MACS0647 Galaxies HST Sersic fits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Sersic profiles to galaxies in a Hubble image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use Astropy Sersic2D to model every object detected in a public HST image:  \n",
    "CLASH WFC3/IR F160W imaging of the galaxy cluster MACS0647+70\n",
    "  \n",
    "It's like a quick and dirty version of GALFIT. Note we're fitting all objects, including stars, each with individual Sersic profile. The Sersic fitting of each galaxy is performed in multiple steps to make it more stable. Otherwise, it's not very fancy.\n",
    "\n",
    "Inputs:  \n",
    "- HST FITS image (retrieved from MAST online archive)\n",
    "- segmentation map (defining the outline of each object; here we use a version manually cleaned of artifacts)\n",
    "- object catalog (here from Astropy Photutils)\n",
    "\n",
    "Outputs:  \n",
    "- Sersic model catalog\n",
    "- model FITS image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "interactive_plots = False\n",
    "\n",
    "if interactive_plots:\n",
    "    %matplotlib notebook\n",
    "    figsize=(9.5,3)\n",
    "else:\n",
    "    %matplotlib inline\n",
    "    figsize=(15,5)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# https://matplotlib.org/tutorials/introductory/customizing.html\n",
    "plt.style.use('/Users/dcoe/p/matplotlibrc.txt')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy\n",
    "from astropy.io import fits\n",
    "from astropy import units as u\n",
    "from astropy.table import QTable\n",
    "\n",
    "import photutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.special as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load HST WFC3/IR F160W image\n",
    "image_file = 'https://archive.stsci.edu/missions/hlsp/clash/macs0647/data/hst/scale_65mas/hlsp_clash_hst_wfc3ir_macs0647_f160w_v1_drz.fits'\n",
    "hdu   = fits.open(image_file)[0]\n",
    "data_count_rate  = hdu.data\n",
    "exposure_time = hdu.header['EXPTIME']\n",
    "data_counts = data_count_rate * exposure_time  # convert from counts/s to counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def HST_zeropoint(header, force_per_second=False, force_total=False):\n",
    "    photflam = header.get('PHOTFLAM', None)  # inverse sensitivity (erg / cm^2 / s / Ang)\n",
    "    photplam = header.get('PHOTPLAM', None)  # pivot wavelength\n",
    "    photplam0 = 5475.4\n",
    "    pzpt     = header.get('PHOTZPT')\n",
    "    exptime  = header.get('EXPTIME')\n",
    "    bunit    = header.get('BUNIT', 'COUNTS')\n",
    "    bunit    = bunit.strip()\n",
    "    if ((bunit[-2:] != '/S') or (force_total == True)) and (force_per_second == False):\n",
    "        photflam = photflam / exptime\n",
    "    \n",
    "    zp = pzpt - 2.5 * np.log10(photflam) - 5 * np.log10(photplam / photplam0)\n",
    "    return zp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.955899912844764"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flux = zp - 2.5 * log10(mag)\n",
    "zeropoint = HST_zeropoint(hdu.header)\n",
    "zeropoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import expanduser\n",
    "home = expanduser(\"~\")\n",
    "catalog_path = os.path.join(home, 'm0647', 'Ryan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load cleaned segmentation map (from Russ)\n",
    "segm_file = os.path.join(catalog_path, 'z11_seg_cleaned.fits')\n",
    "segmhdu = fits.open(segm_file)[0]\n",
    "segm_data = segmhdu.data\n",
    "segm = photutils.segmentation.SegmentationImage(segm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input photutils catalog\n",
    "catalog_file = os.path.join(catalog_path, 'MACS0647.ecsv')\n",
    "\n",
    "tbl = QTable.read(catalog_file)\n",
    "tbl['ra']  = tbl['sky_centroid'].ra.degree\n",
    "tbl['dec'] = tbl['sky_centroid'].dec.degree\n",
    "tbl.remove_column('sky_centroid')  # can't be sorted!\n",
    "tbl.sort('F160W_mag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.modeling import models, fitting\n",
    "from astropy.modeling.models import Sersic2D, Sersic1D\n",
    "fitter = fitting.LevMarLSQFitter()\n",
    "#import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.visualization import LinearStretch, SqrtStretch, LogStretch\n",
    "from astropy.visualization.mpl_normalize import ImageNormalize\n",
    "from astropy.visualization import simple_norm\n",
    "#norm = ImageNormalize(stretch=LinearStretch(), vmin=-0.1)\n",
    "#norm = ImageNormalize(stretch=SqrtStretch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 3000)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Format interactive cursor output for segmentation map (otherwise 4-digit integers truncated in output)\n",
    "\n",
    "plot_data0 = data_count_rate\n",
    "def format_coord0(x, y):\n",
    "    col = int(x + 0.5)\n",
    "    row = int(y + 0.5)\n",
    "    z = plot_data0[row, col]\n",
    "    return 'x=%d, y=%d, data=%d' % (col, row, z)\n",
    "\n",
    "plot_data = segm_data  #segm.data #[segmobj.bbox.slices]\n",
    "def format_coord1(x, y):\n",
    "    col = int(x + 0.5)\n",
    "    row = int(y + 0.5)\n",
    "    z = plot_data[row, col]\n",
    "    return 'x=%d, y=%d, segm=%d' % (col, row, z)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(8,4), sharex=True, sharey=True)\n",
    "\n",
    "ax[0].set_title('Data')\n",
    "norm = simple_norm(data_count_rate, 'asinh', percent=99.9)\n",
    "ax[0].imshow(data_count_rate, norm=norm)\n",
    "ax[0].format_coord = format_coord0\n",
    "\n",
    "ax[1].set_title('Segmentation')\n",
    "cmap = segm.make_cmap(random_state=12345)  # ERROR\n",
    "ax[1].imshow(segm_data, cmap=cmap)\n",
    "ax[1].format_coord = format_coord1\n",
    "\n",
    "plt.xlim(2000,3000)\n",
    "plt.ylim(2000,3000)\n",
    "\n",
    "#plt.savefig('HST_Sersic_fitting.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxiter = 10000  # maximum number of iterations  1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_0 = 1  # initial guess for n_Sersic\n",
    "n_min = 0.25  # minimum allowed\n",
    "n_max = 8     # maximum allowed\n",
    "\n",
    "def initial_guess_from_catalog(tbl_obj):\n",
    "    x = tbl_obj['xcentroid'].value\n",
    "    y = tbl_obj['ycentroid'].value\n",
    "    a = tbl_obj['a'].value\n",
    "    b = tbl_obj['b'].value\n",
    "    A = tbl_obj['area']  # ~ pi r^2\n",
    "    r_Sersic = np.sqrt(A / np.pi).value\n",
    "    n_Sersic = n_0\n",
    "    ellipticity    = tbl_obj['ellipticity']\n",
    "    position_angle = tbl_obj['orientation']\n",
    "    theta = position_angle.to(u.rad)\n",
    "    magnitude = tbl[iobj]['F160W_mag']\n",
    "    flux = zeropoint - 2.5 * np.log10(magnitude)\n",
    "    flux *= exposure_time\n",
    "    #count_rate = magnitude_to_countrate('imaging', 'abmag', magnitude, photfnu)    \n",
    "    b_n = sp.gammaincinv(2*n_Sersic, 0.5)\n",
    "    Sersic_total = 2 * np.pi * a * b * n_Sersic * sp.gamma(2 * n_Sersic) * np.exp(b_n) / \\\n",
    "        (b_n**(2*n_Sersic))\n",
    "    amplitude = 1 / Sersic_total\n",
    "    amplitude *= flux\n",
    "    \n",
    "    initial_guess = Sersic2D(amplitude=amplitude, r_eff=r_Sersic, x_0=x, y_0=y, ellip=ellipticity, theta=theta, n=n_0, bounds=param_bounds, fixed=param_fixed)\n",
    "    #print(initial_guess.render)\n",
    "    return initial_guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_guess_from_model(model):\n",
    "    #global initial_guess\n",
    "    initial_guess = Sersic2D(amplitude=model.amplitude, r_eff=model.r_eff, x_0=model.x_0, y_0=model.y_0, ellip=model.ellip, theta=model.theta, n=model.n, bounds=param_bounds, fixed=param_fixed)\n",
    "    #print(initial_guess)\n",
    "    return initial_guess    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_model_to_data(plot_results=True, verbose=False):\n",
    "    global model, norm, fit_stamp, fitter\n",
    "    model = fitter(initial_guess, xx_stamp, yy_stamp, data_stamp, weights=weight_stamp, maxiter=maxiter)\n",
    "    #print(model)\n",
    "    if verbose:\n",
    "        print(fitter.fit_info['message'])\n",
    "        print(model.render)\n",
    "    fit_stamp = model(xx_stamp, yy_stamp).value\n",
    "    #fit_stamp = np.ma.masked_array(fit_stamp, segm_obj.data_ma.mask)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fit_results(outfile=''):\n",
    "    global norm, ax\n",
    "    #norm = ImageNormalize(stretch=LinearStretch()) #, vmin=-0.1)\n",
    "    #norm = ImageNormalize(stretch=SqrtStretch())\n",
    "    norm = ImageNormalize(stretch=LogStretch())\n",
    "    extent = stamp_extent\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 4, figsize=figsize, sharex=True, sharey=True)\n",
    "\n",
    "    ax[0].imshow(data_stamp, norm=norm, vmin=0, extent=extent)\n",
    "    ax[0].set_title('Data')\n",
    "\n",
    "    ax[1].imshow(fit_stamp, norm=norm, vmin=0, extent=extent)\n",
    "    ax[1].set_title('Fit')\n",
    "\n",
    "    residual = fit_stamp - data_stamp\n",
    "    ax[2].imshow(np.abs(residual), norm=norm, vmin=0, extent=extent)\n",
    "    ax[2].set_title('abs(Residual)')\n",
    "\n",
    "    ax[3].imshow(residual, extent=extent, cmap='bwr')\n",
    "    ax[3].set_title('Residual')\n",
    "    \n",
    "    if outfile:\n",
    "        plt.savefig(outfile)\n",
    "        plt.close()\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fit_results2(outfile=''):\n",
    "    global norm, ax\n",
    "    #norm = ImageNormalize(stretch=LinearStretch()) #, vmin=-0.1)\n",
    "    #norm = ImageNormalize(stretch=SqrtStretch())\n",
    "    norm = ImageNormalize(stretch=LogStretch())\n",
    "    extent = stamp_extent\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 5, figsize=figsize, sharex=True, sharey=True)\n",
    "\n",
    "    ax[0].imshow(data_stamp, norm=norm, vmin=0, extent=extent)\n",
    "    ax[0].set_title('Data')\n",
    "\n",
    "    ax[1].imshow(fit_stamp, norm=norm, vmin=0, extent=extent)\n",
    "    ax[1].set_title('Fit')\n",
    "\n",
    "    residual = fit_stamp - data_stamp\n",
    "    ax[2].imshow(np.abs(residual), norm=norm, vmin=0, extent=extent)\n",
    "    ax[2].set_title('abs(Residual)')\n",
    "\n",
    "    ax[3].imshow(residual, extent=extent, cmap='bwr')\n",
    "    ax[3].set_title('Residual')\n",
    "    \n",
    "    ax[4].imshow(weight_stamp**2, extent=extent)\n",
    "    ax[4].set_title('Weight^2')\n",
    "    \n",
    "    if outfile:\n",
    "        plt.savefig(outfile)\n",
    "        plt.close()\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_fit_results2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmax = 50\n",
    "\n",
    "def keep_close(x, goal, dist):\n",
    "    d = x - goal\n",
    "    dabs = abs(d)\n",
    "    if dabs:\n",
    "        sgn = int(d / dabs)\n",
    "        if dabs > dist:\n",
    "            x = goal + sgn * dist\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_min = 10  # for use with weight map calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_object_to_Sersic(segm_id):\n",
    "    global iobj, segm_obj, tbl_obj, data_stamp, weight_stamp, stamp_extent, xx_stamp, yy_stamp, \\\n",
    "        param_bounds, param_fixed, initial_guess, model\n",
    "    \n",
    "    iobj = segm.get_index(segm_id)\n",
    "    segm_obj = segm[iobj]\n",
    "    #tbl_obj  = tbl[iobj]\n",
    "    tbl_obj = tbl[tbl['id'].data == segm_id]\n",
    "    x = tbl_obj['xcentroid'].value\n",
    "    y = tbl_obj['ycentroid'].value\n",
    "\n",
    "    yslice_obj, xslice_obj = obj_slices = segm_obj.slices\n",
    "    ymin_obj = yslice_obj.start\n",
    "    ymax_obj = yslice_obj.stop - 1\n",
    "    xmin_obj = xslice_obj.start\n",
    "    xmax_obj = xslice_obj.stop - 1\n",
    "    #print('***', ymin_obj, ymax_obj+1, xmin_obj, xmax_obj+1, y, x)\n",
    "\n",
    "    xmin_stamp = keep_close(xmin_obj, int(x), rmax)\n",
    "    xmax_stamp = keep_close(xmax_obj, int(x), rmax)\n",
    "    ymin_stamp = keep_close(ymin_obj, int(y), rmax)\n",
    "    ymax_stamp = keep_close(ymax_obj, int(y), rmax)\n",
    "    #print('***', ymin_stamp, ymax_stamp+1, xmin_stamp, xmax_stamp+1, y, x)\n",
    "\n",
    "    yy_obj, xx_obj = np.mgrid[segm_obj.slices]\n",
    "    xy_within_stamp = (xmin_stamp <= xx_obj) & (xx_obj <= xmax_stamp) \\\n",
    "                    & (ymin_stamp <= yy_obj) & (yy_obj <= ymax_stamp)\n",
    "    #xy_outside_stamp = np.logical_not(xy_within_stamp)\n",
    "\n",
    "    data_within_obj = np.logical_not(segm_obj.data_ma.mask)\n",
    "    all_good = data_within_obj & xy_within_stamp\n",
    "    obj_mask = np.logical_not(all_good)\n",
    "    data_obj = np.ma.masked_array(segm_obj.make_cutout(data_counts), obj_mask)\n",
    "    yy_stamp, xx_stamp = np.mgrid[ymin_stamp:ymax_stamp+1, xmin_stamp:xmax_stamp+1]\n",
    "\n",
    "    xstamp_slice = slice(xmin_stamp-xmin_obj, xmax_stamp-xmin_obj+1)\n",
    "    ystamp_slice = slice(ymin_stamp-ymin_obj, ymax_stamp-ymin_obj+1)\n",
    "    data_stamp = data_obj[ystamp_slice,xstamp_slice]\n",
    "    data_stamp_clipped = np.where(data_stamp < data_min, data_min, data_stamp)\n",
    "    weight_stamp = np.sqrt(data_stamp_clipped)\n",
    "    weight_stamp = np.where(data_stamp.mask, 0, weight_stamp)\n",
    "    stamp_extent = xmin_stamp-0.5, xmax_stamp+0.5, ymin_stamp-0.5, ymax_stamp+0.5\n",
    "\n",
    "    param_bounds = {}\n",
    "    param_fixed = {}\n",
    "    \n",
    "    # Initial guess based on photutils catalog\n",
    "    initial_guess = initial_guess_from_catalog(tbl_obj)\n",
    "    model = initial_guess\n",
    "    fit_stamp = model(xx_stamp, yy_stamp).value\n",
    "    #fit_stamp = np.ma.masked_array(fit_fit_stampobj, segm_obj.data_ma.mask)\n",
    "    \n",
    "    # Restrict effective radius to within object\n",
    "    r_eff_max = np.max([(xmax_obj-xmin_obj)/2, (ymax_obj-ymin_obj)/2])\n",
    "    \n",
    "    # Model #1: Set amplitude free\n",
    "    param_bounds = {'ellip':(0,1), 'n':(n_min,n_max), 'theta':(0,2*np.pi), 'r_eff':(0,r_eff_max), 'x_0':(xmin_stamp,xmax_stamp), 'y_0':(ymin_stamp,ymax_stamp)}\n",
    "    param_fixed = {'r_eff':True, 'ellip':True, 'n':True, 'theta':True, 'x_0':True, 'y_0':True}\n",
    "    initial_guess = initial_guess_from_model(model)  # run this again to set bounds/fixed constraints above\n",
    "    fit_model_to_data()  # verbose=True\n",
    "\n",
    "    # Model #2: Fit amplitude & radius\n",
    "    param_bounds = {'ellip':(0,1), 'n':(n_min,n_max), 'theta':(0,2*np.pi), 'r_eff':(0,r_eff_max), 'x_0':(xmin_stamp,xmax_stamp), 'y_0':(ymin_stamp,ymax_stamp)}\n",
    "    param_fixed = {'ellip':True, 'n':True, 'theta':True, 'x_0':True, 'y_0':True}\n",
    "    initial_guess = initial_guess_from_model(model)\n",
    "    fit_model_to_data()\n",
    "    \n",
    "    # Model #3: Fit amplitude, radius, & centroid\n",
    "    param_fixed = {'ellip':True, 'theta':True, 'r_eff':True}\n",
    "    initial_guess = initial_guess_from_model(model)\n",
    "    fit_model_to_data()\n",
    "    \n",
    "    # Model #4: Fit everything but theta\n",
    "    param_fixed = {'theta':True}\n",
    "    initial_guess = initial_guess_from_model(model)\n",
    "    fit_model_to_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sersic_galaxy(model, floor=None):\n",
    "    model1D = Sersic1D(amplitude=model.amplitude, r_eff=model.r_eff, n=model.n)\n",
    "    radius_factor = 3\n",
    "    stamp_radius = radius_factor * model.r_eff\n",
    "    test_amplitude = model1D(stamp_radius)\n",
    "    if floor != None:\n",
    "        while test_amplitude > floor:\n",
    "            radius_factor *= 1.1\n",
    "            stamp_radius = radius_factor * model.r_eff\n",
    "            test_amplitude = model1D(stamp_radius)\n",
    "            #print(radius_factor, test_radius, test_amplitude)\n",
    "            if stamp_radius > image_size:\n",
    "                break\n",
    "    \n",
    "    #print('%4d %6.1f %6.1f  %g' % (iobj, radius_factor, test_radius, test_amplitude))\n",
    "    #s = '%4d  %4d  %10.3g %4d %4d  %g' % (iobj, id, model.amplitude, radius_factor, stamp_radius, test_amplitude)\n",
    "    s = '%4d  %11.4g %4d %4d  %g' % (segm_id, model.amplitude.value, radius_factor, stamp_radius, test_amplitude)\n",
    "    #print(s)\n",
    "    #fout.write(s+'\\n')\n",
    "    \n",
    "    xmin = int(model.x_0 - stamp_radius)\n",
    "    xmax = int(model.x_0 + stamp_radius + 1)\n",
    "    ymin = int(model.y_0 - stamp_radius)\n",
    "    ymax = int(model.y_0 + stamp_radius + 1)\n",
    "    x_stamp, y_stamp = np.meshgrid(np.arange(xmin, xmax), np.arange(ymin, ymax))\n",
    "    \n",
    "    stamp = model(x_stamp, y_stamp)\n",
    "    stamp = stamp.value\n",
    "    extent = xmin, xmax, ymin, ymax\n",
    "    return stamp, extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_stamp_to_image(image, stamp, extent):\n",
    "    xmin, xmax, ymin, ymax = extent\n",
    "    stamp_within_image = stamp[:]\n",
    "    stamp_ylo = stamp_xlo = 0\n",
    "    stamp_yhi, stamp_xhi = stamp.shape\n",
    "    \n",
    "    if ymin < 0:\n",
    "        stamp_ylo -= ymin\n",
    "        stamp_yhi -= stamp_ylo\n",
    "        ymin = 0\n",
    "        ymax -= stamp_ylo\n",
    "    if ymax > ny_image:\n",
    "        stamp_yhi -= (ymax - ny_image)\n",
    "        ymax = ny_image\n",
    "    if xmin < 0:\n",
    "        stamp_xlo -= xmin\n",
    "        stamp_xhi -= stamp_xlo\n",
    "        xmin = 0\n",
    "        xmax -= stamp_xlo\n",
    "    if xmax > nx_image:\n",
    "        stamp_xhi -= (xmax - nx_image)\n",
    "        xmax = nx_image\n",
    "        \n",
    "    stamp_within_image = stamp[stamp_ylo:stamp_yhi, stamp_xlo:stamp_xhi]\n",
    "    \n",
    "    image[ymin:ymax, xmin:xmax] += stamp_within_image\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_fit(outfile=''):\n",
    "    fig, ax = plt.subplots(1, 3, figsize=figsize, sharex=True, sharey=True)\n",
    "\n",
    "    ax[0].set_title('Data')\n",
    "    norm = simple_norm(data_counts, 'asinh', percent=99.9)\n",
    "    ax[0].imshow(data_counts, norm=norm)\n",
    "\n",
    "    ax[1].set_title('Masked Data')\n",
    "    ax[1].imshow(data_counts_masked, norm=norm)\n",
    "\n",
    "    ax[2].set_title('Model')\n",
    "    ax[2].imshow(image, norm=norm)\n",
    "\n",
    "    plt.xlim(2000,3000)\n",
    "    plt.ylim(2000,3000)\n",
    "    if outfile:\n",
    "        plt.savefig(outfile)\n",
    "        plt.close()\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "segm_id = 786\n",
    "#segm_id = 286  # BCG\n",
    "#segm_id = 425  # BCG\n",
    "segm_id = 16 # bright star requires weights = data (or maybe a smaller stamp!)\n",
    "segm_id = 782\n",
    "#segm_id = 17  # requires more weighting in the center\n",
    "#segm_id = 1536 # spiral\n",
    "\n",
    "# Long arcs:\n",
    "segm_id = tbl['id'][278]\n",
    "\n",
    "fit_object_to_Sersic(segm_id)\n",
    "#plot_fit_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>QTable length=1198</i>\n",
       "<table id=\"table48335812240\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>id</th><th>xcentroid</th><th>ycentroid</th><th>area</th><th>a</th><th>b</th><th>ellipticity</th><th>orientation</th><th>gini</th><th>F105W_flux</th><th>F105W_fluxerr</th><th>F105W_mag</th><th>F105W_magerr</th><th>F125W_flux</th><th>F125W_fluxerr</th><th>F125W_mag</th><th>F125W_magerr</th><th>F140W_flux</th><th>F140W_fluxerr</th><th>F140W_mag</th><th>F140W_magerr</th><th>F160W_flux</th><th>F160W_fluxerr</th><th>F160W_mag</th><th>F160W_magerr</th><th>TOTAL_flux</th><th>TOTAL_fluxerr</th><th>TOTAL_mag</th><th>TOTAL_magerr</th><th>ra</th><th>dec</th></tr></thead>\n",
       "<thead><tr><th></th><th>pix</th><th>pix</th><th>pix2</th><th>pix</th><th>pix</th><th></th><th>deg</th><th></th><th>nJy</th><th>nJy</th><th></th><th></th><th>nJy</th><th>nJy</th><th></th><th></th><th>nJy</th><th>nJy</th><th></th><th></th><th>nJy</th><th>nJy</th><th></th><th></th><th>nJy</th><th>nJy</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>16</td><td>2347.9284951456048</td><td>1674.4920495695358</td><td>56428.0</td><td>10.102895784793382</td><td>9.926894783407528</td><td>0.017420846966546666</td><td>-66.39695975129004</td><td>0.9809265674256349</td><td>20262665.087193694</td><td>45591.14794759686</td><td>13.133258584440595</td><td>0.002440170406087385</td><td>25703098.80664737</td><td>120333624.83129258</td><td>12.875036285689404</td><td>1.8861914879557227</td><td>28139157.22463378</td><td>93137.11232700183</td><td>12.776722284859085</td><td>0.0035877176859105886</td><td>33729793.966923945</td><td>82714.45997373738</td><td>12.579965779053477</td><td>0.0026592557719358153</td><td>27452357.42865264</td><td>47354.4852404677</td><td>12.803550888117174</td><td>0.0018712486144806645</td><td>101.9675239994528</td><td>70.23372398165105</td></tr>\n",
       "<tr><td>431</td><td>2240.9548087465355</td><td>2450.2487104947572</td><td>13094.0</td><td>6.2643221998243765</td><td>6.086594299098593</td><td>0.028371449465157794</td><td>80.44814355490173</td><td>0.9664024723897366</td><td>5956350.683942737</td><td>7307.708718042144</td><td>14.462549352006773</td><td>0.0013312481344658186</td><td>5752224.043225025</td><td>7326.659302606502</td><td>14.500410517428096</td><td>0.0013820319654504014</td><td>6400662.896667364</td><td>7135.473773688252</td><td>14.384437612908975</td><td>0.0012097070606822934</td><td>6264361.660387892</td><td>6056.009134495468</td><td>14.407807942758133</td><td>0.0010491176130362104</td><td>5908572.585036829</td><td>3131.6239373407634</td><td>14.471293562313342</td><td>0.0005753025292492948</td><td>101.9732446746625</td><td>70.24773035192908</td></tr>\n",
       "<tr><td>183</td><td>2806.9860562820145</td><td>2051.7119633871994</td><td>13869.0</td><td>6.517736058478899</td><td>5.942947719051338</td><td>0.08818834243522666</td><td>-23.97702246995355</td><td>0.9688691596182301</td><td>2521471.427795046</td><td>4765.84043817213</td><td>15.395864871777436</td><td>0.002050216190747855</td><td>2702653.2431681105</td><td>4544.194248512845</td><td>15.320524179352017</td><td>0.0018240050118793149</td><td>2608288.027302521</td><td>4636.446681652089</td><td>15.359111130355236</td><td>0.0019282721573319554</td><td>2759108.2602305263</td><td>3849.551190103678</td><td>15.29807814650201</td><td>0.0015137804300050218</td><td>2663774.8982918253</td><td>1976.0299615509443</td><td>15.336256194775569</td><td>0.0008051175429411795</td><td>101.94300960014174</td><td>70.24053432593968</td></tr>\n",
       "<tr><td>5</td><td>2436.5936079863372</td><td>1431.1383240918092</td><td>9708.0</td><td>5.88691516610404</td><td>5.572605335825596</td><td>0.05339126204640965</td><td>-18.470507333187264</td><td>0.9633348952119359</td><td>1592089.7803495573</td><td>3947.1419725338783</td><td>15.895081113463537</td><td>0.002688448442772924</td><td>1867673.350805799</td><td>4041.5982986943</td><td>15.721747694926751</td><td>0.0023469674474675146</td><td>1955390.2851984352</td><td>3932.54752888298</td><td>15.671916367036758</td><td>0.002181365804673554</td><td>2092905.0107976708</td><td>3285.967549216599</td><td>15.59812620557775</td><td>0.0017033243815956223</td><td>1857074.444954015</td><td>1671.0492558036358</td><td>15.727926715630762</td><td>0.0009765375227941227</td><td>101.96278948232992</td><td>70.2293302451883</td></tr>\n",
       "<tr><td>576</td><td>3406.1082469958496</td><td>2405.643691330527</td><td>5848.0</td><td>5.235868568392646</td><td>5.08176784169339</td><td>0.02943174082510691</td><td>-28.134951006854315</td><td>0.947189702502319</td><td>524308.2313939304</td><td>2477.276525341179</td><td>17.10103331003144</td><td>0.005117857127944058</td><td>592689.2871330704</td><td>2203.0768016113466</td><td>16.96793230642177</td><td>0.0040282922645473885</td><td>635851.2803508693</td><td>2704.916629097166</td><td>16.89161112469837</td><td>0.004608934547877236</td><td>720405.2841884176</td><td>2551.148264262367</td><td>16.75605777570339</td><td>0.0038380911544411466</td><td>592019.6748397712</td><td>1159.3326561237743</td><td>16.969159649863478</td><td>0.002124082414762954</td><td>101.91099700770236</td><td>70.24691899217079</td></tr>\n",
       "<tr><td>286</td><td>2464.7802118257696</td><td>2480.704756192045</td><td>19123.0</td><td>24.373945722574952</td><td>16.801838510755726</td><td>0.3106639892451226</td><td>15.364425793981443</td><td>0.7804373675515582</td><td>128801.02756642136</td><td>1486.1545968220905</td><td>18.625201680471445</td><td>0.012455908747633151</td><td>161936.35698280184</td><td>1698.997903293253</td><td>18.376639087746284</td><td>0.01133194447526337</td><td>212160.55035840624</td><td>1766.982400286523</td><td>18.083338416713598</td><td>0.009005122832429746</td><td>253019.84995058243</td><td>1654.6116054934037</td><td>17.89211351538221</td><td>0.007077006949474026</td><td>194373.27503553196</td><td>897.2450452102585</td><td>18.178408619389423</td><td>0.005000326781597704</td><td>101.96128661086044</td><td>70.24828077103126</td></tr>\n",
       "<tr><td>526</td><td>2698.5541809786173</td><td>2653.101554899796</td><td>12149.0</td><td>18.52186182692409</td><td>15.847412084946242</td><td>0.14439421732917612</td><td>-84.24295105755048</td><td>0.7576282327120146</td><td>124171.25035168066</td><td>1101.9206865719775</td><td>18.66494736424208</td><td>0.00959254137559896</td><td>166985.56460880802</td><td>1218.7307706415627</td><td>18.343302676647625</td><td>0.007895381755596809</td><td>204341.8826068604</td><td>1280.0132598113319</td><td>18.1241065244267</td><td>0.0067799221675874126</td><td>245853.88321237132</td><td>1148.8140264890117</td><td>17.923307319315306</td><td>0.005061558634083762</td><td>187925.21603026544</td><td>542.1771255518737</td><td>18.21503735459108</td><td>0.0031279133935039295</td><td>101.94879503801519</td><td>70.2513931847212</td></tr>\n",
       "<tr><td>425</td><td>2543.4404207434463</td><td>2509.386301647606</td><td>10429.0</td><td>21.354208084842217</td><td>17.117981979386897</td><td>0.1983789840683582</td><td>15.500629646478522</td><td>0.6393499365260609</td><td>125807.3149868447</td><td>1282.5301393330571</td><td>18.650735265945833</td><td>0.011012392097668442</td><td>162291.0167561673</td><td>1458.6464468592137</td><td>18.374263797187343</td><td>0.009714835729071959</td><td>206270.71794000213</td><td>1529.6395318283096</td><td>18.113906049549477</td><td>0.008021775358752625</td><td>244231.54148071315</td><td>1435.3058230954364</td><td>17.930495623639096</td><td>0.006362004822224008</td><td>189154.17548336883</td><td>756.5814839920638</td><td>18.207960168582446</td><td>0.004334080678506284</td><td>101.95708393248779</td><td>70.24879862589846</td></tr>\n",
       "<tr><td>840</td><td>2527.2375143468407</td><td>3042.0676426073896</td><td>12461.0</td><td>19.783901738756686</td><td>16.81618547734293</td><td>0.15000662157556088</td><td>50.360761037702794</td><td>0.7096363993497826</td><td>135057.52762238242</td><td>1064.756065990149</td><td>18.573703011961335</td><td>0.008526078289262067</td><td>171206.76213233228</td><td>1133.9784842565348</td><td>18.31619771511152</td><td>0.007167602484988261</td><td>199449.18899891234</td><td>1171.3437336276381</td><td>18.15041931320126</td><td>0.006357761483820544</td><td>223751.8889631298</td><td>1021.3122642338349</td><td>18.02558322396531</td><td>0.004944551118249836</td><td>186448.98105075106</td><td>495.94700622456713</td><td>18.22359996432125</td><td>0.0028841816848741725</td><td>101.95794892946411</td><td>70.25841649267484</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>379</td><td>3204.883569844592</td><td>1979.3730382701074</td><td>9.0</td><td>1.074600205980404</td><td>0.6847882826045061</td><td>0.36275065015482266</td><td>45.75405130304281</td><td>0.14439998471310597</td><td>9.237854361703267</td><td>6.702942529593237</td><td>28.986072222134048</td><td>0.5923472928335444</td><td>10.076078778961321</td><td>7.347747223455168</td><td>28.89177111416584</td><td>0.5946299278845321</td><td>12.588274160695203</td><td>8.019300194151834</td><td>28.650084517832752</td><td>0.5351517066086435</td><td>14.660916402148525</td><td>6.892708310480362</td><td>28.48459720657733</td><td>0.4183979984245855</td><td>12.984041336764752</td><td>3.547669228421263</td><td>28.616475276334825</td><td>0.26226975911188444</td><td>101.92176132735415</td><td>70.23922501186625</td></tr>\n",
       "<tr><td>655</td><td>2258.921588702054</td><td>2796.9599633859552</td><td>7.0</td><td>0.7683382322179925</td><td>0.5900223661847621</td><td>0.23207990772303344</td><td>86.74414242321792</td><td>0.3005030553310426</td><td>-4.006005902619658</td><td>2.6076870408794113</td><td>99.0</td><td>30.359361328146637</td><td>-1.1695320232499675</td><td>2.3778256443060948</td><td>99.0</td><td>30.45954998372546</td><td>3.1542354235916457</td><td>3.5924725497524785</td><td>30.15276473808287</td><td>0.825494519136608</td><td>12.72840899404675</td><td>6.907348502655806</td><td>28.63806469555723</td><td>0.4706838452739071</td><td>4.470893108977029</td><td>1.9207572228546554</td><td>29.77401428302192</td><td>0.38804680301477845</td><td>101.97228868411726</td><td>70.25399048695067</td></tr>\n",
       "<tr><td>240</td><td>2367.774597559875</td><td>2728.835287549326</td><td>8.0</td><td>0.9404638349240196</td><td>0.5592723404006658</td><td>0.4053228634295656</td><td>74.63915869441881</td><td>0.3549450466273938</td><td>-5.5410134172404995</td><td>2.7213871464520265</td><td>99.0</td><td>30.313024176965804</td><td>-1.1143980789908445</td><td>3.6478671568512455</td><td>99.0</td><td>29.994902464028556</td><td>1.1178888855716962</td><td>4.586761938630637</td><td>31.279003404319994</td><td>1.7695760714452524</td><td>12.42808360437964</td><td>7.924042102141857</td><td>28.663989584712873</td><td>0.5355140257903211</td><td>5.974935534727678</td><td>2.9741873142921316</td><td>29.459166940201893</td><td>0.4386181149444831</td><td>101.96647090574416</td><td>70.25276077842739</td></tr>\n",
       "<tr><td>626</td><td>1534.109821353476</td><td>2352.780357293048</td><td>2.0</td><td>2.241211655541869</td><td>0.28867513459481237</td><td>0.8711968439567044</td><td>-63.43494882292201</td><td>0.1098213534760954</td><td>4.151099660618393</td><td>11.08431406306417</td><td>29.85459209961979</td><td>1.411727730012705</td><td>6.197375028645196</td><td>10.267180522521043</td><td>29.419480555275705</td><td>1.0608555846232506</td><td>6.148524801925122</td><td>13.377290068559835</td><td>29.428072676916983</td><td>1.2545955952455758</td><td>7.040366700069294</td><td>12.367296187309059</td><td>29.28101179970541</td><td>1.1009448993526898</td><td>5.425183735466141</td><td>5.796152382159746</td><td>29.563963872294043</td><td>0.7890753002371441</td><td>102.01100420569009</td><td>70.24596365874622</td></tr>\n",
       "<tr><td>837</td><td>2373.208969406837</td><td>2602.751735993285</td><td>36.0</td><td>1.1491119026087135</td><td>0.8936948428278491</td><td>0.22227344369248703</td><td>-69.73692267320581</td><td>0.6113752027998473</td><td>27.988372436166486</td><td>22.5245020858998</td><td>27.782555889109993</td><td>0.6410610978364212</td><td>24.41329010428024</td><td>26.03553173840685</td><td>27.930934220255985</td><td>0.7880617912277361</td><td>24.741413379219363</td><td>26.67353553248674</td><td>27.916438736174797</td><td>0.7941622576884786</td><td>5.597710366214049</td><td>24.797069285087833</td><td>29.52997394081305</td><td>1.836971439035858</td><td>10.233427094127723</td><td>12.278502728153136</td><td>28.874947250318954</td><td>0.8559790659236876</td><td>101.96617975367002</td><td>70.25048428094884</td></tr>\n",
       "<tr><td>160</td><td>1713.0</td><td>1684.48548857332</td><td>2.0</td><td>0.5771678714456009</td><td>0.28867513459481287</td><td>0.4998419890009054</td><td>90.0</td><td>0.029022853359909166</td><td>2.3266585229285184</td><td>9.091042237803723</td><td>30.483168380513526</td><td>1.7271150221924354</td><td>-6.935243764416227e-05</td><td>0.0016746926378365876</td><td>99.0</td><td>38.340162222225025</td><td>4.625667336439803</td><td>9.430641382427341</td><td>29.737064008865644</td><td>1.2067422263821408</td><td>3.7278437345770183</td><td>6.223837880765026</td><td>29.971355752217683</td><td>1.0660969351263596</td><td>2.9511264018407397</td><td>3.296478328598443</td><td>30.225030471297302</td><td>0.8143143339620278</td><td>102.00142301925241</td><td>70.23389970847741</td></tr>\n",
       "<tr><td>502</td><td>1694.0</td><td>2195.0</td><td>1.0</td><td>0.28867513459481287</td><td>0.28867513459481287</td><td>0.0</td><td>0.0</td><td>nan</td><td>2.7534805956824977</td><td>5.694864701697405</td><td>30.300294949325647</td><td>1.2172240889814847</td><td>4.702072517656313</td><td>6.689101459935888</td><td>29.719276693145137</td><td>0.9606979053877811</td><td>4.259483887106769</td><td>6.124685068417927</td><td>29.826607550694266</td><td>0.9675369149395678</td><td>3.1326798078842235</td><td>4.387624757681869</td><td>30.160209980538593</td><td>0.9507985542471777</td><td>3.6096989787758056</td><td>2.529006292498221</td><td>30.006322533537425</td><td>0.576514490366564</td><td>102.00245668396076</td><td>70.24311708938417</td></tr>\n",
       "<tr><td>299</td><td>1933.0</td><td>1924.0</td><td>1.0</td><td>0.28867513459481287</td><td>0.28867513459481287</td><td>0.0</td><td>0.0</td><td>nan</td><td>2.5548250693285284</td><td>4.261865599625735</td><td>30.38159707740065</td><td>1.0655310444584436</td><td>0.021564767227215748</td><td>0.6716538992600086</td><td>35.56563806330369</td><td>3.767813684715555</td><td>0.5019659149348338</td><td>1.3065091368635917</td><td>32.14831442953929</td><td>1.391595734482708</td><td>1.4660953360135356</td><td>2.5453612631849016</td><td>30.984594469601504</td><td>1.0928497142005684</td><td>1.4576586856439597</td><td>1.3726964195300504</td><td>30.99086038808075</td><td>0.7204627052860811</td><td>101.9896835299403</td><td>70.23822662999038</td></tr>\n",
       "<tr><td>774</td><td>2099.0</td><td>2539.0</td><td>1.0</td><td>0.28867513459481287</td><td>0.28867513459481287</td><td>0.0</td><td>0.0</td><td>nan</td><td>1.52212443937018</td><td>3.921336589664117</td><td>30.94387460228645</td><td>1.3835623974244062</td><td>1.519461255452491</td><td>4.21435656856806</td><td>30.945775923655248</td><td>1.4418856495067212</td><td>2.1059886690604763</td><td>4.5711791352914215</td><td>30.591359924492835</td><td>1.2528406511287895</td><td>1.0144327411794805</td><td>2.9154561067580302</td><td>31.384441855566074</td><td>1.4703925232285258</td><td>1.5138716766682223</td><td>1.9405390874898327</td><td>30.949777340618276</td><td>0.8957122863202466</td><td>101.98083042693582</td><td>70.2493320655701</td></tr>\n",
       "<tr><td>827</td><td>2369.7718977228214</td><td>2584.089507293974</td><td>23.0</td><td>0.9861018253133176</td><td>0.6861371841465375</td><td>0.30419235972053016</td><td>-57.77372054973839</td><td>0.5632162358290549</td><td>28.283893484460705</td><td>22.232702806805342</td><td>27.771152017383418</td><td>0.6297372197896168</td><td>17.249954373934475</td><td>23.295411847241745</td><td>28.308030123241213</td><td>0.9278831919540731</td><td>15.153482212779895</td><td>22.867245003663268</td><td>28.44871889116809</td><td>0.9987699393951286</td><td>-4.883770122011478</td><td>20.555650146141907</td><td>99.0</td><td>28.117671956365417</td><td>3.666617783309887</td><td>10.892403663247542</td><td>29.989335898844697</td><td>1.497166363306115</td><td>101.96636329439445</td><td>70.250147317099</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<QTable length=1198>\n",
       "  id      xcentroid      ...         ra                dec       \n",
       "             pix         ...                                     \n",
       "int64      float64       ...      float64            float64     \n",
       "----- ------------------ ... ------------------ -----------------\n",
       "   16 2347.9284951456048 ...  101.9675239994528 70.23372398165105\n",
       "  431 2240.9548087465355 ...  101.9732446746625 70.24773035192908\n",
       "  183 2806.9860562820145 ... 101.94300960014174 70.24053432593968\n",
       "    5 2436.5936079863372 ... 101.96278948232992  70.2293302451883\n",
       "  576 3406.1082469958496 ... 101.91099700770236 70.24691899217079\n",
       "  286 2464.7802118257696 ... 101.96128661086044 70.24828077103126\n",
       "  526 2698.5541809786173 ... 101.94879503801519  70.2513931847212\n",
       "  425 2543.4404207434463 ... 101.95708393248779 70.24879862589846\n",
       "  840 2527.2375143468407 ... 101.95794892946411 70.25841649267484\n",
       "  ...                ... ...                ...               ...\n",
       "  379  3204.883569844592 ... 101.92176132735415 70.23922501186625\n",
       "  655  2258.921588702054 ... 101.97228868411726 70.25399048695067\n",
       "  240  2367.774597559875 ... 101.96647090574416 70.25276077842739\n",
       "  626  1534.109821353476 ... 102.01100420569009 70.24596365874622\n",
       "  837  2373.208969406837 ... 101.96617975367002 70.25048428094884\n",
       "  160             1713.0 ... 102.00142301925241 70.23389970847741\n",
       "  502             1694.0 ... 102.00245668396076 70.24311708938417\n",
       "  299             1933.0 ...  101.9896835299403 70.23822662999038\n",
       "  774             2099.0 ... 101.98083042693582  70.2493320655701\n",
       "  827 2369.7718977228214 ... 101.96636329439445   70.250147317099"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Sersic2D(amplitude=[79.3472892], r_eff=[62.], n=0.25, x_0=[1713.67715464], y_0=[2546.50853317], ellip=[0.95464521] , theta=[1.55854008])>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#image_size = 4000\n",
    "#ny_image = nx_image = image_size\n",
    "#image = np.zeros((ny_image, nx_image))\n",
    "image = data_counts * 0\n",
    "ny_image, nx_image = image.shape\n",
    "image_size = ny_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp, extent = Sersic_galaxy(model)\n",
    "image = add_stamp_to_image(image, stamp, extent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#image = data_counts_initial * 0\n",
    "data_counts_initial = data_count_rate * exposure_time  # re-initialize\n",
    "data_counts_masked = np.where(segm.data_ma.mask, 0, data_counts_initial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_fit_results2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_fit_results('plots/fit_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               id     amplitude          r     ellip   theta    n      x         y\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/astropy/modeling/functional_models.py:2488: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  z = np.sqrt((x_maj / a) ** 2 + (x_min / b) ** 2)\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/astropy/units/quantity.py:464: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 / 1198     16    1.215e+07        3.112   0.039  -1.159  0.41  2347.859  1674.198\n",
      "   1 / 1198     16  The relative error between two consecutive iterates is at most 0.000000\n",
      "   1 / 1198    431    4.169e+06        2.024   0.035   1.404  0.72  2240.915  2449.973\n",
      "   2 / 1198    431  The relative error between two consecutive iterates is at most 0.000000\n",
      "   2 / 1198    183    1.759e+06            2   0.000  -0.418  0.86  2806.736  2051.726\n",
      "   3 / 1198    183  The relative error between two consecutive iterates is at most 0.000000\n",
      "   3 / 1198      5     1.75e+06        1.759   0.000  -0.322  0.64  2436.405  1431.065\n",
      "   4 / 1198      5  The relative error between two consecutive iterates is at most 0.000000\n",
      "   4 / 1198    576    6.105e+05         1.84   0.046  -0.491  0.58  3405.950  2405.584\n",
      "   5 / 1198    576  The relative error between two consecutive iterates is at most 0.000000\n",
      "   5 / 1198    286         1164        31.33   0.158   0.268  1.89  2469.406  2483.242\n",
      "   6 / 1198    286  The relative error between two consecutive iterates is at most 0.000000\n",
      "   6 / 1198    526         7695        7.418   0.012  -1.470  1.15  2699.022  2652.201\n",
      "   7 / 1198    526  The relative error between two consecutive iterates is at most 0.000000\n",
      "   7 / 1198    425        874.5        45.95   0.326   0.271  1.60  2535.836  2507.292\n",
      "   8 / 1198    425  The relative error between two consecutive iterates is at most 0.000000\n",
      "   8 / 1198    840         3507        11.45   0.171   0.879  1.72  2527.522  3040.754\n",
      "   9 / 1198    840  The relative error between two consecutive iterates is at most 0.000000\n",
      "   9 / 1198    567    1.632e+05        1.772   0.000  -0.412  0.65  3302.959  2401.084\n",
      "  10 / 1198    567  The relative error between two consecutive iterates is at most 0.000000\n",
      "  10 / 1198      3    1.124e+04        5.069   0.095  -1.468  1.13  2134.506  1706.821\n",
      "  11 / 1198      3  The relative error between two consecutive iterates is at most 0.000000\n",
      "  11 / 1198     90    1.221e+05        1.923   0.076  -0.444  0.73  1950.867  1645.366\n",
      "  12 / 1198     90  The relative error between two consecutive iterates is at most 0.000000\n",
      "  12 / 1198   1354     1.16e+05        1.858   0.000   1.241  0.74  1977.300  3470.395\n",
      "  13 / 1198   1354  The relative error between two consecutive iterates is at most 0.000000\n",
      "  13 / 1198    132    8.399e+04        2.024   0.034  -0.561  0.64  2148.496  1781.761\n",
      "  14 / 1198    132  The relative error between two consecutive iterates is at most 0.000000\n",
      "  14 / 1198      9    9.406e+04        1.995   0.087  -1.445  0.57  2333.019  1343.753\n",
      "  15 / 1198      9  The relative error between two consecutive iterates is at most 0.000000\n",
      "  15 / 1198     73    8.772e+04         1.96   0.061  -1.071  0.66  2483.092  1743.107\n",
      "  16 / 1198     73  The relative error between two consecutive iterates is at most 0.000000\n",
      "  16 / 1198   1536          668        20.39   0.071  -1.071  2.75  2752.655  2470.535\n",
      "  17 / 1198   1536  The relative error between two consecutive iterates is at most 0.000000\n",
      "  17 / 1198    244    1.499e+04        3.713   0.025   0.089  0.86  2121.157  2043.992\n",
      "  18 / 1198    244  The relative error between two consecutive iterates is at most 0.000000\n",
      "  18 / 1198   1006    8.415e+04        1.808   0.000  -0.392  0.69  1471.357  2972.190\n",
      "  19 / 1198   1006  The relative error between two consecutive iterates is at most 0.000000\n",
      "  19 / 1198   1189    6.888e+04        2.171   0.142  -0.850  0.75  1548.638  3203.888\n",
      "  20 / 1198   1189  The relative error between two consecutive iterates is at most 0.000000\n",
      "  20 / 1198    428    1.066e+04        4.229   0.044   0.155  0.97  2772.597  2242.958\n",
      "  21 / 1198    428  The relative error between two consecutive iterates is at most 0.000000\n",
      "  21 / 1198     25    6.688e+04        1.891   0.004  -0.852  0.64  2398.252  1403.609\n",
      "  22 / 1198     25  The relative error between two consecutive iterates is at most 0.000000\n",
      "  22 / 1198      2         6655        5.958   0.268   0.985  1.05  2596.140  1396.397\n",
      "  23 / 1198      2  The relative error between two consecutive iterates is at most 0.000000\n",
      "  23 / 1198    345         7111        5.231   0.050   0.084  1.08  1547.352  2390.332\n",
      "  24 / 1198    345  The relative error between two consecutive iterates is at most 0.000000\n",
      "  24 / 1198    554         9684        4.661   0.099  -0.515  1.05  1798.742  2385.600\n",
      "  25 / 1198    554  The relative error between two consecutive iterates is at most 0.000000\n",
      "  25 / 1198   1260         8076        4.575   0.143   0.079  1.03  1907.679  3335.671\n",
      "  26 / 1198   1260  The relative error between two consecutive iterates is at most 0.000000\n",
      "  26 / 1198     23    6.919e+04        1.801   0.014   1.098  0.54  1856.837  1371.770\n",
      "  27 / 1198     23  The relative error between two consecutive iterates is at most 0.000000\n",
      "  27 / 1198    366    6.017e+04        1.929   0.079  -0.737  0.61  3309.663  2113.857\n",
      "  28 / 1198    366  The relative error between two consecutive iterates is at most 0.000000\n",
      "  28 / 1198   1099         7879        5.049   0.194   0.870  1.34  3093.925  3189.064\n",
      "  29 / 1198   1099  The relative error between two consecutive iterates is at most 0.000000\n",
      "  29 / 1198   1321    4.945e+04        1.997   0.000  -0.553  0.57  1838.615  3408.158\n",
      "  30 / 1198   1321  The relative error between two consecutive iterates is at most 0.000000\n",
      "  30 / 1198    231         6321        4.982   0.014  -1.297  1.14  1655.629  1919.409\n",
      "  31 / 1198    231  The relative error between two consecutive iterates is at most 0.000000\n",
      "  31 / 1198    698        999.8        18.89   0.340  -0.725  2.01  2695.936  2566.557\n",
      "  32 / 1198    698  The relative error between two consecutive iterates is at most 0.000000\n",
      "  32 / 1198    274         2812        8.112   0.179   0.568  1.12  2209.184  2078.044\n",
      "  33 / 1198    274  The relative error between two consecutive iterates is at most 0.000000\n",
      "  33 / 1198   1527         7286        4.014   0.000  -0.345  1.03  2428.452  2490.665\n",
      "  34 / 1198   1527  The relative error between two consecutive iterates is at most 0.000000\n",
      "  34 / 1198    607         7871        4.485   0.214   0.896  0.97  1951.673  2433.283\n",
      "  35 / 1198    607  The relative error between two consecutive iterates is at most 0.000000\n",
      "  35 / 1198    832         5143        5.061   0.094   0.250  1.15  2669.483  2676.546\n",
      "  36 / 1198    832  The relative error between two consecutive iterates is at most 0.000000\n",
      "  36 / 1198   1483         7574        4.769   0.262  -0.362  0.99  2383.462  1828.563\n",
      "  37 / 1198   1483  The relative error between two consecutive iterates is at most 0.000000\n",
      "  37 / 1198   1210         2874        16.74   0.779  -1.107  0.90  2895.245  3306.240\n",
      "  38 / 1198   1210  The relative error between two consecutive iterates is at most 0.000000\n",
      "  38 / 1198   1097         8092        5.026   0.393   0.133  0.92  2497.510  3010.080\n",
      "  39 / 1198   1097  The relative error between two consecutive iterates is at most 0.000000\n",
      "  39 / 1198    449         5948        4.477   0.177  -0.510  0.99  2146.847  2203.445\n",
      "  40 / 1198    449  The relative error between two consecutive iterates is at most 0.000000\n",
      "  40 / 1198    741         3788        4.439   0.064  -1.535  1.06  2557.578  2530.216\n",
      "  41 / 1198    741  The relative error between two consecutive iterates is at most 0.000000\n",
      "  41 / 1198    782         7196        3.984   0.123   0.676  1.00  2402.286  2658.560\n",
      "  42 / 1198    782  The relative error between two consecutive iterates is at most 0.000000\n",
      "  42 / 1198   1352         1633        10.51   0.390  -1.414  1.40  2822.602  3510.052\n",
      "  43 / 1198   1352  The relative error between two consecutive iterates is at most 0.000000\n",
      "  43 / 1198      1         5023        4.639   0.217  -0.999  1.05  2198.303  1488.565\n",
      "  44 / 1198      1  The relative error between two consecutive iterates is at most 0.000000\n",
      "  44 / 1198   1481         3433        6.552   0.258  -1.135  1.18  2331.569  1767.615\n",
      "  45 / 1198   1481  The relative error between two consecutive iterates is at most 0.000000\n",
      "  45 / 1198     43         3921        4.481   0.000  -1.235  0.98  2723.626  1982.368\n",
      "  46 / 1198     43  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  46 / 1198   1418         1651         10.8   0.396  -0.940  0.73  3097.678  3627.850\n",
      "  47 / 1198   1418  The relative error between two consecutive iterates is at most 0.000000\n",
      "  47 / 1198    471         1537        9.993   0.355   1.045  1.30  2096.297  2247.904\n",
      "  48 / 1198    471  The relative error between two consecutive iterates is at most 0.000000\n",
      "  48 / 1198    990         7267        3.908   0.030  -1.368  0.74  3351.719  2949.033\n",
      "  49 / 1198    990  The relative error between two consecutive iterates is at most 0.000000\n",
      "  49 / 1198    637        76.26         65.5   0.059   1.451  1.83  2761.936  2637.649\n",
      "  50 / 1198    637  The relative error between two consecutive iterates is at most 0.000000\n",
      "  50 / 1198   1032         6742        3.407   0.112  -0.377  0.83  1908.980  2984.423\n",
      "  51 / 1198   1032  The relative error between two consecutive iterates is at most 0.000000\n",
      "  51 / 1198    557         4185        4.236   0.133  -1.313  1.04  1899.872  2330.596\n",
      "  52 / 1198    557  The relative error between two consecutive iterates is at most 0.000000\n",
      "  52 / 1198    647         1263         8.57   0.145  -0.850  1.42  3070.073  2550.587\n",
      "  53 / 1198    647  The relative error between two consecutive iterates is at most 0.000000\n",
      "  53 / 1198    229         3234        4.705   0.117   1.539  1.01  2394.793  2072.849\n",
      "  54 / 1198    229  The relative error between two consecutive iterates is at most 0.000000\n",
      "  54 / 1198    653         6109        4.079   0.267  -1.030  0.89  1646.382  2442.734\n",
      "  55 / 1198    653  The relative error between two consecutive iterates is at most 0.000000\n",
      "  55 / 1198    199         1848        10.45   0.497   1.469  0.59  1802.638  1843.619\n",
      "  56 / 1198    199  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      "  56 / 1198    917         5542        4.302   0.272  -0.204  0.95  2659.696  2824.585\n",
      "  57 / 1198    917  The relative error between two consecutive iterates is at most 0.000000\n",
      "  57 / 1198    551         5177         4.53   0.312  -0.568  0.92  2613.834  2298.677\n",
      "  58 / 1198    551  The relative error between two consecutive iterates is at most 0.000000\n",
      "  58 / 1198    792         4951        3.717   0.000  -0.301  0.94  1860.446  2675.886\n",
      "  59 / 1198    792  The relative error between two consecutive iterates is at most 0.000000\n",
      "  59 / 1198   1530         3885        4.823   0.000  -0.234  1.08  2226.249  2486.007\n",
      "  60 / 1198   1530  The relative error between two consecutive iterates is at most 0.000000\n",
      "  60 / 1198   1317    1.721e+04         2.16   0.046  -1.470  0.93  1984.653  3371.629\n",
      "  61 / 1198   1317  The relative error between two consecutive iterates is at most 0.000000\n",
      "  61 / 1198    297         3440        5.101   0.245   0.690  1.09  2424.036  2063.534\n",
      "  62 / 1198    297  The relative error between two consecutive iterates is at most 0.000000\n",
      "  62 / 1198    548         5840        3.863   0.230  -0.247  0.88  1746.493  2282.355\n",
      "  63 / 1198    548  The relative error between two consecutive iterates is at most 0.000000\n",
      "  63 / 1198    603         2288        5.914   0.169   1.468  1.12  1992.346  2376.720\n",
      "  64 / 1198    603  The relative error between two consecutive iterates is at most 0.000000\n",
      "  64 / 1198     40         5719         3.36   0.009  -1.335  0.86  2171.842  1496.733\n",
      "  65 / 1198     40  The relative error between two consecutive iterates is at most 0.000000\n",
      "  65 / 1198    691         3786        4.152   0.130  -1.520  0.94  1661.638  2519.033\n",
      "  66 / 1198    691  The relative error between two consecutive iterates is at most 0.000000\n",
      "  66 / 1198    466         4066        3.866   0.024   1.303  1.00  3634.657  2222.316\n",
      "  67 / 1198    466  The relative error between two consecutive iterates is at most 0.000000\n",
      "  67 / 1198    259         2665        4.632   0.045   0.045  1.07  2107.281  1930.029\n",
      "  68 / 1198    259  The relative error between two consecutive iterates is at most 0.000000\n",
      "  68 / 1198    746         5188        3.643   0.125   1.344  0.90  1914.724  2588.547\n",
      "  69 / 1198    746  The relative error between two consecutive iterates is at most 0.000000\n",
      "  69 / 1198    788         4048        3.737   0.051   1.090  0.96  2294.640  2688.389\n",
      "  70 / 1198    788  The relative error between two consecutive iterates is at most 0.000000\n",
      "  70 / 1198   1518         2332        5.142   0.000  -0.322  1.24  2147.362  2456.367\n",
      "  71 / 1198   1518  The relative error between two consecutive iterates is at most 0.000000\n",
      "  71 / 1198    141          956        10.25   0.316  -0.522  1.23  2990.527  1740.924\n",
      "  72 / 1198    141  The relative error between two consecutive iterates is at most 0.000000\n",
      "  72 / 1198    457         6053        2.907   0.000  -0.274  0.80  3367.062  2351.069\n",
      "  73 / 1198    457  The relative error between two consecutive iterates is at most 0.000000\n",
      "  73 / 1198    204    2.022e+04        1.751   0.000  -1.349  0.58  3338.151  1787.064\n",
      "  74 / 1198    204  The relative error between two consecutive iterates is at most 0.000000\n",
      "  74 / 1198    394         3139        4.057   0.039   0.361  1.05  2111.309  2079.353\n",
      "  75 / 1198    394  The relative error between two consecutive iterates is at most 0.000000\n",
      "  75 / 1198    581         3310        4.154   0.183  -1.302  1.02  2997.014  2356.652\n",
      "  76 / 1198    581  The relative error between two consecutive iterates is at most 0.000000\n",
      "  76 / 1198      4         1954         10.7   0.676  -0.350  0.82  2332.535  1507.780\n",
      "  77 / 1198      4  The relative error between two consecutive iterates is at most 0.000000\n",
      "  77 / 1198    387         5088        4.172   0.312  -1.457  1.03  3785.190  2068.803\n",
      "  78 / 1198    387  The relative error between two consecutive iterates is at most 0.000000\n",
      "  78 / 1198   1271         2721        4.231   0.000  -0.862  0.93  1548.885  3300.950\n",
      "  79 / 1198   1271  The relative error between two consecutive iterates is at most 0.000000\n",
      "  79 / 1198   1088        825.6        17.56   0.703  -0.752  0.88  1649.256  3033.118\n",
      "  80 / 1198   1088  The relative error between two consecutive iterates is at most 0.000000\n",
      "  80 / 1198    799         4629        3.871   0.299   1.519  0.85  3108.792  2662.776\n",
      "  81 / 1198    799  The relative error between two consecutive iterates is at most 0.000000\n",
      "  81 / 1198   1059         1947        10.55   0.715  -1.393  0.72  1352.664  3019.286\n",
      "  82 / 1198   1059  The relative error between two consecutive iterates is at most 0.000000\n",
      "  82 / 1198    886    1.254e+04        1.978   0.000  -0.303  0.96  2445.189  2745.440\n",
      "  83 / 1198    886  The relative error between two consecutive iterates is at most 0.000000\n",
      "  83 / 1198   1272        316.9        15.14   0.205   1.048  1.81  2335.475  3336.393\n",
      "  84 / 1198   1272  The relative error between two consecutive iterates is at most 0.000000\n",
      "  84 / 1198    447         3078        4.088   0.241   1.000  0.93  3090.358  2185.665\n",
      "  85 / 1198    447  The relative error between two consecutive iterates is at most 0.000000\n",
      "  85 / 1198    738        110.7        42.11   0.436   0.904  1.77  1821.446  2615.568\n",
      "  86 / 1198    738  The relative error between two consecutive iterates is at most 0.000000\n",
      "  86 / 1198    486         5370        2.938   0.000  -0.104  0.81  1682.141  2207.193\n",
      "  87 / 1198    486  The relative error between two consecutive iterates is at most 0.000000\n",
      "  87 / 1198    594         4198        3.443   0.157  -0.747  0.89  3204.508  2363.258\n",
      "  88 / 1198    594  The relative error between two consecutive iterates is at most 0.000000\n",
      "  88 / 1198    452         4079        3.273   0.041   1.145  0.89  2317.471  2273.248\n",
      "  89 / 1198    452  The relative error between two consecutive iterates is at most 0.000000\n",
      "  89 / 1198    597         5219        2.902   0.000   0.854  0.84  2571.574  2348.771\n",
      "  90 / 1198    597  The relative error between two consecutive iterates is at most 0.000000\n",
      "  90 / 1198    283         2701        3.751   0.036   0.199  0.94  3432.634  1964.938\n",
      "  91 / 1198    283  The relative error between two consecutive iterates is at most 0.000000\n",
      "  91 / 1198    861         1769        5.441   0.000   0.551  0.65  3359.084  2730.618\n",
      "  92 / 1198    861  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  92 / 1198    133        911.8        7.282   0.224   1.221  1.26  3021.499  1714.825\n",
      "  93 / 1198    133  The relative error between two consecutive iterates is at most 0.000000\n",
      "  93 / 1198     61         1736         6.62   0.368   1.478  0.71  2766.122  1509.255\n",
      "  94 / 1198     61  The relative error between two consecutive iterates is at most 0.000000\n",
      "  94 / 1198   1515        666.6           14   0.171  -1.383  2.14  2568.305  2575.224\n",
      "  95 / 1198   1515  The relative error between two consecutive iterates is at most 0.000000\n",
      "  95 / 1198    618         5416          4.4   0.535  -0.250  0.49  2781.659  2373.904\n",
      "  96 / 1198    618  The relative error between two consecutive iterates is at most 0.000000\n",
      "  96 / 1198    703    1.344e+04        1.785   0.063  -1.089  0.64  3081.209  2503.860\n",
      "  97 / 1198    703  The relative error between two consecutive iterates is at most 0.000000\n",
      "  97 / 1198    874         1769        5.276   0.182  -1.056  0.97  3095.288  2751.760\n",
      "  98 / 1198    874  The relative error between two consecutive iterates is at most 0.000000\n",
      "  98 / 1198    891         3027        4.455   0.353  -0.818  0.74  3004.918  2786.876\n",
      "  99 / 1198    891  The relative error between two consecutive iterates is at most 0.000000\n",
      "  99 / 1198    685         2751        3.695   0.099  -1.252  0.89  3197.058  2483.186\n",
      " 100 / 1198    685  The relative error between two consecutive iterates is at most 0.000000\n",
      " 100 / 1198    855        328.9        24.67   0.684  -0.393  0.67  3004.872  2767.361\n",
      " 101 / 1198    855  The relative error between two consecutive iterates is at most 0.000000\n",
      " 101 / 1198    967         4408        3.028   0.196   0.177  0.79  2222.285  2869.856\n",
      " 102 / 1198    967  The relative error between two consecutive iterates is at most 0.000000\n",
      " 102 / 1198    312        447.2        10.71   0.257   0.801  1.13  2151.286  2018.221\n",
      " 103 / 1198    312  The relative error between two consecutive iterates is at most 0.000000\n",
      " 103 / 1198   1182        976.6        5.211   0.081   0.338  1.17  1924.449  3187.708\n",
      " 104 / 1198   1182  The relative error between two consecutive iterates is at most 0.000000\n",
      " 104 / 1198    684         2786        5.175   0.499  -0.061  0.70  1990.914  2457.246\n",
      " 105 / 1198    684  The relative error between two consecutive iterates is at most 0.000000\n",
      " 105 / 1198    380         3063        4.157   0.320  -1.157  0.83  2009.845  2060.704\n",
      " 106 / 1198    380  The relative error between two consecutive iterates is at most 0.000000\n",
      " 106 / 1198   1167          243        14.73   0.309   0.094  1.50  2746.706  3133.410\n",
      " 107 / 1198   1167  The relative error between two consecutive iterates is at most 0.000000\n",
      " 107 / 1198    625        439.2        13.08   0.339  -0.374  0.92  1505.885  2503.353\n",
      " 108 / 1198    625  The relative error between two consecutive iterates is at most 0.000000\n",
      " 108 / 1198    610         4032        3.011   0.141  -1.379  0.80  1463.824  2451.873\n",
      " 109 / 1198    610  The relative error between two consecutive iterates is at most 0.000000\n",
      " 109 / 1198    723         2378        4.005   0.145   0.192  0.88  2600.992  2630.856\n",
      " 110 / 1198    723  The relative error between two consecutive iterates is at most 0.000000\n",
      " 110 / 1198    212          456        12.46   0.416  -0.682  1.09  3119.747  1830.010\n",
      " 111 / 1198    212  The relative error between two consecutive iterates is at most 0.000000\n",
      " 111 / 1198   1476         4387        3.204   0.272  -1.107  0.77  2241.045  1621.631\n",
      " 112 / 1198   1476  The relative error between two consecutive iterates is at most 0.000000\n",
      " 112 / 1198    761         3680         3.21   0.190  -0.164  0.87  3551.598  2552.361\n",
      " 113 / 1198    761  The relative error between two consecutive iterates is at most 0.000000\n",
      " 113 / 1198    451         1861        4.256   0.021  -0.960  0.74  1931.499  2294.966\n",
      " 114 / 1198    451  The relative error between two consecutive iterates is at most 0.000000\n",
      " 114 / 1198   1101        329.1         23.5   0.643   0.062  0.85  2493.433  2993.031\n",
      " 115 / 1198   1101  The relative error between two consecutive iterates is at most 0.000000\n",
      " 115 / 1198    867         2227        3.497   0.000  -0.367  0.93  2161.579  2718.965\n",
      " 116 / 1198    867  The relative error between two consecutive iterates is at most 0.000000\n",
      " 116 / 1198    962         2365        3.694   0.183  -1.305  0.79  2518.094  2849.878\n",
      " 117 / 1198    962  The relative error between two consecutive iterates is at most 0.000000\n",
      " 117 / 1198    820        656.8        11.58   0.614   1.234  1.00  1820.555  2687.775\n",
      " 118 / 1198    820  The relative error between two consecutive iterates is at most 0.000000\n",
      " 118 / 1198    454         2648        3.889   0.321  -1.126  0.76  3405.956  2162.033\n",
      " 119 / 1198    454  The relative error between two consecutive iterates is at most 0.000000\n",
      " 119 / 1198    367          231        13.52   0.179  -0.131  1.49  1934.570  2079.728\n",
      " 120 / 1198    367  The relative error between two consecutive iterates is at most 0.000000\n",
      " 120 / 1198    786         3046        3.191   0.074  -1.127  0.91  2474.363  2604.805\n",
      " 121 / 1198    786  The relative error between two consecutive iterates is at most 0.000000\n",
      " 121 / 1198   1118         1622        4.687   0.318   1.444  0.83  2227.881  3182.493\n",
      " 122 / 1198   1118  The relative error between two consecutive iterates is at most 0.000000\n",
      " 122 / 1198   1482        302.2         18.5   0.392  -0.200  0.89  2316.144  1799.832\n",
      " 123 / 1198   1482  The relative error between two consecutive iterates is at most 0.000000\n",
      " 123 / 1198    897         1494        4.667   0.065   0.021  0.67  3036.949  2781.840\n",
      " 124 / 1198    897  The relative error between two consecutive iterates is at most 0.000000\n",
      " 124 / 1198    318         1389        5.109   0.289  -0.297  0.98  2579.179  1970.455\n",
      " 125 / 1198    318  The relative error between two consecutive iterates is at most 0.000000\n",
      " 125 / 1198   1018        453.3        7.629   0.407   1.493  1.02  2591.128  3021.803\n",
      " 126 / 1198   1018  The relative error between two consecutive iterates is at most 0.000000\n",
      " 126 / 1198     81        764.6        8.406   0.506  -1.483  0.78  1996.711  1573.765\n",
      " 127 / 1198     81  The relative error between two consecutive iterates is at most 0.000000\n",
      " 127 / 1198    807         1431        3.854   0.050  -1.147  0.89  2370.825  2699.705\n",
      " 128 / 1198    807  The relative error between two consecutive iterates is at most 0.000000\n",
      " 128 / 1198    717         1617        4.401   0.149   0.836  1.07  2394.635  2575.427\n",
      " 129 / 1198    717  The relative error between two consecutive iterates is at most 0.000000\n",
      " 129 / 1198   1520         3622        2.678   0.092   0.985  0.72  2132.198  2538.390\n",
      " 130 / 1198   1520  The relative error between two consecutive iterates is at most 0.000000\n",
      " 130 / 1198    777        321.8        13.59   0.611  -1.104  1.15  2707.060  2588.478\n",
      " 131 / 1198    777  The relative error between two consecutive iterates is at most 0.000000\n",
      " 131 / 1198    571        182.4           16   0.173  -0.853  1.67  2882.371  2291.431\n",
      " 132 / 1198    571  The relative error between two consecutive iterates is at most 0.000000\n",
      " 132 / 1198    311         1432         4.38   0.223   1.517  0.85  2197.341  2261.682\n",
      " 133 / 1198    311  The relative error between two consecutive iterates is at most 0.000000\n",
      " 133 / 1198   1522         3155        2.888   0.067  -0.878  0.79  2364.447  2443.034\n",
      " 134 / 1198   1522  The relative error between two consecutive iterates is at most 0.000000\n",
      " 134 / 1198    813         1296        4.295   0.163   1.465  0.86  1432.377  2640.146\n",
      " 135 / 1198    813  The relative error between two consecutive iterates is at most 0.000000\n",
      " 135 / 1198    549         1205        4.243   0.101  -1.316  0.95  2076.425  2261.413\n",
      " 136 / 1198    549  The relative error between two consecutive iterates is at most 0.000000\n",
      " 136 / 1198    265         1698        3.933   0.197  -0.919  0.79  2133.588  1888.495\n",
      " 137 / 1198    265  The relative error between two consecutive iterates is at most 0.000000\n",
      " 137 / 1198    249         1929        3.269   0.170  -0.906  0.91  1855.503  1882.448\n",
      " 138 / 1198    249  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 138 / 1198   1326         2085        3.077   0.150   1.057  0.84  1848.891  3370.403\n",
      " 139 / 1198   1326  The relative error between two consecutive iterates is at most 0.000000\n",
      " 139 / 1198   1045         1685        3.533   0.225  -0.580  0.75  2444.056  3031.273\n",
      " 140 / 1198   1045  The relative error between two consecutive iterates is at most 0.000000\n",
      " 140 / 1198    563         1738        3.448   0.101   0.169  0.80  3173.206  2266.198\n",
      " 141 / 1198    563  The relative error between two consecutive iterates is at most 0.000000\n",
      " 141 / 1198     17        591.1        7.822   0.312   0.863  0.83  2313.769  1330.093\n",
      " 142 / 1198     17  The relative error between two consecutive iterates is at most 0.000000\n",
      " 142 / 1198   1426         5036         2.02   0.043  -0.744  0.92  3137.390  3558.724\n",
      " 143 / 1198   1426  The relative error between two consecutive iterates is at most 0.000000\n",
      " 143 / 1198    865         1152        5.797   0.477   0.191  0.85  2946.700  2686.466\n",
      " 144 / 1198    865  The relative error between two consecutive iterates is at most 0.000000\n",
      " 144 / 1198    570        709.8        5.535   0.132  -1.161  0.97  2953.620  2306.259\n",
      " 145 / 1198    570  The relative error between two consecutive iterates is at most 0.000000\n",
      " 145 / 1198    429         1551        3.734   0.223   0.990  0.95  3119.526  2129.369\n",
      " 146 / 1198    429  The relative error between two consecutive iterates is at most 0.000000\n",
      " 146 / 1198   1196        517.7        9.853   0.527  -0.667  0.99  2663.115  3148.806\n",
      " 147 / 1198   1196  The relative error between two consecutive iterates is at most 0.000000\n",
      " 147 / 1198   1217          979        4.216   0.000   1.286  0.87  2444.390  3203.412\n",
      " 148 / 1198   1217  The relative error between two consecutive iterates is at most 0.000000\n",
      " 148 / 1198    162         5512        1.855   0.008   0.928  0.68  2691.022  1687.640\n",
      " 149 / 1198    162  The relative error between two consecutive iterates is at most 0.000000\n",
      " 149 / 1198    871         2909        2.418   0.000   0.058  0.71  3334.877  2698.644\n",
      " 150 / 1198    871  The relative error between two consecutive iterates is at most 0.000000\n",
      " 150 / 1198   1170         1904         2.89   0.076  -0.294  0.86  2845.420  3102.354\n",
      " 151 / 1198   1170  The relative error between two consecutive iterates is at most 0.000000\n",
      " 151 / 1198   1521         1865        3.188   0.000   1.040  0.90  2339.324  2469.270\n",
      " 152 / 1198   1521  The relative error between two consecutive iterates is at most 0.000000\n",
      " 152 / 1198   1200         5439          1.8   0.000  -0.332  0.77  2049.383  3139.685\n",
      " 153 / 1198   1200  The relative error between two consecutive iterates is at most 0.000000\n",
      " 153 / 1198    829         1049        4.156   0.108  -0.747  0.80  2175.810  2686.268\n",
      " 154 / 1198    829  The relative error between two consecutive iterates is at most 0.000000\n",
      " 154 / 1198    445         4682        1.986   0.000  -0.743  0.67  2971.844  2117.248\n",
      " 155 / 1198    445  The relative error between two consecutive iterates is at most 0.000000\n",
      " 155 / 1198    835        591.6        6.107   0.344  -1.526  1.14  2834.365  2719.912\n",
      " 156 / 1198    835  The relative error between two consecutive iterates is at most 0.000000\n",
      " 156 / 1198    708        522.7        6.773   0.310  -0.814  0.91  3581.934  2498.576\n",
      " 157 / 1198    708  The relative error between two consecutive iterates is at most 0.000000\n",
      " 157 / 1198   1183         1143        4.324   0.313   0.732  0.87  3280.549  3129.746\n",
      " 158 / 1198   1183  The relative error between two consecutive iterates is at most 0.000000\n",
      " 158 / 1198    880          919        6.862   0.580  -0.229  0.78  3100.439  2708.081\n",
      " 159 / 1198    880  The relative error between two consecutive iterates is at most 0.000000\n",
      " 159 / 1198   1078        468.9        5.792   0.108  -0.312  1.04  1689.080  2989.486\n",
      " 160 / 1198   1078  The relative error between two consecutive iterates is at most 0.000000\n",
      " 160 / 1198   1396        410.6        7.926   0.386   0.164  1.04  2932.220  3491.843\n",
      " 161 / 1198   1396  The relative error between two consecutive iterates is at most 0.000000\n",
      " 161 / 1198    352        731.1        7.164   0.488   1.429  0.70  1655.459  2024.618\n",
      " 162 / 1198    352  The relative error between two consecutive iterates is at most 0.000000\n",
      " 162 / 1198   1528        38.33          8.5   0.197  -0.602  3.32  2498.649  2512.677\n",
      " 163 / 1198   1528  The relative error between two consecutive iterates is at most 0.000000\n",
      " 163 / 1198    675        281.7        8.784   0.176   0.914  1.02  3472.142  2453.860\n",
      " 164 / 1198    675  The relative error between two consecutive iterates is at most 0.000000\n",
      " 164 / 1198   1108          311        6.597   0.349  -0.179  1.05  2519.780  2996.464\n",
      " 165 / 1198   1108  The relative error between two consecutive iterates is at most 0.000000\n",
      " 165 / 1198    884         1626        2.893   0.000   0.980  0.77  2179.446  2754.174\n",
      " 166 / 1198    884  The relative error between two consecutive iterates is at most 0.000000\n",
      " 166 / 1198   1351        485.9        7.171   0.400  -0.359  1.06  3204.754  3396.324\n",
      " 167 / 1198   1351  The relative error between two consecutive iterates is at most 0.000000\n",
      " 167 / 1198    544        705.1        5.079   0.109  -1.305  0.84  2129.326  2268.283\n",
      " 168 / 1198    544  The relative error between two consecutive iterates is at most 0.000000\n",
      " 168 / 1198   1232         1560        2.801   0.000   0.445  0.89  2871.506  3203.170\n",
      " 169 / 1198   1232  The relative error between two consecutive iterates is at most 0.000000\n",
      " 169 / 1198    877         1237        4.579   0.384   1.566  0.67  3057.493  2727.171\n",
      " 170 / 1198    877  The relative error between two consecutive iterates is at most 0.000000\n",
      " 170 / 1198    633        855.2        4.008   0.011   0.029  1.01  1808.487  2346.639\n",
      " 171 / 1198    633  The relative error between two consecutive iterates is at most 0.000000\n",
      " 171 / 1198   1114         1496        3.658   0.284   1.220  0.75  2637.333  3044.722\n",
      " 172 / 1198   1114  The relative error between two consecutive iterates is at most 0.000000\n",
      " 172 / 1198   1486        575.6         5.61   0.223  -0.961  0.96  2475.062  1845.209\n",
      " 173 / 1198   1486  The relative error between two consecutive iterates is at most 0.000000\n",
      " 173 / 1198    255         1635        3.144   0.126  -0.606  0.81  2786.622  1861.445\n",
      " 174 / 1198    255  The relative error between two consecutive iterates is at most 0.000000\n",
      " 174 / 1198   1049         4158        1.774   0.000   0.872  0.73  2992.208  2934.508\n",
      " 175 / 1198   1049  The relative error between two consecutive iterates is at most 0.000000\n",
      " 175 / 1198    763         1234        3.531   0.143   0.072  0.93  3315.588  2547.462\n",
      " 176 / 1198    763  The relative error between two consecutive iterates is at most 0.000000\n",
      " 176 / 1198   1355         1253        3.701   0.281  -0.951  0.80  2919.535  3409.982\n",
      " 177 / 1198   1355  The relative error between two consecutive iterates is at most 0.000000\n",
      " 177 / 1198    929        471.2        5.141   0.000  -0.942  1.11  2428.157  2821.483\n",
      " 178 / 1198    929  The relative error between two consecutive iterates is at most 0.000000\n",
      " 178 / 1198     10         1658        2.848   0.025  -0.505  0.82  2209.740  1211.510\n",
      " 179 / 1198     10  The relative error between two consecutive iterates is at most 0.000000\n",
      " 179 / 1198    446        603.5        4.474   0.074   0.207  0.94  3158.567  2144.221\n",
      " 180 / 1198    446  The relative error between two consecutive iterates is at most 0.000000\n",
      " 180 / 1198   1475        565.1            7   0.000   0.350  0.90  2601.518  1419.373\n",
      " 181 / 1198   1475  The relative error between two consecutive iterates is at most 0.000000\n",
      " 181 / 1198   1539        518.3           11   0.584   1.049  1.17  2621.711  2449.424\n",
      " 182 / 1198   1539  The relative error between two consecutive iterates is at most 0.000000\n",
      " 182 / 1198    437        455.6        7.708   0.466  -1.362  0.93  1681.426  2248.376\n",
      " 183 / 1198    437  The relative error between two consecutive iterates is at most 0.000000\n",
      " 183 / 1198    664          997        3.828   0.058   1.036  0.83  2595.692  2394.415\n",
      " 184 / 1198    664  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 184 / 1198    386        248.1         10.2   0.462  -0.981  1.03  2581.843  2101.503\n",
      " 185 / 1198    386  The relative error between two consecutive iterates is at most 0.000000\n",
      " 185 / 1198   1144          102         15.9   0.295  -0.169  1.50  2656.365  3077.803\n",
      " 186 / 1198   1144  The relative error between two consecutive iterates is at most 0.000000\n",
      " 186 / 1198    851        55.21        27.27   0.440   1.349  1.43  2865.602  2747.461\n",
      " 187 / 1198    851  The relative error between two consecutive iterates is at most 0.000000\n",
      " 187 / 1198    764         1037        4.159   0.242   1.395  0.77  1625.341  2556.882\n",
      " 188 / 1198    764  The relative error between two consecutive iterates is at most 0.000000\n",
      " 188 / 1198   1507        274.9            9   0.065  -0.901  2.19  2203.397  2411.646\n",
      " 189 / 1198   1507  The relative error between two consecutive iterates is at most 0.000000\n",
      " 189 / 1198   1480          687        6.875   0.499  -0.138  0.85  2163.386  1762.955\n",
      " 190 / 1198   1480  The relative error between two consecutive iterates is at most 0.000000\n",
      " 190 / 1198   1499        260.3         13.5   0.597   0.864  0.89  2878.796  2048.913\n",
      " 191 / 1198   1499  The relative error between two consecutive iterates is at most 0.000000\n",
      " 191 / 1198    961        630.2        5.737   0.468   1.283  0.89  2045.933  2855.526\n",
      " 192 / 1198    961  The relative error between two consecutive iterates is at most 0.000000\n",
      " 192 / 1198    896          998        3.713   0.163  -0.694  0.90  2598.689  2740.542\n",
      " 193 / 1198    896  The relative error between two consecutive iterates is at most 0.000000\n",
      " 193 / 1198    945         1828        2.416   0.000   1.147  0.73  2802.146  2801.138\n",
      " 194 / 1198    945  The relative error between two consecutive iterates is at most 0.000000\n",
      " 194 / 1198    695        596.5        6.789   0.494   0.519  0.96  1422.198  2475.556\n",
      " 195 / 1198    695  The relative error between two consecutive iterates is at most 0.000000\n",
      " 195 / 1198   1459        501.2          6.5   0.101  -0.814  2.77  2800.387  3699.476\n",
      " 196 / 1198   1459  The relative error between two consecutive iterates is at most 0.000000\n",
      " 196 / 1198     70         1819        2.511   0.038   1.385  0.76  1863.225  1515.361\n",
      " 197 / 1198     70  The relative error between two consecutive iterates is at most 0.000000\n",
      " 197 / 1198    293         1917        2.584   0.091  -0.327  0.73  2554.309  1912.659\n",
      " 198 / 1198    293  The relative error between two consecutive iterates is at most 0.000000\n",
      " 198 / 1198   1324        203.9        11.23   0.507  -1.350  0.74  2663.840  3376.421\n",
      " 199 / 1198   1324  The relative error between two consecutive iterates is at most 0.000000\n",
      " 199 / 1198    459        419.2        7.662   0.517  -0.347  0.95  1706.322  2133.593\n",
      " 200 / 1198    459  The relative error between two consecutive iterates is at most 0.000000\n",
      " 200 / 1198    849        549.4        4.664   0.050  -1.140  1.19  2602.457  2705.416\n",
      " 201 / 1198    849  The relative error between two consecutive iterates is at most 0.000000\n",
      " 201 / 1198    507         1558        2.757   0.150   1.357  0.70  3729.950  2198.745\n",
      " 202 / 1198    507  The relative error between two consecutive iterates is at most 0.000000\n",
      " 202 / 1198   1490        238.1        11.51   0.575  -1.334  0.87  2362.261  2114.146\n",
      " 203 / 1198   1490  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      " 203 / 1198    733        425.4        5.223   0.060   1.104  0.85  3353.387  2516.989\n",
      " 204 / 1198    733  The relative error between two consecutive iterates is at most 0.000000\n",
      " 204 / 1198   1485        495.4            8   0.000   0.169  0.82  2375.321  1790.945\n",
      " 205 / 1198   1485  The relative error between two consecutive iterates is at most 0.000000\n",
      " 205 / 1198    455         2439        2.162   0.035   1.224  0.76  2967.359  2111.886\n",
      " 206 / 1198    455  The relative error between two consecutive iterates is at most 0.000000\n",
      " 206 / 1198    100        516.4        6.608   0.029  -1.224  0.92  1717.530  1588.496\n",
      " 207 / 1198    100  The relative error between two consecutive iterates is at most 0.000000\n",
      " 207 / 1198   1382         3342        1.746   0.000   1.260  0.69  3022.988  3433.281\n",
      " 208 / 1198   1382  The relative error between two consecutive iterates is at most 0.000000\n",
      " 208 / 1198    914        822.5        3.554   0.078  -0.361  0.87  1590.729  2771.715\n",
      " 209 / 1198    914  The relative error between two consecutive iterates is at most 0.000000\n",
      " 209 / 1198     98        931.4        3.538   0.130  -0.963  0.91  3238.627  1622.507\n",
      " 210 / 1198     98  The relative error between two consecutive iterates is at most 0.000000\n",
      " 210 / 1198   1516        190.5         11.5   0.128   0.659  2.33  2591.453  2572.502\n",
      " 211 / 1198   1516  The relative error between two consecutive iterates is at most 0.000000\n",
      " 211 / 1198    232        432.8        7.268   0.477   1.152  0.72  2789.112  1831.171\n",
      " 212 / 1198    232  The relative error between two consecutive iterates is at most 0.000000\n",
      " 212 / 1198    957        454.1        5.821   0.178  -0.200  0.73  2558.454  2810.305\n",
      " 213 / 1198    957  The relative error between two consecutive iterates is at most 0.000000\n",
      " 213 / 1198    998         1017        3.174   0.066  -0.020  0.77  2643.054  2849.763\n",
      " 214 / 1198    998  The relative error between two consecutive iterates is at most 0.000000\n",
      " 214 / 1198    893        576.7        4.665   0.189   0.487  0.87  2353.075  2739.302\n",
      " 215 / 1198    893  The relative error between two consecutive iterates is at most 0.000000\n",
      " 215 / 1198    109        279.7           14   0.694   0.868  0.81  1891.476  1606.872\n",
      " 216 / 1198    109  The relative error between two consecutive iterates is at most 0.000000\n",
      " 216 / 1198    369        850.1        3.658   0.192   1.327  0.90  3077.526  2019.898\n",
      " 217 / 1198    369  The relative error between two consecutive iterates is at most 0.000000\n",
      " 217 / 1198    752        411.7        8.171   0.464   0.459  0.83  3579.719  2528.805\n",
      " 218 / 1198    752  The relative error between two consecutive iterates is at most 0.000000\n",
      " 218 / 1198    441        365.1        10.61   0.721   0.729  0.66  3109.257  2137.165\n",
      " 219 / 1198    441  The relative error between two consecutive iterates is at most 0.000000\n",
      " 219 / 1198    649         1068        3.792   0.328  -1.347  0.72  2044.133  2373.710\n",
      " 220 / 1198    649  The relative error between two consecutive iterates is at most 0.000000\n",
      " 220 / 1198    881        300.9        7.716   0.449  -0.307  1.08  1298.435  2712.514\n",
      " 221 / 1198    881  The relative error between two consecutive iterates is at most 0.000000\n",
      " 221 / 1198    327        43.97         20.5   0.000  -0.624  2.04  2758.247  2014.142\n",
      " 222 / 1198    327  The relative error between two consecutive iterates is at most 0.000000\n",
      " 222 / 1198    800        96.39        16.67   0.166  -1.012  1.55  2428.610  2640.760\n",
      " 223 / 1198    800  The relative error between two consecutive iterates is at most 0.000000\n",
      " 223 / 1198   1427        120.7        13.99   0.495  -1.093  1.61  3053.702  3634.546\n",
      " 224 / 1198   1427  The relative error between two consecutive iterates is at most 0.000000\n",
      " 224 / 1198   1004        42.64        20.56   0.008   0.532  1.84  2375.409  2898.764\n",
      " 225 / 1198   1004  The relative error between two consecutive iterates is at most 0.000000\n",
      " 225 / 1198   1543        678.7          5.5   0.029  -1.240  0.52  2679.995  2634.054\n",
      " 226 / 1198   1543  The relative error between two consecutive iterates is at most 0.000000\n",
      " 226 / 1198   1378         2699        1.777   0.000   1.278  0.71  2096.625  3429.989\n",
      " 227 / 1198   1378  The relative error between two consecutive iterates is at most 0.000000\n",
      " 227 / 1198   1531        921.6        3.771   0.134  -0.901  1.09  2286.491  2536.137\n",
      " 228 / 1198   1531  The relative error between two consecutive iterates is at most 0.000000\n",
      " 228 / 1198    278         1202        2.904   0.189  -0.791  0.74  1864.391  1907.294\n",
      " 229 / 1198    278  The relative error between two consecutive iterates is at most 0.000000\n",
      " 229 / 1198   1503        149.4         20.5   0.805  -1.544  0.63  3084.487  2544.475\n",
      " 230 / 1198   1503  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 230 / 1198    334        60.83         18.5   0.223   0.265  1.51  2635.153  2022.539\n",
      " 231 / 1198    334  The relative error between two consecutive iterates is at most 0.000000\n",
      " 231 / 1198   1542        283.5          8.5   0.025  -1.020  0.95  2646.517  2654.843\n",
      " 232 / 1198   1542  The relative error between two consecutive iterates is at most 0.000000\n",
      " 232 / 1198    965        182.8        6.956   0.000   0.140  0.95  1748.041  2839.490\n",
      " 233 / 1198    965  The relative error between two consecutive iterates is at most 0.000000\n",
      " 233 / 1198    171        112.9           12   0.000   0.475  1.41  1720.715  1776.343\n",
      " 234 / 1198    171  The relative error between two consecutive iterates is at most 0.000000\n",
      " 234 / 1198   1303        201.2        8.633   0.340   1.080  1.12  1421.477  3305.624\n",
      " 235 / 1198   1303  The relative error between two consecutive iterates is at most 0.000000\n",
      " 235 / 1198    408         2459        1.768   0.000  -0.809  0.71  2905.894  2052.270\n",
      " 236 / 1198    408  The relative error between two consecutive iterates is at most 0.000000\n",
      " 236 / 1198   1474        241.2         15.5   0.587   0.428  0.87  2295.273  1558.154\n",
      " 237 / 1198   1474  The relative error between two consecutive iterates is at most 0.000000\n",
      " 237 / 1198   1535        601.2        3.804   0.000  -0.539  0.82  2598.364  2493.256\n",
      " 238 / 1198   1535  The relative error between two consecutive iterates is at most 0.000000\n",
      " 238 / 1198    958         2388        1.767   0.000   0.258  0.65  3025.715  2802.386\n",
      " 239 / 1198    958  The relative error between two consecutive iterates is at most 0.000000\n",
      " 239 / 1198    704         1393        2.056   0.000   0.430  0.86  2617.292  2483.539\n",
      " 240 / 1198    704  The relative error between two consecutive iterates is at most 0.000000\n",
      " 240 / 1198   1233        162.8         8.21   0.193  -0.602  1.26  2277.703  3201.570\n",
      " 241 / 1198   1233  The relative error between two consecutive iterates is at most 0.000000\n",
      " 241 / 1198   1092        707.2         3.13   0.000  -0.704  0.80  3194.090  2984.644\n",
      " 242 / 1198   1092  The relative error between two consecutive iterates is at most 0.000000\n",
      " 242 / 1198   1519        223.2        7.305   0.068   0.427  0.97  2156.371  2565.163\n",
      " 243 / 1198   1519  The relative error between two consecutive iterates is at most 0.000000\n",
      " 243 / 1198    939        195.5        8.347   0.331  -0.015  0.77  1263.013  2783.886\n",
      " 244 / 1198    939  The relative error between two consecutive iterates is at most 0.000000\n",
      " 244 / 1198    969        82.78         31.5   0.662  -0.905  1.67  3378.537  2863.948\n",
      " 245 / 1198    969  The relative error between two consecutive iterates is at most 0.000000\n",
      " 245 / 1198    960        89.49        14.95   0.402  -0.253  1.39  2421.317  2811.790\n",
      " 246 / 1198    960  The relative error between two consecutive iterates is at most 0.000000\n",
      " 246 / 1198   1360        704.2        3.679   0.301  -0.780  0.90  2842.513  3401.103\n",
      " 247 / 1198   1360  The relative error between two consecutive iterates is at most 0.000000\n",
      " 247 / 1198   1130        557.4        4.455   0.314  -0.111  0.79  3009.581  3024.203\n",
      " 248 / 1198   1130  The relative error between two consecutive iterates is at most 0.000000\n",
      " 248 / 1198   1365        283.9         6.08   0.146   1.042  0.89  2007.121  3489.160\n",
      " 249 / 1198   1365  The relative error between two consecutive iterates is at most 0.000000\n",
      " 249 / 1198    467        377.5        5.848   0.372   0.759  0.94  1638.054  2154.357\n",
      " 250 / 1198    467  The relative error between two consecutive iterates is at most 0.000000\n",
      " 250 / 1198    569        675.2        3.075   0.000  -0.125  0.96  3501.457  2265.705\n",
      " 251 / 1198    569  The relative error between two consecutive iterates is at most 0.000000\n",
      " 251 / 1198     52        548.9        3.976   0.054  -0.747  0.67  1917.194  1392.536\n",
      " 252 / 1198     52  The relative error between two consecutive iterates is at most 0.000000\n",
      " 252 / 1198    363        363.1        4.681   0.120   0.960  0.88  1844.738  2098.694\n",
      " 253 / 1198    363  The relative error between two consecutive iterates is at most 0.000000\n",
      " 253 / 1198    887        391.9        4.574   0.220  -1.161  0.87  1989.068  2730.676\n",
      " 254 / 1198    887  The relative error between two consecutive iterates is at most 0.000000\n",
      " 254 / 1198     72        812.8        2.962   0.054  -0.496  0.75  2711.511  1463.916\n",
      " 255 / 1198     72  The relative error between two consecutive iterates is at most 0.000000\n",
      " 255 / 1198   1151        238.5        7.795   0.442  -0.958  0.71  3174.259  3080.965\n",
      " 256 / 1198   1151  The relative error between two consecutive iterates is at most 0.000000\n",
      " 256 / 1198    923        210.3        12.76   0.752  -0.160  0.73  1370.223  2755.698\n",
      " 257 / 1198    923  The relative error between two consecutive iterates is at most 0.000000\n",
      " 257 / 1198   1347        145.2        14.73   0.564   0.033  0.88  1442.988  3368.946\n",
      " 258 / 1198   1347  The relative error between two consecutive iterates is at most 0.000000\n",
      " 258 / 1198   1090          613        2.963   0.068   1.014  0.83  1677.833  3028.367\n",
      " 259 / 1198   1090  The relative error between two consecutive iterates is at most 0.000000\n",
      " 259 / 1198    673        165.8        11.88   0.398   1.474  1.53  1504.859  2436.538\n",
      " 260 / 1198    673  The relative error between two consecutive iterates is at most 0.000000\n",
      " 260 / 1198   1262        325.5        5.468   0.314   0.820  0.74  2860.502  3240.475\n",
      " 261 / 1198   1262  The relative error between two consecutive iterates is at most 0.000000\n",
      " 261 / 1198   1479          270        6.669   0.009   0.417  1.31  2220.492  1680.432\n",
      " 262 / 1198   1479  The relative error between two consecutive iterates is at most 0.000000\n",
      " 262 / 1198   1487        199.2         14.5   0.732  -0.793  0.84  2401.344  1867.565\n",
      " 263 / 1198   1487  The relative error between two consecutive iterates is at most 0.000000\n",
      " 263 / 1198    530        172.7        10.18   0.531  -0.605  0.89  2574.097  2226.837\n",
      " 264 / 1198    530  The relative error between two consecutive iterates is at most 0.000000\n",
      " 264 / 1198    527        163.7          9.5   0.000   1.267  2.03  2228.368  2313.552\n",
      " 265 / 1198    527  The relative error between two consecutive iterates is at most 0.000000\n",
      " 265 / 1198    159        91.13         15.5   0.508   0.560  0.87  3329.150  1700.061\n",
      " 266 / 1198    159  The relative error between two consecutive iterates is at most 0.000000\n",
      " 266 / 1198   1488        333.5         6.65   0.495  -0.388  0.89  2496.040  1931.764\n",
      " 267 / 1198   1488  The relative error between two consecutive iterates is at most 0.000000\n",
      " 267 / 1198    650        632.7        3.482   0.186  -1.325  0.80  3112.473  2376.277\n",
      " 268 / 1198    650  The relative error between two consecutive iterates is at most 0.000000\n",
      " 268 / 1198   1505        273.9           11   0.556  -1.302  0.96  1484.707  2379.505\n",
      " 269 / 1198   1505  The relative error between two consecutive iterates is at most 0.000000\n",
      " 269 / 1198    126          381        4.532   0.035  -0.910  0.84  1822.757  1608.065\n",
      " 270 / 1198    126  The relative error between two consecutive iterates is at most 0.000000\n",
      " 270 / 1198    644        569.2        3.563   0.033   1.033  0.92  2554.702  2356.193\n",
      " 271 / 1198    644  The relative error between two consecutive iterates is at most 0.000000\n",
      " 271 / 1198    883        199.9         11.8   0.713   1.380  0.70  1634.582  2753.461\n",
      " 272 / 1198    883  The relative error between two consecutive iterates is at most 0.000000\n",
      " 272 / 1198    131          135        11.53   0.323  -1.351  0.69  2086.331  1640.574\n",
      " 273 / 1198    131  The relative error between two consecutive iterates is at most 0.000000\n",
      " 273 / 1198    671        519.4        4.714   0.000   0.405  1.08  2670.041  2505.744\n",
      " 274 / 1198    671  The relative error between two consecutive iterates is at most 0.000000\n",
      " 274 / 1198    295        568.1        3.882   0.199   1.405  0.70  2830.640  1941.980\n",
      " 275 / 1198    295  The relative error between two consecutive iterates is at most 0.000000\n",
      " 275 / 1198     29         80.5           14   0.086  -1.341  1.23  1793.848  1347.535\n",
      " 276 / 1198     29  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 276 / 1198    892          300         5.18   0.272   1.320  0.76  1965.636  2753.122\n",
      " 277 / 1198    892  The relative error between two consecutive iterates is at most 0.000000\n",
      " 277 / 1198   1266        241.6         10.3   0.740   0.033  0.69  2934.418  3214.662\n",
      " 278 / 1198   1266  The relative error between two consecutive iterates is at most 0.000000\n",
      " 278 / 1198   1506        77.37           62   0.956   1.559  0.25  1713.669  2546.230\n",
      " 279 / 1198   1506  The relative error between two consecutive iterates is at most 0.000000\n",
      " 279 / 1198    853        288.4        6.065   0.165  -0.322  1.03  2380.639  2650.469\n",
      " 280 / 1198    853  The relative error between two consecutive iterates is at most 0.000000\n",
      " 280 / 1198   1489        150.3        9.439   0.429   1.544  1.01  2540.003  1973.233\n",
      " 281 / 1198   1489  The relative error between two consecutive iterates is at most 0.000000\n",
      " 281 / 1198    450        167.2        7.672   0.198  -1.372  0.79  3772.765  2123.812\n",
      " 282 / 1198    450  The relative error between two consecutive iterates is at most 0.000000\n",
      " 282 / 1198   1511        235.9          9.5   0.142  -0.359  0.93  2389.184  2487.314\n",
      " 283 / 1198   1511  The relative error between two consecutive iterates is at most 0.000000\n",
      " 283 / 1198    975        309.7        5.064   0.178  -0.821  0.73  2914.937  2827.380\n",
      " 284 / 1198    975  The relative error between two consecutive iterates is at most 0.000000\n",
      " 284 / 1198    213        506.1        4.057   0.300  -1.196  0.88  2569.576  1772.913\n",
      " 285 / 1198    213  The relative error between two consecutive iterates is at most 0.000000\n",
      " 285 / 1198   1005          401        4.011   0.179  -0.297  0.75  1769.348  2871.144\n",
      " 286 / 1198   1005  The relative error between two consecutive iterates is at most 0.000000\n",
      " 286 / 1198   1021          270          6.5   0.084  -0.938  2.30  2718.353  2868.498\n",
      " 287 / 1198   1021  The relative error between two consecutive iterates is at most 0.000000\n",
      " 287 / 1198   1472          345          5.5   0.229  -0.171  0.98  2335.331  1497.675\n",
      " 288 / 1198   1472  The relative error between two consecutive iterates is at most 0.000000\n",
      " 288 / 1198    634         1342         2.05   0.000   0.763  0.71  3053.683  2322.783\n",
      " 289 / 1198    634  The relative error between two consecutive iterates is at most 0.000000\n",
      " 289 / 1198    434        276.4        4.155   0.162  -0.861  0.83  1929.177  2093.530\n",
      " 290 / 1198    434  The relative error between two consecutive iterates is at most 0.000000\n",
      " 290 / 1198    324          288        5.295   0.345  -0.025  0.86  2669.012  1955.118\n",
      " 291 / 1198    324  The relative error between two consecutive iterates is at most 0.000000\n",
      " 291 / 1198    659          435        3.616   0.029  -0.674  0.82  2760.785  2392.533\n",
      " 292 / 1198    659  The relative error between two consecutive iterates is at most 0.000000\n",
      " 292 / 1198   1532        557.5          6.5   0.219   0.105  0.42  2308.318  2432.164\n",
      " 293 / 1198   1532  The relative error between two consecutive iterates is at most 0.000000\n",
      " 293 / 1198    882        648.5          3.2   0.114  -0.858  0.71  2494.775  2695.130\n",
      " 294 / 1198    882  The relative error between two consecutive iterates is at most 0.000000\n",
      " 294 / 1198   1184        361.5        3.977   0.000  -0.060  0.94  2033.239  3108.399\n",
      " 295 / 1198   1184  The relative error between two consecutive iterates is at most 0.000000\n",
      " 295 / 1198    412          288        4.267   0.000  -1.491  0.86  2056.963  2087.647\n",
      " 296 / 1198    412  The relative error between two consecutive iterates is at most 0.000000\n",
      " 296 / 1198    500        228.3        5.779   0.183  -0.867  0.98  2180.757  2179.597\n",
      " 297 / 1198    500  The relative error between two consecutive iterates is at most 0.000000\n",
      " 297 / 1198    677          101        9.979   0.177   1.318  1.37  1919.625  2448.105\n",
      " 298 / 1198    677  The relative error between two consecutive iterates is at most 0.000000\n",
      " 298 / 1198    147          420        4.788   0.359  -1.465  0.73  1805.367  1646.916\n",
      " 299 / 1198    147  The relative error between two consecutive iterates is at most 0.000000\n",
      " 299 / 1198   1538         1327        2.155   0.065  -0.733  0.66  2572.126  2427.467\n",
      " 300 / 1198   1538  The relative error between two consecutive iterates is at most 0.000000\n",
      " 300 / 1198    357        82.58        11.15   0.267  -0.568  1.28  2066.172  2027.468\n",
      " 301 / 1198    357  The relative error between two consecutive iterates is at most 0.000000\n",
      " 301 / 1198   1172        225.4        5.025   0.066  -0.131  0.83  2396.757  3077.925\n",
      " 302 / 1198   1172  The relative error between two consecutive iterates is at most 0.000000\n",
      " 302 / 1198    553          473        3.464   0.108   0.168  0.83  3144.554  2241.710\n",
      " 303 / 1198    553  The relative error between two consecutive iterates is at most 0.000000\n",
      " 303 / 1198    909        547.2        3.437   0.218   1.503  0.90  3435.636  2749.094\n",
      " 304 / 1198    909  The relative error between two consecutive iterates is at most 0.000000\n",
      " 304 / 1198      8        159.5         9.21   0.499   1.421  0.87  2334.879  1215.307\n",
      " 305 / 1198      8  The relative error between two consecutive iterates is at most 0.000000\n",
      " 305 / 1198     11        387.8        3.832   0.091   0.953  0.77  2401.045  1204.610\n",
      " 306 / 1198     11  The relative error between two consecutive iterates is at most 0.000000\n",
      " 306 / 1198     35        590.7        3.373   0.110  -0.332  0.65  2480.607  1325.776\n",
      " 307 / 1198     35  The relative error between two consecutive iterates is at most 0.000000\n",
      " 307 / 1198   1040        154.1        8.094   0.476  -0.986  0.98  1657.780  2934.299\n",
      " 308 / 1198   1040  The relative error between two consecutive iterates is at most 0.000000\n",
      " 308 / 1198    174         63.7           17   0.548  -0.870  1.20  1952.123  1732.021\n",
      " 309 / 1198    174  The relative error between two consecutive iterates is at most 0.000000\n",
      " 309 / 1198    932        248.3         16.5   0.872   1.127  0.57  1835.580  2784.375\n",
      " 310 / 1198    932  The relative error between two consecutive iterates is at most 0.000000\n",
      " 310 / 1198   1161        280.4        4.038   0.066  -1.417  0.83  2154.121  3075.923\n",
      " 311 / 1198   1161  The relative error between two consecutive iterates is at most 0.000000\n",
      " 311 / 1198    798        669.8        3.374   0.288  -0.663  0.78  2890.085  2582.327\n",
      " 312 / 1198    798  The relative error between two consecutive iterates is at most 0.000000\n",
      " 312 / 1198    329        156.6         10.5   0.684  -1.136  0.86  2601.352  2032.652\n",
      " 313 / 1198    329  The relative error between two consecutive iterates is at most 0.000000\n",
      " 313 / 1198    414        131.3        5.985   0.417  -1.104  0.83  1955.793  2071.386\n",
      " 314 / 1198    414  The relative error between two consecutive iterates is at most 0.000000\n",
      " 314 / 1198    393        236.2        6.848   0.450  -0.969  1.04  1711.001  2044.411\n",
      " 315 / 1198    393  The relative error between two consecutive iterates is at most 0.000000\n",
      " 315 / 1198   1510        312.4         12.5   0.515  -0.293  0.38  2360.572  2483.879\n",
      " 316 / 1198   1510  The relative error between two consecutive iterates is at most 0.000000\n",
      " 316 / 1198   1225        219.8        5.347   0.249   0.697  0.94  1638.900  3170.118\n",
      " 317 / 1198   1225  The relative error between two consecutive iterates is at most 0.000000\n",
      " 317 / 1198   1334        251.4            6   0.095  -0.743  2.24  2583.514  3327.165\n",
      " 318 / 1198   1334  The relative error between two consecutive iterates is at most 0.000000\n",
      " 318 / 1198    396        38.35           15   0.000  -1.239  1.57  2077.948  2069.488\n",
      " 319 / 1198    396  The relative error between two consecutive iterates is at most 0.000000\n",
      " 319 / 1198    346        454.3          5.5   0.416   0.911  1.40  3601.829  1952.382\n",
      " 320 / 1198    346  The relative error between two consecutive iterates is at most 0.000000\n",
      " 320 / 1198   1359        116.3         7.41   0.294   1.556  0.86  2949.984  3426.956\n",
      " 321 / 1198   1359  The relative error between two consecutive iterates is at most 0.000000\n",
      " 321 / 1198    900          406        3.656   0.200  -0.884  0.75  3093.635  2729.768\n",
      " 322 / 1198    900  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 322 / 1198   1512        211.7        7.353   0.000  -0.457  1.18  2408.039  2438.129\n",
      " 323 / 1198   1512  The relative error between two consecutive iterates is at most 0.000000\n",
      " 323 / 1198    904          374        3.822   0.122   1.555  0.91  2762.490  2752.432\n",
      " 324 / 1198    904  The relative error between two consecutive iterates is at most 0.000000\n",
      " 324 / 1198   1226          191        5.114   0.224   0.566  0.91  1968.640  3172.947\n",
      " 325 / 1198   1226  The relative error between two consecutive iterates is at most 0.000000\n",
      " 325 / 1198    905        449.2        3.171   0.018  -0.352  0.80  1923.405  2731.812\n",
      " 326 / 1198    905  The relative error between two consecutive iterates is at most 0.000000\n",
      " 326 / 1198    498        208.7        4.877   0.062   0.389  1.04  3749.414  2172.661\n",
      " 327 / 1198    498  The relative error between two consecutive iterates is at most 0.000000\n",
      " 327 / 1198   1529         1013         2.14   0.000  -0.459  0.77  2502.657  2401.732\n",
      " 328 / 1198   1529  The relative error between two consecutive iterates is at most 0.000000\n",
      " 328 / 1198    987        238.3        6.001   0.494  -0.449  0.78  3109.962  2839.246\n",
      " 329 / 1198    987  The relative error between two consecutive iterates is at most 0.000000\n",
      " 329 / 1198   1316        386.7         3.66   0.200   1.203  0.72  2110.344  3313.274\n",
      " 330 / 1198   1316  The relative error between two consecutive iterates is at most 0.000000\n",
      " 330 / 1198   1492        302.1        5.268   0.354  -1.255  0.91  2510.937  1747.253\n",
      " 331 / 1198   1492  The relative error between two consecutive iterates is at most 0.000000\n",
      " 331 / 1198   1007        109.9        7.812   0.279   0.056  0.86  1748.296  2880.143\n",
      " 332 / 1198   1007  The relative error between two consecutive iterates is at most 0.000000\n",
      " 332 / 1198   1265          313        4.094   0.230   0.519  0.70  1997.784  3232.396\n",
      " 333 / 1198   1265  The relative error between two consecutive iterates is at most 0.000000\n",
      " 333 / 1198   1341        362.6        3.277   0.000   0.977  0.78  2966.608  3360.875\n",
      " 334 / 1198   1341  The relative error between two consecutive iterates is at most 0.000000\n",
      " 334 / 1198   1197        195.5        3.691   0.003   0.113  0.69  2763.513  3121.935\n",
      " 335 / 1198   1197  The relative error between two consecutive iterates is at most 0.000000\n",
      " 335 / 1198    469        386.3        3.519   0.185   1.471  0.76  3471.123  2137.937\n",
      " 336 / 1198    469  The relative error between two consecutive iterates is at most 0.000000\n",
      " 336 / 1198   1008        267.3        4.446   0.177   0.973  0.73  2554.542  2865.885\n",
      " 337 / 1198   1008  The relative error between two consecutive iterates is at most 0.000000\n",
      " 337 / 1198    686        332.2        4.461   0.236   1.124  0.88  2016.436  2424.622\n",
      " 338 / 1198    686  The relative error between two consecutive iterates is at most 0.000000\n",
      " 338 / 1198    812        123.2            8   0.068   0.009  1.65  1975.248  2598.363\n",
      " 339 / 1198    812  The relative error between two consecutive iterates is at most 0.000000\n",
      " 339 / 1198    572        117.6        11.78   0.705   1.315  0.96  3070.868  2338.092\n",
      " 340 / 1198    572  The relative error between two consecutive iterates is at most 0.000000\n",
      " 340 / 1198   1493          100            9   0.165   0.810  1.60  2503.812  1450.563\n",
      " 341 / 1198   1493  The relative error between two consecutive iterates is at most 0.000000\n",
      " 341 / 1198     93          262         5.59   0.413   1.498  0.64  2942.559  1537.426\n",
      " 342 / 1198     93  The relative error between two consecutive iterates is at most 0.000000\n",
      " 342 / 1198    280        209.3        6.547   0.490  -0.456  0.83  2209.259  1902.332\n",
      " 343 / 1198    280  The relative error between two consecutive iterates is at most 0.000000\n",
      " 343 / 1198   1417        209.1        7.582   0.681  -0.800  0.74  2505.952  3530.035\n",
      " 344 / 1198   1417  The relative error between two consecutive iterates is at most 0.000000\n",
      " 344 / 1198     69        369.3        3.802   0.145   0.262  0.96  2324.702  1452.524\n",
      " 345 / 1198     69  The relative error between two consecutive iterates is at most 0.000000\n",
      " 345 / 1198   1002        192.7          4.7   0.141   0.989  0.87  3094.665  2857.595\n",
      " 346 / 1198   1002  The relative error between two consecutive iterates is at most 0.000000\n",
      " 346 / 1198   1120        282.9        4.913   0.403   1.365  0.59  1431.064  3035.563\n",
      " 347 / 1198   1120  The relative error between two consecutive iterates is at most 0.000000\n",
      " 347 / 1198   1524        295.4        4.585   0.066   0.015  0.89  2459.923  2399.337\n",
      " 348 / 1198   1524  The relative error between two consecutive iterates is at most 0.000000\n",
      " 348 / 1198    946        27.32           26   0.290   0.068  1.63  2672.340  2799.821\n",
      " 349 / 1198    946  The relative error between two consecutive iterates is at most 0.000000\n",
      " 349 / 1198    838        385.8        3.474   0.126   0.903  0.76  2961.915  2633.343\n",
      " 350 / 1198    838  The relative error between two consecutive iterates is at most 0.000000\n",
      " 350 / 1198   1513        334.4            6   0.122  -0.358  0.38  2408.123  2461.688\n",
      " 351 / 1198   1513  The relative error between two consecutive iterates is at most 0.000000\n",
      " 351 / 1198    504        76.94           13   0.503  -0.535  0.97  2195.341  2183.864\n",
      " 352 / 1198    504  The relative error between two consecutive iterates is at most 0.000000\n",
      " 352 / 1198   1159        341.4        3.205   0.076  -0.570  0.82  2034.411  3057.937\n",
      " 353 / 1198   1159  The relative error between two consecutive iterates is at most 0.000000\n",
      " 353 / 1198    123        75.75           11   0.424   0.761  1.09  2901.573  1614.287\n",
      " 354 / 1198    123  The relative error between two consecutive iterates is at most 0.000000\n",
      " 354 / 1198    676        192.5        5.878   0.402  -1.153  0.87  3617.829  2403.957\n",
      " 355 / 1198    676  The relative error between two consecutive iterates is at most 0.000000\n",
      " 355 / 1198    924          105        8.636   0.349   0.357  0.84  2772.938  2787.165\n",
      " 356 / 1198    924  The relative error between two consecutive iterates is at most 0.000000\n",
      " 356 / 1198   1523        182.2            7   0.138  -0.100  1.57  2437.301  2399.521\n",
      " 357 / 1198   1523  The relative error between two consecutive iterates is at most 0.000000\n",
      " 357 / 1198   1434         68.7           13   0.343  -0.655  1.14  3102.762  3596.758\n",
      " 358 / 1198   1434  The relative error between two consecutive iterates is at most 0.000000\n",
      " 358 / 1198   1328        219.6        4.156   0.144  -1.007  0.79  2996.553  3338.903\n",
      " 359 / 1198   1328  The relative error between two consecutive iterates is at most 0.000000\n",
      " 359 / 1198    821        215.7        3.828   0.264  -1.258  0.66  1838.159  2611.771\n",
      " 360 / 1198    821  The relative error between two consecutive iterates is at most 0.000000\n",
      " 360 / 1198   1279        258.9        3.659   0.000  -0.467  0.71  2032.620  3251.803\n",
      " 361 / 1198   1279  The relative error between two consecutive iterates is at most 0.000000\n",
      " 361 / 1198   1473        285.3            7   0.336  -0.518  0.89  2352.562  1568.499\n",
      " 362 / 1198   1473  The relative error between two consecutive iterates is at most 0.000000\n",
      " 362 / 1198     26        144.3          9.5   0.542   1.236  0.80  1876.877  1300.225\n",
      " 363 / 1198     26  The relative error between two consecutive iterates is at most 0.000000\n",
      " 363 / 1198   1089        245.4        3.861   0.000  -0.644  0.79  1238.033  2955.208\n",
      " 364 / 1198   1089  The relative error between two consecutive iterates is at most 0.000000\n",
      " 364 / 1198    780        208.7        6.517   0.600  -1.254  0.83  3210.890  2614.217\n",
      " 365 / 1198    780  The relative error between two consecutive iterates is at most 0.000000\n",
      " 365 / 1198    899        95.59        9.108   0.499   1.295  0.83  2682.750  2784.061\n",
      " 366 / 1198    899  The relative error between two consecutive iterates is at most 0.000000\n",
      " 366 / 1198    524        107.9        11.47   0.721   0.370  0.78  3125.357  2206.394\n",
      " 367 / 1198    524  The relative error between two consecutive iterates is at most 0.000000\n",
      " 367 / 1198   1460        75.33          9.5   0.334  -0.297  0.97  2624.069  3731.510\n",
      " 368 / 1198   1460  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 368 / 1198     31        354.4        3.834   0.269   0.487  0.59  2225.373  1303.773\n",
      " 369 / 1198     31  The relative error between two consecutive iterates is at most 0.000000\n",
      " 369 / 1198     77        271.8        4.836   0.324   1.071  0.67  2527.591  1500.215\n",
      " 370 / 1198     77  The relative error between two consecutive iterates is at most 0.000000\n",
      " 370 / 1198   1243        41.57         17.5   0.679  -0.389  0.93  2301.093  3201.108\n",
      " 371 / 1198   1243  The relative error between two consecutive iterates is at most 0.000000\n",
      " 371 / 1198    247        359.5        3.302   0.181  -1.302  0.74  1848.799  1823.232\n",
      " 372 / 1198    247  The relative error between two consecutive iterates is at most 0.000000\n",
      " 372 / 1198   1414        69.62        9.617   0.472  -0.358  0.98  2389.556  3515.877\n",
      " 373 / 1198   1414  The relative error between two consecutive iterates is at most 0.000000\n",
      " 373 / 1198    580        354.1        2.955   0.019   1.486  0.70  3112.927  2267.149\n",
      " 374 / 1198    580  The relative error between two consecutive iterates is at most 0.000000\n",
      " 374 / 1198    402        269.8        3.425   0.000   0.288  0.86  1921.568  2036.504\n",
      " 375 / 1198    402  The relative error between two consecutive iterates is at most 0.000000\n",
      " 375 / 1198   1534        322.8        2.267   0.000  -0.154  0.76  2594.856  2513.734\n",
      " 376 / 1198   1534  The relative error between two consecutive iterates is at most 0.000000\n",
      " 376 / 1198   1342        217.6        3.567   0.000   1.340  0.77  2550.085  3359.303\n",
      " 377 / 1198   1342  The relative error between two consecutive iterates is at most 0.000000\n",
      " 377 / 1198   1398        342.8        3.017   0.019  -0.070  0.70  2384.641  3457.208\n",
      " 378 / 1198   1398  The relative error between two consecutive iterates is at most 0.000000\n",
      " 378 / 1198   1495        112.5          7.5   0.205  -0.844  1.27  2662.388  1793.308\n",
      " 379 / 1198   1495  The relative error between two consecutive iterates is at most 0.000000\n",
      " 379 / 1198    355        150.7        6.199   0.424  -0.241  0.83  3154.387  1972.675\n",
      " 380 / 1198    355  The relative error between two consecutive iterates is at most 0.000000\n",
      " 380 / 1198    376        132.3          6.5   0.104  -0.273  1.66  2644.515  2000.472\n",
      " 381 / 1198    376  The relative error between two consecutive iterates is at most 0.000000\n",
      " 381 / 1198    815        218.7        5.562   0.079   0.819  0.65  2327.505  2610.209\n",
      " 382 / 1198    815  The relative error between two consecutive iterates is at most 0.000000\n",
      " 382 / 1198    127        275.4        3.743   0.069  -0.515  0.84  2132.040  1603.896\n",
      " 383 / 1198    127  The relative error between two consecutive iterates is at most 0.000000\n",
      " 383 / 1198    689        240.5        3.742   0.018   0.041  0.84  3053.164  2427.905\n",
      " 384 / 1198    689  The relative error between two consecutive iterates is at most 0.000000\n",
      " 384 / 1198    241        196.7        6.797   0.625   0.430  0.72  2991.483  1808.115\n",
      " 385 / 1198    241  The relative error between two consecutive iterates is at most 0.000000\n",
      " 385 / 1198   1287        109.9           10   0.577   0.526  1.09  1905.676  3259.690\n",
      " 386 / 1198   1287  The relative error between two consecutive iterates is at most 0.000000\n",
      " 386 / 1198    595        108.4         6.18   0.135  -1.048  0.85  3260.601  2305.826\n",
      " 387 / 1198    595  The relative error between two consecutive iterates is at most 0.000000\n",
      " 387 / 1198    237        61.76        7.967   0.034  -1.059  1.13  3038.999  1812.867\n",
      " 388 / 1198    237  The relative error between two consecutive iterates is at most 0.000000\n",
      " 388 / 1198    596        138.6        5.866   0.164  -0.874  0.87  1991.188  2284.145\n",
      " 389 / 1198    596  The relative error between two consecutive iterates is at most 0.000000\n",
      " 389 / 1198    947          259        3.637   0.218  -1.190  0.77  2252.081  2790.223\n",
      " 390 / 1198    947  The relative error between two consecutive iterates is at most 0.000000\n",
      " 390 / 1198   1463        437.8        2.879   0.138  -0.513  0.74  2786.365  3800.720\n",
      " 391 / 1198   1463  The relative error between two consecutive iterates is at most 0.000000\n",
      " 391 / 1198   1160        160.2        4.741   0.098   1.241  0.78  2140.130  3067.882\n",
      " 392 / 1198   1160  The relative error between two consecutive iterates is at most 0.000000\n",
      " 392 / 1198    652        263.2            5   0.000   0.342  1.02  1910.493  2341.926\n",
      " 393 / 1198    652  The relative error between two consecutive iterates is at most 0.000000\n",
      " 393 / 1198    630        353.7        3.401   0.043   1.497  0.90  2346.259  2323.364\n",
      " 394 / 1198    630  The relative error between two consecutive iterates is at most 0.000000\n",
      " 394 / 1198   1336        120.6           11   0.705   0.758  0.68  1404.493  3349.860\n",
      " 395 / 1198   1336  The relative error between two consecutive iterates is at most 0.000000\n",
      " 395 / 1198    298        99.53        6.597   0.101   1.322  1.33  2286.352  1919.529\n",
      " 396 / 1198    298  The relative error between two consecutive iterates is at most 0.000000\n",
      " 396 / 1198    680        126.4        7.912   0.511  -0.890  1.04  3006.061  2424.501\n",
      " 397 / 1198    680  The relative error between two consecutive iterates is at most 0.000000\n",
      " 397 / 1198    372        65.83        8.976   0.286   1.245  1.00  3271.193  2016.903\n",
      " 398 / 1198    372  The relative error between two consecutive iterates is at most 0.000000\n",
      " 398 / 1198   1187        118.6        5.171   0.000   0.646  0.64  3067.735  3100.307\n",
      " 399 / 1198   1187  The relative error between two consecutive iterates is at most 0.000000\n",
      " 399 / 1198   1540        151.8            8   0.317  -1.422  0.71  2670.426  2726.806\n",
      " 400 / 1198   1540  The relative error between two consecutive iterates is at most 0.000000\n",
      " 400 / 1198    730        96.48        7.817   0.303   1.031  1.14  1902.360  2493.024\n",
      " 401 / 1198    730  The relative error between two consecutive iterates is at most 0.000000\n",
      " 401 / 1198   1500        156.5          7.5   0.073  -0.378  0.66  2764.130  2219.578\n",
      " 402 / 1198   1500  The relative error between two consecutive iterates is at most 0.000000\n",
      " 402 / 1198    719        259.6        3.604   0.129  -0.857  0.86  2971.369  2470.095\n",
      " 403 / 1198    719  The relative error between two consecutive iterates is at most 0.000000\n",
      " 403 / 1198   1010        169.4        4.066   0.000  -0.603  0.94  2938.360  2858.614\n",
      " 404 / 1198   1010  The relative error between two consecutive iterates is at most 0.000000\n",
      " 404 / 1198    506        88.63        7.686   0.292  -0.539  1.12  2565.022  2174.416\n",
      " 405 / 1198    506  The relative error between two consecutive iterates is at most 0.000000\n",
      " 405 / 1198    857        184.1         4.36   0.104   0.072  1.03  1322.922  2642.378\n",
      " 406 / 1198    857  The relative error between two consecutive iterates is at most 0.000000\n",
      " 406 / 1198   1332        73.66           11   0.490  -0.961  1.13  3104.360  3340.578\n",
      " 407 / 1198   1332  The relative error between two consecutive iterates is at most 0.000000\n",
      " 407 / 1198    619        167.6        4.222   0.130   0.306  0.84  3222.763  2303.209\n",
      " 408 / 1198    619  The relative error between two consecutive iterates is at most 0.000000\n",
      " 408 / 1198    217        156.6        5.023   0.250   0.773  0.86  1899.368  1762.835\n",
      " 409 / 1198    217  The relative error between two consecutive iterates is at most 0.000000\n",
      " 409 / 1198   1244        119.3        4.649   0.220  -0.959  1.04  2265.077  3199.532\n",
      " 410 / 1198   1244  The relative error between two consecutive iterates is at most 0.000000\n",
      " 410 / 1198    916        201.3        6.001   0.577  -0.472  0.67  1469.546  2746.067\n",
      " 411 / 1198    916  The relative error between two consecutive iterates is at most 0.000000\n",
      " 411 / 1198   1236        172.8        3.961   0.347  -0.858  0.74  3048.555  3205.212\n",
      " 412 / 1198   1236  The relative error between two consecutive iterates is at most 0.000000\n",
      " 412 / 1198    915        68.32           13   0.653  -0.975  1.29  3422.441  2780.091\n",
      " 413 / 1198    915  The relative error between two consecutive iterates is at most 0.000000\n",
      " 413 / 1198     89        321.1        3.551   0.251   0.383  0.67  2539.344  1504.035\n",
      " 414 / 1198     89  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 414 / 1198   1107        227.2          5.5   0.132  -0.206  1.09  1255.519  3005.655\n",
      " 415 / 1198   1107  The relative error between two consecutive iterates is at most 0.000000\n",
      " 415 / 1198    206          301          3.4   0.230   0.809  0.87  3385.550  1735.460\n",
      " 416 / 1198    206  The relative error between two consecutive iterates is at most 0.000000\n",
      " 416 / 1198   1176        46.65         14.5   0.607  -0.545  1.46  3239.219  3089.478\n",
      " 417 / 1198   1176  The relative error between two consecutive iterates is at most 0.000000\n",
      " 417 / 1198   1256        195.6        4.111   0.259  -1.146  0.77  2184.075  3207.700\n",
      " 418 / 1198   1256  The relative error between two consecutive iterates is at most 0.000000\n",
      " 418 / 1198   1044        314.9        3.792   0.375  -0.943  0.71  1251.627  2901.820\n",
      " 419 / 1198   1044  The relative error between two consecutive iterates is at most 0.000000\n",
      " 419 / 1198   1547        85.56            7   0.410  -0.861  1.07  2796.043  2459.965\n",
      " 420 / 1198   1547  The relative error between two consecutive iterates is at most 0.000000\n",
      " 420 / 1198    784        172.5        6.546   0.586  -0.932  0.62  3043.521  2575.009\n",
      " 421 / 1198    784  The relative error between two consecutive iterates is at most 0.000000\n",
      " 421 / 1198    114        224.8        3.835   0.106  -0.754  0.62  1863.810  1584.490\n",
      " 422 / 1198    114  The relative error between two consecutive iterates is at most 0.000000\n",
      " 422 / 1198   1370        364.4        3.004   0.086  -0.483  0.40  2833.287  3386.587\n",
      " 423 / 1198   1370  The relative error between two consecutive iterates is at most 0.000000\n",
      " 423 / 1198    405        53.38           11   0.434  -1.116  0.89  3268.285  2056.313\n",
      " 424 / 1198    405  The relative error between two consecutive iterates is at most 0.000000\n",
      " 424 / 1198   1491        42.77           11   0.103   0.804  1.51  2420.695  2013.030\n",
      " 425 / 1198   1491  The relative error between two consecutive iterates is at most 0.000000\n",
      " 425 / 1198   1423        313.3        3.361   0.221  -1.054  0.72  3150.462  3535.973\n",
      " 426 / 1198   1423  The relative error between two consecutive iterates is at most 0.000000\n",
      " 426 / 1198    920          229        3.645   0.076  -0.365  0.83  2797.587  2760.699\n",
      " 427 / 1198    920  The relative error between two consecutive iterates is at most 0.000000\n",
      " 427 / 1198   1083        138.3          6.5   0.350  -0.950  1.09  2625.998  2942.109\n",
      " 428 / 1198   1083  The relative error between two consecutive iterates is at most 0.000000\n",
      " 428 / 1198   1533          188            8   0.062   0.716  0.85  2280.376  2389.007\n",
      " 429 / 1198   1533  The relative error between two consecutive iterates is at most 0.000000\n",
      " 429 / 1198    656        224.1        3.766   0.000   0.306  0.89  2620.219  2359.297\n",
      " 430 / 1198    656  The relative error between two consecutive iterates is at most 0.000000\n",
      " 430 / 1198   1350        277.5            3   0.120  -1.511  0.90  2344.513  3344.829\n",
      " 431 / 1198   1350  The relative error between two consecutive iterates is at most 0.000000\n",
      " 431 / 1198    568        171.9        4.174   0.000   0.490  0.90  2015.193  2242.091\n",
      " 432 / 1198    568  The relative error between two consecutive iterates is at most 0.000000\n",
      " 432 / 1198   1070        294.3        2.721   0.000   1.107  0.69  2435.770  2924.055\n",
      " 433 / 1198   1070  The relative error between two consecutive iterates is at most 0.000000\n",
      " 433 / 1198   1301        266.4        3.435   0.159  -0.167  0.54  2188.947  3265.971\n",
      " 434 / 1198   1301  The relative error between two consecutive iterates is at most 0.000000\n",
      " 434 / 1198    846        76.61        2.708   0.288   0.963  0.25  1815.214  2635.529\n",
      " 435 / 1198    846  The relative error between two consecutive iterates is at most 0.000000\n",
      " 435 / 1198   1383        211.8        3.828   0.207  -1.334  0.66  2005.846  3427.339\n",
      " 436 / 1198   1383  The relative error between two consecutive iterates is at most 0.000000\n",
      " 436 / 1198   1537          213        3.833   0.220  -0.079  0.71  2774.202  2577.017\n",
      " 437 / 1198   1537  The relative error between two consecutive iterates is at most 0.000000\n",
      " 437 / 1198    112        325.3         2.87   0.060  -0.199  0.79  2597.065  1572.695\n",
      " 438 / 1198    112  The relative error between two consecutive iterates is at most 0.000000\n",
      " 438 / 1198    285        215.3         4.04   0.373   1.250  0.79  3416.101  1913.452\n",
      " 439 / 1198    285  The relative error between two consecutive iterates is at most 0.000000\n",
      " 439 / 1198   1140        55.47            8   0.033  -0.200  1.38  3050.315  3037.955\n",
      " 440 / 1198   1140  The relative error between two consecutive iterates is at most 0.000000\n",
      " 440 / 1198   1454        120.9        6.421   0.442   0.015  1.51  2801.787  3664.416\n",
      " 441 / 1198   1454  The relative error between two consecutive iterates is at most 0.000000\n",
      " 441 / 1198   1023        74.28        4.448   0.023  -1.341  1.24  2365.416  2882.325\n",
      " 442 / 1198   1023  The relative error between two consecutive iterates is at most 0.000000\n",
      " 442 / 1198   1435        182.5        5.696   0.516  -1.229  0.71  3032.361  3594.503\n",
      " 443 / 1198   1435  The relative error between two consecutive iterates is at most 0.000000\n",
      " 443 / 1198    321        195.8        3.979   0.000   0.739  0.79  1860.122  1968.058\n",
      " 444 / 1198    321  The relative error between two consecutive iterates is at most 0.000000\n",
      " 444 / 1198   1385        104.5        5.576   0.282  -0.357  0.76  2835.485  3435.574\n",
      " 445 / 1198   1385  The relative error between two consecutive iterates is at most 0.000000\n",
      " 445 / 1198    868        152.4            7   0.453  -0.177  0.86  1437.757  2651.056\n",
      " 446 / 1198    868  The relative error between two consecutive iterates is at most 0.000000\n",
      " 446 / 1198    389        68.58        10.89   0.617   1.045  1.14  3189.202  2048.941\n",
      " 447 / 1198    389  The relative error between two consecutive iterates is at most 0.000000\n",
      " 447 / 1198    211        275.2        3.175   0.253   0.663  0.73  3021.663  1758.409\n",
      " 448 / 1198    211  The relative error between two consecutive iterates is at most 0.000000\n",
      " 448 / 1198   1407        266.3        3.247   0.186   0.352  0.75  3126.074  3478.688\n",
      " 449 / 1198   1407  The relative error between two consecutive iterates is at most 0.000000\n",
      " 449 / 1198    921        85.91          7.5   0.361   0.765  1.15  1548.308  2754.301\n",
      " 450 / 1198    921  The relative error between two consecutive iterates is at most 0.000000\n",
      " 450 / 1198   1477        79.03            7   0.000  -1.079  1.36  2153.114  1504.235\n",
      " 451 / 1198   1477  The relative error between two consecutive iterates is at most 0.000000\n",
      " 451 / 1198    978        111.6            7   0.400   0.664  1.04  3145.217  2812.494\n",
      " 452 / 1198    978  The relative error between two consecutive iterates is at most 0.000000\n",
      " 452 / 1198   1546        60.04          7.5   0.094  -0.066  1.50  2809.392  2457.018\n",
      " 453 / 1198   1546  The relative error between two consecutive iterates is at most 0.000000\n",
      " 453 / 1198    383        88.83        3.908   0.000  -0.745  1.01  2624.872  2007.504\n",
      " 454 / 1198    383  The relative error between two consecutive iterates is at most 0.000000\n",
      " 454 / 1198   1544        89.35        6.004   0.000   0.177  1.22  2654.994  2577.463\n",
      " 455 / 1198   1544  The relative error between two consecutive iterates is at most 0.000000\n",
      " 455 / 1198    888          177        5.881   0.553  -1.448  0.74  2974.665  2707.225\n",
      " 456 / 1198    888  The relative error between two consecutive iterates is at most 0.000000\n",
      " 456 / 1198    611        239.5        4.954   0.586   1.474  0.59  3163.355  2305.369\n",
      " 457 / 1198    611  The relative error between two consecutive iterates is at most 0.000000\n",
      " 457 / 1198    209        56.87         10.5   0.433  -0.755  0.78  2670.617  1757.776\n",
      " 458 / 1198    209  The relative error between two consecutive iterates is at most 0.000000\n",
      " 458 / 1198    411        184.4        3.542   0.000  -0.034  0.79  2920.139  2082.021\n",
      " 459 / 1198    411  The relative error between two consecutive iterates is at most 0.000000\n",
      " 459 / 1198    350        75.43        9.118   0.519  -1.141  1.27  1871.400  1981.807\n",
      " 460 / 1198    350  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 460 / 1198    186        48.91        6.776   0.263   1.548  1.15  1964.584  1718.106\n",
      " 461 / 1198    186  The relative error between two consecutive iterates is at most 0.000000\n",
      " 461 / 1198    609        224.8        3.612   0.103  -0.009  0.74  1780.037  2283.244\n",
      " 462 / 1198    609  The relative error between two consecutive iterates is at most 0.000000\n",
      " 462 / 1198    997        137.3        3.807   0.026   0.342  0.78  1859.114  2829.685\n",
      " 463 / 1198    997  The relative error between two consecutive iterates is at most 0.000000\n",
      " 463 / 1198   1028         87.7        6.219   0.295   1.465  0.79  2207.763  2897.770\n",
      " 464 / 1198   1028  The relative error between two consecutive iterates is at most 0.000000\n",
      " 464 / 1198      7        74.22          9.5   0.482   0.355  0.91  2296.561  1166.978\n",
      " 465 / 1198      7  The relative error between two consecutive iterates is at most 0.000000\n",
      " 465 / 1198   1514        527.1        1.452   0.025   0.951  0.44  2527.781  2453.899\n",
      " 466 / 1198   1514  The relative error between two consecutive iterates is at most 0.000000\n",
      " 466 / 1198    836        96.66        4.828   0.064   1.177  1.02  3347.441  2624.487\n",
      " 467 / 1198    836  The relative error between two consecutive iterates is at most 0.000000\n",
      " 467 / 1198    222        164.9            5   0.180   1.487  1.60  1736.740  1756.144\n",
      " 468 / 1198    222  The relative error between two consecutive iterates is at most 0.000000\n",
      " 468 / 1198    986        109.8        6.358   0.610   0.453  0.77  1846.438  2814.777\n",
      " 469 / 1198    986  The relative error between two consecutive iterates is at most 0.000000\n",
      " 469 / 1198    938        75.65        10.12   0.674  -1.262  0.97  3087.742  2821.653\n",
      " 470 / 1198    938  The relative error between two consecutive iterates is at most 0.000000\n",
      " 470 / 1198    288        120.6          5.5   0.152   0.136  1.33  3425.540  1891.855\n",
      " 471 / 1198    288  The relative error between two consecutive iterates is at most 0.000000\n",
      " 471 / 1198    624        131.8            5   0.000   1.545  1.84  3134.705  2302.629\n",
      " 472 / 1198    624  The relative error between two consecutive iterates is at most 0.000000\n",
      " 472 / 1198    140        63.02          7.5   0.215   1.297  1.62  2959.429  1628.348\n",
      " 473 / 1198    140  The relative error between two consecutive iterates is at most 0.000000\n",
      " 473 / 1198    579        91.68          6.5   0.148  -0.811  1.15  2363.628  2255.325\n",
      " 474 / 1198    579  The relative error between two consecutive iterates is at most 0.000000\n",
      " 474 / 1198    805        157.2        3.991   0.148   1.407  0.86  1642.125  2593.255\n",
      " 475 / 1198    805  The relative error between two consecutive iterates is at most 0.000000\n",
      " 475 / 1198   1112        103.6        4.883   0.250  -1.092  0.82  1995.972  3002.170\n",
      " 476 / 1198   1112  The relative error between two consecutive iterates is at most 0.000000\n",
      " 476 / 1198   1185        693.7        1.461   0.000   1.039  0.53  2635.492  3077.270\n",
      " 477 / 1198   1185  The relative error between two consecutive iterates is at most 0.000000\n",
      " 477 / 1198   1015        211.4        3.308   0.197   0.114  0.75  2858.716  2858.846\n",
      " 478 / 1198   1015  The relative error between two consecutive iterates is at most 0.000000\n",
      " 478 / 1198    509        83.66        6.013   0.323  -0.601  0.91  3170.258  2171.683\n",
      " 479 / 1198    509  The relative error between two consecutive iterates is at most 0.000000\n",
      " 479 / 1198   1121        110.6          8.5   0.678   0.110  0.68  1614.956  3001.594\n",
      " 480 / 1198   1121  The relative error between two consecutive iterates is at most 0.000000\n",
      " 480 / 1198    216          141        3.749   0.100  -1.339  0.92  3074.392  1757.277\n",
      " 481 / 1198    216  The relative error between two consecutive iterates is at most 0.000000\n",
      " 481 / 1198    694        176.4        4.164   0.212  -0.963  0.83  1699.001  2426.063\n",
      " 482 / 1198    694  The relative error between two consecutive iterates is at most 0.000000\n",
      " 482 / 1198     60        106.4            8   0.503   0.322  0.77  2685.955  1414.882\n",
      " 483 / 1198     60  The relative error between two consecutive iterates is at most 0.000000\n",
      " 483 / 1198   1322        226.6        3.104   0.161  -1.476  0.83  1394.807  3309.662\n",
      " 484 / 1198   1322  The relative error between two consecutive iterates is at most 0.000000\n",
      " 484 / 1198     83         84.2           11   0.456   0.927  1.27  2405.570  1528.756\n",
      " 485 / 1198     83  The relative error between two consecutive iterates is at most 0.000000\n",
      " 485 / 1198   1242        271.3        3.062   0.301  -0.273  0.55  1838.171  3175.000\n",
      " 486 / 1198   1242  The relative error between two consecutive iterates is at most 0.000000\n",
      " 486 / 1198   1380        123.8            7   0.284  -0.982  0.72  3242.156  3418.298\n",
      " 487 / 1198   1380  The relative error between two consecutive iterates is at most 0.000000\n",
      " 487 / 1198   1285        170.8        4.274   0.427   1.285  0.58  1810.081  3251.362\n",
      " 488 / 1198   1285  The relative error between two consecutive iterates is at most 0.000000\n",
      " 488 / 1198   1298         69.8            8   0.382   1.001  0.88  3205.113  3271.997\n",
      " 489 / 1198   1298  The relative error between two consecutive iterates is at most 0.000000\n",
      " 489 / 1198     53        75.71            7   0.427  -0.548  1.33  1843.181  1380.391\n",
      " 490 / 1198     53  The relative error between two consecutive iterates is at most 0.000000\n",
      " 490 / 1198    971        45.04          9.5   0.153  -1.255  1.38  2627.224  2820.218\n",
      " 491 / 1198    971  The relative error between two consecutive iterates is at most 0.000000\n",
      " 491 / 1198   1313        96.58        4.982   0.226  -0.468  0.86  2489.347  3278.576\n",
      " 492 / 1198   1313  The relative error between two consecutive iterates is at most 0.000000\n",
      " 492 / 1198    202        83.37        6.346   0.373  -1.098  1.02  2704.945  1742.700\n",
      " 493 / 1198    202  The relative error between two consecutive iterates is at most 0.000000\n",
      " 493 / 1198    194         73.3        9.181   0.546  -0.266  0.97  1940.047  1704.783\n",
      " 494 / 1198    194  The relative error between two consecutive iterates is at most 0.000000\n",
      " 494 / 1198   1174        90.61        6.473   0.414  -0.311  0.90  1918.692  3071.620\n",
      " 495 / 1198   1174  The relative error between two consecutive iterates is at most 0.000000\n",
      " 495 / 1198    726        227.5        2.791   0.000   0.854  0.77  3564.833  2464.667\n",
      " 496 / 1198    726  The relative error between two consecutive iterates is at most 0.000000\n",
      " 496 / 1198   1036        234.5        2.896   0.164  -0.142  0.68  2429.146  2888.639\n",
      " 497 / 1198   1036  The relative error between two consecutive iterates is at most 0.000000\n",
      " 497 / 1198    564        145.5        3.626   0.000   1.363  0.84  2476.756  2233.623\n",
      " 498 / 1198    564  The relative error between two consecutive iterates is at most 0.000000\n",
      " 498 / 1198    954        57.15            4   0.595  -1.394  0.48  2987.263  2773.358\n",
      " 499 / 1198    954  The relative error between two consecutive iterates is at most 0.000000\n",
      " 499 / 1198    995          107            5   0.186  -1.384  2.07  1589.504  2817.414\n",
      " 500 / 1198    995  The relative error between two consecutive iterates is at most 0.000000\n",
      " 500 / 1198   1300        99.13           10   0.326  -1.125  0.76  3277.000  3281.669\n",
      " 501 / 1198   1300  The relative error between two consecutive iterates is at most 0.000000\n",
      " 501 / 1198    667        219.2        3.153   0.175   1.288  0.67  3094.274  2387.782\n",
      " 502 / 1198    667  The relative error between two consecutive iterates is at most 0.000000\n",
      " 502 / 1198    743         72.8            9   0.409   0.875  0.76  2886.717  2510.864\n",
      " 503 / 1198    743  The relative error between two consecutive iterates is at most 0.000000\n",
      " 503 / 1198    910        85.95        5.926   0.172  -0.814  0.95  2160.664  2735.471\n",
      " 504 / 1198    910  The relative error between two consecutive iterates is at most 0.000000\n",
      " 504 / 1198   1247        143.1        2.648   0.087   0.266  0.67  2290.907  3192.162\n",
      " 505 / 1198   1247  The relative error between two consecutive iterates is at most 0.000000\n",
      " 505 / 1198    715        96.98            6   0.215  -0.403  1.01  1506.418  2450.370\n",
      " 506 / 1198    715  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 506 / 1198     44        257.5            4   0.275  -0.016  0.89  2348.642  1339.887\n",
      " 507 / 1198     44  The relative error between two consecutive iterates is at most 0.000000\n",
      " 507 / 1198    714        24.98        17.75   0.326   0.408  1.47  3035.758  2474.477\n",
      " 508 / 1198    714  The relative error between two consecutive iterates is at most 0.000000\n",
      " 508 / 1198   1541         86.8          7.5   0.056   0.852  0.75  2639.452  2717.070\n",
      " 509 / 1198   1541  The relative error between two consecutive iterates is at most 0.000000\n",
      " 509 / 1198   1443        240.3        3.099   0.324  -0.624  0.54  2831.371  3611.960\n",
      " 510 / 1198   1443  The relative error between two consecutive iterates is at most 0.000000\n",
      " 510 / 1198   1100        78.49          6.5   0.325  -0.748  1.09  2881.949  2974.441\n",
      " 511 / 1198   1100  The relative error between two consecutive iterates is at most 0.000000\n",
      " 511 / 1198    111        105.1        4.764   0.122   1.097  0.86  1863.430  1573.788\n",
      " 512 / 1198    111  The relative error between two consecutive iterates is at most 0.000000\n",
      " 512 / 1198   1117        45.51         10.5   0.528   0.952  1.05  2645.477  3016.028\n",
      " 513 / 1198   1117  The relative error between two consecutive iterates is at most 0.000000\n",
      " 513 / 1198   1261        152.2        3.612   0.268   1.542  0.95  2079.572  3206.343\n",
      " 514 / 1198   1261  The relative error between two consecutive iterates is at most 0.000000\n",
      " 514 / 1198   1498        48.14            7   0.000  -0.498  1.58  2711.517  2012.087\n",
      " 515 / 1198   1498  The relative error between two consecutive iterates is at most 0.000000\n",
      " 515 / 1198    578        109.8          5.5   0.328   1.147  1.21  2885.247  2249.514\n",
      " 516 / 1198    578  The relative error between two consecutive iterates is at most 0.000000\n",
      " 516 / 1198    585        113.3          8.5   0.401   0.377  0.56  2305.739  2256.517\n",
      " 517 / 1198    585  The relative error between two consecutive iterates is at most 0.000000\n",
      " 517 / 1198     46        224.2            4   0.361  -0.327  0.96  2317.297  1347.635\n",
      " 518 / 1198     46  The relative error between two consecutive iterates is at most 0.000000\n",
      " 518 / 1198   1169        81.68          5.5   0.160  -1.190  1.33  3189.593  3055.792\n",
      " 519 / 1198   1169  The relative error between two consecutive iterates is at most 0.000000\n",
      " 519 / 1198    657        95.37        7.123   0.279   0.993  0.86  1643.019  2364.337\n",
      " 520 / 1198    657  The relative error between two consecutive iterates is at most 0.000000\n",
      " 520 / 1198   1294        145.3          4.5   0.311  -0.303  1.06  1382.836  3244.426\n",
      " 521 / 1198   1294  The relative error between two consecutive iterates is at most 0.000000\n",
      " 521 / 1198    167         60.3          6.5   0.017   1.118  1.44  2648.486  1675.534\n",
      " 522 / 1198    167  The relative error between two consecutive iterates is at most 0.000000\n",
      " 522 / 1198   1164        105.4            5   0.293   1.513  1.44  3016.515  3047.069\n",
      " 523 / 1198   1164  The relative error between two consecutive iterates is at most 0.000000\n",
      " 523 / 1198   1135        26.79         11.5   0.439  -1.244  1.06  2795.005  3049.869\n",
      " 524 / 1198   1135  The relative error between two consecutive iterates is at most 0.000000\n",
      " 524 / 1198    712        146.6        4.907   0.506  -1.467  0.72  3126.911  2481.403\n",
      " 525 / 1198    712  The relative error between two consecutive iterates is at most 0.000000\n",
      " 525 / 1198   1213         80.2          5.5   0.221  -0.685  1.28  3159.450  3132.122\n",
      " 526 / 1198   1213  The relative error between two consecutive iterates is at most 0.000000\n",
      " 526 / 1198   1025        165.6        3.026   0.135  -1.038  0.96  2466.481  2882.089\n",
      " 527 / 1198   1025  The relative error between two consecutive iterates is at most 0.000000\n",
      " 527 / 1198   1150         69.1         5.42   0.116  -0.709  1.00  2343.485  3040.793\n",
      " 528 / 1198   1150  The relative error between two consecutive iterates is at most 0.000000\n",
      " 528 / 1198   1494        73.47            7   0.056   1.325  1.10  2574.063  1534.217\n",
      " 529 / 1198   1494  The relative error between two consecutive iterates is at most 0.000000\n",
      " 529 / 1198   1061        158.5        3.718   0.306   0.910  0.81  1245.180  2919.243\n",
      " 530 / 1198   1061  The relative error between two consecutive iterates is at most 0.000000\n",
      " 530 / 1198   1471        103.7          9.5   0.000  -1.311  0.72  2337.550  1537.380\n",
      " 531 / 1198   1471  The relative error between two consecutive iterates is at most 0.000000\n",
      " 531 / 1198   1330        101.6          4.5   0.000  -1.331  2.11  2800.682  3314.476\n",
      " 532 / 1198   1330  The relative error between two consecutive iterates is at most 0.000000\n",
      " 532 / 1198    309        53.01          6.5   0.049  -0.578  1.32  3136.897  1917.596\n",
      " 533 / 1198    309  The relative error between two consecutive iterates is at most 0.000000\n",
      " 533 / 1198    739        70.59          6.5   0.081  -0.462  1.33  2953.846  2485.743\n",
      " 534 / 1198    739  The relative error between two consecutive iterates is at most 0.000000\n",
      " 534 / 1198    665        92.15          5.5   0.046  -0.699  1.22  2682.999  2365.321\n",
      " 535 / 1198    665  The relative error between two consecutive iterates is at most 0.000000\n",
      " 535 / 1198   1109        89.89        4.716   0.297  -0.593  0.81  2006.425  2991.885\n",
      " 536 / 1198   1109  The relative error between two consecutive iterates is at most 0.000000\n",
      " 536 / 1198    107        91.02           15   0.703   0.330  0.72  2220.449  1566.418\n",
      " 537 / 1198    107  The relative error between two consecutive iterates is at most 0.000000\n",
      " 537 / 1198    928        61.41            9   0.394  -0.833  1.19  2726.520  2769.138\n",
      " 538 / 1198    928  The relative error between two consecutive iterates is at most 0.000000\n",
      " 538 / 1198    436        127.7        4.871   0.446   0.766  0.74  2909.668  2089.107\n",
      " 539 / 1198    436  The relative error between two consecutive iterates is at most 0.000000\n",
      " 539 / 1198   1291        54.89          7.5   0.418   0.605  1.00  2071.744  3254.230\n",
      " 540 / 1198   1291  The relative error between two consecutive iterates is at most 0.000000\n",
      " 540 / 1198   1502        92.75         14.5   0.837   1.444  0.25  3218.151  2396.193\n",
      " 541 / 1198   1502  The relative error between two consecutive iterates is at most 0.000000\n",
      " 541 / 1198    188        57.78          6.5   0.147   1.433  1.14  2755.813  1723.738\n",
      " 542 / 1198    188  The relative error between two consecutive iterates is at most 0.000000\n",
      " 542 / 1198    911        45.62          8.5   0.316   1.357  1.08  1504.090  2798.290\n",
      " 543 / 1198    911  The relative error between two consecutive iterates is at most 0.000000\n",
      " 543 / 1198    531        57.04         10.5   0.505  -1.078  1.10  1700.728  2225.829\n",
      " 544 / 1198    531  The relative error between two consecutive iterates is at most 0.000000\n",
      " 544 / 1198   1046        24.04         11.5   0.315   1.315  1.46  2855.254  2928.718\n",
      " 545 / 1198   1046  The relative error between two consecutive iterates is at most 0.000000\n",
      " 545 / 1198   1093        80.26         4.57   0.107   1.452  0.94  2819.123  2966.522\n",
      " 546 / 1198   1093  The relative error between two consecutive iterates is at most 0.000000\n",
      " 546 / 1198    976        71.57        4.518   0.086   1.427  1.12  1986.000  2813.552\n",
      " 547 / 1198    976  The relative error between two consecutive iterates is at most 0.000000\n",
      " 547 / 1198   1158        43.72        7.285   0.256  -0.849  1.14  2073.094  3066.101\n",
      " 548 / 1198   1158  The relative error between two consecutive iterates is at most 0.000000\n",
      " 548 / 1198    134        30.09           16   0.135   0.605  1.25  2123.810  1634.343\n",
      " 549 / 1198    134  The relative error between two consecutive iterates is at most 0.000000\n",
      " 549 / 1198   1022        102.6          5.5   0.401   1.385  0.95  1339.614  2867.596\n",
      " 550 / 1198   1022  The relative error between two consecutive iterates is at most 0.000000\n",
      " 550 / 1198   1241         65.7        4.797   0.014   0.808  0.76  2195.008  3179.024\n",
      " 551 / 1198   1241  The relative error between two consecutive iterates is at most 0.000000\n",
      " 551 / 1198   1063        64.76          7.5   0.523  -0.580  0.95  1975.057  2923.181\n",
      " 552 / 1198   1063  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 552 / 1198   1026        65.83        6.061   0.310   0.169  1.21  2449.536  2882.415\n",
      " 553 / 1198   1026  The relative error between two consecutive iterates is at most 0.000000\n",
      " 553 / 1198   1329        107.8        4.064   0.284  -1.045  0.77  2856.506  3326.095\n",
      " 554 / 1198   1329  The relative error between two consecutive iterates is at most 0.000000\n",
      " 554 / 1198    234         97.7            6   0.245   0.725  0.86  1679.079  1784.580\n",
      " 555 / 1198    234  The relative error between two consecutive iterates is at most 0.000000\n",
      " 555 / 1198   1549        76.37        4.021   0.076  -0.130  0.60  2731.113  2425.230\n",
      " 556 / 1198   1549  The relative error between two consecutive iterates is at most 0.000000\n",
      " 556 / 1198   1251         46.7          9.5   0.553  -1.335  0.95  2238.945  3205.780\n",
      " 557 / 1198   1251  The relative error between two consecutive iterates is at most 0.000000\n",
      " 557 / 1198    816        58.28          5.5   0.069  -0.548  1.26  2794.116  2592.200\n",
      " 558 / 1198    816  The relative error between two consecutive iterates is at most 0.000000\n",
      " 558 / 1198    214        133.5          4.5   0.265   1.279  0.97  3357.932  1735.037\n",
      " 559 / 1198    214  The relative error between two consecutive iterates is at most 0.000000\n",
      " 559 / 1198    930         35.5          9.5   0.000  -0.124  1.17  2713.688  2777.696\n",
      " 560 / 1198    930  The relative error between two consecutive iterates is at most 0.000000\n",
      " 560 / 1198   1147        79.55         5.21   0.404  -1.429  0.96  1753.564  3048.096\n",
      " 561 / 1198   1147  The relative error between two consecutive iterates is at most 0.000000\n",
      " 561 / 1198    404        129.5          3.5   0.001   1.041  0.75  2272.935  2078.058\n",
      " 562 / 1198    404  The relative error between two consecutive iterates is at most 0.000000\n",
      " 562 / 1198    754        231.6        2.801   0.207  -1.512  0.67  1748.921  2503.981\n",
      " 563 / 1198    754  The relative error between two consecutive iterates is at most 0.000000\n",
      " 563 / 1198    196        102.6        4.159   0.165  -1.009  1.01  2731.217  1713.445\n",
      " 564 / 1198    196  The relative error between two consecutive iterates is at most 0.000000\n",
      " 564 / 1198    935        76.23            6   0.220  -0.058  0.95  2554.434  2759.393\n",
      " 565 / 1198    935  The relative error between two consecutive iterates is at most 0.000000\n",
      " 565 / 1198   1041        41.82            8   0.176   0.895  1.01  1562.669  2900.438\n",
      " 566 / 1198   1041  The relative error between two consecutive iterates is at most 0.000000\n",
      " 566 / 1198    561        62.87          7.5   0.226   0.831  0.79  2732.304  2239.024\n",
      " 567 / 1198    561  The relative error between two consecutive iterates is at most 0.000000\n",
      " 567 / 1198    742        131.2            4   0.125  -1.087  1.74  2026.429  2475.421\n",
      " 568 / 1198    742  The relative error between two consecutive iterates is at most 0.000000\n",
      " 568 / 1198    740        92.98        4.262   0.099   0.109  0.74  3304.818  2481.083\n",
      " 569 / 1198    740  The relative error between two consecutive iterates is at most 0.000000\n",
      " 569 / 1198    201        79.82          5.5   0.238  -0.982  1.15  1937.973  1715.419\n",
      " 570 / 1198    201  The relative error between two consecutive iterates is at most 0.000000\n",
      " 570 / 1198    503        52.08            9   0.388  -0.410  0.83  2646.571  2157.660\n",
      " 571 / 1198    503  The relative error between two consecutive iterates is at most 0.000000\n",
      " 571 / 1198    233        123.9          6.5   0.555  -0.957  0.25  1673.784  1779.982\n",
      " 572 / 1198    233  The relative error between two consecutive iterates is at most 0.000000\n",
      " 572 / 1198   1220        49.74            4   0.049  -0.483  1.22  2763.158  3132.386\n",
      " 573 / 1198   1220  The relative error between two consecutive iterates is at most 0.000000\n",
      " 573 / 1198     88        142.1            6   0.498  -0.101  0.87  2276.288  1496.283\n",
      " 574 / 1198     88  The relative error between two consecutive iterates is at most 0.000000\n",
      " 574 / 1198    358         76.7        6.117   0.332   0.284  1.11  2393.383  1987.772\n",
      " 575 / 1198    358  The relative error between two consecutive iterates is at most 0.000000\n",
      " 575 / 1198   1404        29.88        8.464   0.332   0.010  1.46  2568.499  3466.456\n",
      " 576 / 1198   1404  The relative error between two consecutive iterates is at most 0.000000\n",
      " 576 / 1198   1449        212.3            4   0.273  -0.720  0.58  3190.074  3636.631\n",
      " 577 / 1198   1449  The relative error between two consecutive iterates is at most 0.000000\n",
      " 577 / 1198    808        150.2        2.941   0.000  -0.246  0.82  1893.366  2581.957\n",
      " 578 / 1198    808  The relative error between two consecutive iterates is at most 0.000000\n",
      " 578 / 1198   1193          132        3.114   0.075   0.168  0.75  2744.818  3086.952\n",
      " 579 / 1198   1193  The relative error between two consecutive iterates is at most 0.000000\n",
      " 579 / 1198   1375        100.8        3.956   0.137   0.143  0.83  3117.998  3399.710\n",
      " 580 / 1198   1375  The relative error between two consecutive iterates is at most 0.000000\n",
      " 580 / 1198    330        33.91          9.5   0.207  -0.014  1.51  2772.684  1941.465\n",
      " 581 / 1198    330  The relative error between two consecutive iterates is at most 0.000000\n",
      " 581 / 1198   1020        100.4        2.857   0.010   0.847  0.67  2703.149  2870.818\n",
      " 582 / 1198   1020  The relative error between two consecutive iterates is at most 0.000000\n",
      " 582 / 1198    532        62.49            8   0.561  -0.506  0.92  2456.949  2200.889\n",
      " 583 / 1198    532  The relative error between two consecutive iterates is at most 0.000000\n",
      " 583 / 1198   1068        84.83        4.024   0.000   0.466  0.74  1894.048  2921.646\n",
      " 584 / 1198   1068  The relative error between two consecutive iterates is at most 0.000000\n",
      " 584 / 1198    481        130.7        3.085   0.125   0.144  0.74  3440.590  2121.918\n",
      " 585 / 1198    481  The relative error between two consecutive iterates is at most 0.000000\n",
      " 585 / 1198   1411        56.59        4.935   0.077   0.569  1.01  2418.070  3495.442\n",
      " 586 / 1198   1411  The relative error between two consecutive iterates is at most 0.000000\n",
      " 586 / 1198    870        39.18          9.5   0.326   0.125  1.24  3468.639  2658.236\n",
      " 587 / 1198    870  The relative error between two consecutive iterates is at most 0.000000\n",
      " 587 / 1198    988        48.41        6.414   0.339   1.375  1.25  1521.569  2830.816\n",
      " 588 / 1198    988  The relative error between two consecutive iterates is at most 0.000000\n",
      " 588 / 1198   1127        48.24          9.5   0.630  -0.942  0.77  1512.532  3022.966\n",
      " 589 / 1198   1127  The relative error between two consecutive iterates is at most 0.000000\n",
      " 589 / 1198   1295        122.6        3.907   0.497  -0.906  0.54  2672.844  3270.093\n",
      " 590 / 1198   1295  The relative error between two consecutive iterates is at most 0.000000\n",
      " 590 / 1198   1258        28.16            8   0.000  -0.844  1.12  2375.310  3205.012\n",
      " 591 / 1198   1258  The relative error between two consecutive iterates is at most 0.000000\n",
      " 591 / 1198    604          108        3.714   0.229   1.041  1.12  3512.517  2276.762\n",
      " 592 / 1198    604  The relative error between two consecutive iterates is at most 0.000000\n",
      " 592 / 1198    818        80.43            5   0.087  -0.275  1.19  2070.018  2585.585\n",
      " 593 / 1198    818  The relative error between two consecutive iterates is at most 0.000000\n",
      " 593 / 1198   1344        51.45          6.5   0.338   1.229  1.27  1579.611  3349.961\n",
      " 594 / 1198   1344  The relative error between two consecutive iterates is at most 0.000000\n",
      " 594 / 1198   1191        53.69        4.998   0.081  -1.452  0.79  3008.525  3093.559\n",
      " 595 / 1198   1191  The relative error between two consecutive iterates is at most 0.000000\n",
      " 595 / 1198   1302        56.91          6.5   0.232  -0.077  0.80  1606.799  3262.398\n",
      " 596 / 1198   1302  The relative error between two consecutive iterates is at most 0.000000\n",
      " 596 / 1198    514        77.98        4.675   0.310   1.215  0.80  3427.209  2180.116\n",
      " 597 / 1198    514  The relative error between two consecutive iterates is at most 0.000000\n",
      " 597 / 1198   1353        57.68          7.5   0.388   0.421  0.84  1606.028  3357.367\n",
      " 598 / 1198   1353  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 598 / 1198    354        75.39            6   0.378   1.116  0.90  2234.398  1969.742\n",
      " 599 / 1198    354  The relative error between two consecutive iterates is at most 0.000000\n",
      " 599 / 1198   1343        55.78            5   0.401   0.950  0.89  1395.975  3340.828\n",
      " 600 / 1198   1343  The relative error between two consecutive iterates is at most 0.000000\n",
      " 600 / 1198     13        170.5        3.043   0.226  -0.903  0.63  2218.988  1217.878\n",
      " 601 / 1198     13  The relative error between two consecutive iterates is at most 0.000000\n",
      " 601 / 1198   1389        123.9        3.217   0.214   1.327  0.56  2366.996  3432.020\n",
      " 602 / 1198   1389  The relative error between two consecutive iterates is at most 0.000000\n",
      " 602 / 1198    640        95.36          7.5   0.398   1.522  0.86  2349.618  2340.852\n",
      " 603 / 1198    640  The relative error between two consecutive iterates is at most 0.000000\n",
      " 603 / 1198   1371        75.56          5.5   0.402  -1.119  0.95  2693.166  3387.095\n",
      " 604 / 1198   1371  The relative error between two consecutive iterates is at most 0.000000\n",
      " 604 / 1198    803        64.92          8.5   0.685  -1.240  0.88  3221.690  2591.653\n",
      " 605 / 1198    803  The relative error between two consecutive iterates is at most 0.000000\n",
      " 605 / 1198   1497        46.08          6.5   0.298   0.509  1.28  2697.581  2010.933\n",
      " 606 / 1198   1497  The relative error between two consecutive iterates is at most 0.000000\n",
      " 606 / 1198    699         82.1            5   0.152  -0.621  0.99  2884.223  2423.041\n",
      " 607 / 1198    699  The relative error between two consecutive iterates is at most 0.000000\n",
      " 607 / 1198     22        70.14          5.5   0.129  -0.675  0.97  2371.806  1251.211\n",
      " 608 / 1198     22  The relative error between two consecutive iterates is at most 0.000000\n",
      " 608 / 1198    539        66.58            8   0.316  -1.329  0.74  2792.658  2214.941\n",
      " 609 / 1198    539  The relative error between two consecutive iterates is at most 0.000000\n",
      " 609 / 1198     50        180.5          3.5   0.316  -0.731  0.78  1870.305  1362.896\n",
      " 610 / 1198     50  The relative error between two consecutive iterates is at most 0.000000\n",
      " 610 / 1198   1438        141.7        3.574   0.402   1.086  0.66  2420.717  3600.717\n",
      " 611 / 1198   1438  The relative error between two consecutive iterates is at most 0.000000\n",
      " 611 / 1198    802        62.55          5.5   0.000  -1.284  1.36  1484.319  2574.555\n",
      " 612 / 1198    802  The relative error between two consecutive iterates is at most 0.000000\n",
      " 612 / 1198    944        54.95          5.5   0.057   0.810  1.19  1854.989  2765.717\n",
      " 613 / 1198    944  The relative error between two consecutive iterates is at most 0.000000\n",
      " 613 / 1198    956        191.5        2.749   0.242  -0.380  0.64  3328.412  2780.370\n",
      " 614 / 1198    956  The relative error between two consecutive iterates is at most 0.000000\n",
      " 614 / 1198   1199        70.38            6   0.457  -0.856  0.94  3222.973  3115.055\n",
      " 615 / 1198   1199  The relative error between two consecutive iterates is at most 0.000000\n",
      " 615 / 1198    982         37.2          5.5   0.203  -1.544  0.74  2436.182  2806.968\n",
      " 616 / 1198    982  The relative error between two consecutive iterates is at most 0.000000\n",
      " 616 / 1198   1171        100.1            5   0.441   1.512  0.91  3286.297  3055.811\n",
      " 617 / 1198   1171  The relative error between two consecutive iterates is at most 0.000000\n",
      " 617 / 1198    268        96.92        3.595   0.202   0.453  0.72  3006.075  1852.971\n",
      " 618 / 1198    268  The relative error between two consecutive iterates is at most 0.000000\n",
      " 618 / 1198    168        279.2        2.008   0.156  -1.539  0.75  2910.541  1666.305\n",
      " 619 / 1198    168  The relative error between two consecutive iterates is at most 0.000000\n",
      " 619 / 1198   1204        149.6        1.693   0.023   1.345  0.51  2731.079  3117.799\n",
      " 620 / 1198   1204  The relative error between two consecutive iterates is at most 0.000000\n",
      " 620 / 1198   1067        34.84          7.5   0.291  -0.959  1.14  2064.474  2928.238\n",
      " 621 / 1198   1067  The relative error between two consecutive iterates is at most 0.000000\n",
      " 621 / 1198    381        91.43         3.93   0.156   1.532  0.83  1991.996  2003.265\n",
      " 622 / 1198    381  The relative error between two consecutive iterates is at most 0.000000\n",
      " 622 / 1198     12        88.31          4.5   0.118   1.090  1.00  2286.451  1191.338\n",
      " 623 / 1198     12  The relative error between two consecutive iterates is at most 0.000000\n",
      " 623 / 1198    790        59.19          8.5   0.174   1.375  0.99  2217.669  2577.178\n",
      " 624 / 1198    790  The relative error between two consecutive iterates is at most 0.000000\n",
      " 624 / 1198    559        77.66        5.796   0.542  -1.277  1.00  1695.622  2237.850\n",
      " 625 / 1198    559  The relative error between two consecutive iterates is at most 0.000000\n",
      " 625 / 1198   1146        230.5        2.042   0.156   0.427  0.71  1527.865  3023.621\n",
      " 626 / 1198   1146  The relative error between two consecutive iterates is at most 0.000000\n",
      " 626 / 1198    137        40.66          7.5   0.000   0.200  1.37  3126.178  1612.196\n",
      " 627 / 1198    137  The relative error between two consecutive iterates is at most 0.000000\n",
      " 627 / 1198    606        67.53          5.5   0.208  -1.078  0.75  2082.205  2276.848\n",
      " 628 / 1198    606  The relative error between two consecutive iterates is at most 0.000000\n",
      " 628 / 1198   1126        68.49            6   0.425   1.021  0.65  1517.564  3002.205\n",
      " 629 / 1198   1126  The relative error between two consecutive iterates is at most 0.000000\n",
      " 629 / 1198    362        190.8        2.718   0.235  -0.970  0.64  2060.053  1973.108\n",
      " 630 / 1198    362  The relative error between two consecutive iterates is at most 0.000000\n",
      " 630 / 1198    725           65        5.213   0.000  -1.522  1.16  1948.167  2474.282\n",
      " 631 / 1198    725  The relative error between two consecutive iterates is at most 0.000000\n",
      " 631 / 1198    287        59.23        4.804   0.087   0.912  0.86  3456.757  1896.022\n",
      " 632 / 1198    287  The relative error between two consecutive iterates is at most 0.000000\n",
      " 632 / 1198     39        86.63          5.5   0.405   0.411  0.99  2445.324  1325.611\n",
      " 633 / 1198     39  The relative error between two consecutive iterates is at most 0.000000\n",
      " 633 / 1198   1235        87.57            5   0.136   0.156  0.92  3299.329  3157.295\n",
      " 634 / 1198   1235  The relative error between two consecutive iterates is at most 0.000000\n",
      " 634 / 1198    529        86.45          4.5   0.232  -0.734  0.95  3561.556  2190.257\n",
      " 635 / 1198    529  The relative error between two consecutive iterates is at most 0.000000\n",
      " 635 / 1198    983           63            6   0.409  -1.125  0.41  2635.232  2811.123\n",
      " 636 / 1198    983  The relative error between two consecutive iterates is at most 0.000000\n",
      " 636 / 1198   1027        63.78            8   0.701  -0.722  0.68  2912.171  2885.454\n",
      " 637 / 1198   1027  The relative error between two consecutive iterates is at most 0.000000\n",
      " 637 / 1198    693        58.54            7   0.107  -0.152  0.92  1691.516  2417.745\n",
      " 638 / 1198    693  The relative error between two consecutive iterates is at most 0.000000\n",
      " 638 / 1198    973        66.88            5   0.042   0.451  1.05  2512.646  2795.928\n",
      " 639 / 1198    973  The relative error between two consecutive iterates is at most 0.000000\n",
      " 639 / 1198    223        107.3          4.5   0.364  -0.605  0.76  3348.264  1748.927\n",
      " 640 / 1198    223  The relative error between two consecutive iterates is at most 0.000000\n",
      " 640 / 1198   1357        71.13            4   0.000  -1.024  1.78  1852.462  3360.523\n",
      " 641 / 1198   1357  The relative error between two consecutive iterates is at most 0.000000\n",
      " 641 / 1198    110        138.8        3.479   0.322   1.090  0.54  2663.679  1566.700\n",
      " 642 / 1198    110  The relative error between two consecutive iterates is at most 0.000000\n",
      " 642 / 1198    152        105.3        3.863   0.264  -0.050  0.76  2005.049  1627.004\n",
      " 643 / 1198    152  The relative error between two consecutive iterates is at most 0.000000\n",
      " 643 / 1198   1188        69.69          8.5   0.592   1.398  0.95  3313.038  3081.043\n",
      " 644 / 1198   1188  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 644 / 1198    648        39.09            7   0.000  -1.413  0.79  1910.572  2357.498\n",
      " 645 / 1198    648  The relative error between two consecutive iterates is at most 0.000000\n",
      " 645 / 1198   1205        60.93          6.5   0.385   0.443  0.54  1734.475  3121.318\n",
      " 646 / 1198   1205  The relative error between two consecutive iterates is at most 0.000000\n",
      " 646 / 1198    252        57.46            8   0.376   0.017  0.58  2475.101  1820.274\n",
      " 647 / 1198    252  The relative error between two consecutive iterates is at most 0.000000\n",
      " 647 / 1198    422        68.88          5.5   0.193  -0.019  0.89  2090.435  2110.186\n",
      " 648 / 1198    422  The relative error between two consecutive iterates is at most 0.000000\n",
      " 648 / 1198    758        71.16        4.693   0.000   1.557  0.91  1684.861  2512.285\n",
      " 649 / 1198    758  The relative error between two consecutive iterates is at most 0.000000\n",
      " 649 / 1198   1039        79.83         3.91   0.172   0.255  0.62  2478.499  2886.979\n",
      " 650 / 1198   1039  The relative error between two consecutive iterates is at most 0.000000\n",
      " 650 / 1198    128        81.83            6   0.341   1.286  0.75  2521.586  1595.203\n",
      " 651 / 1198    128  The relative error between two consecutive iterates is at most 0.000000\n",
      " 651 / 1198   1440        54.35            5   0.176  -0.412  1.41  3079.290  3590.404\n",
      " 652 / 1198   1440  The relative error between two consecutive iterates is at most 0.000000\n",
      " 652 / 1198    984        41.82            6   0.119   1.421  0.94  3365.025  2812.287\n",
      " 653 / 1198    984  The relative error between two consecutive iterates is at most 0.000000\n",
      " 653 / 1198    501        46.26            6   0.286   0.647  0.91  3286.676  2152.877\n",
      " 654 / 1198    501  The relative error between two consecutive iterates is at most 0.000000\n",
      " 654 / 1198    768        64.27          5.5   0.015   0.919  1.08  1473.463  2524.023\n",
      " 655 / 1198    768  The relative error between two consecutive iterates is at most 0.000000\n",
      " 655 / 1198    258        88.89          7.5   0.674  -1.041  0.53  2196.435  1841.790\n",
      " 656 / 1198    258  The relative error between two consecutive iterates is at most 0.000000\n",
      " 656 / 1198    950        65.89          4.5   0.142  -0.538  1.35  1577.460  2772.650\n",
      " 657 / 1198    950  The relative error between two consecutive iterates is at most 0.000000\n",
      " 657 / 1198   1057          102        1.825   0.000  -0.966  0.67  2362.560  2915.027\n",
      " 658 / 1198   1057  The relative error between two consecutive iterates is at most 0.000000\n",
      " 658 / 1198   1038        112.7        3.317   0.309   1.242  0.76  1637.361  2887.923\n",
      " 659 / 1198   1038  The relative error between two consecutive iterates is at most 0.000000\n",
      " 659 / 1198    707        88.97        3.932   0.133  -0.497  0.86  3583.940  2431.418\n",
      " 660 / 1198    707  The relative error between two consecutive iterates is at most 0.000000\n",
      " 660 / 1198   1043        35.09           12   0.687  -1.128  0.25  2372.016  2934.341\n",
      " 661 / 1198   1043  The relative error between two consecutive iterates is at most 0.000000\n",
      " 661 / 1198    277        63.65          4.5   0.000   0.420  1.09  2827.635  1863.508\n",
      " 662 / 1198    277  The relative error between two consecutive iterates is at most 0.000000\n",
      " 662 / 1198    225        82.83            5   0.336  -0.864  0.57  1812.327  1758.291\n",
      " 663 / 1198    225  The relative error between two consecutive iterates is at most 0.000000\n",
      " 663 / 1198   1230        39.29        2.969   0.000  -0.409  0.94  2764.223  3149.487\n",
      " 664 / 1198   1230  The relative error between two consecutive iterates is at most 0.000000\n",
      " 664 / 1198   1087        66.35          4.5   0.118   0.905  1.23  1844.679  2937.656\n",
      " 665 / 1198   1087  The relative error between two consecutive iterates is at most 0.000000\n",
      " 665 / 1198   1103         70.6          4.5   0.263  -0.129  1.11  1566.097  2968.726\n",
      " 666 / 1198   1103  The relative error between two consecutive iterates is at most 0.000000\n",
      " 666 / 1198   1154        24.87          8.5   0.253  -1.024  1.29  2081.956  3057.675\n",
      " 667 / 1198   1154  The relative error between two consecutive iterates is at most 0.000000\n",
      " 667 / 1198    573        82.98          4.5   0.069  -1.184  0.86  1737.047  2239.630\n",
      " 668 / 1198    573  The relative error between two consecutive iterates is at most 0.000000\n",
      " 668 / 1198    943        92.74        3.216   0.023   0.039  0.73  2543.167  2756.409\n",
      " 669 / 1198    943  The relative error between two consecutive iterates is at most 0.000000\n",
      " 669 / 1198   1013         54.9            5   0.136   0.805  0.95  2850.258  2845.362\n",
      " 670 / 1198   1013  The relative error between two consecutive iterates is at most 0.000000\n",
      " 670 / 1198    540        46.72            5   0.052  -0.677  1.15  3504.698  2199.770\n",
      " 671 / 1198    540  The relative error between two consecutive iterates is at most 0.000000\n",
      " 671 / 1198    314        80.09         4.06   0.197  -0.153  0.90  2312.002  1917.180\n",
      " 672 / 1198    314  The relative error between two consecutive iterates is at most 0.000000\n",
      " 672 / 1198   1017        82.94        3.832   0.321  -1.141  0.68  1593.815  2859.525\n",
      " 673 / 1198   1017  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000 and the relative error between two consecutive iterates is at \n",
      "  most 0.000000\n",
      " 673 / 1198    279        145.8        2.808   0.325   0.451  0.59  3211.692  1865.221\n",
      " 674 / 1198    279  The relative error between two consecutive iterates is at most 0.000000\n",
      " 674 / 1198   1545        26.12          7.5   0.197   0.330  2.36  2804.265  2495.560\n",
      " 675 / 1198   1545  The relative error between two consecutive iterates is at most 0.000000\n",
      " 675 / 1198    351        75.34           24   0.644  -1.062  0.39  2863.169  1975.568\n",
      " 676 / 1198    351  The relative error between two consecutive iterates is at most 0.000000\n",
      " 676 / 1198    635        60.19            5   0.000   0.078  1.07  2526.629  2313.305\n",
      " 677 / 1198    635  The relative error between two consecutive iterates is at most 0.000000\n",
      " 677 / 1198    577        47.36          5.5   0.188   1.007  0.86  3082.298  2250.079\n",
      " 678 / 1198    577  The relative error between two consecutive iterates is at most 0.000000\n",
      " 678 / 1198    737        192.9            2   0.216   1.243  0.56  3472.585  2462.083\n",
      " 679 / 1198    737  The relative error between two consecutive iterates is at most 0.000000\n",
      " 679 / 1198    593        75.33          4.5   0.286   1.006  0.87  2949.684  2261.654\n",
      " 680 / 1198    593  The relative error between two consecutive iterates is at most 0.000000\n",
      " 680 / 1198    139        113.4        3.384   0.150  -0.927  0.71  1790.218  1611.480\n",
      " 681 / 1198    139  The relative error between two consecutive iterates is at most 0.000000\n",
      " 681 / 1198    822        54.75          5.5   0.127  -0.876  1.02  1560.136  2597.142\n",
      " 682 / 1198    822  The relative error between two consecutive iterates is at most 0.000000\n",
      " 682 / 1198    197        72.59          4.5   0.257  -1.050  0.90  3380.222  1703.503\n",
      " 683 / 1198    197  The relative error between two consecutive iterates is at most 0.000000\n",
      " 683 / 1198    443        65.71            5   0.209   0.256  0.85  2239.544  2074.720\n",
      " 684 / 1198    443  The relative error between two consecutive iterates is at most 0.000000\n",
      " 684 / 1198   1142        63.36            5   0.449  -0.837  1.03  2030.136  3026.432\n",
      " 685 / 1198   1142  The relative error between two consecutive iterates is at most 0.000000\n",
      " 685 / 1198    804        67.96            6   0.000   0.235  0.32  2982.748  2579.374\n",
      " 686 / 1198    804  The relative error between two consecutive iterates is at most 0.000000\n",
      " 686 / 1198   1034        95.04         2.06   0.226  -1.561  0.55  2355.273  2899.931\n",
      " 687 / 1198   1034  The relative error between two consecutive iterates is at most 0.000000\n",
      " 687 / 1198   1252        109.2        3.212   0.345  -0.410  0.63  2835.384  3179.665\n",
      " 688 / 1198   1252  The relative error between two consecutive iterates is at most 0.000000\n",
      " 688 / 1198   1096        146.7        2.243   0.015   1.450  0.60  1696.421  2957.724\n",
      " 689 / 1198   1096  The relative error between two consecutive iterates is at most 0.000000\n",
      " 689 / 1198     32        118.9        3.459   0.323   0.375  0.85  2467.379  1295.317\n",
      " 690 / 1198     32  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 690 / 1198   1180        105.9        2.575   0.171  -1.292  0.61  1927.773  3073.626\n",
      " 691 / 1198   1180  The relative error between two consecutive iterates is at most 0.000000\n",
      " 691 / 1198   1054        43.95        4.886   0.000   0.023  1.16  2431.651  2904.448\n",
      " 692 / 1198   1054  The relative error between two consecutive iterates is at most 0.000000\n",
      " 692 / 1198    902        58.05            5   0.075  -1.498  0.89  2384.685  2712.092\n",
      " 693 / 1198    902  The relative error between two consecutive iterates is at most 0.000000\n",
      " 693 / 1198   1548        38.92          7.5   0.531  -1.163  0.92  1555.267  3050.937\n",
      " 694 / 1198   1548  The relative error between two consecutive iterates is at most 0.000000\n",
      " 694 / 1198    348        63.06            4   0.084   1.334  1.53  2986.476  1952.461\n",
      " 695 / 1198    348  The relative error between two consecutive iterates is at most 0.000000\n",
      " 695 / 1198   1504        50.02            5   0.000  -0.576  0.99  2591.815  2075.947\n",
      " 696 / 1198   1504  The relative error between two consecutive iterates is at most 0.000000\n",
      " 696 / 1198    511        53.92            5   0.073  -0.511  0.95  2299.557  2161.026\n",
      " 697 / 1198    511  The relative error between two consecutive iterates is at most 0.000000\n",
      " 697 / 1198    378        60.82          4.5   0.275  -1.175  0.85  1865.189  1995.326\n",
      " 698 / 1198    378  The relative error between two consecutive iterates is at most 0.000000\n",
      " 698 / 1198    980        48.29            5   0.094  -0.974  0.98  2964.991  2823.614\n",
      " 699 / 1198    980  The relative error between two consecutive iterates is at most 0.000000\n",
      " 699 / 1198   1335        41.72        5.297   0.408  -0.468  0.87  2812.819  3322.954\n",
      " 700 / 1198   1335  The relative error between two consecutive iterates is at most 0.000000\n",
      " 700 / 1198   1323        30.13            6   0.000  -0.941  1.07  2708.568  3308.910\n",
      " 701 / 1198   1323  The relative error between two consecutive iterates is at most 0.000000\n",
      " 701 / 1198   1273        78.43        3.503   0.218  -0.374  0.80  1895.896  3214.323\n",
      " 702 / 1198   1273  The relative error between two consecutive iterates is at most 0.000000\n",
      " 702 / 1198   1000        50.85            5   0.201  -1.504  1.03  1515.371  2854.366\n",
      " 703 / 1198   1000  The relative error between two consecutive iterates is at most 0.000000\n",
      " 703 / 1198    300        50.94          4.5   0.000   0.611  1.00  2909.304  1901.699\n",
      " 704 / 1198    300  The relative error between two consecutive iterates is at most 0.000000\n",
      " 704 / 1198    523        87.97          3.2   0.060  -0.806  0.97  3595.554  2180.879\n",
      " 705 / 1198    523  The relative error between two consecutive iterates is at most 0.000000\n",
      " 705 / 1198   1470        119.6            6   0.507   0.060  0.25  2358.452  1413.148\n",
      " 706 / 1198   1470  The relative error between two consecutive iterates is at most 0.000000\n",
      " 706 / 1198    842        58.84          5.5   0.131  -0.124  0.80  2155.809  2613.840\n",
      " 707 / 1198    842  The relative error between two consecutive iterates is at most 0.000000\n",
      " 707 / 1198   1450        65.78          4.5   0.278  -0.743  0.85  2544.682  3650.611\n",
      " 708 / 1198   1450  The relative error between two consecutive iterates is at most 0.000000\n",
      " 708 / 1198   1234        37.01            5   0.000  -1.567  1.11  2912.178  3160.106\n",
      " 709 / 1198   1234  The relative error between two consecutive iterates is at most 0.000000\n",
      " 709 / 1198    413        21.37            6   0.000  -0.566  1.86  2620.177  2037.592\n",
      " 710 / 1198    413  The relative error between two consecutive iterates is at most 0.000000\n",
      " 710 / 1198    317        28.37            8   0.227   1.441  0.96  3059.719  1935.304\n",
      " 711 / 1198    317  The relative error between two consecutive iterates is at most 0.000000\n",
      " 711 / 1198    823        114.5        2.497   0.224  -1.374  0.75  3214.685  2598.126\n",
      " 712 / 1198    823  The relative error between two consecutive iterates is at most 0.000000\n",
      " 712 / 1198     99        86.53          4.5   0.366  -0.828  0.85  2082.347  1527.862\n",
      " 713 / 1198     99  The relative error between two consecutive iterates is at most 0.000000\n",
      " 713 / 1198    934        84.25            5   0.517   1.304  0.75  1649.121  2755.819\n",
      " 714 / 1198    934  The relative error between two consecutive iterates is at most 0.000000\n",
      " 714 / 1198    281        31.32            7   0.135   0.947  0.90  3333.101  1893.738\n",
      " 715 / 1198    281  The relative error between two consecutive iterates is at most 0.000000\n",
      " 715 / 1198    866        49.66            5   0.055   1.341  0.98  3483.107  2647.847\n",
      " 716 / 1198    866  The relative error between two consecutive iterates is at most 0.000000\n",
      " 716 / 1198    444        51.65            5   0.093   1.053  0.87  2008.096  2082.071\n",
      " 717 / 1198    444  The relative error between two consecutive iterates is at most 0.000000\n",
      " 717 / 1198    966         39.2          7.5   0.180  -1.448  0.36  2450.000  2804.461\n",
      " 718 / 1198    966  The relative error between two consecutive iterates is at most 0.000000\n",
      " 718 / 1198   1098        50.98          4.5   0.000   1.027  1.15  2990.236  2960.620\n",
      " 719 / 1198   1098  The relative error between two consecutive iterates is at most 0.000000\n",
      " 719 / 1198    727        51.39          6.5   0.501  -1.566  0.56  3017.992  2468.096\n",
      " 720 / 1198    727  The relative error between two consecutive iterates is at most 0.000000\n",
      " 720 / 1198   1325        46.35          4.5   0.082   0.361  0.89  2653.456  3306.586\n",
      " 721 / 1198   1325  The relative error between two consecutive iterates is at most 0.000000\n",
      " 721 / 1198    191         71.6          6.5   0.129  -0.183  0.25  3285.097  1692.640\n",
      " 722 / 1198    191  The relative error between two consecutive iterates is at most 0.000000\n",
      " 722 / 1198    643        58.68            8   0.594   0.477  0.55  2708.282  2332.201\n",
      " 723 / 1198    643  The relative error between two consecutive iterates is at most 0.000000\n",
      " 723 / 1198    622        47.65            8   0.350  -0.560  0.61  3570.282  2302.205\n",
      " 724 / 1198    622  The relative error between two consecutive iterates is at most 0.000000\n",
      " 724 / 1198   1525        99.07          8.5   0.093   1.326  0.98  2394.220  2403.592\n",
      " 725 / 1198   1525  The relative error between two consecutive iterates is at most 0.000000\n",
      " 725 / 1198    895        92.97            4   0.149  -0.378  0.81  2560.591  2694.287\n",
      " 726 / 1198    895  The relative error between two consecutive iterates is at most 0.000000\n",
      " 726 / 1198    912        58.61          5.5   0.493   1.041  0.67  1644.329  2732.872\n",
      " 727 / 1198    912  The relative error between two consecutive iterates is at most 0.000000\n",
      " 727 / 1198    462        108.5        2.888   0.123   0.054  0.60  1877.685  2104.409\n",
      " 728 / 1198    462  The relative error between two consecutive iterates is at most 0.000000\n",
      " 728 / 1198    608        53.22          4.5   0.003   0.188  1.18  2627.646  2273.173\n",
      " 729 / 1198    608  The relative error between two consecutive iterates is at most 0.000000\n",
      " 729 / 1198   1080        29.07          7.5   0.437   0.274  1.13  1695.534  2925.706\n",
      " 730 / 1198   1080  The relative error between two consecutive iterates is at most 0.000000\n",
      " 730 / 1198    873        73.56            5   0.252   1.541  0.72  2351.728  2663.261\n",
      " 731 / 1198    873  The relative error between two consecutive iterates is at most 0.000000\n",
      " 731 / 1198   1447        37.81            6   0.415   0.237  0.79  2763.409  3626.647\n",
      " 732 / 1198   1447  The relative error between two consecutive iterates is at most 0.000000\n",
      " 732 / 1198   1079        78.37          3.5   0.137  -0.205  1.18  1236.361  2921.359\n",
      " 733 / 1198   1079  The relative error between two consecutive iterates is at most 0.000000\n",
      " 733 / 1198   1141        44.56        4.829   0.227  -0.179  0.92  2639.692  3024.724\n",
      " 734 / 1198   1141  The relative error between two consecutive iterates is at most 0.000000\n",
      " 734 / 1198   1011        102.4        2.434   0.015   0.783  0.62  1998.990  2837.034\n",
      " 735 / 1198   1011  The relative error between two consecutive iterates is at most 0.000000\n",
      " 735 / 1198    847           62            5   0.450   0.694  0.86  1993.871  2619.481\n",
      " 736 / 1198    847  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 736 / 1198   1131        60.42          4.5   0.368   1.459  0.88  1774.835  3007.181\n",
      " 737 / 1198   1131  The relative error between two consecutive iterates is at most 0.000000\n",
      " 737 / 1198    510        58.59          4.5   0.117  -0.566  0.99  2199.518  2157.832\n",
      " 738 / 1198    510  The relative error between two consecutive iterates is at most 0.000000\n",
      " 738 / 1198   1240        43.34            6   0.463  -0.570  0.87  2840.213  3170.294\n",
      " 739 / 1198   1240  The relative error between two consecutive iterates is at most 0.000000\n",
      " 739 / 1198     62        69.43          4.5   0.459  -1.291  0.86  2675.936  1420.303\n",
      " 740 / 1198     62  The relative error between two consecutive iterates is at most 0.000000\n",
      " 740 / 1198   1065        39.76        3.202   0.335  -1.196  0.55  2367.142  2922.283\n",
      " 741 / 1198   1065  The relative error between two consecutive iterates is at most 0.000000\n",
      " 741 / 1198   1228        58.44          4.5   0.335  -0.060  0.72  2393.767  3143.547\n",
      " 742 / 1198   1228  The relative error between two consecutive iterates is at most 0.000000\n",
      " 742 / 1198    776        46.77          6.5   0.269   0.689  0.97  1613.865  2541.467\n",
      " 743 / 1198    776  The relative error between two consecutive iterates is at most 0.000000\n",
      " 743 / 1198    749        38.64          5.5   0.248   0.774  1.03  3394.016  2501.960\n",
      " 744 / 1198    749  The relative error between two consecutive iterates is at most 0.000000\n",
      " 744 / 1198    468        64.29          4.5   0.268   1.063  0.51  1841.614  2111.604\n",
      " 745 / 1198    468  The relative error between two consecutive iterates is at most 0.000000\n",
      " 745 / 1198   1305        36.28          6.5   0.548  -0.428  0.99  1884.407  3259.157\n",
      " 746 / 1198   1305  The relative error between two consecutive iterates is at most 0.000000\n",
      " 746 / 1198   1237        35.59          4.5   0.000  -1.394  1.28  2724.667  3159.903\n",
      " 747 / 1198   1237  The relative error between two consecutive iterates is at most 0.000000\n",
      " 747 / 1198    913        28.94          7.5   0.459   1.336  1.13  2144.611  2745.348\n",
      " 748 / 1198    913  The relative error between two consecutive iterates is at most 0.000000\n",
      " 748 / 1198   1445        47.56          4.5   0.000   1.119  0.71  2452.733  3621.152\n",
      " 749 / 1198   1445  The relative error between two consecutive iterates is at most 0.000000\n",
      " 749 / 1198    598         59.1          5.5   0.186   0.049  0.55  2736.967  2266.097\n",
      " 750 / 1198    598  The relative error between two consecutive iterates is at most 0.000000\n",
      " 750 / 1198    306        30.06            8   0.217  -1.567  1.07  2079.357  1917.880\n",
      " 751 / 1198    306  The relative error between two consecutive iterates is at most 0.000000\n",
      " 751 / 1198   1442        38.35            5   0.000   1.002  0.56  2595.012  3602.137\n",
      " 752 / 1198   1442  The relative error between two consecutive iterates is at most 0.000000\n",
      " 752 / 1198    344        63.05            5   0.353  -0.612  0.68  2302.411  1941.689\n",
      " 753 / 1198    344  The relative error between two consecutive iterates is at most 0.000000\n",
      " 753 / 1198   1526        69.44            9   0.074  -0.314  1.03  2413.810  2406.148\n",
      " 754 / 1198   1526  The relative error between two consecutive iterates is at most 0.000000\n",
      " 754 / 1198    631        52.33          6.5   0.000  -1.217  1.01  2136.562  2315.648\n",
      " 755 / 1198    631  The relative error between two consecutive iterates is at most 0.000000\n",
      " 755 / 1198    828        51.84          4.5   0.063  -0.165  0.91  3106.375  2594.059\n",
      " 756 / 1198    828  The relative error between two consecutive iterates is at most 0.000000\n",
      " 756 / 1198   1436        41.07            7   0.529  -1.296  0.82  2843.916  3600.056\n",
      " 757 / 1198   1436  The relative error between two consecutive iterates is at most 0.000000\n",
      " 757 / 1198    765        47.22            6   0.218  -1.234  1.03  1525.437  2527.892\n",
      " 758 / 1198    765  The relative error between two consecutive iterates is at most 0.000000\n",
      " 758 / 1198    121        64.13            5   0.405  -0.083  0.91  2577.647  1576.473\n",
      " 759 / 1198    121  The relative error between two consecutive iterates is at most 0.000000\n",
      " 759 / 1198    783        45.23          6.5   0.067   0.967  1.21  2469.428  2584.533\n",
      " 760 / 1198    783  The relative error between two consecutive iterates is at most 0.000000\n",
      " 760 / 1198   1314        55.92            4   0.097  -0.939  0.89  2993.220  3274.304\n",
      " 761 / 1198   1314  The relative error between two consecutive iterates is at most 0.000000\n",
      " 761 / 1198    550        71.12          3.5   0.000   0.441  1.06  2072.708  2203.087\n",
      " 762 / 1198    550  The relative error between two consecutive iterates is at most 0.000000\n",
      " 762 / 1198   1215        7.395           24   0.119  -1.437  1.86  2066.071  3145.475\n",
      " 763 / 1198   1215  The relative error between two consecutive iterates is at most 0.000000\n",
      " 763 / 1198   1179        37.61          5.5   0.350   0.941  0.99  1577.569  3069.040\n",
      " 764 / 1198   1179  The relative error between two consecutive iterates is at most 0.000000\n",
      " 764 / 1198    301        55.68          4.5   0.051  -1.012  0.97  2181.714  1899.547\n",
      " 765 / 1198    301  The relative error between two consecutive iterates is at most 0.000000\n",
      " 765 / 1198     37        47.56            5   0.330  -0.493  1.38  1971.274  1306.410\n",
      " 766 / 1198     37  The relative error between two consecutive iterates is at most 0.000000\n",
      " 766 / 1198   1224        27.13            6   0.314  -0.934  1.22  2860.163  3151.318\n",
      " 767 / 1198   1224  The relative error between two consecutive iterates is at most 0.000000\n",
      " 767 / 1198   1309        27.57            7   0.387  -0.321  1.49  2546.659  3264.464\n",
      " 768 / 1198   1309  The relative error between two consecutive iterates is at most 0.000000\n",
      " 768 / 1198    322         42.6            7   0.349   1.422  0.62  2802.405  1939.270\n",
      " 769 / 1198    322  The relative error between two consecutive iterates is at most 0.000000\n",
      " 769 / 1198    266        44.36          4.5   0.000   0.908  0.60  2997.236  1840.529\n",
      " 770 / 1198    266  The relative error between two consecutive iterates is at most 0.000000\n",
      " 770 / 1198     41        104.8          4.5   0.355   0.961  0.25  2560.878  1335.469\n",
      " 771 / 1198     41  The relative error between two consecutive iterates is at most 0.000000\n",
      " 771 / 1198   1496         76.3        2.835   0.134  -0.426  0.66  2869.556  1871.232\n",
      " 772 / 1198   1496  The relative error between two consecutive iterates is at most 0.000000\n",
      " 772 / 1198   1123        39.28          5.5   0.522  -0.489  0.88  1512.919  2991.730\n",
      " 773 / 1198   1123  The relative error between two consecutive iterates is at most 0.000000\n",
      " 773 / 1198    439        596.6            7   0.889  -0.118  0.35  3774.223  2057.231\n",
      " 774 / 1198    439  The relative error between two consecutive iterates is at most 0.000000\n",
      " 774 / 1198   1484        53.31          5.5   0.196  -0.417  1.27  2420.607  1820.790\n",
      " 775 / 1198   1484  The relative error between two consecutive iterates is at most 0.000000\n",
      " 775 / 1198    937        48.19            5   0.082   0.515  0.44  3267.063  2750.991\n",
      " 776 / 1198    937  The relative error between two consecutive iterates is at most 0.000000\n",
      " 776 / 1198    505          207          2.5   0.000  -0.257  0.26  1689.548  2137.834\n",
      " 777 / 1198    505  The relative error between two consecutive iterates is at most 0.000000\n",
      " 777 / 1198    972        97.37        2.353   0.000   0.518  0.69  1674.168  2789.148\n",
      " 778 / 1198    972  The relative error between two consecutive iterates is at most 0.000000\n",
      " 778 / 1198    850        94.91            3   0.104  -1.202  1.24  3139.500  2618.015\n",
      " 779 / 1198    850  The relative error between two consecutive iterates is at most 0.000000\n",
      " 779 / 1198   1304        41.28            4   0.000   0.744  1.26  1598.339  3254.332\n",
      " 780 / 1198   1304  The relative error between two consecutive iterates is at most 0.000000\n",
      " 780 / 1198    534        57.76          4.5   0.323  -0.457  0.63  3071.337  2188.675\n",
      " 781 / 1198    534  The relative error between two consecutive iterates is at most 0.000000\n",
      " 781 / 1198   1406        33.59          5.5   0.242   0.424  0.91  2647.901  3469.497\n",
      " 782 / 1198   1406  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 782 / 1198    970       -213.5        3.291   0.000  -1.391  0.25  1838.000  2794.039\n",
      " 783 / 1198    970  The relative error between two consecutive iterates is at most 0.000000\n",
      " 783 / 1198     86        61.28          7.5   0.199  -0.329  0.62  2371.500  1496.570\n",
      " 784 / 1198     86  The relative error between two consecutive iterates is at most 0.000000\n",
      " 784 / 1198    600           53          3.5   0.104   0.631  1.19  2875.335  2261.324\n",
      " 785 / 1198    600  The relative error between two consecutive iterates is at most 0.000000\n",
      " 785 / 1198    438        11.58          6.5   0.042   0.080  1.29  1964.944  2083.249\n",
      " 786 / 1198    438  The relative error between two consecutive iterates is at most 0.000000\n",
      " 786 / 1198     67        67.47            5   0.197  -0.089  0.76  2352.127  1428.912\n",
      " 787 / 1198     67  The relative error between two consecutive iterates is at most 0.000000\n",
      " 787 / 1198    513        30.16          5.5   0.169   0.223  0.92  3271.560  2164.657\n",
      " 788 / 1198    513  The relative error between two consecutive iterates is at most 0.000000\n",
      " 788 / 1198    582        50.32          4.5   0.346   0.964  0.65  3337.295  2247.031\n",
      " 789 / 1198    582  The relative error between two consecutive iterates is at most 0.000000\n",
      " 789 / 1198   1452        74.49            3   0.122   1.052  1.21  3096.287  3655.542\n",
      " 790 / 1198   1452  The relative error between two consecutive iterates is at most 0.000000\n",
      " 790 / 1198   1165        41.78            6   0.545  -0.348  0.87  3206.905  3045.677\n",
      " 791 / 1198   1165  The relative error between two consecutive iterates is at most 0.000000\n",
      " 791 / 1198   1468        82.22            4   0.350  -0.318  0.78  2482.476  3730.765\n",
      " 792 / 1198   1468  The relative error between two consecutive iterates is at most 0.000000\n",
      " 792 / 1198    338        86.82            3   0.000  -0.153  1.00  3583.619  1931.757\n",
      " 793 / 1198    338  The relative error between two consecutive iterates is at most 0.000000\n",
      " 793 / 1198   1263        58.71            4   0.401  -0.809  0.74  3005.705  3197.203\n",
      " 794 / 1198   1263  The relative error between two consecutive iterates is at most 0.000000\n",
      " 794 / 1198    556        63.07          3.5   0.226   1.374  0.94  3316.364  2213.510\n",
      " 795 / 1198    556  The relative error between two consecutive iterates is at most 0.000000\n",
      " 795 / 1198    395        58.18           19   0.829   1.476  0.90  2276.948  2005.973\n",
      " 796 / 1198    395  The relative error between two consecutive iterates is at most 0.000000\n",
      " 796 / 1198    276        65.89          4.5   0.288  -1.003  0.65  2211.042  1865.338\n",
      " 797 / 1198    276  The relative error between two consecutive iterates is at most 0.000000\n",
      " 797 / 1198    845        44.39            5   0.000   0.894  0.72  1538.116  2623.550\n",
      " 798 / 1198    845  The relative error between two consecutive iterates is at most 0.000000\n",
      " 798 / 1198    687        82.27          5.5   0.094  -0.917  0.30  1648.102  2406.038\n",
      " 799 / 1198    687  The relative error between two consecutive iterates is at most 0.000000\n",
      " 799 / 1198   1119        30.75            5   0.109   0.449  1.07  2037.726  2993.589\n",
      " 800 / 1198   1119  The relative error between two consecutive iterates is at most 0.000000\n",
      " 800 / 1198    879        8.882           13   0.000  -1.148  1.95  1903.539  2671.819\n",
      " 801 / 1198    879  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000 and the relative error between two consecutive iterates is at \n",
      "  most 0.000000\n",
      " 801 / 1198   1024        46.69          5.5   0.424   0.526  0.45  1888.444  2871.002\n",
      " 802 / 1198   1024  The relative error between two consecutive iterates is at most 0.000000\n",
      " 802 / 1198   1202        34.17          6.5   0.542   0.554  0.71  3078.164  3112.828\n",
      " 803 / 1198   1202  The relative error between two consecutive iterates is at most 0.000000\n",
      " 803 / 1198    262        53.46            4   0.197   0.666  0.77  2812.114  1831.641\n",
      " 804 / 1198    262  The relative error between two consecutive iterates is at most 0.000000\n",
      " 804 / 1198     71        64.59          4.5   0.255   0.790  0.57  2178.277  1435.135\n",
      " 805 / 1198     71  The relative error between two consecutive iterates is at most 0.000000\n",
      " 805 / 1198   1223        28.19            7   0.161  -1.432  0.52  2224.076  3151.705\n",
      " 806 / 1198   1223  The relative error between two consecutive iterates is at most 0.000000\n",
      " 806 / 1198    101        45.18            5   0.391   0.160  0.91  2754.010  1528.341\n",
      " 807 / 1198    101  The relative error between two consecutive iterates is at most 0.000000\n",
      " 807 / 1198    175        52.07          4.5   0.013  -0.844  0.86  2571.224  1673.444\n",
      " 808 / 1198    175  The relative error between two consecutive iterates is at most 0.000000\n",
      " 808 / 1198   1231        150.5        1.675   0.087  -0.324  0.55  2202.738  3144.318\n",
      " 809 / 1198   1231  The relative error between two consecutive iterates is at most 0.000000\n",
      " 809 / 1198    178        55.47            5   0.344   0.214  0.76  1884.206  1667.140\n",
      " 810 / 1198    178  The relative error between two consecutive iterates is at most 0.000000\n",
      " 810 / 1198   1421        40.49        3.068   0.347   1.134  0.76  2379.774  3515.679\n",
      " 811 / 1198   1421  The relative error between two consecutive iterates is at most 0.000000\n",
      " 811 / 1198    948         32.7          5.5   0.288  -0.188  1.11  3255.810  2764.392\n",
      " 812 / 1198    948  The relative error between two consecutive iterates is at most 0.000000\n",
      " 812 / 1198   1207        44.04          4.5   0.228  -1.192  0.94  1576.365  3117.471\n",
      " 813 / 1198   1207  The relative error between two consecutive iterates is at most 0.000000\n",
      " 813 / 1198    356        172.4            3   0.000  -0.939  0.60  1633.289  1950.893\n",
      " 814 / 1198    356  The relative error between two consecutive iterates is at most 0.000000\n",
      " 814 / 1198   1387        176.5        1.678   0.089  -0.674  0.51  3170.131  3416.954\n",
      " 815 / 1198   1387  The relative error between two consecutive iterates is at most 0.000000\n",
      " 815 / 1198     27        91.68            3   0.134  -0.914  0.65  2194.817  1274.950\n",
      " 816 / 1198     27  The relative error between two consecutive iterates is at most 0.000000\n",
      " 816 / 1198    612        38.77            5   0.000   0.286  0.83  2820.935  2278.763\n",
      " 817 / 1198    612  The relative error between two consecutive iterates is at most 0.000000\n",
      " 817 / 1198    722        30.65          6.5   0.130  -0.266  1.41  2773.797  2543.561\n",
      " 818 / 1198    722  The relative error between two consecutive iterates is at most 0.000000\n",
      " 818 / 1198   1084        77.08            3   0.183  -0.586  0.75  2444.368  2924.729\n",
      " 819 / 1198   1084  The relative error between two consecutive iterates is at most 0.000000\n",
      " 819 / 1198    933        29.33            6   0.679   1.116  0.25  1824.269  2754.968\n",
      " 820 / 1198    933  The relative error between two consecutive iterates is at most 0.000000\n",
      " 820 / 1198   1168        76.45          3.5   0.407  -1.131  0.58  1533.914  3045.629\n",
      " 821 / 1198   1168  The relative error between two consecutive iterates is at most 0.000000\n",
      " 821 / 1198   1094        10.43           12   0.000  -0.402  2.06  2078.032  2949.377\n",
      " 822 / 1198   1094  The relative error between two consecutive iterates is at most 0.000000\n",
      " 822 / 1198   1137        59.56            4   0.379  -0.410  0.65  1915.186  3016.781\n",
      " 823 / 1198   1137  The relative error between two consecutive iterates is at most 0.000000\n",
      " 823 / 1198   1132        49.07           11   0.420   0.553  0.25  3043.918  3014.568\n",
      " 824 / 1198   1132  The relative error between two consecutive iterates is at most 0.000000\n",
      " 824 / 1198   1250        34.37        3.268   0.000  -0.300  1.34  2844.560  3177.040\n",
      " 825 / 1198   1250  The relative error between two consecutive iterates is at most 0.000000\n",
      " 825 / 1198    949        47.48            5   0.197   0.486  0.59  2600.413  2773.423\n",
      " 826 / 1198    949  The relative error between two consecutive iterates is at most 0.000000\n",
      " 826 / 1198    872        45.23            4   0.161   0.908  0.87  3232.365  2652.334\n",
      " 827 / 1198    872  The relative error between two consecutive iterates is at most 0.000000\n",
      " 827 / 1198    325        38.23          4.5   0.000  -0.332  0.48  2924.625  1925.740\n",
      " 828 / 1198    325  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 828 / 1198    521        57.23          3.5   0.000  -0.747  0.69  2003.779  2174.889\n",
      " 829 / 1198    521  The relative error between two consecutive iterates is at most 0.000000\n",
      " 829 / 1198   1281        47.53            6   0.568   0.082  0.25  2341.323  3219.397\n",
      " 830 / 1198   1281  The relative error between two consecutive iterates is at most 0.000000\n",
      " 830 / 1198    555        21.41            6   0.539   0.224  0.88  3142.583  2209.439\n",
      " 831 / 1198    555  The relative error between two consecutive iterates is at most 0.000000\n",
      " 831 / 1198    591        54.44            4   0.364  -0.357  0.99  3613.472  2250.484\n",
      " 832 / 1198    591  The relative error between two consecutive iterates is at most 0.000000\n",
      " 832 / 1198    843        45.72            3   0.046  -0.425  2.35  1869.536  2609.477\n",
      " 833 / 1198    843  The relative error between two consecutive iterates is at most 0.000000\n",
      " 833 / 1198   1030         54.8           20   0.732   0.931  0.25  1684.367  2897.374\n",
      " 834 / 1198   1030  The relative error between two consecutive iterates is at most 0.000000\n",
      " 834 / 1198   1361         63.9          3.5   0.000   0.831  0.25  1416.221  3363.824\n",
      " 835 / 1198   1361  The relative error between two consecutive iterates is at most 0.000000\n",
      " 835 / 1198   1280        83.33        2.623   0.393  -1.415  0.59  1988.245  3226.604\n",
      " 836 / 1198   1280  The relative error between two consecutive iterates is at most 0.000000\n",
      " 836 / 1198    163        63.59          3.5   0.084  -0.827  0.61  3171.479  1647.521\n",
      " 837 / 1198    163  The relative error between two consecutive iterates is at most 0.000000\n",
      " 837 / 1198    400        76.26          7.5   0.513  -1.020  0.25  2383.290  2033.186\n",
      " 838 / 1198    400  The relative error between two consecutive iterates is at most 0.000000\n",
      " 838 / 1198     68         55.1          5.5   0.522  -0.478  0.72  2244.599  1423.721\n",
      " 839 / 1198     68  The relative error between two consecutive iterates is at most 0.000000\n",
      " 839 / 1198   1275        23.37          4.5   0.214   0.198  0.90  2296.327  3208.946\n",
      " 840 / 1198   1275  The relative error between two consecutive iterates is at most 0.000000\n",
      " 840 / 1198   1222        49.92            4   0.265   0.235  0.59  1674.502  3135.449\n",
      " 841 / 1198   1222  The relative error between two consecutive iterates is at most 0.000000\n",
      " 841 / 1198   1069        48.48          3.5   0.208   0.291  0.71  2443.451  2907.782\n",
      " 842 / 1198   1069  The relative error between two consecutive iterates is at most 0.000000\n",
      " 842 / 1198     14        173.5          3.5   0.477  -1.366  0.25  2172.447  1207.563\n",
      " 843 / 1198     14  The relative error between two consecutive iterates is at most 0.000000\n",
      " 843 / 1198   1157        64.84        2.764   0.243  -0.075  0.67  2280.840  3027.068\n",
      " 844 / 1198   1157  The relative error between two consecutive iterates is at most 0.000000\n",
      " 844 / 1198   1446        83.16            3   0.289  -0.275  0.73  2920.809  3612.051\n",
      " 845 / 1198   1446  The relative error between two consecutive iterates is at most 0.000000\n",
      " 845 / 1198   1072        43.57            6   0.650  -1.123  0.67  3195.491  2927.629\n",
      " 846 / 1198   1072  The relative error between two consecutive iterates is at most 0.000000\n",
      " 846 / 1198    170        63.04        2.393   0.165  -1.417  0.76  3285.336  1682.934\n",
      " 847 / 1198    170  The relative error between two consecutive iterates is at most 0.000000\n",
      " 847 / 1198   1400        67.91            3   0.067  -1.234  1.10  2748.945  3446.935\n",
      " 848 / 1198   1400  The relative error between two consecutive iterates is at most 0.000000\n",
      " 848 / 1198    401        41.26            4   0.218   1.569  0.70  3453.629  2016.186\n",
      " 849 / 1198    401  The relative error between two consecutive iterates is at most 0.000000\n",
      " 849 / 1198    964        33.46          4.5   0.202  -1.316  1.00  3210.413  2784.230\n",
      " 850 / 1198    964  The relative error between two consecutive iterates is at most 0.000000\n",
      " 850 / 1198    616        36.91          6.5   0.585   1.478  0.85  3229.082  2291.630\n",
      " 851 / 1198    616  The relative error between two consecutive iterates is at most 0.000000\n",
      " 851 / 1198   1009        17.92            6   0.000  -0.238  1.75  2613.559  2829.079\n",
      " 852 / 1198   1009  The relative error between two consecutive iterates is at most 0.000000\n",
      " 852 / 1198   1227        51.45          4.5   0.118  -1.276  0.33  2836.422  3145.419\n",
      " 853 / 1198   1227  The relative error between two consecutive iterates is at most 0.000000\n",
      " 853 / 1198    122        15.74          5.5   0.498   0.015  1.38  2214.636  1566.938\n",
      " 854 / 1198    122  The relative error between two consecutive iterates is at most 0.000000\n",
      " 854 / 1198    535        45.71          3.5   0.028  -1.476  0.81  2452.197  2189.964\n",
      " 855 / 1198    535  The relative error between two consecutive iterates is at most 0.000000\n",
      " 855 / 1198   1056        37.65          4.5   0.196   1.161  0.75  1835.606  2904.341\n",
      " 856 / 1198   1056  The relative error between two consecutive iterates is at most 0.000000\n",
      " 856 / 1198   1238        38.47          5.5   0.368  -1.330  0.25  2852.958  3164.326\n",
      " 857 / 1198   1238  The relative error between two consecutive iterates is at most 0.000000\n",
      " 857 / 1198    382        43.73          3.5   0.152  -0.419  0.88  3250.383  1990.094\n",
      " 858 / 1198    382  The relative error between two consecutive iterates is at most 0.000000\n",
      " 858 / 1198    239        60.95            4   0.113   0.762  0.46  2073.529  1780.849\n",
      " 859 / 1198    239  The relative error between two consecutive iterates is at most 0.000000\n",
      " 859 / 1198    787        53.71          3.5   0.000  -0.566  0.65  3479.725  2548.743\n",
      " 860 / 1198    787  The relative error between two consecutive iterates is at most 0.000000\n",
      " 860 / 1198    706        78.66          3.5   0.038   0.030  0.66  2049.527  2421.106\n",
      " 861 / 1198    706  The relative error between two consecutive iterates is at most 0.000000\n",
      " 861 / 1198   1003           41            4   0.211   0.569  0.79  1668.126  2826.622\n",
      " 862 / 1198   1003  The relative error between two consecutive iterates is at most 0.000000\n",
      " 862 / 1198   1047        57.52            4   0.445  -1.500  0.61  1163.677  2892.342\n",
      " 863 / 1198   1047  The relative error between two consecutive iterates is at most 0.000000\n",
      " 863 / 1198    826        35.12          5.5   0.423  -0.396  0.74  3420.334  2585.765\n",
      " 864 / 1198    826  The relative error between two consecutive iterates is at most 0.000000\n",
      " 864 / 1198    543        58.57          3.5   0.092  -1.362  0.49  2619.102  2194.283\n",
      " 865 / 1198    543  The relative error between two consecutive iterates is at most 0.000000\n",
      " 865 / 1198    326        54.66            4   0.131  -0.666  0.51  2342.644  1923.447\n",
      " 866 / 1198    326  The relative error between two consecutive iterates is at most 0.000000\n",
      " 866 / 1198    993        54.79        3.039   0.180   0.019  1.07  1469.231  2806.572\n",
      " 867 / 1198    993  The relative error between two consecutive iterates is at most 0.000000\n",
      " 867 / 1198   1192        41.68            4   0.362   1.106  0.98  2014.286  3078.582\n",
      " 868 / 1198   1192  The relative error between two consecutive iterates is at most 0.000000\n",
      " 868 / 1198     97        52.51            4   0.286  -0.091  0.53  2017.744  1515.650\n",
      " 869 / 1198     97  The relative error between two consecutive iterates is at most 0.000000\n",
      " 869 / 1198   1073        31.76        2.062   0.593  -0.316  0.25  2378.970  2915.906\n",
      " 870 / 1198   1073  The relative error between two consecutive iterates is at most 0.000000\n",
      " 870 / 1198    492        39.25            5   0.071   0.329  0.54  3487.985  2132.954\n",
      " 871 / 1198    492  The relative error between two consecutive iterates is at most 0.000000\n",
      " 871 / 1198   1246        39.96        3.725   0.310  -1.230  1.00  2160.480  3172.079\n",
      " 872 / 1198   1246  The relative error between two consecutive iterates is at most 0.000000\n",
      " 872 / 1198   1143        40.38          7.5   0.715   1.114  0.32  1775.531  3034.182\n",
      " 873 / 1198   1143  The relative error between two consecutive iterates is at most 0.000000\n",
      " 873 / 1198    541        32.72            5   0.000   1.502  1.15  2802.398  2200.192\n",
      " 874 / 1198    541  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 874 / 1198   1320        39.22            4   0.335   1.407  0.80  3052.329  3291.831\n",
      " 875 / 1198   1320  The relative error between two consecutive iterates is at most 0.000000\n",
      " 875 / 1198   1296        40.26            4   0.361  -1.094  0.91  3082.642  3248.366\n",
      " 876 / 1198   1296  The relative error between two consecutive iterates is at most 0.000000\n",
      " 876 / 1198    769        52.38            4   0.352  -1.558  0.86  3246.946  2524.206\n",
      " 877 / 1198    769  The relative error between two consecutive iterates is at most 0.000000\n",
      " 877 / 1198   1175        38.53            4   0.165   0.301  1.12  1600.385  3053.216\n",
      " 878 / 1198   1175  The relative error between two consecutive iterates is at most 0.000000\n",
      " 878 / 1198    528        55.42          3.5   0.422   0.112  0.73  3129.756  2182.512\n",
      " 879 / 1198    528  The relative error between two consecutive iterates is at most 0.000000\n",
      " 879 / 1198    862        49.37          4.5   0.498   1.066  0.80  3341.051  2642.056\n",
      " 880 / 1198    862  The relative error between two consecutive iterates is at most 0.000000\n",
      " 880 / 1198    668        53.17          3.5   0.000  -0.669  0.64  2723.549  2362.431\n",
      " 881 / 1198    668  The relative error between two consecutive iterates is at most 0.000000\n",
      " 881 / 1198    104        37.52            4   0.120   1.023  1.06  2740.469  1540.750\n",
      " 882 / 1198    104  The relative error between two consecutive iterates is at most 0.000000\n",
      " 882 / 1198    981        56.03         12.5   0.813   1.207  0.25  3085.223  2797.146\n",
      " 883 / 1198    981  The relative error between two consecutive iterates is at most 0.000000\n",
      " 883 / 1198    284        38.69            6   0.690   0.918  0.25  3323.078  1887.728\n",
      " 884 / 1198    284  The relative error between two consecutive iterates is at most 0.000000\n",
      " 884 / 1198   1053         54.4            3   0.026  -0.899  0.63  3267.112  2895.323\n",
      " 885 / 1198   1053  The relative error between two consecutive iterates is at most 0.000000\n",
      " 885 / 1198     15        91.64            3   0.395  -0.382  0.33  2265.110  1209.998\n",
      " 886 / 1198     15  The relative error between two consecutive iterates is at most 0.000000\n",
      " 886 / 1198    307        50.07          3.5   0.074   0.947  0.75  2452.234  1899.377\n",
      " 887 / 1198    307  The relative error between two consecutive iterates is at most 0.000000\n",
      " 887 / 1198    485        44.21            5   0.049   1.460  0.55  2762.609  2131.182\n",
      " 888 / 1198    485  The relative error between two consecutive iterates is at most 0.000000\n",
      " 888 / 1198    806        42.17          4.5   0.119  -0.488  0.55  3202.089  2567.545\n",
      " 889 / 1198    806  The relative error between two consecutive iterates is at most 0.000000\n",
      " 889 / 1198    796        62.91            3   0.191  -0.741  0.76  3221.446  2551.285\n",
      " 890 / 1198    796  The relative error between two consecutive iterates is at most 0.000000\n",
      " 890 / 1198   1337        69.77          3.5   0.311   1.198  0.25  3190.037  3318.450\n",
      " 891 / 1198   1337  The relative error between two consecutive iterates is at most 0.000000\n",
      " 891 / 1198    941        54.13          3.5   0.419  -1.559  0.75  3287.805  2753.964\n",
      " 892 / 1198    941  The relative error between two consecutive iterates is at most 0.000000\n",
      " 892 / 1198   1338        50.27          3.5   0.483  -0.433  0.59  2824.966  3318.114\n",
      " 893 / 1198   1338  The relative error between two consecutive iterates is at most 0.000000\n",
      " 893 / 1198   1297        61.47        2.373   0.196  -0.461  0.73  2811.492  3243.043\n",
      " 894 / 1198   1297  The relative error between two consecutive iterates is at most 0.000000\n",
      " 894 / 1198    375        39.75          3.5   0.070  -0.657  1.01  2943.459  1980.749\n",
      " 895 / 1198    375  The relative error between two consecutive iterates is at most 0.000000\n",
      " 895 / 1198   1290         36.1          4.5   0.584  -0.178  0.72  2538.812  3228.464\n",
      " 896 / 1198   1290  The relative error between two consecutive iterates is at most 0.000000\n",
      " 896 / 1198    420        14.59            4   0.410   0.936  1.25  2641.232  2038.651\n",
      " 897 / 1198    420  The relative error between two consecutive iterates is at most 0.000000\n",
      " 897 / 1198    520        68.03            3   0.180  -0.749  0.68  2255.685  2168.127\n",
      " 898 / 1198    520  The relative error between two consecutive iterates is at most 0.000000\n",
      " 898 / 1198    639         44.6          3.5   0.056  -1.209  0.72  3032.377  2317.549\n",
      " 899 / 1198    639  The relative error between two consecutive iterates is at most 0.000000\n",
      " 899 / 1198   1134        29.39          4.5   0.173   0.172  0.67  2631.108  3010.613\n",
      " 900 / 1198   1134  The relative error between two consecutive iterates is at most 0.000000\n",
      " 900 / 1198   1455        37.61          3.5   0.318  -0.480  1.08  2542.784  3657.958\n",
      " 901 / 1198   1455  The relative error between two consecutive iterates is at most 0.000000\n",
      " 901 / 1198    421        54.59          3.5   0.388   0.735  0.42  3425.689  2035.836\n",
      " 902 / 1198    421  The relative error between two consecutive iterates is at most 0.000000\n",
      " 902 / 1198   1292        68.64            3   0.332  -0.932  0.44  3150.429  3233.393\n",
      " 903 / 1198   1292  The relative error between two consecutive iterates is at most 0.000000\n",
      " 903 / 1198    629        46.19          2.5   0.021   0.524  0.63  3579.256  2297.609\n",
      " 904 / 1198    629  The relative error between two consecutive iterates is at most 0.000000\n",
      " 904 / 1198   1267        128.3          1.5   0.071  -0.553  0.27  2271.499  3193.891\n",
      " 905 / 1198   1267  The relative error between two consecutive iterates is at most 0.000000\n",
      " 905 / 1198    270        60.69            3   0.104   0.983  0.52  2004.743  1845.528\n",
      " 906 / 1198    270  The relative error between two consecutive iterates is at most 0.000000\n",
      " 906 / 1198   1037        46.48            4   0.290  -1.250  0.52  2551.469  2883.875\n",
      " 907 / 1198   1037  The relative error between two consecutive iterates is at most 0.000000\n",
      " 907 / 1198   1333        46.41          6.5   0.000   0.631  0.25  2958.222  3314.325\n",
      " 908 / 1198   1333  The relative error between two consecutive iterates is at most 0.000000\n",
      " 908 / 1198   1156        41.88          4.5   0.774  -0.388  0.33  2645.872  3029.856\n",
      " 909 / 1198   1156  The relative error between two consecutive iterates is at most 0.000000\n",
      " 909 / 1198   1381        89.81            3   0.444   0.424  0.37  1690.216  3394.823\n",
      " 910 / 1198   1381  The relative error between two consecutive iterates is at most 0.000000\n",
      " 910 / 1198   1379        54.55            3   0.000  -0.963  0.68  3178.766  3394.159\n",
      " 911 / 1198   1379  The relative error between two consecutive iterates is at most 0.000000\n",
      " 911 / 1198   1145        45.41        2.127   0.298  -1.567  0.44  3058.297  3027.966\n",
      " 912 / 1198   1145  The relative error between two consecutive iterates is at most 0.000000\n",
      " 912 / 1198   1402        25.16          3.5   0.000  -0.249  0.91  2842.289  3456.082\n",
      " 913 / 1198   1402  The relative error between two consecutive iterates is at most 0.000000\n",
      " 913 / 1198   1153        62.28            3   0.361  -1.465  0.73  1811.877  3026.520\n",
      " 914 / 1198   1153  The relative error between two consecutive iterates is at most 0.000000\n",
      " 914 / 1198    491        56.93            3   0.110   0.725  0.69  2900.831  2125.436\n",
      " 915 / 1198    491  The relative error between two consecutive iterates is at most 0.000000\n",
      " 915 / 1198    435        67.75          2.5   0.051  -0.670  0.98  3634.419  2062.586\n",
      " 916 / 1198    435  The relative error between two consecutive iterates is at most 0.000000\n",
      " 916 / 1198   1058        44.82          8.5   0.359  -1.342  0.25  2109.521  2926.512\n",
      " 917 / 1198   1058  The relative error between two consecutive iterates is at most 0.000000\n",
      " 917 / 1198   1501        40.36          5.5   0.258   1.272  0.25  3424.354  2480.185\n",
      " 918 / 1198   1501  The relative error between two consecutive iterates is at most 0.000000\n",
      " 918 / 1198    860         45.4            4   0.499   1.151  0.44  3347.616  2638.400\n",
      " 919 / 1198    860  The relative error between two consecutive iterates is at most 0.000000\n",
      " 919 / 1198    218        24.03            4   0.482   1.497  0.72  1952.535  1743.459\n",
      " 920 / 1198    218  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 920 / 1198    108        56.14            3   0.086   0.311  0.69  1962.587  1544.822\n",
      " 921 / 1198    108  The relative error between two consecutive iterates is at most 0.000000\n",
      " 921 / 1198   1152        44.92          3.5   0.206  -0.633  0.53  1534.788  3028.841\n",
      " 922 / 1198   1152  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      " 922 / 1198    349        51.99          3.5   0.155  -0.841  0.55  1795.017  1950.459\n",
      " 923 / 1198    349  The relative error between two consecutive iterates is at most 0.000000\n",
      " 923 / 1198   1208           50            4   0.507  -0.322  0.25  3230.852  3105.883\n",
      " 924 / 1198   1208  The relative error between two consecutive iterates is at most 0.000000\n",
      " 924 / 1198    589        43.88            4   0.006   1.159  0.25  3113.234  2253.290\n",
      " 925 / 1198    589  The relative error between two consecutive iterates is at most 0.000000\n",
      " 925 / 1198   1115        31.47          3.5   0.000  -1.226  0.99  3039.059  2978.294\n",
      " 926 / 1198   1115  The relative error between two consecutive iterates is at most 0.000000\n",
      " 926 / 1198   1394        115.4          2.5   0.324   1.105  0.43  1729.354  3427.578\n",
      " 927 / 1198   1394  The relative error between two consecutive iterates is at most 0.000000\n",
      " 927 / 1198    959        52.33            3   0.332   1.032  0.74  1247.267  2773.937\n",
      " 928 / 1198    959  The relative error between two consecutive iterates is at most 0.000000\n",
      " 928 / 1198   1327        49.97            3   0.210  -0.919  0.56  2077.709  3301.466\n",
      " 929 / 1198   1327  The relative error between two consecutive iterates is at most 0.000000\n",
      " 929 / 1198    745        62.99            3   0.000  -1.205  0.53  1844.021  2474.756\n",
      " 930 / 1198    745  The relative error between two consecutive iterates is at most 0.000000\n",
      " 930 / 1198    615        80.96          3.5   0.067   0.839  0.43  2235.706  2278.357\n",
      " 931 / 1198    615  The relative error between two consecutive iterates is at most 0.000000\n",
      " 931 / 1198    651        76.32            3   0.115  -1.330  0.46  2612.894  2338.085\n",
      " 932 / 1198    651  The relative error between two consecutive iterates is at most 0.000000\n",
      " 932 / 1198   1340        52.29            3   0.241  -0.612  0.75  3161.511  3324.036\n",
      " 933 / 1198   1340  The relative error between two consecutive iterates is at most 0.000000\n",
      " 933 / 1198   1257        31.45          2.5   0.000  -0.104  0.78  2189.013  3179.276\n",
      " 934 / 1198   1257  The relative error between two consecutive iterates is at most 0.000000\n",
      " 934 / 1198   1310        41.82          4.5   0.000  -1.545  0.39  1832.863  3269.485\n",
      " 935 / 1198   1310  The relative error between two consecutive iterates is at most 0.000000\n",
      " 935 / 1198   1051        50.23            3   0.479  -0.445  0.70  2458.756  2885.969\n",
      " 936 / 1198   1051  The relative error between two consecutive iterates is at most 0.000000\n",
      " 936 / 1198   1349        54.08          9.5   0.380   0.758  0.25  1604.734  3351.379\n",
      " 937 / 1198   1349  The relative error between two consecutive iterates is at most 0.000000\n",
      " 937 / 1198   1177        47.96          3.5   0.443  -0.670  0.76  2914.315  3051.594\n",
      " 938 / 1198   1177  The relative error between two consecutive iterates is at most 0.000000\n",
      " 938 / 1198     48        56.35          2.5   0.000  -0.243  0.91  1941.735  1348.322\n",
      " 939 / 1198     48  The relative error between two consecutive iterates is at most 0.000000\n",
      " 939 / 1198    623        49.12          3.5   0.475  -1.376  0.58  3263.464  2292.562\n",
      " 940 / 1198    623  The relative error between two consecutive iterates is at most 0.000000\n",
      " 940 / 1198    198        82.08            3   0.436  -1.444  0.54  3411.105  1696.229\n",
      " 941 / 1198    198  The relative error between two consecutive iterates is at most 0.000000\n",
      " 941 / 1198    642         92.4            9   0.312  -1.122  0.25  1670.036  2349.668\n",
      " 942 / 1198    642  The relative error between two consecutive iterates is at most 0.000000\n",
      " 942 / 1198    814        54.74          4.5   0.173  -1.063  0.46  3006.084  2584.351\n",
      " 943 / 1198    814  The relative error between two consecutive iterates is at most 0.000000\n",
      " 943 / 1198   1403        27.12          3.5   0.112   0.531  0.80  2579.643  3453.858\n",
      " 944 / 1198   1403  The relative error between two consecutive iterates is at most 0.000000\n",
      " 944 / 1198    926        57.25          2.5   0.000  -0.774  0.90  1430.604  2733.914\n",
      " 945 / 1198    926  The relative error between two consecutive iterates is at most 0.000000\n",
      " 945 / 1198   1016        40.65          3.5   0.344  -0.797  0.59  2066.720  2848.432\n",
      " 946 / 1198   1016  The relative error between two consecutive iterates is at most 0.000000\n",
      " 946 / 1198    894        28.42            4   0.310  -0.598  0.79  1899.757  2693.238\n",
      " 947 / 1198    894  The relative error between two consecutive iterates is at most 0.000000\n",
      " 947 / 1198    522        50.63            4   0.396  -0.890  0.58  1856.205  2177.457\n",
      " 948 / 1198    522  The relative error between two consecutive iterates is at most 0.000000\n",
      " 948 / 1198   1315        38.25            3   0.000   1.290  0.97  1649.886  3270.821\n",
      " 949 / 1198   1315  The relative error between two consecutive iterates is at most 0.000000\n",
      " 949 / 1198    968        50.31            4   0.074  -1.218  0.31  2364.958  2781.557\n",
      " 950 / 1198    968  The relative error between two consecutive iterates is at most 0.000000\n",
      " 950 / 1198   1376        73.29          2.5   0.278  -1.221  0.41  1759.055  3388.322\n",
      " 951 / 1198   1376  The relative error between two consecutive iterates is at most 0.000000\n",
      " 951 / 1198    701         77.7          3.5   0.010  -1.325  0.35  2671.215  2421.037\n",
      " 952 / 1198    701  The relative error between two consecutive iterates is at most 0.000000\n",
      " 952 / 1198    560        85.44          2.5   0.173   1.556  0.50  1744.794  2210.524\n",
      " 953 / 1198    560  The relative error between two consecutive iterates is at most 0.000000\n",
      " 953 / 1198    536        35.05          3.5   0.399   0.320  0.74  2552.772  2182.246\n",
      " 954 / 1198    536  The relative error between two consecutive iterates is at most 0.000000\n",
      " 954 / 1198   1186         34.3            4   0.521  -0.947  0.69  2012.711  3069.716\n",
      " 955 / 1198   1186  The relative error between two consecutive iterates is at most 0.000000\n",
      " 955 / 1198   1308        33.82          4.5   0.499   1.409  0.44  3010.119  3271.243\n",
      " 956 / 1198   1308  The relative error between two consecutive iterates is at most 0.000000\n",
      " 956 / 1198    901        58.27          4.5   0.632  -0.745  0.39  2181.738  2708.114\n",
      " 957 / 1198    901  The relative error between two consecutive iterates is at most 0.000000\n",
      " 957 / 1198    898        57.43          3.5   0.250  -0.484  0.25  1712.586  2694.558\n",
      " 958 / 1198    898  The relative error between two consecutive iterates is at most 0.000000\n",
      " 958 / 1198   1052        53.72          3.5   0.521  -0.597  0.33  3225.376  2890.744\n",
      " 959 / 1198   1052  The relative error between two consecutive iterates is at most 0.000000\n",
      " 959 / 1198   1293        58.65            3   0.252  -1.043  0.46  3156.372  3234.658\n",
      " 960 / 1198   1293  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      " 960 / 1198    721        58.76            4   0.177  -1.515  0.89  2663.242  2449.368\n",
      " 961 / 1198    721  The relative error between two consecutive iterates is at most 0.000000\n",
      " 961 / 1198    922         61.4            8   0.344   0.431  0.41  1963.855  2739.728\n",
      " 962 / 1198    922  The relative error between two consecutive iterates is at most 0.000000\n",
      " 962 / 1198    844        44.32          2.5   0.000  -0.631  0.74  3413.026  2609.270\n",
      " 963 / 1198    844  The relative error between two consecutive iterates is at most 0.000000\n",
      " 963 / 1198   1431        38.68            5   0.667  -0.118  0.38  2570.125  3543.997\n",
      " 964 / 1198   1431  The relative error between two consecutive iterates is at most 0.000000\n",
      " 964 / 1198   1071         32.1          2.5   0.254   0.235  1.12  1569.344  2905.738\n",
      " 965 / 1198   1071  The relative error between two consecutive iterates is at most 0.000000\n",
      " 965 / 1198    304        54.53            3   0.411   0.545  0.58  2184.803  1892.260\n",
      " 966 / 1198    304  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 966 / 1198   1139        47.86          2.5   0.060   1.025  1.17  2303.564  3012.953\n",
      " 967 / 1198   1139  The relative error between two consecutive iterates is at most 0.000000\n",
      " 967 / 1198    951         60.7          2.5   0.000  -0.627  0.45  2953.590  2764.117\n",
      " 968 / 1198    951  The relative error between two consecutive iterates is at most 0.000000\n",
      " 968 / 1198   1075         55.3            3   0.426  -1.424  0.56  3261.915  2918.214\n",
      " 969 / 1198   1075  The relative error between two consecutive iterates is at most 0.000000\n",
      " 969 / 1198   1439        54.56          2.5   0.144  -0.886  0.49  2307.191  3582.605\n",
      " 970 / 1198   1439  The relative error between two consecutive iterates is at most 0.000000\n",
      " 970 / 1198    702         69.1          3.5   0.084  -0.982  0.25  2664.030  2419.877\n",
      " 971 / 1198    702  The relative error between two consecutive iterates is at most 0.000000\n",
      " 971 / 1198   1456        52.92          2.5   0.250  -0.088  0.76  2535.460  3662.866\n",
      " 972 / 1198   1456  The relative error between two consecutive iterates is at most 0.000000\n",
      " 972 / 1198   1105        23.97          3.5   0.000   0.391  1.29  2447.624  2965.800\n",
      " 973 / 1198   1105  The relative error between two consecutive iterates is at most 0.000000\n",
      " 973 / 1198   1372        37.07            3   0.353  -0.919  1.06  3014.739  3377.892\n",
      " 974 / 1198   1372  The relative error between two consecutive iterates is at most 0.000000\n",
      " 974 / 1198    927        41.03          3.5   0.111  -0.252  0.53  3030.581  2735.619\n",
      " 975 / 1198    927  The relative error between two consecutive iterates is at most 0.000000\n",
      " 975 / 1198    617        60.34          2.5   0.251  -0.308  0.52  3273.574  2274.428\n",
      " 976 / 1198    617  The relative error between two consecutive iterates is at most 0.000000\n",
      " 976 / 1198    166        70.69          2.5   0.289   1.241  0.66  1835.996  1647.171\n",
      " 977 / 1198    166  The relative error between two consecutive iterates is at most 0.000000\n",
      " 977 / 1198   1012        48.05          3.5   0.336   1.355  0.74  2939.374  2832.246\n",
      " 978 / 1198   1012  The relative error between two consecutive iterates is at most 0.000000\n",
      " 978 / 1198    493        50.56          2.5   0.044   0.514  1.23  2922.224  2123.590\n",
      " 979 / 1198    493  The relative error between two consecutive iterates is at most 0.000000\n",
      " 979 / 1198    940        39.73            4   0.232  -1.296  0.74  2739.336  2755.717\n",
      " 980 / 1198    940  The relative error between two consecutive iterates is at most 0.000000\n",
      " 980 / 1198    994        74.25          2.5   0.309   0.608  0.46  1394.464  2800.916\n",
      " 981 / 1198    994  The relative error between two consecutive iterates is at most 0.000000\n",
      " 981 / 1198    118         55.8          3.5   0.206  -0.805  0.25  2049.634  1573.077\n",
      " 982 / 1198    118  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      " 982 / 1198   1358        54.77            3   0.271  -0.387  0.60  3184.820  3350.214\n",
      " 983 / 1198   1358  The relative error between two consecutive iterates is at most 0.000000\n",
      " 983 / 1198    473        52.37          3.5   0.195  -0.824  0.25  2988.312  2105.423\n",
      " 984 / 1198    473  The relative error between two consecutive iterates is at most 0.000000\n",
      " 984 / 1198    426           41            3   0.238   0.348  0.64  2660.155  2040.924\n",
      " 985 / 1198    426  The relative error between two consecutive iterates is at most 0.000000\n",
      " 985 / 1198    315        58.15          2.5   0.000   0.625  0.56  2015.457  1907.407\n",
      " 986 / 1198    315  The relative error between two consecutive iterates is at most 0.000000\n",
      " 986 / 1198   1136         28.8          3.5   0.062  -0.419  0.88  2390.516  3011.620\n",
      " 987 / 1198   1136  The relative error between two consecutive iterates is at most 0.000000\n",
      " 987 / 1198   1307        32.63          3.5   0.000   1.480  0.65  2720.926  3261.331\n",
      " 988 / 1198   1307  The relative error between two consecutive iterates is at most 0.000000\n",
      " 988 / 1198   1384         36.8            3   0.329  -1.475  0.81  2853.166  3405.280\n",
      " 989 / 1198   1384  The relative error between two consecutive iterates is at most 0.000000\n",
      " 989 / 1198   1162         36.1          3.5   0.448  -0.351  0.65  2731.169  3032.226\n",
      " 990 / 1198   1162  The relative error between two consecutive iterates is at most 0.000000\n",
      " 990 / 1198   1311        75.96            4   0.660  -1.555  0.25  3274.868  3265.175\n",
      " 991 / 1198   1311  The relative error between two consecutive iterates is at most 0.000000\n",
      " 991 / 1198    875        104.6          2.5   0.033  -0.920  0.30  2512.976  2654.847\n",
      " 992 / 1198    875  The relative error between two consecutive iterates is at most 0.000000\n",
      " 992 / 1198    185        46.01         15.5   0.754   1.122  0.25  3049.864  1745.791\n",
      " 993 / 1198    185  The relative error between two consecutive iterates is at most 0.000000\n",
      " 993 / 1198   1458        63.74            3   0.348  -0.984  0.25  2529.145  3681.557\n",
      " 994 / 1198   1458  The relative error between two consecutive iterates is at most 0.000000\n",
      " 994 / 1198   1082        47.89          3.5   0.363   0.185  0.25  2925.234  2922.148\n",
      " 995 / 1198   1082  The relative error between two consecutive iterates is at most 0.000000\n",
      " 995 / 1198    291        48.82            3   0.513   0.819  0.38  3432.345  1881.650\n",
      " 996 / 1198    291  The relative error between two consecutive iterates is at most 0.000000\n",
      " 996 / 1198    859         99.3          3.5   0.300  -0.826  0.51  2488.776  2634.303\n",
      " 997 / 1198    859  The relative error between two consecutive iterates is at most 0.000000\n",
      " 997 / 1198    839        49.95            4   0.279  -1.240  0.25  3327.155  2604.780\n",
      " 998 / 1198    839  The relative error between two consecutive iterates is at most 0.000000\n",
      " 998 / 1198   1415        33.07          4.5   0.266  -0.395  0.25  2259.087  3482.949\n",
      " 999 / 1198   1415  The relative error between two consecutive iterates is at most 0.000000\n",
      " 999 / 1198   1345        55.48            2   0.068  -0.516  0.65  2511.241  3326.322\n",
      "1000 / 1198   1345  The relative error between two consecutive iterates is at most 0.000000\n",
      "1000 / 1198   1198        23.36          3.5   0.000  -1.095  0.74  2367.521  3095.702\n",
      "1001 / 1198   1198  The relative error between two consecutive iterates is at most 0.000000\n",
      "1001 / 1198    628        35.53          2.5   0.000   1.203  0.76  3581.609  2294.515\n",
      "1002 / 1198    628  The relative error between two consecutive iterates is at most 0.000000\n",
      "1002 / 1198   1339        22.95            8   0.604   0.156  0.88  2977.000  3313.787\n",
      "1003 / 1198   1339  The relative error between two consecutive iterates is at most 0.000000\n",
      "1003 / 1198   1239        40.52            4   0.306  -0.517  0.25  2594.142  3155.925\n",
      "1004 / 1198   1239  The relative error between two consecutive iterates is at most 0.000000\n",
      "1004 / 1198   1299        44.29            3   0.459  -1.536  0.59  2491.819  3249.439\n",
      "1005 / 1198   1299  The relative error between two consecutive iterates is at most 0.000000\n",
      "1005 / 1198    574        50.81            3   0.000  -0.059  0.45  3667.740  2230.213\n",
      "1006 / 1198    574  The relative error between two consecutive iterates is at most 0.000000\n",
      "1006 / 1198    448        63.39            3   0.436   0.219  0.30  3595.450  2070.814\n",
      "1007 / 1198    448  The relative error between two consecutive iterates is at most 0.000000\n",
      "1007 / 1198   1019         43.6            3   0.286   0.158  0.56  2367.045  2844.869\n",
      "1008 / 1198   1019  The relative error between two consecutive iterates is at most 0.000000\n",
      "1008 / 1198    751        37.76          3.5   0.199   0.765  0.80  2834.175  2489.654\n",
      "1009 / 1198    751  The relative error between two consecutive iterates is at most 0.000000\n",
      "1009 / 1198   1362        33.53          3.5   0.463  -0.157  0.64  2440.942  3358.816\n",
      "1010 / 1198   1362  The relative error between two consecutive iterates is at most 0.000000\n",
      "1010 / 1198    272        24.95            4   0.222  -0.036  0.91  3044.476  1846.131\n",
      "1011 / 1198    272  The relative error between two consecutive iterates is at most 0.000000\n",
      "1011 / 1198    696        82.26          3.5   0.231   0.213  0.25  2016.526  2405.231\n",
      "1012 / 1198    696  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1012 / 1198    724        67.79            3   0.270  -1.554  0.58  1685.509  2449.408\n",
      "1013 / 1198    724  The relative error between two consecutive iterates is at most 0.000000\n",
      "1013 / 1198    778        68.07          2.5   0.096  -0.676  0.25  1827.841  2527.892\n",
      "1014 / 1198    778  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      "1014 / 1198    310        40.83          3.5   0.158  -1.086  0.25  3167.907  1906.093\n",
      "1015 / 1198    310  The relative error between two consecutive iterates is at most 0.000000\n",
      "1015 / 1198   1377        30.42            3   0.185  -0.090  1.04  2561.709  3385.901\n",
      "1016 / 1198   1377  The relative error between two consecutive iterates is at most 0.000000\n",
      "1016 / 1198   1441        64.06          2.5   0.517   0.381  0.40  2450.668  3586.291\n",
      "1017 / 1198   1441  The relative error between two consecutive iterates is at most 0.000000\n",
      "1017 / 1198    565        108.2            2   0.100   1.205  0.28  2182.737  2217.869\n",
      "1018 / 1198    565  The relative error between two consecutive iterates is at most 0.000000\n",
      "1018 / 1198   1124        31.91            4   0.000   1.036  0.25  1409.840  2991.907\n",
      "1019 / 1198   1124  The relative error between two consecutive iterates is at most 0.000000\n",
      "1019 / 1198    876        43.05          3.5   0.503  -1.500  0.76  3295.625  2660.454\n",
      "1020 / 1198    876  The relative error between two consecutive iterates is at most 0.000000\n",
      "1020 / 1198   1033        46.84          2.5   0.146  -0.410  0.63  2454.034  2871.488\n",
      "1021 / 1198   1033  The relative error between two consecutive iterates is at most 0.000000\n",
      "1021 / 1198    852        92.38          2.5   0.408   0.241  0.25  3522.458  2609.713\n",
      "1022 / 1198    852  The relative error between two consecutive iterates is at most 0.000000\n",
      "1022 / 1198   1268        53.72            2   0.163   0.813  0.62  1759.449  3197.861\n",
      "1023 / 1198   1268  The relative error between two consecutive iterates is at most 0.000000\n",
      "1023 / 1198   1076        31.58          2.5   0.122  -1.168  0.65  2068.163  2915.627\n",
      "1024 / 1198   1076  The relative error between two consecutive iterates is at most 0.000000\n",
      "1024 / 1198   1122        52.87          2.5   0.351   1.253  0.77  1842.032  2982.316\n",
      "1025 / 1198   1122  The relative error between two consecutive iterates is at most 0.000000\n",
      "1025 / 1198   1331        23.59         10.5   0.625   0.146  0.74  2933.840  3302.958\n",
      "1026 / 1198   1331  The relative error between two consecutive iterates is at most 0.000000\n",
      "1026 / 1198    487        49.56          3.5   0.632  -1.077  0.25  3474.120  2124.234\n",
      "1027 / 1198    487  The relative error between two consecutive iterates is at most 0.000000\n",
      "1027 / 1198   1395         30.4          3.5   0.235  -0.451  0.52  2738.086  3432.177\n",
      "1028 / 1198   1395  The relative error between two consecutive iterates is at most 0.000000\n",
      "1028 / 1198   1211        42.75          2.5   0.408  -0.970  0.74  3213.026  3111.876\n",
      "1029 / 1198   1211  The relative error between two consecutive iterates is at most 0.000000\n",
      "1029 / 1198   1428        36.98          2.5   0.000   1.388  0.75  2544.613  3540.527\n",
      "1030 / 1198   1428  The relative error between two consecutive iterates is at most 0.000000\n",
      "1030 / 1198    558         73.1            2   0.059   0.931  0.27  2501.909  2206.917\n",
      "1031 / 1198    558  The relative error between two consecutive iterates is at most 0.000000\n",
      "1031 / 1198    336        35.04            4   0.634  -1.216  0.53  2779.879  1933.754\n",
      "1032 / 1198    336  The relative error between two consecutive iterates is at most 0.000000\n",
      "1032 / 1198   1035        42.85        1.396   0.324  -1.435  0.25  2707.357  2880.444\n",
      "1033 / 1198   1035  The relative error between two consecutive iterates is at most 0.000000\n",
      "1033 / 1198   1386        25.04          3.5   0.204   1.240  0.68  2822.380  3413.827\n",
      "1034 / 1198   1386  The relative error between two consecutive iterates is at most 0.000000\n",
      "1034 / 1198   1216        42.49            4   0.610   0.504  0.25  2100.176  3121.834\n",
      "1035 / 1198   1216  The relative error between two consecutive iterates is at most 0.000000\n",
      "1035 / 1198     34        67.31            3   0.401  -1.129  0.25  2160.672  1297.172\n",
      "1036 / 1198     34  The relative error between two consecutive iterates is at most 0.000000\n",
      "1036 / 1198    762        80.04            2   0.000   0.551  0.43  1832.638  2499.304\n",
      "1037 / 1198    762  The relative error between two consecutive iterates is at most 0.000000\n",
      "1037 / 1198    834        70.99            3   0.501   1.044  0.25  3462.880  2599.954\n",
      "1038 / 1198    834  The relative error between two consecutive iterates is at most 0.000000\n",
      "1038 / 1198     74        54.94          3.5   0.226  -1.559  0.25  2132.447  1438.809\n",
      "1039 / 1198     74  The relative error between two consecutive iterates is at most 0.000000\n",
      "1039 / 1198    499        43.04          3.5   0.201  -1.170  0.25  3246.384  2133.040\n",
      "1040 / 1198    499  The relative error between two consecutive iterates is at most 0.000000\n",
      "1040 / 1198   1283        21.96           10   0.744   0.197  0.81  2970.000  3217.509\n",
      "1041 / 1198   1283  The relative error between two consecutive iterates is at most 0.000000\n",
      "1041 / 1198    263         55.6          2.5   0.371  -1.156  0.33  3262.670  1827.332\n",
      "1042 / 1198    263  The relative error between two consecutive iterates is at most 0.000000\n",
      "1042 / 1198   1219         31.9          2.5   0.087   0.553  0.76  1790.054  3128.457\n",
      "1043 / 1198   1219  The relative error between two consecutive iterates is at most 0.000000\n",
      "1043 / 1198    303        70.78          2.5   0.165  -0.733  0.30  2332.192  1891.421\n",
      "1044 / 1198    303  The relative error between two consecutive iterates is at most 0.000000\n",
      "1044 / 1198   1190        39.46            3   0.434  -0.222  0.40  2094.942  3065.234\n",
      "1045 / 1198   1190  The relative error between two consecutive iterates is at most 0.000000\n",
      "1045 / 1198   1356        47.26            2   0.295  -0.756  0.55  1614.643  3348.838\n",
      "1046 / 1198   1356  The relative error between two consecutive iterates is at most 0.000000\n",
      "1046 / 1198     55        51.88            3   0.387   0.100  0.54  2176.773  1368.818\n",
      "1047 / 1198     55  The relative error between two consecutive iterates is at most 0.000000\n",
      "1047 / 1198    602        64.13            3   0.234   0.810  0.25  2348.434  2261.639\n",
      "1048 / 1198    602  The relative error between two consecutive iterates is at most 0.000000\n",
      "1048 / 1198   1289        36.53            5   0.496   1.304  0.25  2949.592  3239.678\n",
      "1049 / 1198   1289  The relative error between two consecutive iterates is at most 0.000000\n",
      "1049 / 1198   1106        54.83            3   0.526  -1.011  0.28  3122.628  2968.158\n",
      "1050 / 1198   1106  The relative error between two consecutive iterates is at most 0.000000\n",
      "1050 / 1198   1166        34.11            3   0.307  -1.029  0.25  2331.638  3040.728\n",
      "1051 / 1198   1166  The relative error between two consecutive iterates is at most 0.000000\n",
      "1051 / 1198   1214        37.54            3   0.307  -0.915  0.33  3131.916  3121.057\n",
      "1052 / 1198   1214  The relative error between two consecutive iterates is at most 0.000000\n",
      "1052 / 1198   1410        28.32            3   0.383  -1.261  0.48  2729.658  3477.504\n",
      "1053 / 1198   1410  The relative error between two consecutive iterates is at most 0.000000\n",
      "1053 / 1198    817        39.68          3.5   0.101   0.551  0.74  2997.742  2578.013\n",
      "1054 / 1198    817  The relative error between two consecutive iterates is at most 0.000000\n",
      "1054 / 1198    398        34.69            3   0.135   0.568  0.53  3558.586  2004.283\n",
      "1055 / 1198    398  The relative error between two consecutive iterates is at most 0.000000\n",
      "1055 / 1198    461        85.01            3   0.517   0.709  0.25  2333.407  2090.560\n",
      "1056 / 1198    461  The relative error between two consecutive iterates is at most 0.000000\n",
      "1056 / 1198    165        51.87            3   0.223  -1.046  0.49  3231.509  1648.657\n",
      "1057 / 1198    165  The relative error between two consecutive iterates is at most 0.000000\n",
      "1057 / 1198   1181         50.6          3.5   0.614   1.183  0.25  1812.912  3064.242\n",
      "1058 / 1198   1181  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1058 / 1198    323        71.82            2   0.125  -0.291  0.27  1783.255  1915.375\n",
      "1059 / 1198    323  The relative error between two consecutive iterates is at most 0.000000\n",
      "1059 / 1198   1409        7.139         11.5   0.620  -1.525  1.37  2411.829  3474.070\n",
      "1060 / 1198   1409  The relative error between two consecutive iterates is at most 0.000000\n",
      "1060 / 1198   1229         43.6          2.5   0.200  -1.556  0.48  2928.457  3137.511\n",
      "1061 / 1198   1229  The relative error between two consecutive iterates is at most 0.000000\n",
      "1061 / 1198   1348        27.99            4   0.580  -1.424  0.65  3046.566  3347.135\n",
      "1062 / 1198   1348  The relative error between two consecutive iterates is at most 0.000000\n",
      "1062 / 1198    566        74.57            2   0.208  -0.489  0.65  2348.009  2217.482\n",
      "1063 / 1198    566  The relative error between two consecutive iterates is at most 0.000000\n",
      "1063 / 1198    991        21.51          3.5   0.518  -0.257  0.57  2416.748  2798.000\n",
      "1064 / 1198    991  The relative error between two consecutive iterates is at most 0.000000\n",
      "1064 / 1198    153        3.614        2.932   0.649  -0.168  1.00  2767.957  2160.041\n",
      "1065 / 1198    153  The cosine of the angle between func(x) and any column of the\n",
      "  Jacobian is at most 0.000000 in absolute value\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.\n",
      "  dv = (np.float64(self.norm.vmax) -\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/astropy/visualization/mpl_normalize.py:113: RuntimeWarning: invalid value encountered in true_divide\n",
      "  np.true_divide(values, self.vmax - self.vmin, out=values)\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/matplotlib/colors.py:527: RuntimeWarning: invalid value encountered in less\n",
      "  xa[xa < 0] = -1\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.\n",
      "  np.float64(self.norm.vmin))\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.\n",
      "  a_min = np.float64(newmin)\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.\n",
      "  a_max = np.float64(newmax)\n",
      "<string>:6: UserWarning: Warning: converting a masked element to nan.\n",
      "/Users/dcoe/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1065 / 1198   1212        26.97          3.5   0.000  -0.876  0.68  2545.886  3123.019\n",
      "1066 / 1198   1212  The relative error between two consecutive iterates is at most 0.000000\n",
      "1066 / 1198   1282        10.39          2.5   0.111  -0.522  1.35  2278.495  3219.006\n",
      "1067 / 1198   1282  The relative error between two consecutive iterates is at most 0.000000\n",
      "1067 / 1198     66        46.54          2.5   0.183   0.320  0.55  2086.629  1414.287\n",
      "1068 / 1198     66  The relative error between two consecutive iterates is at most 0.000000\n",
      "1068 / 1198    683        64.12          2.5   0.382  -0.890  0.50  2974.964  2385.392\n",
      "1069 / 1198    683  The relative error between two consecutive iterates is at most 0.000000\n",
      "1069 / 1198    819        62.35            2   0.268  -0.600  0.44  1743.458  2574.881\n",
      "1070 / 1198    819  The relative error between two consecutive iterates is at most 0.000000\n",
      "1070 / 1198   1091        47.34            3   0.460   1.385  0.32  1855.713  2940.860\n",
      "1071 / 1198   1091  The relative error between two consecutive iterates is at most 0.000000\n",
      "1071 / 1198   1245        48.96            2   0.000   1.082  0.72  2094.851  3162.196\n",
      "1072 / 1198   1245  The relative error between two consecutive iterates is at most 0.000000\n",
      "1072 / 1198   1422         56.4          2.5   0.401  -0.484  0.41  3149.513  3500.469\n",
      "1073 / 1198   1422  The relative error between two consecutive iterates is at most 0.000000\n",
      "1073 / 1198    809        34.91            3   0.176   0.019  0.62  3340.842  2562.854\n",
      "1074 / 1198    809  The relative error between two consecutive iterates is at most 0.000000\n",
      "1074 / 1198     65        61.98          2.5   0.000  -0.062  0.25  1990.180  1412.906\n",
      "1075 / 1198     65  The relative error between two consecutive iterates is at most 0.000000\n",
      "1075 / 1198   1259        65.57            2   0.438  -0.770  0.51  2900.230  3181.092\n",
      "1076 / 1198   1259  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000 and the relative error between two consecutive iterates is at \n",
      "  most 0.000000\n",
      "1076 / 1198    331        36.59          2.5   0.000   0.292  0.61  3160.754  1921.961\n",
      "1077 / 1198    331  The relative error between two consecutive iterates is at most 0.000000\n",
      "1077 / 1198    484        57.94          2.5   0.159  -1.307  0.43  1767.224  2114.412\n",
      "1078 / 1198    484  Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      "1078 / 1198    430        53.59            2   0.165   1.118  0.51  3437.038  2050.960\n",
      "1079 / 1198    430  The relative error between two consecutive iterates is at most 0.000000\n",
      "1079 / 1198   1138        35.84          2.5   0.279   0.556  0.82  2215.911  3006.616\n",
      "1080 / 1198   1138  The relative error between two consecutive iterates is at most 0.000000\n",
      "1080 / 1198    785        6.082        2.646   0.720  -0.230  1.00  1680.862  2858.882\n",
      "1081 / 1198    785  The cosine of the angle between func(x) and any column of the\n",
      "  Jacobian is at most 0.000000 in absolute value\n",
      "1081 / 1198    253        71.93            2   0.003  -0.287  0.25  3399.170  1804.082\n",
      "1082 / 1198    253  The relative error between two consecutive iterates is at most 0.000000\n",
      "1082 / 1198    646        68.61          2.5   0.247  -1.337  0.61  1754.725  2324.666\n",
      "1083 / 1198    646  The relative error between two consecutive iterates is at most 0.000000\n",
      "1083 / 1198    305        49.44          2.5   0.000   0.048  0.25  3262.199  1889.017\n",
      "1084 / 1198    305  The relative error between two consecutive iterates is at most 0.000000\n",
      "1084 / 1198    797        75.44            2   0.153   0.555  0.69  2040.902  2547.823\n",
      "1085 / 1198    797  The relative error between two consecutive iterates is at most 0.000000\n",
      "1085 / 1198    113        64.59          2.5   0.459   0.063  0.32  2960.707  1554.514\n",
      "1086 / 1198    113  The relative error between two consecutive iterates is at most 0.000000\n",
      "1086 / 1198   1201        30.94          2.5   0.128   0.928  0.50  2306.225  3101.530\n",
      "1087 / 1198   1201  The relative error between two consecutive iterates is at most 0.000000\n",
      "1087 / 1198    772        72.48          2.5   0.411   0.754  0.25  1701.650  2517.023\n",
      "1088 / 1198    772  The relative error between two consecutive iterates is at most 0.000000\n",
      "1088 / 1198    793        50.72          2.5   0.231   1.280  0.53  3196.293  2549.393\n",
      "1089 / 1198    793  The relative error between two consecutive iterates is at most 0.000000\n",
      "1089 / 1198   1286        39.78            3   0.524  -0.612  0.40  1750.898  3222.752\n",
      "1090 / 1198   1286  The relative error between two consecutive iterates is at most 0.000000\n",
      "1090 / 1198    537        71.48            2   0.398  -0.657  0.43  2967.362  2180.447\n",
      "1091 / 1198    537  The relative error between two consecutive iterates is at most 0.000000\n",
      "1091 / 1198    424         65.6          3.5   0.087  -0.069  0.25  3657.207  2028.135\n",
      "1092 / 1198    424  The relative error between two consecutive iterates is at most 0.000000\n",
      "1092 / 1198    403        190.1          6.5   0.000   0.621  8.00  2324.425  2015.000\n",
      "1093 / 1198    403  The relative error between two consecutive iterates is at most 0.000000\n",
      "1093 / 1198   1066        45.27            2   0.081   1.045  0.53  1730.539  2899.273\n",
      "1094 / 1198   1066  The relative error between two consecutive iterates is at most 0.000000\n",
      "1094 / 1198     42        26.76          2.5   0.312   0.159  0.25  1781.136  1335.283\n",
      "1095 / 1198     42  The relative error between two consecutive iterates is at most 0.000000\n",
      "1095 / 1198    512        56.92          2.5   0.351   1.225  0.25  3014.748  2153.926\n",
      "1096 / 1198    512  The relative error between two consecutive iterates is at most 0.000000\n",
      "1096 / 1198    546        79.16            2   0.384  -0.001  0.25  3539.745  2187.386\n",
      "1097 / 1198    546  The relative error between two consecutive iterates is at most 0.000000\n",
      "1097 / 1198    931        52.25            2   0.061   0.439  0.66  1410.100  2731.333\n",
      "1098 / 1198    931  The relative error between two consecutive iterates is at most 0.000000\n",
      "1098 / 1198   1077         38.4            2   0.000   1.541  0.73  1598.282  2913.469\n",
      "1099 / 1198   1077  The relative error between two consecutive iterates is at most 0.000000\n",
      "1099 / 1198    519        105.2          1.5   0.188  -0.083  0.28  2095.455  2157.662\n",
      "1100 / 1198    519  The relative error between two consecutive iterates is at most 0.000000\n",
      "1100 / 1198    359        36.19            2   0.000  -1.356  0.70  3265.540  1952.974\n",
      "1101 / 1198    359  The relative error between two consecutive iterates is at most 0.000000\n",
      "1101 / 1198   1102        42.43          3.5   0.543   1.463  0.25  1761.694  2963.060\n",
      "1102 / 1198   1102  The relative error between two consecutive iterates is at most 0.000000\n",
      "1102 / 1198    195        76.79            2   0.396  -1.270  0.50  1779.241  1687.919\n",
      "1103 / 1198    195  The relative error between two consecutive iterates is at most 0.000000\n",
      "1103 / 1198   1095        43.48          2.5   0.000  -0.847  0.25  2011.024  2946.755\n",
      "1104 / 1198   1095  The relative error between two consecutive iterates is at most 0.000000\n",
      "1104 / 1198    750        73.85            3   0.532   1.339  0.25  1930.313  2488.284\n",
      "1105 / 1198    750  The relative error between two consecutive iterates is at most 0.000000\n",
      "1105 / 1198    599        57.37            3   0.000  -0.874  0.27  2475.864  2257.450\n",
      "1106 / 1198    599  The relative error between two consecutive iterates is at most 0.000000\n",
      "1106 / 1198    906        72.48            2   0.231   0.473  0.25  2436.866  2703.208\n",
      "1107 / 1198    906  The relative error between two consecutive iterates is at most 0.000000\n",
      "1107 / 1198    601        41.11            2   0.000   0.994  0.60  3123.753  2255.962\n",
      "1108 / 1198    601  The relative error between two consecutive iterates is at most 0.000000\n",
      "1108 / 1198   1086        33.43          2.5   0.075   0.276  0.72  1736.990  2919.943\n",
      "1109 / 1198   1086  The relative error between two consecutive iterates is at most 0.000000\n",
      "1109 / 1198   1346        27.31          2.5   0.446  -0.820  0.90  2139.708  3338.826\n",
      "1110 / 1198   1346  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1110 / 1198   1209        38.22          3.5   0.437   0.410  0.26  3005.489  3109.595\n",
      "1111 / 1198   1209  The relative error between two consecutive iterates is at most 0.000000\n",
      "1111 / 1198    824        21.59            2   0.000   0.506  0.84  3002.278  2580.101\n",
      "1112 / 1198    824  The relative error between two consecutive iterates is at most 0.000000\n",
      "1112 / 1198     82        65.89            2   0.000   0.193  0.83  2117.761  1480.154\n",
      "1113 / 1198     82  The relative error between two consecutive iterates is at most 0.000000\n",
      "1113 / 1198    397         37.5          2.5   0.265  -1.370  0.71  3364.053  2006.131\n",
      "1114 / 1198    397  The relative error between two consecutive iterates is at most 0.000000\n",
      "1114 / 1198    508        61.49          2.5   0.311  -0.240  0.25  2552.248  2143.205\n",
      "1115 / 1198    508  The relative error between two consecutive iterates is at most 0.000000\n",
      "1115 / 1198   1288        37.78          2.5   0.000  -1.200  0.25  2195.662  3224.981\n",
      "1116 / 1198   1288  The relative error between two consecutive iterates is at most 0.000000\n",
      "1116 / 1198   1062           38          2.5   0.000  -1.413  0.34  1915.929  2901.208\n",
      "1117 / 1198   1062  The relative error between two consecutive iterates is at most 0.000000\n",
      "1117 / 1198    620        42.79            2   0.063   1.347  0.58  3208.089  2276.358\n",
      "1118 / 1198    620  The relative error between two consecutive iterates is at most 0.000000\n",
      "1118 / 1198   1278        38.63            2   0.163  -0.923  0.72  1801.229  3213.361\n",
      "1119 / 1198   1278  The relative error between two consecutive iterates is at most 0.000000\n",
      "1119 / 1198    645        75.27            2   0.000   0.110  0.25  1972.867  2317.467\n",
      "1120 / 1198    645  The relative error between two consecutive iterates is at most 0.000000\n",
      "1120 / 1198    773        80.18          2.5   0.490  -1.343  0.25  3482.625  2520.202\n",
      "1121 / 1198    773  The relative error between two consecutive iterates is at most 0.000000\n",
      "1121 / 1198    269        44.76            2   0.000   1.407  0.28  3293.523  1836.640\n",
      "1122 / 1198    269  The relative error between two consecutive iterates is at most 0.000000\n",
      "1122 / 1198   1111        75.24            2   0.000   1.183  0.25  1381.565  2969.085\n",
      "1123 / 1198   1111  The relative error between two consecutive iterates is at most 0.000000\n",
      "1123 / 1198   1014         67.9          1.5   0.000  -0.964  0.25  1262.399  2829.872\n",
      "1124 / 1198   1014  The relative error between two consecutive iterates is at most 0.000000\n",
      "1124 / 1198    692         48.9          2.5   0.434  -0.844  0.58  3074.156  2399.176\n",
      "1125 / 1198    692  The relative error between two consecutive iterates is at most 0.000000\n",
      "1125 / 1198    720         80.7          1.5   0.182   0.906  0.39  3561.802  2434.997\n",
      "1126 / 1198    720  The relative error between two consecutive iterates is at most 0.000000\n",
      "1126 / 1198   1318        80.97          1.5   0.010  -1.113  0.31  3233.065  3275.446\n",
      "1127 / 1198   1318  The relative error between two consecutive iterates is at most 0.000000\n",
      "1127 / 1198    878        50.45            2   0.000   1.564  0.25  3220.428  2660.206\n",
      "1128 / 1198    878  The relative error between two consecutive iterates is at most 0.000000\n",
      "1128 / 1198   1221        41.17            2   0.279  -0.429  0.60  1849.485  3128.240\n",
      "1129 / 1198   1221  The relative error between two consecutive iterates is at most 0.000000\n",
      "1129 / 1198    744        116.4          2.5   0.213  -1.346  0.25  1503.083  2471.943\n",
      "1130 / 1198    744  The relative error between two consecutive iterates is at most 0.000000\n",
      "1130 / 1198   1457        60.12          1.5   0.000  -0.774  0.27  2407.716  3658.929\n",
      "1131 / 1198   1457  The relative error between two consecutive iterates is at most 0.000000\n",
      "1131 / 1198    542        48.62            2   0.438   0.952  0.68  3445.917  2186.083\n",
      "1132 / 1198    542  The relative error between two consecutive iterates is at most 0.000000\n",
      "1132 / 1198   1397        47.27            2   0.432   0.076  0.25  2748.291  3431.686\n",
      "1133 / 1198   1397  The relative error between two consecutive iterates is at most 0.000000\n",
      "1133 / 1198    273        36.81            3   0.671  -1.175  0.67  3210.510  1851.882\n",
      "1134 / 1198    273  The relative error between two consecutive iterates is at most 0.000000\n",
      "1134 / 1198    290        58.19          2.5   0.145   0.393  0.25  2086.141  1874.433\n",
      "1135 / 1198    290  The relative error between two consecutive iterates is at most 0.000000\n",
      "1135 / 1198    251         51.8            2   0.207  -0.937  0.86  2055.460  1799.526\n",
      "1136 / 1198    251  The relative error between two consecutive iterates is at most 0.000000\n",
      "1136 / 1198   1451        42.88          1.5   0.000   0.756  0.63  2395.803  3641.856\n",
      "1137 / 1198   1451  The relative error between two consecutive iterates is at most 0.000000\n",
      "1137 / 1198   1306        32.77            2   0.000   0.145  0.41  3021.818  3248.191\n",
      "1138 / 1198   1306  The relative error between two consecutive iterates is at most 0.000000\n",
      "1138 / 1198    592        27.74          2.5   0.371  -1.366  0.90  3272.818  2250.527\n",
      "1139 / 1198    592  The relative error between two consecutive iterates is at most 0.000000\n",
      "1139 / 1198   1364        43.24          2.5   0.353   0.217  0.25  2632.916  3361.107\n",
      "1140 / 1198   1364  The relative error between two consecutive iterates is at most 0.000000\n",
      "1140 / 1198    105          123          2.5   0.553   0.136  0.25  2440.378  1532.013\n",
      "1141 / 1198    105  The relative error between two consecutive iterates is at most 0.000000\n",
      "1141 / 1198    985        37.38          2.5   0.212   1.473  0.53  2828.016  2795.146\n",
      "1142 / 1198    985  The relative error between two consecutive iterates is at most 0.000000\n",
      "1142 / 1198   1373        93.34          1.5   0.372   0.931  0.25  3127.220  3373.409\n",
      "1143 / 1198   1373  The relative error between two consecutive iterates is at most 0.000000\n",
      "1143 / 1198    517        77.52          1.5   0.091   1.460  0.45  1752.013  2157.787\n",
      "1144 / 1198    517  The relative error between two consecutive iterates is at most 0.000000\n",
      "1144 / 1198   1194        45.24          1.5   0.038   0.561  0.54  2993.223  3075.395\n",
      "1145 / 1198   1194  The relative error between two consecutive iterates is at most 0.000000\n",
      "1145 / 1198   1399        35.18          2.5   0.000  -1.268  0.25  2274.813  3438.828\n",
      "1146 / 1198   1399  The relative error between two consecutive iterates is at most 0.000000\n",
      "1146 / 1198   1277         46.1          1.5   0.212  -0.922  0.77  2319.521  3207.732\n",
      "1147 / 1198   1277  The relative error between two consecutive iterates is at most 0.000000\n",
      "1147 / 1198   1401        50.11            2   0.546  -0.684  0.25  2356.809  3445.691\n",
      "1148 / 1198   1401  The relative error between two consecutive iterates is at most 0.000000\n",
      "1148 / 1198    974        47.56            2   0.379   0.953  0.28  1979.177  2783.843\n",
      "1149 / 1198    974  The relative error between two consecutive iterates is at most 0.000000\n",
      "1149 / 1198   1425        26.51          1.5   0.000   0.639  0.60  2401.169  3515.111\n",
      "1150 / 1198   1425  The relative error between two consecutive iterates is at most 0.000000\n",
      "1150 / 1198    903        44.63            3   0.547  -1.272  0.25  3253.811  2703.426\n",
      "1151 / 1198    903  The relative error between two consecutive iterates is at most 0.000000\n",
      "1151 / 1198   1081        44.35          1.5   0.000  -1.373  0.58  1752.314  2915.629\n",
      "1152 / 1198   1081  The relative error between two consecutive iterates is at most 0.000000\n",
      "1152 / 1198   1149        39.93          2.5   0.601  -0.291  0.52  1850.680  3015.942\n",
      "1153 / 1198   1149  The relative error between two consecutive iterates is at most 0.000000\n",
      "1153 / 1198   1432        51.84            2   0.608   1.350  0.25  2664.951  3552.606\n",
      "1154 / 1198   1432  The relative error between two consecutive iterates is at most 0.000000\n",
      "1154 / 1198   1419        31.95            2   0.207  -0.369  0.59  2351.024  3496.993\n",
      "1155 / 1198   1419  The relative error between two consecutive iterates is at most 0.000000\n",
      "1155 / 1198    889        49.81            2   0.221   1.350  0.26  2042.008  2676.856\n",
      "1156 / 1198    889  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1156 / 1198    391        49.46          1.5   0.083   0.825  0.28  3354.086  1993.542\n",
      "1157 / 1198    391  The relative error between two consecutive iterates is at most 0.000000\n",
      "1157 / 1198   1001         47.7          1.5   0.000   1.206  0.25  2132.229  2811.638\n",
      "1158 / 1198   1001  The relative error between two consecutive iterates is at most 0.000000\n",
      "1158 / 1198    271        55.62          1.5   0.089  -0.681  0.25  3072.431  1837.384\n",
      "1159 / 1198    271  The relative error between two consecutive iterates is at most 0.000000\n",
      "1159 / 1198   1368        25.03            2   0.355  -0.757  0.71  2614.902  3366.744\n",
      "1160 / 1198   1368  The relative error between two consecutive iterates is at most 0.000000\n",
      "1160 / 1198    296        43.03            2   0.000   0.684  0.25  3350.796  1886.193\n",
      "1161 / 1198    296  The relative error between two consecutive iterates is at most 0.000000\n",
      "1161 / 1198    525        48.44          1.5   0.042  -0.641  0.41  3358.072  2170.620\n",
      "1162 / 1198    525  The relative error between two consecutive iterates is at most 0.000000\n",
      "1162 / 1198   1060        31.16            2   0.243  -1.082  0.75  3322.579  2894.812\n",
      "1163 / 1198   1060  The relative error between two consecutive iterates is at most 0.000000\n",
      "1163 / 1198    590        64.31          1.5   0.062  -0.149  0.46  2440.839  2238.904\n",
      "1164 / 1198    590  The relative error between two consecutive iterates is at most 0.000000\n",
      "1164 / 1198    992        45.72          1.5   0.000   0.938  0.49  3181.689  2797.126\n",
      "1165 / 1198    992  The relative error between two consecutive iterates is at most 0.000000\n",
      "1165 / 1198    228        24.53            2   0.000  -1.520  0.59  2679.520  1750.801\n",
      "1166 / 1198    228  The relative error between two consecutive iterates is at most 0.000000\n",
      "1166 / 1198    864         40.6            2   0.000   1.134  0.39  1651.596  2629.915\n",
      "1167 / 1198    864  The relative error between two consecutive iterates is at most 0.000000\n",
      "1167 / 1198   1424        37.64          1.5   0.000  -1.481  0.58  2709.490  3515.192\n",
      "1168 / 1198   1424  The relative error between two consecutive iterates is at most 0.000000\n",
      "1168 / 1198    368        417.6        1.871   0.990  -1.562  1.00  1995.146  2759.274\n",
      "1169 / 1198    368  The cosine of the angle between func(x) and any column of the\n",
      "  Jacobian is at most 0.000000 in absolute value\n",
      "1169 / 1198     92        80.41          1.5   0.000   0.176  0.25  2267.371  1852.914\n",
      "1170 / 1198     92  The relative error between two consecutive iterates is at most 0.000000\n",
      "1170 / 1198   1392        31.66          2.5   0.580  -1.325  0.25  2143.167  3420.730\n",
      "1171 / 1198   1392  The relative error between two consecutive iterates is at most 0.000000\n",
      "1171 / 1198    952        37.32          2.5   0.318   0.476  0.25  1876.132  2760.354\n",
      "1172 / 1198    952  The relative error between two consecutive iterates is at most 0.000000\n",
      "1172 / 1198   1469        50.53          1.5   0.000  -0.101  0.25  2792.692  3748.041\n",
      "1173 / 1198   1469  The relative error between two consecutive iterates is at most 0.000000\n",
      "1173 / 1198   1249        52.19          1.5   0.235  -0.354  0.44  2928.278  3160.066\n",
      "1174 / 1198   1249  The relative error between two consecutive iterates is at most 0.000000\n",
      "1174 / 1198   1420        20.21            2   0.005   0.230  0.82  2713.622  3495.640\n",
      "1175 / 1198   1420  The relative error between two consecutive iterates is at most 0.000000\n",
      "1175 / 1198   1433         26.9          2.5   0.000   0.207  0.33  2593.184  3550.615\n",
      "1176 / 1198   1433  The relative error between two consecutive iterates is at most 0.000000\n",
      "1176 / 1198   1178        51.23          1.5   0.320   1.053  0.25  2772.949  3048.376\n",
      "1177 / 1198   1178  The relative error between two consecutive iterates is at most 0.000000\n",
      "1177 / 1198   1195        38.95          1.5   0.191   0.806  0.25  2947.276  3078.641\n",
      "1178 / 1198   1195  The relative error between two consecutive iterates is at most 0.000000\n",
      "1178 / 1198    341        38.33            2   0.449  -1.288  0.45  3254.080  1930.743\n",
      "1179 / 1198    341  The relative error between two consecutive iterates is at most 0.000000\n",
      "1179 / 1198    996         65.8            1   0.000  -0.192  0.25  2220.087  2797.067\n",
      "1180 / 1198    996  The relative error between two consecutive iterates is at most 0.000000\n",
      "1180 / 1198    388        51.83          1.5   0.458  -0.993  0.25  3235.084  1990.554\n",
      "1181 / 1198    388  The relative error between two consecutive iterates is at most 0.000000\n",
      "1181 / 1198    494        167.3            4   0.604   0.091  8.00  1571.286  2199.000\n",
      "1182 / 1198    494  The relative error between two consecutive iterates is at most 0.000000\n",
      "1182 / 1198   1206        42.33          1.5   0.349  -1.241  0.25  2946.212  3101.317\n",
      "1183 / 1198   1206  The relative error between two consecutive iterates is at most 0.000000\n",
      "1183 / 1198   1104        35.26            2   0.000   1.257  0.25  2053.493  2959.754\n",
      "1184 / 1198   1104  The relative error between two consecutive iterates is at most 0.000000\n",
      "1184 / 1198   1163        46.29            1   0.000   0.634  0.25  3140.974  3026.186\n",
      "1185 / 1198   1163  The relative error between two consecutive iterates is at most 0.000000\n",
      "1185 / 1198    538        35.61          1.5   0.325  -0.197  0.59  3447.180  2178.206\n",
      "1186 / 1198    538  The relative error between two consecutive iterates is at most 0.000000\n",
      "1186 / 1198   1128        47.58            2   0.646  -1.020  0.25  2646.632  2989.813\n",
      "1187 / 1198   1128  The relative error between two consecutive iterates is at most 0.000000\n",
      "1187 / 1198   1312        52.81          1.5   0.733   0.227  8.00  2632.102  3253.417\n",
      "1188 / 1198   1312  The relative error between two consecutive iterates is at most 0.000000\n",
      "1188 / 1198    379        37.44          1.5   0.000   0.799  0.25  3204.924  1979.359\n",
      "1189 / 1198    379  The relative error between two consecutive iterates is at most 0.000000\n",
      "1189 / 1198    655        63.45            1   0.275   1.514  0.25  2258.884  2797.087\n",
      "1190 / 1198    655  The relative error between two consecutive iterates is at most 0.000000\n",
      "1190 / 1198    240         44.1          1.5   0.211   1.303  0.47  2367.715  2728.872\n",
      "1191 / 1198    240  The relative error between two consecutive iterates is at most 0.000000\n",
      "1191 / 1198    626        282.7            2   0.871  -1.107  0.25  1534.016  2352.973\n",
      "1192 / 1198    626  The relative error between two consecutive iterates is at most 0.000000\n",
      "1192 / 1198    837        62.59            4   0.083  -1.217  0.54  2372.849  2602.652\n",
      "1193 / 1198    837  The relative error between two consecutive iterates is at most 0.000000\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Improper input: N=4 must not exceed M=2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-17a8b0f74856>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitbl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msegm_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mdata_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_counts_initial\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mfit_object_to_Sersic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msegm_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%4d  '\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msegm_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-23-e72d63689b40>\u001b[0m in \u001b[0;36mfit_object_to_Sersic\u001b[0;34m(segm_id)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0mparam_fixed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'ellip'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'theta'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r_eff'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0minitial_guess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minitial_guess_from_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mfit_model_to_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;31m# Model #4: Fit everything but theta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-17-02c9b7ea9c0e>\u001b[0m in \u001b[0;36mfit_model_to_data\u001b[0;34m(plot_results, verbose)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit_model_to_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_results\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_stamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_guess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxx_stamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy_stamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_stamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mweight_stamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;31m#print(model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/astropy/modeling/fitting.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, model, x, y, z, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m                     \u001b[0mmodel_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m                     \u001b[0mmodel_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mydata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m                 \u001b[0;31m# And finally we add back units to the parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/astropy/modeling/fitting.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, model, x, y, z, weights, maxiter, acc, epsilon, estimate_jacobian)\u001b[0m\n\u001b[1;32m    883\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfarg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDfun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m             \u001b[0mcol_deriv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_copy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcol_fit_deriv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxfev\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsfcn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m             xtol=acc, full_output=True)\n\u001b[0m\u001b[1;32m    886\u001b[0m         \u001b[0m_fitter_to_model_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_copy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdinfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/astroconda3p/lib/python3.7/site-packages/scipy/optimize/minpack.py\u001b[0m in \u001b[0;36mleastsq\u001b[0;34m(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Improper input: N=%s must not exceed M=%s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mepsfcn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Improper input: N=4 must not exceed M=2"
     ]
    }
   ],
   "source": [
    "s = '               id     amplitude          r     ellip   theta    n      x         y'\n",
    "print(s)\n",
    "\n",
    "fout = open('MACS0647_Sersic_fits.cat', 'w')\n",
    "fout.write(s+'\\n')\n",
    "\n",
    "fout_messages = open('MACS0647_Sersic_fit_messages.txt', 'w')\n",
    "\n",
    "data_counts_initial = data_count_rate * exposure_time  # re-initialize\n",
    "image = data_counts_initial * 0\n",
    "\n",
    "ids = tbl['id']\n",
    "\n",
    "for itbl, segm_id in enumerate(ids):\n",
    "    data_counts = data_counts_initial - image\n",
    "    fit_object_to_Sersic(segm_id)\n",
    "    \n",
    "    s = '%4d  ' % segm_id\n",
    "    model_values = model.amplitude.value, model.r_eff.value, model.ellip.value, model.theta.value, model.n.value, model.x_0.value, model.y_0.value\n",
    "    #s += '%8.3f % 10.3f  %5.2f  %5.2f  %4.2f  %5.1f  %5.1f' % model_values\n",
    "    s += '%11.4g  % 11.4g  %6.3f  %6.3f  %4.2f  %7.3f  %7.3f' % model_values\n",
    "    print('%4d / %4d  ' % (itbl, len(tbl)), s)\n",
    "    fout.write(s+'\\n')\n",
    "    \n",
    "    stamp, extent = Sersic_galaxy(model, floor=1e-7)  # should probably multiply floor by exposure time...\n",
    "    image = add_stamp_to_image(image, stamp, extent)\n",
    "    \n",
    "    sid = '%4d / %4d   %4d  ' % (itbl+1, len(tbl), segm_id)\n",
    "    sm = sid + fitter.fit_info['message']\n",
    "    print(sm)\n",
    "    fout_messages.write(sm+'\\n')\n",
    "    \n",
    "    #plot_file = 'plots/image_%04d_%d.png' % (itbl, segm_id)\n",
    "    #show_fit(plot_file)\n",
    "\n",
    "    plot_file = 'plots/fit_%04d_%d.png' % (itbl, segm_id)\n",
    "    plot_fit_results(plot_file)\n",
    "    #wait = input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu_model = fits.open(image_file)[0]\n",
    "hdu_model.data = image\n",
    "hdu_model.writeto('MACS0647_Sersic.fits', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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