OsKFVfkJb/7MSN1tOMFNZcJVEO6AIM933EoUbfAvtH7wI4XAF31hMkYA4DJpPHls0bzll1dox94SzXAEwQhZES8CEOC4CIv6lh2ssXvOSHjTBtlxyjR4tXrxTDrJuWAycRRZ6QhO9lCQp4aPuhTvBiddQew0siTm+aP4Y07zuW2BWODv8gcC8NnAJBvL8eua7yd9JmAl7jLpH2IF1BVPrlF0N5A/Md/5IIpKvXm2QU47BjipWvkBWD8RZCQqX6vxhDGrkZdD1qsnfytTnVHzju1WfljDD5VAo8Zn/e65pqSkeyPmcBJPQ3N1tTzWAJBEHwSNeJFSqWjm1DSRtnJ4Y+8pJud8408Iy/NzsiLYdat9Yg0OKdL+yyXds006oexAEGgaRqThqV0K+vtEadpF2CNYzYb63ykgzwwxMtoX2kjUD6SC3+mtj96mKvHqc3Xd/RT6qjTqtrzg+/mhTEWmH6N8xtd/Q4Lz/N7u41H6jjkGMZhrQDN0ekeTlm5U6UGzRblpfEgLSGWK88o4C27kTqSqiNB6A1RI16kVDq6GTzx4iyVRkVVvDwvrsiLs0za06CamOku0/Ysl7Z3QrVzLMAAVhqFBafvBeBv9sXsLm/0e2qrrZOqRvX78Bt5AdU/ZfQ5YLdyztEHSbKYKW9oZ5tz2nVYMXq4mOP89qVxpY4ASr4asIGgMc/oSPZCtcMY1Pjpc+p14sU+S/W/Mm80bzurjjr3vqY6FQuCEBJRI16E6GawIy8JDiVMyuvb3FEBV48XZ4O6rmkSI/riWS5dexDsVohNGpCxAGGlcAFYkrHnz+ZDRxGVje1+500drVViLz0xlvTEAGk8TYOLfwlomHc+z1fHKAP06k/7IXXkShkN819Cn38GjFmgIjOzvhTwdsY8I3PRZWrHwbfUfKQdL6jvjeZ3XZg8LBXrqAW067HENB53e6AEQQgaES/CkCCY+UYnm5yel5Twe14sHc2YTRoddp1qpzHYZdh1po0Mg6orTZLpzIN4Vhy5mtMNzFiAsJKaD3fswHzTfxmVqQTangrf0ZeAZt2u5Be7BiTe3PwIoPP6zsoeuymHTJOfMmlPNA1ufAXu2AFJWX5Pq2uxqS7DwJSSc5WHxtYM7/5KpQrj01VUyQ/Xnz2ZdQ5l2O7c81rIb0UQTneG2L+ewulKljFZunVw0kaatYHhafGAh2m3S3fdI7VGaXDXyIuHaXcQOuuGlcRMsCRR5JyHtNuPeHH7fwL7YlxccA/EJpJRt5VrLB9zor6NbeGuOnJNk/Zh1vVE03z3dfHgI+cgxol5yeSmJqqqJIAPV6nXqVcFrMS6qCiPTZYzAWja/krPaxcEwQsRL8KQwOjQWtc8OGkj2hsZkZ4AqA+uNpvdy7Db0NbhSmm5mrJlOSMvnuJlkCuNwsWUHsSL3wZ1/kgdrjrvAj+yPEcctqCqjkKKzgSqNAqR9U6/y3xjJIAxqBHnejy9Mz6INZvIm6165GTU7+g+U0kQhICIeBGGBEbkpcnaibXT3u24MRoAICec4iXOKV46Whibqe77uzf3M+Pnb3Di+BEAdtTHsdf5IZ6TEkdynLOzb6YP8TIIYwH6gynDVTfZPRVNPo8bUaiAZt2uzPsmpI4ks7OKW8yrWb0jcOrofzsrmPvrt/nT2weCu38waaMg2XBIRV7Odk7qZsy5rhQj6aOh4Mwe73HFOWew3aH+jBz/+KU+r0kQTidEvAhDgtT4WNf04/rWjm7Hm62dWDvVaICwel6caSOAZfNyubp4BMPT4umw66R0KnPpHa+Vc90jHwFd0iSZTkOuUS7dWuf+AM0d2LEA4aYoX/1cDlY3Yet0dDvu9v+EIF4sia7S6WUx/8FWX8724w0+T31vXzXf+udWTjZZWbFmP69+Wt7z/YNNG/VAeX0bpTUtmDQ1SRpQZdaTlqjtmdcHNVMrJyWO47kL1dK2/bdPaxpQOm1SISUMOiJehCGByaSRkRgLQK2P1JHRoC7JYibREoaZRgbmWIhVgmRkYgcrrpvFhh+ez9o755GqqeiC2aMD68yR6e5rvcqlD7tn5qSP9hJFQ5ER6QmkxsfQYdc5WN3sdazF2ukyNReGIl5A9VkZMZtEzcr/i3nBZ+poY2kdS5/eQoddd6Xyvv/ip+yv8h0FcuFKG/norhsCRspo+sh0UuNj3QcuuR+ueMA9tykICs9RowPGNm3i1Kl+HkoZDqr3wK/z1URwQRhEoka8SJO66CdQubTL7xLO0QAGRjqgXUUBNE1jVJzzA9ts4Y0fXsa67y/iiZvmcOdFE7ss2iN1ZKSMhg38WIBwo2kak52+l64VR0aZdEZiLGmJsd2u7eHGSgQA15rf58C2D7xSRztPNHDzk5to73CwaGIm79w4jHPHptBqs7P071tobO8elQPA4XDPNfI1GiAEXCmjcV2qkRIz4YwbVBQmSKbMOJMqUx7xWgcfvf3vPq1rQDi2UU0T3/gIWJt7Pl8Q+omoES/SpC76yXD2C6nzUXFkzDUKq1nXwDDtWj0+pF1m3Tw0k4mCzEQWTc4lKa5L1MeoOKo9NPQrjbrgr+LI6KwbtFm3KwVz6Cz6HCZN5+vtf2WHs2HdweomvvL4RnRrIz/LfZ+/Ni4l7i/z+Mvw18hPi+dwTQv/7/ntvn0yrbXqQxct4KyintB1nQ2HVOTF5XfpA5rJROOoCwFo2/EKZbWtPVwxyHS0ul/3vjq4axFOa6JGvAjRT1ayUXFk7XbMXWkURr+LgZHiafcULx5zjQLhqjjyFC+DMxYg3BT5ibyUhtLjxQ8xi3+OTbNwlmkPB9b+k2N1rfzg0Zf5lvVRNiZ8i5sa/4Kp/ggACWXv8+CXS7CYTby5u4qH3vcxybvxhHpNylGpwF5y6GQLVY1WLDEmSkZ3757bG8bOU1VHM/W9LHvmE5+G9IjBc9TF9mcHbx3CaY+IF2HIEChtdNI1lLEfIy/tHubRlmr1mtzDZGYj8lJzwD0WYIiXSRtM8RAvntGOkBrU+SO9gKOTbgZg3oE/ULbqCl6wfYuvxrxBot4G2ZNUbxiAmv3MGhbPz65QovB3b+zjgwM13vdrCo9Z14i6zB6dQXxs4F4wwWLOnwnAGFMl+0+c5L7Ve8Ny3//trORn/91Fh727oToQ+6uauOVvm9lYWtf9YIdHZKj0fbcJWhAGGBEvwpAhM1DaqD96vBgYnhevtJEhXnqIvBiel+Ob1VgASzKkjwn7EgeDCXnJmE0ap1o7qGxsd+03PC9jsoNsUOeHkZfdRbWeQT7VnG3fhEnTsRZeCF/+Nyz7GM5ZrgzRuh1O7uX6uQV8fvZIHDp865+feE8Ad5p1K/Us7nxuG7N/+RaX/Xkdm474+ID2Q1ltK//ceAyA+V39Ln0hOQ8SMjGjM0E7zpMbjvC/nX0XBT9/ZRdPbjjCu3urQ7ruyQ1HeGtPFV99YiM7T3Sp9rJ5/Ex1h3sUgiAMMCJehCFDQMNuUz8adn1FXrrMNfJLljPyojtTAblDcCyAH+JjzYzL6T4mwGu6dh9ISE7jxRHf45BjOM+bllBxwwfE3fgvGH+BMvZqmts/VLUTTdO497PTmD4ijVOtHXzj6U/YW9nIY+sO8+/3lRduzXETL209QU2zlZ0nGrn24Q/50Us7aGjzY/QFOu0O/vL+IRavfJ89FY0kxJq5bEbfTL9eaJorlbh0ihKB33vxU47V9d7/0tDaQUWDutenfsrN/fGp02PUYrNz0xObvH04Hc60UVqB8+Tner1GQegL0fGvqHBakOmMqvgulTYa1A2U5yXItFFChvdk4ShJGRm4U0eqTLnZ2ulqFthrw64Hl159E0+VvMjsbzzG8HE+qrSMyi1n5+L4WDMPffkM0hNj2XGigUtWruOXr+3BXq88Lx1Jw/j6uWN5+uYz+cIc9QH8zMdlXLTifV7fUdHN7Lv9WD1XPLCe+17fS3uHg/njslj9nQVheW9eOPv+LMmtp2R0Bk3tnXzzmU989tAJhn0eZeOfdo2eBKC9w86+SnXt6KxEapqt3PjERmoNn5kReZl5PZgtysdldI0WhAFExIswZDDSRr7mG500xEt/lkpbPSMvTvHSk2EX3KkjiJpKIwNXxVG5EnZHnVGXzCQLaQm9N8YajMlO4uefncbYnGTfJxg/T6OHDjAyI5E/faGYWLNGrFljwYRs5ueqPzNfW3I2d106hXMmZHP/52bw7G1nMTY7ieomK9/4xyfc+tQWyuvbaLF2cu8ru7nqwfXsrmgkPTGW314zg3/ccmZoXYODJU+JF/PJ3fzp+mLSE2PZfryB+17f06vbeYmX4/VBj1HYW9lEh10nIzGW578+jxHpCZTWtPC1JzfRaut0e15S892DJz8V464w8Ih4EYYMgdNGEWrYBbdpF6JOvEzpUnF0pMYYTtk3v0vQGJGsqh1eXV/PnZjD+h+ez9Z7FvP3m89khLleHegy1+issSqS8u3zxxNr1nhrTxUXrXifC1e8z+PrS3HocOWsfN5afh7Xzi5AC6Jzbq8wOi5X7WZEegK/v1aZeJ9Yf4Q3doU+92hfpTtKWN/awfFTbUFdZ5SlTx+ZTl5qPE/dPJcMp5Ba9o9PcBjVRpYk9/ymHS+CI4IrpISoRMSLMGQwSqVPtXbgcLg/qFqsnbR1qH88+1e8+Eob5fZ8fZZn5GVojwXoiiFeSmtbaLV1unu89NHvEjQ5k8EUo4Rlw3GvQ7kp8e45U0Z3XR8N6uJjzSxfPInXvr2AM0al02KzU9HQzsiMBJ786hxWfqG4f/5ceS12inptroTWOi6Yksdt5yrR+70XtofsfzFSPwbB+l6M82aOVH/mx+Uk89eb5hAfa+LdfScpq3RWccUmqshLfLqq5Cp9P6T1CUJfiUjx8uqrrzJp0iQmTJjAY489NtjLESKEdGe3VrtD9+qkavhdEmLN3ZvEhYOukRdbC9ic3UWDES9G2ihjDMSlhH15g0lOShw5KXHouko5hDxNuq/ExKmyaXD30emKtdldKRagu+7EvBReXDqf/7tmBt+/ZBJv3nkuCycF8fsNB3EpkD5KbTs7MX/v4kkUj0qnsb2T//fC9qBvpeu6S7ycMSodcJtwe8IQL9NHpLn2nTEqg1VfPAOzSaOtxflztCSqn/20q9X328W4KwwsESdeOjs7Wb58Oe+88w6ffPIJv/nNb6irC76cUYhe4mLMpDjFSa1H6sg9GqAfzLrQvVTaiLrEJqrS556YcBGMXRTSzJuhhGfqqM/ddXuDkTryZxw1erxYUnoUjyaTxudnF3D7wvHhnZEVDLnO5oXVuwGINZv40xeKAfi4tC5gRZQnlY3tNLZ3YjZpXFWsJmgHE3lptXVyoFqJnpkF6V7HLpiSx6+unEYi6u/aO4ec4n2GM3W05xXvBnaC0M9EnHjZuHEjU6dOZcSIEaSkpHDppZfyxhtvDPayhAgh00gdeYiXk/3pd4Hu1UaeZt1gPBAJ6fCVl+GMr/TH6gadKcOVINhT0Uip0/MS8kDGvpDn4XvxhdFdt48N6vodI6VozMACCjITGZWp/EM7gkz9GFGXCVkW5g5XAmzniQavVKsvdpc34tAhNyWOvNT4bse/MHcU2XGdAPxnj3MtBXMho1CVUO+RcQHCwBF28bJ27Vouv/xy8vPz0TSNl19+uds5Dz74IIWFhcTHx1NSUsK6detcx8rLyxkxYoTr+5EjR3LixIlwL1MYohjzjXxGXvpNvHikjXQ9NLPuaYBRcbSp9JTrdzG6jw3qQmJY94ojLxrDM5Cx3zFMu87Ii8EMp/9ke5CpH0O83Kf/kYlPTmOV5c/k2Y64+u/4Y7tTHBnP80WCM/Kyr9auxJCmwYzr1EGpOhIGkLCLl5aWFmbOnMkDDzzg8/hzzz3HHXfcwd13383WrVtZsGABS5YsoaysDMBnSV+/OfyFIUeWj4qjfhcvRtrI0QGd7R4N6gbIDxHhGOLFKM/NSrKQGt/3MumgyXP2eqkr9T3puMlp1k2JcPFizLyq3qOmYDuZ5UzhbD9WH9Rt9lU1kUg7M1rWo6HzGdOHvGn5AZaXb3GPqPCBUWk0Y2S67xN0Hc1ZKl1ri+GY0cF4xufV6+H3oCn0yqge2fc6PHeDd7WfcNoTdvGyZMkSfvnLX3L11Vf7PL5ixQpuvvlmbrnlFqZMmcLKlSspKCjgoYceAmDEiBFekZbjx48zfLj/cK/VaqWxsdHrS4heMgKIl35pUAfK16I5/6q0N3hMlBbxAlCYnYQlxv1PyYD6XUCNaEgeBujdohaAR6VRhKeNssaDKVaZwRuOuXYbYiLYiqF9lU0Umw5g1u2QnMfu9IWYNJ2CE6/Dg2fBizfDyX3drnOZdf1FXjrb0VD/uWwjztWYkKxxMHKuc1zAi0G+2RB488ew57+w89/hv7cwZBlQz4vNZmPLli0sXrzYa//ixYvZsGEDAHPnzmXnzp2cOHGCpqYmVq9ezcUXX+z3nvfddx9paWmur4KCgn59D8Lg4jPy4vS89EuDOlDt/A2jZ3tj8KMBThNizCYmD3MbYUcPVI8XTwKljoy0UUqEixdzLOQ4K6c8RNi0EamYNGXErfKYIeWLTruDA9XNnGlyNrcbu5B9561iifU+PoqbD+iw80VYdSb861aXj6uxvYPDzkqxGSP8iBePuUatxHuXY8/sp9TRqaNQe1BtG6+CwACLl5qaGux2O3l53v/o5+XlUVmpwo0xMTH8/ve/Z9GiRRQXF/O9732PrCz/Q9DuuusuGhoaXF/Hjh3ze64w9PHVqO5kf6eNAOI8fC8tzshLMN11TxOmDEt1bQ+oWdfAY8ZRN4y0UeqI7scijdzupt1ESwwT85Q47Cl1dLSuFVung3lmZ2Rl9NlMH5HOHn00N7V+m85b18LkywAddjwPu14CcA1gHJGeQJa/v0fOuUadJgsOTOyr8ohyT71aRY0qd0CVj+hXbzn0tntbxIvgwaBUG3X1sOi67rXviiuuYP/+/Rw8eJDbbrst4L3i4uJITU31+hKil0Bpo34ZymhgmHatDRJ58YFRcQSDkDaCbjOOvHAZdiM88gLuZnV+TLs9pY72VTYRh42ZmvODfvTZjM1OIjkuhvYOBwdMhfCFf8B0p0+lvd7rvoHMukbkRY9RkbW9FR6Rl8RMmOiMkIcz+nLoHfe2iBfBgwEVL9nZ2ZjNZleUxaC6urpbNCZUVq1aRVFREXPmzOnTfYTIxnfaaAAiL57l0mLY7UZRvvtDr19m//SEK/Kyy8vsir3D/fuKdMMuuE27VV3FSzrQc8XRvsomZmqHsNABSbmQNQ6TSWPaCPXn11Vubfx5dvZm2eESL+n+b+6MvJji1O/3SG0LbTaPsQBG1dHGR+FwGDru2jvh8Fr396eOqN+nIDDA4sVisVBSUsKaNWu89q9Zs4b58+f36d7Lli1j9+7dbNq0qU/3ESKbrmmjNpudFpsxGqCfDLvgXS4tht1uTB6e4hqEOCiel6zxYI5TH7CnSt37m6sAXY0QGAppPiNtVHsAOt0CfaaHaTfQkMV9lU3MNTkrisac7epD5Lr+RL06ZjRXdFZnGfuDibyY4pLITLLg0HE1tQNg0qUw7nw1vPGZz8OBNX5uFCQnNqtIZ3y6agjp6IT6sr7dU4gawi5empub2bZtG9u2bQOgtLSUbdu2uUqhly9fzmOPPcbjjz/Onj17uPPOOykrK2Pp0qV9eq5EXk4PuooXI2UUF2Nyz7HpD4xy6cYT0Okccpck4sUgNT6Wv9xQwkNfKiFlIMukDcwx7iZvnqZdT7OuKeJ6cnYnbaTyVzk6oWa/a/ekYSlYYkw0tHVwtNb/nKP9VU1us+7os137p3dNOxnixdZMXYuNY3Xqz/Q0f2ZdcE2U1mITXQbtvZ6mXXMMfOGfMHGJainwz+th93+Dets+MVJG4xa5R2yEkjpy2OHgW1JiHaWE/W/z5s2bKS4uprhYtbVevnw5xcXF3HPPPQBcd911rFy5knvvvZdZs2axdu1aVq9ezejRo/v0XIm8nB4Y4qWtw06bze5l1u3XfkBG5KXmgHq1pKj5LoKL8yfncWHRIPqAfJl2je66kV5pZKBpPn0vlhiTq5+Ov9RRe4ed47UNlJicf0Y9xMuMEemA6oJs7bSrqdAAthZ2OM26hdlJpCUEEJ4eE6UnOcVL1wGQxMbDdX+HqVepvkgv3ASfPh/4PfvjoNOsO+5893DTUMTLjhfh6c/Bmz/p3fOFiCbs/1VduHBhwLAmwO23387tt98e7kcLpwHJcTFYzCZsdge1LVa336U/zbrg9gjUHnIuRKIuEYcv064x1yjSu+t6klcExz7yqjgCNel527F6th9r4LOzuldOHahqpogjJGpW9IQMtJzJrmMFmQmkJ8ZS39rBvsomZsS5Iy+fOiuYpgeKuoAr8kJsoqu6bG+lj75a5lj43F9VqmfbP+Dft6lrS24K6u0D0FoH5Z+o7XEXqJJpCE28HN+oXk98Evw1wpBhCMRRg0PSRqcHmqaRkaT+d3iqpYOaZmePl/70u4A7bWT84yniJfLwGXkxyqSHkHjxMybAGJbob0L0Po+UkTZqvleaTNM0lzj59HiDV+Tl0xNBVBqBu8+LJdF/5MXAZIYrHoA5twA6vPId+OihwPf3pPR91fQuZzKkjVCeJghNvBim55r9KoUkRBVRI14kbXT6kJmkoiy1LVZ3d91+j7w4/2E3/C4iXiIPo1Kn4Ri0nVLbhngZKmkj8B4T4IFRCbSzvIFOu4Ou7Kts9DbrdsFt+q338rwEVWkErmojYpOYmJeCpkFNs42TzuhnN0wmuPR3MP9b6vv//RA+WBn4GQaeKSPwEC+HvE57Y1clJb9Yw4eHar2v13Wodkau7FaoPxrccwGHQ+cnL+9k5Vv7ez5ZGDSiRrwIpw+e5dL9PtfIIL5L/yAx60YeCemQNkptG6mjoZg2MjwvDce8zKZjs5NIcfZr2V/VfYbT/soG5hjiZXT36k0v064z8tLZ3kxlYzsmDabm99AjyyPykmAxM8bZjNBv9AWUh+eiX8DCu9T3b/20myjrhq57mHUvUK+G56XxhNt7Azy5/gi1LTb+/clx73s0lnsbdX2MQ/DH7opG/v7RUVa+dcCrJYMQWYh4EYYcno3qTg5EjxdwR14MpEFdZDKsS+poKKaNEjLc3YA9PuhNJs1DgNR3u8xRsYNUrQ17bLJ7WKUHRuTlQHUz7SZlNu9sU56V8bnJJPVUrefheQGYlGdUHPUwT07TYOEPYcrl6vt1vw98fs1+JVLMcW4RlpgJCZlqu+4woNokbDmqImy7K7qsoeuMqxDEy1aPLsabjtQFfZ0wsESNeBHPy+nDoERe4rqKF4m8RCSepl1dH5ppI3BHX7qYdt3N6rzLf+tbbYxv+xQAveAsVbbchbzUOHJS4rA7dA7WO9NOzijGdGc1UkA8qo2Ann0vXTn3++p157+6pX+8MFJGo+d5V/R18b1sOlKHzZk+21/VhK3TI5XW5ecWkngpO+Xa3lgq4iVSiRrxIp6X04dML/Giwrr92qAOuqeNRLxEJi7T7g7le7E7/RhDTrz4Me06Iy9dZxztq2ziTGfKKKawu98FlGnXuH7nSWVgjbW3AnrPZl3oFnkxRkLsDVa8DJ8BEy9RRtxA0RdjnpGRMjLoIl4+OFjjXppd926YZ/zchs1QrzXBi5dtgxF5OfEJPHq+6ksjBEXUiBfh9MEzbTRwpdISeRkSGGmj6r3ubqyJWar/yFDCz5gAo+JoX1UT7R3uChpl1nWmmMac4/e2RoRlW5W61oyDODqCEy+uyIszbeQsl95f1YTdEbg9hgsj+rL9WdXuvysd7XBkvdoe31W8GL1eVNTmgwNKvMSYVH+nXeUeqSMj8jL1KvV6cr+KxPVAQ2sHh0+6PTW7yhtpsXb2eJ3Bq5+Wc+dz22hoDWGMgcOhqrFObIENfw7+utMcES/CkMNIG1U0tNPk/Iel/9NGYtgdEqSPUZU0diuUOufiDIWZRl1xRV52eX3oDk+LJztZpX48P6zrju4gU2umwxQHw2f5ve2MAiVStpS3ufalmaxMGR7EQFtX5EWljUZlJpIQa8ba6eBobUuACz0YWaIqiHS778qjsg9VRV/yMPfPwMAj8lLbbHX5XC6boaJqu42fh73DnSaacgVoZrA1uVOIAdjm9BKNzkpkRHoCdofOJx5ppEDous7PX9nNS1tP8MC7B4K6BoCdL0KlSvlR9jF0+qneEryIGvEinpfTByNtdLBaVVxYzCZS4/txNACo/7mbPQSSRF4iE5PJHbU48KZ6HQrTpLuSM0l96LY3eH3oeqZ+PFNHieUfAVCfOQti/KdQjV4vB2vb6TQnADAtx0x8rLnnNXlUGwGYTRoT81TJddCpI3BHX7b9AxpOeB875FEi3bVjtod42eAsjZ48LIVzJ6qZVS7xUntQdfe1JEPmWHfE5uTeHpe2rawegFkF6cwtVAbhTUH6XnaVN7oKCJ768ChVje09X9RphXd+4fF9m4rACD0SNeJFPC+nD54jAkD5Xfp1NICB4XuJT4eYfo70CL3H8L2Ufaheh1KlkUFMnPvDuovvZYZnvxbU//hHNm1VBwOkjEBFKEekJ6Dr0OxQf4Zn5gYhXMCrz4vBJF8zjnpi9DwYfQ7YbbD+j97HDr2rXrumjEAJEYC2U3yyR6WOFkzIZqpzovnuikYcDt2dMsqdosRsziT1fRCm3W3HVJSluCCdOWOUeNkYpO/l/f0nXdvWTgd/fieI6Mumx1R6M2U4TLhY7StdF9TzTneiRrwIpw+GeDHod7+LgZE6kqhLZGNUHDmcXoWhmDYC96DJbp12vYcsVtS3UaKrc9InL+zxtoa/pd6u/t5MyQoyatkl8gIw2el72ddTuXRXzvueev3kb9BUpbabKp0l7hqMXdj9GksipI4E4MRhNXzz7PHZjMtJwhJjotnaSVldq/vnZaSdsp3ipQfTrq7rLrPurFEZzC3MAGBrWb13JZMf3ttXDcAVM9Wft2c3HuNYnf8hmrTVw9rfqu1FP4KJi9X2kdDEy4aDNby9pyo0n00UIOJFGHKkdxkel9PffhcDw7QrPV4im2FdepwMxbQRQK5v064ReTlc00JDWwdlB3eRp9VjI4bY0T2nzY1eMa0oE/PEjCCjll2qjQDf06WDofA8GDlXTZ/e8Ce1z2hMN3wmJGX7vs6ZAkppPorFbGJuYSYxZpNrHbvKG90/LyN9aMx46iHycrS2lVOtHVjMJqYMT2FcTjKZSRasnQ7X8Ep/NLR2uHrOfP+SSSyYkE2nQ2flWwGiL+tXqoq4nMkw84vqZwJwbCN0tPm/zoN391Xzxcc+5ua/bWbWL97kkpVr+cnLO3lle3lwaashjIgXYcgRYzaRnugWMP1u1jUw0kZJOQPzPKF35E4BPD6Qh2LaCDwiL949SzKTLBRkKr/KjuMNtB1SxuSyhCkQm9DjbY1mdS2ovzcjknqOKgAekZfuaaOyutaQqnLQNDjP6X3Z/Di01LrFi6+UkYEzlVZoquCM0ekkWlTUyOgOvKu8wf3zMiIvORPV68m9ASuOjKjL1BGpxMWY0TSN2aNV9KWnkul1B0/i0GFCbjIjMxL57mIV7Xlp63EOVvsQdg3H3bOeLvyZ6suTNV4Zle1WJWB6oKm9gx/9W0WgspIs6LoSkX//6Cjf+udWzvz125z7f+/y+zf39TgseSgSNeJFDLunF56po+yUfu7xYiCRl6GBJclt0oShmzYyPnxP7ge7tzCY6WpWV09yxccA1GfPDuq205ym3RZdCZ3YziArhVyeF3fkJStZNb7TdVUyHRLjL1SVUR2t8OGfPUYCnO//GkO8aBWcM94dnSly+l4On6h0l8gbkZesCYCmohwtNfjDaE43y1mODgRt2n13r/K7LJyU47rHRUV5OHT4wxof0Zd371NRp1HzVe8bUIKucIHaDiJ1dN/re6loaGd0ViIf/OB8Nt19IQ996Qy+evYYpo1IxaQpUfnndw56l5FHCVEjXsSwe3qRmeghXgYq8mK0bM8YMzDPE3qPYdqFoZs2Sh+tzLF2K9R5d6R1iZdj9RQ0bQNA68Gsa5CWEEthdpIr8uI5K8gvnTa3h8iz6y3u1FHQnXYNNA3OdXpfNvwZWmtVhdDIuX4vsWcqUTpWq+RsD/FiRF5s5c6oS/IwNVLAWG+6c+ZVAN+LEXkpHpXh2meYdjcfPaXMwD5wOHSXWXfhJLcf7ruLJ6Jp8NqOCnZ6pp2qdsP2Z9T2Rfd6V1WNcYqXHky7Gw7V8MzHSqTdf/UMEixmclLiWDJ9OD+9fCqvfmsB23+62CWmXv20IuD9hiJRI16E0wuvyMtAiZdzlsMVf4YzbhiY5wm9x2hWF5uoqsOGIiYT5Dr9Gt3GBKhIQ0XZAYY5qujUTeQWnRv0rRdNyqVVdzbus3Uf8tiNDg+B41FtBH3wvQBMulR5ewxhVHhuwFLvfZ0q6jnGVMn0/BTX/inDVKQhr90p8vK69Ihx+V58l0u3d9hdfWOKPSIvU/NTSbSYaWjrYL+v9A+qyqmm2UqSxczsMW7hM3lYKpfPUFG/FWs8JlS/9TPVZbjos1DQJVNgRF5ObPErKlttnfzwXypd9OWzRjFvXJbP81LiY7mmRBmcX9tRHnWpIxEvwpAkK3kQxEtyDpzxFa+cvxChGI3a0kZ27xcylPAzJmDaiDRMGoxrVc3NdlPIiLzgvVg/WDKJxcXO1Jo1CPFi+F1MMd3EhdFpt8cBjb4wmeDc/+f+PlDKCHivKp4O3UwCNmKa3dGEBIuZwuwkJmnH1I6uDe5cvpf9+GJXeSMddp2sJAsjM9y+oRiziTOckRh/qaN396oqo/njs4mL8S47v/OiiZhNGu/srVaG3tJ1cOAN9XO84Kfdb5ZRqCqqHB1Q9pHP5/3ujf2U1bWSnxbPDy6Z7PMcg/Mn55IQa+ZYXVuPpuOhhogXYUiS4ZE2yhkoz4swdBi7CM6+Ay6+b7BX0jf8jAlIiothQm6KayTAocRZmEzBi7S4GDNpac4oQTBpoy7ddT3xTBv16n/3RZ+F/GKVMpq0JOCp6w7WU6Y7UzPOGUcGU/PTmGxyihfj52bQQ+TFnTJK79Yzyt3vxXen3fecKaNFHikjg8LsJK45Q0U/fve/vbDmHnWg5CZvX5ZBD76XLUfreGJDKQC/vno6KfGx3c7xJNESw/lT1LrCljo6sQVeWhqUqbg/EfEiDEkGJW0kDB3MMXDRz2HChYO9kr5hRBD2vQY/z4Rf5cNvCuH3U/hH21KuMqs5QA05vShUMCKIwaSNusw18mR8bjImDU61drg6zIaEyQw3vQbf+VRFyvzQZrOz5egpDutOD1NX8TI8xX/kJTtwozpXfxePlJHBnEJ35KWrOKtvtbmMvoa/pCvfvnACFrOJjKOrofwTJdLO+4HPcwG/vpf2Djvff/FTdB2uKRnp5a8JxGXT1c/rtU8rghaXO4438ItXd9Psq4Js8+Ow/Z/qdRAR8SIMSYy0UaxZIy0h8P8+BGHIMqLEbTbV7cp70lYHTeVk206QoNlo1BMwF84P/d4W1do/tMhLd/ESH6tSNgB7euN7ASWkknx7Nww2HqnDZndw0lKgdtR6m5iLM6xkaM3Y8eiqa2CkjZorVXO4LrgrjTK6HSsuyCDWrFHZ2M7xU979V9YeqMGhw8S8ZPLTfZepj0hP4ItnjuJys+r4rM/9euBGl0bkpXwrWN0/zz+9fYBDJ1vISYnjJ58p8nNxdxZNziXRYuZEfZvXxGx/2B063352K3/9oJTH1h32PtjeCDv/rbbPuDHoNfQHUSNepFT69MJIG2Unxw3MaABBGAzikuFbW+H7pbB8L3x7G9z+Mdz2Pocu/zfX2+5mifV+xhaM6N29IcjIS/fuup70utNuCKw/qMqcLXlOIdIl8jLFrKIuRxx5NNm7dA2OT3OXzNd4+15qmq0cP9WGprkHV3qSYDG7yss3dvG9GF11faWMPLl90TgmmNSMqr8cHeaew+SL9FGqolG3w1EleHaeaOAva5WQ+OWV00hLDP4/bPGxZi6coozOwaSO/rezktKaFtf5XtGaHS8oIZs9CUadFfQa+oOoES9SKn16UVyQwdjsJK6YNUR7eAhCsJhjVNlv6nDILFQVSPmzKJi5iL3xs6iNzQtuKnRXXGmjYCIv3ecaedKniqMg+eCAEi/DxjorybqIl5QGJUr26gXsqfCxDpdp1zt1ZAxjHJ+TTKofD4mr34tHszqHQ+f9fcrvcp6flJFBbqKZQlMlAE8diOPSP63jC498yJu7KrH7KsF2po46D7/PxtI6/t8L27E7dC6bMZyLpw4L+CxfGJO3V++o8FvyDWpEwoPvuX+uB6ub2Wf079F12PKE2i65adCN8P08ilcQ+oe0xFje+X8LB3sZgjBoWGJMPHvbPKyd9m7zvoK7QfgiL64Bjb5EQxioaba6SpmnTDsDPgDqj6r+M0b1k7Mia59jFCfLG1yCw0XOZDj8XjfT7lZjGOOodL/Pnzsmk7+8f9gr8rKzvIHaFpsqkR6d6fdaAE4dwazbscckUjJ9KlW7qvnocB0fHa5jVGYiN84fw+dnj6SpvZMtR09hOzWWzwF7NrzG599TKcHMJAs/v2Jq4Of44dyJOaTExVDR0M4nZaeYPcb3etceqGFXeSMJsWamj0xjY2kdr26vUJG18q1QuQPMFpj5hV6tI5yIeBEEQRiiGKKhVxjiJZhSaR/ddT0x0kYHTzbTaXcQYw5vUH/DoVoApgxPJSuvQK3d1gynjrgjKs5eOPv0AlJ9pWWyned1SRu5zbrd/S4Gs0dnomlqntTJJis5KXG854y6nDMhG0tMD+/XGe0x50zgz1+azV31bfz9o6P8c2MZZXWt/OLV3fx69R5XFCaXXD4XD0XaEcYmdzJpzEi+sXAcWb0sToiPNXNRUR7/3nqCVz+t8CteHnJGXa6fO4qZBU7x8mm5arj3yd/USUWfdTcAHESiJm0kCIIghEAoaSMfc408GZmRQJLFjK3TwZHaIMcNhMB6Z8ronPFZKl1hlBkbqSN7p0sg7NULfLfD91EubXfobD+m+p/4qjQySEuMZVKeEorla5+Etb/l3b1qGnZQVT9GZ19n1VN+egI/uGQyH/7wAn591XQm5CZjd+iYTRrTR6Rx6fximpPHYNZ03r4mhoe+XOIayNlbPtND6uiTslN8dLiOWLPGrecWcuGUPOJjTRypbWXP0QrY8aI6seSmPq0jXEjkRRAE4XQkJM+L/2ojAJNJY+KwFLaW1bOnoonxuX2ICHVB13U+cJp1XSMBssZDxXa3eKk7DHYrjpgEytpzialuwtbp8I6IGBVI9cfUe7YkcehkM83WThItZibmJQdcx5wxmeytbGTylp+Bo5VW22+AAr8l0l7UOOcbGdEfJwkWM188cxTXzy3gaG0rualxrmGTvLIItjyBVroOJn+m52f0wIIJOaTEx1DdZGXTkTrOHOtd3fXgu6p666riEQxPU5VT50/OZfWOSo68/xRFtmb1cx99dp/XEg4k8iIIgnA64ul56an/h6vPi//u0u6Ko/D6Xo7UtnKivg2L2eT2sTgHNLrEi3OStJY7hdSEODrsOge6tvNPyobELEB3iQnDrDt9RFqPqa45hZkkYiXOoYRcEUeYPCzF9UEfEMMknDPR52FN0xiTneQWLhDSkMZgsMSYXGbf13Z4Vx3tq2zirT1VaBp8/Tx387zLnOMNxhx5Qe0448ZBN+oaiHgRBEE4HTFKpdHdkRV/9BB5Ac+Ko/CWSxtRlzNGp7s/3F3ixdnrxdmBWMsroshZeeUzdWQ0q3P6XrYafpcAZl2DuWMyydLcLfYnm8p6rDIClDD0E3kJiNGsrmontNQGf10A3FVH3lVOD7+vfo6XTB3GuBx3BGrRpFzOsByjSD+IwxQLs74YlnWEAxEvgiAIpyMxCYDzf9E9pY56qDYCXOXaHxyscZU1h4MPDxl+F/cU6W6eF2P2U+5U14Rpn71UjNSR0/diNKcrDmDWNRiWFk9RqruD8BStrMf+LgA0VYCtCTQzZI7t+XyD5FzImaK2j34Q/HUBOHt8NumJsdQ0W/m4VAmiY3Wt/He76kFz+8LxXucnWMzcmal6zexJO1dFryKEqBEv0qROEAQhBEym4EcE9NDnBWD26AwWTsqhvcPB157cxJu7KsOyzLI6Z5om36OXTaZTvDRXqi60xtTtvCKmjjAiLz4GEbpMu/tosXay39nDJFCZtCdzcx2u7ammMkpG9yx6XNVNGWMgJsRqoULfowJ6S6zZxCVG6sjZsO6RtYexO3QWTMhm+sguTfpsLZzV/BYADzUtCNgjZqCJGvEiTeoEQRBCJNhy6SAiLyaTxl9uKOGSqcOw2R184x+f8J9tJ/q8RGNeUm5KvHtnQjokOVM2lTtUyTRA7lSKhqsP4N3ljd0/bD0a1e040YBDh/y0ePJS4wmG6ek213aW1kBs68kg3oBTvHQdWRAMY8LrewF31dH/dlZS1djO85tVZ+JvLPQxKHLXS8R2NnNMz+O15vFsPup7OOVgEDXiRRAEQQiRYCuOgvC8gJpW/cAXi7n6jBHYHTp3PLeNZz4u6/XyHA6dmmYlGHJSukQtDN/LnlcBXYmZ5BzG5SQRF2OixWZ3RW1cGJGXusNsP6KERzB+F4Nxid6zjaja0fNFRuQle0LQz3Ex5hxAU2mu5urQr/fBvLFZZCZZqG2xsewfn2DtdDCrIJ15Y33Mltqiert8mvdZdEy8+ml5WNYQDkS8CIIgnK4EK16CqDYyiDGb+N01M7nhrNHoOvzopR08uvZwj9f54lSrDbtDR9Po3kXY8L3s+a96dU6SjjGbXObhbqbdlOFgSQHdTkXpTiBwf5euZNIlFWWkqwLRpcdLSCRmQp5zHEKYoi8xZhOXTFOpIyOScvvCcd1nxFXthuMbwRRD+vybgO5G38FExIsgCMLpiqtcuofy5iAjLwYmk8a9n53KUmfZ7a9W7+EPa/Z7D/kLgpPNKmWUmWghtmspsxF5aVBpD/LcrfOL8lXqqJvvRdNc6RtruTL5Fo8KwrdiXN6izMOOBGeUonJnzxcZlUa9SRuB2/dy+P3eXe+Dy6YPd21PyE12DW70wuioO2kJc6dPcRt9D4en8qmviHgRBEE4XXFNlu57tVFXNE3jh0sm872L1Yf2H98+wO/e3NfDVd5UNyrx0i1lBG7xYuCMvACuiiPfnXbVenLaj2I2aUzL7z5J2i8tKtVkGrdQfV/Vg3hpb1DVRtC7tBHAuAvU6/Z/BieWguDMsVmun+nS88ZhMnWJunS0qecBlNzkZfR9JYjJ1AOBiBdBEITTlaA9Lz1XG/lj2aLx/PRyJSweeu8QzdbOoK81zLpBiZc8t3gp8iNemq2dvF2rIi0TTMeZPTqDBIs56PXgjLwwdpF6rdkPnVb/59c4S7mTh0F8CCLJk/EXwMQlYLfBi19zC8k+YDZpPPzlM/j5FVO5qnhE9xN2/0cJr7RRMPZ8wN2w7n87K+iwO7pfM8CIeBEEQThdMcSLtYe0US8iL5589exCkuNicOhQ1dge9HVG2ijH10DCjEJcfWrQ3D1RgCnDUjFpahp1dVM7uq7zn20nOP937/GPQ6qyaFZCNSu/MCu0N+KMvDB8phIjjs5uU6q9cPldehl1AZXq+uwqJYBq9sEbP+r9vTwoGZ3JjfPHdI+6NFXC2/eq7TO+okrqgbPGZpKVZOFUa4drUOZgIuJFEAThdMXinEEUKPLisIPdGV3oReTFIC9VCZCqhhDES6DIS2w8pBeo7cxCL2GVYDEz1tkp9uWtJ/jCIx/xnWe3Ud1kpS1NRWwK7CcYnhwb/BtwOKDVGXlJzoW86Wo7UCqnpg9l0p4kZcFVDwMabHkC9rzSt/v5o6MNnv0iNJ6ArAlw5m2uQzFmE0umO1NH2we/6kjEiyAIwulKMGkjz2O9jLwArl4qVU1hEi/gTh15+F0MDN/Lr1fv5ePSOuJjTfy/xRN54o7Pqe7CdivUHw3+DbSdAt2ZLknMgmHOKqBAFUdGj5dQxgL4Y9wiOPvbavu/34KGvvfQ8ULX4T/fhBNbID4dvvhct1TX5c7U0Ru7KrF22sP7/BCJSPFy1VVXkZGRwTXXXDPYSxEEQYheghEvrrlHGsQE18zNFy7x0hjAI9KFHsXLsBnqdeTsboemj3B/8C6ZNoy3lp/HN8+fQHycBbKdoudkCAZiI2WUkAHmWHcJc6BeLzVhFC8Ai34M+cVKSL30dRUVCxfrfgc7XwRTDFz3d3cpugdzxmSSlxpHU3sn6/aHbwREb4hI8fLtb3+bp556arCXIQiCEN0EUyrt2eOlDxOF3eIlTJ4XgAXfhWufhLlf73bo2pICvn7eWP5+81we+nIJIzM8okauMQEB/CpdMcSL0dnXiLxU7vQ9lbvTBnXO/jbhEi8xFvjcX1X67sg6+OAP4bnv7v/AO79U25f+FgrP9XmayaRxqbPMerAb1kWkeFm0aBEpKSmDvQxBEIToJphS6RB7vPjD5XkJRbz0FHmJT4WpV/lMZ6UlxnLXkiksmOBj8nOX6dJB0VW85EwBzQRtde5yaE/qDoNuV76i1Pzgn9MTWeOUwAB499dwfHPf7le+DV5aqrbPXAqzvxbw9M+dMZJvnT+eb3QZ4jjQhCxe1q5dy+WXX05+fj6apvHyyy93O+fBBx+ksLCQ+Ph4SkpKWLcufHMZBEEQhDARlOelb5VGBqGmjayddhraOoAA4qW3uKZLh5I2cqZJjMnKsfHK1Aq+TbueYwH6ELHyyawvwrTPKXH04teg3Uc/m2BoqoR/Xq8E6rgLYPGverxk2og0vrt4EpOGDW6AIWTx0tLSwsyZM3nggQd8Hn/uuee44447uPvuu9m6dSsLFixgyZIllJW551uUlJQwbdq0bl/l5YPvYBYEQThtcJVKBxjM2IceL56EGnkxZhrFmjXSEkKoCgqGjNHqteF48NcYkZfEbPe+YQF8L64y6TCljDzRNPjMCtWHpf4ovP6D0O9hVBY1las1XvsEmGPCv9Z+IuSVLlmyhCVLlvg9vmLFCm6++WZuueUWAFauXMkbb7zBQw89xH333QfAli1bernc7litVqxWt5JvbOylAhUEQTjdcJVKBxAvYY68VDda0XW9+yydLrhSRslxPZ4bMqnOxmwt1cqbEmMJfD50TxuBMu3u/JfviiPXWIB+EC+gJmt/7jF4fLHqhnv+3ZA2MvjrX/++qixKyIDrn+19E71BIqyeF5vNxpYtW1i8eLHX/sWLF7Nhw4ZwPsrFfffdR1pamuuroKCgX54jCIIQdYRSbdRHz4uR+rHZHZxq7ejx/B79Ln0hMQvMTsHSXBncNS7x4hl5CdDr5WQ/Rl4MRp0Jo88GdHc7/2BorICt/1Db1z7ps7Io0gmreKmpqcFut5OX5z3kKS8vj8rKIP+AABdffDHXXnstq1evZuTIkWzatMnvuXfddRcNDQ2ur2PHjvV6/YIgCKcVofR5CWKidCDiYsyuydDBpI76VbxompowDdAYpF3B5XnpEnkBqD2g0jAGuu6OvPRmmnQoFH9ZvW79h++qJ19se1r5ZUbNg7EL+21p/Um/VBt1DfEFEyL05I033uDkyZO0trZy/Phx5syZ4/fcuLg4UlNTvb4EQRCEIDBKpTtaVAdZX4Qp8gLu1FHlYIsXcKeOGoNs9tbqQ7ykDIOETNW8rnqPe3/jCfUzNcWo7r/9SdFn1e/xVCkcDSLD4XDAFmcrkpKb+nVp/UlYxUt2djZms7lblKW6urpbNCbcrFq1iqKiooBCRxAEQfDAKJUGtzG3K2HyvIDbtFsdjHhpVuf47fHSV4zy5aAjLz48L5rmYdr1SB0ZlUaZY1VDu/7EkqTKxQG2Pt3z+YfegYYy1UW36LP9urT+JKzixWKxUFJSwpo1a7z2r1mzhvnz54fzUd1YtmwZu3fvDphiEgRBEDyIiVe9SsB/6ihM1UYAeSnBl0u7Ii+pve/qG5BUI23ko0dLVzptasoyeHtewD3jyNO0G86xAMFQfIN63f1yz0M2tzyhXmdeD7EJ/bqs/iTkaqPm5mYOHjzo+r60tJRt27aRmZnJqFGjWL58OTfccAOzZ89m3rx5PPLII5SVlbF06dKwLrwrq1atYtWqVdjtgztvQRAEYcigaSrlYG1U5dK+WneEM/KS1ou0Ub9FXkJIGxkpI1OMilh44tlp1yDcYwF6omCu6jlTewB2vaSmQfuisQL2va62h3DKCHoRedm8eTPFxcUUFxcDsHz5coqLi7nnnnsAuO6661i5ciX33nsvs2bNYu3ataxevZrRo0eHd+VdkMiLIAhCL3CNCPBTLu2KvAx02qi/PS8hpI08e7yYunxses44MgyzAy1eNA2Kv6S2jSoiX3gadXMnD8za+omQIy8LFy5E78HRfPvtt3P77bf3elGCIAjCANFTxZEr8jJwaSNd112Rl9x+N+yGIF48/S4GOZNURKa9QTW9Sy9wi5f+6vHii5nXw9u/gGMfqUqn7Anexx32qDDqGkTkbCNBEARhgHCJF3+Rl/BVGw0LMm3UbO2kvUNVP2X3V9rIKJVurux5OnPX0QCexMS5IyxVO6GtHpqr1PcDFXkBVfk0/kK1vc1H9OXQu1Fh1DWIGvEi1UaCIAi9IK6HLrth6vMCkOtMG9U0W+m0+ynNxu13SYmLIcFi7vNzfZKcp8zKjk53ZMUfgSIv4E4dVe5093dJyXf/bAcKo+fLtn+CvdP7WJQYdQ2iRryI50UQBKEX9JQ2CmPkJSspDrNJUz3cnLOLfNHvPV5AzfFJHqa2ezLt9iRePMuljZlGA5kyMph4ieoe3FwJh9527/cy6t448OvqB6JGvAiCIAi9IGjPS9/Fi9mkuTwsgVJH1U7xkt2f4gWCN+0GShuBh2l358CbdT2JscCM69S2Z88Xw6hbcBbkThn4dfUDUSNeJG0kCILQC3qaLB3GPi8AuamGade/eBmQyAsE3+ulx8iLs9dL7SEo36a2B0O8gDt1tO91aKn1NurO/urgrKkfiBrxImkjQRCEXtDTZOkwRl4A8lJ6Lpd2lUn3l1nXINheLz2Jl+Rc5zEdjnyg9g2WeMmbCsNngaMDdjwfdUZdg6gRL4IgCEIvGEDPCwRXcTRwkZdQ00Z+xAu4U0e6s3Ipp58HMgbCNazx6agz6hqIeBEEQTidCVQq7XC4xUsYqo3APZwxUK+XgRMvzshLU4C0ka57RF78eF7AbdoFiEtV1UyDxfRrwBynPDh7X1P7osSoaxA14kU8L4IgCL0gLkCH3c4293aYIi+GYTciPC9Gr5dAaSNbM3Q61xpIvBgzjkCljDSt7+vrLQkZMOUy5zd6VBl1DaJGvIjnRRAEoRe4xgP4SBsZfhcIe9qoOlDkZcA8Lx5pI3+d442oS2xS4OiTZ+RlsPwunhipI4gqo65ByOMBBEEQhCgikOfFqDSKSeg+06eXGGkjf54Xu0OntrmfRwMYGJGXznZoOwWJmd3P6alM2iBrAphilVF2MHq8dKXwPBg1X02ZjiKjroGIF0EQhNOZQKXSYa40Avd8o4a2Dto77MTHenfQrWux4dBV1iUzyRK25/okNl4NW2ytUdEXn+IlCL8LqB4rw6ZD+Sdu8+5gYjLD114f7FX0G1GTNhIEQRB6QaBSaVelUXjMugCpCTHExaiPHl+pI8PvkpVkIcY8AB9Rrl4vfiqOeiqT9uSKP8GS/4Nx54dnbYJfoka8iGFXEAShFwRKG7nmGoUv8qJpWsByacPv0m8DGbvSU6+XYCMvoCIvZ35dRT2EfiVqxIsYdgVBEHpBoFLpMPd4MTBSR74qjozIi9GJt9/pqddLMD1ehAEnasSLIAiC0AuMyced7d0nEYdxorQnxnTpQOKl3yuNDAzx0hSGtJEwYIh4EQRBOJ3xFCYdXVJH/RR5GRZgvtGA9XgxSOkp8iLiJRIR8SIIgnA6Y7aAyVl42rXiqB+qjSBwl11Xj5eBEi9Bp42C8LwIA4aIF0EQhNMZTfNv2g3zRGmDwGkjtW/gxIth2JXIy1BCxIsgCMLpjr9y6X6KvASVNhowz4uzVNraqBq6eeKwQ2ut2hbxElFEjXiRUmlBEIRe4jfy0k/VRh5pI71LW/4B97zEpahBigCNXQY0tp0C3aG2E7MGZj1CUESNeJFSaUEQhF7ir1y6n6qNDPHS1mGnyequcGrvsNPYrr4fMPECHr6XLr1eDL9LQgaYYwduPUKPRI14EQRBEHpJnJ/hjP0UeUmwmEmNVybhqgZ36qjGada1xJhcxwcEV7l0l8iL+F0iFhEvgiAIpzuuydID43kB3xVHnn4XTdPC/ky/pPiLvIh4iVREvAiCIJzu+BvO2E/VRuApXtyRlwH3uxj4K5eWMumIRcSLIAjC6Y4/w64r8tJ/4sVzvlF1xIkXibxEKiJeBEEQTnf8pY06+jNtpARKdUREXvz0ehHxErGIeBEEQTjdsfgx7NoGIm3k4XlpHuAeLwZGrxe/4kXSRpGGiBdBEITTHX+l0v0aeemeNhr0yEtrDXR4NM6TidIRS9SIF2lSJwiC0Ev8lUrb+qdUGiIsbZSQATFKTHmVS0vaKGKJGvEiTeoEQRB6iS/Pi667q4360bBb3WTF4VBddgdNvGia714vEnmJWKJGvAiCIAi9xFepdKfV3Rq/HyIvOSlxaBp0OnTqWm3ouj54nhfw6PXi9L10WsHaoLZlNEDEIeJFEAThdMdXqbThd/E8HkZizSaykpRIqWxop7G9E1unEksDHnmB7iMCjKiLKQbi0wd+PUJARLwIgiCc7rimSnuIF2PbHAcmc7881uV7aWp3pYxS42OIj+2f5wWka68Xw++SmA0m+aiMNOQ3IgiCcLrjirw0uff1Y6WRgWe59KD5XQy69noRv0tEM4CTrwRBEISIxFfaqB97vBi4yqUb2kmKUx9HgydeuvR6kR4vEY2IF0EQhNMdo1TaboNOG8RYBijy4k4bpSbEApCTEt9vzwuIv7SRRF4iEhEvgiAIpzue0ZWOFiVe+rHHi4Fn2igtwQIMUqURuNNGzVVg7xTxEuGI50UQBOF0J8YCZiUeXOXS/djjxWCYR9po0D0vSTmqski3Q0u1TJSOcES8CIIgCN3nGw1A5CXXs9qoeZDFi8kMycPUdmO5RF4inIgTL8eOHWPhwoUUFRUxY8YMXnjhhcFekiAIQvTTVbwMYLVRTbONivo2YBDFC3j3ehHxEtFEnOclJiaGlStXMmvWLKqrqznjjDO49NJLSUrqv9ClIAjCaU/XcukBqDbKTLQQa9bosOscOqnSVYPmeQEP8VIhpdIRTsRFXoYPH86sWbMAyM3NJTMzk7q6usFdlCAIQrTTtVx6ACIvJpNGrrO6yDneaJAjL0avl+NSKh3hhCxe1q5dy+WXX05+fj6apvHyyy93O+fBBx+ksLCQ+Ph4SkpKWLduXa8Wt3nzZhwOBwUFBb26XhAEQQiSrpOlXZGX/hMv4Pa9AJg0yEyy9OvzAmL0ejm5H+zKgyPiJTIJOW3U0tLCzJkz+epXv8rnPve5bsefe+457rjjDh588EHOPvts/vKXv7BkyRJ2797NqFGjACgpKcFqtXa79s033yQ/X4Xtamtr+cpXvsJjjz0WcD1Wq9XrXo2NjaG+JUEQBKHrZGlX5KV/U/ZGxRFAVnIcZpPWr88LiJE2qtiuXmOT+v39C70jZPGyZMkSlixZ4vf4ihUruPnmm7nlllsAWLlyJW+88QYPPfQQ9913HwBbtmwJ+Ayr1cpVV13FXXfdxfz58wOee9999/Hzn/88xHchCIIgeNF1svQAVBuB27QLg+x3AY9eL5XqVaIuEUtYPS82m40tW7awePFir/2LFy9mw4YNQd1D13Vuuukmzj//fG644YYez7/rrrtoaGhwfR07dqxXaxcEQTit6VZtZPR5Gbi00aD6XQBShnt/L2bdiCWs1UY1NTXY7Xby8vK89ufl5VFZWRnUPdavX89zzz3HjBkzXH6av//970yfPt3n+XFxccTFDfIfeEEQhKGOy7DbNfIycGkjES9CsPRLqbSmeecsdV3vts8f55xzDg6HI+Rnrlq1ilWrVmG320O+VhAE4bTHr+dlANNGgy1eYiyQlKs67IKkjSKYsKaNsrOzMZvN3aIs1dXV3aIx4WbZsmXs3r2bTZs29etzBEEQopKupdID0OcF3MMZIQI8L+A27YJEXiKYsIoXi8VCSUkJa9as8dq/Zs2aHo23giAIwiDStVR6gCIvuZEUeQERL0OEkNNGzc3NHDx40PV9aWkp27ZtIzMzk1GjRrF8+XJuuOEGZs+ezbx583jkkUcoKytj6dKlYV14VyRtJAiC0AeMtJHV6LA7MNVGKXExJFrMtNrs5EaceJG0UaQSsnjZvHkzixYtcn2/fPlyAG688UaefPJJrrvuOmpra7n33nupqKhg2rRprF69mtGjR4dv1T5YtmwZy5Yto7GxkbS0tH59liAIQtTRrcNu/0+VBuWRPH9yLpuO1DF5eGq/PisoRLwMCUIWLwsXLkTX9YDn3H777dx+++29XpQgCIIwwAzCVGmDP19fjENncBvUGRi9XkDSRhFMxM026i2rVq2iqKiIOXPmDPZSBEEQhh6ekRd7Bzg6nPv7X7xomhYZwgW8y6VFvEQsUSNepNpIEAShD7giL03u6Av0e7VRxOEZeUnMGrx1CAHplz4vgiAIwhDDM/JiVBqZYlTvk9OJzLEw9WrlfTHHDvZqBD9EjXiRaiNBEIQ+YJRKOzqh7ZTaPt2iLgAmE1z7xGCvQugBSRsJgiAI3kKl2dlhdgD8LoLQG6JGvAiCIAh9wBwDMc6GcYZ4GYBKI0HoDSJeBEEQBIVh2m2RyIsQ2USNeJFSaUEQhD5imHZdkZfT0PMiDAmiRryI50UQBKGPuCIvJ53fS+RFiEyiRrwIgiAIfcQVealSr+J5ESIUES+CIAiCwiiXbjYiL5I2EiITES+CIAiCwhArLVJtJEQ2Il4EQRAERTfPi0RehMgkasSLVBsJgiD0EUO86A71KpEXIUKJGvEi1UaCIAh9pGukRaqNhAglasSLIAiC0EeMyIuB9HkRIhQRL4IgCIIirot4kciLEKGIeBEEQRAUXdNG4nkRIhQRL4IgCIKim+dF0kZCZBI14kWqjQRBEPpIN8+LRF6EyCRqxItUGwmCIPSRruJFPC9ChBI14kUQBEHoI908L5I2EiITES+CIAiCQiIvwhBBxIsgCIKg6FoqLZEXIUIR8SIIgiAopMOuMEQQ8SIIgiAovKqLNIiJH7SlCEIgRLwIgiAICpPZLWAsSaBpg7seQfCDiBdBEATBjWHalR4vQgQj4kUQBEFwY/hexO8iRDBRI16kw64gCEIYcEVepNJIiFyiRrxIh11BEIQwYJRLS+RFiGCiRrwIgiAIYcBIG4nnRYhgRLwIgiAIblyeF0kbCZGLiBdBEATBjVQbCUMAES+CIAiCG4t4XoTIR8SLIAiC4CY5V70m5Q7uOgQhADGDvQBBEAQhgphzM8SnwdSrBnslguAXES+CIAiCm4QMmHvrYK9CEAIiaSNBEARBEIYUESdempqamDNnDrNmzWL69Ok8+uijg70kQRAEQRAiiIhLGyUmJvL++++TmJhIa2sr06ZN4+qrryYrK2uwlyYIgiAIQgQQcZEXs9lMYqIq0Wtvb8dut6Pr+iCvShAEQRCESCFk8bJ27Vouv/xy8vPz0TSNl19+uds5Dz74IIWFhcTHx1NSUsK6detCekZ9fT0zZ85k5MiRfP/73yc7OzvUZQqCIAiCEKWELF5aWlqYOXMmDzzwgM/jzz33HHfccQd33303W7duZcGCBSxZsoSysjLXOSUlJUybNq3bV3l5OQDp6els376d0tJSnnnmGaqqqnr59gRBEARBiDY0vQ85GU3TeOmll7jyyitd+84880zOOOMMHnroIde+KVOmcOWVV3LfffeF/IxvfOMbnH/++Vx77bU+j1utVqxWq+v7xsZGCgoKaGhoIDU1NeTnCYIgCIIw8DQ2NpKWlhbU53dYPS82m40tW7awePFir/2LFy9mw4YNQd2jqqqKxsZGQL2RtWvXMmnSJL/n33fffaSlpbm+CgoKev8GBEEQBEGIeMIqXmpqarDb7eTl5Xntz8vLo7KyMqh7HD9+nHPPPZeZM2dyzjnn8M1vfpMZM2b4Pf+uu+6ioaHB9XXs2LE+vQdBEARBECKbfimV1jTN63td17vt80dJSQnbtm0L+llxcXHExcWxatUqVq1ahd1uD2WpgiAIgiAMMcIaecnOzsZsNneLslRXV3eLxoSbZcuWsXv3bjZt2tSvzxEEQRAEYXAJq3ixWCyUlJSwZs0ar/1r1qxh/vz54XyUIAiCIAinKSGnjZqbmzl48KDr+9LSUrZt20ZmZiajRo1i+fLl3HDDDcyePZt58+bxyCOPUFZWxtKlS8O68K5I2kgQBEEQTg9CLpV+7733WLRoUbf9N954I08++SSgmtT93//9HxUVFUybNo0//OEPnHvuuWFZcE80NDSQnp7OsWPHpFRaEARBEIYIRquT+vp60tLSAp7bpz4vkcjx48elXFoQBEEQhijHjh1j5MiRAc+JOvHicDgoLy8nJSUl6AonA0P1RXPURt5jdCDvMTqQ9xgdyHsMD7qu09TURH5+PiZTYEtuxE2V7ismk6lHxdYTqampUfsH0EDeY3Qg7zE6kPcYHch77Ds9pYsMIm6qtCAIgiAIQiBEvAiCIAiCMKQQ8eJBXFwcP/3pT4mLixvspfQb8h6jA3mP0YG8x+hA3uPAE3WGXUEQBEEQohuJvAiCIAiCMKQQ8SIIgiAIwpBCxIsgCIIgCEMKES+CIAiCIAwpokq8PPjggxQWFhIfH09JSQnr1q0LeP77779PSUkJ8fHxjB07locffrjbOf/6178oKioiLi6OoqIiXnrppT4/ty+E+z0++uijLFiwgIyMDDIyMrjwwgvZuHGj1zk/+9nP0DTN62vYsGFhf28G4X6PTz75ZLf1a5pGe3t7n57bF8L9HhcuXOjzPX7mM59xnRPJv8eKigq++MUvMmnSJEwmE3fccYfP84by38dg3uNQ//sYzHsc6n8fg3mPQ/3v47///e//3979hTT1v3EAf2w7B2lMoTLTohVdNCr7o2abBrupWeBVFzqkMCmoq2YFsa5mBEP7Q1CMLsZY68KZ2UVd6bxZFy2T6nRhGyWtgsAhCoEmmNbzvfnt4HFTz8487Xz2e14guM+enc95/Jw3ftQdhOPHj0NZWRmUlJSA1WqFwcHBtLq85hELRG9vL3Ichz6fD2OxGDqdTjQYDPj9+/eM9YlEAtevX49OpxNjsRj6fD7kOA77+/vFmmg0ijqdDj0eD8bjcfR4PKjX63F4eFjxvFrrsbW1Fb1eLwqCgPF4HNvb27G0tBR//Pgh1rjdbty7dy+Oj4+LHxMTE2ven1o9BgIBLCkpkZz/+Ph4TvNqrcepqSlJb6Ojo6jT6TAQCIg1Wl7Hr1+/4qVLlzAYDOLBgwfR6XSm1bCeRzk9sp5HOT2ynkc5PbKeR6fTid3d3TgyMoKfP3/G69evI8dx+P79e7Em33ksmM1LXV0dXrx4UTJmNpvR5XJlrL927RqazWbJ2IULF9BisYiPm5ub8cSJE5KaxsZGdDgciufNhRo9LrWwsIBGoxGDwaA45na78cCBA8pPPAtq9BgIBLC0tHRN583Fv1jHe/fuodFoxJmZGXFMy+u4mM1my/gNgfU8LrZcj0uxlsfFluuR9TwuJncdWc5jyp49e/DGjRvi43znsSD+bPT792949+4d2O12ybjdbodoNJrxNa9fv06rb2xshLdv38L8/PyKNaljKplXKbV6XGp2dhbm5+dhw4YNkvGxsTGorKyEnTt3gsPhgEQikUM3manZ48zMDJhMJti2bRs0NTWBIAg5zavUv1pHv98PDocDDAaDZFyr6ygH63lUgrU8ysVyHpVgPY9///6F6elpyXWY7zwWxOZlcnIS/vz5A+Xl5ZLx8vJySCaTGV+TTCYz1i8sLMDk5OSKNaljKplXKbV6XMrlcsHWrVvh2LFj4tiRI0fg8ePHMDg4CD6fD5LJJNTX18PU1FSOXUmp1aPZbIZHjx7BixcvIBQKQXFxMTQ0NMDY2JjieZX6F+s4MjICo6OjcP78ecm4ltdRDtbzqARreZSD9TxmqxDyePfuXfj16xc0NzeLY/nOY0H9V+mioiLJY0RMG1utfum4nGNmO28u1Ogx5datWxAKhSASiUBxcbE4fvLkSfHzqqoqsFqtsGvXLggGg3DlyhVFfaxkrXu0WCxgsVjE5xsaGqC6uhoePHgA9+/fVzxvLtRcR7/fD/v27YO6ujrJuNbXca2OqeV1zAareVxNIeQxG6znMRQKQWdnJzx//hw2b96c9THV+toWxG9eNm3aBDqdLm03NzExkbbrS9myZUvGer1eDxs3blyxJnVMJfMqpVaPKXfu3AGPxwPhcBj279+/4rkYDAaoqqoSf1JaK2r3mLJu3To4fPiweP6FtI6zs7PQ29ub9lNeJlpaRzlYz2M2WM2jEqzlMRus5/HJkydw7tw56Ovrk/z2DyD/eSyIzQvP81BTUwNDQ0OS8aGhIaivr8/4GqvVmlYfDoehtrYWOI5bsSZ1TCXzKqVWjwAAt2/fhps3b8LAwADU1tauei5zc3MQj8ehoqJCQSfLU7PHxRARPnz4IJ5/oawjAEBfXx/Mzc3B6dOnVz0XLa2jHKznUS6W86gEa3nMBst5DIVCcPbsWejp6ZHc4p2S9zzm/JZfjUjdkuX3+zEWi2FHRwcaDAb89u0bIiK6XC48c+aMWJ+6/fTy5csYi8XQ7/en3X766tUr1Ol02NXVhfF4HLu6upa9FWy5ebXeY3d3N/I8j/39/ZJb9qanp8Waq1evYiQSwUQigcPDw9jU1IRGo5GZHjs7O3FgYAC/fPmCgiBge3s76vV6fPPmjex5td5jytGjR7GlpSXjvFpeR0REQRBQEASsqanB1tZWFAQBP378KD7Peh7l9Mh6HuX0yHoe5fSYwmoee3p6UK/Xo9frlVyHP3/+FGvynceC2bwgInq9XjSZTMjzPFZXV+PLly/F59ra2tBms0nqI5EIHjp0CHmexx07duDDhw/Tjvn06VPcvXs3chyHZrMZnz17ltW8a22tezSZTAgAaR9ut1usaWlpwYqKCuQ4DisrK/HUqVMZg6rVHjs6OnD79u3I8zyWlZWh3W7HaDSa1bxrTY1r9dOnTwgAGA6HM86p9XXMdB2aTCZJDet5XK3HQsjjaj0WQh7lXKss59Fms2Xssa2tTXLMfOaxCPF/7/wjhBBCCGFAQbznhRBCCCH/P2jzQgghhBCm0OaFEEIIIUyhzQshhBBCmEKbF0IIIYQwhTYvhBBCCGEKbV4IIYQQwhTavBBCCCGEKbR5IYQQQghTaPNCCCGEEKbQ5oUQQgghTKHNCyGEEEKY8h9V+sotGYvcxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "t=0\n",
    "ax.plot(bk_gadget['ksamp'], np.abs(bk_gadget['B'][t]), label='gadget')\n",
    "# ax.plot(bk_gadget_fnl0['ksamp'], bk_gadget_fnl0['B'][t], label='gadget fnl0')\n",
    "ax.plot(bk_abacus['ksamp'], np.abs(bk_abacus['B'][t]), label='abacus')\n",
    "# ax.plot(bk_abacus_fnl0['ksamp'], bk_abacus_fnl0['B'][t], label='abacus fnl0')\n",
    "ax.legend()\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(pk_abacus['k_mid'][1:], pk_abacus['power'][1:], label='abacus')\n",
    "ax.plot(bk_abacus['ksamp'], bk_abacus['Pk'][0], label='bk')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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