Bipod_Run_v4_thbodlocked.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "a76235ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
     ]
    }
   ],
   "source": [
    "%reset\n",
    "from pyomo.environ import*\n",
    "from pyomo.opt import SolverFactory\n",
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "import sympy as sym\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "from sympy import pprint\n",
    "\n",
    "import dill\n",
    "dill.load_session('simplified_bipod_thbodlocked_final_mark.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "6744cb0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "func_map = {'sin':sin, 'cos':cos} \n",
    "\n",
    "sym_list = [g] + [BLD] + [foot_angle] + [foot_length] +\\\n",
    "           [cg] + [lx_boom] + [ly_boom] +\\\n",
    "           [y_boomOffset] + [y_bodyOffset] + [x_footOffset] +\\\n",
    "           [COMR[link,leg] for leg in legs for link in links]+\\\n",
    "           [masses[base] for base in bases] +\\\n",
    "           [masses[link,leg] for leg in legs for link in links] +\\\n",
    "           [lengths[base] for base in bases] +\\\n",
    "           [lengths[link,leg] for leg in legs for link in links] +\\\n",
    "           [inertias['body']] + [inertias['boom','X']] + [inertias['boom','Y']] +\\\n",
    "           [inertias[link,leg] for leg in legs for link in links] +\\\n",
    "           [x_body]+[y_body]+ [th[link,leg] for leg in legs for link in links] +\\\n",
    "           [dx_body]+[dy_body]+[dth[link,leg] for leg in legs for link in links] +\\\n",
    "           [ddx_body]+[ddy_body] + [ddth[link,leg] for leg in legs for link in links] +\\\n",
    "           [tau['left',leg] for leg in legs] + [tau['right',leg] for leg in legs] +\\\n",
    "           [lamda['x',leg] for leg in legs] + [lamda['y',leg] for leg in legs] +\\\n",
    "           [GRF['x',leg] for leg in legs] + [GRF['y',leg] for leg in legs]\n",
    "\n",
    "lamb_EOMs = {}\n",
    "\n",
    "DOFs = ['x_body'] + ['y_body']\n",
    "\n",
    "for leg in legs:\n",
    "    for link in links:\n",
    "        DOFs = DOFs + ['theta_{0}'.format(link+leg)]\n",
    "\n",
    "# for dof_i,dof in enumerate(DOFs):\n",
    "#     print(dof_i)\n",
    "#    1\n",
    "\n",
    "\n",
    "        \n",
    "# print(len(DOFs))\n",
    "# print(len(EOMs))     \n",
    "for dof_i,dof in enumerate(DOFs):\n",
    "    lamb_EOMs.update({dof: sym.lambdify(sym_list,EOMs[dof_i],modules = [func_map])})\n",
    "# print(EOMs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "88822ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PARAMETERS:\n",
    "\n",
    "COMR_ = {'ULA': 41.14/174.5,\n",
    "         'LLA': 132.2/300,\n",
    "         'URA': 41.14/174.5,\n",
    "         'LRA': 190.4/295.5,\n",
    "         'ULB': 41.14/174.5,\n",
    "         'LLB': 132.2/300,\n",
    "         'URB': 41.14/174.5,         \n",
    "         'LRB': 190.4/295.5}\n",
    "\n",
    "masses_ = {'body': 5756.78/1000,\n",
    "           'boom': 2470.66/1000,\n",
    "           'ULA': 153.81/1000,\n",
    "           'LLA': 346.45/1000,\n",
    "           'URA': 153.81/1000,\n",
    "           'LRA': 499.95/1000,\n",
    "           'ULB': 153.81/1000,\n",
    "           'LLB': 346.45/1000,\n",
    "           'URB': 153.81/1000,\n",
    "           'LRB': 499.95/1000}\n",
    "\n",
    "lengths_ = {'body': 253/1000,\n",
    "            'boom': None,\n",
    "            'ULA': 174.5/1000,\n",
    "            'LLA': 300.0/1000,\n",
    "            'URA': 174.5/1000,\n",
    "            'LRA': 295.5/1000,\n",
    "            'ULB': 174.5/1000,\n",
    "            'LLB': 300.0/1000,\n",
    "            'URB': 174.5/1000,\n",
    "            'LRB': 295.5/1000}\n",
    "\n",
    "''' ****** '''\n",
    "inertias_ = {'body': 32124078.52*1e-9, # body rotate about its center \n",
    "             'boom_X': 1947254622.19*1e-9,\n",
    "             'boom_Y': 1947254622.19*1e-9,\n",
    "             'ULA': 600933.68*1e-9, # rotates as pendulumn\n",
    "             'LLA': 4659658.63*1e-9,\n",
    "             'URA': 600933.68*1e-9,\n",
    "             'LRA': 7369968.50*1e-9,\n",
    "             'ULB': 600933.68*1e-9, # rotates as pendulumn \n",
    "             'LLB': 4659658.63*1e-9,\n",
    "             'URB': 600933.68*1e-9,\n",
    "             'LRB': 7369968.50*1e-9}\n",
    "\n",
    "all_links = []\n",
    "all_links_inertia = []\n",
    "leg_links = []\n",
    "[leg_links.append(link+leg) for leg in legs for link in links]\n",
    "[all_links.append(base) for base in bases]\n",
    "[all_links.append(link+leg) for leg in legs for link in links]\n",
    "all_links_inertia.append('body')\n",
    "all_links_inertia.append('boom_X')\n",
    "all_links_inertia.append('boom_Y')\n",
    "[all_links_inertia.append(link+leg) for leg in legs for link in links]\n",
    "\n",
    "\n",
    "total_mass = 0.0\n",
    "for mass in masses_:\n",
    "    total_mass += masses_[mass]\n",
    "total_mass\n",
    "\n",
    "WDOFs = ['X','Y'] \n",
    "signs = ['-ve','+ve'] \n",
    "ground_constraints = ['contact','friction','slip_+ve','slip_-ve'] # Group all constraints occuring at the ground\n",
    "sides = ['left','right']\n",
    "coordinates = {'X':0 , 'Y':1}\n",
    "motor_w_angle = {'left': 'UL' ,'right': 'UR'}\n",
    "\n",
    "def get_var_list_zeros(m,n):\n",
    "    var_list = [m.g] + [m.BLD] + [m.foot_angle] + [m.foot_length] +\\\n",
    "               [m.cg] + [m.lx_boom] + [m.ly_boom] +\\\n",
    "               [m.y_boomOffset] + [m.y_bodyOffset] + [m.x_footOffset] +\\\n",
    "               [m.COMR[link+leg] for leg in legs for link in links]+\\\n",
    "               [m.masses[base] for base in bases] +\\\n",
    "               [m.masses[link+leg] for leg in legs for link in links] +\\\n",
    "               [m.lengths[base] for base in bases] +\\\n",
    "               [m.lengths[link+leg] for leg in legs for link in links] +\\\n",
    "               [m.inertias['body']] + [m.inertias['boom_X']] + [m.inertias['boom_Y']] +\\\n",
    "               [m.inertias[link+leg] for leg in legs for link in links] +\\\n",
    "               [m.q[n,dof] for dof in DOFs] +\\\n",
    "               [m.dq[n,dof] for dof in DOFs] +\\\n",
    "               [m.ddq[n,dof] for dof in DOFs] +\\\n",
    "               [0 for leg in legs] + [0 for leg in legs] +\\\n",
    "               [0 for leg in legs] + [0 for leg in legs] +\\\n",
    "               [0 for leg in legs] + [0 for leg in legs]\n",
    "    return var_list\n",
    "\n",
    "def get_var_list_values(m,n):\n",
    "    \n",
    "    BW = 1\n",
    "    tau_value = {}\n",
    "    connection_force_value = {}\n",
    "    GRF_X_value = {}\n",
    "    GRF_Y_value = {}\n",
    "    \n",
    "    for leg in legs:\n",
    "        \n",
    "        [tau_value.update({(side,leg): BW*(m.tau_torque[n,side,leg])}) for side in sides]\n",
    "        #[connection_force_value.update({(wdof,leg):BW*(m.connect_force[n,'+ve',wdof,leg]-m.connect_force[n,'-ve',wdof,leg])}) for wdof in WDOFs]\n",
    "        #[connection_force_value.update({(wdof,leg):0.0})for wdof in WDOFs]\n",
    "        [connection_force_value.update({(wdof,leg):BW*(m.connect_force[n,wdof,leg])}) for wdof in WDOFs]\n",
    "        GRF_X_value.update({(leg):BW*(m.GRF_x[n,'+ve',leg]-m.GRF_x[n,'-ve',leg])})\n",
    "        GRF_Y_value.update({(leg):BW*(m.GRF_y[n,leg])})\n",
    "\n",
    "    var_list = [m.g] + [m.BLD] + [m.foot_angle] + [m.foot_length] +\\\n",
    "               [m.cg] + [m.lx_boom] + [m.ly_boom] +\\\n",
    "               [m.y_boomOffset] + [m.y_bodyOffset] + [m.x_footOffset] +\\\n",
    "               [m.COMR[link+leg] for leg in legs for link in links]+\\\n",
    "               [m.masses[base] for base in bases] +\\\n",
    "               [m.masses[link+leg] for leg in legs for link in links] +\\\n",
    "               [m.lengths[base] for base in bases] +\\\n",
    "               [m.lengths[link+leg] for leg in legs for link in links] +\\\n",
    "               [m.inertias['body']] + [m.inertias['boom_X']] + [m.inertias['boom_Y']] +\\\n",
    "               [m.inertias[link+leg] for leg in legs for link in links] +\\\n",
    "               [m.q[n,dof] for dof in DOFs] +\\\n",
    "               [m.dq[n,dof] for dof in DOFs] +\\\n",
    "               [m.ddq[n,dof] for dof in DOFs] +\\\n",
    "               [tau_value['left',leg] for leg in legs] + [tau_value['right',leg] for leg in legs] +\\\n",
    "               [connection_force_value['X',leg] for leg in legs] + [connection_force_value['Y',leg] for leg in legs] +\\\n",
    "               [GRF_X_value[leg] for leg in legs] + [GRF_Y_value[leg] for leg in legs]\n",
    "    return var_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7648c860",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------\n",
      "Stall torque =  140.4238410596027\n",
      "Max speed =  55.8\n",
      "--------------\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plot Torque Speed Curve'''\n",
    "import matplotlib.pyplot as plt\n",
    "x_p = np.linspace(0, 54, num=54)\n",
    "x_n = np.linspace(-54, 0, num=54)\n",
    "no_load_speed = 55.8\n",
    "# from graph\n",
    "max_torque = 38.0\n",
    "speed_max_torque = 40.7\n",
    "\n",
    "m_grad = (max_torque-0.0)/(speed_max_torque-no_load_speed)\n",
    "\n",
    "stall_torque = 0.0 - m_grad*no_load_speed\n",
    "\n",
    "y_p = m_grad*x_p+stall_torque\n",
    "y_n = m_grad*x_n-stall_torque\n",
    "\n",
    "stall_torque_p = []\n",
    "stall_torque_n = []\n",
    "rated_torque_p = []\n",
    "rated_torque_n = []\n",
    "\n",
    "for ii in range(len(x_p)):\n",
    "    stall_torque_p.append(stall_torque)\n",
    "    stall_torque_n.append(-stall_torque)\n",
    "    rated_torque_p.append(max_torque)\n",
    "    rated_torque_n.append(-max_torque)\n",
    "\n",
    "plt.grid()\n",
    "plt.plot(x_p, y_p,'b')\n",
    "plt.plot(x_n, stall_torque_p,'b')\n",
    "\n",
    "plt.fill_between(x_p, y_p, alpha=0.4, color = 'cyan')\n",
    "plt.fill_between(x_n, stall_torque_p, alpha=0.4,color = 'cyan')\n",
    "\n",
    "plt.plot(x_n, y_n,'r')\n",
    "plt.plot(x_p, stall_torque_n,'r')\n",
    "\n",
    "plt.fill_between(x_n, y_n, alpha=0.4,color = 'pink')\n",
    "plt.fill_between(x_p, stall_torque_n, alpha=0.4,color = 'pink')\n",
    "\n",
    "plt.axis((-60,60,-160,160))\n",
    "plt.title('New Torque Speed Curve')\n",
    "plt.xlabel('Speed (rad/s)')\n",
    "plt.ylabel('Torque (Nm)')\n",
    "print('--------------')\n",
    "\n",
    "print('Stall torque = ',stall_torque)\n",
    "print('Max speed = ',no_load_speed)\n",
    "\n",
    "print('--------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1a4acc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "tests = {0:'High_Drop', 1:'Low_Drop', 2:'Hop',3:'Drop_Hop', 4:'Sprint', 5:'Start_Periodic_Walk',\\\n",
    "         6:'Periodic_Walk', 7:'Periodic_Walk_Stop'}\n",
    "\n",
    "test = tests[6]\n",
    "#V_avg = 0.5\n",
    "mu_value = 1\n",
    "# V_avg = 0.8\n",
    "\n",
    "body_height = 0.42\n",
    "#body height originally 0.38 for test attempts <=9\n",
    "#height changed to 45 for test10\n",
    "#height 42 for test 11\n",
    "stride_length = 0.4\n",
    "speed_factor = 0.4\n",
    "V_avg = speed_factor*sqrt(body_height*9.81)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "if test == 'High_Drop' or test == 'Low_Drop':\n",
    "    N = 50\n",
    "    TT = 0.2\n",
    "elif test == 'Hop' or test == 'Drop_Hop':\n",
    "    N = 50\n",
    "    TT = 1.0\n",
    "elif test == 'Sprint':\n",
    "    N = 100\n",
    "    TT = 1.5\n",
    "    x_final = 5.0\n",
    "elif test == 'Start_Periodic_Walk':\n",
    "    N = 50\n",
    "    TT = 1.0\n",
    "    \n",
    "#     xfinal/V_avg = TT\n",
    "elif test == 'Periodic_Walk':\n",
    "    N = 100\n",
    "#     x_final = stride_length\n",
    "    TT = 0.5\n",
    "elif test =='Periodic_Walk_Stop':\n",
    "    N = 50\n",
    "    TT = 0.3\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea59b71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e032aa7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: DEPRECATED: The default domain for Param objects is 'Any'.  However,\n",
      "    we will be changing that default to 'Reals' in the future.  If you really\n",
      "    intend the domain of this Param (lengths) to be 'Any', you can suppress\n",
      "    this warning by explicitly specifying 'within=Any' to the Param\n",
      "    constructor.  (deprecated in 5.6.9, will be removed in 6.0) (called from\n",
      "    /home/mark/miniconda3/lib/python3.9/site-\n",
      "    packages/pyomo/core/base/param.py:77)\n"
     ]
    }
   ],
   "source": [
    "m = ConcreteModel('Biped')\n",
    "\n",
    "#SETS-------------------------\n",
    "m.N = RangeSet(N)\n",
    "m.ground_constraints = Set(initialize = ground_constraints)\n",
    "m.legs = Set(initialize = legs)\n",
    "m.links = Set(initialize = links)\n",
    "m.bases = Set(initialize = bases)\n",
    "m.all_links = Set(initialize = all_links)\n",
    "m.leg_links = Set(initialize = leg_links)\n",
    "m.all_links_inertia = Set(initialize = all_links_inertia)\n",
    "m.DOFs = Set(initialize = DOFs)\n",
    "m.WDOFs = Set(initialize = WDOFs)\n",
    "m.signs = Set(initialize = signs)\n",
    "m.sides = Set(initialize = sides)\n",
    "\n",
    "#Params--------------------------\n",
    "m.g = Param(initialize = 9.81)\n",
    "m.BLD = Param(initialize = 60/1000)\n",
    "m.foot_angle = Param(initialize = 135*(np.pi/180))\n",
    "m.foot_length = Param(initialize = 42.5/1000)\n",
    "m.cg = Param(initialize = 1.149)\n",
    "m.lx_boom = Param(initialize = 2.575)\n",
    "m.ly_boom = Param(initialize = 2.493)\n",
    "m.y_boomOffset = Param(initialize = 0.101)\n",
    "m.y_bodyOffset = Param(initialize = 8.36/1000)\n",
    "m.x_footOffset = Param(initialize = 1.63/1000)\n",
    "\n",
    "m.COMR = Param(m.leg_links, initialize = COMR_)\n",
    "m.masses = Param(m.all_links, initialize = masses_)\n",
    "m.lengths = Param(m.all_links, initialize = lengths_)\n",
    "m.inertias = Param(m.all_links_inertia, initialize = inertias_)\n",
    "\n",
    "\n",
    "#VARIABLES----------------------\n",
    "#gen coordinates\n",
    "m.q = Var(m.N,m.DOFs)\n",
    "m.dq = Var(m.N,m.DOFs)\n",
    "m.ddq = Var(m.N,m.DOFs)\n",
    "\n",
    "''' CONSTRAINTS  - Integration'''\n",
    "# variable timestep\n",
    "hm = TT/N # master timestep\n",
    "m.h = Var(m.N, bounds = (0.8,1.2)) #m.h = Var(m.N, bounds = (0.5,1.5))\n",
    "\n",
    "def BwEuler_p(m,n,dof):\n",
    "    if n > 1:\n",
    "        return m.q[n,dof] == m.q[n-1,dof] + hm*m.h[n]*m.dq[n,dof]\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "m.integrate_p = Constraint(m.N, m.DOFs, rule = BwEuler_p)\n",
    "\n",
    "def BwEuler_v(m,n,dof):\n",
    "    if n > 1:\n",
    "        return m.dq[n,dof] == m.dq[n-1,dof] + hm*m.h[n]*m.ddq[n,dof]\n",
    "    else:\n",
    "        return Constraint.Skip \n",
    "m.integrate_v = Constraint(m.N, m.DOFs, rule = BwEuler_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a4845a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "''' CONSTRAINTS  - CONNECTION POINT'''\n",
    "m.connect_position = Var(m.N, m.WDOFs, m.sides, m.legs) # connection point\n",
    "m.connect_force = Var(m.N, m.WDOFs, m.legs,bounds = (-10*total_mass*9.81,10*total_mass*9.81)) # connection force\n",
    "\n",
    "# Lamdify\n",
    "lamb_connect_position = {} # (WDOFs,sides,leg)\n",
    "[lamb_connect_position.update({(wdof,side,leg): sym.lambdify(sym_list,connect_position[side,leg][coordinates[wdof]],modules = [func_map])}) for leg in legs for side in sides for wdof in WDOFs]\n",
    "\n",
    "# Equate the connection equations from the previous script to the model m\n",
    "def def_connect_position_equation(m,n,wdof,side,leg):\n",
    "    var_list = get_var_list_zeros(m,n)    \n",
    "    return m.connect_position[n,wdof,side,leg] == lamb_connect_position[wdof,side,leg](*var_list)\n",
    "m.connect_position_equation = Constraint(m.N, m.WDOFs, m.sides, m.legs, rule = def_connect_position_equation)\n",
    "\n",
    "# Ensure that there is no distance between the X (and Y) of link connection points\n",
    "def def_connect_position(m,n,wdof,leg):\n",
    "    return  m.connect_position[n,wdof,'right',leg] - m.connect_position[n,wdof,'left',leg] == 0.0\n",
    "m.connect_position_constraint = Constraint(m.N, m.WDOFs, m.legs, rule= def_connect_position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d2ac6d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variable of foot position\n",
    "m.foot_position = Var(m.N, m.WDOFs, m.legs)\n",
    "for n in range(1,N+1):\n",
    "    for leg in legs:\n",
    "        m.foot_position[n,'Y',leg].setlb(0.0)\n",
    "\n",
    "#Get the foot position in WDOF\n",
    "lamb_foot_position = {}\n",
    "[lamb_foot_position.update({(wdof,leg): sym.lambdify(sym_list,foot_position['right',leg][coordinates[wdof]],modules = [func_map])}) for leg in legs for wdof in WDOFs]\n",
    "\n",
    "def def_foot_position(m,n,wdof,leg):\n",
    "    var_list = get_var_list_zeros(m,n)\n",
    "    return m.foot_position[n,wdof,leg] == lamb_foot_position[wdof,leg](*var_list)\n",
    "m.def_foot_position = Constraint(m.N, m.WDOFs, m.legs, rule = def_foot_position)\n",
    "\n",
    "m.mu = Param(initialize = mu_value,within=Any)\n",
    "\n",
    "# Variables\n",
    "m.foot_velocity = Var(m.N, m.WDOFs, m.signs, m.legs, bounds = (0.0,None)) # the sign will account for -ve velocities\n",
    "m.friction_cone = Var(m.N, m.legs, bounds = (0.0,None)) \n",
    "m.GRF_y = Var(m.N, m.legs, bounds = (0.0,10*total_mass*m.g.value)) # ground reaction forces\n",
    "m.GRF_x = Var(m.N, m.signs, m.legs, bounds = (0.0,10*total_mass*m.g.value)) # ground reaction forces\n",
    "\n",
    "'''******'''\n",
    "m.ground_penalty = Var(m.N, m.ground_constraints, m.legs, bounds = (0.0,10.0)) # penalty\n",
    "\n",
    "lamb_foot_velocity_x = {}\n",
    "\n",
    "# Get the foot position in WDOF\n",
    "[lamb_foot_velocity_x.update({(leg): sym.lambdify(sym_list,(foot_position['right',leg].jacobian(q)*dq)[0].simplify(),modules = [func_map])}) for leg in legs]\n",
    "\n",
    "# CONSTRAINTS\n",
    "def def_foot_velocity(m,n,wdof,leg):\n",
    "    if wdof == 'X':\n",
    "        var_list = get_var_list_zeros(m,n)\n",
    "        return m.foot_velocity[n,'X','+ve',leg]-m.foot_velocity[n,'X','-ve',leg] == lamb_foot_velocity_x[leg](*var_list)\n",
    "    else: return Constraint.Skip\n",
    "m.foot_velocity_constraint = Constraint(m.N, m.WDOFs, m.legs, rule = def_foot_velocity)\n",
    "\n",
    "def def_friction_cone(m,n,leg):\n",
    "    return m.friction_cone[n,leg] == m.mu*m.GRF_y[n,leg] - (m.GRF_x[n,'+ve',leg] + m.GRF_x[n,'-ve',leg])\n",
    "m.friction_cone_constraint = Constraint(m.N, m.legs, rule = def_friction_cone)\n",
    "\n",
    "# constraints: complementarity\n",
    "\n",
    "# contact\n",
    "def def_ground_contact(m,n,leg):\n",
    "    if n < N:\n",
    "        return m.foot_position[n+1,'Y',leg]*m.GRF_y[n,leg] <= m.ground_penalty[n,'contact',leg]\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "m.ground_contact_constraint = Constraint(m.N, m.legs, rule = def_ground_contact)\n",
    "\n",
    "# friction\n",
    "def def_ground_friction(m,n,leg):\n",
    "    return (m.foot_velocity[n,'X','+ve',leg]+m.foot_velocity[n,'X','-ve',leg])*m.friction_cone[n,leg] <= m.ground_penalty[n,'friction',leg]\n",
    "m.ground_friction_constraint = Constraint(m.N, m.legs, rule = def_ground_friction)\n",
    "\n",
    "# slipping\n",
    "def def_ground_slip_positive(m,n,leg):\n",
    "    return m.foot_velocity[n,'X','+ve',leg]*m.GRF_x[n,'+ve',leg] <= m.ground_penalty[n,'slip_+ve',leg]\n",
    "m.ground_slip_positive_constraint = Constraint(m.N, m.legs, rule = def_ground_slip_positive)\n",
    "\n",
    "def def_ground_slip_negative(m,n,leg):\n",
    "    return m.foot_velocity[n,'X','-ve',leg]*m.GRF_x[n,'-ve',leg] <= m.ground_penalty[n,'slip_-ve',leg]\n",
    "m.ground_slip_negative_constraint = Constraint(m.N, m.legs, rule = def_ground_slip_negative)\n",
    "\n",
    "#bound contact forces at last node\n",
    "for leg in legs:\n",
    "    m.GRF_y[N,leg].fix(0.0)\n",
    "    for sgn in signs:\n",
    "        m.GRF_x[N,sgn,leg].fix(0.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "912e52e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Torque Speed - Speed Torque Curve'''\n",
    "\n",
    "m.tau_torque = Var(m.N, m.sides, m.legs, bounds = (-max_torque,max_torque))\n",
    "m.tau_velocity = Var(m.N, m.sides, m.legs, bounds = (-no_load_speed,no_load_speed))\n",
    "\n",
    "lamb_tau_velocity = {}\n",
    "[lamb_tau_velocity.update({(side,leg): sym.lambdify(sym_list,w_links[motor_w_angle[side],leg],modules = [func_map])}) for leg in legs for side in sides]\n",
    "\n",
    "def def_angular_velocity(m,n,side,leg):\n",
    "    var_list = get_var_list_zeros(m,n)\n",
    "    return m.tau_velocity[n,side,leg] == lamb_tau_velocity[side,leg](*var_list)\n",
    "m.left_angular_velocity_constraint = Constraint(m.N, m.sides, m.legs, rule = def_angular_velocity)\n",
    "\n",
    "# assume straight line\n",
    "def def_TW_right(m,n,side,leg):\n",
    "    return m.tau_torque[n,side,leg] <= m.tau_velocity[n,side,leg]*m_grad + stall_torque\n",
    "m.TW_right_constraint = Constraint(m.N, m.sides, m.legs, rule = def_TW_right)\n",
    "\n",
    "def def_TW_left(m,n,side,leg):\n",
    "    return m.tau_torque[n,side,leg] >= m.tau_velocity[n,side,leg]*m_grad - stall_torque\n",
    "m.TW_left_constraint = Constraint(m.N, m.sides, m.legs, rule = def_TW_left)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "f25f28ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def def_foot_on_floor(m,n):\n",
    "#     return m.foot_position[n,'Y','A'] <= 0 or m.foot_position[n,'Y','B'] <= 0\n",
    "# m.foot_on_floor = Constraint(m.N, rule = def_foot_on_floor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f3893956",
   "metadata": {},
   "outputs": [],
   "source": [
    "def def_max_change_torque(m,n,side,leg):\n",
    "    if n<(N-1):\n",
    "        return abs(m.tau_torque[n+1,side,leg] - m.tau_torque[n,side,leg]) <= 5 \n",
    "    else:\n",
    "        return abs(m.tau_torque[n,side,leg] - m.tau_torque[n-1,side,leg]) <= 5\n",
    "m.max_change_torque = Constraint(m.N, m.sides, m.legs, rule = def_max_change_torque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "77f9b021",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def def_max_change_footpos(m,n,leg):\n",
    "#     if n<(N-1):\n",
    "#         return abs(m.foot_position[n+1,'Y',leg] - m.foot_position[n,'Y',leg]) <= 0.01 \n",
    "#     else:\n",
    "#         return abs(m.foot_position[n,'Y',leg] - m.foot_position[n-1,'Y',leg]) <= 0.01\n",
    "# m.max_change_footpos = Constraint(m.N, m.legs, rule = def_max_change_footpos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d7a938a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the angles for the knees - only use this when actuated \n",
    "# This ensures links does not cross\n",
    "m.knee_angle = Var(m.N, m.sides, m.legs, bounds = (0.0,130*(np.pi)/180))\n",
    "\n",
    "def def_knee_angle(m,n,side,leg):\n",
    "    if side == 'left':\n",
    "        return m.knee_angle[n,side,leg] == 2*np.pi - m.q[n,'theta_LL'+leg]\n",
    "    else:\n",
    "        return m.knee_angle[n,side,leg] == m.q[n,'theta_LR'+leg]   \n",
    "m.knee_angle_constraint = Constraint(m.N, m.sides, m.legs, rule = def_knee_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1a779464",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the foot position in WDOF\n",
    "if test == \"Periodic_Walk\" or test == \"Periodic_Hop\":\n",
    "    \n",
    "    lamb_body_position_x = sym.lambdify(sym_list,(r_bases['body'])[0].simplify(),modules = [func_map])\n",
    "    m.body_position = Var(m.N, m.WDOFs) # the sign will account for -ve velocities\n",
    "\n",
    "    # CONSTRAINTS\n",
    "    def def_body_position(m,n,wdof):\n",
    "        var_list = get_var_list_zeros(m,n)\n",
    "        if wdof == 'X':\n",
    "            return m.body_position[n,'X'] == lamb_body_position_x(*var_list)\n",
    "        else: return Constraint.Skip\n",
    "    m.body_position_constraint = Constraint(m.N, m.WDOFs, rule = def_body_position)\n",
    "    \n",
    "    \n",
    "    if test == \"Periodic_Walk\":\n",
    "        \n",
    "#         def def_hold_foot(m,n):\n",
    "#             return m.foot_position[1,'X','B'] - m.foot_position[N,'X','B'] == 0\n",
    "#         m.def_hold_foot_constraint = Constraint(m.N, rule = def_hold_foot)\n",
    "#         def def_foot_velocity(m,n,wdof,leg):\n",
    "#             if wdof == 'Y':\n",
    "#                 return m.foot_velocity[1,'Y','+ve',l]-m.foot_velocity[n,'Y','-ve',leg] == 0\n",
    "#             else: return Constraint.Skip\n",
    "#         m.foot_velocity_constraint = Constraint(m.N, m.WDOFs, m.legs, rule = def_foot_velocity)\n",
    "    \n",
    "    \n",
    "        # Average velocities\n",
    "        def def_body_position_average(m,wdof):\n",
    "            if wdof == 'X':\n",
    "                return (m.body_position[N,'X'] - m.body_position[1,'X'])/(sum(hm*m.h[n] for n in range(1,N+1))) == V_avg\n",
    "            else: return Constraint.Skip\n",
    "        m.def_body_position_average_constraint = Constraint(m.WDOFs, rule = def_body_position_average)\n",
    "        \n",
    "        # Equate states\n",
    "        def def_equate_position_states(m,n,dof_i):\n",
    "            \n",
    "            if dof_i == 'y_body':\n",
    "                return m.q[1,dof_i] == m.q[N,dof_i]\n",
    "            \n",
    "            elif dof_i == 'theta_ULA':\n",
    "                return m.q[1,'theta_ULA'] == m.q[N,'theta_ULB']\n",
    "            elif dof_i == 'theta_ULB':\n",
    "                return m.q[1,'theta_ULB'] == m.q[N,'theta_ULA']\n",
    "            \n",
    "            elif dof_i == 'theta_URA':\n",
    "                return m.q[1,'theta_URA'] == m.q[N,'theta_URB']\n",
    "            elif dof_i == 'theta_URB':\n",
    "                return m.q[1,'theta_URB'] == m.q[N,'theta_URA']\n",
    "            \n",
    "            elif dof_i == 'theta_LLA':\n",
    "                return m.q[1,'theta_LLA'] == m.q[N,'theta_LLB']\n",
    "            elif dof_i == 'theta_LLB':\n",
    "                return m.q[1,'theta_LLB'] == m.q[N,'theta_LLA']\n",
    "            \n",
    "            elif dof_i == 'theta_LRA':\n",
    "                return m.q[1,'theta_LRA'] == m.q[N,'theta_LRB']\n",
    "\n",
    "#             elif dof_i == 'theta_ULA':\n",
    "#                 return m.q[1,'theta_ULA'] == m.q[N,'theta_ULA']\n",
    "#             elif dof_i == 'theta_ULB':\n",
    "#                 return m.q[1,'theta_ULB'] == m.q[N,'theta_ULB']\n",
    "            \n",
    "#             elif dof_i == 'theta_URA':\n",
    "#                 return m.q[1,'theta_URA'] == m.q[N,'theta_URA']\n",
    "#             elif dof_i == 'theta_URB':\n",
    "#                 return m.q[1,'theta_URB'] == m.q[N,'theta_URB']\n",
    "            \n",
    "#             elif dof_i == 'theta_LLA':\n",
    "#                 return m.q[1,'theta_LLA'] == m.q[N,'theta_LLA']\n",
    "#             elif dof_i == 'theta_LLB':\n",
    "#                 return m.q[1,'theta_LLB'] == m.q[N,'theta_LLB']\n",
    "            \n",
    "#             elif dof_i == 'theta_LRA':\n",
    "#                 return m.q[1,'theta_LRA'] == m.q[N,'theta_LRA']\n",
    "            \n",
    "#             elif dof_i == 'theta_LRB':\n",
    "#                 return m.q[1,'theta_LRB'] == m.q[N,'theta_LRB']\n",
    "            else: \n",
    "                 return Constraint.Skip\n",
    "        m.def_equate_position_states_constraint = Constraint(m.N, m.DOFs, rule = def_equate_position_states) \n",
    "\n",
    "        \n",
    "        def def_equate_vel_states(m,n,dof_i):\n",
    "            return m.dq[1,dof_i] == m.dq[N,dof_i]\n",
    "        m.def_equate_vel_states_constraint = Constraint(m.N, m.DOFs,rule = def_equate_vel_states)\n",
    "  \n",
    "    \n",
    "        # Condition where the GRF can't increase twice from zero          \n",
    "    \n",
    "    \n",
    "    else: \n",
    "        \n",
    "        def def_equate_position_states(m,n,dof_i):\n",
    "            return m.q[1,dof_i] == m.q[N,dof_i]\n",
    "        m.def_equate_position_states_constraint = Constraint(m.N, m.DOFs,rule = def_equate_position_states)\n",
    "        \n",
    "        def def_equate_vel_states(m,n,dof_i):\n",
    "            return m.dq[1,dof_i] == m.dq[N,dof_i]\n",
    "        m.def_equate_vel_states_constraint = Constraint(m.N, m.DOFs,rule = def_equate_vel_states)\n",
    "\n",
    "else: pass\n",
    "                                    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "58a09f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test code\n",
    "\n",
    "sign_symbols = ['+','-']\n",
    "boundaries = ['setub','setlb']\n",
    "\n",
    "for n in range(1,N+1):\n",
    "    for leg in legs:\n",
    "        for index in range(len(boundaries)):\n",
    "            eval('m.q[n,\\'theta_UL\\'+leg].{0}(np.pi*(0.5{1}1/1.5))'.format(boundaries[index],sign_symbols[index]))\n",
    "            eval('m.q[n,\\'theta_LL\\'+leg].{0}(np.pi*(1{1}1/2))'.format(boundaries[index],sign_symbols[index]))  \n",
    "            eval('m.q[n,\\'theta_UR\\'+leg].{0}(np.pi*(0.5{1}1/1.5))'.format(boundaries[index],sign_symbols[index]))    \n",
    "            eval('m.q[n,\\'theta_LR\\'+leg].{0}(np.pi*(1{1}1/2))'.format(boundaries[index],sign_symbols[index]))\n",
    "\n",
    "\n",
    "# for n in range(1,N+1):    \n",
    "    if test == 'High_Drop' or test == 'Low_Drop' or test == 'Drop_Hop' or test == 'Hop':\n",
    "        x_low_lim = -1.0\n",
    "        x_high_lim = 1.0\n",
    "        y_low_lim = -0.5\n",
    "        y_high_lim = 2.0\n",
    "        \n",
    "        if test == 'Drop_Hop' or test == 'Hop':\n",
    "            [m.tau_torque[1,side,leg].fix(0.0) for side in sides for leg in legs]\n",
    "        else:\n",
    "            [m.tau_torque[n,side,leg].fix(0.0) for side in sides for leg in legs]\n",
    "            \n",
    "    else:\n",
    "        x_low_lim = -1.0\n",
    "        x_high_lim = 2.0\n",
    "        y_low_lim = -0.5\n",
    "        y_high_lim = 1.5\n",
    "        \n",
    "        if test == 'Sprint' or test == 'Periodic_Walk' or test == 'Periodic_Walk_Stop' or test == 'Start_Periodic_Walk':\n",
    "            pass\n",
    "        else:\n",
    "            [m.tau_torque[1,side,leg].fix(0.0) for side in sides for leg in legs]\n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "372f7e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "if test == 'High_Drop':\n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "    m.q[1,'y_body'].fix(1.0)\n",
    "\n",
    "\n",
    "    m.q[1,'theta_ULA'].fix(np.pi*(0.5-1/7))\n",
    "    m.q[1,'theta_URA'].fix(np.pi*(0.5+1/7))\n",
    "\n",
    "    m.q[1,'theta_ULB'].fix(np.pi*(0.5-1/10))\n",
    "    m.q[1,'theta_URB'].fix(np.pi*(0.5+1/10))\n",
    "    \n",
    "elif test == 'Low_Drop':\n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "    m.q[1,'y_body'].fix(1.0)\n",
    "    \n",
    "    m.q[1,'theta_ULA'].fix(np.pi*(0.5-1/7))\n",
    "    m.q[1,'theta_URA'].fix(np.pi*(0.5+1/7))\n",
    "\n",
    "    m.q[1,'theta_ULB'].fix(np.pi*(0.5-1/10))\n",
    "    m.q[1,'theta_URB'].fix(np.pi*(0.5+1/10))\n",
    "    \n",
    "elif test == 'Hop':\n",
    "    m.q[1,'y_body'].fix(0.35)\n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "\n",
    "    m.foot_position[1, 'Y', 'A'].fix(0.0)\n",
    "    m.foot_position[1, 'Y', 'B'].fix(0.0)\n",
    "    m.foot_position[1, 'X', 'A'].fix(0.0)\n",
    "    m.foot_position[1, 'X', 'B'].fix(0.0)\n",
    "        \n",
    "    m.q[N,'y_body'].setlb(0.8)\n",
    "    \n",
    "elif test == 'Drop_Hop':\n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "    m.q[1,'y_body'].fix(1.0)\n",
    "\n",
    "    \n",
    "#     m.q[1,'theta_ullA'].fix(np.pi*(0.5-1/7))\n",
    "#     m.q[1,'theta_urlA'].fix(np.pi*(0.5+1/7))\n",
    "\n",
    "#     m.q[1,'theta_ullB'].fix(np.pi*(0.5-1/7))\n",
    "#     m.q[1,'theta_urlB'].fix(np.pi*(0.5+1/7))\n",
    "        \n",
    "    m.foot_position[round(N/5), 'Y', 'A'].setub(0.05)\n",
    "    m.foot_position[round(N/5), 'Y', 'B'].setub(0.05)\n",
    "        \n",
    "    m.q[N,'y_body'].setlb(0.8)\n",
    "    m.q[N,'x_body'].setub(0.7)\n",
    "    m.q[N,'x_body'].setlb(0.3)\n",
    "    \n",
    "elif test == 'Sprint':\n",
    "    guide = np.linspace(0,x_final,N)\n",
    "    for n in range(1,N+1):\n",
    "        m.GRF_y[n,'A'].value = np.random.uniform(0.0,1)\n",
    "        m.GRF_x[n,'+ve','A'].value = m.mu.value*m.GRF_y[n,'A'].value\n",
    "        m.GRF_y[n,'B'].value = np.random.uniform(0.0,1)\n",
    "        m.GRF_x[n,'+ve','B'].value = m.mu.value*m.GRF_y[n,'B'].value\n",
    "        \n",
    "        m.q[n,'y_body'].setub(0.6)\n",
    "        m.q[n,'x_body'].setlb(0.0)\n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "\n",
    "    \n",
    "    \n",
    "    m.q[1,'theta_ULA'].fix(np.pi*(0.5))\n",
    "    m.q[1,'theta_URA'].fix(np.pi*(0.5))\n",
    "    m.q[1,'theta_ULB'].fix(np.pi*(0.5))\n",
    "    m.q[1,'theta_URB'].fix(np.pi*(0.5))\n",
    "        \n",
    "    m.foot_position[1, 'Y', 'A'].fix(0.0)\n",
    "    m.foot_position[1, 'Y', 'B'].fix(0.0)\n",
    "\n",
    "    # final condition\n",
    "    m.q[N,'x_body'].setlb(x_final)\n",
    "\n",
    "    m.dq[N,'x_body'].fix(0.0)\n",
    "\n",
    "    \n",
    "    m.dq[N,'theta_ULA'].fix(0.0)\n",
    "    m.dq[N,'theta_URA'].fix(0.0)\n",
    "    m.dq[N,'theta_ULB'].fix(0.0)\n",
    "    m.dq[N,'theta_URB'].fix(0.0)\n",
    "\n",
    "\n",
    "\n",
    "    m.foot_position[N, 'Y', 'A'].fix(0.0)\n",
    "    m.foot_position[N, 'Y', 'B'].fix(0.0)\n",
    "    \n",
    "    \n",
    "elif test == 'Start_Periodic_Walk':\n",
    "    #init conditions\n",
    "    #match mid walk node1 conditions\n",
    "    with open('periodic_walk_N1_data_v10.pkl','rb') as f:\n",
    "        start_data = pickle.load(f)\n",
    "    \n",
    "#     guide = np.linspace(0,TT*V_avg,N)\n",
    "    for n in range(1,N+1):\n",
    "        m.q[n,'y_body'].setub(0.5)\n",
    "        m.q[n,'x_body'].setub(1.0)\n",
    "        m.q[n,'x_body'].setlb(0.0)\n",
    "#         m.foot_position[n,'X','A'].setlb(0.0)\n",
    "#         m.foot_position[n,'X','B'].setlb(0.0)\n",
    "#         m.foot_position[n,'Y','A'].setub(0.06)\n",
    "#         m.foot_position[n,'Y','B'].setub(0.06)\n",
    "\n",
    "#         m.q[n,'x_body'].setub(1.0)\n",
    "#         m.q[n,'x_body'].value = guide[n-1]\n",
    "        \n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "\n",
    "    \n",
    "    m.foot_position[1,'X','A'].fix(0.0)\n",
    "    m.foot_position[1,'X','B'].fix(0.0)\n",
    "    m.foot_position[1,'Y','A'].fix(0.0)\n",
    "    m.foot_position[1,'Y','B'].fix(0.0)\n",
    "    \n",
    "#     m.foot_position[N/2,'Y','A'].fix(0.0)\n",
    "#     m.foot_position[N/2,'Y','B'].fix(0.0)\n",
    "    \n",
    "#     m.foot_position[N,'Y','B'].fix(0.0)\n",
    "    \n",
    "#     for n in range(1,N+1):\n",
    "#         if n<(N/2):\n",
    "# #             m.foot_position[n,'Y','B'].fix(0.0)\n",
    "# #             m.foot_position[n,'X','B'].fix(0.0)\n",
    "# #             m.GRF_y[n,'B'].fix(0.0)\n",
    "#             m.GRF_y[n,'A'].setlb(0.1)\n",
    "# # #             m.foot_position[n,'X','A'].fix(0.0)\n",
    "    \n",
    "#             if n>1:\n",
    "#                 m.GRF_y[n,'B'].fix(0.0)\n",
    "\n",
    "#         elif n>(N/2):\n",
    "#             m.GRF_y[n,'A'].fix(0.0)\n",
    "#             m.foot_position[n,'Y','B'].fix(0.0)\n",
    "#             if n>(N-10):\n",
    "#                 m.foot_position[n,'Y','A'].setlb(0.02)\n",
    "\n",
    "    \n",
    "    m.q[N,'y_body'].fix(start_data['ybod'])\n",
    "    m.dq[N,'y_body'].fix(start_data['dybod'])\n",
    "    m.dq[N,'x_body'].fix(start_data['dxbod'])\n",
    "    \n",
    "    m.q[N,'theta_ULA'].fix(start_data['thULA'])\n",
    "    m.q[N,'theta_LLA'].fix(start_data['thLLA'])\n",
    "    m.q[N,'theta_URA'].fix(start_data['thURA'])\n",
    "    m.q[N,'theta_LRA'].fix(start_data['thLRA'])\n",
    "    \n",
    "    m.q[N,'theta_ULB'].fix(start_data['thULB'])\n",
    "    m.q[N,'theta_LLB'].fix(start_data['thLLB'])\n",
    "    m.q[N,'theta_URB'].fix(start_data['thURB'])\n",
    "    m.q[N,'theta_LRB'].fix(start_data['thLRB'])\n",
    "    \n",
    "    m.dq[N,'theta_ULA'].fix(start_data['dthULA'])\n",
    "    m.dq[N,'theta_LLA'].fix(start_data['dthLLA'])\n",
    "    m.dq[N,'theta_URA'].fix(start_data['dthURA'])\n",
    "    m.dq[N,'theta_LRA'].fix(start_data['dthLRA'])\n",
    "    \n",
    "    m.dq[N,'theta_ULB'].fix(start_data['dthULB'])\n",
    "    m.dq[N,'theta_LLB'].fix(start_data['dthLLB'])\n",
    "    m.dq[N,'theta_URB'].fix(start_data['dthURB'])\n",
    "    m.dq[N,'theta_LRB'].fix(start_data['dthLRB'])\n",
    "    \n",
    "    \n",
    "\n",
    "elif test == 'Periodic_Walk':\n",
    "    guide = np.linspace(0,TT*V_avg,N)\n",
    "\n",
    "    for n in range(1,N+1):\n",
    "        m.q[n,'x_body'].value = guide[n-1]\n",
    "        m.q[n,'y_body'].setub(0.42)\n",
    "\n",
    "    m.foot_position[1,'Y','B'].fix(0.0)\n",
    "    \n",
    "    m.q[1,'x_body'].fix(0.0)\n",
    "\n",
    "\n",
    "    \n",
    "    m.foot_position[N/2,'Y','A'].fix(0.0)\n",
    "    m.foot_position[N/2,'Y','B'].fix(0.0)\n",
    "    \n",
    "    for n in range(1,N+1):\n",
    "        if n<(N/2):\n",
    "            m.GRF_y[n,'A'].fix(0.0)\n",
    "            m.GRF_y[n,'B'].setlb(1)\n",
    "            m.foot_position[n,'X','B'].fix(0.0)\n",
    "            \n",
    "            if n<(N/2 - 5):\n",
    "                m.foot_position[n,'Y','A'].setlb(0.04)\n",
    "                m.foot_position[n,'Y','A'].setub(0.06)\n",
    "                \n",
    "        elif n>(N/2):\n",
    "            m.GRF_y[n,'B'].fix(0.0)\n",
    "            m.GRF_y[n,'A'].setlb(1)\n",
    "            \n",
    "            if n>(N/2 + 15):\n",
    "                m.foot_position[n,'Y','B'].setlb(0.04)\n",
    "                m.foot_position[n,'Y','B'].setub(0.06)\n",
    "                \n",
    "\n",
    "        else:\n",
    "            pass\n",
    "            \n",
    "    m.foot_position[N,'Y','A'].fix(0.0)\n",
    "    \n",
    "\n",
    "    \n",
    "elif test == 'Periodic_Walk_Stop':\n",
    "    \n",
    "    with open('periodic_walk_mid_v1.pkl','rb') as f:\n",
    "        start_data = pickle.load(f)\n",
    "    \n",
    "    m.q[1,'y_body'].fix(start_data['ybod'])\n",
    "    m.dq[1,'x_body'].fix(start_data['dxbod'])\n",
    "    \n",
    "    m.dq[1,'theta_ULA'].fix(start_data['dthULA'])\n",
    "    m.dq[1,'theta_LLA'].fix(start_data['dthLLA'])\n",
    "    m.dq[1,'theta_URA'].fix(start_data['dthURA'])\n",
    "    m.dq[1,'theta_LRA'].fix(start_data['dthLRA'])\n",
    "    \n",
    "    m.dq[1,'theta_ULB'].fix(start_data['dthULB'])\n",
    "    m.dq[1,'theta_LLB'].fix(start_data['dthLLB'])\n",
    "    m.dq[1,'theta_URB'].fix(start_data['dthURB'])\n",
    "    m.dq[1,'theta_LRB'].fix(start_data['dthLRB'])\n",
    "    \n",
    "    m.q[1,'x_body'].fix(0.0) # initial condition\n",
    "\n",
    "#     m.q[1,'y_body'].fix(body_height)\n",
    "    \n",
    "\n",
    "#     m.dq[1,'y_body'].fix(0.0)\n",
    "\n",
    "    m.foot_position[1,'X','A'].fix(0.0-(stride_length/2))\n",
    "    m.foot_position[1,'X','B'].fix(0.0+(stride_length/2))\n",
    "    m.foot_position[1,'Y','A'].fix(0.0)\n",
    "    m.foot_position[1,'Y','B'].fix(0.0)\n",
    "#     m.q[1,'y_body'].fix(body_height)\n",
    "    \n",
    "    for n in range(1,N+1):\n",
    "        m.foot_position[n,'X','B'].fix(stride_length/2)\n",
    "        m.foot_position[n,'Y','B'].fix(0.0)\n",
    "    \n",
    "    for n5 in range(N-5,N+1): # last 5 nodes \n",
    "        for dof_i in DOFs:\n",
    "            if dof_i == 'x_body':\n",
    "                m.dq[n5,dof_i].setub(0.0)\n",
    "            else:\n",
    "                pass\n",
    "#                 m.dq[n5,dof_i].setub(0.05*m.dq[1,dof_i])\n",
    "                \n",
    "        m.foot_position[n5, 'Y', 'A'].fix(0.0)\n",
    "        m.foot_position[n5, 'Y', 'B'].fix(0.0)\n",
    "        \n",
    "    m.q[N,'x_body'].fix(stride_length/2)\n",
    "#     m.q[N,'y_body'].fix(body_height)\n",
    "\n",
    "    \n",
    "    m.dq[N,'x_body'].fix(0.0)\n",
    "    m.dq[N,'y_body'].fix(0.0)\n",
    "\n",
    "    \n",
    "    m.dq[N,'theta_ULA'].fix(0.0)\n",
    "    m.dq[N,'theta_URA'].fix(0.0)\n",
    "    m.dq[N,'theta_LLA'].fix(0.0)\n",
    "    m.dq[N,'theta_LRA'].fix(0.0)\n",
    "    \n",
    "    m.dq[N,'theta_ULB'].fix(0.0)\n",
    "    m.dq[N,'theta_URB'].fix(0.0)\n",
    "    m.dq[N,'theta_LLB'].fix(0.0)\n",
    "    m.dq[N,'theta_LRB'].fix(0.0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    m.foot_position[N,'X','A'].fix(stride_length/2)\n",
    "    m.foot_position[N,'X','B'].fix(stride_length/2)\n",
    "    \n",
    "    \n",
    "    \n",
    "#     m.foot_position[N,'X','A'].fix(0.75)\n",
    "#     m.foot_position[N,'X','B'].fix(0.25)\n",
    "    \n",
    "else:\n",
    "    pass\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "0b53951b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# COST FUNCTION -------------------------------------------------------------------------------------------------------------\n",
    "def CostFun(m):\n",
    "    row = 1e5 # scale the penalty\n",
    "    tau_sum = 0.0\n",
    "    pen_sum = 0.0\n",
    "    transport_sum = 0.0\n",
    "    tau_diff = 0.0\n",
    "   \n",
    "    \n",
    "    T = sum(m.h[n] for n in range(1,N+1))\n",
    "    \n",
    "    for n in range(1,N+1):\n",
    "        for leg in legs:\n",
    "            for side in sides:\n",
    "                tau_sum += (m.tau_torque[n,side,leg])**2\n",
    "#                 tau_diff = sum(diff(m.tau_torque[n,side,leg]))\n",
    "#                 if n<N:\n",
    "#                     tau_diff += (m.tau_torque[n+1,side,leg] - m.tau_torque[n,side,leg])**2\n",
    "                \n",
    "            for gc in ground_constraints:\n",
    "                for leg in legs:\n",
    "                    pen_sum += m.ground_penalty[n,gc,leg] \n",
    "                    \n",
    "    #return T + row*pen_sum + tau_sum\n",
    "    return row*pen_sum + tau_sum/10 #+ tau_diff\n",
    "m.Cost = Objective(rule = CostFun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "18ba8f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Turning EOM back to pyomo (from sympy) '''\n",
    "#create the constraints\n",
    "def EOM_x_body(m,n):\n",
    "    var_list = get_var_list_values(m,n)\n",
    "    return lamb_EOMs['x_body'](*var_list) == 0\n",
    "m.EOM_x_body_constraint = Constraint(m.N, rule = EOM_x_body)\n",
    "\n",
    "def EOM_y_body(m,n):\n",
    "    var_list = get_var_list_values(m,n)\n",
    "    return lamb_EOMs['y_body'](*var_list) == 0\n",
    "m.EOM_y_body_constraint = Constraint(m.N, rule = EOM_y_body)\n",
    "\n",
    "\n",
    "def EOM_th_ULA(m,n):\n",
    "    var_list = get_var_list_values(m,n)\n",
    "    return lamb_EOMs['theta_ULA'](*var_list) == 0\n",
    "m.EOM_th_ULA_constraint = Constraint(m.N, rule = EOM_th_ULA)\n",
    "\n",
    "def EOM_th_LLA(m,n):\n",
    "    var_list = get_var_list_values(m,n)\n",
    "    return lamb_EOMs['theta_LLA'](*var_list) == 0\n",
    "m.EOM_th_LLA_constraint = Constraint(m.N, rule = EOM_th_LLA)\n",
    "\n",
    "def EOM_th_URA(m,n):\n",
    "    var_list = get_var_list_values(m,n)   \n",
    "    return lamb_EOMs['theta_URA'](*var_list) == 0\n",
    "m.EOM_th_URA_constraint = Constraint(m.N, rule = EOM_th_URA)\n",
    "\n",
    "def EOM_th_LRA(m,n):\n",
    "    var_list = get_var_list_values(m,n)    \n",
    "    return lamb_EOMs['theta_LRA'](*var_list) == 0\n",
    "m.EOM_th_LRA_constraint = Constraint(m.N, rule = EOM_th_LRA)\n",
    "\n",
    "def EOM_th_ULB(m,n):\n",
    "    var_list = get_var_list_values(m,n)\n",
    "    return lamb_EOMs['theta_ULB'](*var_list) == 0\n",
    "m.EOM_th_ULB_constraint = Constraint(m.N, rule = EOM_th_ULB)\n",
    "\n",
    "def EOM_th_LLB(m,n):\n",
    "    var_list = get_var_list_values(m,n)\n",
    "    return lamb_EOMs['theta_LLB'](*var_list) == 0\n",
    "m.EOM_th_LLB_constraint = Constraint(m.N, rule = EOM_th_LLB)\n",
    "\n",
    "def EOM_th_URB(m,n):\n",
    "    var_list = get_var_list_values(m,n)   \n",
    "    return lamb_EOMs['theta_URB'](*var_list) == 0\n",
    "m.EOM_th_URB_constraint = Constraint(m.N, rule = EOM_th_URB)\n",
    "\n",
    "def EOM_th_LRB(m,n):\n",
    "    var_list = get_var_list_values(m,n)    \n",
    "    return lamb_EOMs['theta_LRB'](*var_list) == 0\n",
    "m.EOM_th_LRB_constraint = Constraint(m.N, rule = EOM_th_LRB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "212b52d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ipopt 3.14.10: linear_solver=ma86\n",
      "expect_infeasible_problem=yes\n",
      "print_level=5\n",
      "max_iter=600000\n",
      "max_cpu_time=12000\n",
      "Tol=1e-06\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.14.10, running with linear solver ma86.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:    35943\n",
      "Number of nonzeros in inequality constraint Jacobian.:     4786\n",
      "Number of nonzeros in Lagrangian Hessian.............:    24776\n",
      "\n",
      "Total number of variables............................:     7842\n",
      "                     variables with only lower bounds:      717\n",
      "                variables with lower and upper bounds:     3875\n",
      "                     variables with only upper bounds:      100\n",
      "Total number of equality constraints.................:     7681\n",
      "Total number of inequality constraints...............:     1998\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:     1998\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  1.7239058e+08 5.61e+00 1.00e+02  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  1.5077279e+08 5.66e+00 9.98e+01  -1.0 1.01e+04    -  2.79e-03 2.35e-03f  1\n",
      "   2  1.3101060e+08 5.64e+00 9.94e+01  -1.0 8.72e+03    -  1.51e-02 3.13e-03f  1\n",
      "   3  1.1805353e+08 5.62e+00 9.90e+01  -1.0 5.88e+03    -  2.21e-02 4.54e-03f  1\n",
      "   4  9.5323493e+07 5.54e+00 9.76e+01  -1.0 3.71e+03    -  1.55e-02 1.40e-02f  1\n",
      "   5  7.8783865e+07 5.45e+00 9.60e+01  -1.0 2.49e+03    -  1.30e-02 1.58e-02f  1\n",
      "   6  6.7958165e+07 5.38e+00 9.46e+01  -1.0 2.01e+03    -  2.87e-02 1.43e-02f  1\n",
      "   7  5.6874282e+07 5.27e+00 9.27e+01  -1.0 1.94e+03    -  1.76e-02 2.05e-02f  1\n",
      "   8  4.9260907e+07 5.18e+00 6.08e+02  -1.0 1.75e+03    -  6.10e-02 1.79e-02f  1\n",
      "   9  4.1848095e+07 5.05e+00 6.62e+02  -1.0 1.59e+03    -  8.66e-03 2.39e-02f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  3.4012351e+07 4.91e+00 3.65e+02  -1.0 1.50e+03    -  5.83e-02 2.94e-02f  1\n",
      "  11  2.6107819e+07 4.72e+00 9.89e+02  -1.0 1.07e+03    -  9.46e-02 3.98e-02f  1\n",
      "  12  1.8388049e+07 4.44e+00 9.19e+02  -1.0 9.35e+02    -  6.26e-02 5.89e-02f  1\n",
      "  13  1.3828599e+07 4.19e+00 7.36e+02  -1.0 1.18e+03    -  1.15e-01 5.70e-02f  1\n",
      "  14  9.6679530e+06 3.79e+00 1.53e+03  -1.0 7.04e+02    -  1.99e-01 9.61e-02f  1\n",
      "  15  7.3098069e+06 3.34e+00 1.40e+03  -1.0 3.78e+02    -  1.13e-01 1.18e-01f  1\n",
      "  16  5.0330372e+06 2.75e+00 9.09e+02  -1.0 3.21e+02    -  2.16e-01 1.76e-01f  1\n",
      "  17  4.8543791e+06 2.69e+00 8.82e+02  -1.0 2.93e+03    -  1.84e-02 2.41e-02f  1\n",
      "  18  3.7202697e+06 2.25e+00 7.15e+02  -1.0 7.10e+02    -  1.28e-01 1.63e-01f  1\n",
      "  19  2.9528951e+06 1.89e+00 6.11e+02  -1.0 7.94e+02    -  1.69e-01 1.57e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  2.3578079e+06 1.57e+00 5.14e+02  -1.0 5.06e+02    -  1.75e-01 1.69e-01f  1\n",
      "  21  1.9793899e+06 1.34e+00 8.84e+02  -1.0 5.10e+02    -  1.95e-01 1.49e-01f  1\n",
      "  22  1.1811113e+06 7.93e-01 1.30e+03  -1.0 3.78e+02    -  1.76e-01 4.06e-01f  1\n",
      "  23  9.3046927e+05 5.88e-01 6.71e+02  -1.0 1.97e+02    -  4.31e-01 2.57e-01f  1\n",
      "  24  7.2491651e+05 3.98e-01 1.34e+03  -1.0 2.97e+02    -  6.42e-01 3.20e-01f  1\n",
      "  25  5.2991911e+05 3.13e-01 4.59e+02  -1.0 4.60e+02    -  3.95e-01 4.90e-01f  1\n",
      "  26  4.4225040e+05 1.75e-01 4.84e+02  -1.0 5.54e+01  -4.0 4.83e-01 4.51e-01h  1\n",
      "  27  4.3453712e+05 1.59e-01 1.78e+03  -1.0 7.97e+02    -  5.08e-01 7.81e-02h  4\n",
      "  28  4.1437969e+05 1.26e-01 4.63e+03  -1.0 1.03e+02  -4.5 9.56e-01 2.25e-01h  2\n",
      "  29  4.1338802e+05 1.24e-01 4.43e+03  -1.0 4.75e+02  -5.0 5.85e-02 1.46e-02h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30  4.1140669e+05 1.20e-01 4.91e+03  -1.0 1.07e+02  -4.5 2.95e-01 2.96e-02h  4\n",
      "  31  3.5797822e+05 1.02e-02 4.39e+02  -1.0 1.36e+01  -3.2 1.00e+00 9.53e-01h  1\n",
      "  32  3.6661114e+05 7.98e-03 3.60e+04  -1.0 3.99e+01  -3.7 1.00e+00 1.65e-01f  2\n",
      "  33  3.6912238e+05 7.77e-03 2.00e+04  -1.0 8.96e+01  -4.2 1.00e+00 2.73e-02h  6\n",
      "  34  4.2068746e+05 3.44e+00 5.11e+03  -1.0 4.25e+01  -3.7 1.00e+00 7.90e-01H  1\n",
      "  35  4.2430436e+05 9.44e-01 3.74e+03  -1.0 1.00e+02  -4.2 4.20e-01 2.96e-01h  2\n",
      "  36  4.2523356e+05 1.22e-02 4.06e+03  -1.0 5.46e+01  -3.8 7.33e-01 1.20e-01h  4\n",
      "  37  4.2562349e+05 1.20e-02 3.94e+03  -1.0 1.16e+02  -4.3 3.33e-01 6.25e-02h  5\n",
      "  38  4.2578442e+05 1.18e-02 4.34e+03  -1.0 5.84e+01  -3.8 1.00e+00 2.60e-02h  6\n",
      "  39  4.2591658e+05 1.16e-02 4.55e+03  -1.0 1.43e+02  -4.3 2.69e-01 2.22e-02h  6\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  40r 4.2591658e+05 1.16e-02 1.00e+03   0.3 0.00e+00  -3.9 0.00e+00 1.00e+00R  1\n",
      "  41r 4.4074599e+05 1.15e-02 1.28e+04   0.3 1.93e+03    -  2.57e-02 9.98e-04f  1\n",
      "  42r 8.3104115e+05 1.00e-02 8.71e+03   0.3 8.94e+02    -  5.72e-02 2.67e-02f  1\n",
      "  43r 2.1600154e+06 3.47e-02 5.59e+03   0.3 4.56e+02    -  4.40e-02 8.87e-02f  1\n",
      "  44r 5.2682723e+06 1.05e-01 4.33e+03   0.3 3.92e+02    -  2.68e-01 1.96e-01f  1\n",
      "  45r 2.6773921e+07 1.17e+01 1.09e+04   0.3 5.63e+02    -  3.22e-01 1.00e+00f  1\n",
      "  46r 5.8544890e+07 1.26e+01 1.36e+03   0.3 1.02e+03    -  6.35e-01 9.29e-01f  1\n",
      "  47r 1.3175129e+08 1.11e+01 8.93e+02   0.3 9.03e+02    -  6.25e-01 1.00e+00f  1\n",
      "  48r 2.0838029e+08 1.53e+01 3.94e+02   0.3 8.84e+02    -  6.86e-01 1.00e+00f  1\n",
      "  49r 2.4496215e+08 1.36e+00 1.13e+02   0.3 8.97e+02    -  9.20e-01 5.99e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  50r 2.8551359e+08 6.14e+00 3.90e+01   0.3 6.25e+02    -  1.00e+00 9.83e-01h  1\n",
      "  51r 2.8578737e+08 1.09e+01 1.05e+02   0.3 9.40e+02    -  6.25e-01 1.00e+00f  1\n",
      "  52r 2.8602733e+08 8.55e+00 8.46e+01   0.3 8.81e+02    -  5.53e-01 1.00e+00f  1\n",
      "  53r 2.8600522e+08 7.29e-01 1.28e+02   0.3 9.09e+02    -  8.74e-01 5.13e-01f  1\n",
      "  54r 2.8567683e+08 6.08e+00 1.24e+01   0.3 3.52e+02    -  1.00e+00 1.00e+00h  1\n",
      "  55r 2.8569925e+08 1.29e+00 8.16e+01   0.3 6.72e+02    -  8.14e-01 5.00e-01f  2\n",
      "  56r 2.8551863e+08 1.03e+01 5.72e+01   0.3 6.55e+02    -  8.51e-01 6.11e-01H  1\n",
      "  57r 2.8552377e+08 3.07e+00 4.08e+01   0.3 8.47e+02    -  6.45e-01 3.80e-01f  1\n",
      "  58r 1.7513367e+08 4.30e-01 6.57e+01  -0.4 3.49e+02    -  8.30e-01 1.00e+00f  1\n",
      "  59r 1.8352299e+08 1.50e+00 8.82e+01  -0.4 3.66e+02    -  7.86e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  60r 1.8917342e+08 3.79e+00 1.60e+01  -0.4 4.83e+02    -  9.37e-01 1.00e+00f  1\n",
      "  61r 1.8995530e+08 5.49e+00 1.31e+01  -0.4 5.03e+02    -  1.00e+00 1.00e+00f  1\n",
      "  62r 1.8994755e+08 4.51e+00 1.22e+01  -0.4 5.44e+02    -  1.00e+00 1.00e+00f  1\n",
      "  63r 1.8993668e+08 4.88e+00 4.52e+01  -0.4 5.52e+02    -  8.08e-01 1.00e+00f  1\n",
      "  64r 1.8998545e+08 6.21e+00 1.43e+01  -0.4 5.58e+02    -  9.92e-01 1.00e+00f  1\n",
      "  65r 1.8993300e+08 5.56e+00 1.51e+01  -0.4 5.60e+02    -  9.53e-01 1.00e+00f  1\n",
      "  66r 1.8994792e+08 4.84e+00 1.29e+01  -0.4 5.56e+02    -  1.00e+00 1.00e+00h  1\n",
      "  67r 1.8993013e+08 5.52e+00 1.23e+01  -0.4 5.54e+02    -  1.00e+00 1.00e+00f  1\n",
      "  68r 1.8993788e+08 5.95e+00 1.41e+01  -0.4 5.56e+02    -  1.00e+00 1.00e+00f  1\n",
      "  69r 1.8991905e+08 5.98e+00 1.50e+01  -0.4 5.57e+02    -  9.74e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  70r 1.8993648e+08 5.49e+00 1.42e+01  -0.4 5.55e+02    -  1.00e+00 1.00e+00h  1\n",
      "  71r 1.8992389e+08 5.74e+00 1.38e+01  -0.4 5.54e+02    -  1.00e+00 1.00e+00h  1\n",
      "  72r 1.8993500e+08 5.77e+00 1.42e+01  -0.4 5.55e+02    -  1.00e+00 1.00e+00f  1\n",
      "  73r 1.8991761e+08 5.91e+00 1.39e+01  -0.4 5.57e+02    -  1.00e+00 1.00e+00f  1\n",
      "  74r 1.8992437e+08 2.23e-01 1.19e+02  -0.4 5.55e+02    -  1.00e+00 5.00e-01f  2\n",
      "  75r 1.8994618e+08 3.75e+00 2.51e+00  -0.4 1.53e+02    -  1.00e+00 1.00e+00h  1\n",
      "  76r 1.8994785e+08 2.45e+00 4.67e+00  -0.4 3.23e+02    -  8.07e-01 7.62e-01f  1\n",
      "  77r 1.8986886e+08 7.27e+00 2.55e+01  -0.4 4.17e+02    -  9.38e-01 7.51e-01H  1\n",
      "  78r 1.8972412e+08 8.53e+00 8.93e+01  -0.4 5.14e+02    -  3.24e-01 7.15e-01H  1\n",
      "  79r 1.8985160e+08 1.28e+01 4.84e+01  -0.4 4.86e+02    -  7.05e-01 6.21e-01F  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  80r 1.8987496e+08 2.43e-01 2.40e+01  -0.4 3.39e+02    -  7.12e-01 6.07e-01f  1\n",
      "  81r 1.8995867e+08 8.92e+00 1.81e+01  -0.4 3.62e+02    -  9.21e-01 8.76e-01H  1\n",
      "  82r 1.8994232e+08 2.35e+00 5.66e+01  -0.4 5.62e+02    -  4.92e-01 8.64e-01f  1\n",
      "  83r 1.8985423e+08 1.19e+01 2.95e+01  -0.4 3.62e+02    -  8.37e-01 6.76e-01H  1\n",
      "  84r 1.8988478e+08 1.64e+00 7.02e+01  -0.4 4.95e+02    -  5.11e-01 1.00e+00f  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  85r 1.8986938e+08 1.24e+01 3.49e+01  -0.4 4.21e+02    -  6.99e-01 6.18e-01H  1\n",
      "  86r 1.8965062e+08 1.35e+01 2.13e+02  -0.4 4.59e+02    -  1.14e-01 6.06e-01F  1\n",
      "  87r 1.8985460e+08 3.77e+00 1.14e+02  -0.4 8.55e+02    -  3.18e-01 4.25e-01f  1\n",
      "  88r 1.8993159e+08 2.04e-01 6.39e+01  -0.4 3.48e+02    -  1.00e+00 5.00e-01f  2\n",
      "  89r 1.8994214e+08 1.09e-01 4.57e+01  -0.4 3.37e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  90r 1.8993615e+08 9.37e-02 3.55e+01  -0.4 3.65e+02    -  9.16e-01 4.32e-01h  2\n",
      "  91r 1.8993919e+08 7.77e-02 5.12e+01  -0.4 3.12e+02    -  1.00e+00 2.50e-01h  3\n",
      "  92r 1.8996675e+08 2.06e-02 1.73e+00  -0.4 1.66e+02    -  1.00e+00 1.00e+00h  1\n",
      "  93r 1.8996293e+08 2.41e-02 4.50e+01  -0.4 3.13e+02    -  1.00e+00 5.00e-01f  2\n",
      "  94r 1.8994884e+08 2.60e-02 4.08e+01  -0.4 3.92e+02    -  1.00e+00 5.00e-01h  2\n",
      "  95r 1.8994515e+08 2.24e-02 3.95e+01  -0.4 3.48e+02    -  8.24e-01 2.50e-01h  3\n",
      "  96r 1.8994119e+08 8.99e+00 4.37e+00  -0.4 2.61e+02    -  1.00e+00 1.00e+00H  1\n",
      "  97r 1.8995197e+08 1.01e+00 6.69e+01  -0.4 5.66e+02    -  9.92e-01 5.00e-01f  2\n",
      "  98r 1.8996019e+08 4.83e-02 6.73e+01  -0.4 3.70e+02    -  9.99e-01 2.50e-01h  3\n",
      "  99r 1.8995240e+08 2.42e-02 1.63e+01  -0.4 5.08e+01    -  9.65e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 100r 1.8994791e+08 1.86e-02 6.92e+01  -0.4 2.34e+02    -  1.00e+00 2.50e-01h  3\n",
      " 101r 1.8991741e+08 5.55e+00 4.31e+01  -0.4 2.52e+02    -  1.00e+00 6.63e-01H  1\n",
      " 102r 1.8989078e+08 1.29e-01 2.17e+01  -0.4 4.62e+02    -  6.77e-01 4.57e-01F  1\n",
      " 103r 1.8984098e+08 4.66e+00 4.56e+01  -0.4 3.97e+02    -  6.11e-01 3.75e-01H  1\n",
      " 104r 1.8981445e+08 3.61e+00 1.74e+01  -0.4 2.93e+02    -  5.40e-01 3.66e-01F  1\n",
      " 105r 1.8984562e+08 5.12e-01 2.16e+01  -0.4 2.63e+02    -  5.95e-01 2.50e-01f  3\n",
      " 106r 1.8989950e+08 4.19e-02 2.91e+01  -0.4 1.79e+02    -  8.67e-01 4.27e-01h  2\n",
      " 107r 1.8991175e+08 3.11e-02 5.00e+01  -0.4 2.23e+02    -  1.00e+00 2.50e-01h  3\n",
      " 108r 1.8992501e+08 2.30e-02 5.09e+01  -0.4 3.09e+02    -  1.00e+00 2.50e-01h  3\n",
      " 109r 1.8993422e+08 2.02e-02 4.73e+01  -0.4 2.87e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 110r 1.8993729e+08 1.55e-02 4.98e+01  -0.4 3.09e+02    -  1.00e+00 2.50e-01h  3\n",
      " 111r 1.8994195e+08 1.32e-02 5.19e+01  -0.4 2.91e+02    -  1.00e+00 2.50e-01h  3\n",
      " 112r 1.8991121e+08 5.59e+00 1.37e+01  -0.4 3.04e+02    -  1.00e+00 8.83e-01H  1\n",
      " 113r 1.8992053e+08 2.69e+00 4.18e+01  -0.4 5.01e+02    -  6.53e-01 2.50e-01f  3\n",
      " 114r 1.8996875e+08 3.52e+00 4.81e+01  -0.4 6.09e+02    -  1.00e+00 6.22e-01f  1\n",
      " 115r 1.8992060e+08 5.66e+00 4.05e+01  -0.4 3.23e+02    -  6.43e-01 1.00e+00f  1\n",
      " 116r 1.8997677e+08 2.10e-01 1.93e+01  -0.4 5.14e+02    -  6.92e-01 6.31e-01f  1\n",
      " 117r 1.8986171e+08 1.13e+01 6.39e+00  -0.4 3.26e+02    -  1.00e+00 9.60e-01H  1\n",
      " 118r 1.8969042e+08 1.75e+01 1.37e+02  -0.4 5.97e+02    -  2.70e-01 5.82e-01F  1\n",
      " 119r 1.8987674e+08 4.03e+00 8.50e+01  -0.4 6.23e+02    -  7.25e-01 5.00e-01f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 120r 1.8991096e+08 7.42e+00 2.96e+01  -0.4 3.18e+02    -  6.13e-01 7.81e-01H  1\n",
      " 121r 1.8994416e+08 1.52e-01 1.72e+01  -0.4 5.06e+02    -  5.34e-01 5.00e-01f  2\n",
      " 122r 1.8984728e+08 1.33e+01 2.53e+01  -0.4 2.77e+02    -  8.63e-01 7.41e-01H  1\n",
      " 123r 1.8987567e+08 4.64e+00 5.03e+01  -0.4 5.43e+02    -  2.78e-01 5.00e-01f  2\n",
      " 124r 1.8988947e+08 3.69e+00 1.62e+01  -0.4 3.73e+02    -  7.30e-01 5.91e-01H  1\n",
      " 125r 1.8988390e+08 1.59e+00 8.02e+01  -0.4 3.87e+02    -  9.42e-01 4.42e-01F  1\n",
      " 126r 1.8990259e+08 5.84e-02 4.12e+01  -0.4 3.04e+02    -  9.36e-01 5.00e-01f  2\n",
      " 127r 1.8992343e+08 4.22e-02 5.00e+01  -0.4 3.53e+02    -  1.00e+00 4.42e-01h  2\n",
      " 128r 1.8986097e+08 1.19e+01 1.53e+01  -0.4 3.34e+02    -  7.15e-01 6.98e-01H  1\n",
      " 129r 1.8991230e+08 3.84e+00 2.40e+01  -0.4 4.81e+02    -  6.79e-01 5.00e-01f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 130r 1.8992525e+08 3.37e-02 5.23e+01  -0.4 3.88e+02    -  7.85e-01 2.50e-01f  3\n",
      " 131r 1.8993958e+08 2.61e-02 7.58e+01  -0.4 2.53e+02    -  1.00e+00 2.50e-01h  3\n",
      " 132r 1.8987573e+08 9.07e+00 2.02e+01  -0.4 3.24e+02    -  7.85e-01 6.86e-01H  1\n",
      " 133r 1.8989273e+08 5.45e+00 5.02e+01  -0.4 4.86e+02    -  7.63e-01 2.50e-01f  3\n",
      " 134r 1.8992865e+08 3.89e-01 5.10e+01  -0.4 5.48e+02    -  4.29e-01 2.50e-01f  3\n",
      " 135r 1.8994260e+08 1.76e-02 7.10e+01  -0.4 3.91e+02    -  1.00e+00 2.50e-01h  3\n",
      " 136r 1.8995012e+08 1.52e-02 3.92e+01  -0.4 2.76e+02    -  8.28e-01 2.50e-01h  3\n",
      " 137r 1.8994981e+08 1.35e-02 5.41e+01  -0.4 2.58e+02    -  1.00e+00 2.50e-01h  3\n",
      " 138r 1.8994975e+08 1.27e-02 4.73e+01  -0.4 2.86e+02    -  1.00e+00 2.50e-01h  3\n",
      " 139r 1.8994784e+08 1.20e-02 4.69e+01  -0.4 2.63e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 140r 1.8991012e+08 1.13e+01 3.65e+01  -0.4 2.86e+02    -  9.97e-01 6.77e-01H  1\n",
      " 141r 1.8988141e+08 3.82e+00 3.74e+01  -0.4 4.81e+02    -  8.06e-01 4.58e-01F  1\n",
      " 142r 1.8992326e+08 3.47e-02 2.70e+01  -0.4 3.92e+02    -  6.77e-01 5.00e-01f  2\n",
      " 143r 1.8993681e+08 5.75e-02 4.43e+01  -0.4 3.68e+02    -  1.00e+00 5.00e-01h  2\n",
      " 144r 1.8992960e+08 5.01e-02 3.25e+01  -0.4 3.73e+02    -  9.27e-01 5.00e-01h  2\n",
      " 145r 1.8993271e+08 5.95e-02 4.07e+01  -0.4 3.69e+02    -  1.00e+00 5.00e-01h  2\n",
      " 146r 1.8993404e+08 4.99e-02 4.11e+01  -0.4 3.69e+02    -  1.00e+00 5.00e-01h  2\n",
      " 147r 1.8993942e+08 2.89e-02 3.83e+01  -0.4 3.71e+02    -  1.00e+00 5.00e-01h  2\n",
      " 148r 1.8993763e+08 2.89e-02 3.92e+01  -0.4 3.65e+02    -  1.00e+00 5.00e-01h  2\n",
      " 149r 1.8993945e+08 2.87e-02 3.82e+01  -0.4 3.74e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 150r 1.8985758e+08 1.36e+01 9.64e+00  -0.4 3.72e+02    -  7.38e-01 7.13e-01H  1\n",
      " 151r 1.8989965e+08 4.59e+00 1.65e+01  -0.4 4.97e+02    -  3.79e-01 5.00e-01f  2\n",
      " 152r 1.8993220e+08 4.94e-02 7.89e+00  -0.4 3.09e+02    -  6.20e-01 5.00e-01h  2\n",
      " 153r 1.8993893e+08 3.80e-02 4.98e+01  -0.4 3.27e+02    -  8.94e-01 2.50e-01h  3\n",
      " 154r 1.8997309e+08 5.84e-02 2.90e+00  -0.4 2.94e+02    -  1.00e+00 1.00e+00h  1\n",
      " 155r 1.8996488e+08 6.73e-02 9.42e+01  -0.4 4.59e+02    -  1.00e+00 5.00e-01f  2\n",
      " 156r 1.8995836e+08 5.04e-02 6.16e+01  -0.4 1.78e+02    -  9.95e-01 2.50e-01h  3\n",
      " 157r 1.8992655e+08 1.00e+01 7.40e+00  -0.4 2.51e+02    -  1.00e+00 1.00e+00H  1\n",
      " 158r 1.8993824e+08 8.12e-01 1.67e+01  -0.4 5.70e+02    -  5.87e-01 5.00e-01f  2\n",
      " 159r 1.8994811e+08 5.49e-02 7.19e+01  -0.4 2.78e+02    -  8.84e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 160r 1.8990889e+08 9.52e+00 2.37e+01  -0.4 2.93e+02    -  6.70e-01 6.73e-01H  1\n",
      " 161r 1.8992504e+08 2.26e+00 1.69e+01  -0.4 3.96e+02    -  6.56e-01 5.00e-01f  2\n",
      " 162r 1.8993698e+08 7.00e-02 3.98e+01  -0.4 2.49e+02    -  7.85e-01 2.43e-01h  3\n",
      " 163r 1.8994724e+08 5.36e-02 6.31e+01  -0.4 2.21e+02    -  8.10e-01 2.50e-01h  3\n",
      " 164r 1.8994393e+08 4.23e+00 7.77e+00  -0.4 2.80e+02    -  1.00e+00 1.00e+00H  1\n",
      " 165r 1.8994856e+08 1.01e+00 1.08e+02  -0.4 5.16e+02    -  8.97e-01 2.50e-01f  3\n",
      " 166r 1.8996642e+08 4.00e-02 1.11e+02  -0.4 4.54e+02    -  1.00e+00 2.32e-01f  3\n",
      " 167r 1.8997467e+08 3.49e-02 1.27e+02  -0.4 3.40e+02    -  1.00e+00 2.50e-01h  3\n",
      " 168r 1.8997273e+08 2.91e-02 6.45e+01  -0.4 2.57e+02    -  1.00e+00 2.50e-01h  3\n",
      " 169r 1.8996861e+08 2.47e-02 6.89e+01  -0.4 2.69e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 170r 1.8996174e+08 2.24e-02 7.69e+01  -0.4 2.58e+02    -  9.53e-01 2.50e-01h  3\n",
      " 171r 1.8996006e+08 2.06e-02 6.63e+01  -0.4 2.62e+02    -  1.00e+00 2.50e-01h  3\n",
      " 172r 1.8995661e+08 1.76e-02 6.59e+01  -0.4 2.62e+02    -  1.00e+00 2.50e-01h  3\n",
      " 173r 1.8995405e+08 1.57e-02 8.04e+01  -0.4 2.66e+02    -  1.00e+00 1.25e-01h  4\n",
      " 174r 1.8995947e+08 1.21e-02 3.78e+01  -0.4 7.34e+01    -  1.00e+00 2.50e-01h  3\n",
      " 175r 1.8995370e+08 2.88e+00 2.46e+00  -0.4 9.42e+01    -  1.00e+00 1.00e+00w  1\n",
      " 176r 1.8995648e+08 3.89e+00 9.98e+00  -0.4 1.59e+02    -  8.69e-01 1.00e+00w  1\n",
      " 177r 1.8996952e+08 4.25e+00 1.49e+01  -0.4 2.92e+02    -  8.02e-01 1.00e+00h  1\n",
      " 178r 1.8996736e+08 4.70e+00 1.46e+01  -0.4 4.81e+02    -  9.34e-01 8.14e-01f  1\n",
      " 179r 1.8994050e+08 4.21e+00 4.60e+00  -0.4 4.73e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 180r 1.8994082e+08 1.08e-01 6.67e+01  -0.4 5.67e+02    -  1.00e+00 5.00e-01f  2\n",
      " 181r 1.8994379e+08 7.24e-02 2.96e+01  -0.4 1.48e+02    -  8.46e-01 5.00e-01h  2\n",
      " 182r 1.8994316e+08 6.69e-02 4.76e+01  -0.4 2.58e+02    -  7.38e-01 2.25e-01h  3\n",
      " 183r 1.8995532e+08 3.42e-02 3.39e+01  -0.4 2.58e+02    -  1.00e+00 5.00e-01h  2\n",
      " 184r 1.8995239e+08 2.63e-02 5.05e+01  -0.4 3.47e+02    -  1.00e+00 2.50e-01h  3\n",
      " 185r 1.8996329e+08 2.21e-02 2.95e+01  -0.4 2.69e+02    -  1.00e+00 5.00e-01h  2\n",
      " 186r 1.8995907e+08 2.76e-02 6.74e+01  -0.4 3.87e+02    -  9.01e-01 2.16e-01h  3\n",
      " 187r 1.8995825e+08 2.19e-02 4.88e+01  -0.4 3.02e+02    -  1.00e+00 2.50e-01h  3\n",
      " 188r 1.8995496e+08 1.72e-02 4.83e+01  -0.4 2.95e+02    -  1.00e+00 2.50e-01h  3\n",
      " 189r 1.8995219e+08 1.55e-02 5.60e+01  -0.4 3.03e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 190r 1.8996345e+08 9.93e-03 1.65e+00  -0.4 5.94e+01    -  1.00e+00 1.00e+00w  1\n",
      " 191r 1.8994848e+08 2.80e+00 7.49e+00  -0.4 2.31e+02    -  9.67e-01 9.75e-01w  1\n",
      " 192r 1.8995732e+08 2.98e+00 1.76e+01  -0.4 3.26e+02    -  7.92e-01 1.00e+00w  1\n",
      " 193r 1.8995501e+08 1.21e-02 2.89e+01  -0.4 4.88e+02    -  1.00e+00 2.50e-01h  2\n",
      " 194r 1.8995496e+08 1.10e-02 5.79e+01  -0.4 8.92e+01    -  1.00e+00 1.25e-01h  4\n",
      " 195r 1.8995400e+08 8.45e-03 3.34e+01  -0.4 1.30e+02    -  1.00e+00 2.50e-01h  3\n",
      " 196r 1.8995271e+08 7.42e-03 7.55e+01  -0.4 2.42e+02    -  1.00e+00 1.25e-01h  4\n",
      " 197r 1.8995472e+08 6.79e-03 5.08e+01  -0.4 1.05e+02    -  1.00e+00 1.25e-01h  4\n",
      " 198r 1.8995488e+08 7.69e-03 4.09e+01  -0.4 1.17e+02    -  1.00e+00 1.25e-01h  4\n",
      " 199r 1.8995466e+08 7.39e-03 3.09e+01  -0.4 1.09e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 200r 1.8990182e+08 1.04e+01 3.00e+01  -0.4 8.82e+01    -  1.00e+00 7.07e-01H  1\n",
      " 201r 1.8990850e+08 6.57e+00 5.54e+01  -0.4 2.10e+02    -  8.32e-01 2.50e-01f  3\n",
      " 202r 1.8991828e+08 4.38e+00 6.51e+01  -0.4 2.25e+02    -  5.38e-01 8.12e-02f  4\n",
      " 203r 1.8994701e+08 4.87e-03 4.97e+01  -0.4 2.10e+02    -  6.52e-01 2.50e-01f  3\n",
      " 204r 1.8994877e+08 5.24e-03 8.66e+01  -0.4 2.48e+02    -  8.59e-01 6.25e-02h  5\n",
      " 205r 1.8995517e+08 7.55e+00 1.77e+01  -0.4 2.33e+02    -  1.00e+00 8.59e-01H  1\n",
      " 206r 1.8995196e+08 4.87e+00 5.30e+01  -0.4 4.23e+02    -  5.67e-01 2.50e-01f  3\n",
      " 207r 1.8995479e+08 3.32e+00 1.29e+02  -0.4 3.80e+02    -  6.38e-01 9.83e-02f  4\n",
      " 208r 1.8997743e+08 4.21e-01 8.08e+01  -0.4 3.36e+02    -  7.68e-01 2.50e-01f  3\n",
      " 209r 1.8998430e+08 8.16e-03 6.94e+01  -0.4 2.54e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 210r 1.8998188e+08 1.55e-02 6.69e+01  -0.4 2.07e+02    -  1.00e+00 2.50e-01h  3\n",
      " 211r 1.8996836e+08 2.23e-02 2.08e+01  -0.4 1.10e+02    -  1.00e+00 5.00e-01h  2\n",
      " 212r 1.8994339e+08 3.52e+00 1.54e+01  -0.4 2.49e+02    -  1.00e+00 8.36e-01f  1\n",
      " 213r 1.8993873e+08 5.89e+00 5.13e+01  -0.4 4.13e+02    -  6.59e-01 1.00e+00f  1\n",
      " 214r 1.8999959e+08 4.24e+00 1.40e+01  -0.4 4.57e+02    -  8.54e-01 1.00e+00h  1\n",
      " 215r 1.8995857e+08 4.73e+00 2.88e+01  -0.4 4.54e+02    -  7.75e-01 1.00e+00f  1\n",
      " 216r 1.8997652e+08 5.33e+00 1.06e+01  -0.4 4.34e+02    -  1.00e+00 9.78e-01h  1\n",
      " 217r 1.8992907e+08 5.15e+00 1.04e+01  -0.4 4.22e+02    -  1.00e+00 1.00e+00f  1\n",
      " 218r 1.8993544e+08 5.65e+00 1.30e+01  -0.4 4.33e+02    -  1.00e+00 1.00e+00h  1\n",
      " 219r 1.8993570e+08 1.81e-01 1.06e+02  -0.4 4.32e+02    -  1.00e+00 5.00e-01f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 220r 1.8994685e+08 1.06e-01 4.11e+01  -0.4 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      " 221r 1.8994455e+08 1.04e-01 3.93e+01  -0.4 2.60e+02    -  1.00e+00 5.00e-01h  2\n",
      " 222r 1.8993805e+08 8.28e-02 4.23e+01  -0.4 3.55e+02    -  1.00e+00 5.00e-01h  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 223r 1.8993688e+08 6.94e-02 5.50e+01  -0.4 3.41e+02    -  1.00e+00 2.50e-01h  3\n",
      " 224r 1.8995104e+08 3.53e-02 3.42e+01  -0.4 2.17e+02    -  1.00e+00 5.00e-01h  2\n",
      " 225r 1.8995098e+08 2.72e-02 9.29e+01  -0.4 3.00e+02    -  1.00e+00 2.50e-01h  3\n",
      " 226r 1.8994897e+08 2.04e-02 6.05e+01  -0.4 3.06e+02    -  1.00e+00 2.50e-01h  3\n",
      " 227r 1.8994749e+08 1.21e-02 2.95e+01  -0.4 1.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 228r 1.8994540e+08 9.21e-03 6.35e+01  -0.4 2.58e+02    -  1.00e+00 2.50e-01h  3\n",
      " 229r 1.8997158e+08 2.21e+00 2.04e+00  -0.4 3.15e+02    -  1.00e+00 1.00e+00w  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 230r 1.8995665e+08 2.74e+00 2.75e+01  -0.4 5.00e+02    -  1.00e+00 7.86e-01w  1\n",
      " 231r 1.8993766e+08 5.43e+00 5.31e+00  -0.4 3.93e+02    -  1.00e+00 1.00e+00w  1\n",
      " 232r 1.8995195e+08 8.96e-03 6.17e+01  -0.4 5.06e+02    -  1.00e+00 2.50e-01h  2\n",
      " 233r 1.8995153e+08 9.65e-03 5.27e+01  -0.4 2.83e+02    -  1.00e+00 2.50e-01h  3\n",
      " 234r 1.8995086e+08 9.48e-03 6.10e+01  -0.4 3.04e+02    -  1.00e+00 2.50e-01h  3\n",
      " 235r 1.8995151e+08 9.85e-03 5.22e+01  -0.4 2.85e+02    -  1.00e+00 2.50e-01h  3\n",
      " 236r 1.8995046e+08 9.91e-03 6.24e+01  -0.4 3.01e+02    -  1.00e+00 2.50e-01h  3\n",
      " 237r 1.8995031e+08 9.90e-03 5.49e+01  -0.4 2.90e+02    -  1.00e+00 2.50e-01h  3\n",
      " 238r 1.8994970e+08 9.20e-03 6.67e+01  -0.4 2.97e+02    -  1.00e+00 1.25e-01h  4\n",
      " 239r 1.8995793e+08 4.57e-03 2.22e+01  -0.4 6.93e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 240r 1.8995653e+08 4.69e-03 1.42e+02  -0.4 2.33e+02    -  1.00e+00 1.25e-01h  4\n",
      " 241r 1.8996058e+08 5.85e-03 3.86e+01  -0.4 2.24e+02    -  1.00e+00 2.50e-01h  3\n",
      " 242r 1.8995582e+08 2.64e+00 1.85e+00  -0.4 3.34e+02    -  1.00e+00 1.00e+00w  1\n",
      " 243r 1.8995413e+08 2.73e+00 7.23e+00  -0.4 5.33e+02    -  8.63e-01 7.97e-01w  1\n",
      " 244r 1.8995565e+08 4.76e+00 5.43e+00  -0.4 4.21e+02    -  1.00e+00 1.00e+00w  1\n",
      " 245r 1.8995938e+08 5.98e-03 5.72e+01  -0.4 5.25e+02    -  1.00e+00 2.50e-01h  2\n",
      " 246r 1.8995772e+08 6.63e-03 7.99e+01  -0.4 2.89e+02    -  1.00e+00 1.25e-01h  4\n",
      " 247r 1.8995791e+08 5.66e-03 4.91e+01  -0.4 1.14e+02    -  1.00e+00 2.50e-01h  3\n",
      " 248r 1.8995630e+08 6.81e-03 5.19e+01  -0.4 2.28e+02    -  1.00e+00 2.50e-01h  3\n",
      " 249r 1.8995381e+08 6.28e-03 7.19e+01  -0.4 2.63e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 250r 1.8995589e+08 5.82e-03 3.29e+01  -0.4 6.06e+01    -  1.00e+00 1.25e-01h  4\n",
      " 251r 1.8995619e+08 5.21e-03 4.17e+01  -0.4 7.13e+01    -  1.00e+00 1.25e-01h  4\n",
      " 252r 1.8995498e+08 4.67e-03 5.91e+01  -0.4 8.84e+01    -  1.00e+00 1.25e-01h  4\n",
      " 253r 1.8992074e+08 4.30e+00 1.82e+01  -0.4 1.16e+02    -  1.00e+00 8.20e-01H  1\n",
      " 254r 1.8992789e+08 2.07e+00 6.44e+01  -0.4 3.21e+02    -  6.23e-01 2.50e-01f  3\n",
      " 255r 1.8994061e+08 2.75e-02 8.64e+01  -0.4 3.36e+02    -  5.22e-01 2.38e-01f  3\n",
      " 256r 1.8994868e+08 2.07e-02 5.34e+01  -0.4 2.02e+02    -  1.00e+00 2.50e-01h  3\n",
      " 257r 1.8995037e+08 1.76e-02 4.58e+01  -0.4 2.28e+02    -  9.57e-01 2.50e-01h  3\n",
      " 258r 1.8994888e+08 4.05e-02 3.10e+01  -0.4 2.87e+02    -  1.00e+00 5.00e-01h  2\n",
      " 259r 1.8994682e+08 4.11e+00 7.73e+00  -0.4 3.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 260r 1.8994859e+08 4.63e+00 1.10e+01  -0.4 4.80e+02    -  8.48e-01 9.20e-01f  1\n",
      " 261r 1.8995545e+08 2.43e-01 5.56e+01  -0.4 4.65e+02    -  9.77e-01 5.00e-01f  2\n",
      " 262r 1.8995555e+08 1.11e-01 2.89e+01  -0.4 2.84e+02    -  9.51e-01 5.00e-01h  2\n",
      " 263r 1.8995139e+08 8.84e-02 4.11e+01  -0.4 3.61e+02    -  1.00e+00 5.00e-01h  2\n",
      " 264r 1.8994488e+08 6.74e-02 3.54e+01  -0.4 3.18e+02    -  1.00e+00 5.00e-01h  2\n",
      " 265r 1.8993851e+08 6.68e-02 5.51e+01  -0.4 3.35e+02    -  1.00e+00 5.00e-01h  2\n",
      " 266r 1.8994114e+08 6.40e-02 7.19e+01  -0.4 3.05e+02    -  1.00e+00 5.00e-01h  2\n",
      " 267r 1.8994009e+08 5.45e-02 1.02e+02  -0.4 3.27e+02    -  1.00e+00 2.50e-01h  3\n",
      " 268r 1.8995347e+08 3.92e-02 3.89e+01  -0.4 2.39e+02    -  1.00e+00 5.00e-01h  2\n",
      " 269r 1.8994829e+08 6.02e-02 4.35e+01  -0.4 3.87e+02    -  9.00e-01 4.41e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 270r 1.8994665e+08 4.94e-02 6.76e+01  -0.4 3.34e+02    -  1.00e+00 2.50e-01h  3\n",
      " 271r 1.8995872e+08 5.62e-01 1.69e+00  -0.4 1.59e+02    -  1.00e+00 1.00e+00w  1\n",
      " 272r 1.8994860e+08 3.06e+00 4.11e+00  -0.4 3.42e+02    -  8.43e-01 8.09e-01w  1\n",
      " 273r 1.8995786e+08 6.46e+00 5.03e+00  -0.4 4.32e+02    -  1.00e+00 1.00e+00w  1\n",
      " 274r 1.8995268e+08 4.08e-02 2.41e+01  -0.4 5.15e+02    -  1.00e+00 5.00e-01h  1\n",
      " 275r 1.8994919e+08 3.95e-02 5.15e+01  -0.4 2.52e+02    -  1.00e+00 2.50e-01h  3\n",
      " 276r 1.8995474e+08 3.36e-02 4.77e+01  -0.4 2.64e+02    -  1.00e+00 2.50e-01h  3\n",
      " 277r 1.8995437e+08 3.18e-02 4.15e+01  -0.4 2.78e+02    -  1.00e+00 2.50e-01h  3\n",
      " 278r 1.8995276e+08 2.74e-02 5.22e+01  -0.4 3.18e+02    -  1.00e+00 2.50e-01h  3\n",
      " 279r 1.8995165e+08 2.43e-02 5.61e+01  -0.4 2.84e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 280r 1.8995549e+08 2.07e-02 3.45e+01  -0.4 1.40e+02    -  1.00e+00 2.50e-01h  3\n",
      " 281r 1.8995360e+08 2.07e-02 7.74e+01  -0.4 2.59e+02    -  9.28e-01 1.07e-01h  4\n",
      " 282r 1.8995784e+08 1.74e-02 4.64e+01  -0.4 2.00e+02    -  1.00e+00 2.50e-01h  3\n",
      " 283r 1.8995635e+08 1.54e-02 6.24e+01  -0.4 2.94e+02    -  1.00e+00 1.25e-01h  4\n",
      " 284r 1.8996114e+08 3.16e+00 3.90e+00  -0.4 2.38e+02    -  1.00e+00 1.00e+00H  1\n",
      " 285r 1.8996089e+08 5.12e-02 1.25e+02  -0.4 5.74e+02    -  8.62e-01 2.50e-01f  3\n",
      " 286r 1.8997082e+08 4.77e-02 1.65e+02  -0.4 5.34e+02    -  1.00e+00 2.50e-01f  3\n",
      " 287r 1.8997014e+08 3.90e-02 8.13e+01  -0.4 2.77e+02    -  1.00e+00 2.50e-01h  3\n",
      " 288r 1.8996374e+08 3.08e-02 5.38e+01  -0.4 3.31e+02    -  1.00e+00 2.50e-01h  3\n",
      " 289r 1.8995953e+08 2.45e-02 5.90e+01  -0.4 2.92e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 290r 1.8995331e+08 2.23e-02 3.48e+01  -0.4 3.34e+02    -  1.00e+00 5.00e-01h  2\n",
      " 291r 1.8994267e+08 5.76e+00 1.36e+01  -0.4 3.83e+02    -  9.26e-01 1.00e+00h  1\n",
      " 292r 1.8996230e+08 3.80e+00 1.17e+01  -0.4 5.52e+02    -  1.00e+00 1.00e+00f  1\n",
      " 293r 1.8995025e+08 4.29e+00 1.08e+01  -0.4 5.43e+02    -  9.14e-01 1.00e+00f  1\n",
      " 294r 1.8995631e+08 4.90e+00 2.04e+01  -0.4 5.54e+02    -  8.27e-01 1.00e+00f  1\n",
      " 295r 1.8997385e+08 6.07e+00 1.56e+01  -0.4 5.61e+02    -  9.02e-01 1.00e+00f  1\n",
      " 296r 1.8996367e+08 4.78e+00 1.19e+01  -0.4 5.60e+02    -  1.00e+00 1.00e+00h  1\n",
      " 297r 1.8993379e+08 5.48e+00 1.52e+01  -0.4 5.61e+02    -  8.82e-01 1.00e+00f  1\n",
      " 298r 1.8995984e+08 5.34e+00 1.80e+01  -0.4 5.64e+02    -  8.42e-01 1.00e+00f  1\n",
      " 299r 1.8996432e+08 5.76e+00 1.45e+01  -0.4 5.64e+02    -  9.10e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 300r 1.8995456e+08 4.69e+00 1.18e+01  -0.4 5.61e+02    -  9.25e-01 1.00e+00h  1\n",
      " 301r 1.8994446e+08 5.45e+00 1.16e+01  -0.4 5.62e+02    -  9.19e-01 1.00e+00f  1\n",
      " 302r 1.8994630e+08 1.17e-01 4.66e+01  -0.4 5.65e+02    -  8.49e-01 5.00e-01f  2\n",
      " 303r 1.8995108e+08 5.77e-02 4.06e+01  -0.4 1.39e+02    -  1.00e+00 5.00e-01h  2\n",
      " 304r 1.8994266e+08 2.92e-02 3.83e+01  -0.4 2.71e+02    -  1.00e+00 5.00e-01h  2\n",
      " 305r 1.8993398e+08 2.73e-02 4.23e+01  -0.4 3.55e+02    -  1.00e+00 5.00e-01h  2\n",
      " 306r 1.8993766e+08 2.81e-02 3.67e+01  -0.4 3.32e+02    -  1.00e+00 5.00e-01h  2\n",
      " 307r 1.8987204e+08 1.21e+01 1.15e+01  -0.4 3.63e+02    -  9.31e-01 1.00e+00H  1\n",
      " 308r 1.8978889e+08 5.90e+00 4.90e+01  -0.4 6.01e+02    -  4.09e-01 7.38e-01F  1\n",
      " 309r 1.8984086e+08 6.43e+00 5.13e+01  -0.4 3.85e+02    -  6.38e-01 5.44e-01F  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 310r 1.8982089e+08 6.02e+00 4.20e+01  -0.4 1.29e+02  -4.0 7.24e-02 1.62e-02h  1\n",
      " 311r 1.8990720e+08 8.99e-01 3.09e+01  -0.4 4.07e+02    -  7.34e-01 5.26e-01f  1\n",
      " 312r 1.8993295e+08 2.39e-02 2.76e+01  -0.4 3.12e+02    -  4.98e-01 4.17e-01f  1\n",
      " 313r 1.8988657e+08 9.64e+00 1.84e+01  -0.4 3.62e+02    -  7.50e-01 6.39e-01H  1\n",
      " 314r 1.8991107e+08 2.27e+00 2.31e+01  -0.4 4.61e+02    -  6.89e-01 5.00e-01f  2\n",
      " 315r 1.8993078e+08 1.15e-01 4.87e+01  -0.4 3.20e+02    -  7.23e-01 3.62e-01f  2\n",
      " 316r 1.8994056e+08 2.83e+00 5.93e+00  -0.4 3.14e+02    -  1.00e+00 1.00e+00H  1\n",
      " 317r 1.8994779e+08 6.78e-02 3.97e+01  -0.4 5.95e+02    -  7.09e-01 5.00e-01f  2\n",
      " 318r 1.8995483e+08 5.15e-02 6.51e+01  -0.4 3.72e+02    -  1.00e+00 5.00e-01h  2\n",
      " 319r 1.8994766e+08 3.85e-02 3.88e+01  -0.4 3.40e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 320r 1.8994515e+08 3.54e-02 3.02e+01  -0.4 3.68e+02    -  9.37e-01 5.00e-01h  2\n",
      " 321r 1.8994325e+08 3.34e-02 7.21e+01  -0.4 3.38e+02    -  1.00e+00 5.00e-01h  2\n",
      " 322r 1.8994242e+08 3.57e-02 3.46e+01  -0.4 3.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 323r 1.8994159e+08 5.01e-02 3.91e+01  -0.4 3.59e+02    -  1.00e+00 5.00e-01h  2\n",
      " 324r 1.8994172e+08 4.64e-02 4.00e+01  -0.4 3.49e+02    -  1.00e+00 5.00e-01h  2\n",
      " 325r 1.8993769e+08 5.54e-02 4.09e+01  -0.4 3.47e+02    -  8.86e-01 3.81e-01h  2\n",
      " 326r 1.8994028e+08 4.61e-02 3.37e+01  -0.4 3.07e+02    -  1.00e+00 5.00e-01h  2\n",
      " 327r 1.8994145e+08 3.40e+00 1.08e+01  -0.4 3.70e+02    -  9.03e-01 1.00e+00w  1\n",
      " 328r 1.8996488e+08 4.96e+00 1.09e+01  -0.4 5.33e+02    -  1.00e+00 1.00e+00w  1\n",
      " 329r 1.8995111e+08 4.87e+00 9.78e+00  -0.4 5.25e+02    -  9.94e-01 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 330r 1.8994813e+08 1.05e-01 5.60e+01  -0.4 5.18e+02    -  1.00e+00 5.00e-01f  2\n",
      " 331r 1.8996234e+08 5.18e-02 3.95e+01  -0.4 1.41e+02    -  1.00e+00 5.00e-01h  2\n",
      " 332r 1.8995605e+08 3.03e-02 4.05e+01  -0.4 2.69e+02    -  1.00e+00 5.00e-01h  2\n",
      " 333r 1.8985445e+08 1.05e+01 1.43e+01  -0.4 3.59e+02    -  1.00e+00 8.79e-01H  1\n",
      " 334r 1.8980146e+08 1.88e+00 3.16e+01  -0.4 5.83e+02    -  3.81e-01 5.25e-01F  1\n",
      " 335r 1.8988522e+08 2.06e-02 2.55e+01  -0.4 1.58e+02    -  7.77e-01 5.00e-01f  2\n",
      " 336r 1.8986622e+08 8.02e+00 2.04e+01  -0.4 2.38e+02    -  7.48e-01 6.26e-01H  1\n",
      " 337r 1.8989923e+08 1.65e+00 6.85e+00  -0.4 4.45e+02    -  5.98e-01 5.00e-01f  2\n",
      " 338r 1.8993471e+08 3.06e-02 5.86e+01  -0.4 2.44e+02    -  9.82e-01 5.00e-01h  2\n",
      " 339r 1.8994570e+08 3.42e-02 4.31e+01  -0.4 2.80e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 340r 1.8995002e+08 3.91e-02 5.09e+01  -0.4 2.78e+02    -  1.00e+00 5.00e-01h  2\n",
      " 341r 1.8994590e+08 2.11e-02 5.17e+01  -0.4 2.76e+02    -  1.00e+00 5.00e-01h  2\n",
      " 342r 1.8994005e+08 2.20e-02 1.01e+02  -0.4 2.84e+02    -  9.02e-01 2.50e-01h  3\n",
      " 343r 1.8995588e+08 2.17e-02 3.01e+01  -0.4 9.15e+01    -  1.00e+00 5.00e-01h  2\n",
      " 344r 1.8994914e+08 5.78e-02 4.29e+01  -0.4 2.21e+02    -  1.00e+00 4.80e-01h  2\n",
      " 345r 1.8994639e+08 5.03e-02 3.73e+01  -0.4 2.38e+02    -  8.97e-01 2.50e-01h  3\n",
      " 346r 1.8995274e+08 4.14e-02 5.08e+01  -0.4 2.96e+02    -  1.00e+00 2.50e-01h  3\n",
      " 347r 1.8995515e+08 3.02e+00 1.99e+00  -0.4 2.58e+02    -  1.00e+00 1.00e+00w  1\n",
      " 348r 1.8995834e+08 3.39e+00 6.02e+00  -0.4 5.01e+02    -  9.33e-01 9.83e-01w  1\n",
      " 349r 1.8995615e+08 4.31e+00 1.97e+01  -0.4 5.10e+02    -  7.98e-01 1.00e+00w  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 350r 1.8995394e+08 2.24e-02 3.21e+01  -0.4 5.18e+02    -  1.00e+00 5.00e-01h  1\n",
      " 351r 1.8995197e+08 1.97e-02 5.52e+01  -0.4 3.73e+02    -  1.00e+00 2.50e-01h  3\n",
      " 352r 1.8995601e+08 2.40e-02 7.23e+01  -0.4 2.70e+02    -  1.00e+00 2.50e-01h  3\n",
      " 353r 1.8988159e+08 9.53e+00 2.60e+01  -0.4 2.49e+02    -  9.84e-01 7.42e-01H  1\n",
      " 354r 1.8982835e+08 2.47e+00 2.14e+01  -0.4 4.94e+02    -  6.59e-01 4.97e-01F  1\n",
      " 355r 1.8982577e+08 4.26e-02 1.52e+01  -0.4 2.50e+02    -  2.91e-01 2.91e-01F  1\n",
      " 356r 1.8989097e+08 1.97e-02 2.92e+01  -0.4 2.85e+02    -  7.80e-01 5.00e-01f  2\n",
      " 357r 1.8990686e+08 1.70e-02 7.05e+01  -0.4 2.98e+02    -  1.00e+00 2.50e-01h  3\n",
      " 358r 1.8989080e+08 7.07e+00 1.33e+01  -0.4 2.75e+02    -  8.36e-01 8.04e-01H  1\n",
      " 359r 1.8990653e+08 4.13e+00 4.70e+01  -0.4 4.77e+02    -  6.67e-01 2.50e-01f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 360r 1.8993163e+08 1.81e-02 9.46e+01  -0.4 5.19e+02    -  7.20e-01 2.50e-01f  3\n",
      " 361r 1.8995044e+08 1.61e-02 6.88e+01  -0.4 3.44e+02    -  1.00e+00 2.50e-01h  3\n",
      " 362r 1.8995543e+08 1.34e-02 6.86e+01  -0.4 1.65e+02    -  1.00e+00 2.50e-01h  3\n",
      " 363r 1.8995732e+08 1.23e-02 4.84e+01  -0.4 2.24e+02    -  1.00e+00 2.50e-01h  3\n",
      " 364r 1.8995679e+08 1.11e-02 5.64e+01  -0.4 3.32e+02    -  1.00e+00 2.50e-01h  3\n",
      " 365r 1.8995748e+08 1.02e-02 4.97e+01  -0.4 2.89e+02    -  1.00e+00 1.25e-01h  4\n",
      " 366r 1.8995848e+08 7.66e-03 3.32e+01  -0.4 1.04e+02    -  1.00e+00 2.50e-01h  3\n",
      " 367r 1.8995686e+08 7.03e-03 1.28e+02  -0.4 2.38e+02    -  1.00e+00 1.25e-01h  4\n",
      " 368r 1.8991270e+08 6.11e+00 2.87e+01  -0.4 2.27e+02    -  9.45e-01 6.90e-01H  1\n",
      " 369r 1.8991653e+08 3.11e+00 2.15e+01  -0.4 4.47e+02    -  6.20e-01 2.50e-01f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 370r 1.8993175e+08 7.26e-03 3.14e+01  -0.4 5.26e+02    -  3.41e-01 1.82e-01f  3\n",
      " 371r 1.8994501e+08 7.59e-03 6.27e+01  -0.4 4.11e+02    -  7.39e-01 2.50e-01h  3\n",
      " 372r 1.8998065e+08 5.31e+00 6.08e+00  -0.4 1.80e+02    -  1.00e+00 1.00e+00h  1\n",
      " 373r 1.8995081e+08 3.85e+00 2.11e+01  -0.4 3.17e+02    -  7.70e-01 1.00e+00f  1\n",
      " 374r 1.8998020e+08 6.19e+00 1.15e+01  -0.4 4.94e+02    -  9.67e-01 1.00e+00f  1\n",
      " 375r 1.8995296e+08 5.55e+00 1.18e+01  -0.4 5.25e+02    -  1.00e+00 1.00e+00f  1\n",
      " 376r 1.8994384e+08 4.38e+00 1.23e+01  -0.4 5.21e+02    -  8.92e-01 1.00e+00f  1\n",
      " 377r 1.8995510e+08 9.02e-02 6.50e+01  -0.4 5.74e+02    -  1.00e+00 5.00e-01f  2\n",
      " 378r 1.8995334e+08 3.92e+00 2.36e+00  -0.4 1.49e+02    -  1.00e+00 1.00e+00h  1\n",
      " 379r 1.8994646e+08 2.85e+00 4.60e+00  -0.4 3.36e+02    -  8.13e-01 7.64e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 380r 1.8994703e+08 1.45e-01 4.47e+01  -0.4 4.14e+02    -  9.49e-01 5.00e-01f  2\n",
      " 381r 1.8994795e+08 8.26e-02 4.40e+01  -0.4 3.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 382r 1.8994288e+08 7.22e-02 4.57e+01  -0.4 3.33e+02    -  1.00e+00 5.00e-01h  2\n",
      " 383r 1.8994004e+08 5.76e-02 4.03e+01  -0.4 3.29e+02    -  1.00e+00 5.00e-01h  2\n",
      " 384r 1.8991622e+08 5.56e+00 8.86e+01  -0.4 3.17e+02    -  1.00e+00 4.72e-01H  1\n",
      " 385r 1.8991619e+08 9.32e-01 2.08e+01  -0.4 4.27e+02    -  6.15e-01 5.00e-01f  2\n",
      " 386r 1.8992708e+08 8.21e-02 5.71e+01  -0.4 2.91e+02    -  1.00e+00 2.50e-01h  3\n",
      " 387r 1.8994556e+08 3.97e-02 2.87e+01  -0.4 2.94e+02    -  1.00e+00 5.00e-01h  2\n",
      " 388r 1.8994479e+08 3.09e-02 4.11e+01  -0.4 3.84e+02    -  1.00e+00 5.00e-01h  2\n",
      " 389r 1.8993998e+08 3.34e-02 7.86e+01  -0.4 3.24e+02    -  9.47e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 390r 1.8995105e+08 2.68e-02 1.41e+01  -0.4 1.41e+02    -  9.14e-01 5.00e-01h  2\n",
      " 391r 1.8994899e+08 4.55e-02 3.69e+01  -0.4 2.69e+02    -  1.00e+00 5.00e-01h  2\n",
      " 392r 1.8994506e+08 4.70e-02 3.68e+01  -0.4 3.72e+02    -  1.00e+00 5.00e-01h  2\n",
      " 393r 1.8994290e+08 4.40e-02 3.68e+01  -0.4 3.39e+02    -  8.29e-01 2.50e-01h  3\n",
      " 394r 1.8995240e+08 3.49e-02 4.10e+01  -0.4 2.45e+02    -  1.00e+00 5.00e-01h  2\n",
      " 395r 1.8994339e+08 4.45e+00 3.30e+00  -0.4 3.36e+02    -  1.00e+00 1.00e+00w  1\n",
      " 396r 1.8994857e+08 7.16e+00 5.20e+01  -0.4 4.57e+02    -  6.61e-01 1.00e+00w  1\n",
      " 397r 1.9000029e+08 5.27e+00 1.60e+01  -0.4 4.57e+02    -  8.53e-01 1.00e+00w  1\n",
      " 398r 1.8995014e+08 3.76e-02 8.66e+01  -0.4 4.35e+02    -  1.00e+00 2.50e-01h  2\n",
      " 399r 1.8995158e+08 3.06e-02 4.89e+01  -0.4 2.55e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 400r 1.8994998e+08 2.34e-02 5.41e+01  -0.4 3.38e+02    -  1.00e+00 2.50e-01h  3\n",
      " 401r 1.8995143e+08 2.03e-02 4.10e+01  -0.4 2.83e+02    -  1.00e+00 2.50e-01h  3\n",
      " 402r 1.8995104e+08 1.81e-02 5.76e+01  -0.4 3.11e+02    -  1.00e+00 1.25e-01h  4\n",
      " 403r 1.8995595e+08 1.51e-02 2.46e+01  -0.4 7.18e+01    -  1.00e+00 2.50e-01h  3\n",
      " 404r 1.8995174e+08 1.93e-02 4.97e+01  -0.4 1.31e+02    -  1.00e+00 2.50e-01h  3\n",
      " 405r 1.8995012e+08 1.77e-02 4.24e+01  -0.4 2.47e+02    -  1.00e+00 2.50e-01h  3\n",
      " 406r 1.8994988e+08 1.75e-02 7.29e+01  -0.4 3.17e+02    -  1.00e+00 1.25e-01h  4\n",
      " 407r 1.8995265e+08 1.87e-02 1.69e+01  -0.4 1.30e+02    -  1.00e+00 5.00e-01h  2\n",
      " 408r 1.8993835e+08 4.68e+00 5.11e+00  -0.4 2.68e+02    -  9.09e-01 8.49e-01w  1\n",
      " 409r 1.8995775e+08 3.59e+00 9.82e+00  -0.4 4.65e+02    -  1.00e+00 1.00e+00w  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 410r 1.8994822e+08 4.23e+00 1.03e+01  -0.4 5.21e+02    -  9.79e-01 1.00e+00w  1\n",
      " 411r 1.8995086e+08 1.90e-02 7.79e+01  -0.4 5.20e+02    -  9.09e-01 1.06e-01h  3\n",
      " 412r 1.8996004e+08 1.57e-02 5.20e+01  -0.4 2.45e+02    -  1.00e+00 2.50e-01h  3\n",
      " 413r 1.8995934e+08 1.38e-02 4.67e+01  -0.4 2.92e+02    -  1.00e+00 2.50e-01h  3\n",
      " 414r 1.8995778e+08 1.23e-02 7.38e+01  -0.4 2.70e+02    -  1.00e+00 1.25e-01h  4\n",
      " 415r 1.8995773e+08 1.12e-02 4.69e+01  -0.4 2.54e+02    -  1.00e+00 1.25e-01h  4\n",
      " 416r 1.8995791e+08 9.22e-03 5.23e+01  -0.4 2.93e+02    -  1.00e+00 2.50e-01h  3\n",
      " 417r 1.8995645e+08 8.21e-03 6.14e+01  -0.4 3.04e+02    -  1.00e+00 1.25e-01h  4\n",
      " 418r 1.8995647e+08 7.23e-03 5.27e+01  -0.4 2.43e+02    -  1.00e+00 1.25e-01h  4\n",
      " 419r 1.8995579e+08 6.42e-03 3.99e+01  -0.4 1.45e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 420r 1.8995523e+08 7.67e-03 8.21e+01  -0.4 2.13e+02    -  9.32e-01 1.25e-01h  4\n",
      " 421r 1.8996816e+08 5.59e-01 1.56e+00  -0.4 1.00e+02    -  1.00e+00 1.00e+00w  1\n",
      " 422r 1.8995268e+08 1.84e+00 2.04e+01  -0.4 3.16e+02    -  9.33e-01 7.38e-01w  1\n",
      " 423r 1.8995189e+08 3.09e+00 1.01e+01  -0.4 3.67e+02    -  9.21e-01 1.00e+00w  1\n",
      " 424r 1.8995846e+08 8.43e-03 4.89e+01  -0.4 4.33e+02    -  1.00e+00 2.50e-01h  2\n",
      " 425r 1.8995736e+08 9.31e-03 5.62e+01  -0.4 1.90e+02    -  1.00e+00 1.25e-01h  4\n",
      " 426r 1.8995795e+08 8.69e-03 5.47e+01  -0.4 2.49e+02    -  1.00e+00 1.25e-01h  4\n",
      " 427r 1.8995749e+08 7.76e-03 4.94e+01  -0.4 2.61e+02    -  1.00e+00 1.25e-01h  4\n",
      " 428r 1.8995658e+08 7.23e-03 4.93e+01  -0.4 2.62e+02    -  1.00e+00 1.25e-01h  4\n",
      " 429r 1.8995577e+08 7.45e-03 8.25e+01  -0.4 2.52e+02    -  8.48e-01 5.73e-02h  5\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 430r 1.8997074e+08 2.17e+00 3.48e+00  -0.4 6.93e+01    -  1.00e+00 1.00e+00H  1\n",
      " 431r 1.8996755e+08 4.48e-02 4.65e+01  -0.4 2.78e+02    -  7.53e-01 2.16e-01f  3\n",
      " 432r 1.8996914e+08 4.63e-02 1.81e+02  -0.4 4.48e+02    -  1.00e+00 8.46e-02f  4\n",
      " 433r 1.8997816e+08 4.57e-02 7.22e+01  -0.4 2.08e+02    -  1.00e+00 2.50e-01h  3\n",
      " 434r 1.8997499e+08 3.77e-02 4.05e+01  -0.4 2.49e+02    -  8.12e-01 2.50e-01h  3\n",
      " 435r 1.8997096e+08 3.57e-02 6.12e+01  -0.4 2.25e+02    -  1.00e+00 1.25e-01h  4\n",
      " 436r 1.8993360e+08 2.68e+00 4.94e+00  -0.4 2.44e+02    -  1.00e+00 1.00e+00H  1\n",
      " 437r 1.8995430e+08 6.31e-01 4.08e+01  -0.4 6.00e+02    -  7.17e-01 1.00e+00f  1\n",
      " 438r 1.8999042e+08 1.17e+00 7.23e+00  -0.4 4.39e+02    -  9.03e-01 1.00e+00f  1\n",
      " 439r 1.8995643e+08 5.39e+00 1.95e+01  -0.4 5.00e+02    -  8.50e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 440r 1.8996771e+08 2.15e+00 1.96e+01  -0.4 5.01e+02    -  8.41e-01 1.00e+00f  1\n",
      " 441r 1.8997244e+08 5.37e+00 1.00e+01  -0.4 5.05e+02    -  9.54e-01 9.91e-01f  1\n",
      " 442r 1.8995815e+08 3.03e+00 2.49e+01  -0.4 5.03e+02    -  8.17e-01 1.00e+00f  1\n",
      " 443r 1.8997997e+08 4.69e+00 2.03e+01  -0.4 5.10e+02    -  8.28e-01 1.00e+00f  1\n",
      " 444r 1.8996668e+08 6.36e+00 3.93e+01  -0.4 5.05e+02    -  7.87e-01 1.00e+00f  1\n",
      " 445r 1.8998186e+08 4.31e+00 1.12e+01  -0.4 5.06e+02    -  1.00e+00 1.00e+00h  1\n",
      " 446r 1.8994236e+08 5.70e+00 1.04e+01  -0.4 5.10e+02    -  1.00e+00 1.00e+00f  1\n",
      " 447r 1.8993945e+08 6.03e+00 3.79e+01  -0.4 5.07e+02    -  7.64e-01 1.00e+00h  1\n",
      " 448r 1.8996606e+08 7.83e-02 7.14e+01  -0.4 5.09e+02    -  9.88e-01 5.00e-01f  2\n",
      " 449r 1.8996949e+08 3.96e-02 3.59e+01  -0.4 1.35e+02    -  9.18e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 450r 1.8995380e+08 3.20e-02 3.93e+01  -0.4 2.56e+02    -  1.00e+00 5.00e-01h  2\n",
      " 451r 1.8994139e+08 2.84e-02 4.27e+01  -0.4 3.58e+02    -  1.00e+00 5.00e-01h  2\n",
      " 452r 1.8993968e+08 3.20e-02 3.62e+01  -0.4 3.31e+02    -  1.00e+00 5.00e-01h  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 453r 1.8989218e+08 4.35e+00 3.06e+01  -0.4 3.66e+02    -  9.31e-01 5.92e-01H  1\n",
      " 454r 1.8987530e+08 5.06e-02 2.91e+01  -0.4 4.28e+02    -  7.16e-01 4.20e-01F  1\n",
      " 455r 1.8991569e+08 1.24e-01 9.26e+01  -0.4 3.69e+02    -  9.57e-01 5.00e-01f  2\n",
      " 456r 1.8989744e+08 5.47e+00 3.78e+01  -0.4 2.57e+02    -  8.95e-01 6.04e-01H  1\n",
      " 457r 1.8985835e+08 3.31e+00 2.67e+01  -0.4 4.60e+02    -  7.07e-01 5.27e-01F  1\n",
      " 458r 1.8990449e+08 7.98e-02 4.58e+01  -0.4 2.84e+02    -  8.98e-01 5.00e-01f  2\n",
      " 459r 1.8992848e+08 5.90e-02 2.40e+01  -0.4 3.63e+02    -  6.30e-01 4.51e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 460r 1.8993876e+08 5.42e-01 6.45e+00  -0.4 3.40e+02    -  5.50e-01 5.00e-01h  2\n",
      " 461r 1.8990465e+08 1.11e+01 3.27e+01  -0.4 3.55e+02    -  9.53e-01 6.78e-01H  1\n",
      " 462r 1.8983491e+08 2.60e+00 7.81e+00  -0.4 4.55e+02    -  7.25e-01 7.15e-01F  1\n",
      " 463r 1.8990346e+08 9.00e-02 5.93e+01  -0.4 4.00e+02    -  9.53e-01 5.00e-01f  2\n",
      " 464r 1.8980891e+08 1.02e+01 2.16e+01  -0.4 2.64e+02    -  6.67e-01 5.93e-01H  1\n",
      " 465r 1.8987391e+08 2.96e+00 1.56e+01  -0.4 4.30e+02    -  6.39e-01 5.00e-01f  2\n",
      " 466r 1.8992506e+08 5.50e-01 3.27e+01  -0.4 2.73e+02    -  7.01e-01 5.00e-01f  2\n",
      " 467r 1.8992802e+08 2.93e-02 5.91e+01  -0.4 3.93e+02    -  7.79e-01 2.50e-01h  3\n",
      " 468r 1.8993654e+08 1.05e+01 6.96e+00  -0.4 2.91e+02    -  9.54e-01 1.00e+00H  1\n",
      " 469r 1.8993977e+08 1.14e+00 4.57e+00  -0.4 6.09e+02    -  4.59e-01 5.00e-01f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 470r 1.8995679e+08 6.13e-02 3.63e+01  -0.4 3.65e+02    -  1.00e+00 5.00e-01h  2\n",
      " 471r 1.8995572e+08 4.76e-02 5.28e+01  -0.4 3.26e+02    -  1.00e+00 2.50e-01h  3\n",
      " 472r 1.8996216e+08 3.39e+00 1.58e+00  -0.4 1.42e+02    -  1.00e+00 1.00e+00h  1\n",
      " 473r 1.8990898e+08 2.71e+00 5.24e+01  -0.4 3.35e+02    -  7.65e-01 5.09e-01H  1\n",
      " 474r 1.8991484e+08 4.28e-02 1.66e+01  -0.4 3.98e+02    -  7.03e-01 5.00e-01f  2\n",
      " 475r 1.8993134e+08 9.47e-02 4.82e+01  -0.4 3.21e+02    -  7.07e-01 3.70e-01h  2\n",
      " 476r 1.8988745e+08 4.62e+00 3.62e+01  -0.4 3.38e+02    -  1.00e+00 7.83e-01H  1\n",
      " 477r 1.8980954e+08 2.04e+00 2.25e+01  -0.4 4.68e+02    -  6.15e-01 6.61e-01F  1\n",
      " 478r 1.8989122e+08 1.23e-01 6.04e+01  -0.4 2.72e+02    -  9.86e-01 5.00e-01f  2\n",
      " 479r 1.8990892e+08 9.44e-02 7.45e+01  -0.4 2.81e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 480r 1.8991847e+08 7.12e-02 7.48e+01  -0.4 3.03e+02    -  8.16e-01 2.50e-01h  3\n",
      " 481r 1.8992572e+08 5.63e-02 5.19e+01  -0.4 3.07e+02    -  1.00e+00 2.50e-01h  3\n",
      " 482r 1.8989221e+08 5.34e+00 2.60e+01  -0.4 2.99e+02    -  1.00e+00 8.23e-01H  1\n",
      " 483r 1.8987412e+08 3.10e+00 7.90e+00  -0.4 4.80e+02    -  5.98e-01 5.62e-01F  1\n",
      " 484r 1.8980382e+08 5.68e+00 2.89e+01  -0.4 3.58e+02    -  3.59e-01 4.41e-01H  1\n",
      " 485r 1.8987056e+08 2.36e+00 2.87e+01  -0.4 4.26e+02    -  6.63e-01 5.56e-01F  1\n",
      " 486r 1.8988701e+08 7.84e-01 4.82e+01  -0.4 3.63e+02    -  6.74e-01 2.50e-01f  3\n",
      " 487r 1.8990349e+08 8.86e-02 1.45e+02  -0.4 3.43e+02    -  1.00e+00 2.37e-01h  3\n",
      " 488r 1.8991770e+08 6.57e-02 8.13e+01  -0.4 2.71e+02    -  1.00e+00 2.50e-01h  3\n",
      " 489r 1.8994940e+08 4.10e+00 5.27e+00  -0.4 2.10e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 490r 1.8992011e+08 1.15e-01 6.70e+01  -0.4 4.73e+02    -  9.59e-01 5.05e-01F  1\n",
      " 491r 1.8992878e+08 8.74e-02 1.18e+02  -0.4 3.55e+02    -  1.00e+00 2.50e-01f  3\n",
      " 492r 1.8994122e+08 6.68e-02 7.97e+01  -0.4 2.86e+02    -  1.00e+00 2.50e-01h  3\n",
      " 493r 1.8988818e+08 1.28e+01 1.86e+01  -0.4 3.01e+02    -  9.04e-01 7.38e-01H  1\n",
      " 494r 1.8990988e+08 4.34e+00 6.03e+00  -0.4 5.09e+02    -  5.76e-01 5.00e-01f  2\n",
      " 495r 1.8991727e+08 1.90e+00 6.28e+01  -0.4 4.31e+02    -  7.85e-01 1.25e-01h  4\n",
      " 496r 1.8993964e+08 3.16e-02 5.33e+01  -0.4 3.96e+02    -  6.39e-01 2.50e-01h  3\n",
      " 497r 1.8994693e+08 2.46e-02 7.21e+01  -0.4 2.27e+02    -  8.11e-01 2.50e-01h  3\n",
      " 498r 1.8995332e+08 2.08e-02 4.80e+01  -0.4 2.85e+02    -  1.00e+00 2.50e-01h  3\n",
      " 499r 1.8989102e+08 5.26e+00 1.38e+01  -0.4 3.01e+02    -  8.91e-01 9.66e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 500r 1.8992928e+08 5.33e-02 1.78e+01  -0.4 5.36e+02    -  6.22e-01 5.00e-01f  2\n",
      " 501r 1.8993689e+08 4.79e-02 9.76e+01  -0.4 5.53e+02    -  1.00e+00 1.60e-01f  3\n",
      " 502r 1.8995469e+08 4.06e-02 7.84e+01  -0.4 3.81e+02    -  1.00e+00 2.50e-01h  3\n",
      " 503r 1.8995462e+08 3.65e-02 7.93e+01  -0.4 3.08e+02    -  9.00e-01 1.25e-01h  4\n",
      " 504r 1.8996566e+08 3.31e+00 4.42e+00  -0.4 1.49e+02    -  1.00e+00 1.00e+00H  1\n",
      " 505r 1.8991778e+08 5.31e-02 8.59e+00  -0.4 3.64e+02    -  4.34e-01 5.08e-01F  1\n",
      " 506r 1.8993051e+08 3.98e-02 4.05e+01  -0.4 3.29e+02    -  8.48e-01 2.50e-01f  3\n",
      " 507r 1.8995097e+08 2.67e-02 3.82e+01  -0.4 2.49e+02    -  1.00e+00 5.00e-01h  2\n",
      " 508r 1.8995178e+08 2.81e-02 8.81e+01  -0.4 3.40e+02    -  1.00e+00 2.50e-01h  3\n",
      " 509r 1.8995264e+08 2.52e-02 4.82e+01  -0.4 2.31e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 510r 1.8995244e+08 2.21e-02 6.14e+01  -0.4 2.49e+02    -  1.00e+00 1.25e-01h  4\n",
      " 511r 1.8989139e+08 6.99e+00 1.16e+01  -0.4 6.84e+01    -  8.53e-01 6.76e-01H  1\n",
      " 512r 1.8990492e+08 4.33e+00 3.71e+01  -0.4 1.44e+02    -  7.83e-01 2.50e-01f  3\n",
      " 513r 1.8992615e+08 1.41e+00 2.80e+01  -0.4 2.41e+02    -  2.31e-01 1.71e-01f  3\n",
      " 514r 1.8993920e+08 9.52e-03 4.79e+01  -0.4 2.11e+02    -  1.00e+00 2.50e-01h  3\n",
      " 515r 1.8994312e+08 9.12e-03 7.32e+01  -0.4 2.68e+02    -  9.94e-01 1.25e-01h  4\n",
      " 516r 1.8995726e+08 8.62e+00 3.40e+01  -0.4 2.55e+02    -  1.00e+00 6.88e-01H  1\n",
      " 517r 1.8992372e+08 1.52e-01 4.79e+01  -0.4 4.23e+02    -  7.07e-01 1.00e+00f  1\n",
      " 518r 1.8997941e+08 2.93e+00 2.11e+01  -0.4 3.90e+02    -  7.44e-01 9.67e-01f  1\n",
      " 519r 1.8996694e+08 3.81e+00 1.05e+01  -0.4 5.23e+02    -  8.61e-01 7.84e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 520r 1.8995536e+08 1.48e-01 4.39e+01  -0.4 4.06e+02    -  9.86e-01 5.00e-01f  2\n",
      " 521r 1.8995609e+08 8.95e-02 3.83e+01  -0.4 3.05e+02    -  1.00e+00 5.00e-01h  2\n",
      " 522r 1.8994786e+08 7.85e-02 3.93e+01  -0.4 3.35e+02    -  1.00e+00 5.00e-01h  2\n",
      " 523r 1.8993430e+08 6.67e-02 2.23e+01  -0.4 3.15e+02    -  8.83e-01 5.00e-01h  2\n",
      " 524r 1.8993231e+08 6.34e-02 4.65e+01  -0.4 3.31e+02    -  7.99e-01 2.50e-01h  3\n",
      " 525r 1.8994866e+08 4.48e-02 2.82e+01  -0.4 2.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 526r 1.8994752e+08 5.39e-02 4.12e+01  -0.4 3.89e+02    -  1.00e+00 5.00e-01h  2\n",
      " 527r 1.8994942e+08 5.03e-02 4.19e+01  -0.4 3.55e+02    -  1.00e+00 5.00e-01h  2\n",
      " 528r 1.8994666e+08 4.00e-02 5.98e+01  -0.4 3.74e+02    -  9.86e-01 2.50e-01h  3\n",
      " 529r 1.8995961e+08 3.02e-02 2.16e+01  -0.4 9.07e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 530r 1.8994250e+08 4.08e+00 1.57e+01  -0.4 2.50e+02    -  1.00e+00 8.38e-01w  1\n",
      " 531r 1.8994897e+08 5.93e+00 5.19e+01  -0.4 3.58e+02    -  6.44e-01 1.00e+00w  1\n",
      " 532r 1.8999705e+08 3.38e+00 1.45e+01  -0.4 4.90e+02    -  8.66e-01 1.00e+00w  1\n",
      " 533r 1.8995533e+08 3.29e-02 7.68e+01  -0.4 5.03e+02    -  1.00e+00 2.09e-01h  2\n",
      " 534r 1.8992411e+08 6.16e+00 9.68e+00  -0.4 2.57e+02    -  8.87e-01 8.09e-01H  1\n",
      " 535r 1.8989907e+08 3.12e+00 2.09e+01  -0.4 5.15e+02    -  5.62e-01 4.59e-01F  1\n",
      " 536r 1.8993242e+08 7.21e-02 3.64e+01  -0.4 3.72e+02    -  8.43e-01 5.00e-01f  2\n",
      " 537r 1.8993726e+08 5.60e-02 6.76e+01  -0.4 3.15e+02    -  1.00e+00 2.50e-01h  3\n",
      " 538r 1.8992504e+08 9.90e+00 6.30e+00  -0.4 1.45e+02    -  1.00e+00 1.00e+00H  1\n",
      " 539r 1.8995662e+08 2.43e+00 4.65e+01  -0.4 3.58e+02    -  6.01e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 540r 1.8999949e+08 3.27e+00 1.05e+01  -0.4 4.39e+02    -  8.44e-01 1.00e+00f  1\n",
      " 541r 1.8995917e+08 4.78e+00 7.54e+00  -0.4 5.08e+02    -  9.19e-01 1.00e+00f  1\n",
      " 542r 1.8995166e+08 1.88e+00 1.12e+01  -0.4 5.19e+02    -  8.07e-01 7.19e-01f  1\n",
      " 543r 1.8994726e+08 1.50e-01 1.71e+01  -0.4 3.79e+02    -  7.38e-01 5.00e-01f  2\n",
      " 544r 1.8995184e+08 8.45e-02 3.38e+01  -0.4 3.44e+02    -  1.00e+00 5.00e-01h  2\n",
      " 545r 1.8994397e+08 7.32e-02 3.89e+01  -0.4 3.66e+02    -  1.00e+00 5.00e-01h  2\n",
      " 546r 1.8994283e+08 6.54e-02 3.96e+01  -0.4 3.46e+02    -  1.00e+00 5.00e-01h  2\n",
      " 547r 1.8994317e+08 7.61e-02 2.95e+01  -0.4 3.49e+02    -  8.03e-01 4.42e-01h  2\n",
      " 548r 1.8991069e+08 1.17e+01 6.95e+00  -0.4 3.30e+02    -  1.00e+00 1.00e+00H  1\n",
      " 549r 1.8982813e+08 4.92e+00 2.87e+01  -0.4 5.82e+02    -  3.84e-01 5.36e-01F  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 550r 1.8990488e+08 1.12e-01 4.60e+01  -0.4 2.66e+02    -  8.48e-01 5.00e-01f  2\n",
      " 551r 1.8993451e+08 6.66e-02 2.01e+01  -0.4 2.19e+02    -  8.39e-01 5.00e-01h  2\n",
      " 552r 1.8993623e+08 6.36e-02 4.29e+01  -0.4 3.76e+02    -  1.00e+00 5.00e-01h  2\n",
      " 553r 1.8993933e+08 5.68e-02 3.78e+01  -0.4 3.30e+02    -  1.00e+00 5.00e-01h  2\n",
      " 554r 1.8993835e+08 7.20e-02 2.83e+01  -0.4 3.62e+02    -  7.95e-01 4.43e-01h  2\n",
      " 555r 1.8994614e+08 5.54e-02 3.25e+01  -0.4 3.30e+02    -  1.00e+00 5.00e-01h  2\n",
      " 556r 1.8994279e+08 4.17e-02 5.69e+01  -0.4 3.63e+02    -  9.70e-01 2.50e-01h  3\n",
      " 557r 1.8996893e+08 8.13e-02 2.28e+00  -0.4 1.37e+02    -  1.00e+00 1.00e+00h  1\n",
      " 558r 1.8996296e+08 3.86e-02 9.52e+01  -0.4 3.28e+02    -  1.00e+00 5.00e-01f  2\n",
      " 559r 1.8983889e+08 8.84e+00 4.18e+01  -0.4 3.54e+02    -  7.90e-01 5.45e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 560r 1.8987719e+08 1.42e+00 1.86e+01  -0.4 3.87e+02    -  7.34e-01 5.00e-01f  2\n",
      " 561r 1.8987488e+08 3.06e-01 2.53e+01  -0.4 2.40e+02    -  5.09e-01 3.46e-01H  1\n",
      " 562r 1.8991194e+08 3.70e-02 9.37e+00  -0.4 3.38e+02    -  5.36e-01 5.00e-01f  2\n",
      " 563r 1.8993300e+08 2.85e+00 4.61e+01  -0.4 3.11e+02    -  1.00e+00 4.32e-01h  2\n",
      " 564r 1.8984783e+08 1.14e+01 4.60e+01  -0.4 3.33e+02    -  4.70e-01 6.47e-01H  1\n",
      " 565r 1.8994264e+08 1.96e-02 1.85e+01  -0.4 4.90e+02    -  3.78e-01 5.00e-01f  2\n",
      " 566r 1.8994519e+08 1.85e-02 6.97e+01  -0.4 2.25e+02    -  9.72e-01 2.50e-01h  3\n",
      " 567r 1.8990358e+08 9.97e+00 7.73e+00  -0.4 1.42e+02    -  1.00e+00 1.00e+00H  1\n",
      " 568r 1.8992735e+08 1.29e+00 1.52e+01  -0.4 3.74e+02    -  4.22e-01 5.00e-01f  2\n",
      " 569r 1.8995034e+08 2.06e-02 5.62e+01  -0.4 2.80e+02    -  9.94e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 570r 1.8994749e+08 1.72e-02 1.13e+02  -0.4 2.89e+02    -  1.00e+00 2.50e-01h  3\n",
      " 571r 1.8994667e+08 4.08e+00 4.93e+00  -0.4 2.44e+02    -  1.00e+00 1.00e+00H  1\n",
      " 572r 1.8991275e+08 1.92e-01 6.01e+01  -0.4 5.84e+02    -  9.20e-01 4.90e-01F  1\n",
      " 573r 1.8993054e+08 6.07e-02 4.73e+01  -0.4 3.36e+02    -  8.39e-01 5.00e-01f  2\n",
      " 574r 1.8993443e+08 4.56e-02 6.65e+01  -0.4 2.81e+02    -  8.69e-01 2.50e-01h  3\n",
      " 575r 1.8996777e+08 5.17e-02 1.50e+00  -0.4 2.60e+02    -  1.00e+00 1.00e+00h  1\n",
      " 576r 1.8996389e+08 4.89e-02 1.39e+02  -0.4 4.63e+02    -  1.00e+00 2.50e-01f  3\n",
      " 577r 1.8996764e+08 4.14e-02 8.82e+01  -0.4 3.44e+02    -  1.00e+00 2.50e-01h  3\n",
      " 578r 1.8996505e+08 3.21e-02 5.58e+01  -0.4 2.50e+02    -  1.00e+00 2.50e-01h  3\n",
      " 579r 1.8992298e+08 4.00e+00 4.01e+00  -0.4 3.34e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 580r 1.8994421e+08 8.14e-02 6.86e+01  -0.4 6.33e+02    -  1.00e+00 5.00e-01f  2\n",
      " 581r 1.8995493e+08 6.56e-02 6.78e+01  -0.4 4.27e+02    -  1.00e+00 2.50e-01h  3\n",
      " 582r 1.8995620e+08 5.04e-02 5.34e+01  -0.4 2.44e+02    -  1.00e+00 2.50e-01h  3\n",
      " 583r 1.8989920e+08 9.38e+00 2.86e+01  -0.4 2.64e+02    -  9.16e-01 6.22e-01H  1\n",
      " 584r 1.8991366e+08 2.08e+00 1.05e+01  -0.4 4.01e+02    -  6.90e-01 5.00e-01f  2\n",
      " 585r 1.8992760e+08 2.66e-02 4.50e+01  -0.4 3.21e+02    -  8.12e-01 2.30e-01h  3\n",
      " 586r 1.8993118e+08 1.09e+01 1.96e+01  -0.4 1.69e+02    -  6.76e-01 6.32e-01H  1\n",
      " 587r 1.8993093e+08 7.01e+00 4.08e+01  -0.4 3.55e+02    -  5.94e-01 2.50e-01f  3\n",
      " 588r 1.8993923e+08 1.78e+00 2.96e+01  -0.4 6.26e+02    -  2.53e-01 4.27e-01F  1\n",
      " 589r 1.8994578e+08 1.43e-01 5.93e+01  -0.4 2.64e+02    -  1.00e+00 2.50e-01f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 590r 1.8994906e+08 1.08e-01 4.64e+01  -0.4 3.12e+02    -  1.00e+00 2.50e-01h  3\n",
      " 591r 1.8988712e+08 7.85e+00 1.98e+01  -0.4 3.09e+02    -  8.07e-01 6.02e-01H  1\n",
      " 592r 1.8989841e+08 4.89e+00 5.02e+01  -0.4 4.65e+02    -  7.70e-01 2.44e-01f  3\n",
      " 593r 1.8991708e+08 7.15e-01 5.25e+01  -0.4 5.10e+02    -  2.61e-01 1.58e-01f  3\n",
      " 594r 1.8993655e+08 4.89e-02 6.73e+01  -0.4 4.23e+02    -  1.00e+00 2.50e-01h  3\n",
      " 595r 1.8994607e+08 3.73e-02 5.37e+01  -0.4 2.82e+02    -  9.45e-01 2.50e-01h  3\n",
      " 596r 1.8994912e+08 2.81e-02 5.88e+01  -0.4 3.04e+02    -  1.00e+00 2.50e-01h  3\n",
      " 597r 1.8994919e+08 2.46e-02 6.42e+01  -0.4 2.88e+02    -  1.00e+00 2.50e-01h  3\n",
      " 598r 1.8994801e+08 3.23e+00 4.49e+00  -0.4 2.98e+02    -  1.00e+00 1.00e+00H  1\n",
      " 599r 1.8995113e+08 4.58e-02 1.14e+02  -0.4 5.76e+02    -  1.00e+00 2.50e-01f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 600r 1.8996475e+08 4.23e-02 9.66e+01  -0.4 5.23e+02    -  1.00e+00 2.50e-01f  3\n",
      " 601r 1.8996392e+08 3.61e-02 6.88e+01  -0.4 3.67e+02    -  1.00e+00 2.50e-01h  3\n",
      " 602r 1.8995871e+08 2.81e-02 4.95e+01  -0.4 2.22e+02    -  1.00e+00 2.50e-01h  3\n",
      " 603r 1.8995609e+08 2.07e-02 4.71e+01  -0.4 2.60e+02    -  1.00e+00 2.50e-01h  3\n",
      " 604r 1.8995634e+08 1.82e-02 5.34e+01  -0.4 2.85e+02    -  1.00e+00 1.25e-01h  4\n",
      " 605r 1.8995741e+08 1.59e-02 4.98e+01  -0.4 1.23e+02    -  1.00e+00 1.25e-01h  4\n",
      " 606r 1.8995786e+08 1.39e-02 5.66e+01  -0.4 8.98e+01    -  1.00e+00 1.25e-01h  4\n",
      " 607r 1.8995722e+08 1.31e-02 3.47e+01  -0.4 1.24e+02    -  1.00e+00 6.25e-02h  5\n",
      " 608r 1.8995608e+08 2.40e+00 4.58e+00  -0.4 2.32e+02    -  1.00e+00 1.00e+00H  1\n",
      " 609r 1.8995554e+08 4.85e-02 6.01e+01  -0.4 5.86e+02    -  7.34e-01 2.50e-01f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 610r 1.8996131e+08 4.42e-02 9.82e+01  -0.4 5.29e+02    -  1.00e+00 1.41e-01h  3\n",
      " 611r 1.8997109e+08 3.81e-02 8.92e+01  -0.4 4.13e+02    -  1.00e+00 2.50e-01h  3\n",
      " 612r 1.8996466e+08 3.04e-02 5.92e+01  -0.4 2.01e+02    -  1.00e+00 2.50e-01h  3\n",
      " 613r 1.8995814e+08 2.31e-02 6.09e+01  -0.4 1.98e+02    -  1.00e+00 2.50e-01h  3\n",
      " 614r 1.8988254e+08 1.02e+01 3.64e+01  -0.4 2.66e+02    -  9.46e-01 6.78e-01H  1\n",
      " 615r 1.8992390e+08 1.41e+00 5.63e+01  -0.4 4.76e+02    -  7.26e-01 1.00e+00f  1\n",
      " 616r 1.8998714e+08 5.58e+00 1.32e+01  -0.4 3.58e+02    -  8.57e-01 1.00e+00f  1\n",
      " 617r 1.8996028e+08 5.69e+00 2.15e+01  -0.4 5.31e+02    -  8.65e-01 1.00e+00f  1\n",
      " 618r 1.8995935e+08 5.54e+00 2.94e+01  -0.4 5.22e+02    -  7.90e-01 1.00e+00h  1\n",
      " 619r 1.8997764e+08 6.11e+00 1.21e+01  -0.4 5.73e+02    -  1.00e+00 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 620r 1.8994253e+08 5.53e+00 1.22e+01  -0.4 5.73e+02    -  9.36e-01 1.00e+00h  1\n",
      " 621r 1.8995343e+08 5.52e+00 1.22e+01  -0.4 5.70e+02    -  9.88e-01 1.00e+00f  1\n",
      " 622r 1.8994619e+08 3.42e-01 6.26e+01  -0.4 5.70e+02    -  8.84e-01 5.00e-01h  2\n",
      " 623r 1.8995606e+08 6.95e-02 3.79e+01  -0.4 1.55e+02    -  1.00e+00 5.00e-01h  2\n",
      " 624r 1.8994899e+08 6.25e-02 4.34e+01  -0.4 2.58e+02    -  1.00e+00 5.00e-01h  2\n",
      " 625r 1.8994483e+08 6.33e-02 3.90e+01  -0.4 3.65e+02    -  1.00e+00 5.00e-01h  2\n",
      " 626r 1.8994562e+08 5.54e-02 5.43e+01  -0.4 3.31e+02    -  1.00e+00 2.50e-01h  3\n",
      " 627r 1.8995210e+08 2.76e-02 4.12e+01  -0.4 2.45e+02    -  1.00e+00 5.00e-01h  2\n",
      " 628r 1.8994886e+08 2.13e-02 5.88e+01  -0.4 3.30e+02    -  1.00e+00 5.00e-01h  2\n",
      " 629r 1.8994283e+08 1.86e-02 9.50e+01  -0.4 2.72e+02    -  9.24e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 630r 1.8995593e+08 1.12e-02 1.98e+01  -0.4 7.93e+01    -  1.00e+00 5.00e-01h  2\n",
      " 631r 1.8995194e+08 1.98e-02 6.97e+01  -0.4 2.66e+02    -  9.58e-01 2.15e-01h  3\n",
      " 632r 1.8996608e+08 3.24e+00 2.24e+00  -0.4 2.51e+02    -  1.00e+00 1.00e+00w  1\n",
      " 633r 1.8994875e+08 3.59e+00 2.20e+01  -0.4 5.07e+02    -  1.00e+00 8.26e-01w  1\n",
      " 634r 1.8994198e+08 5.33e+00 9.07e+00  -0.4 4.09e+02    -  1.00e+00 1.00e+00w  1\n",
      " 635r 1.8995901e+08 1.75e-02 3.05e+01  -0.4 4.95e+02    -  1.00e+00 5.00e-01h  1\n",
      " 636r 1.8995442e+08 2.22e-02 7.16e+01  -0.4 3.80e+02    -  1.00e+00 2.50e-01h  3\n",
      " 637r 1.8995483e+08 1.95e-02 3.77e+01  -0.4 1.12e+02    -  1.00e+00 2.50e-01h  3\n",
      " 638r 1.8995013e+08 2.35e-02 4.05e+01  -0.4 1.34e+02    -  1.00e+00 2.50e-01h  3\n",
      " 639r 1.8991961e+08 3.17e+00 3.46e+00  -0.4 2.40e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 640r 1.8990135e+08 1.31e+00 9.03e+01  -0.4 6.03e+02    -  1.00e+00 4.88e-01F  1\n",
      " 641r 1.8991522e+08 7.28e-02 6.28e+01  -0.4 3.30e+02    -  9.34e-01 2.50e-01f  3\n",
      " 642r 1.8994905e+08 4.59e-02 4.42e+01  -0.4 2.63e+02    -  1.00e+00 5.00e-01h  2\n",
      " 643r 1.8994793e+08 4.57e-02 6.35e+01  -0.4 3.63e+02    -  1.00e+00 2.50e-01h  3\n",
      " 644r 1.8995042e+08 3.59e-02 6.58e+01  -0.4 2.72e+02    -  1.00e+00 2.50e-01h  3\n",
      " 645r 1.8989121e+08 5.45e+00 1.64e+01  -0.4 2.45e+02    -  9.35e-01 7.79e-01H  1\n",
      " 646r 1.8994693e+08 1.91e-01 3.23e+01  -0.4 5.05e+02    -  7.71e-01 1.00e+00f  1\n",
      " 647r 1.8996853e+08 5.81e+00 3.84e+01  -0.4 3.92e+02    -  6.65e-01 1.00e+00f  1\n",
      " 648r 1.8998514e+08 3.88e+00 2.02e+01  -0.4 5.24e+02    -  8.26e-01 1.00e+00f  1\n",
      " 649r 1.8996586e+08 4.40e+00 1.40e+01  -0.4 5.10e+02    -  8.87e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 650r 1.8996385e+08 4.97e+00 1.15e+01  -0.4 5.14e+02    -  1.00e+00 9.21e-01f  1\n",
      " 651r 1.8992019e+08 5.72e+00 1.09e+01  -0.4 4.73e+02    -  9.52e-01 9.75e-01f  1\n",
      " 652r 1.8994829e+08 4.17e+00 1.22e+01  -0.4 5.03e+02    -  1.00e+00 9.14e-01f  1\n",
      " 653r 1.8993822e+08 1.61e-01 6.80e+01  -0.4 4.71e+02    -  1.00e+00 5.00e-01f  2\n",
      " 654r 1.8994168e+08 6.85e-02 4.46e+01  -0.4 2.42e+02    -  1.00e+00 5.00e-01h  2\n",
      " 655r 1.8994237e+08 3.92e-02 5.56e+01  -0.4 3.36e+02    -  1.00e+00 5.00e-01h  2\n",
      " 656r 1.8994406e+08 3.43e-02 5.00e+01  -0.4 2.89e+02    -  1.00e+00 5.00e-01h  2\n",
      " 657r 1.8994288e+08 2.89e-02 8.46e+01  -0.4 3.09e+02    -  1.00e+00 2.50e-01h  3\n",
      " 658r 1.8996664e+08 1.04e+00 2.18e+00  -0.4 1.39e+02    -  1.00e+00 1.00e+00h  1\n",
      " 659r 1.8993375e+08 2.89e+00 8.27e+01  -0.4 3.37e+02    -  1.00e+00 4.90e-01F  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 660r 1.8990209e+08 3.09e+00 4.35e+01  -0.4 3.78e+02    -  9.12e-01 5.31e-01F  1\n",
      " 661r 1.8991303e+08 5.58e-02 4.92e+01  -0.4 3.70e+02    -  7.33e-01 3.53e-01f  2\n",
      " 662r 1.8992581e+08 3.61e-02 1.18e+02  -0.4 3.78e+02    -  1.00e+00 2.50e-01h  3\n",
      " 663r 1.8996731e+08 9.96e-02 2.23e+00  -0.4 2.63e+02    -  1.00e+00 1.00e+00h  1\n",
      " 664r 1.8993669e+08 3.14e+00 7.61e+01  -0.4 5.23e+02    -  1.00e+00 4.97e-01F  1\n",
      " 665r 1.8993167e+08 9.77e-01 7.48e+01  -0.4 2.64e+02    -  8.81e-01 2.36e-01f  3\n",
      " 666r 1.8995593e+08 4.91e+00 1.05e+02  -0.4 3.30e+02    -  1.00e+00 5.57e-01H  1\n",
      " 667r 1.8994696e+08 1.38e+00 2.39e+01  -0.4 4.06e+02    -  6.46e-01 2.50e-01f  3\n",
      " 668r 1.8994721e+08 1.46e-01 1.27e+02  -0.4 5.63e+02    -  7.91e-01 2.50e-01f  3\n",
      " 669r 1.8994881e+08 1.01e+00 9.41e+01  -0.4 4.86e+02    -  6.82e-01 3.61e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 670r 1.8988561e+08 1.29e+01 3.82e+01  -0.4 2.83e+02    -  1.00e+00 7.21e-01H  1\n",
      " 671r 1.8982673e+08 3.77e+00 8.61e+00  -0.4 3.99e+02    -  6.31e-01 5.58e-01F  1\n",
      " 672r 1.8986148e+08 7.71e-01 4.57e+01  -0.4 2.87e+02    -  6.75e-01 2.50e-01f  3\n",
      " 673r 1.8992203e+08 6.39e-02 4.14e+01  -0.4 3.08e+02    -  1.00e+00 5.00e-01h  2\n",
      " 674r 1.8988538e+08 6.24e+00 9.47e+00  -0.4 3.70e+02    -  7.69e-01 7.54e-01H  1\n",
      " 675r 1.8992174e+08 8.41e-01 1.95e+01  -0.4 4.95e+02    -  6.19e-01 5.00e-01f  2\n",
      " 676r 1.8988099e+08 1.83e+00 1.12e+02  -0.4 3.70e+02    -  6.80e-01 2.80e-01H  1\n",
      " 677r 1.8991164e+08 2.03e-02 2.19e+01  -0.4 2.43e+02    -  6.50e-01 5.00e-01f  2\n",
      " 678r 1.8992494e+08 5.04e-02 3.86e+01  -0.4 2.80e+02    -  8.66e-01 3.96e-01h  2\n",
      " 679r 1.8992722e+08 1.35e-02 6.55e+01  -0.4 2.99e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 680r 1.8995020e+08 1.03e-02 2.77e+01  -0.4 1.15e+02    -  1.00e+00 5.00e-01h  2\n",
      " 681r 1.8994851e+08 1.77e-02 7.68e+01  -0.4 2.66e+02    -  1.00e+00 2.10e-01h  3\n",
      " 682r 1.8994888e+08 1.56e-02 7.05e+01  -0.4 2.49e+02    -  1.00e+00 2.50e-01h  3\n",
      " 683r 1.8989655e+08 7.34e+00 4.15e+01  -0.4 2.49e+02    -  9.86e-01 6.79e-01H  1\n",
      " 684r 1.8990407e+08 4.34e+00 3.94e+01  -0.4 4.02e+02    -  6.60e-01 2.50e-01f  3\n",
      " 685r 1.8992861e+08 1.61e-02 8.58e+01  -0.4 3.97e+02    -  8.94e-01 2.98e-01f  2\n",
      " 686r 1.8993916e+08 2.08e-02 7.81e+01  -0.4 2.66e+02    -  8.74e-01 2.50e-01h  3\n",
      " 687r 1.8992141e+08 5.78e+00 6.78e+00  -0.4 3.29e+02    -  9.26e-01 9.19e-01H  1\n",
      " 688r 1.8990547e+08 5.97e-02 4.58e+00  -0.4 5.61e+02    -  5.16e-01 5.07e-01F  1\n",
      " 689r 1.1880108e+08 3.58e-01 1.08e+01  -1.1 3.41e+02    -  8.69e-01 9.51e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 690r 1.2343269e+08 6.14e-01 3.99e+00  -1.1 7.01e+01    -  1.00e+00 1.00e+00f  1\n",
      " 691r 1.2658107e+08 2.55e+00 4.08e+00  -1.1 1.56e+02    -  9.59e-01 1.00e+00h  1\n",
      " 692r 1.2654591e+08 1.60e+00 8.21e-01  -1.1 2.21e+02    -  1.00e+00 1.00e+00h  1\n",
      " 693r 1.2654245e+08 2.22e+00 8.62e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      " 694r 1.2654121e+08 3.48e-02 9.31e+01  -1.1 2.42e+02    -  1.00e+00 5.00e-01h  2\n",
      " 695r 1.2653953e+08 5.24e-03 2.75e-01  -1.1 6.74e+01    -  1.00e+00 1.00e+00h  1\n",
      " 696r 1.2653894e+08 5.63e-03 5.26e+01  -1.1 1.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 697r 1.2651420e+08 8.17e+00 2.68e+01  -1.1 1.50e+02    -  1.00e+00 8.31e-01H  1\n",
      " 698r 1.2653268e+08 4.30e-02 7.47e+01  -1.1 2.41e+02    -  6.52e-01 1.00e+00f  1\n",
      " 699r 1.2654038e+08 2.62e-02 7.80e+01  -1.1 2.05e+02    -  9.92e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 700r 1.2653431e+08 8.67e-03 4.07e+00  -1.1 1.07e+02    -  9.77e-01 1.00e+00h  1\n",
      " 701r 1.2653821e+08 9.65e-03 9.19e+01  -1.1 2.00e+02    -  1.00e+00 5.00e-01h  2\n",
      " 702r 1.2653210e+08 4.79e-03 5.67e+01  -1.1 6.22e+01    -  1.00e+00 5.00e-01h  2\n",
      " 703r 1.2652398e+08 4.61e+00 9.92e-01  -1.1 1.11e+02    -  1.00e+00 1.00e+00H  1\n",
      " 704r 1.2653027e+08 8.77e-03 8.22e+01  -1.1 2.30e+02    -  1.00e+00 5.00e-01h  2\n",
      " 705r 1.2652307e+08 5.93e+00 3.27e+01  -1.1 1.08e+02    -  1.00e+00 7.24e-01H  1\n",
      " 706r 1.2651441e+08 9.17e-01 7.52e+00  -1.1 1.55e+02    -  5.72e-01 5.30e-01F  1\n",
      " 707r 1.2650724e+08 1.90e+00 1.77e+01  -1.1 1.37e+02    -  5.58e-01 4.56e-01H  1\n",
      " 708r 1.2652148e+08 6.05e-03 4.39e+00  -1.1 1.11e+02    -  5.73e-01 5.00e-01f  2\n",
      " 709r 1.2652929e+08 3.85e-03 4.50e+01  -1.1 1.04e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 710r 1.2651639e+08 2.72e+00 1.45e+00  -1.1 1.09e+02    -  1.00e+00 9.90e-01H  1\n",
      " 711r 1.2652303e+08 8.65e-01 7.53e+01  -1.1 2.12e+02    -  7.05e-01 2.50e-01f  3\n",
      " 712r 1.2653558e+08 1.47e-02 8.26e+01  -1.1 1.89e+02    -  7.88e-01 3.50e-01h  2\n",
      " 713r 1.2653191e+08 9.87e-03 5.30e+01  -1.1 9.75e+01    -  1.00e+00 5.00e-01h  2\n",
      " 714r 1.2653382e+08 4.70e-03 3.94e+01  -1.1 1.00e+02    -  1.00e+00 5.00e-01h  2\n",
      " 715r 1.2651312e+08 7.48e+00 1.67e+01  -1.1 8.36e+01    -  1.00e+00 8.25e-01H  1\n",
      " 716r 1.2646002e+08 2.10e+00 5.81e+01  -1.1 1.44e+02    -  4.00e-01 7.40e-01H  1\n",
      " 717r 1.2657841e+08 1.77e-02 3.74e+01  -1.1 1.96e+02    -  6.16e-01 1.00e+00f  1\n",
      " 718r 1.2654387e+08 1.66e+00 1.85e+00  -1.1 1.97e+02    -  9.82e-01 1.00e+00h  1\n",
      " 719r 1.2654054e+08 9.81e-01 1.86e+01  -1.1 2.21e+02    -  1.00e+00 8.79e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 720r 1.2653868e+08 3.40e-02 5.91e+01  -1.1 1.95e+02    -  9.07e-01 5.00e-01h  2\n",
      " 721r 1.2653830e+08 3.01e-02 3.00e+01  -1.1 1.18e+02    -  1.00e+00 5.00e-01h  2\n",
      " 722r 1.2651510e+08 6.55e+00 1.86e+01  -1.1 1.07e+02    -  1.00e+00 7.97e-01H  1\n",
      " 723r 1.2652281e+08 1.49e+00 2.28e+01  -1.1 2.05e+02    -  7.01e-01 5.00e-01h  2\n",
      " 724r 1.2651977e+08 4.47e-01 5.90e+01  -1.1 1.57e+02    -  1.00e+00 4.31e-01H  1\n",
      " 725r 1.2652715e+08 5.70e-03 2.11e+01  -1.1 1.20e+02    -  5.78e-01 5.00e-01f  2\n",
      " 726r 1.2651856e+08 1.05e+00 1.12e+01  -1.1 1.41e+02    -  4.50e-01 4.30e-01h  2\n",
      " 727r 1.2652832e+08 3.03e-03 4.51e+01  -1.1 9.94e+01    -  1.00e+00 5.00e-01h  2\n",
      " 728r 1.2653222e+08 2.84e-03 4.21e+01  -1.1 1.04e+02    -  1.00e+00 5.00e-01h  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 729r 1.2653342e+08 2.89e-03 2.39e+01  -1.1 8.27e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 730r 1.2653371e+08 2.87e-03 3.64e+01  -1.1 1.02e+02    -  1.00e+00 5.00e-01h  2\n",
      " 731r 1.2653386e+08 2.79e-03 8.61e+01  -1.1 1.58e+02    -  1.00e+00 2.50e-01h  3\n",
      " 732r 1.2654162e+08 4.77e-03 2.09e-01  -1.1 2.77e+01    -  1.00e+00 1.00e+00h  1\n",
      " 733r 1.2654062e+08 2.27e-02 7.61e+01  -1.1 1.76e+02    -  1.00e+00 4.56e-01h  2\n",
      " 734r 1.2653632e+08 1.44e-02 3.52e+01  -1.1 1.12e+02    -  1.00e+00 5.00e-01h  2\n",
      " 735r 1.2653625e+08 1.12e-02 8.48e+01  -1.1 1.58e+02    -  1.00e+00 2.50e-01h  3\n",
      " 736r 1.2653863e+08 1.35e-03 2.36e-01  -1.1 3.56e+01    -  1.00e+00 1.00e+00h  1\n",
      " 737r 1.2653830e+08 1.83e-03 7.85e+01  -1.1 8.14e+01    -  1.00e+00 2.50e-01h  3\n",
      " 738r 1.2652643e+08 3.71e+00 2.94e+01  -1.1 9.88e+01    -  1.00e+00 7.09e-01H  1\n",
      " 739r 1.2652580e+08 1.62e+00 6.52e+01  -1.1 1.96e+02    -  7.66e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 740r 1.2653192e+08 4.56e-03 7.17e+01  -1.1 1.64e+02    -  5.92e-01 2.17e-01h  3\n",
      " 741r 1.2653778e+08 8.04e+00 1.16e+01  -1.1 1.02e+02    -  1.00e+00 9.00e-01H  1\n",
      " 742r 1.2653852e+08 6.53e+00 6.26e+01  -1.1 1.76e+02    -  5.62e-01 1.25e-01f  4\n",
      " 743r 1.2654020e+08 3.50e+00 6.87e+01  -1.1 2.65e+02    -  3.65e-01 1.70e-01f  3\n",
      " 744r 1.2655762e+08 3.20e+00 4.03e+00  -1.1 1.83e+02    -  9.17e-01 9.32e-01h  1\n",
      " 745r 1.2654439e+08 9.15e-01 4.35e+00  -1.1 1.82e+02    -  9.66e-01 9.33e-01h  1\n",
      " 746r 1.2654381e+08 1.68e+00 1.61e+01  -1.1 1.65e+02    -  8.93e-01 1.00e+00h  1\n",
      " 747r 1.2653800e+08 9.03e-01 8.78e-01  -1.1 1.94e+02    -  1.00e+00 1.00e+00h  1\n",
      " 748r 1.2654362e+08 4.37e-01 3.38e+01  -1.1 1.87e+02    -  1.00e+00 8.18e-01h  1\n",
      " 749r 1.2653856e+08 4.11e-02 6.82e+01  -1.1 1.24e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 750r 1.2653525e+08 1.35e+00 2.85e-01  -1.1 1.07e+02    -  1.00e+00 1.00e+00h  1\n",
      " 751r 1.2652665e+08 3.61e+00 6.54e+01  -1.1 1.90e+02    -  1.00e+00 6.28e-01H  1\n",
      " 752r 1.2650780e+08 2.24e+00 2.59e+01  -1.1 1.89e+02    -  8.35e-01 6.34e-01H  1\n",
      " 753r 1.2652172e+08 3.00e-02 6.73e+01  -1.1 1.51e+02    -  1.00e+00 4.66e-01h  2\n",
      " 754r 1.2654054e+08 8.50e-01 3.02e-01  -1.1 8.62e+01    -  1.00e+00 1.00e+00h  1\n",
      " 755r 1.2654204e+08 7.18e-03 6.96e+01  -1.1 1.72e+02    -  1.00e+00 5.00e-01h  2\n",
      " 756r 1.2653981e+08 5.23e-03 3.57e+01  -1.1 8.94e+01    -  1.00e+00 5.00e-01h  2\n",
      " 757r 1.2654033e+08 4.82e-03 4.34e+01  -1.1 9.15e+01    -  1.00e+00 5.00e-01h  2\n",
      " 758r 1.2653811e+08 4.59e-03 8.30e+01  -1.1 1.38e+02    -  1.00e+00 2.50e-01h  3\n",
      " 759r 1.2653546e+08 2.69e-03 3.68e+01  -1.1 1.04e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 760r 1.2653524e+08 5.31e-03 8.78e+01  -1.1 1.58e+02    -  1.00e+00 2.39e-01h  3\n",
      " 761r 1.2653477e+08 3.93e+00 7.38e-01  -1.1 4.20e+01    -  1.00e+00 1.00e+00H  1\n",
      " 762r 1.2652384e+08 1.52e+00 6.02e+01  -1.1 1.59e+02    -  8.35e-01 5.20e-01H  1\n",
      " 763r 1.2652675e+08 3.32e-02 5.41e+01  -1.1 1.40e+02    -  9.81e-01 5.00e-01h  2\n",
      " 764r 1.2652836e+08 2.65e-02 1.25e+02  -1.1 2.22e+02    -  9.87e-01 2.44e-01h  3\n",
      " 765r 1.2652727e+08 5.60e+00 8.32e-01  -1.1 7.95e+01    -  1.00e+00 1.00e+00H  1\n",
      " 766r 1.2653218e+08 5.71e-03 7.07e+01  -1.1 1.90e+02    -  1.00e+00 5.00e-01h  2\n",
      " 767r 1.2653222e+08 2.26e+00 2.30e+01  -1.1 1.10e+02    -  9.69e-01 8.07e-01H  1\n",
      " 768r 1.2652546e+08 4.73e-02 1.80e+01  -1.1 1.65e+02    -  7.53e-01 6.41e-01F  1\n",
      " 769r 1.2651959e+08 1.29e+00 6.15e+01  -1.1 1.40e+02    -  1.00e+00 5.35e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 770r 1.2652425e+08 6.37e-03 2.72e+01  -1.1 1.12e+02    -  5.34e-01 5.00e-01f  2\n",
      " 771r 1.2652734e+08 4.86e-03 4.14e+01  -1.1 1.63e+02    -  6.21e-01 2.34e-01h  3\n",
      " 772r 1.2652458e+08 6.89e+00 3.34e+01  -1.1 8.09e+01    -  1.00e+00 6.71e-01H  1\n",
      " 773r 1.2652436e+08 2.10e+00 2.77e+01  -1.1 1.57e+02    -  7.73e-01 5.00e-01h  2\n",
      " 774r 1.2653033e+08 2.89e-03 4.77e+01  -1.1 1.50e+02    -  8.74e-01 4.02e-01h  2\n",
      " 775r 1.2653073e+08 2.57e-03 7.09e+01  -1.1 1.32e+02    -  9.75e-01 2.50e-01h  3\n",
      " 776r 1.2652847e+08 9.81e-01 5.56e-01  -1.1 1.05e+02    -  1.00e+00 1.00e+00H  1\n",
      " 777r 1.2653930e+08 4.31e-01 4.91e+00  -1.1 2.25e+02    -  9.02e-01 9.32e-01h  1\n",
      " 778r 1.2654372e+08 1.77e+00 5.93e-01  -1.1 1.99e+02    -  1.00e+00 1.00e+00h  1\n",
      " 779r 1.2653787e+08 2.10e+00 8.25e-01  -1.1 2.25e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 780r 1.2653725e+08 1.78e+00 8.00e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      " 781r 1.2653677e+08 2.12e+00 9.58e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      " 782r 1.2653742e+08 1.67e+00 8.98e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      " 783r 1.2653778e+08 2.17e+00 8.20e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      " 784r 1.2653755e+08 1.71e+00 8.49e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      " 785r 1.2653708e+08 3.70e-02 9.75e+01  -1.1 2.41e+02    -  1.00e+00 5.00e-01h  2\n",
      " 786r 1.2653763e+08 1.99e-02 2.99e+01  -1.1 6.75e+01    -  1.00e+00 5.00e-01h  2\n",
      " 787r 1.2653768e+08 8.54e-03 3.34e+01  -1.1 1.08e+02    -  1.00e+00 5.00e-01h  2\n",
      " 788r 1.2653744e+08 6.42e-03 8.78e+01  -1.1 1.62e+02    -  1.00e+00 2.50e-01h  3\n",
      " 789r 1.2653889e+08 3.34e-03 2.46e-01  -1.1 2.48e+01    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 790r 1.2653808e+08 2.19e-02 5.67e+01  -1.1 1.74e+02    -  1.00e+00 4.59e-01h  2\n",
      " 791r 1.2653796e+08 1.41e-02 4.02e+01  -1.1 1.10e+02    -  1.00e+00 5.00e-01h  2\n",
      " 792r 1.2653647e+08 1.10e-02 8.52e+01  -1.1 1.57e+02    -  1.00e+00 2.50e-01h  3\n",
      " 793r 1.2653748e+08 1.39e-03 1.50e-01  -1.1 3.56e+01    -  1.00e+00 1.00e+00h  1\n",
      " 794r 1.2653703e+08 1.87e-03 7.85e+01  -1.1 8.14e+01    -  1.00e+00 2.50e-01h  3\n",
      " 795r 1.2652602e+08 2.64e+00 8.33e-01  -1.1 9.65e+01    -  1.00e+00 1.00e+00H  1\n",
      " 796r 1.2652348e+08 8.23e-02 1.68e+01  -1.1 2.32e+02    -  6.64e-01 5.76e-01F  1\n",
      " 797r 1.2651852e+08 9.50e-01 6.45e+01  -1.1 1.37e+02    -  7.97e-01 4.40e-01H  1\n",
      " 798r 1.2652505e+08 3.25e-02 2.62e+01  -1.1 1.82e+02    -  5.32e-01 4.96e-01f  2\n",
      " 799r 1.2652736e+08 2.60e-02 4.01e+01  -1.1 1.49e+02    -  6.06e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 800r 1.2653028e+08 5.87e-03 5.01e-01  -1.1 9.95e+01    -  1.00e+00 1.00e+00H  1\n",
      " 801r 1.2653246e+08 6.16e-03 1.24e+02  -1.1 2.31e+02    -  1.00e+00 2.50e-01h  3\n",
      " 802r 1.2653136e+08 2.85e+00 7.71e+01  -1.1 1.93e+02    -  1.00e+00 5.60e-01H  1\n",
      " 803r 1.2653060e+08 2.04e-03 4.34e+01  -1.1 1.57e+02    -  9.01e-01 5.00e-01f  2\n",
      " 804r 1.2653325e+08 1.65e-03 4.00e+01  -1.1 9.74e+01    -  7.48e-01 2.34e-01h  3\n",
      " 805r 1.2653311e+08 1.30e-03 5.02e+01  -1.1 9.33e+01    -  1.00e+00 2.50e-01h  3\n",
      " 806r 1.2653388e+08 1.22e-03 3.49e+01  -1.1 8.08e+01    -  1.00e+00 2.50e-01h  3\n",
      " 807r 1.2652160e+08 2.52e+00 2.12e+01  -1.1 2.47e+01    -  1.00e+00 7.63e-01H  1\n",
      " 808r 1.2652876e+08 2.45e-03 1.63e+01  -1.1 7.85e+01    -  6.57e-01 5.00e-01f  2\n",
      " 809r 1.2653091e+08 1.81e-03 8.90e+01  -1.1 1.14e+02    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 810r 1.2653859e+08 1.47e-03 3.46e+01  -1.1 5.05e+01    -  1.00e+00 5.00e-01h  2\n",
      " 811r 1.2653695e+08 1.46e-03 2.64e+01  -1.1 5.63e+01    -  1.00e+00 2.50e-01h  3\n",
      " 812r 1.2652512e+08 3.62e+00 1.67e+01  -1.1 9.70e+01    -  1.00e+00 8.33e-01H  1\n",
      " 813r 1.2652830e+08 1.90e+00 4.75e+01  -1.1 2.11e+02    -  6.06e-01 2.50e-01f  3\n",
      " 814r 1.2653115e+08 2.33e-02 6.48e+01  -1.1 4.78e+02    -  3.60e-01 1.08e-01f  3\n",
      " 815r 1.2653328e+08 2.38e-02 7.41e+01  -1.1 1.97e+02    -  6.36e-01 2.09e-01h  3\n",
      " 816r 1.2654719e+08 2.92e+00 9.23e+00  -1.1 1.05e+02    -  9.26e-01 1.00e+00h  1\n",
      " 817r 1.2653940e+08 1.00e+00 3.28e+01  -1.1 1.74e+02    -  7.74e-01 1.00e+00h  1\n",
      " 818r 1.2654240e+08 1.63e-02 7.29e+01  -1.1 1.90e+02    -  9.06e-01 5.00e-01h  2\n",
      " 819r 1.2653598e+08 3.98e-02 2.85e-01  -1.1 8.81e+01    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 820r 1.2653755e+08 2.11e-02 5.33e+01  -1.1 1.52e+02    -  1.00e+00 5.00e-01h  2\n",
      " 821r 1.2653489e+08 1.07e-02 5.66e+01  -1.1 1.52e+02    -  1.00e+00 5.00e-01h  2\n",
      " 822r 1.2651647e+08 1.43e+00 2.13e+01  -1.1 1.49e+02    -  1.00e+00 8.51e-01H  1\n",
      " 823r 1.2651854e+08 7.89e-01 6.54e+01  -1.1 2.21e+02    -  6.06e-01 1.25e-01f  4\n",
      " 824r 1.2652182e+08 2.29e-03 8.41e+01  -1.1 2.19e+02    -  3.97e-01 1.09e-01f  4\n",
      " 825r 1.2653761e+08 4.51e-03 5.66e+01  -1.1 1.97e+02    -  7.81e-01 5.00e-01h  2\n",
      " 826r 1.2653733e+08 3.52e-03 4.22e+01  -1.1 1.23e+02    -  1.00e+00 5.00e-01h  2\n",
      " 827r 1.2653661e+08 4.60e-03 5.76e+01  -1.1 1.62e+02    -  1.00e+00 5.00e-01h  2\n",
      " 828r 1.2653653e+08 3.68e-03 4.61e+01  -1.1 1.26e+02    -  1.00e+00 5.00e-01h  2\n",
      " 829r 1.2653405e+08 3.14e-03 3.13e+01  -1.1 9.10e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 830r 1.2653252e+08 3.85e-03 3.28e+01  -1.1 1.03e+02    -  1.00e+00 5.00e-01h  2\n",
      " 831r 1.2653303e+08 1.52e-02 6.56e+01  -1.1 1.63e+02    -  1.00e+00 4.49e-01h  2\n",
      " 832r 1.2653704e+08 1.60e-02 1.27e-01  -1.1 1.36e+02    -  1.00e+00 1.00e+00h  1\n",
      " 833r 1.2651835e+08 6.44e+00 5.15e+01  -1.1 2.26e+02    -  9.39e-01 6.64e-01H  1\n",
      " 834r 1.2653532e+08 3.53e+00 4.04e+01  -1.1 2.70e+02    -  7.62e-01 1.00e+00f  1\n",
      " 835r 1.2651253e+08 4.69e+00 7.11e+01  -1.1 2.52e+02    -  1.00e+00 6.62e-01H  1\n",
      " 836r 1.2652469e+08 8.12e-01 7.27e+01  -1.1 1.89e+02    -  6.67e-01 1.00e+00f  1\n",
      " 837r 1.2654939e+08 1.83e+00 1.61e+01  -1.1 2.17e+02    -  8.37e-01 1.00e+00h  1\n",
      " 838r 1.2654640e+08 4.97e-02 9.32e+01  -1.1 2.39e+02    -  1.00e+00 5.00e-01h  2\n",
      " 839r 1.2653263e+08 1.03e+00 2.69e-01  -1.1 7.90e+01    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 840r 1.2653285e+08 3.70e-02 5.67e+01  -1.1 1.88e+02    -  1.00e+00 4.62e-01h  2\n",
      " 841r 1.2651400e+08 5.21e+00 1.17e+00  -1.1 1.48e+02    -  1.00e+00 1.00e+00H  1\n",
      " 842r 1.2651964e+08 2.11e+00 1.19e+02  -1.1 2.47e+02    -  9.25e-01 2.50e-01h  3\n",
      " 843r 1.2651987e+08 2.19e+00 9.92e+01  -1.1 2.04e+02    -  1.00e+00 4.94e-01H  1\n",
      " 844r 1.2651979e+08 4.11e-03 1.30e+01  -1.1 1.25e+02    -  8.09e-01 5.00e-01f  2\n",
      " 845r 1.2651128e+08 5.09e+00 7.15e+00  -1.1 1.11e+02    -  4.55e-01 4.57e-01H  1\n",
      " 846r 1.2652481e+08 1.04e+00 4.57e+00  -1.1 1.54e+02    -  5.64e-01 5.00e-01f  2\n",
      " 847r 1.2653439e+08 1.20e-02 4.71e+01  -1.1 1.23e+02    -  1.00e+00 5.00e-01h  2\n",
      " 848r 1.2650833e+08 2.98e+00 1.71e+00  -1.1 1.15e+02    -  1.00e+00 1.00e+00H  1\n",
      " 849r 1.2651575e+08 1.06e+00 5.96e+01  -1.1 1.92e+02    -  6.61e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 850r 1.2649568e+08 8.95e-01 1.07e+02  -1.1 1.51e+02    -  8.94e-01 4.12e-01H  1\n",
      " 851r 1.2651924e+08 1.55e-03 2.95e+01  -1.1 1.40e+02    -  6.73e-01 5.00e-01f  2\n",
      " 852r 1.2652115e+08 1.89e-03 2.34e+01  -1.1 1.22e+02    -  5.75e-01 3.83e-01h  2\n",
      " 853r 1.2652874e+08 2.60e-03 4.95e+01  -1.1 1.23e+02    -  1.00e+00 5.00e-01h  2\n",
      " 854r 1.2653121e+08 4.61e-03 5.34e+01  -1.1 1.36e+02    -  1.00e+00 5.00e-01h  2\n",
      " 855r 1.2653332e+08 4.55e-03 5.03e+01  -1.1 1.40e+02    -  1.00e+00 5.00e-01h  2\n",
      " 856r 1.2653590e+08 4.43e-03 5.14e+01  -1.1 1.52e+02    -  1.00e+00 5.00e-01h  2\n",
      " 857r 1.2653434e+08 3.80e-03 7.48e+01  -1.1 1.44e+02    -  1.00e+00 2.50e-01h  3\n",
      " 858r 1.2653481e+08 2.88e-03 2.76e+01  -1.1 9.30e+01    -  1.00e+00 2.50e-01h  3\n",
      " 859r 1.2653412e+08 2.82e-03 3.59e+01  -1.1 9.85e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 860r 1.2653384e+08 2.77e-03 8.71e+01  -1.1 1.61e+02    -  1.00e+00 2.50e-01h  3\n",
      " 861r 1.2653739e+08 4.51e-03 2.03e-01  -1.1 2.88e+01    -  1.00e+00 1.00e+00w  1\n",
      " 862r 1.2653861e+08 1.89e+00 8.48e+00  -1.1 1.70e+02    -  1.00e+00 9.19e-01w  1\n",
      " 863r 1.2653607e+08 1.34e+00 9.23e-01  -1.1 1.44e+02    -  1.00e+00 1.00e+00w  1\n",
      " 864r 1.2653562e+08 1.91e-03 1.55e+01  -1.1 2.05e+02    -  1.00e+00 5.00e-01h  1\n",
      " 865r 1.2653564e+08 2.34e-03 4.35e+01  -1.1 6.12e+01    -  1.00e+00 2.50e-01h  3\n",
      " 866r 1.2653457e+08 2.01e-03 5.13e+01  -1.1 9.54e+01    -  1.00e+00 2.50e-01h  3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 867r 1.2652439e+08 2.74e+00 5.80e-01  -1.1 7.18e+01    -  1.00e+00 1.00e+00H  1\n",
      " 868r 1.2652589e+08 1.86e+00 8.86e+01  -1.1 1.59e+02    -  7.26e-01 1.25e-01h  4\n",
      " 869r 1.2653273e+08 2.42e-03 1.60e+02  -1.1 1.60e+02    -  9.00e-01 2.06e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 870r 1.2654281e+08 1.80e-03 6.59e+01  -1.1 1.19e+02    -  9.42e-01 2.50e-01h  3\n",
      " 871r 1.2654065e+08 1.33e-03 7.06e+01  -1.1 1.25e+02    -  1.00e+00 2.50e-01h  3\n",
      " 872r 1.2652991e+08 1.41e+00 8.02e-01  -1.1 9.73e+01    -  1.00e+00 1.00e+00H  1\n",
      " 873r 1.2654147e+08 3.14e-02 3.02e+01  -1.1 2.31e+02    -  8.16e-01 1.00e+00h  1\n",
      " 874r 1.2654534e+08 3.48e-01 4.58e-01  -1.1 2.03e+02    -  1.00e+00 1.00e+00h  1\n",
      " 875r 1.2654153e+08 2.01e+00 1.07e+00  -1.1 2.21e+02    -  1.00e+00 1.00e+00h  1\n",
      " 876r 1.2654006e+08 7.10e-01 1.16e+01  -1.1 2.37e+02    -  9.48e-01 8.85e-01h  1\n",
      " 877r 1.2654066e+08 3.37e-02 6.30e+01  -1.1 2.16e+02    -  8.88e-01 5.00e-01h  2\n",
      " 878r 1.2653711e+08 2.85e-02 3.49e+01  -1.1 1.15e+02    -  1.00e+00 5.00e-01h  2\n",
      " 879r 1.2653451e+08 9.31e-01 3.96e-01  -1.1 1.31e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 880r 1.2653646e+08 1.56e-02 7.28e+01  -1.1 1.92e+02    -  1.00e+00 5.00e-01h  2\n",
      " 881r 1.2653764e+08 1.30e-02 5.71e+01  -1.1 6.03e+01    -  1.00e+00 5.00e-01h  2\n",
      " 882r 1.2653688e+08 7.63e-03 3.69e+01  -1.1 5.84e+01    -  1.00e+00 5.00e-01h  2\n",
      " 883r 1.2653570e+08 6.56e-03 3.55e+01  -1.1 1.13e+02    -  1.00e+00 5.00e-01h  2\n",
      " 884r 1.2653659e+08 7.68e-03 5.73e+01  -1.1 1.56e+02    -  1.00e+00 5.00e-01h  2\n",
      " 885r 1.2653348e+08 5.26e-03 5.08e+01  -1.1 1.44e+02    -  1.00e+00 5.00e-01h  2\n",
      " 886r 1.2653321e+08 4.27e-03 5.06e+01  -1.1 1.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 887r 1.2653350e+08 4.30e-03 7.66e+01  -1.1 1.47e+02    -  1.00e+00 2.50e-01h  3\n",
      " 888r 1.2653431e+08 2.96e-03 1.18e+01  -1.1 3.17e+01    -  1.00e+00 5.00e-01h  2\n",
      " 889r 1.2653489e+08 3.08e-03 3.68e+01  -1.1 6.00e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 890r 1.2653503e+08 7.29e-03 1.75e-01  -1.1 9.75e+01    -  1.00e+00 1.00e+00w  1\n",
      " 891r 1.2653989e+08 1.90e+00 1.25e+01  -1.1 2.09e+02    -  1.00e+00 9.17e-01w  1\n",
      " 892r 1.2654084e+08 1.22e+00 8.07e-01  -1.1 2.05e+02    -  1.00e+00 1.00e+00w  1\n",
      " 893r 1.2653496e+08 2.58e-03 3.51e+01  -1.1 2.20e+02    -  1.00e+00 5.00e-01h  1\n",
      " 894r 1.2653556e+08 2.88e-03 8.66e+01  -1.1 1.60e+02    -  1.00e+00 2.50e-01h  3\n",
      " 895r 1.2653487e+08 2.39e-03 4.34e+01  -1.1 2.72e+01    -  1.00e+00 2.50e-01h  3\n",
      " 896r 1.2653411e+08 1.70e-03 3.69e+01  -1.1 6.42e+01    -  1.00e+00 2.50e-01h  3\n",
      " 897r 1.2653377e+08 1.67e-03 5.05e+01  -1.1 9.48e+01    -  1.00e+00 2.50e-01h  3\n",
      " 898r 1.2653435e+08 1.30e-03 3.16e+01  -1.1 2.31e+01    -  1.00e+00 2.50e-01h  3\n",
      " 899r 1.2653428e+08 1.17e-03 3.52e+01  -1.1 6.84e+01    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 900r 1.2653444e+08 1.03e-03 3.30e+01  -1.1 2.16e+01    -  1.00e+00 1.25e-01h  4\n",
      " 901r 1.2653469e+08 7.77e-04 2.64e+01  -1.1 3.08e+01    -  1.00e+00 2.50e-01h  3\n",
      " 902r 1.2653482e+08 6.82e-04 4.92e+01  -1.1 2.63e+01    -  1.00e+00 1.25e-01h  4\n",
      " 903r 1.2653313e+08 2.49e-01 2.47e-01  -1.1 5.94e+01    -  1.00e+00 1.00e+00H  1\n",
      " 904r 1.2653461e+08 4.89e-03 7.88e+01  -1.1 1.61e+02    -  1.00e+00 2.50e-01h  3\n",
      " 905r 1.2653768e+08 5.51e-03 5.98e+01  -1.1 1.35e+02    -  1.00e+00 2.50e-01h  3\n",
      " 906r 1.2653778e+08 4.59e-03 6.86e+01  -1.1 1.19e+02    -  1.00e+00 2.50e-01h  3\n",
      " 907r 1.2653670e+08 3.54e-03 5.29e+01  -1.1 1.01e+02    -  1.00e+00 2.50e-01h  3\n",
      " 908r 1.2653674e+08 2.91e-03 3.35e+01  -1.1 7.32e+01    -  1.00e+00 2.50e-01h  3\n",
      " 909r 1.2653411e+08 2.33e-03 1.61e+01  -1.1 3.46e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 910r 1.2653139e+08 2.30e+00 1.26e+00  -1.1 1.57e+02    -  1.00e+00 9.75e-01h  1\n",
      " 911r 1.2653341e+08 1.45e+00 8.30e-01  -1.1 1.18e+02    -  1.00e+00 1.00e+00h  1\n",
      " 912r 1.2653414e+08 2.20e-02 8.09e+01  -1.1 1.51e+02    -  1.00e+00 5.00e-01h  2\n",
      " 913r 1.2653789e+08 1.12e-02 5.51e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      " 914r 1.2653616e+08 5.30e-03 5.20e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      " 915r 1.2653583e+08 4.55e-03 5.13e+01  -1.1 1.48e+02    -  1.00e+00 5.00e-01h  2\n",
      " 916r 1.2653623e+08 4.10e-03 7.60e+01  -1.1 1.48e+02    -  1.00e+00 2.50e-01h  3\n",
      " 917r 1.2653549e+08 4.94e-03 1.71e-01  -1.1 6.03e+01    -  1.00e+00 1.00e+00h  1\n",
      " 918r 1.2653569e+08 8.96e-03 7.30e+01  -1.1 1.75e+02    -  1.00e+00 2.27e-01h  3\n",
      " 919r 1.2653556e+08 5.85e+00 1.60e+00  -1.1 1.13e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 920r 1.2653087e+08 4.89e-01 3.75e+01  -1.1 2.31e+02    -  7.70e-01 1.00e+00h  1\n",
      " 921r 1.2654642e+08 1.52e+00 5.24e-01  -1.1 2.15e+02    -  1.00e+00 1.00e+00h  1\n",
      " 922r 1.2653235e+08 1.85e+00 8.62e-01  -1.1 2.21e+02    -  1.00e+00 1.00e+00h  1\n",
      " 923r 1.2653495e+08 2.47e-02 9.24e+01  -1.1 2.38e+02    -  1.00e+00 5.00e-01h  2\n",
      " 924r 1.2653619e+08 1.24e-02 4.48e+01  -1.1 6.73e+01    -  1.00e+00 5.00e-01h  2\n",
      " 925r 1.2653684e+08 6.41e-03 3.39e+01  -1.1 1.09e+02    -  1.00e+00 5.00e-01h  2\n",
      " 926r 1.2653682e+08 5.27e-03 5.94e+01  -1.1 1.59e+02    -  1.00e+00 5.00e-01h  2\n",
      " 927r 1.2652236e+08 7.63e+00 2.47e+01  -1.1 1.45e+02    -  1.00e+00 8.24e-01H  1\n",
      " 928r 1.2652871e+08 2.29e+00 2.36e+01  -1.1 2.19e+02    -  6.98e-01 5.00e-01f  2\n",
      " 929r 1.2653504e+08 2.06e-01 2.01e+01  -1.1 8.31e+01    -  7.00e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 930r 1.2653431e+08 4.73e-03 8.87e+01  -1.1 1.09e+02    -  1.00e+00 2.50e-01h  3\n",
      " 931r 1.2653151e+08 3.76e+00 6.91e-01  -1.1 6.81e+01    -  1.00e+00 1.00e+00H  1\n",
      " 932r 1.2653328e+08 2.41e-02 4.75e+01  -1.1 1.76e+02    -  9.18e-01 4.67e-01h  2\n",
      " 933r 1.2653028e+08 1.27e+00 4.13e+01  -1.1 1.28e+02    -  9.41e-01 5.97e-01H  1\n",
      " 934r 1.2653416e+08 4.06e-03 1.37e+01  -1.1 1.59e+02    -  5.45e-01 5.00e-01f  2\n",
      " 935r 1.2651760e+08 1.90e+00 4.36e+01  -1.1 9.81e+01    -  9.07e-01 4.22e-01H  1\n",
      " 936r 1.2652307e+08 6.30e-02 2.49e+01  -1.1 1.43e+02    -  6.58e-01 5.00e-01f  2\n",
      " 937r 1.2652412e+08 3.32e-03 2.41e+01  -1.1 1.17e+02    -  6.15e-01 3.24e-01h  2\n",
      " 938r 1.2652985e+08 2.99e-03 1.92e+01  -1.1 7.60e+01    -  9.51e-01 5.00e-01h  2\n",
      " 939r 1.2652950e+08 5.16e-03 3.31e+01  -1.1 1.07e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 940r 1.2653047e+08 3.75e-03 5.86e+01  -1.1 1.62e+02    -  1.00e+00 5.00e-01h  2\n",
      " 941r 1.2652899e+08 4.00e-03 5.06e+01  -1.1 1.45e+02    -  1.00e+00 5.00e-01h  2\n",
      " 942r 1.2651424e+08 3.94e+00 3.25e+01  -1.1 1.51e+02    -  1.00e+00 7.74e-01H  1\n",
      " 943r 1.2651736e+08 1.89e+00 4.84e+01  -1.1 2.07e+02    -  6.64e-01 2.50e-01h  3\n",
      " 944r 1.2652288e+08 3.09e-03 6.50e+01  -1.1 1.78e+02    -  6.05e-01 2.39e-01h  3\n",
      " 945r 1.2654439e+08 2.08e+00 5.15e-01  -1.1 1.38e+02    -  1.00e+00 1.00e+00h  1\n",
      " 946r 1.2653351e+08 1.26e+00 9.63e-01  -1.1 2.26e+02    -  1.00e+00 1.00e+00h  1\n",
      " 947r 1.2653207e+08 9.87e-01 7.51e-01  -1.1 2.37e+02    -  1.00e+00 1.00e+00h  1\n",
      " 948r 1.2653679e+08 1.88e+00 8.41e-01  -1.1 2.45e+02    -  1.00e+00 1.00e+00h  1\n",
      " 949r 1.2653446e+08 1.25e+00 9.28e-01  -1.1 2.51e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 950r 1.2653807e+08 1.50e+00 8.40e-01  -1.1 2.50e+02    -  1.00e+00 1.00e+00h  1\n",
      " 951r 1.2653209e+08 1.36e+00 7.83e-01  -1.1 2.50e+02    -  1.00e+00 1.00e+00h  1\n",
      " 952r 1.2653618e+08 1.70e+00 8.53e-01  -1.1 2.50e+02    -  1.00e+00 1.00e+00h  1\n",
      " 953r 1.2653292e+08 1.25e+00 9.37e-01  -1.1 2.51e+02    -  1.00e+00 1.00e+00h  1\n",
      " 954r 1.2653614e+08 1.33e+00 8.07e-01  -1.1 2.51e+02    -  1.00e+00 1.00e+00h  1\n",
      " 955r 1.2653404e+08 3.04e-02 9.45e+01  -1.1 2.50e+02    -  1.00e+00 5.00e-01h  2\n",
      " 956r 1.2653603e+08 1.53e-02 2.19e+01  -1.1 7.03e+01    -  1.00e+00 5.00e-01h  2\n",
      " 957r 1.2653416e+08 1.20e+00 5.54e-01  -1.1 1.10e+02    -  1.00e+00 1.00e+00h  1\n",
      " 958r 1.2653504e+08 9.31e-03 7.62e+01  -1.1 2.12e+02    -  1.00e+00 5.00e-01h  2\n",
      " 959r 1.2653890e+08 4.67e-03 5.68e+01  -1.1 1.08e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 960r 1.2653727e+08 3.60e-03 4.51e+01  -1.1 6.36e+01    -  1.00e+00 2.50e-01h  3\n",
      " 961r 1.2653563e+08 3.69e-03 3.37e+01  -1.1 9.84e+01    -  1.00e+00 5.00e-01h  2\n",
      " 962r 1.2653566e+08 3.22e-03 8.71e+01  -1.1 1.61e+02    -  1.00e+00 2.50e-01h  3\n",
      " 963r 1.2653559e+08 2.42e-03 2.55e+01  -1.1 3.99e+01    -  1.00e+00 2.50e-01h  3\n",
      " 964r 1.2652033e+08 2.56e+00 6.34e-01  -1.1 2.22e+01    -  1.00e+00 1.00e+00H  1\n",
      " 965r 1.2652410e+08 7.92e-01 7.01e+01  -1.1 9.82e+01    -  7.28e-01 2.50e-01h  3\n",
      " 966r 1.2653121e+08 2.39e-03 1.35e+02  -1.1 9.76e+01    -  1.00e+00 2.50e-01h  3\n",
      " 967r 1.2654058e+08 1.31e-03 3.44e+01  -1.1 7.01e+01    -  1.00e+00 5.00e-01h  2\n",
      " 968r 1.2653811e+08 1.45e-03 5.40e+01  -1.1 1.16e+02    -  1.00e+00 2.50e-01h  3\n",
      " 969r 1.2652789e+08 1.61e+00 7.64e-01  -1.1 1.38e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 970r 1.2653089e+08 2.87e-02 7.95e+01  -1.1 2.49e+02    -  6.97e-01 2.41e-01h  3\n",
      " 971r 1.2653427e+08 1.92e-02 1.14e+02  -1.1 2.27e+02    -  7.97e-01 1.95e-01h  3\n",
      " 972r 1.2653720e+08 1.42e-02 1.01e+02  -1.1 1.84e+02    -  9.25e-01 2.50e-01h  3\n",
      " 973r 1.2653183e+08 1.58e+00 4.41e-01  -1.1 1.19e+02    -  1.00e+00 1.00e+00h  1\n",
      " 974r 1.2653654e+08 1.59e+00 1.54e+01  -1.1 2.12e+02    -  1.00e+00 8.98e-01h  1\n",
      " 975r 1.2653411e+08 5.05e-02 7.80e+01  -1.1 2.02e+02    -  1.00e+00 5.00e-01h  2\n",
      " 976r 1.2653296e+08 3.20e-02 3.73e+01  -1.1 1.26e+02    -  1.00e+00 5.00e-01h  2\n",
      " 977r 1.2653484e+08 1.66e-02 3.74e+01  -1.1 1.06e+02    -  1.00e+00 5.00e-01h  2\n",
      " 978r 1.2653640e+08 1.15e-02 5.74e+01  -1.1 1.65e+02    -  1.00e+00 5.00e-01h  2\n",
      " 979r 1.2653540e+08 9.97e-03 5.05e+01  -1.1 1.48e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 980r 1.2653516e+08 6.85e-03 5.16e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      " 981r 1.2653712e+08 4.93e-03 5.15e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      " 982r 1.2653736e+08 5.97e-03 5.66e+01  -1.1 1.53e+02    -  1.00e+00 5.00e-01h  2\n",
      " 983r 1.2653698e+08 5.02e-03 8.18e+01  -1.1 1.50e+02    -  1.00e+00 2.50e-01h  3\n",
      " 984r 1.2653554e+08 4.05e-03 1.80e+01  -1.1 6.23e+01    -  1.00e+00 5.00e-01h  2\n",
      " 985r 1.2653119e+08 1.59e+00 5.83e-01  -1.1 1.16e+02    -  1.00e+00 1.00e+00w  1\n",
      " 986r 1.2653950e+08 8.12e-01 8.05e-01  -1.1 2.10e+02    -  1.00e+00 1.00e+00w  1\n",
      " 987r 1.2654238e+08 1.54e+00 8.55e-01  -1.1 2.19e+02    -  1.00e+00 1.00e+00h  1\n",
      " 988r 1.2653365e+08 1.96e+00 8.88e-01  -1.1 2.16e+02    -  1.00e+00 1.00e+00h  1\n",
      " 989r 1.2654070e+08 3.25e+00 8.82e-01  -1.1 2.35e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 990r 1.2653886e+08 5.82e-02 9.15e+01  -1.1 2.36e+02    -  1.00e+00 5.00e-01h  2\n",
      " 991r 1.2653908e+08 3.19e-02 3.09e-01  -1.1 1.08e+02    -  1.00e+00 1.00e+00h  1\n",
      " 992r 1.2653904e+08 3.01e-02 8.15e+01  -1.1 1.82e+02    -  1.00e+00 2.32e-01h  3\n",
      " 993r 1.2653852e+08 4.84e+00 1.47e+00  -1.1 1.13e+02    -  1.00e+00 1.00e+00H  1\n",
      " 994r 1.2654166e+08 1.73e+00 3.75e+01  -1.1 2.13e+02    -  7.41e-01 1.00e+00h  1\n",
      " 995r 1.2654713e+08 4.57e-02 7.71e+01  -1.1 1.79e+02    -  1.00e+00 5.00e-01h  2\n",
      " 996r 1.2654319e+08 2.35e-02 3.03e+01  -1.1 7.73e+01    -  1.00e+00 5.00e-01h  2\n",
      " 997r 1.2654364e+08 1.85e-02 5.62e+01  -1.1 1.42e+02    -  1.00e+00 2.50e-01h  3\n",
      " 998r 1.2653963e+08 1.11e-02 1.95e+01  -1.1 5.47e+01    -  1.00e+00 5.00e-01h  2\n",
      " 999r 1.2653750e+08 9.11e-03 4.58e+01  -1.1 5.96e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1000r 1.2653798e+08 6.82e-03 3.22e+01  -1.1 9.53e+01    -  1.00e+00 5.00e-01h  2\n",
      "1001r 1.2651699e+08 6.10e+00 2.90e+01  -1.1 1.62e+02    -  1.00e+00 8.39e-01H  1\n",
      "1002r 1.2650399e+08 3.44e+00 9.44e+01  -1.1 2.19e+02    -  4.86e-01 8.26e-01F  1\n",
      "1003r 1.2652431e+08 2.90e-02 7.47e+01  -1.1 2.04e+02    -  7.86e-01 5.00e-01f  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1004r 1.2653393e+08 6.99e-01 1.09e+00  -1.1 8.68e+01    -  1.00e+00 1.00e+00H  1\n",
      "1005r 1.2653902e+08 5.61e-03 5.34e+01  -1.1 1.61e+02    -  1.00e+00 5.00e-01h  2\n",
      "1006r 1.2652582e+08 3.66e+00 4.13e+01  -1.1 1.31e+02    -  1.00e+00 7.16e-01H  1\n",
      "1007r 1.2652004e+08 3.47e-02 1.74e+01  -1.1 1.76e+02    -  5.69e-01 5.67e-01F  1\n",
      "1008r 1.2651513e+08 1.65e+00 4.82e+01  -1.1 1.29e+02    -  7.09e-01 4.65e-01H  1\n",
      "1009r 1.2652719e+08 1.29e-02 3.17e+01  -1.1 1.61e+02    -  8.22e-01 5.00e-01f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1010r 1.2653048e+08 1.10e-02 4.08e+01  -1.1 9.03e+01    -  1.00e+00 5.00e-01h  2\n",
      "1011r 1.2653016e+08 8.50e-03 5.03e+01  -1.1 1.03e+02    -  1.00e+00 2.50e-01h  3\n",
      "1012r 1.2653410e+08 4.74e-03 2.35e+01  -1.1 7.21e+01    -  1.00e+00 5.00e-01h  2\n",
      "1013r 1.2653430e+08 3.52e-03 5.07e+01  -1.1 1.07e+02    -  1.00e+00 2.50e-01h  3\n",
      "1014r 1.2652060e+08 2.07e+00 4.61e-01  -1.1 1.21e+02    -  1.00e+00 1.00e+00H  1\n",
      "1015r 1.2652021e+08 4.52e-02 3.97e+01  -1.1 2.13e+02    -  8.16e-01 6.01e-01F  1\n",
      "1016r 1.2654081e+08 1.53e-01 4.07e-01  -1.1 1.35e+02    -  1.00e+00 1.00e+00f  1\n",
      "1017r 1.2654672e+08 1.45e+00 9.34e-01  -1.1 1.94e+02    -  1.00e+00 1.00e+00h  1\n",
      "1018r 1.2654241e+08 1.30e+00 7.96e-01  -1.1 1.83e+02    -  1.00e+00 1.00e+00h  1\n",
      "1019r 1.2654312e+08 1.67e+00 7.77e-01  -1.1 1.79e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1020r 1.2653878e+08 1.81e+00 8.83e-01  -1.1 1.79e+02    -  1.00e+00 1.00e+00h  1\n",
      "1021r 1.2654507e+08 1.52e+00 9.29e-01  -1.1 1.80e+02    -  1.00e+00 1.00e+00h  1\n",
      "1022r 1.2654031e+08 1.37e+00 8.23e-01  -1.1 1.79e+02    -  1.00e+00 1.00e+00h  1\n",
      "1023r 1.2654229e+08 2.03e+00 7.98e-01  -1.1 1.79e+02    -  1.00e+00 1.00e+00h  1\n",
      "1024r 1.2654130e+08 7.09e-01 1.22e+01  -1.1 1.79e+02    -  9.45e-01 8.70e-01h  1\n",
      "1025r 1.2654390e+08 1.49e+00 1.34e+01  -1.1 1.58e+02    -  9.18e-01 1.00e+00h  1\n",
      "1026r 1.2654287e+08 1.36e+00 8.60e-01  -1.1 1.80e+02    -  1.00e+00 1.00e+00h  1\n",
      "1027r 1.2654173e+08 2.37e-01 3.27e+01  -1.1 1.80e+02    -  1.00e+00 8.01e-01h  1\n",
      "1028r 1.2652811e+08 1.78e+00 5.56e-01  -1.1 1.45e+02    -  1.00e+00 1.00e+00h  1\n",
      "1029r 1.2654330e+08 1.47e+00 8.26e-01  -1.1 1.81e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1030r 1.2650758e+08 6.69e+00 2.03e+00  -1.1 1.80e+02    -  1.00e+00 1.00e+00H  1\n",
      "1031r 1.2653639e+08 5.25e-02 1.59e+01  -1.1 1.97e+02    -  7.10e-01 7.95e-01h  1\n",
      "1032r 1.2654086e+08 2.11e+00 4.36e-01  -1.1 1.39e+02    -  1.00e+00 1.00e+00h  1\n",
      "1033r 1.2651495e+08 6.43e+00 6.63e+00  -1.1 1.85e+02    -  9.65e-01 1.00e+00H  1\n",
      "1034r 1.2653521e+08 8.33e-01 7.94e+01  -1.1 2.03e+02    -  5.84e-01 1.00e+00h  1\n",
      "1035r 1.2651905e+08 6.60e+00 5.40e+01  -1.1 1.72e+02    -  1.00e+00 7.34e-01H  1\n",
      "1036r 1.2651900e+08 3.79e-02 7.55e+01  -1.1 1.51e+02    -  6.07e-01 1.00e+00f  1\n",
      "1037r 1.2652916e+08 5.72e+00 1.61e+00  -1.1 1.68e+02    -  1.00e+00 1.00e+00H  1\n",
      "1038r 1.2644647e+08 8.04e+00 5.55e+01  -1.1 1.99e+02    -  4.77e-01 6.66e-01H  1\n",
      "1039r 1.2656399e+08 2.47e-02 3.31e+01  -1.1 1.29e+02    -  7.19e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1040r 1.2652386e+08 5.47e+00 5.49e+01  -1.1 1.58e+02    -  9.16e-01 6.46e-01H  1\n",
      "1041r 1.2650960e+08 1.99e-02 2.57e+01  -1.1 1.66e+02    -  5.52e-01 5.59e-01F  1\n",
      "1042r 1.2652642e+08 3.25e-02 7.09e+01  -1.1 1.60e+02    -  1.00e+00 5.00e-01f  2\n",
      "1043r 1.2652716e+08 8.32e+00 1.74e+01  -1.1 1.55e+02    -  1.00e+00 8.91e-01H  1\n",
      "1044r 1.2652990e+08 2.07e+00 4.09e+01  -1.1 2.25e+02    -  7.71e-01 5.00e-01f  2\n",
      "1045r 1.2651762e+08 1.19e-02 1.58e+02  -1.1 1.30e+02    -  1.00e+00 2.50e-01H  1\n",
      "1046r 1.2653384e+08 9.31e-03 5.91e+01  -1.1 8.48e+01    -  7.00e-01 5.00e-01h  2\n",
      "1047r 1.2651653e+08 6.79e-01 4.47e+01  -1.1 1.08e+02    -  4.22e-01 2.27e-01H  1\n",
      "1048r 1.2650952e+08 3.09e+00 3.11e+01  -1.1 4.26e+01    -  5.50e-01 7.99e-01H  1\n",
      "1049r 1.2652835e+08 3.04e-02 1.52e+01  -1.1 1.72e+02    -  5.14e-01 5.00e-01f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1050r 1.2650230e+08 2.22e+00 7.27e+01  -1.1 1.10e+02    -  1.00e+00 5.66e-01H  1\n",
      "1051r 1.2651114e+08 6.79e-03 1.46e+01  -1.1 1.76e+02    -  7.10e-01 5.00e-01f  2\n",
      "1052r 1.2651937e+08 1.16e-02 2.55e+01  -1.1 1.24e+02    -  7.28e-01 4.33e-01h  2\n",
      "1053r 1.2652477e+08 5.10e+00 1.00e+00  -1.1 1.51e+02    -  1.00e+00 1.00e+00H  1\n",
      "1054r 1.2653860e+08 2.94e-02 3.34e+01  -1.1 2.43e+02    -  8.01e-01 1.00e+00h  1\n",
      "1055r 1.2654296e+08 2.00e-02 7.81e+01  -1.1 1.86e+02    -  1.00e+00 5.00e-01h  2\n",
      "1056r 1.2653729e+08 1.12e-02 4.94e+01  -1.1 1.11e+02    -  1.00e+00 5.00e-01h  2\n",
      "1057r 1.2653425e+08 9.13e-03 6.71e+01  -1.1 1.44e+02    -  1.00e+00 5.00e-01h  2\n",
      "1058r 1.2653296e+08 7.54e-03 8.01e+01  -1.1 1.41e+02    -  1.00e+00 2.50e-01h  3\n",
      "1059r 1.2653400e+08 4.96e-03 2.18e+01  -1.1 7.74e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1060r 1.2653368e+08 7.22e-03 3.55e+01  -1.1 1.06e+02    -  1.00e+00 5.00e-01h  2\n",
      "1061r 1.2652393e+08 5.49e+00 1.14e+00  -1.1 1.58e+02    -  1.00e+00 1.00e+00H  1\n",
      "1062r 1.2652889e+08 2.19e+00 1.32e+02  -1.1 2.49e+02    -  1.00e+00 2.50e-01h  3\n",
      "1063r 1.2652981e+08 2.14e+00 1.07e+02  -1.1 2.05e+02    -  1.00e+00 4.55e-01H  1\n",
      "1064r 1.2652840e+08 2.74e-03 1.07e+01  -1.1 1.16e+02    -  7.21e-01 5.00e-01f  2\n",
      "1065r 1.2651917e+08 2.28e+00 4.12e+01  -1.1 1.25e+02    -  6.75e-01 3.81e-01H  1\n",
      "1066r 1.2652545e+08 1.44e-02 2.44e+01  -1.1 1.44e+02    -  7.73e-01 5.00e-01f  2\n",
      "1067r 1.2652847e+08 1.16e-02 8.50e+01  -1.1 1.25e+02    -  1.00e+00 2.50e-01h  3\n",
      "1068r 1.2653493e+08 8.26e-03 1.42e+01  -1.1 6.21e+01    -  1.00e+00 5.00e-01h  2\n",
      "1069r 1.2653502e+08 7.05e-03 4.40e+01  -1.1 6.03e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1070r 1.2653426e+08 5.69e-03 4.95e+01  -1.1 9.66e+01    -  1.00e+00 2.50e-01h  3\n",
      "1071r 1.2652773e+08 1.08e+00 4.05e-01  -1.1 7.27e+01    -  1.00e+00 1.00e+00H  1\n",
      "1072r 1.2652806e+08 2.70e-02 1.27e+02  -1.1 1.62e+02    -  1.00e+00 1.25e-01h  4\n",
      "1073r 1.2653589e+08 2.99e-03 7.22e+01  -1.1 1.60e+02    -  1.00e+00 2.50e-01h  3\n",
      "1074r 1.2653688e+08 3.37e-03 3.18e+01  -1.1 1.06e+02    -  1.00e+00 5.00e-01h  2\n",
      "1075r 1.2653659e+08 3.13e-03 9.18e+01  -1.1 1.69e+02    -  1.00e+00 2.50e-01h  3\n",
      "1076r 1.2652824e+08 6.85e-01 3.05e-01  -1.1 2.99e+01    -  1.00e+00 1.00e+00H  1\n",
      "1077r 1.2653062e+08 8.92e-03 7.70e+01  -1.1 1.63e+02    -  8.69e-01 2.50e-01h  3\n",
      "1078r 1.2653607e+08 9.16e-03 5.40e+01  -1.1 1.02e+02    -  1.00e+00 2.50e-01h  3\n",
      "1079r 1.2653653e+08 7.56e-03 3.23e+01  -1.1 7.22e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1080r 1.2652883e+08 1.41e+00 6.49e-01  -1.1 4.30e+01    -  1.00e+00 1.00e+00H  1\n",
      "1081r 1.2653435e+08 3.58e-02 2.25e+01  -1.1 1.96e+02    -  7.14e-01 5.00e-01h  2\n",
      "1082r 1.2653626e+08 2.99e-02 8.54e+01  -1.1 1.15e+02    -  1.00e+00 2.50e-01h  3\n",
      "1083r 1.2653510e+08 2.30e-02 4.14e+01  -1.1 5.00e+01    -  1.00e+00 2.50e-01h  3\n",
      "1084r 1.2653507e+08 1.72e-02 3.56e+01  -1.1 3.16e+01    -  1.00e+00 2.50e-01h  3\n",
      "1085r 1.2653413e+08 1.28e-02 4.03e+01  -1.1 5.82e+01    -  1.00e+00 2.50e-01h  3\n",
      "1086r 1.2651339e+08 1.59e+00 1.64e+01  -1.1 9.75e+01    -  1.00e+00 8.34e-01H  1\n",
      "1087r 1.2651908e+08 2.86e-01 4.89e+01  -1.1 2.13e+02    -  6.12e-01 2.50e-01f  3\n",
      "1088r 1.2652641e+08 1.18e-03 8.62e+01  -1.1 1.81e+02    -  7.59e-01 2.50e-01h  3\n",
      "1089r 1.2653134e+08 8.88e-04 3.98e+01  -1.1 8.46e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1090r 1.2653583e+08 5.79e-04 2.55e+01  -1.1 3.03e+01    -  1.00e+00 5.00e-01h  2\n",
      "1091r 1.2653051e+08 1.09e+00 7.49e-01  -1.1 5.74e+01    -  1.00e+00 1.00e+00h  1\n",
      "1092r 1.2652995e+08 1.53e+00 1.05e+00  -1.1 1.49e+02    -  1.00e+00 1.00e+00h  1\n",
      "1093r 1.2653487e+08 1.48e+00 1.17e+00  -1.1 2.07e+02    -  1.00e+00 1.00e+00h  1\n",
      "1094r 1.2653364e+08 2.05e+00 1.11e+00  -1.1 2.24e+02    -  1.00e+00 1.00e+00h  1\n",
      "1095r 1.2653267e+08 1.75e+00 1.11e+00  -1.1 2.20e+02    -  1.00e+00 1.00e+00h  1\n",
      "1096r 1.2652959e+08 1.98e+00 1.14e+00  -1.1 2.38e+02    -  1.00e+00 1.00e+00h  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1097r 1.2653432e+08 1.78e+00 1.10e+00  -1.1 2.46e+02    -  1.00e+00 1.00e+00h  1\n",
      "1098r 1.2653205e+08 2.01e+00 1.12e+00  -1.1 2.45e+02    -  1.00e+00 1.00e+00h  1\n",
      "1099r 1.2653493e+08 1.78e+00 1.14e+00  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1100r 1.2653240e+08 2.00e+00 1.12e+00  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1101r 1.2653371e+08 1.77e+00 1.09e+00  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1102r 1.2653311e+08 2.24e-02 9.36e+01  -1.1 2.41e+02    -  1.00e+00 5.00e-01h  2\n",
      "1103r 1.2653569e+08 1.13e-02 2.87e+01  -1.1 6.76e+01    -  1.00e+00 5.00e-01h  2\n",
      "1104r 1.2653580e+08 9.48e-03 3.38e+01  -1.1 1.09e+02    -  1.00e+00 5.00e-01h  2\n",
      "1105r 1.2653576e+08 6.95e-03 5.85e+01  -1.1 1.64e+02    -  1.00e+00 5.00e-01h  2\n",
      "1106r 1.2653649e+08 5.25e-03 5.21e+01  -1.1 1.49e+02    -  1.00e+00 5.00e-01h  2\n",
      "1107r 1.2652263e+08 4.56e+00 9.02e-01  -1.1 1.45e+02    -  1.00e+00 1.00e+00H  1\n",
      "1108r 1.2653752e+08 9.10e-01 2.96e+01  -1.1 2.59e+02    -  8.51e-01 1.00e+00h  1\n",
      "1109r 1.2650085e+08 5.51e+00 5.91e+01  -1.1 2.22e+02    -  1.00e+00 7.28e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1110r 1.2652349e+08 2.21e-02 6.88e+01  -1.1 1.78e+02    -  6.45e-01 1.00e+00f  1\n",
      "1111r 1.2654640e+08 1.59e+00 2.14e+01  -1.1 2.03e+02    -  1.00e+00 8.59e-01h  1\n",
      "1112r 1.2654367e+08 5.37e-02 7.24e+01  -1.1 1.96e+02    -  1.00e+00 5.00e-01h  2\n",
      "1113r 1.2653707e+08 3.43e-02 3.74e+01  -1.1 1.25e+02    -  1.00e+00 5.00e-01h  2\n",
      "1114r 1.2651192e+08 6.51e+00 2.18e+01  -1.1 1.06e+02    -  1.00e+00 7.67e-01H  1\n",
      "1115r 1.2649096e+08 5.79e+00 3.83e+01  -1.1 2.02e+02    -  4.93e-01 6.48e-01F  1\n",
      "1116r 1.2652055e+08 7.04e-01 3.46e+01  -1.1 1.63e+02    -  7.18e-01 5.00e-01f  2\n",
      "1117r 1.2650145e+08 7.10e+00 3.97e+01  -1.1 8.99e+01    -  1.00e+00 6.83e-01H  1\n",
      "1118r 1.2651323e+08 7.59e-01 1.27e+01  -1.1 1.21e+02    -  6.85e-01 5.00e-01f  2\n",
      "1119r 1.2650494e+08 3.85e+00 4.54e+01  -1.1 1.45e+02    -  8.15e-01 5.65e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1120r 1.2650079e+08 4.23e-02 6.77e+01  -1.1 1.77e+02    -  4.87e-01 7.40e-01F  1\n",
      "1121r 1.2650487e+08 7.05e+00 4.80e+01  -1.1 1.93e+02    -  7.71e-01 6.21e-01H  1\n",
      "1122r 1.2651682e+08 2.18e+00 1.14e+01  -1.1 1.82e+02    -  6.75e-01 5.00e-01f  2\n",
      "1123r 1.2652682e+08 3.25e-03 4.05e+01  -1.1 7.48e+01    -  6.05e-01 3.69e-01h  2\n",
      "1124r 1.2653150e+08 3.33e-03 3.25e+01  -1.1 9.96e+01    -  1.00e+00 5.00e-01h  2\n",
      "1125r 1.2653036e+08 3.84e-03 5.90e+01  -1.1 1.59e+02    -  1.00e+00 5.00e-01h  2\n",
      "1126r 1.2653042e+08 4.70e-03 5.00e+01  -1.1 1.46e+02    -  1.00e+00 5.00e-01h  2\n",
      "1127r 1.2652895e+08 1.86e-02 5.58e+01  -1.1 1.58e+02    -  1.00e+00 4.81e-01h  2\n",
      "1128r 1.2653035e+08 1.27e-02 5.11e+01  -1.1 1.46e+02    -  1.00e+00 5.00e-01h  2\n",
      "1129r 1.2651762e+08 3.38e+00 1.03e+00  -1.1 1.53e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1130r 1.2652320e+08 1.10e-01 3.01e+01  -1.1 2.45e+02    -  7.96e-01 6.38e-01F  1\n",
      "1131r 1.2652827e+08 4.47e-02 1.00e+02  -1.1 1.41e+02    -  1.00e+00 2.50e-01h  3\n",
      "1132r 1.2653157e+08 3.80e-02 8.63e+01  -1.1 2.32e+02    -  1.00e+00 5.00e-01h  2\n",
      "1133r 1.2652350e+08 6.79e+00 1.60e+01  -1.1 1.35e+02    -  1.00e+00 8.86e-01H  1\n",
      "1134r 1.2652951e+08 1.81e+00 1.20e+01  -1.1 1.98e+02    -  6.19e-01 5.00e-01f  2\n",
      "1135r 1.2651720e+08 2.47e+00 1.98e+01  -1.1 1.55e+02    -  6.04e-01 4.71e-01H  1\n",
      "1136r 1.2652314e+08 6.73e-01 2.94e+01  -1.1 1.45e+02    -  5.64e-01 2.50e-01f  3\n",
      "1137r 1.2652590e+08 6.33e-03 2.56e+01  -1.1 1.49e+02    -  3.90e-01 1.63e-01h  3\n",
      "1138r 1.2654591e+08 6.41e-01 7.07e-01  -1.1 8.26e+01    -  1.00e+00 1.00e+00H  1\n",
      "1139r 1.2654423e+08 5.36e-03 7.91e+01  -1.1 1.88e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1140r 1.2652633e+08 3.39e+00 2.29e+01  -1.1 1.28e+02    -  1.00e+00 8.28e-01H  1\n",
      "1141r 1.2652374e+08 2.25e-02 4.19e+01  -1.1 1.95e+02    -  4.95e-01 6.59e-01F  1\n",
      "1142r 1.2653183e+08 2.48e-02 6.47e+01  -1.1 1.81e+02    -  9.19e-01 5.00e-01f  2\n",
      "1143r 1.2653414e+08 1.22e-02 3.41e+01  -1.1 7.73e+01    -  1.00e+00 5.00e-01h  2\n",
      "1144r 1.2653327e+08 9.18e-03 5.03e+01  -1.1 1.06e+02    -  1.00e+00 2.50e-01h  3\n",
      "1145r 1.2653468e+08 4.72e-03 1.74e+01  -1.1 4.19e+01    -  1.00e+00 5.00e-01h  2\n",
      "1146r 1.2653475e+08 3.98e-03 3.49e+01  -1.1 3.42e+01    -  1.00e+00 2.50e-01h  3\n",
      "1147r 1.2653481e+08 3.65e-03 4.07e+01  -1.1 6.12e+01    -  1.00e+00 2.50e-01h  3\n",
      "1148r 1.2652318e+08 2.99e+00 6.04e-01  -1.1 9.47e+01    -  1.00e+00 1.00e+00H  1\n",
      "1149r 1.2653000e+08 5.93e-03 4.09e+01  -1.1 2.32e+02    -  7.47e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1150r 1.2653075e+08 5.58e-03 1.18e+02  -1.1 1.06e+02    -  1.00e+00 6.25e-02h  5\n",
      "1151r 1.2653742e+08 4.44e-03 4.18e+01  -1.1 8.29e+01    -  8.75e-01 2.50e-01h  3\n",
      "1152r 1.2653692e+08 3.33e-03 3.75e+01  -1.1 3.53e+01    -  1.00e+00 2.50e-01h  3\n",
      "1153r 1.2653820e+08 1.12e-03 4.15e-01  -1.1 3.27e+01    -  1.00e+00 1.00e+00H  1\n",
      "1154r 1.2653843e+08 1.23e-03 7.67e+01  -1.1 7.52e+01    -  1.00e+00 2.50e-01h  3\n",
      "1155r 1.2654112e+08 1.05e-03 5.29e+01  -1.1 1.01e+02    -  1.00e+00 2.50e-01h  3\n",
      "1156r 1.2653976e+08 8.71e-04 3.23e+01  -1.1 7.20e+01    -  1.00e+00 2.50e-01h  3\n",
      "1157r 1.2653820e+08 8.91e-04 2.61e+01  -1.1 3.71e+01    -  1.00e+00 2.50e-01h  3\n",
      "1158r 1.2653419e+08 8.37e-03 3.18e-01  -1.1 2.74e+01    -  1.00e+00 1.00e+00H  1\n",
      "1159r 1.2653461e+08 1.27e-02 6.03e+01  -1.1 1.78e+02    -  8.47e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1160r 1.2653748e+08 1.20e-02 5.41e+01  -1.1 1.02e+02    -  1.00e+00 2.50e-01h  3\n",
      "1161r 1.2651725e+08 2.52e+00 1.57e+01  -1.1 7.22e+01    -  1.00e+00 7.98e-01H  1\n",
      "1162r 1.2652233e+08 1.23e+00 2.59e+01  -1.1 1.51e+02    -  5.95e-01 2.50e-01h  3\n",
      "1163r 1.2652715e+08 1.66e-02 5.56e+01  -1.1 2.23e+02    -  8.26e-01 2.22e-01h  3\n",
      "1164r 1.2654866e+08 1.22e+00 3.13e+01  -1.1 1.20e+02    -  7.63e-01 1.00e+00h  1\n",
      "1165r 1.2653885e+08 1.07e+00 8.01e-01  -1.1 1.88e+02    -  1.00e+00 1.00e+00h  1\n",
      "1166r 1.2653700e+08 2.07e+00 8.44e-01  -1.1 2.08e+02    -  1.00e+00 1.00e+00h  1\n",
      "1167r 1.2653545e+08 3.61e-02 9.77e+01  -1.1 2.26e+02    -  1.00e+00 5.00e-01h  2\n",
      "1168r 1.2654007e+08 8.65e-03 3.37e-01  -1.1 6.68e+01    -  1.00e+00 1.00e+00h  1\n",
      "1169r 1.2653842e+08 8.88e-03 5.47e+01  -1.1 1.49e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1170r 1.2653786e+08 4.30e-03 5.77e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      "1171r 1.2653708e+08 4.43e-03 5.73e+01  -1.1 1.53e+02    -  1.00e+00 5.00e-01h  2\n",
      "1172r 1.2653599e+08 4.22e-03 7.94e+01  -1.1 1.54e+02    -  1.00e+00 2.50e-01h  3\n",
      "1173r 1.2653283e+08 9.94e-01 4.76e-01  -1.1 9.50e+01    -  1.00e+00 1.00e+00h  1\n",
      "1174r 1.2653442e+08 8.42e-03 1.18e+02  -1.1 2.09e+02    -  1.00e+00 2.31e-01h  3\n",
      "1175r 1.2653736e+08 6.24e-03 8.70e+01  -1.1 1.59e+02    -  1.00e+00 2.50e-01h  3\n",
      "1176r 1.2653746e+08 3.64e-03 4.30e+01  -1.1 1.02e+02    -  1.00e+00 5.00e-01h  2\n",
      "1177r 1.2653620e+08 2.89e-03 5.05e+01  -1.1 1.24e+02    -  1.00e+00 5.00e-01h  2\n",
      "1178r 1.2653556e+08 2.70e-03 8.17e+01  -1.1 1.57e+02    -  1.00e+00 2.50e-01h  3\n",
      "1179r 1.2653460e+08 2.02e-03 3.35e+01  -1.1 8.60e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1180r 1.2653564e+08 5.68e-03 7.57e+01  -1.1 1.59e+02    -  1.00e+00 2.39e-01h  3\n",
      "1181r 1.2653757e+08 6.79e-03 2.57e-01  -1.1 4.03e+01    -  1.00e+00 1.00e+00h  1\n",
      "1182r 1.2653727e+08 1.02e-02 7.66e+01  -1.1 1.64e+02    -  1.00e+00 2.32e-01h  3\n",
      "1183r 1.2653798e+08 7.82e-03 5.22e+01  -1.1 9.80e+01    -  1.00e+00 2.50e-01h  3\n",
      "1184r 1.2653707e+08 5.83e-03 4.03e+01  -1.1 6.91e+01    -  1.00e+00 2.50e-01h  3\n",
      "1185r 1.2653593e+08 2.78e-03 2.30e+01  -1.1 2.08e+01    -  1.00e+00 5.00e-01h  2\n",
      "1186r 1.2653702e+08 8.46e-04 2.56e+01  -1.1 3.49e+01    -  1.00e+00 5.00e-01h  2\n",
      "1187r 1.2653560e+08 9.63e-04 4.10e+01  -1.1 5.56e+01    -  1.00e+00 2.50e-01h  3\n",
      "1188r 1.2653566e+08 1.34e-03 4.99e+01  -1.1 9.94e+01    -  1.00e+00 2.50e-01h  3\n",
      "1189r 1.2653652e+08 1.60e-03 2.09e+01  -1.1 6.96e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1190r 1.2653537e+08 2.11e-03 5.13e+01  -1.1 1.10e+02    -  1.00e+00 2.50e-01h  3\n",
      "1191r 1.2653509e+08 1.87e-03 6.92e+01  -1.1 1.23e+02    -  1.00e+00 2.50e-01h  3\n",
      "1192r 1.2653328e+08 5.93e-03 2.48e-01  -1.1 9.92e+01    -  1.00e+00 1.00e+00w  1\n",
      "1193r 1.2653173e+08 9.17e-01 1.02e+00  -1.1 2.11e+02    -  1.00e+00 1.00e+00w  1\n",
      "1194r 1.2654130e+08 1.28e-01 7.66e-01  -1.1 2.23e+02    -  1.00e+00 1.00e+00w  1\n",
      "1195r 1.2653418e+08 1.98e-03 3.59e+01  -1.1 2.20e+02    -  1.00e+00 5.00e-01h  1\n",
      "1196r 1.2653325e+08 1.87e-03 8.69e+01  -1.1 1.60e+02    -  1.00e+00 2.50e-01h  3\n",
      "1197r 1.2653333e+08 1.41e-03 6.17e+01  -1.1 9.69e+01    -  1.00e+00 2.50e-01h  3\n",
      "1198r 1.2653397e+08 1.22e-03 3.14e+01  -1.1 6.63e+01    -  1.00e+00 2.50e-01h  3\n",
      "1199r 1.2653424e+08 1.17e-03 5.75e+01  -1.1 9.43e+01    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1200r 1.2652328e+08 2.40e+00 4.79e-01  -1.1 2.22e+01    -  1.00e+00 1.00e+00H  1\n",
      "1201r 1.2653116e+08 4.57e-03 4.04e+00  -1.1 9.56e+01    -  5.27e-01 5.00e-01h  2\n",
      "1202r 1.2653324e+08 3.33e-03 8.39e+01  -1.1 1.16e+02    -  1.00e+00 2.50e-01h  3\n",
      "1203r 1.2653524e+08 2.55e-03 5.10e+01  -1.1 4.50e+01    -  1.00e+00 2.50e-01h  3\n",
      "1204r 1.2653582e+08 1.87e-03 2.23e+01  -1.1 3.74e+01    -  1.00e+00 2.50e-01h  3\n",
      "1205r 1.2653537e+08 1.52e-03 4.10e+01  -1.1 5.84e+01    -  1.00e+00 2.50e-01h  3\n",
      "1206r 1.2653468e+08 1.15e-03 4.67e+01  -1.1 9.82e+01    -  1.00e+00 2.50e-01h  3\n",
      "1207r 1.2653510e+08 9.81e-04 7.63e+01  -1.1 1.38e+02    -  1.00e+00 2.50e-01h  3\n",
      "1208r 1.2653538e+08 1.20e-03 5.57e+01  -1.1 1.03e+02    -  1.00e+00 2.50e-01h  3\n",
      "1209r 1.2653501e+08 1.10e-03 4.23e+01  -1.1 7.02e+01    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1210r 1.2653536e+08 8.35e-04 3.01e+01  -1.1 2.13e+01    -  1.00e+00 2.50e-01h  3\n",
      "1211r 1.2653224e+08 9.00e-01 4.85e-01  -1.1 2.61e+01    -  1.00e+00 1.00e+00w  1\n",
      "1212r 1.2653507e+08 7.63e-01 8.04e-01  -1.1 6.62e+01    -  1.00e+00 1.00e+00w  1\n",
      "1213r 1.2653396e+08 1.55e+00 8.39e-01  -1.1 1.49e+02    -  1.00e+00 1.00e+00w  1\n",
      "1214r 1.2653497e+08 7.34e-04 3.32e+01  -1.1 2.06e+02    -  1.00e+00 1.25e-01h  3\n",
      "1215r 1.2653353e+08 5.52e-04 2.40e-01  -1.1 1.90e+01    -  1.00e+00 1.00e+00H  1\n",
      "1216r 1.2653441e+08 4.00e-03 6.25e+01  -1.1 8.77e+01    -  1.00e+00 5.00e-01h  2\n",
      "1217r 1.2653424e+08 3.54e-03 6.19e+01  -1.1 1.11e+02    -  1.00e+00 1.25e-01h  4\n",
      "1218r 1.2653462e+08 3.13e-03 5.56e+01  -1.1 9.91e+01    -  1.00e+00 1.25e-01h  4\n",
      "1219r 1.2653498e+08 2.45e-03 3.02e+01  -1.1 3.01e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1220r 1.2653465e+08 1.77e-03 3.92e+01  -1.1 2.57e+01    -  1.00e+00 2.50e-01h  3\n",
      "1221r 1.2652630e+08 2.07e+00 4.47e-01  -1.1 5.83e+01    -  1.00e+00 1.00e+00H  1\n",
      "1222r 1.2652911e+08 3.59e-01 6.03e+01  -1.1 1.64e+02    -  8.16e-01 2.50e-01h  3\n",
      "1223r 1.2653490e+08 2.41e-03 6.95e+01  -1.1 1.43e+02    -  1.00e+00 2.50e-01h  3\n",
      "1224r 1.2653638e+08 2.21e-03 4.47e+01  -1.1 9.82e+01    -  1.00e+00 2.50e-01h  3\n",
      "1225r 1.2654087e+08 1.05e-01 2.82e-01  -1.1 1.36e+02    -  1.00e+00 1.00e+00h  1\n",
      "1226r 1.2653902e+08 7.21e-01 8.16e-01  -1.1 1.89e+02    -  1.00e+00 1.00e+00h  1\n",
      "1227r 1.2653911e+08 1.32e+00 7.88e-01  -1.1 2.08e+02    -  1.00e+00 1.00e+00h  1\n",
      "1228r 1.2653329e+08 1.59e+00 7.27e-01  -1.1 2.24e+02    -  1.00e+00 1.00e+00h  1\n",
      "1229r 1.2653696e+08 2.16e-02 9.93e+01  -1.1 2.20e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1230r 1.2653779e+08 1.08e-02 3.53e+01  -1.1 7.91e+01    -  1.00e+00 5.00e-01h  2\n",
      "1231r 1.2653764e+08 1.25e+00 5.04e-01  -1.1 1.14e+02    -  1.00e+00 1.00e+00h  1\n",
      "1232r 1.2653977e+08 1.07e-02 7.69e+01  -1.1 2.04e+02    -  1.00e+00 5.00e-01h  2\n",
      "1233r 1.2653794e+08 7.19e-03 3.98e+01  -1.1 1.06e+02    -  1.00e+00 5.00e-01h  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1234r 1.2653505e+08 5.54e-03 4.87e+01  -1.1 1.10e+02    -  1.00e+00 5.00e-01h  2\n",
      "1235r 1.2653341e+08 4.77e-03 5.99e+01  -1.1 1.34e+02    -  1.00e+00 5.00e-01h  2\n",
      "1236r 1.2651583e+08 1.68e+00 2.94e+01  -1.1 1.51e+02    -  1.00e+00 8.12e-01H  1\n",
      "1237r 1.2652165e+08 5.43e-01 2.65e+01  -1.1 2.20e+02    -  4.85e-01 2.50e-01f  3\n",
      "1238r 1.2652403e+08 5.04e+00 9.24e+01  -1.1 4.05e+02    -  3.94e-01 1.00e+00f  1\n",
      "1239r 1.2656128e+08 8.10e-01 5.52e+00  -1.1 3.79e+02    -  9.51e-01 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1240r 1.2653695e+08 1.45e+00 9.46e-01  -1.1 2.42e+02    -  1.00e+00 1.00e+00h  1\n",
      "1241r 1.2653714e+08 1.66e+00 1.11e+00  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1242r 1.2653555e+08 2.15e+00 1.16e+00  -1.1 2.42e+02    -  1.00e+00 1.00e+00h  1\n",
      "1243r 1.2653673e+08 1.61e+00 1.12e+00  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1244r 1.2653506e+08 1.74e+00 1.09e+00  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1245r 1.2653924e+08 1.86e+00 1.13e+00  -1.1 2.42e+02    -  1.00e+00 1.00e+00h  1\n",
      "1246r 1.2649484e+08 6.72e+00 1.87e+00  -1.1 2.42e+02    -  1.00e+00 1.00e+00H  1\n",
      "1247r 1.2653429e+08 1.93e+00 1.27e+00  -1.1 2.61e+02    -  1.00e+00 1.00e+00h  1\n",
      "1248r 1.2653486e+08 3.34e+00 9.37e-01  -1.1 2.20e+02    -  1.00e+00 1.00e+00h  1\n",
      "1249r 1.2653656e+08 1.83e+00 1.16e+00  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1250r 1.2653594e+08 2.27e-02 9.34e+01  -1.1 2.41e+02    -  1.00e+00 5.00e-01h  2\n",
      "1251r 1.2653952e+08 6.72e-01 2.21e-01  -1.1 6.98e+01    -  1.00e+00 1.00e+00h  1\n",
      "1252r 1.2653884e+08 2.34e-02 5.69e+01  -1.1 1.76e+02    -  1.00e+00 4.56e-01h  2\n",
      "1253r 1.2653049e+08 3.83e-01 3.36e-01  -1.1 1.39e+02    -  1.00e+00 1.00e+00h  1\n",
      "1254r 1.2653237e+08 2.18e-02 9.01e+01  -1.1 2.35e+02    -  1.00e+00 5.00e-01h  2\n",
      "1255r 1.2653767e+08 1.79e-02 2.63e-01  -1.1 6.24e+01    -  1.00e+00 1.00e+00h  1\n",
      "1256r 1.2653941e+08 2.56e-02 8.14e+01  -1.1 1.63e+02    -  1.00e+00 4.24e-01h  2\n",
      "1257r 1.2651211e+08 5.97e+00 1.26e+00  -1.1 1.37e+02    -  1.00e+00 1.00e+00H  1\n",
      "1258r 1.2652054e+08 2.00e-02 8.36e+01  -1.1 2.65e+02    -  9.12e-01 5.00e-01h  2\n",
      "1259r 1.2653402e+08 8.31e+00 1.85e+00  -1.1 1.31e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1260r 1.2651926e+08 8.37e-01 1.25e+01  -1.1 2.38e+02    -  5.79e-01 5.20e-01H  1\n",
      "1261r 1.2651273e+08 6.54e+00 1.38e+01  -1.1 1.33e+02    -  6.72e-01 5.76e-01H  1\n",
      "1262r 1.2652474e+08 1.91e+00 2.59e+01  -1.1 1.31e+02    -  7.16e-01 5.00e-01f  2\n",
      "1263r 1.2651591e+08 1.19e-02 2.11e+01  -1.1 1.25e+02    -  5.35e-01 2.85e-01H  1\n",
      "1264r 1.2652544e+08 6.00e-03 1.13e+01  -1.1 9.62e+01    -  4.81e-01 5.00e-01f  2\n",
      "1265r 1.2651950e+08 4.64e-03 8.70e+01  -1.1 1.66e+02    -  7.73e-01 2.20e-01H  1\n",
      "1266r 1.2652627e+08 2.14e-03 2.96e+01  -1.1 9.19e+01    -  9.16e-01 5.00e-01f  2\n",
      "1267r 1.2653110e+08 2.00e-03 5.61e+01  -1.1 1.20e+02    -  9.55e-01 3.41e-01h  2\n",
      "1268r 1.2653029e+08 3.40e-03 3.68e+01  -1.1 1.23e+02    -  9.41e-01 5.00e-01h  2\n",
      "1269r 1.2653204e+08 6.64e-03 5.34e+01  -1.1 1.46e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1270r 1.2653258e+08 4.69e-03 5.33e+01  -1.1 1.52e+02    -  1.00e+00 5.00e-01h  2\n",
      "1271r 1.2653417e+08 4.76e-03 5.02e+01  -1.1 1.46e+02    -  1.00e+00 5.00e-01h  2\n",
      "1272r 1.2653432e+08 6.06e-03 5.01e+01  -1.1 1.47e+02    -  1.00e+00 5.00e-01h  2\n",
      "1273r 1.2653398e+08 4.46e-03 5.33e+01  -1.1 1.49e+02    -  1.00e+00 5.00e-01h  2\n",
      "1274r 1.2653177e+08 4.88e-03 5.71e+01  -1.1 1.30e+02    -  1.00e+00 5.00e-01h  2\n",
      "1275r 1.2652740e+08 2.66e+00 3.58e+01  -1.1 1.56e+02    -  1.00e+00 5.37e-01H  1\n",
      "1276r 1.2652382e+08 2.30e+00 2.50e+01  -1.1 1.73e+02    -  9.21e-01 6.33e-01H  1\n",
      "1277r 1.2653031e+08 2.22e-02 3.03e+01  -1.1 1.85e+02    -  7.31e-01 4.52e-01h  2\n",
      "1278r 1.2653593e+08 2.32e-02 4.69e+01  -1.1 1.22e+02    -  1.00e+00 5.00e-01h  2\n",
      "1279r 1.2653356e+08 1.14e-02 3.85e+01  -1.1 1.14e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1280r 1.2653283e+08 8.63e-03 8.93e+01  -1.1 1.63e+02    -  1.00e+00 2.50e-01h  3\n",
      "1281r 1.2653830e+08 7.42e-03 2.02e-01  -1.1 3.86e+01    -  1.00e+00 1.00e+00h  1\n",
      "1282r 1.2653802e+08 2.38e-02 5.64e+01  -1.1 1.67e+02    -  1.00e+00 4.62e-01h  2\n",
      "1283r 1.2651331e+08 5.34e+00 3.84e+01  -1.1 1.10e+02    -  9.72e-01 6.03e-01H  1\n",
      "1284r 1.2651711e+08 3.40e+00 5.09e+01  -1.1 1.81e+02    -  7.29e-01 2.50e-01h  3\n",
      "1285r 1.2652164e+08 4.20e-03 5.32e+01  -1.1 1.52e+02    -  3.51e-01 1.67e-01h  3\n",
      "1286r 1.2653262e+08 2.27e-03 3.00e+01  -1.1 7.31e+01    -  1.00e+00 5.00e-01h  2\n",
      "1287r 1.2653308e+08 2.96e-03 3.41e+01  -1.1 1.06e+02    -  1.00e+00 5.00e-01h  2\n",
      "1288r 1.2653296e+08 2.36e-03 8.65e+01  -1.1 1.60e+02    -  1.00e+00 2.50e-01h  3\n",
      "1289r 1.2653108e+08 5.18e+00 3.36e-01  -1.1 3.63e+01    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1290r 1.2653285e+08 1.99e+00 6.69e+01  -1.1 7.60e+01    -  8.76e-01 2.50e-01h  3\n",
      "1291r 1.2652796e+08 2.36e+00 4.92e+01  -1.1 1.15e+02    -  9.56e-01 5.27e-01H  1\n",
      "1292r 1.2652645e+08 5.17e-03 1.84e+01  -1.1 1.69e+02    -  5.49e-01 5.00e-01f  2\n",
      "1293r 1.2653033e+08 4.58e-03 6.08e+01  -1.1 1.27e+02    -  1.00e+00 2.50e-01h  3\n",
      "1294r 1.2653319e+08 3.96e-03 7.31e+01  -1.1 1.06e+02    -  1.00e+00 2.50e-01h  3\n",
      "1295r 1.2653443e+08 3.22e-03 2.48e+01  -1.1 4.07e+01    -  1.00e+00 2.50e-01h  3\n",
      "1296r 1.2653503e+08 3.06e-03 3.14e+01  -1.1 3.65e+01    -  1.00e+00 2.50e-01h  3\n",
      "1297r 1.2651921e+08 4.03e+00 1.58e+01  -1.1 3.17e+01    -  1.00e+00 8.02e-01H  1\n",
      "1298r 1.2652232e+08 2.11e+00 4.28e+01  -1.1 1.48e+02    -  6.81e-01 2.50e-01h  3\n",
      "1299r 1.2652504e+08 1.14e-01 9.53e+01  -1.1 2.31e+02    -  8.75e-01 1.12e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1300r 1.2653378e+08 6.26e-03 5.19e+01  -1.1 4.46e+01    -  7.10e-01 2.50e-01h  3\n",
      "1301r 1.2653781e+08 5.00e-03 4.16e+01  -1.1 5.70e+01    -  1.00e+00 2.50e-01h  3\n",
      "1302r 1.2653745e+08 3.91e-03 3.26e+01  -1.1 1.01e+02    -  1.00e+00 5.00e-01h  2\n",
      "1303r 1.2653396e+08 7.68e-01 4.41e-01  -1.1 1.62e+02    -  1.00e+00 1.00e+00h  1\n",
      "1304r 1.2653541e+08 3.11e+00 7.46e-01  -1.1 2.21e+02    -  1.00e+00 1.00e+00h  1\n",
      "1305r 1.2653242e+08 2.04e+00 8.30e-01  -1.1 2.21e+02    -  1.00e+00 1.00e+00h  1\n",
      "1306r 1.2653348e+08 1.76e+00 8.20e-01  -1.1 2.38e+02    -  1.00e+00 1.00e+00h  1\n",
      "1307r 1.2653472e+08 2.07e+00 8.87e-01  -1.1 2.46e+02    -  1.00e+00 1.00e+00h  1\n",
      "1308r 1.2653365e+08 3.54e+00 9.35e-01  -1.1 2.45e+02    -  1.00e+00 1.00e+00h  1\n",
      "1309r 1.2653412e+08 8.25e-02 9.38e+01  -1.1 2.40e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1310r 1.2653739e+08 4.92e-02 2.88e+01  -1.1 1.16e+02    -  1.00e+00 5.00e-01h  2\n",
      "1311r 1.2653877e+08 1.59e+00 5.21e-01  -1.1 1.08e+02    -  1.00e+00 1.00e+00h  1\n",
      "1312r 1.2653951e+08 1.63e-02 7.22e+01  -1.1 2.09e+02    -  1.00e+00 5.00e-01h  2\n",
      "1313r 1.2654341e+08 8.28e-03 5.69e+01  -1.1 1.15e+02    -  1.00e+00 5.00e-01h  2\n",
      "1314r 1.2653730e+08 4.16e-03 2.27e+01  -1.1 5.93e+01    -  1.00e+00 5.00e-01h  2\n",
      "1315r 1.2653453e+08 4.35e-03 3.62e+01  -1.1 1.13e+02    -  1.00e+00 5.00e-01h  2\n",
      "1316r 1.2652019e+08 3.20e+00 9.93e-01  -1.1 1.56e+02    -  1.00e+00 1.00e+00H  1\n",
      "1317r 1.2649927e+08 4.09e+00 1.50e+01  -1.1 2.47e+02    -  4.31e-01 4.86e-01F  1\n",
      "1318r 1.2651871e+08 6.49e-01 2.96e+01  -1.1 9.17e+01    -  7.32e-01 5.00e-01f  2\n",
      "1319r 1.2652767e+08 4.30e-03 2.42e+01  -1.1 9.95e+01    -  8.20e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1320r 1.2651790e+08 5.16e+00 6.65e+01  -1.1 1.70e+02    -  1.00e+00 5.92e-01H  1\n",
      "1321r 1.2652266e+08 1.35e+00 1.10e+01  -1.1 1.67e+02    -  7.37e-01 5.00e-01h  2\n",
      "1322r 1.2652588e+08 9.81e-03 2.62e+01  -1.1 7.59e+01    -  6.16e-01 2.32e-01h  3\n",
      "1323r 1.2652998e+08 8.29e-03 5.18e+01  -1.1 9.43e+01    -  1.00e+00 2.50e-01h  3\n",
      "1324r 1.2653333e+08 4.30e-03 4.96e+01  -1.1 1.36e+02    -  1.00e+00 5.00e-01h  2\n",
      "1325r 1.2653337e+08 4.35e-03 5.49e+01  -1.1 1.55e+02    -  1.00e+00 5.00e-01h  2\n",
      "1326r 1.2653499e+08 4.14e-03 4.92e+01  -1.1 1.45e+02    -  1.00e+00 5.00e-01h  2\n",
      "1327r 1.2653389e+08 4.31e-03 5.06e+01  -1.1 1.48e+02    -  1.00e+00 5.00e-01h  2\n",
      "1328r 1.2653527e+08 4.30e-03 5.01e+01  -1.1 1.46e+02    -  1.00e+00 5.00e-01h  2\n",
      "1329r 1.2653521e+08 4.76e-03 5.18e+01  -1.1 1.49e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1330r 1.2653426e+08 4.55e-03 5.44e+01  -1.1 1.51e+02    -  1.00e+00 5.00e-01h  2\n",
      "1331r 1.2653179e+08 1.18e-02 1.69e-01  -1.1 1.30e+02    -  1.00e+00 1.00e+00w  1\n",
      "1332r 1.2653974e+08 1.39e+00 2.41e+01  -1.1 2.06e+02    -  1.00e+00 8.58e-01w  1\n",
      "1333r 1.2653401e+08 1.28e+00 7.47e-01  -1.1 1.93e+02    -  1.00e+00 1.00e+00w  1\n",
      "1334r 1.2653804e+08 9.38e-01 7.51e-01  -1.1 2.21e+02    -  1.00e+00 1.00e+00h  1\n",
      "1335r 1.2653088e+08 1.76e+00 8.36e-01  -1.1 2.37e+02    -  1.00e+00 1.00e+00h  1\n",
      "1336r 1.2653741e+08 9.54e-01 3.39e+01  -1.1 2.44e+02    -  1.00e+00 8.17e-01h  1\n",
      "1337r 1.2653626e+08 4.43e-02 7.11e+01  -1.1 1.83e+02    -  1.00e+00 5.00e-01h  2\n",
      "1338r 1.2653415e+08 2.93e-02 2.52e+01  -1.1 1.10e+02    -  1.00e+00 5.00e-01h  2\n",
      "1339r 1.2650852e+08 6.62e+00 2.01e+01  -1.1 1.07e+02    -  1.00e+00 7.76e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1340r 1.2651892e+08 1.39e+00 3.17e+01  -1.1 2.05e+02    -  7.59e-01 5.00e-01h  2\n",
      "1341r 1.2651360e+08 1.03e+00 2.81e+01  -1.1 1.20e+02    -  6.97e-01 3.72e-01H  1\n",
      "1342r 1.2652502e+08 1.53e-02 1.63e+01  -1.1 1.28e+02    -  4.77e-01 5.00e-01f  2\n",
      "1343r 1.2651308e+08 2.63e+00 5.36e+01  -1.1 1.41e+02    -  8.45e-01 4.25e-01H  1\n",
      "1344r 1.2652160e+08 4.00e-01 1.53e+01  -1.1 9.44e+01    -  6.58e-01 5.00e-01f  2\n",
      "1345r 1.2652595e+08 4.38e-03 4.22e+01  -1.1 1.20e+02    -  7.57e-01 3.26e-01h  2\n",
      "1346r 1.2652875e+08 3.56e-03 3.63e+01  -1.1 9.35e+01    -  9.39e-01 5.00e-01h  2\n",
      "1347r 1.2653159e+08 6.18e-03 4.43e+01  -1.1 1.09e+02    -  1.00e+00 5.00e-01h  2\n",
      "1348r 1.2651184e+08 6.25e+00 1.72e+01  -1.1 8.00e+01    -  1.00e+00 7.59e-01H  1\n",
      "1349r 1.2651963e+08 1.38e+00 1.58e+01  -1.1 1.41e+02    -  6.68e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1350r 1.2651516e+08 3.83e+00 2.72e+01  -1.1 1.41e+02    -  7.71e-01 5.55e-01H  1\n",
      "1351r 1.2651962e+08 2.00e+00 2.28e+01  -1.1 1.76e+02    -  5.31e-01 2.50e-01f  3\n",
      "1352r 1.2652298e+08 5.14e-03 1.70e+01  -1.1 1.22e+02    -  3.69e-01 3.29e-01h  2\n",
      "1353r 1.2653085e+08 4.36e+00 2.19e+00  -1.1 7.33e+01    -  1.00e+00 1.00e+00H  1\n",
      "1354r 1.2653560e+08 6.46e-03 3.23e+01  -1.1 1.61e+02    -  8.05e-01 5.00e-01h  2\n",
      "1355r 1.2653700e+08 5.80e-03 8.50e+01  -1.1 1.33e+02    -  1.00e+00 2.50e-01h  3\n",
      "1356r 1.2653573e+08 4.20e-03 3.06e+01  -1.1 8.14e+01    -  1.00e+00 5.00e-01h  2\n",
      "1357r 1.2653313e+08 1.77e-02 5.76e+01  -1.1 1.58e+02    -  1.00e+00 4.58e-01h  2\n",
      "1358r 1.2652228e+08 5.51e+00 9.23e-01  -1.1 1.37e+02    -  1.00e+00 1.00e+00H  1\n",
      "1359r 1.2652415e+08 3.93e+00 1.61e+02  -1.1 2.26e+02    -  1.00e+00 1.25e-01h  4\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1360r 1.2652342e+08 2.82e-03 1.69e+02  -1.1 2.17e+02    -  1.00e+00 2.52e-01H  1\n",
      "1361r 1.2652501e+08 2.35e-03 6.12e+01  -1.1 1.33e+02    -  7.48e-01 5.00e-01f  2\n",
      "1362r 1.2652958e+08 1.99e-03 7.00e+01  -1.1 1.18e+02    -  1.00e+00 2.50e-01h  3\n",
      "1363r 1.2653033e+08 1.52e-03 3.56e+01  -1.1 3.15e+01    -  1.00e+00 2.50e-01h  3\n",
      "1364r 1.2653770e+08 7.94e-04 4.13e-01  -1.1 2.60e+01    -  1.00e+00 1.00e+00H  1\n",
      "1365r 1.2653838e+08 9.07e-04 7.58e+01  -1.1 8.69e+01    -  1.00e+00 2.50e-01h  3\n",
      "1366r 1.2654166e+08 8.07e-04 5.20e+01  -1.1 1.02e+02    -  1.00e+00 2.50e-01h  3\n",
      "1367r 1.2653969e+08 6.52e-04 3.26e+01  -1.1 7.16e+01    -  1.00e+00 2.50e-01h  3\n",
      "1368r 1.2653899e+08 5.23e-04 2.71e+01  -1.1 2.21e+01    -  1.00e+00 2.50e-01h  3\n",
      "1369r 1.2653839e+08 4.80e-04 3.98e+01  -1.1 3.12e+01    -  1.00e+00 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1370r 1.2653671e+08 4.81e-04 2.92e+01  -1.1 2.45e+01    -  1.00e+00 2.50e-01h  3\n",
      "1371r 1.2652584e+08 2.38e+00 3.54e+00  -1.1 5.68e+01    -  1.00e+00 9.47e-01H  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1372r 1.2652737e+08 1.35e+00 4.43e+01  -1.1 1.79e+02    -  5.68e-01 1.25e-01h  4\n",
      "1373r 1.2653105e+08 2.39e-02 6.02e+01  -1.1 3.11e+02    -  6.04e-01 1.66e-01h  3\n",
      "1374r 1.2653533e+08 2.54e-02 5.22e+01  -1.1 1.84e+02    -  6.06e-01 2.48e-01h  3\n",
      "1375r 1.2653763e+08 1.91e-02 6.78e+01  -1.1 1.19e+02    -  1.00e+00 2.50e-01h  3\n",
      "1376r 1.2651800e+08 3.10e+00 2.25e+01  -1.1 1.01e+02    -  1.00e+00 7.88e-01H  1\n",
      "1377r 1.2652971e+08 1.74e-02 8.52e+01  -1.1 2.04e+02    -  4.93e-01 1.00e+00f  1\n",
      "1378r 1.2654234e+08 1.77e-02 9.04e-01  -1.1 2.01e+02    -  1.00e+00 1.00e+00h  1\n",
      "1379r 1.2653718e+08 1.83e+00 1.42e+00  -1.1 2.22e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1380r 1.2653666e+08 6.13e-01 1.31e+00  -1.1 2.39e+02    -  1.00e+00 1.00e+00h  1\n",
      "1381r 1.2653921e+08 1.59e+00 2.40e+01  -1.1 2.45e+02    -  1.00e+00 8.71e-01h  1\n",
      "1382r 1.2653960e+08 1.25e+00 1.03e+00  -1.1 1.98e+02    -  1.00e+00 1.00e+00h  1\n",
      "1383r 1.2654421e+08 2.36e+00 1.31e+00  -1.1 2.24e+02    -  1.00e+00 1.00e+00h  1\n",
      "1384r 1.2654146e+08 2.75e-02 9.41e+01  -1.1 2.40e+02    -  1.00e+00 5.00e-01h  2\n",
      "1385r 1.2653985e+08 1.91e-02 2.86e+01  -1.1 6.62e+01    -  1.00e+00 5.00e-01h  2\n",
      "1386r 1.2653800e+08 1.38e-02 3.44e+01  -1.1 1.08e+02    -  1.00e+00 5.00e-01h  2\n",
      "1387r 1.2653791e+08 1.00e-02 5.80e+01  -1.1 1.62e+02    -  1.00e+00 5.00e-01h  2\n",
      "1388r 1.2653884e+08 4.60e-03 5.19e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      "1389r 1.2649546e+08 6.25e+00 2.89e+01  -1.1 1.48e+02    -  1.00e+00 8.12e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1390r 1.2644556e+08 5.70e+00 6.99e+01  -1.1 2.30e+02    -  4.51e-01 6.36e-01H  1\n",
      "1391r 1.2655280e+08 1.52e+00 4.39e+01  -1.1 1.81e+02    -  5.49e-01 1.00e+00f  1\n",
      "1392r 1.2655355e+08 2.69e+00 1.44e+00  -1.1 1.82e+02    -  1.00e+00 1.00e+00h  1\n",
      "1393r 1.2654513e+08 1.37e+00 1.01e+00  -1.1 2.22e+02    -  1.00e+00 1.00e+00h  1\n",
      "1394r 1.2654000e+08 9.70e-01 9.14e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1395r 1.2654342e+08 1.88e+00 9.43e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1396r 1.2653732e+08 3.54e+00 9.76e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1397r 1.2653832e+08 1.17e+00 6.21e+00  -1.1 2.42e+02    -  9.67e-01 1.00e+00h  1\n",
      "1398r 1.2653793e+08 1.78e+00 8.49e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1399r 1.2654037e+08 2.19e-02 9.37e+01  -1.1 2.40e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1400r 1.2653859e+08 3.77e-03 2.52e-01  -1.1 6.87e+01    -  1.00e+00 1.00e+00h  1\n",
      "1401r 1.2653832e+08 3.63e-03 7.91e+01  -1.1 1.48e+02    -  1.00e+00 2.50e-01h  3\n",
      "1402r 1.2653792e+08 1.19e+00 5.20e-01  -1.1 1.11e+02    -  1.00e+00 1.00e+00H  1\n",
      "1403r 1.2654264e+08 4.30e-02 9.75e-01  -1.1 2.13e+02    -  1.00e+00 1.00e+00h  1\n",
      "1404r 1.2654239e+08 2.78e-02 7.67e+01  -1.1 1.78e+02    -  1.00e+00 5.00e-01h  2\n",
      "1405r 1.2653978e+08 1.48e-02 3.36e+01  -1.1 7.85e+01    -  1.00e+00 5.00e-01h  2\n",
      "1406r 1.2653960e+08 1.22e-02 5.27e+01  -1.1 1.08e+02    -  1.00e+00 2.50e-01h  3\n",
      "1407r 1.2653962e+08 2.18e-03 2.61e-01  -1.1 2.46e+01    -  1.00e+00 1.00e+00h  1\n",
      "1408r 1.2653737e+08 3.02e-03 5.23e+01  -1.1 8.28e+01    -  1.00e+00 5.00e-01h  2\n",
      "1409r 1.2653596e+08 2.86e-03 5.65e+01  -1.1 1.15e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1410r 1.2653399e+08 3.03e-03 5.67e+01  -1.1 1.56e+02    -  1.00e+00 5.00e-01h  2\n",
      "1411r 1.2653321e+08 3.68e-03 5.04e+01  -1.1 1.46e+02    -  1.00e+00 5.00e-01h  2\n",
      "1412r 1.2651486e+08 4.65e+00 2.74e+01  -1.1 1.48e+02    -  1.00e+00 8.04e-01H  1\n",
      "1413r 1.2652085e+08 4.93e-03 4.28e+01  -1.1 2.09e+02    -  8.29e-01 5.00e-01f  2\n",
      "1414r 1.2651751e+08 2.57e-03 3.91e+01  -1.1 9.97e+01    -  6.67e-01 3.80e-01H  1\n",
      "1415r 1.2652484e+08 2.33e-03 1.73e+01  -1.1 3.91e+01    -  5.39e-01 5.00e-01f  2\n",
      "1416r 1.2652762e+08 2.18e-03 1.42e+01  -1.1 6.16e+01    -  5.53e-01 2.50e-01h  3\n",
      "1417r 1.2652918e+08 2.01e-03 4.70e+01  -1.1 9.50e+01    -  1.00e+00 2.50e-01h  3\n",
      "1418r 1.2653088e+08 1.39e-03 7.63e+01  -1.1 1.39e+02    -  1.00e+00 2.50e-01h  3\n",
      "1419r 1.2652078e+08 6.33e+00 7.54e+00  -1.1 1.03e+02    -  1.00e+00 9.23e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1420r 1.2652578e+08 3.86e+00 6.43e+01  -1.1 2.18e+02    -  6.78e-01 2.50e-01f  3\n",
      "1421r 1.2653070e+08 6.66e-03 5.48e+01  -1.1 1.87e+02    -  3.53e-01 2.50e-01h  3\n",
      "1422r 1.2653403e+08 4.69e-03 4.91e+01  -1.1 9.10e+01    -  1.00e+00 2.50e-01h  3\n",
      "1423r 1.2652546e+08 4.52e+00 9.50e+00  -1.1 3.81e+01    -  1.00e+00 8.53e-01H  1\n",
      "1424r 1.2652343e+08 3.97e-02 9.17e+00  -1.1 1.75e+02    -  7.03e-01 5.98e-01F  1\n",
      "1425r 1.2653960e+08 8.80e+00 4.51e+00  -1.1 1.31e+02    -  1.00e+00 1.00e+00f  1\n",
      "1426r 1.2653063e+08 3.12e+00 1.08e+02  -1.1 2.08e+02    -  2.60e-01 1.00e+00h  1\n",
      "1427r 1.2657718e+08 1.36e+00 2.10e+01  -1.1 2.28e+02    -  7.49e-01 1.00e+00h  1\n",
      "1428r 1.2653629e+08 3.39e+00 1.16e+00  -1.1 2.21e+02    -  9.92e-01 1.00e+00h  1\n",
      "1429r 1.2653731e+08 7.18e-02 9.18e+01  -1.1 2.39e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1430r 1.2654046e+08 3.35e-02 3.48e-01  -1.1 1.12e+02    -  1.00e+00 1.00e+00h  1\n",
      "1431r 1.2653963e+08 4.11e-02 5.61e+01  -1.1 1.84e+02    -  1.00e+00 4.66e-01h  2\n",
      "1432r 1.2652163e+08 4.64e+00 1.10e+00  -1.1 1.41e+02    -  1.00e+00 1.00e+00H  1\n",
      "1433r 1.2652857e+08 1.89e-02 1.02e+02  -1.1 2.67e+02    -  1.00e+00 5.00e-01h  2\n",
      "1434r 1.2651661e+08 7.50e+00 2.28e+01  -1.1 1.35e+02    -  1.00e+00 8.28e-01H  1\n",
      "1435r 1.2650730e+08 2.08e+00 1.36e+01  -1.1 2.23e+02    -  5.12e-01 5.24e-01F  1\n",
      "1436r 1.2649553e+08 8.83e-03 3.78e+01  -1.1 1.37e+02    -  4.08e-01 2.22e-01H  1\n",
      "1437r 1.2653472e+08 2.60e-02 2.65e-01  -1.1 1.15e+02    -  1.00e+00 1.00e+00f  1\n",
      "1438r 1.2653620e+08 3.08e-02 4.95e+01  -1.1 1.81e+02    -  1.00e+00 4.40e-01h  2\n",
      "1439r 1.2653191e+08 4.40e+00 1.26e+00  -1.1 1.48e+02    -  1.00e+00 1.00e+00H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1440r 1.2653655e+08 1.76e-02 8.82e+01  -1.1 2.49e+02    -  1.00e+00 5.00e-01h  2\n",
      "1441r 1.2652424e+08 4.81e+00 1.72e+00  -1.1 1.33e+02    -  1.00e+00 1.00e+00H  1\n",
      "1442r 1.2653387e+08 2.05e-02 6.17e+01  -1.1 2.17e+02    -  8.78e-01 5.00e-01h  2\n",
      "1443r 1.2652952e+08 3.76e+00 2.98e+01  -1.1 8.80e+01    -  1.00e+00 7.15e-01H  1\n",
      "1444r 1.2653045e+08 4.33e-03 1.01e+01  -1.1 1.33e+02    -  6.43e-01 5.00e-01f  2\n",
      "1445r 1.2651249e+08 4.45e+00 3.43e+01  -1.1 1.40e+02    -  7.78e-01 5.43e-01H  1\n",
      "1446r 1.2651558e+08 2.61e+00 4.98e+01  -1.1 1.75e+02    -  7.05e-01 2.50e-01f  3\n",
      "1447r 1.2652342e+08 4.61e-03 3.10e+01  -1.1 1.44e+02    -  3.19e-01 3.49e-01h  2\n",
      "1448r 1.2653040e+08 6.21e-03 3.09e+01  -1.1 7.15e+01    -  1.00e+00 5.00e-01h  2\n",
      "1449r 1.2651078e+08 5.83e+00 2.23e+01  -1.1 1.06e+02    -  1.00e+00 7.67e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1450r 1.2652763e+08 5.96e-01 5.98e+01  -1.1 2.04e+02    -  6.47e-01 1.00e+00f  1\n",
      "1451r 1.2653832e+08 4.19e-02 7.75e+01  -1.1 2.07e+02    -  1.00e+00 5.00e-01h  2\n",
      "1452r 1.2653607e+08 2.66e-02 2.87e+01  -1.1 8.46e+01    -  1.00e+00 5.00e-01h  2\n",
      "1453r 1.2653471e+08 1.99e-02 5.65e+01  -1.1 1.08e+02    -  1.00e+00 2.50e-01h  3\n",
      "1454r 1.2653413e+08 9.72e-03 2.24e+01  -1.1 2.33e+01    -  1.00e+00 5.00e-01h  2\n",
      "1455r 1.2653351e+08 7.18e-03 4.30e+01  -1.1 5.97e+01    -  1.00e+00 2.50e-01h  3\n",
      "1456r 1.2653295e+08 9.25e-03 2.11e-01  -1.1 9.39e+01    -  1.00e+00 1.00e+00h  1\n",
      "1457r 1.2653428e+08 1.04e-02 1.17e+02  -1.1 2.10e+02    -  1.00e+00 2.27e-01h  3\n",
      "1458r 1.2653936e+08 5.89e+00 1.58e+00  -1.1 1.62e+02    -  1.00e+00 1.00e+00H  1\n",
      "1459r 1.2653847e+08 2.71e+00 8.62e+01  -1.1 2.45e+02    -  7.52e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1460r 1.2654673e+08 8.28e-03 7.39e+01  -1.1 1.99e+02    -  1.00e+00 5.00e-01h  2\n",
      "1461r 1.2654000e+08 5.53e-03 5.54e+01  -1.1 9.61e+01    -  1.00e+00 5.00e-01h  2\n",
      "1462r 1.2653883e+08 4.18e-03 5.88e+01  -1.1 1.12e+02    -  1.00e+00 2.50e-01h  3\n",
      "1463r 1.2653938e+08 4.99e-03 4.60e+01  -1.1 1.23e+02    -  1.00e+00 5.00e-01h  2\n",
      "1464r 1.2653821e+08 4.61e-03 7.72e+01  -1.1 1.47e+02    -  1.00e+00 2.50e-01h  3\n",
      "1465r 1.2652120e+08 7.09e+00 6.87e+00  -1.1 7.38e+01    -  1.00e+00 8.78e-01H  1\n",
      "1466r 1.2652727e+08 2.14e+00 2.81e+01  -1.1 1.70e+02    -  7.43e-01 4.78e-01f  2\n",
      "1467r 1.2652251e+08 1.63e-02 1.99e+01  -1.1 1.23e+02    -  4.65e-01 2.55e-01H  1\n",
      "1468r 1.2653567e+08 6.98e-01 4.95e+01  -1.1 1.17e+02    -  5.60e-01 1.00e+00f  1\n",
      "1469r 1.2653889e+08 1.66e+00 3.30e+00  -1.1 2.02e+02    -  9.71e-01 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1470r 1.2653763e+08 1.60e+00 9.99e-01  -1.1 2.25e+02    -  1.00e+00 1.00e+00h  1\n",
      "1471r 1.2653702e+08 2.57e-02 9.91e+01  -1.1 2.20e+02    -  1.00e+00 5.00e-01h  2\n",
      "1472r 1.2653913e+08 1.65e-02 3.51e-01  -1.1 7.91e+01    -  1.00e+00 1.00e+00h  1\n",
      "1473r 1.2653926e+08 2.62e-02 5.26e+01  -1.1 1.65e+02    -  1.00e+00 5.00e-01h  2\n",
      "1474r 1.2653927e+08 1.71e-02 5.77e+01  -1.1 1.50e+02    -  1.00e+00 5.00e-01h  2\n",
      "1475r 1.2651544e+08 4.43e+00 3.37e+01  -1.1 1.44e+02    -  1.00e+00 7.65e-01H  1\n",
      "1476r 1.2652401e+08 8.48e-02 2.07e+01  -1.1 2.02e+02    -  7.09e-01 5.00e-01h  2\n",
      "1477r 1.2652395e+08 1.18e+00 2.58e+01  -1.1 5.61e+01    -  1.00e+00 6.37e-01H  1\n",
      "1478r 1.2652832e+08 1.97e-02 9.46e+00  -1.1 1.48e+02    -  5.10e-01 4.79e-01f  2\n",
      "1479r 1.2652467e+08 1.03e-01 9.24e+00  -1.1 1.15e+02    -  4.92e-01 4.28e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1480r 1.2652592e+08 5.48e+00 2.63e+00  -1.1 1.45e+02    -  1.00e+00 1.00e+00H  1\n",
      "1481r 1.2652253e+08 4.06e-02 1.45e+01  -1.1 2.51e+02    -  5.16e-01 5.84e-01F  1\n",
      "1482r 1.2652569e+08 3.01e-02 3.44e+01  -1.1 1.30e+02    -  7.14e-01 5.00e-01h  2\n",
      "1483r 1.2652412e+08 2.95e+00 7.81e-01  -1.1 1.04e+02    -  1.00e+00 1.00e+00H  1\n",
      "1484r 1.2652854e+08 1.12e+00 7.39e+01  -1.1 2.33e+02    -  6.90e-01 2.50e-01h  3\n",
      "1485r 1.2653972e+08 5.55e-02 8.36e+01  -1.1 2.63e+02    -  1.00e+00 4.20e-01h  2\n",
      "1486r 1.2652795e+08 2.33e+00 1.89e+00  -1.1 1.07e+02    -  9.88e-01 1.00e+00H  1\n",
      "1487r 1.2652550e+08 2.50e-02 3.35e+00  -1.1 1.90e+02    -  5.21e-01 5.42e-01H  1\n",
      "1488r 1.2653398e+08 1.78e-02 6.70e+01  -1.1 1.62e+02    -  1.00e+00 5.00e-01f  2\n",
      "1489r 1.2653414e+08 9.08e-03 4.73e+01  -1.1 1.27e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1490r 1.2653470e+08 4.45e-03 5.59e+01  -1.1 1.60e+02    -  1.00e+00 5.00e-01h  2\n",
      "1491r 1.2653354e+08 4.68e-03 5.19e+01  -1.1 1.45e+02    -  1.00e+00 5.00e-01h  2\n",
      "1492r 1.2653192e+08 3.73e-03 7.16e+01  -1.1 1.31e+02    -  1.00e+00 2.50e-01h  3\n",
      "1493r 1.2653316e+08 4.33e-03 2.00e-01  -1.1 8.66e+01    -  1.00e+00 1.00e+00h  1\n",
      "1494r 1.2653696e+08 2.07e-02 7.45e+01  -1.1 1.80e+02    -  1.00e+00 4.60e-01h  2\n",
      "1495r 1.2653803e+08 1.64e-02 6.39e+01  -1.1 1.14e+02    -  1.00e+00 2.50e-01h  3\n",
      "1496r 1.2653912e+08 1.18e-02 3.54e+01  -1.1 9.83e+01    -  1.00e+00 5.00e-01h  2\n",
      "1497r 1.2653777e+08 9.22e-03 5.80e+01  -1.1 1.58e+02    -  1.00e+00 5.00e-01h  2\n",
      "1498r 1.2653730e+08 7.44e-03 7.63e+01  -1.1 1.45e+02    -  1.00e+00 2.50e-01h  3\n",
      "1499r 1.2653691e+08 6.16e-03 1.47e+01  -1.1 4.71e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1500r 1.2653558e+08 5.99e-03 4.39e+01  -1.1 9.93e+01    -  1.00e+00 2.50e-01h  3\n",
      "1501r 1.2652081e+08 7.45e+00 1.53e+01  -1.1 9.66e+01    -  1.00e+00 8.47e-01H  1\n",
      "1502r 1.2652426e+08 4.88e+00 7.35e+01  -1.1 2.13e+02    -  7.49e-01 2.39e-01h  3\n",
      "1503r 1.2654707e+08 1.13e+01 4.00e+01  -1.1 1.82e+02    -  3.65e-01 7.28e-01f  1\n",
      "1504r 1.2654869e+08 2.43e+00 8.45e+01  -1.1 1.76e+02    -  3.23e-01 1.00e+00h  1\n",
      "1505r 1.2656234e+08 3.20e+00 2.16e+00  -1.1 2.19e+02    -  9.45e-01 9.61e-01h  1\n",
      "1506r 1.2653877e+08 1.64e+00 8.66e-01  -1.1 2.29e+02    -  1.00e+00 1.00e+00h  1\n",
      "1507r 1.2654182e+08 1.49e+00 7.88e-01  -1.1 2.47e+02    -  1.00e+00 1.00e+00h  1\n",
      "1508r 1.2654028e+08 2.09e+00 8.71e-01  -1.1 2.45e+02    -  1.00e+00 1.00e+00h  1\n",
      "1509r 1.2654167e+08 1.76e+00 8.22e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1510r 1.2653862e+08 2.01e+00 8.44e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1511r 1.2653958e+08 1.92e+00 8.24e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1512r 1.2654145e+08 1.26e+00 8.63e+00  -1.1 2.41e+02    -  9.42e-01 8.96e-01h  1\n",
      "1513r 1.2654145e+08 1.71e+00 2.06e+01  -1.1 2.14e+02    -  8.85e-01 1.00e+00h  1\n",
      "1514r 1.2650893e+08 5.39e+00 6.18e+01  -1.1 2.39e+02    -  1.00e+00 7.23e-01H  1\n",
      "1515r 1.2651828e+08 3.07e-02 5.05e+01  -1.1 1.98e+02    -  7.17e-01 9.31e-01h  1\n",
      "1516r 1.2653192e+08 2.36e-02 8.69e+01  -1.1 2.26e+02    -  1.00e+00 5.00e-01h  2\n",
      "1517r 1.2653307e+08 1.06e-02 3.13e-01  -1.1 7.41e+01    -  1.00e+00 1.00e+00h  1\n",
      "1518r 1.2652082e+08 4.92e+00 5.17e+01  -1.1 1.77e+02    -  1.00e+00 6.61e-01H  1\n",
      "1519r 1.2652486e+08 6.44e-02 7.30e+01  -1.1 1.74e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1520r 1.2653126e+08 4.24e-02 6.18e+01  -1.1 1.83e+02    -  1.00e+00 5.00e-01h  2\n",
      "1521r 1.2652157e+08 4.38e+00 1.24e+00  -1.1 1.10e+02    -  1.00e+00 1.00e+00H  1\n",
      "1522r 1.2653432e+08 8.91e-03 6.72e+01  -1.1 2.31e+02    -  9.11e-01 5.00e-01h  2\n",
      "1523r 1.2654071e+08 1.35e-02 4.74e+01  -1.1 1.09e+02    -  1.00e+00 5.00e-01h  2\n",
      "1524r 1.2653730e+08 1.77e-02 3.56e+01  -1.1 1.12e+02    -  9.16e-01 5.00e-01h  2\n",
      "1525r 1.2653597e+08 1.43e-02 7.95e+01  -1.1 1.12e+02    -  1.00e+00 2.50e-01h  3\n",
      "1526r 1.2653621e+08 1.63e-02 2.13e-01  -1.1 6.21e+01    -  1.00e+00 1.00e+00h  1\n",
      "1527r 1.2653672e+08 1.78e-02 1.07e+02  -1.1 1.80e+02    -  1.00e+00 2.30e-01h  3\n",
      "1528r 1.2653619e+08 5.88e+00 1.63e+00  -1.1 1.13e+02    -  1.00e+00 1.00e+00H  1\n",
      "1529r 1.2653573e+08 2.71e+00 8.07e+01  -1.1 2.31e+02    -  7.45e-01 2.50e-01h  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1530r 1.2654003e+08 3.73e-03 9.84e+01  -1.1 1.80e+02    -  1.00e+00 2.50e-01h  3\n",
      "1531r 1.2654135e+08 3.18e-03 5.54e+01  -1.1 1.05e+02    -  9.01e-01 2.50e-01h  3\n",
      "1532r 1.2653904e+08 2.50e-01 1.22e+00  -1.1 3.12e+01    -  1.00e+00 1.00e+00H  1\n",
      "1533r 1.2654022e+08 2.72e-02 4.37e+01  -1.1 1.84e+02    -  8.79e-01 4.66e-01h  2\n",
      "1534r 1.2653382e+08 3.73e+00 3.47e+01  -1.1 1.16e+02    -  1.00e+00 6.70e-01H  1\n",
      "1535r 1.2653234e+08 1.66e+00 4.42e+01  -1.1 1.86e+02    -  6.60e-01 2.50e-01h  3\n",
      "1536r 1.2653529e+08 4.78e-03 6.06e+01  -1.1 1.49e+02    -  5.55e-01 2.09e-01h  3\n",
      "1537r 1.2653639e+08 4.01e-03 4.81e+01  -1.1 7.61e+01    -  1.00e+00 2.50e-01h  3\n",
      "1538r 1.2652457e+08 6.07e+00 1.74e+01  -1.1 9.36e+01    -  7.00e-01 6.37e-01H  1\n",
      "1539r 1.2653196e+08 1.84e+00 1.82e+01  -1.1 1.89e+02    -  6.38e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1540r 1.2653984e+08 4.53e+00 3.12e+00  -1.1 1.05e+02    -  1.00e+00 1.00e+00h  1\n",
      "1541r 1.2652957e+08 2.84e+00 2.06e+00  -1.1 1.75e+02    -  1.00e+00 1.00e+00h  1\n",
      "1542r 1.2653857e+08 9.21e-01 1.30e+00  -1.1 2.33e+02    -  1.00e+00 1.00e+00h  1\n",
      "1543r 1.2653855e+08 1.47e+00 8.81e-01  -1.1 2.46e+02    -  1.00e+00 1.00e+00h  1\n",
      "1544r 1.2653148e+08 8.96e-01 7.93e-01  -1.1 2.49e+02    -  1.00e+00 1.00e+00h  1\n",
      "1545r 1.2654372e+08 1.68e+00 9.19e-01  -1.1 2.50e+02    -  1.00e+00 1.00e+00h  1\n",
      "1546r 1.2653472e+08 1.31e+00 9.60e-01  -1.1 2.51e+02    -  1.00e+00 1.00e+00h  1\n",
      "1547r 1.2654328e+08 1.32e+00 8.11e-01  -1.1 2.51e+02    -  1.00e+00 1.00e+00h  1\n",
      "1548r 1.2653143e+08 1.25e+00 8.08e-01  -1.1 2.50e+02    -  1.00e+00 1.00e+00h  1\n",
      "1549r 1.2653685e+08 3.26e-02 9.48e+01  -1.1 2.50e+02    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1550r 1.2653239e+08 5.23e-03 2.61e-01  -1.1 7.06e+01    -  1.00e+00 1.00e+00h  1\n",
      "1551r 1.2653457e+08 2.34e-02 5.23e+01  -1.1 1.60e+02    -  1.00e+00 5.00e-01h  2\n",
      "1552r 1.2653178e+08 1.57e-02 5.73e+01  -1.1 1.57e+02    -  1.00e+00 5.00e-01h  2\n",
      "1553r 1.2653185e+08 1.09e-02 5.18e+01  -1.1 1.53e+02    -  1.00e+00 5.00e-01h  2\n",
      "1554r 1.2653248e+08 9.05e-03 7.71e+01  -1.1 1.47e+02    -  1.00e+00 2.50e-01h  3\n",
      "1555r 1.2653562e+08 5.80e-03 1.44e+01  -1.1 3.44e+01    -  1.00e+00 5.00e-01h  2\n",
      "1556r 1.2653542e+08 6.96e-03 3.06e+01  -1.1 5.71e+01    -  1.00e+00 5.00e-01h  2\n",
      "1557r 1.2653415e+08 4.75e-03 3.60e+01  -1.1 1.14e+02    -  1.00e+00 5.00e-01h  2\n",
      "1558r 1.2653471e+08 3.46e-03 8.52e+01  -1.1 1.56e+02    -  1.00e+00 2.50e-01h  3\n",
      "1559r 1.2653926e+08 5.04e-03 1.40e-01  -1.1 2.57e+01    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1560r 1.2654015e+08 2.30e-02 5.72e+01  -1.1 1.77e+02    -  1.00e+00 4.53e-01h  2\n",
      "1561r 1.2653902e+08 2.03e-02 6.94e+01  -1.1 1.10e+02    -  1.00e+00 1.25e-01h  4\n",
      "1562r 1.2654025e+08 1.46e-02 3.36e+01  -1.1 9.80e+01    -  1.00e+00 5.00e-01h  2\n",
      "1563r 1.2653887e+08 1.09e-02 8.89e+01  -1.1 1.64e+02    -  1.00e+00 2.50e-01h  3\n",
      "1564r 1.2653190e+08 6.89e+00 1.50e+01  -1.1 3.11e+01    -  1.00e+00 8.35e-01H  1\n",
      "1565r 1.2653432e+08 1.75e+00 1.47e+01  -1.1 6.12e+01    -  6.41e-01 5.00e-01h  2\n",
      "1566r 1.2653419e+08 1.05e-02 4.49e+01  -1.1 1.13e+02    -  6.82e-01 2.40e-01h  3\n",
      "1567r 1.2653540e+08 8.69e-03 4.17e+01  -1.1 6.96e+01    -  1.00e+00 2.50e-01h  3\n",
      "1568r 1.2653573e+08 7.01e-03 2.47e+01  -1.1 4.35e+01    -  1.00e+00 2.50e-01h  3\n",
      "1569r 1.2651798e+08 1.87e+00 1.38e+01  -1.1 3.47e+01    -  1.00e+00 8.00e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1570r 1.2652193e+08 6.11e-01 3.65e+01  -1.1 1.44e+02    -  6.19e-01 2.50e-01h  3\n",
      "1571r 1.2650745e+08 4.55e-03 4.68e+01  -1.1 1.09e+02    -  6.53e-01 3.41e-01H  1\n",
      "1572r 1.2651379e+08 3.59e-03 2.52e+01  -1.1 1.48e+02    -  5.76e-01 2.50e-01f  3\n",
      "1573r 1.2652051e+08 1.33e+00 1.58e+01  -1.1 9.65e+01    -  3.41e-01 3.32e-01f  2\n",
      "1574r 1.2652599e+08 2.61e-03 4.28e+01  -1.1 6.20e+01    -  1.00e+00 2.50e-01h  3\n",
      "1575r 1.2653048e+08 2.23e-03 5.09e+01  -1.1 9.59e+01    -  1.00e+00 2.50e-01h  3\n",
      "1576r 1.2653131e+08 2.02e-03 3.82e+01  -1.1 7.15e+01    -  1.00e+00 1.25e-01h  4\n",
      "1577r 1.2654135e+08 5.03e-04 3.59e-01  -1.1 2.60e+01    -  1.00e+00 1.00e+00H  1\n",
      "1578r 1.2654049e+08 1.12e-03 9.64e+01  -1.1 8.78e+01    -  1.00e+00 2.50e-01h  3\n",
      "1579r 1.2653353e+08 2.33e+00 3.98e+01  -1.1 1.03e+02    -  1.00e+00 6.31e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1580r 1.2653272e+08 5.24e-01 5.42e+01  -1.1 1.81e+02    -  7.33e-01 2.50e-01f  3\n",
      "1581r 1.2653599e+08 7.66e-04 3.78e+01  -1.1 9.84e+01    -  5.70e-01 2.09e-01h  3\n",
      "1582r 1.2653858e+08 5.75e-04 4.06e+01  -1.1 7.91e+01    -  1.00e+00 2.50e-01h  3\n",
      "1583r 1.2653676e+08 7.10e-04 2.01e+01  -1.1 2.66e+01    -  9.80e-01 5.00e-01h  2\n",
      "1584r 1.2653628e+08 5.92e-04 3.78e+01  -1.1 3.25e+01    -  1.00e+00 2.50e-01h  3\n",
      "1585r 1.2653040e+08 7.10e-01 7.64e-01  -1.1 5.89e+01    -  1.00e+00 1.00e+00H  1\n",
      "1586r 1.2653272e+08 1.81e-02 5.00e+01  -1.1 1.87e+02    -  7.21e-01 2.46e-01h  3\n",
      "1587r 1.2653561e+08 2.26e-02 7.16e+01  -1.1 2.56e+02    -  1.00e+00 1.75e-01h  3\n",
      "1588r 1.2653783e+08 1.68e-02 5.05e+01  -1.1 1.12e+02    -  9.11e-01 2.50e-01h  3\n",
      "1589r 1.2653889e+08 7.06e-01 2.61e-01  -1.1 1.29e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1590r 1.2654006e+08 9.55e-01 7.16e-01  -1.1 1.89e+02    -  1.00e+00 1.00e+00h  1\n",
      "1591r 1.2653165e+08 1.28e+00 7.84e-01  -1.1 2.08e+02    -  1.00e+00 1.00e+00h  1\n",
      "1592r 1.2652961e+08 1.52e+00 7.62e-01  -1.1 2.26e+02    -  1.00e+00 1.00e+00h  1\n",
      "1593r 1.2653522e+08 1.74e+00 8.69e-01  -1.1 2.19e+02    -  1.00e+00 1.00e+00h  1\n",
      "1594r 1.2653256e+08 2.74e-02 1.01e+02  -1.1 2.21e+02    -  1.00e+00 5.00e-01h  2\n",
      "1595r 1.2653498e+08 1.39e-02 2.19e+01  -1.1 6.95e+01    -  1.00e+00 5.00e-01h  2\n",
      "1596r 1.2653466e+08 7.04e-03 3.39e+01  -1.1 1.11e+02    -  1.00e+00 5.00e-01h  2\n",
      "1597r 1.2653527e+08 6.15e-03 5.92e+01  -1.1 1.59e+02    -  1.00e+00 5.00e-01h  2\n",
      "1598r 1.2653681e+08 5.02e-03 4.93e+01  -1.1 1.45e+02    -  1.00e+00 5.00e-01h  2\n",
      "1599r 1.2650929e+08 7.30e+00 2.78e+01  -1.1 1.48e+02    -  1.00e+00 8.17e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1600r 1.2652534e+08 8.62e-02 7.39e+01  -1.1 2.30e+02    -  6.39e-01 1.00e+00f  1\n",
      "1601r 1.2654665e+08 3.36e-01 5.92e-01  -1.1 2.15e+02    -  1.00e+00 1.00e+00h  1\n",
      "1602r 1.2654212e+08 3.15e+00 1.11e+00  -1.1 2.42e+02    -  1.00e+00 1.00e+00h  1\n",
      "1603r 1.2653975e+08 1.56e+00 8.35e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1604r 1.2654391e+08 1.49e+00 8.97e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1605r 1.2653673e+08 2.00e+00 8.78e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1606r 1.2654265e+08 3.56e+00 9.72e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1607r 1.2653866e+08 1.70e+00 9.94e-01  -1.1 2.39e+02    -  1.00e+00 1.00e+00h  1\n",
      "1608r 1.2654422e+08 1.64e+00 9.40e-01  -1.1 2.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1609r 1.2653726e+08 1.98e+00 8.95e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1610r 1.2654231e+08 3.56e+00 9.60e-01  -1.1 2.41e+02    -  1.00e+00 1.00e+00h  1\n",
      "1611r 1.2653860e+08 1.70e+00 9.99e-01  -1.1 2.39e+02    -  1.00e+00 1.00e+00h  1\n",
      "1612r 1.2654143e+08 5.87e-02 9.35e+01  -1.1 2.40e+02    -  1.00e+00 5.00e-01h  2\n",
      "1613r 1.2653382e+08 2.00e-01 3.76e-01  -1.1 6.62e+01    -  1.00e+00 1.00e+00h  1\n",
      "1614r 1.2653579e+08 3.01e-02 5.79e+01  -1.1 1.68e+02    -  1.00e+00 4.49e-01h  2\n",
      "1615r 1.2653340e+08 1.77e-02 5.12e+01  -1.1 1.39e+02    -  1.00e+00 5.00e-01h  2\n",
      "1616r 1.2652208e+08 6.18e+00 1.79e+00  -1.1 1.52e+02    -  1.00e+00 1.00e+00H  1\n",
      "1617r 1.2650503e+08 2.82e+00 3.10e+01  -1.1 2.49e+02    -  7.54e-01 6.01e-01H  1\n",
      "1618r 1.2651420e+08 4.71e-02 9.52e+01  -1.1 1.49e+02    -  9.48e-01 2.50e-01h  3\n",
      "1619r 1.2652127e+08 5.78e+00 2.93e+01  -1.1 1.69e+02    -  1.00e+00 7.38e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1620r 1.2649718e+08 2.18e+00 1.50e+01  -1.1 1.69e+02    -  7.67e-01 6.46e-01F  1\n",
      "1621r 1.2650828e+08 5.30e-02 9.05e+01  -1.1 1.77e+02    -  9.30e-01 2.50e-01h  3\n",
      "1622r 1.2651704e+08 4.26e-02 7.89e+01  -1.1 3.47e+02    -  5.85e-01 2.73e-01h  2\n",
      "1623r 1.2653688e+08 1.05e-02 2.53e-01  -1.1 7.97e+01    -  1.00e+00 1.00e+00h  1\n",
      "1624r 1.2653337e+08 5.48e-03 5.05e+01  -1.1 1.52e+02    -  1.00e+00 5.00e-01h  2\n",
      "1625r 1.2651546e+08 7.42e+00 2.66e+01  -1.1 1.52e+02    -  9.73e-01 7.93e-01H  1\n",
      "1626r 1.2652029e+08 4.83e+00 5.51e+01  -1.1 2.10e+02    -  6.44e-01 2.50e-01h  3\n",
      "1627r 1.2653200e+08 3.52e-03 3.91e+01  -1.1 1.87e+02    -  4.49e-01 3.87e-01h  2\n",
      "1628r 1.2653170e+08 3.07e-03 6.00e+01  -1.1 1.02e+02    -  1.00e+00 2.50e-01h  3\n",
      "1629r 1.2653520e+08 2.55e-03 2.38e+01  -1.1 8.09e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1630r 1.2653407e+08 3.54e-03 3.25e+01  -1.1 1.04e+02    -  1.00e+00 5.00e-01h  2\n",
      "1631r 1.2651916e+08 6.62e+00 3.16e+01  -1.1 1.61e+02    -  1.00e+00 8.03e-01H  1\n",
      "1632r 1.2651409e+08 2.48e+00 8.63e+00  -1.1 2.13e+02    -  6.51e-01 5.50e-01F  1\n",
      "1633r 1.2653153e+08 2.70e+00 2.71e+01  -1.1 1.46e+02    -  6.42e-01 1.00e+00f  1\n",
      "1634r 1.2655284e+08 2.11e+00 2.83e+00  -1.1 1.40e+02    -  1.00e+00 1.00e+00h  1\n",
      "1635r 1.2654390e+08 9.48e-01 3.40e+00  -1.1 2.26e+02    -  9.45e-01 9.24e-01h  1\n",
      "1636r 1.2654520e+08 1.15e+00 1.60e+01  -1.1 2.29e+02    -  9.10e-01 1.00e+00h  1\n",
      "1637r 1.2654057e+08 1.30e+00 9.59e-01  -1.1 2.43e+02    -  1.00e+00 1.00e+00h  1\n",
      "1638r 1.2654220e+08 2.61e-01 3.76e+01  -1.1 2.42e+02    -  1.00e+00 8.00e-01h  1\n",
      "1639r 1.2653250e+08 1.56e+00 6.25e-01  -1.1 1.90e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1640r 1.2650711e+08 5.61e+00 6.27e+01  -1.1 2.39e+02    -  1.00e+00 7.44e-01H  1\n",
      "1641r 1.2652992e+08 2.38e-02 7.38e+01  -1.1 2.06e+02    -  6.70e-01 1.00e+00f  1\n",
      "1642r 1.2651431e+08 5.56e+00 8.20e+01  -1.1 2.18e+02    -  1.00e+00 6.40e-01H  1\n",
      "1643r 1.2651053e+08 6.76e-01 5.65e+01  -1.1 1.93e+02    -  7.54e-01 1.00e+00f  1\n",
      "1644r 1.2653102e+08 5.86e+00 8.13e-01  -1.1 2.08e+02    -  1.00e+00 1.00e+00H  1\n",
      "1645r 1.2653843e+08 1.20e+00 1.75e+01  -1.1 2.50e+02    -  9.07e-01 1.00e+00h  1\n",
      "1646r 1.2650487e+08 5.45e+00 7.31e+01  -1.1 2.28e+02    -  1.00e+00 6.79e-01H  1\n",
      "1647r 1.2648118e+08 1.21e+01 6.06e+01  -1.1 1.93e+02    -  5.30e-01 6.63e-01F  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1648r 1.2650510e+08 2.64e+00 2.71e+01  -1.1 1.72e+02    -  6.42e-01 6.09e-01F  1\n",
      "1649r 1.2649500e+08 6.74e-01 5.18e+01  -1.1 1.68e+02    -  7.03e-01 4.20e-01H  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1650r 1.2651336e+08 3.32e-02 3.10e+01  -1.1 2.08e+02    -  6.56e-01 4.65e-01f  2\n",
      "1651r 1.2652395e+08 2.17e-02 3.68e+01  -1.1 1.51e+02    -  1.00e+00 5.00e-01h  2\n",
      "1652r 1.2650554e+08 7.10e+00 5.74e+00  -1.1 1.03e+02    -  8.45e-01 8.03e-01H  1\n",
      "1653r 1.2652185e+08 2.70e-01 2.51e+01  -1.1 2.06e+02    -  6.73e-01 5.00e-01f  2\n",
      "1654r 1.2651091e+08 5.26e-01 2.53e+01  -1.1 1.01e+02    -  8.19e-01 5.68e-01H  1\n",
      "1655r 1.2652711e+08 7.77e-01 5.65e-01  -1.1 1.41e+02    -  1.00e+00 1.00e+00f  1\n",
      "1656r 1.2653180e+08 1.29e-02 8.94e+01  -1.1 2.29e+02    -  1.00e+00 5.00e-01h  2\n",
      "1657r 1.2652954e+08 6.41e-03 6.63e+01  -1.1 6.64e+01    -  1.00e+00 5.00e-01h  2\n",
      "1658r 1.2652214e+08 4.37e+00 3.86e+01  -1.1 1.06e+02    -  1.00e+00 7.58e-01H  1\n",
      "1659r 1.2653071e+08 2.29e-01 1.95e+01  -1.1 1.97e+02    -  6.89e-01 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1660r 1.2653708e+08 1.13e-02 6.63e+01  -1.1 1.16e+02    -  1.00e+00 5.00e-01h  2\n",
      "1661r 1.2653276e+08 6.28e-03 5.34e+01  -1.1 5.85e+01    -  9.89e-01 5.00e-01h  2\n",
      "1662r 1.2651172e+08 3.71e+00 2.55e+01  -1.1 1.09e+02    -  1.00e+00 7.60e-01H  1\n",
      "1663r 1.2651541e+08 3.19e-02 1.21e+01  -1.1 1.87e+02    -  5.13e-01 5.10e-01F  1\n",
      "1664r 1.2651234e+08 7.34e-03 4.67e+01  -1.1 1.45e+02    -  5.82e-01 3.26e-01H  1\n",
      "1665r 1.2652397e+08 3.75e-03 2.21e+01  -1.1 1.40e+02    -  5.36e-01 5.00e-01f  2\n",
      "1666r 1.2652886e+08 3.28e-03 2.27e+01  -1.1 1.39e+02    -  6.96e-01 5.00e-01h  2\n",
      "1667r 1.2651788e+08 5.06e+00 3.38e+01  -1.1 1.51e+02    -  9.30e-01 6.88e-01H  1\n",
      "1668r 1.2652492e+08 9.51e-01 1.37e+01  -1.1 1.95e+02    -  6.41e-01 5.00e-01f  2\n",
      "1669r 1.2652633e+08 6.18e-03 1.64e+01  -1.1 8.41e+01    -  6.48e-01 4.59e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1670r 1.2651190e+08 4.09e+00 2.81e+01  -1.1 1.27e+02    -  1.00e+00 7.91e-01H  1\n",
      "1671r 1.2652387e+08 4.40e-03 1.39e+01  -1.1 1.85e+02    -  6.33e-01 5.00e-01f  2\n",
      "1672r 1.2649756e+08 2.24e-01 9.23e+01  -1.1 1.11e+02    -  8.08e-01 3.50e-01H  1\n",
      "1673r 1.2650509e+08 2.01e-03 3.93e+01  -1.1 1.39e+02    -  4.88e-01 2.50e-01f  3\n",
      "1674r 1.2650999e+08 1.48e-03 2.81e+01  -1.1 9.75e+01    -  2.90e-01 2.33e-01h  3\n",
      "1675r 1.2651553e+08 5.53e+00 4.55e+00  -1.1 6.67e+01    -  8.26e-01 7.71e-01H  1\n",
      "1676r 1.2652720e+08 1.41e+00 1.44e+01  -1.1 1.44e+02    -  6.72e-01 5.00e-01f  2\n",
      "1677r 1.2653103e+08 7.95e-03 2.54e+01  -1.1 1.40e+02    -  7.20e-01 4.73e-01h  2\n",
      "1678r 1.2653007e+08 1.09e-02 6.60e+01  -1.1 1.52e+02    -  9.09e-01 2.50e-01h  3\n",
      "1679r 1.2653447e+08 5.50e-03 3.48e+01  -1.1 9.57e+01    -  1.00e+00 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1680r 1.2652097e+08 2.23e+00 3.41e+01  -1.1 1.61e+02    -  1.00e+00 7.88e-01H  1\n",
      "1681r 1.2652315e+08 9.29e-01 4.10e+01  -1.1 2.09e+02    -  5.87e-01 2.50e-01f  3\n",
      "1682r 1.2652834e+08 1.56e-03 6.23e+01  -1.1 1.85e+02    -  6.24e-01 2.50e-01h  3\n",
      "1683r 1.2654074e+08 1.00e-02 2.09e-01  -1.1 1.26e+02    -  1.00e+00 1.00e+00h  1\n",
      "1684r 1.2654148e+08 5.63e-01 8.18e-01  -1.1 2.10e+02    -  1.00e+00 1.00e+00h  1\n",
      "1685r 1.2653773e+08 1.60e-02 8.32e+01  -1.1 2.21e+02    -  1.00e+00 5.00e-01h  2\n",
      "1686r 1.2653879e+08 6.74e-01 4.54e-01  -1.1 6.49e+01    -  1.00e+00 1.00e+00h  1\n",
      "1687r 1.2653892e+08 2.37e-02 5.84e+01  -1.1 1.76e+02    -  1.00e+00 4.55e-01h  2\n",
      "1688r 1.2653418e+08 6.12e-01 3.94e-01  -1.1 1.56e+02    -  1.00e+00 1.00e+00h  1\n",
      "1689r 1.2653733e+08 2.02e+00 1.13e+00  -1.1 2.22e+02    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "1690r 1.2653944e+08 6.37e-01 1.03e+01  -1.1 2.20e+02    -  9.36e-01 8.72e-01h  1\n",
      "1691r 1.2653996e+08 3.33e-02 5.74e+01  -1.1 1.93e+02    -  8.94e-01 5.00e-01h  2\n",
      "1692r 1.2653806e+08 2.95e-02 3.57e+01  -1.1 1.15e+02    -  1.00e+00 5.00e-01h  2\n",
      "1693r 1.2653597e+08 1.44e-02 3.35e+01  -1.1 1.06e+02    -  1.00e+00 5.00e-01h  2\n"
     ]
    }
   ],
   "source": [
    "# solving\n",
    "#opt = SolverFactory('ipopt',executable = '/home/uct/Trajectory_Optimization/IPOPT/build/bin/ipopt')\n",
    "opt = SolverFactory('ipopt')\n",
    "opt.options[\"linear_solver\"] = 'ma86'\n",
    "\n",
    "# solver options\n",
    "opt.options[\"expect_infeasible_problem\"] = 'yes'\n",
    "#pt.options[\"linear_system_scaling\"] = 'none'\n",
    "#opt.options[\"mu_strategy\"] = \"adaptive\"\n",
    "opt.options[\"print_level\"] = 5     # prints a log with each iteration (you want to this - it's the only way to see progress.)\n",
    "opt.options[\"max_iter\"] = 30000*20   # maximum number of iterations\n",
    "opt.options[\"max_cpu_time\"] = 600*20 # maximum cpu time in seconds\n",
    "opt.options[\"Tol\"] = 1e-6          # the tolerance for feasibility. Considers constraints satisfied when they're within this margin.\n",
    "\n",
    "results = opt.solve(m, tee = True)\n",
    "\n",
    "# model.piston_complementarity.deactivate()\n",
    "# result = default_solver('/usr/local/bin/ipopt', approximate_hessian=False).solve(model, tee=True)\n",
    "# if result.solver.termination_condition != TerminationCondition.optimal: quit()\n",
    "# # solve for discrete piston control3\n",
    "# model.piston_complementarity.activate()\n",
    "# result = default_solver('/usr/local/bin/ipopt', approximate_hessian=False, warm_start=True).solve(model, tee=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2dbf94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(results.solver.termination_condition) # check if optimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "800c8acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOT COST FUNCTION -------------------------------------------------------------------------------------------------------------\n",
    "# T = sum(m.h[n] for n in range(1,N+1))\n",
    "pen_sum = 0.0\n",
    "for n in range(1,N+1):\n",
    "        for gc in ground_constraints:\n",
    "            for leg in legs:\n",
    "                pen_sum += m.ground_penalty[n,gc,leg].value\n",
    "print(pen_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "15693749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tau_sum = 0.0\n",
    "# for n in range(1,N+1):\n",
    "#     for leg in legs:\n",
    "#         for side in sides:\n",
    "#             tau_sum += (m.tau_torque[n,side,leg])**2\n",
    "# print(tau_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "82ced369",
   "metadata": {},
   "outputs": [
    {
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       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#animate it\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as ani\n",
    "from IPython.display import HTML\n",
    "%matplotlib inline\n",
    "\n",
    "fig1, ax1 = plt.subplots(1,1) #create axes\n",
    "ax1.set_aspect('equal')\n",
    "\n",
    "# arrays to store values\n",
    "N_time = []\n",
    "\n",
    "def plot_robot(i,m,ax): #update function for animation\n",
    "    ax.clear()\n",
    "    ax.set_xlim([x_low_lim,x_high_lim])\n",
    "    ax.set_ylim([y_low_lim,y_high_lim])\n",
    "    N_time.append(i)\n",
    "     \n",
    "    plot_body = {}\n",
    "    plot_boom = {}\n",
    "    plot_boom_COM = {}\n",
    "    plot_UR = {}\n",
    "    plot_LR = {}\n",
    "    plot_UL = {}\n",
    "    plot_LL = {}\n",
    "    plot_foot = {}\n",
    "    abs_angles = {}\n",
    "    bod_angle = 0\n",
    "    \n",
    "    # plot body\n",
    "    plot_body.update({('x_back'):m.q[i,'x_body'].value - 0.5*m.lengths['body']*cos(bod_angle)})\n",
    "    plot_body.update({('y_back'):m.q[i,'y_body'].value - m.y_bodyOffset.value - 0.5*m.lengths['body']*sin(bod_angle)})\n",
    "    plot_body.update({('x_front'):m.q[i,'x_body'].value + 0.5*m.lengths['body']*cos(bod_angle)})\n",
    "    plot_body.update({('y_front'):m.q[i,'y_body'].value - m.y_bodyOffset.value + 0.5*m.lengths['body']*sin(bod_angle)})  \n",
    "    ax.plot([plot_body['x_back'],plot_body['x_front']],[plot_body['y_back'],plot_body['y_front']],color='xkcd:green')\n",
    "    \n",
    "    # plot boom\n",
    "    plot_boom.update({('x_back'): 0.0})\n",
    "    plot_boom.update({('y_back'): m.y_bodyOffset.value})\n",
    "    plot_boom.update({('x_front'):m.q[i,'x_body'].value })\n",
    "    plot_boom.update({('y_front'):m.q[i,'y_body'].value })  \n",
    "    ax.plot([plot_boom['x_back'],plot_boom['x_front']],[plot_boom['y_back'],plot_boom['y_front']],color='xkcd:black')\n",
    "\n",
    "    # plot boom COM\n",
    "    plot_boom_COM.update({('x'): (m.cg.value/m.lx_boom.value)*m.q[i,'x_body'].value})\n",
    "    plot_boom_COM.update({('y'): (m.cg.value/m.ly_boom.value)*m.q[i,'y_body'].value})\n",
    "    ax.plot([plot_boom_COM['x']],[plot_boom_COM['y']],color='xkcd:purple',marker=\"o\")\n",
    "\n",
    "    for leg in legs:\n",
    "        if leg == 'A':\n",
    "            link_colour = ['blue','red']\n",
    "        else:\n",
    "            link_colour = ['green','purple']\n",
    "        \n",
    "        \n",
    "        #plot upper right\n",
    "        abs_angles.update({('RU',leg): bod_angle + m.q[i,'theta_UR'+leg].value})\n",
    "        plot_UR.update({('top','X',leg): m.q[i,'x_body'].value + m.BLD.value*cos(bod_angle)})\n",
    "        plot_UR.update({('top','Y',leg): m.q[i,'y_body'].value - m.y_bodyOffset.value + m.BLD.value*sin(bod_angle)})\n",
    "        plot_UR.update({('bot','X',leg): plot_UR['top','X',leg] - m.lengths['UR'+leg]*cos(abs_angles['RU',leg])})\n",
    "        plot_UR.update({('bot','Y',leg): plot_UR['top','Y',leg] - m.lengths['UR'+leg]*sin(abs_angles['RU',leg])})\n",
    "        ax.plot([plot_UR['top','X',leg],plot_UR['bot','X',leg]],[plot_UR['top','Y',leg],plot_UR['bot','Y',leg]],color='xkcd:{0}'.format(link_colour[0]))\n",
    "        \n",
    "        #plot lower right\n",
    "        abs_angles.update({('RL',leg): m.q[i,'theta_LR'+leg].value - (np.pi-abs_angles['RU',leg])})\n",
    "        plot_LR.update({('top','X',leg): plot_UR['bot','X',leg]})\n",
    "        plot_LR.update({('top','Y',leg): plot_UR['bot','Y',leg]})\n",
    "        plot_LR.update({('bot','X',leg): plot_UR['bot','X',leg] - m.lengths['LR'+leg]*cos(abs_angles['RL',leg])})\n",
    "        plot_LR.update({('bot','Y',leg): plot_UR['bot','Y',leg] - m.lengths['LR'+leg]*sin(abs_angles['RL',leg])})\n",
    "        ax.plot([plot_LR['top','X',leg],plot_LR['bot','X',leg]],[plot_LR['top','Y',leg],plot_LR['bot','Y',leg]],color='xkcd:{0}'.format(link_colour[0]))\n",
    "        \n",
    "        #plot foot\n",
    "        plot_foot.update({('top','X',leg): plot_LR['bot','X',leg]})\n",
    "        plot_foot.update({('top','Y',leg): plot_LR['bot','Y',leg]})\n",
    "        plot_foot.update({('bot','X',leg): plot_LR['bot','X',leg] + m.foot_length.value*cos(m.foot_angle.value - (np.pi)/2)})\n",
    "        plot_foot.update({('bot','Y',leg): plot_LR['bot','Y',leg] - m.foot_length.value*sin(m.foot_angle.value - (np.pi)/2)})\n",
    "        ax.plot([plot_foot['top','X',leg],plot_foot['bot','X',leg]],[plot_foot['top','Y',leg],plot_foot['bot','Y',leg]],color='xkcd:{0}'.format(link_colour[0]))\n",
    "\n",
    "        #plot upper left\n",
    "        abs_angles.update({('LU',leg): bod_angle + m.q[i,'theta_UL'+leg].value})\n",
    "        plot_UL.update({('top','X',leg): m.q[i,'x_body'].value - m.BLD.value*cos(bod_angle)})\n",
    "        plot_UL.update({('top','Y',leg): m.q[i,'y_body'].value - m.y_bodyOffset.value - m.BLD.value*sin(bod_angle)})\n",
    "        plot_UL.update({('bot','X',leg): plot_UL['top','X',leg] - m.lengths['UL'+leg]*cos(abs_angles['LU',leg])})\n",
    "        plot_UL.update({('bot','Y',leg): plot_UL['top','Y',leg]- m.lengths['UL'+leg]*sin(abs_angles['LU',leg])})\n",
    "        ax.plot([plot_UL['top','X',leg],plot_UL['bot','X',leg]],[plot_UL['top','Y',leg],plot_UL['bot','Y',leg]],color='xkcd:{0}'.format(link_colour[0]))\n",
    "\n",
    "        #plot lower left\n",
    "        abs_angles.update({('LL',leg):m.q[i,'theta_LL'+leg].value - (np.pi-abs_angles['LU',leg])})\n",
    "        plot_LL.update({('top','X',leg): plot_UL['bot','X',leg]})\n",
    "        plot_LL.update({('top','Y',leg): plot_UL['bot','Y',leg]})\n",
    "        plot_LL.update({('bot','X',leg): plot_UL['bot','X',leg] - m.lengths['LL'+leg]*cos(abs_angles['LL',leg])})\n",
    "        plot_LL.update({('bot','Y',leg): plot_UL['bot','Y',leg] - m.lengths['LL'+leg]*sin(abs_angles['LL',leg])})\n",
    "        ax.plot([plot_LL['top','X',leg],plot_LL['bot','X',leg]],[plot_LL['top','Y',leg],plot_LL['bot','Y',leg]],color='xkcd:{0}'.format(link_colour[0]))     \n",
    "    \n",
    "    \n",
    "    if m.GRF_y[i,'A'].value > 0.0:\n",
    "        #print('GRF_footR = ',m.GRF_y[i,'footR'].value)\n",
    "        ax.arrow(plot_LL['bot','X','A'], plot_LL['bot','Y','A'], 0, 0.01*m.GRF_y[i,'A'].value,color='xkcd:{0}'.format('red'),head_width = 0.1)\n",
    "        \n",
    "    if m.GRF_y[i,'B'].value > 0.0:\n",
    "        #print('GRF_footL = ',m.GRF_y[i,'footL'].value)\n",
    "        ax.arrow(plot_LR['bot','X','B'], plot_LR['bot','Y','B'], 0, 0.01*m.GRF_y[i,'B'].value,color='xkcd:{0}'.format('red'),head_width = 0.1)\n",
    "    \n",
    "#     if m.GRF_x[i,'-ve','B'].value > 0.0:\n",
    "#         #print('GRF_footL = ',m.GRF_y[i,'footL'].value)\n",
    "#         ax.arrow(plot_LR['bot','X','B'], plot_LR['bot','Y','B'], 0, 0.01*m.GRF_x[i,'-ve','B'].value,color='xkcd:{0}'.format('blue'),head_width = 0.1)\n",
    "    \n",
    "    \n",
    "    ax.legend([\"Body\",\"GRF*0.01\"], frameon=True)\n",
    "    \n",
    "    ax.plot([-10,10],[0,0]) # plot the ground\n",
    "    \n",
    "    \n",
    "update = lambda i: plot_robot(i,m,ax1) # lambdify update function\n",
    "\n",
    "animate = ani.FuncAnimation(fig1,update,range(1,N+1),interval = 50,repeat=True)\n",
    "\n",
    "HTML(animate.to_html5_video()) #you need to convert the animation to HTML5 to embed it in the notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "986d9a54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbd724728b0>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,N+1,1)\n",
    "LeftA = []\n",
    "RightA = []\n",
    "LeftB = []\n",
    "RightB = []\n",
    "\n",
    "for n in range(1,N+1):\n",
    "    LeftA.append(m.tau_torque[n,'left','A'].value)\n",
    "    RightA.append(m.tau_torque[n,'right','A'].value)\n",
    "    LeftB.append(m.tau_torque[n,'left','B'].value)\n",
    "    RightB.append(m.tau_torque[n,'right','B'].value)\n",
    "    \n",
    "line1 = plt.plot(x,LeftA,label = 'A Left')\n",
    "line2 = plt.plot(x,LeftB,label = 'A Right')\n",
    "line3 = plt.plot(x,RightA,label = 'B Left')\n",
    "line4 = plt.plot(x,RightB,label = 'B Right')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0b8026e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6493c22f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # abs(m.tau_torque[25,'right','B'].value-m.tau_torque[24,'right','B'].value)#<=10\n",
    "# for n in range(1,N+1):\n",
    "#     if n<N:\n",
    "#         print(abs(m.tau_torque[n+1,'right','B'].value-m.tau_torque[n,'right','B'].value))\n",
    "    \n",
    "# # np.diff(m.tau_torque[n,'left','A'] for n in range(1,N+1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "56f3f7e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbd809d8760>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,N+1,1)\n",
    "LeftA = []\n",
    "RightA = []\n",
    "LeftB = []\n",
    "RightB = []\n",
    "\n",
    "for n in range(1,N+1):\n",
    "    LeftA.append(m.knee_angle[n,'left','A'].value*180/np.pi)\n",
    "    RightA.append(m.knee_angle[n,'right','A'].value*180/np.pi)\n",
    "    LeftB.append(m.knee_angle[n,'left','B'].value*180/np.pi)\n",
    "    RightB.append(m.knee_angle[n,'right','B'].value*180/np.pi)\n",
    "    \n",
    "line1 = plt.plot(x,LeftA,label = 'A Left')\n",
    "line2 = plt.plot(x,LeftB,label = 'A Right')\n",
    "line3 = plt.plot(x,RightA,label = 'B Left')\n",
    "line4 = plt.plot(x,RightB,label = 'B Right')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "5d2ba65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbd80943a60>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,N+1,1)\n",
    "\n",
    "bGRFy = []\n",
    "bFootPos = []\n",
    "aGRFy = []\n",
    "aFootPos = []\n",
    "footScale = 1\n",
    "\n",
    "for n in range(1,N+1):\n",
    "    bGRFy.append(m.GRF_y[n,'B'].value)\n",
    "    bFootPos.append(m.foot_position[n,'Y','B'].value*footScale)\n",
    "    aGRFy.append(m.GRF_y[n,'A'].value)\n",
    "    aFootPos.append(m.foot_position[n,'Y','A'].value*footScale)\n",
    "\n",
    "# bGRFyline = plt.plot(x,bGRFy,label = 'B GRFy')\n",
    "bFootline = plt.plot(x,bFootPos,label = 'B Foot Pos (y)')\n",
    "# aGRFyline = plt.plot(x,aGRFy,label = 'A GRFy')\n",
    "aFootline = plt.plot(x,aFootPos,label = 'A Foot Pos (y)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "20ac3be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for n in range(1,N+1):\n",
    "#     print(m.GRF_y[n,'B'].value,m.foot_position[n,'X','B'].value,m.q[n,'y_body'].value)\n",
    "# # m.dq[N,'x_body'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c8740966",
   "metadata": {},
   "outputs": [],
   "source": [
    "# guide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "463c8ec3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.40678674979176427\n",
      "0.40666072647451745\n",
      "0.40637868868285965\n",
      "0.40604405169380947\n",
      "0.40566914158957956\n",
      "0.40525883237992305\n",
      "0.40481098858964587\n",
      "0.4043289589582235\n",
      "0.40381731682996297\n",
      "0.403287437821939\n",
      "0.40274718651145375\n",
      "0.4022033639067313\n",
      "0.40166736399869213\n",
      "0.40114154590551454\n",
      "0.4006341269460062\n",
      "0.400149926941824\n",
      "0.3996947300870021\n",
      "0.399265951720559\n",
      "0.39886368790168064\n",
      "0.39848791466718614\n",
      "0.39813600993206655\n",
      "0.3978049193903581\n",
      "0.3974877199132124\n",
      "0.39717840989139147\n",
      "0.3968728675760571\n",
      "0.3965695237909778\n",
      "0.3962652847423424\n",
      "0.3959629988898129\n",
      "0.3956666394054334\n",
      "0.39537183672941173\n",
      "0.39506941886718866\n",
      "0.3947547993307093\n",
      "0.39442408566491677\n",
      "0.3940814622193989\n",
      "0.39373409828437006\n",
      "0.3934014141936995\n",
      "0.393113639524269\n",
      "0.39290471629388535\n",
      "0.39283470453300606\n",
      "0.3929630372902751\n",
      "0.3933301154110299\n",
      "0.393996861217714\n",
      "0.3949750796449461\n",
      "0.39629093456050624\n",
      "0.3978381303504703\n",
      "0.3996540825109027\n",
      "0.401439755905381\n",
      "0.4029591033869736\n",
      "0.4040522825653004\n",
      "0.40426651435201016\n",
      "0.4059696633921537\n",
      "0.4069559258349402\n",
      "0.40738025781669135\n",
      "0.40739711712021504\n",
      "0.40708787763882764\n",
      "0.406519598860463\n",
      "0.4057696933017685\n",
      "0.40489099871322953\n",
      "0.40392561324045084\n",
      "0.4029065398695058\n",
      "0.40188566852784385\n",
      "0.4009487070368702\n",
      "0.3999662798150348\n",
      "0.3990826638952392\n",
      "0.3983455186257618\n",
      "0.3978054096148353\n",
      "0.3974434801776018\n",
      "0.3972845329019342\n",
      "0.3973388987533984\n",
      "0.3975790927349875\n",
      "0.3980022145676407\n",
      "0.39858160440704354\n",
      "0.3992900077016163\n",
      "0.40009579659096706\n",
      "0.4009683422617386\n",
      "0.4018756979472403\n",
      "0.4027947152235047\n",
      "0.40370030120702444\n",
      "0.4045707743229441\n",
      "0.40539608199117333\n",
      "0.4061670157744462\n",
      "0.40687390746184326\n",
      "0.4075127125213162\n",
      "0.40808098807577864\n",
      "0.4085780685469053\n",
      "0.40900733053162636\n",
      "0.4093711253045135\n",
      "0.40967705972974355\n",
      "0.4099326703248705\n",
      "0.4101456642019754\n",
      "0.4103185873043862\n",
      "0.4104531485011863\n",
      "0.41054472066590547\n",
      "0.4105854670352193\n",
      "0.4105525497563602\n",
      "0.41041080892745624\n",
      "0.4100982390985114\n",
      "0.40949827023377233\n",
      "0.40851037370657384\n",
      "0.4067868865606556\n"
     ]
    }
   ],
   "source": [
    "for n in range(1,N+1):\n",
    "    print(m.q[n,'y_body'].value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "e626f53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for n in range(1,N+1):\n",
    "#     print(m.knee_angle[n,'left','B'].value*180/np.pi)\n",
    "#m.foot_position[2,'X','B'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "58e08b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# thULA = []\n",
    "# thURA = []\n",
    "# thULB = []\n",
    "# thURB = []\n",
    "# dthULA = []\n",
    "# dthURA = []\n",
    "# dthULB = []\n",
    "# dthURB = []\n",
    "# LeftA = []\n",
    "# RightA = []\n",
    "# LeftB = []\n",
    "# RightB = []\n",
    "\n",
    "# for n in range(1,N+1):\n",
    "#     thULA.append(m.q[n,'theta_ULA'].value)\n",
    "#     thURA.append(m.q[n,'theta_URA'].value)\n",
    "#     thULB.append(m.q[n,'theta_ULB'].value)\n",
    "#     thURB.append(m.q[n,'theta_URB'].value)\n",
    "#     dthULA.append(m.dq[n,'theta_ULA'].value)\n",
    "#     dthURA.append(m.dq[n,'theta_URA'].value)\n",
    "#     dthULB.append(m.dq[n,'theta_ULB'].value)\n",
    "#     dthURB.append(m.dq[n,'theta_URB'].value)\n",
    "#     LeftA.append(m.tau_torque[n,'left','A'].value)\n",
    "#     RightA.append(m.tau_torque[n,'right','A'].value)\n",
    "#     LeftB.append(m.tau_torque[n,'left','B'].value)\n",
    "#     RightB.append(m.tau_torque[n,'right','B'].value)\n",
    "    \n",
    "# #     print(m.q[n,'theta_ULA'].value)\n",
    "# # position_data = {'thULA':thULA,'thURA':thURA,'thULB':thULB,'thURB':thURB}\n",
    "# # with open('position_data_test1.pkl','wb') as f:\n",
    "# #     pickle.dump(position_data, f)\n",
    "# # print(position_data['thULA'])\n",
    "# heading0 = ['node']\n",
    "# heading1 = ['thULA','thURA','thULB','thURB','dthULA','dthURA','dthULB','dthURB','tLeftA','tRightA','tLeftB','tRightB']\n",
    "# heading = heading0+heading1\n",
    "# import csv\n",
    "# with open('mid_walk_thbod_locked_v10_.csv','w') as f:\n",
    "#     writer = csv.writer(f)\n",
    "#     writer.writerow(heading)\n",
    "#     for n in range(N):\n",
    "#         node = [n]\n",
    "#         data1 = [thULA[n],thURA[n],thULB[n],thURB[n],dthULA[n],dthURA[n],dthULB[n],dthURB[n],LeftA[n],RightA[n],LeftB[n],RightB[n]]\n",
    "#         data = node+data1\n",
    "#         writer.writerow(data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "92174121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "80da3a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.dq[1,'theta_URA'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "cf755fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for n in range(1,N+1):\n",
    "#     print(m.q[n,'y_body'].value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a7e8eb9c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.811931031553789"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#m.dq[N,'y_body'].value\n",
    "V_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7336db3f",
   "metadata": {},
   "source": [
    "<!-- 1.4=72cm\n",
    "x=45cm -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "d6ac0cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.foot_position[N,'Y','B'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "9b02b07c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save1 = False\n",
    "# if save1 == True:\n",
    "#     data =  {'ybod' : m.q[N,'y_body'].value,\n",
    "#         'dxbod' : m.dq[N,'x_body'].value,\n",
    "#         'dthULA' : m.dq[N,'theta_ULA'].value,\n",
    "#         'dthLLA' : m.dq[N,'theta_LLA'].value,\n",
    "#         'dthURA' : m.dq[N,'theta_URA'].value,\n",
    "#         'dthLRA' : m.dq[N,'theta_LRA'].value,\n",
    "\n",
    "#         'dthULB' : m.dq[N,'theta_ULB'].value,\n",
    "#         'dthLLB' : m.dq[N,'theta_LLB'].value,\n",
    "#         'dthURB' : m.dq[N,'theta_URB'].value,\n",
    "#         'dthLRB' : m.dq[N,'theta_LRB'].value}\n",
    "#     with open('periodic_walk_start_v7.pkl', 'wb') as f: \n",
    "#         pickle.dump(data, f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "314da3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "save2 = False\n",
    "if save2 == True:\n",
    "    data =  {'ybod' : m.q[1,'y_body'].value,\n",
    "             'dybod' : m.dq[1,'y_body'].value,\n",
    "        'dxbod' : m.dq[1,'x_body'].value,\n",
    "             \n",
    "        'thULA' : m.q[1,'theta_ULA'].value,\n",
    "        'thLLA' : m.q[1,'theta_LLA'].value,\n",
    "        'thURA' : m.q[1,'theta_URA'].value,\n",
    "        'thLRA' : m.q[1,'theta_LRA'].value,\n",
    "\n",
    "        'thULB' : m.q[1,'theta_ULB'].value,\n",
    "        'thLLB' : m.q[1,'theta_LLB'].value,\n",
    "        'thURB' : m.q[1,'theta_URB'].value,\n",
    "        'thLRB' : m.q[1,'theta_LRB'].value,\n",
    "             \n",
    "             \n",
    "        'dthULA' : m.dq[1,'theta_ULA'].value,\n",
    "        'dthLLA' : m.dq[1,'theta_LLA'].value,\n",
    "        'dthURA' : m.dq[1,'theta_URA'].value,\n",
    "        'dthLRA' : m.dq[1,'theta_LRA'].value,\n",
    "\n",
    "        'dthULB' : m.dq[1,'theta_ULB'].value,\n",
    "        'dthLLB' : m.dq[1,'theta_LLB'].value,\n",
    "        'dthURB' : m.dq[1,'theta_URB'].value,\n",
    "        'dthLRB' : m.dq[1,'theta_LRB'].value}\n",
    "    with open('periodic_walk_N1_data_v10.pkl', 'wb') as f: \n",
    "        pickle.dump(data, f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "64021090",
   "metadata": {},
   "outputs": [],
   "source": [
    "save2 = False\n",
    "if save2 == True:\n",
    "    data =  {'ybod' : m.q[N,'y_body'].value,\n",
    "             'dybod' : m.dq[N,'y_body'].value,\n",
    "        'dxbod' : m.dq[N,'x_body'].value,\n",
    "             \n",
    "        'thULA' : m.q[N,'theta_ULA'].value,\n",
    "        'thLLA' : m.q[N,'theta_LLA'].value,\n",
    "        'thURA' : m.q[N,'theta_URA'].value,\n",
    "        'thLRA' : m.q[N,'theta_LRA'].value,\n",
    "\n",
    "        'thULB' : m.q[N,'theta_ULB'].value,\n",
    "        'thLLB' : m.q[N,'theta_LLB'].value,\n",
    "        'thURB' : m.q[N,'theta_URB'].value,\n",
    "        'thLRB' : m.q[N,'theta_LRB'].value,\n",
    "             \n",
    "             \n",
    "        'dthULA' : m.dq[N,'theta_ULA'].value,\n",
    "        'dthLLA' : m.dq[N,'theta_LLA'].value,\n",
    "        'dthURA' : m.dq[N,'theta_URA'].value,\n",
    "        'dthLRA' : m.dq[N,'theta_LRA'].value,\n",
    "\n",
    "        'dthULB' : m.dq[N,'theta_ULB'].value,\n",
    "        'dthLLB' : m.dq[N,'theta_LLB'].value,\n",
    "        'dthURB' : m.dq[N,'theta_URB'].value,\n",
    "        'dthLRB' : m.dq[N,'theta_LRB'].value}\n",
    "    with open('periodic_walk_finalN_data_v10.pkl', 'wb') as f: \n",
    "        pickle.dump(data, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "0c4a79ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for n in range(1,N+1):\n",
    "#     print(m.knee_angle[n,'left','B'].value*180/np.pi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "ee0e8c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('periodic_walk_start_v.pkl','rb') as f:\n",
    "#     start = pickle.load(f)\n",
    "# start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "582834a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# start[0]['xbod']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1cc54b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34295717",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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