Testing_Likelihood_J0340.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from cycler import cycler\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy.linalg as sl\n",
    "import scipy.stats as sps\n",
    "from scipy.constants import golden_ratio\n",
    "\n",
    "import json, pickle, os, corner, glob, sys\n",
    "from collections import defaultdict, OrderedDict\n",
    "from copy import deepcopy\n",
    "import pandas as pd\n",
    "from astropy.time import Time\n",
    "import astropy.units as u\n",
    "\n",
    "from PTMCMCSampler.PTMCMCSampler import PTSampler as ptmcmc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_cycle_wong = ['#000000','#E69F00','#009E73','#56B4E9','#0072B2','#F0E442','#D55E00','#CC79A7']\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler(color=color_cycle_wong)\n",
    "def get_fig_size(width=15,scale=1.):\n",
    "    #width = 3.36 # 242 pt\n",
    "    base_size = np.array([1, 1/scale/golden_ratio])\n",
    "    fig_size = width * base_size\n",
    "    return(fig_size)\n",
    "mpl.rcParams['figure.figsize'] = get_fig_size()\n",
    "mpl.rcParams['text.usetex'] = False\n",
    "mpl.rc('font',**{'family':'serif','serif':['Times New Roman']})\n",
    "mpl.rcParams['lines.linewidth'] = 3\n",
    "mpl.rcParams['lines.markersize'] = 10\n",
    "mpl.rcParams['axes.labelsize'] = 30\n",
    "mpl.rcParams['xtick.labelsize'] = 20\n",
    "mpl.rcParams['ytick.labelsize'] = 20\n",
    "mpl.rcParams['legend.fontsize'] = 20\n",
    "mpl.rcParams['axes.titlesize'] = 20\n",
    "#mpl.rcParams[\"figure.dpi\"] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_path = os.getcwd()\n",
    "splt_path = current_path.split(\"/\")\n",
    "#top_path_idx = splt_path.index(\"akaiser\")\n",
    "top_path_idx = splt_path.index(\"nanograv\")\n",
    "top_dir = \"/\".join(splt_path[0 : top_path_idx + 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "la_f_path = top_dir + \"/la_forge\"\n",
    "e_e_path = top_dir + \"/enterprise_extensions/\"\n",
    "enterprise_path = top_dir + \"/enterprise\"\n",
    "sys.path.insert(0, la_f_path)\n",
    "sys.path.insert(0, e_e_path)\n",
    "sys.path.insert(0, top_dir + \"/enterprise_timing\")\n",
    "sys.path.insert(0, enterprise_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PINT not installed. Will use libstempo instead.\n"
     ]
    }
   ],
   "source": [
    "import enterprise\n",
    "from enterprise.pulsar import Pulsar\n",
    "from enterprise.signals import utils\n",
    "from enterprise.signals import parameter\n",
    "from enterprise.signals import white_signals\n",
    "from enterprise.signals import signal_base\n",
    "from enterprise.signals import selections\n",
    "from enterprise.signals import deterministic_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import la_forge\n",
    "import la_forge.diagnostics as dg\n",
    "from la_forge.core import TimingCore\n",
    "from la_forge.core import Core\n",
    "\n",
    "import nltm_plot_utils_v4 as nltm_plt\n",
    "from nltm_J0340_setup_pta_v1 import pta_setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ducks 2!\n",
      "/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/ent_t2_desmat.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Could not find pulsar distance for PSR J0340+4130. Setting value to 1 with 20% uncertainty.\n"
     ]
    }
   ],
   "source": [
    "psr_name = 'J0340+4130'#'J1640+2224'#'J2043+1711'#'J2317+1439' 'J1909-3744'\n",
    "parfile = top_dir + f\"/9yr/par/{psr_name}_NANOGrav_9yv1.gls.par\"\n",
    "timfile = top_dir + f\"/9yr/tim/{psr_name}_NANOGrav_9yv1.tim\"\n",
    "ephem = 'DE421'\n",
    "exclude_params = ['lnpost','lnlike','chain_accept','pt_chain_accept']\n",
    "\n",
    "psr = Pulsar(parfile, timfile, ephem=ephem, clk=None, drop_t2pulsar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptypes = [\"normalized\" for ii in range(len(psr.t2pulsar.pars()))]\n",
    "tm_params_orig = OrderedDict(\n",
    "        zip(\n",
    "            psr.t2pulsar.pars(),\n",
    "            map(\n",
    "                list,\n",
    "                zip(\n",
    "                    np.longdouble(psr.t2pulsar.vals()),\n",
    "                    np.longdouble(psr.t2pulsar.errs()),\n",
    "                    ptypes,\n",
    "                ),\n",
    "            ),\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELONG 1.0928215436402511194 0.0\n",
      "ELAT 0.372356825973707325 nan\n",
      "F0 303.0909794334667708 5.760699999999999879e-12\n",
      "F1 -6.501604154599e-16 2.1133269239020001296e-18\n",
      "PMELONG -2.4185 0.8314000000000000279\n",
      "PMELAT -4.2499 1.3380000000000000782\n",
      "PX 0.6837 0.69620000000000004103\n",
      "FD1 3.21433466e-05 1.853543770000000083e-06\n",
      "DMX_0001 -0.00568289474 0.00032399321199999997923\n",
      "DMX_0002 -0.00552085223 0.00029977501100000001916\n",
      "DMX_0003 -0.00548448238 0.000280141254999999984\n",
      "DMX_0004 -0.0052418574 0.0002743026410000000176\n",
      "DMX_0005 -0.00546321846 0.00027164663500000001822\n",
      "DMX_0006 -0.0058250848 0.0004933091900000000432\n",
      "DMX_0007 -0.00530310515 0.00029648043799999999198\n",
      "DMX_0008 -0.00526781796 0.00028518873099999997355\n",
      "DMX_0009 -0.00517494804 0.00028266689699999998728\n",
      "DMX_0010 -0.0050663524 0.0002701798849999999903\n",
      "DMX_0011 -0.00478031918 0.0002806008360000000106\n",
      "DMX_0012 -0.0050208651 0.00026573736999999997884\n",
      "DMX_0013 -0.00505935183 0.0002586487010000000121\n",
      "DMX_0014 -0.00509190447 0.0002561736220000000101\n",
      "DMX_0015 -0.00516529749 0.00025988915899999997941\n",
      "DMX_0016 -0.00507480475 0.00028150625200000002103\n",
      "DMX_0017 -0.0050515162 0.00027584791899999997703\n",
      "DMX_0018 -0.00498892016 0.00028898848400000002289\n",
      "DMX_0019 -0.00483972156 0.0003065586760000000243\n",
      "DMX_0020 -0.00486491944 0.00030898006899999998134\n",
      "DMX_0021 -0.00463044232 0.0003054490159999999994\n",
      "DMX_0022 -0.00434833581 0.00032017814800000001484\n",
      "DMX_0023 -0.00447875203 0.00061232973599999998473\n",
      "DMX_0024 -0.00399314623 0.00031041997000000001077\n",
      "DMX_0025 -0.00435106576 0.0002877625810000000099\n",
      "DMX_0026 -0.00404593118 0.000287672562000000024\n",
      "DMX_0027 -0.00445715532 0.00031498618299999998297\n",
      "JUMP1 -2.0070000000000000119e-06 0.0\n"
     ]
    }
   ],
   "source": [
    "[print(p,z,e) for p,z,e in zip(psr.t2pulsar.pars(),psr.t2pulsar.vals(),psr.t2pulsar.errs())];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tmp = psr.t2pulsar.vals()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nltm_plt.refit_errs(psr,tm_params_orig)\n",
    "for p,t,pt2v in zip(psr.t2pulsar.pars(),tmp,psr.t2pulsar.vals()):\n",
    "    print(p,t,pt2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6837\n",
      "0.69620000000000004103\n",
      "1393.083700000000082\n"
     ]
    }
   ],
   "source": [
    "par = 'PX'\n",
    "value = 2000\n",
    "print(tm_params_orig[par][0])\n",
    "print(tm_params_orig[par][1])\n",
    "print(tm_params_orig[par][0]+tm_params_orig[par][1]*value)\n",
    "tm_delay_kwargs = {par:value}\n",
    "#psr.tm_params_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'PX': 1393.083700000000082}\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1080x667.477 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 596,
       "width": 934
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nltm_plt.tm_delay(psr.t2pulsar,tm_params_orig,**tm_delay_kwargs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "```\n",
    "value = 1000.\n",
    "for par in psr.t2pulsar.pars():\n",
    "    tm_delay_kwargs = {par:value}\n",
    "    nltm_plt.tm_delay(psr.t2pulsar,tm_params_orig,**tm_delay_kwargs);\n",
    "    plt.show()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading runs for MLVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending PTMCMCSampler sampling parameters to end of parameter list.\n",
      "If unwanted please provide a parameter list.\n",
      "No burn specified. Burn set to 25% of chain length, 24975\n",
      "You may change the burn length with core.set_burn()\n",
      "Red noise frequencies must be set before plotting most red noise figures.\n",
      "Please use core.set_rn_freqs() to set, if needed.\n"
     ]
    }
   ],
   "source": [
    "psr_dir = current_path + f'/{psr_name}/chains/'\n",
    "chaindir_core_e_e_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3 = psr_dir+f'9yr/{psr_name}_uniform_DE421_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3'\n",
    "burn=None\n",
    "\n",
    "core_e_e_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3 = TimingCore('_'.join(chaindir_core_e_e_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3.split('/')[-2:]),\n",
    "                              chaindir=chaindir_core_e_e_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3,\n",
    "                              tm_pars_path=chaindir_core_e_e_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3+'/orig_timing_pars.pkl',\n",
    "                              burn=burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "burn=None\n",
    "#mark9\n",
    "chaindir_PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1 = top_dir + f'/enterprise_timing/{psr_name}/ark_PAL2/PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1'\n",
    "enterprise_equiv_pars = ['J0340+4130_Rcvr1_2_GUPPI_efac', 'J0340+4130_Rcvr_800_GUPPI_efac',\n",
    "                        'J0340+4130_Rcvr1_2_GUPPI_log10_ecorr', 'J0340+4130_Rcvr_800_GUPPI_log10_ecorr',\n",
    "                        'J0340+4130_Rcvr1_2_GUPPI_log10_equad', 'J0340+4130_Rcvr_800_GUPPI_log10_equad',\n",
    "                        'J0340+4130_timing_model_PMELONG', 'J0340+4130_timing_model_PMELAT',\n",
    "                        'J0340+4130_timing_model_PX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending PTMCMCSampler sampling parameters to end of parameter list.\n",
      "If unwanted please provide a parameter list.\n",
      "No burn specified. Burn set to 25% of chain length, 11350\n",
      "You may change the burn length with core.set_burn()\n",
      "Red noise frequencies must be set before plotting most red noise figures.\n",
      "Please use core.set_rn_freqs() to set, if needed.\n"
     ]
    }
   ],
   "source": [
    "#mark9\n",
    "chain_PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1 = pd.read_csv(chaindir_PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1+'/chain_1.txt',\n",
    "                                                                        sep='\\t', dtype=float, header=None).values\n",
    "core_PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1 = Core('PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1',\n",
    "                                                                chain=chain_PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1,\n",
    "                                                                params=enterprise_equiv_pars.copy(),\n",
    "                                                                burn=burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_PAL2 = core_PAL2_9yr_noRN_vary_WN_noise_only_1\n",
    "c_PAL2 = core_PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1\n",
    "#c_PAL2 = core_PAL2_noRN_varyWN_full_nltm_mark9_1\n",
    "#c_e_e = core_9yr_DE421_noRN_varyWN_noise_only_1\n",
    "c_e_e = core_e_e_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3\n",
    "#c_e_e = core_9yr_DE421_noRN_varyWN_noise_only_no_tm_1\n",
    "#c_e_e = core_9yr_DE421_noRN_varyWN_full_nltm_500_sigma_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chain_accept'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_e_e.params[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 308,
       "width": 727
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32491.138411, 32460.271804]\n"
     ]
    }
   ],
   "source": [
    "hist_core_kwargs = {'hist':True, 'ncols':3,'title_y':1.03,\n",
    "                'hist_kwargs':dict(fill=False),'linewidth':3.}\n",
    "trace_core_kwargs = {'hist':False, 'ncols':3,'title_y':1.05,\n",
    "                'hist_kwargs':dict(fill=False),'linewidth':3.}\n",
    "dg.plot_chains(c_e_e,\n",
    "               pars=['lnlike','lnpost'],\n",
    "               real_tm_pars=False,\n",
    "               **hist_core_kwargs)\n",
    "print([np.max(c_e_e.get_param(x)) for x in ['lnlike','lnpost']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_e_e_titles = nltm_plt.get_titles(psr_name,c_e_e)\n",
    "map_idx_e_e = np.argmax(c_e_e.get_param('lnpost',to_burn=True))\n",
    "map_dict_e_e = OrderedDict()\n",
    "map_dict_e_e_scaled = OrderedDict()\n",
    "mean_dict_e_e = OrderedDict()\n",
    "for par in c_e_e.params:\n",
    "    unscaled_param = c_e_e.get_param(par,to_burn=True)\n",
    "    scaled_param = c_e_e.get_param(par,to_burn=True,tm_convert=False)\n",
    "    map_dict_e_e_scaled[par] = scaled_param[map_idx_e_e]\n",
    "    map_dict_e_e[par] = unscaled_param[map_idx_e_e]\n",
    "    mean_dict_e_e[par] = np.mean(unscaled_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6837, 0.69620000000000004103, 'normalized']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_params_orig['PX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('J0340+4130_Rcvr1_2_GUPPI_efac', 1.1653083923340508),\n",
       "             ('J0340+4130_Rcvr1_2_GUPPI_log10_ecorr', -7.933830522466152),\n",
       "             ('J0340+4130_Rcvr1_2_GUPPI_log10_equad', -9.975839783512333),\n",
       "             ('J0340+4130_Rcvr_800_GUPPI_efac', 1.0918043418926164),\n",
       "             ('J0340+4130_Rcvr_800_GUPPI_log10_ecorr', -8.314049329096257),\n",
       "             ('J0340+4130_Rcvr_800_GUPPI_log10_equad', -7.377330422172056),\n",
       "             ('J0340+4130_timing_model_PMELAT', -4.17865177914288),\n",
       "             ('J0340+4130_timing_model_PMELONG', -2.3390302472236604),\n",
       "             ('J0340+4130_timing_model_PX', 0.6475440470430858),\n",
       "             ('lnpost', 32460.271804),\n",
       "             ('lnlike', 32491.138411),\n",
       "             ('chain_accept', 0.325388),\n",
       "             ('pt_chain_accept', 0.24721)])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_dict_e_e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "hist_core_kwargs = {'hist':True, 'ncols':3,'title_y':1.03,\n",
    "                'hist_kwargs':dict(fill=False),'linewidth':3.}\n",
    "dg.plot_chains(c_e_e,\n",
    "               pars=c_e_e.params,\n",
    "               titles=c_e_e_titles,\n",
    "               real_tm_pars=True,\n",
    "               **hist_core_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('ELONG', [1.0928215436402511194, 0.0, 'normalized']), ('ELAT', [0.372356825973707325, nan, 'normalized']), ('F0', [303.0909794334667708, 5.760699999999999879e-12, 'normalized']), ('F1', [-6.501604154599e-16, 2.1133269239020001296e-18, 'normalized']), ('PMELONG', [-2.4185, 0.8314000000000000279, 'physical']), ('PMELAT', [-4.2499, 1.3380000000000000782, 'physical']), ('PX', [0.6837, 0.69620000000000004103, 'physical']), ('FD1', [3.21433466e-05, 1.853543770000000083e-06, 'normalized']), ('DMX_0001', [-0.00568289474, 0.00032399321199999997923, 'normalized']), ('DMX_0002', [-0.00552085223, 0.00029977501100000001916, 'normalized']), ('DMX_0003', [-0.00548448238, 0.000280141254999999984, 'normalized']), ('DMX_0004', [-0.0052418574, 0.0002743026410000000176, 'normalized']), ('DMX_0005', [-0.00546321846, 0.00027164663500000001822, 'normalized']), ('DMX_0006', [-0.0058250848, 0.0004933091900000000432, 'normalized']), ('DMX_0007', [-0.00530310515, 0.00029648043799999999198, 'normalized']), ('DMX_0008', [-0.00526781796, 0.00028518873099999997355, 'normalized']), ('DMX_0009', [-0.00517494804, 0.00028266689699999998728, 'normalized']), ('DMX_0010', [-0.0050663524, 0.0002701798849999999903, 'normalized']), ('DMX_0011', [-0.00478031918, 0.0002806008360000000106, 'normalized']), ('DMX_0012', [-0.0050208651, 0.00026573736999999997884, 'normalized']), ('DMX_0013', [-0.00505935183, 0.0002586487010000000121, 'normalized']), ('DMX_0014', [-0.00509190447, 0.0002561736220000000101, 'normalized']), ('DMX_0015', [-0.00516529749, 0.00025988915899999997941, 'normalized']), ('DMX_0016', [-0.00507480475, 0.00028150625200000002103, 'normalized']), ('DMX_0017', [-0.0050515162, 0.00027584791899999997703, 'normalized']), ('DMX_0018', [-0.00498892016, 0.00028898848400000002289, 'normalized']), ('DMX_0019', [-0.00483972156, 0.0003065586760000000243, 'normalized']), ('DMX_0020', [-0.00486491944, 0.00030898006899999998134, 'normalized']), ('DMX_0021', [-0.00463044232, 0.0003054490159999999994, 'normalized']), ('DMX_0022', [-0.00434833581, 0.00032017814800000001484, 'normalized']), ('DMX_0023', [-0.00447875203, 0.00061232973599999998473, 'normalized']), ('DMX_0024', [-0.00399314623, 0.00031041997000000001077, 'normalized']), ('DMX_0025', [-0.00435106576, 0.0002877625810000000099, 'normalized']), ('DMX_0026', [-0.00404593118, 0.000287672562000000024, 'normalized']), ('DMX_0027', [-0.00445715532, 0.00031498618299999998297, 'normalized']), ('JUMP1', [-2.0070000000000000119e-06, 0.0, 'normalized'])])\n",
      "{'PMELONG': -2.3390302472236603748, 'PMELAT': -4.1786517791428803292, 'PX': 0.6475440470430857687}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x667.477 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 596,
       "width": 934
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use_map = True\n",
    "use_map = True\n",
    "use_zeros = False\n",
    "if use_map:\n",
    "    e_e_dict = deepcopy(map_dict_e_e)\n",
    "    if use_zeros:\n",
    "        for p in e_e_dict.keys():\n",
    "            if 'timing' in p:\n",
    "                e_e_dict.update({p:tm_params_orig[p.split('_')[-1]][0]})\n",
    "else:\n",
    "    e_e_dict = deepcopy(mean_dict_e_e)\n",
    "    if use_zeros:\n",
    "        for p in e_e_dict.keys():\n",
    "            if 'timing' in p:\n",
    "                e_e_dict.update({p:tm_params_orig[p.split('_')[-1]][0]})\n",
    "try:\n",
    "    chain_tm_params_orig = deepcopy(tm_params_orig)\n",
    "    chain_tm_delay_kwargs = {}\n",
    "    for par in psr.t2pulsar.pars():\n",
    "        if par in c_e_e_titles:\n",
    "            chain_tm_params_orig[par][-1] = 'physical'\n",
    "            chain_tm_delay_kwargs[par] = e_e_dict[c_e_e.params[c_e_e_titles.index(par)]]\n",
    "        elif par == 'SINI' and 'COSI' in c_e_e_titles:\n",
    "            print(\"COSI added to tm_params_orig for to work with tm_delay.\")\n",
    "            sin_val, sin_err, _ = chain_tm_params_orig[par]\n",
    "            val = np.longdouble(np.sqrt(1 - sin_val ** 2))\n",
    "            err = np.longdouble(\n",
    "                np.sqrt((np.abs(sin_val / val)) ** 2 * sin_err ** 2)\n",
    "            )\n",
    "            chain_tm_params_orig['COSI'] = [val, err, \"physical\"]\n",
    "            chain_tm_delay_kwargs['COSI'] = e_e_dict[c_e_e.params[c_e_e_titles.index('COSI')]]\n",
    "    print(chain_tm_params_orig)\n",
    "    nltm_plt.tm_delay(psr.t2pulsar,chain_tm_params_orig,**chain_tm_delay_kwargs);\n",
    "    plt.show()\n",
    "except:\n",
    "    print(\"Noise only run? Can't do it.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_PAL2_titles = nltm_plt.get_titles(psr_name,c_PAL2)\n",
    "map_idx_PAL2 = np.argmax(c_PAL2.get_param('lnpost',to_burn=True))\n",
    "map_dict_PAL2 = OrderedDict()\n",
    "mean_dict_PAL2 = OrderedDict()\n",
    "pert_map_dict_PAL2 = OrderedDict()\n",
    "for par in c_PAL2.params:\n",
    "    unscaled_param = c_PAL2.get_param(par,to_burn=True)\n",
    "    pert_map_dict_PAL2[par] = unscaled_param[map_idx_PAL2]+.1*unscaled_param[map_idx_PAL2]\n",
    "    map_dict_PAL2[par] = unscaled_param[map_idx_PAL2]\n",
    "    mean_dict_PAL2[par] = np.mean(unscaled_param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "hist_core_kwargs = {'hist':True, 'ncols':3,'title_y':1.03,\n",
    "                'hist_kwargs':dict(fill=False),'linewidth':3.}\n",
    "dg.plot_chains(c_PAL2,\n",
    "               pars=c_PAL2.params,\n",
    "               titles=c_PAL2_titles,\n",
    "               real_tm_pars=False,\n",
    "               **hist_core_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'PMELONG': -2.3719113964803866246, 'PMELAT': -4.0930183759458964587, 'PX': 0.896001675010787868}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x667.477 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 596,
       "width": 934
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use_map = True\n",
    "use_map = False\n",
    "if use_map:\n",
    "    PAL2_dict = deepcopy(map_dict_PAL2)\n",
    "else:\n",
    "    PAL2_dict = deepcopy(mean_dict_PAL2)\n",
    "try:\n",
    "    PAL2_tm_params_orig = deepcopy(tm_params_orig)\n",
    "    PAL2_tm_delay_kwargs = {}\n",
    "    for par in psr.t2pulsar.pars():\n",
    "        if par in c_PAL2_titles:\n",
    "            PAL2_tm_params_orig[par][-1] = 'physical'\n",
    "            PAL2_tm_delay_kwargs[par] = PAL2_dict[c_PAL2.params[c_PAL2_titles.index(par)]]\n",
    "        elif par == 'SINI' and 'COSI' in c_PAL2_titles:\n",
    "            print(\"COSI added to tm_params_orig for to work with tm_delay.\")\n",
    "            sin_val, sin_err, _ = PAL2_tm_params_orig[par]\n",
    "            val = np.longdouble(np.sqrt(1 - sin_val ** 2))\n",
    "            err = np.longdouble(\n",
    "                np.sqrt((np.abs(sin_val / val)) ** 2 * sin_err ** 2)\n",
    "            )\n",
    "            PAL2_tm_params_orig['COSI'] = [val, err, \"physical\"]\n",
    "            PAL2_tm_delay_kwargs['COSI'] = PAL2_dict[c_PAL2.params[c_PAL2_titles.index('COSI')]]\n",
    "\n",
    "    nltm_plt.tm_delay(psr.t2pulsar,PAL2_tm_params_orig,**PAL2_tm_delay_kwargs);\n",
    "    plt.show()\n",
    "except:\n",
    "    print(\"Noise only run? Can't do it.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_1\n",
      "9yr_J0340+4130_uniform_DE421_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_500_sigma_3\n"
     ]
    }
   ],
   "source": [
    "print(c_PAL2.label)\n",
    "print(c_e_e.label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save map_dicts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "nltm_timing_run_name = 'PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_like_test_1'\n",
    "nltm_timing_outDir=\"/home/andrew/Documents/Research/nanograv/enterprise_timing/{}/ark_PAL2/{}\".format(psr_name,nltm_timing_run_name)\n",
    "with open(nltm_timing_outDir+'/map_dict_PAL2.json','w') as f:\n",
    "    json.dump(map_dict_PAL2,f)\n",
    "    \n",
    "with open(nltm_timing_outDir+'/pert_map_dict_PAL2.json','w') as f:\n",
    "    json.dump(pert_map_dict_PAL2,f)\n",
    "    \n",
    "copy_tm_params_orig = deepcopy(tm_params_orig)\n",
    "for key, val in copy_tm_params_orig.items():\n",
    "    for i,num in enumerate(val[:-1]):\n",
    "        copy_tm_params_orig[key][i] = np.double(num)\n",
    "with open(nltm_timing_outDir+'/tm_params_orig.json','w') as f:\n",
    "    json.dump(copy_tm_params_orig,f)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Likelihood Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ducks 2!\n",
      "/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/ent_t2_desmat.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Could not find pulsar distance for PSR J0340+4130. Setting value to 1 with 20% uncertainty.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-linearly varying these values:  ['PMELONG', 'PMELAT', 'PX'] \n",
      " in pulsar  J0340+4130\n",
      "Linearly varying these values:  ['Offset', 'ELONG', 'ELAT', 'F0', 'F1', 'FD1', 'DMX_0001', 'DMX_0002', 'DMX_0003', 'DMX_0004', 'DMX_0005', 'DMX_0006', 'DMX_0007', 'DMX_0008', 'DMX_0009', 'DMX_0010', 'DMX_0011', 'DMX_0012', 'DMX_0013', 'DMX_0014', 'DMX_0015', 'DMX_0016', 'DMX_0017', 'DMX_0018', 'DMX_0019', 'DMX_0020', 'DMX_0021', 'DMX_0022', 'DMX_0023', 'DMX_0024', 'DMX_0025', 'DMX_0026', 'DMX_0027', 'JUMP1']\n",
      "Using  uniform  prior.\n",
      "OrderedDict([('ELONG', [1.0928215436402511194, 8.312475227234983854e-10, 'normalized']), ('ELAT', [0.372356825973707325, 2.6585994223154536922e-09, 'normalized']), ('F0', [303.0909794334667708, 5.760699999999999879e-12, 'normalized']), ('F1', [-6.501604154599e-16, 2.1133269239020001296e-18, 'normalized']), ('PMELONG', [-2.4185, 0.8314000000000000279, 'normalized']), ('PMELAT', [-4.2499, 1.3380000000000000782, 'normalized']), ('PX', [0.6837, 0.69620000000000004103, 'physical']), ('FD1', [3.21433466e-05, 1.853543770000000083e-06, 'normalized']), ('DMX_0001', [-0.00568289474, 0.00032399321199999997923, 'normalized']), ('DMX_0002', [-0.00552085223, 0.00029977501100000001916, 'normalized']), ('DMX_0003', [-0.00548448238, 0.000280141254999999984, 'normalized']), ('DMX_0004', [-0.0052418574, 0.0002743026410000000176, 'normalized']), ('DMX_0005', [-0.00546321846, 0.00027164663500000001822, 'normalized']), ('DMX_0006', [-0.0058250848, 0.0004933091900000000432, 'normalized']), ('DMX_0007', [-0.00530310515, 0.00029648043799999999198, 'normalized']), ('DMX_0008', [-0.00526781796, 0.00028518873099999997355, 'normalized']), ('DMX_0009', [-0.00517494804, 0.00028266689699999998728, 'normalized']), ('DMX_0010', [-0.0050663524, 0.0002701798849999999903, 'normalized']), ('DMX_0011', [-0.00478031918, 0.0002806008360000000106, 'normalized']), ('DMX_0012', [-0.0050208651, 0.00026573736999999997884, 'normalized']), ('DMX_0013', [-0.00505935183, 0.0002586487010000000121, 'normalized']), ('DMX_0014', [-0.00509190447, 0.0002561736220000000101, 'normalized']), ('DMX_0015', [-0.00516529749, 0.00025988915899999997941, 'normalized']), ('DMX_0016', [-0.00507480475, 0.00028150625200000002103, 'normalized']), ('DMX_0017', [-0.0050515162, 0.00027584791899999997703, 'normalized']), ('DMX_0018', [-0.00498892016, 0.00028898848400000002289, 'normalized']), ('DMX_0019', [-0.00483972156, 0.0003065586760000000243, 'normalized']), ('DMX_0020', [-0.00486491944, 0.00030898006899999998134, 'normalized']), ('DMX_0021', [-0.00463044232, 0.0003054490159999999994, 'normalized']), ('DMX_0022', [-0.00434833581, 0.00032017814800000001484, 'normalized']), ('DMX_0023', [-0.00447875203, 0.00061232973599999998473, 'normalized']), ('DMX_0024', [-0.00399314623, 0.00031041997000000001077, 'normalized']), ('DMX_0025', [-0.00435106576, 0.0002877625810000000099, 'normalized']), ('DMX_0026', [-0.00404593118, 0.000287672562000000024, 'normalized']), ('DMX_0027', [-0.00445715532, 0.00031498618299999998297, 'normalized']), ('JUMP1', [-2.0070000000000000119e-06, 5.978592235113711761e-07, 'normalized'])])\n"
     ]
    }
   ],
   "source": [
    "if 'noise_only' in c_PAL2.label:\n",
    "    tm_var=False\n",
    "else:\n",
    "    tm_var=True\n",
    "\n",
    "if 'no_tm' in c_PAL2.label:\n",
    "    incTimingModel=False\n",
    "else:\n",
    "    incTimingModel=True\n",
    "    \n",
    "if 'full' in c_PAL2.label:\n",
    "    lin_dmx_jump_fd=False\n",
    "else:\n",
    "    lin_dmx_jump_fd=True\n",
    "\n",
    "psr_reload = Pulsar(parfile, timfile, ephem=ephem, clk=None, drop_t2pulsar=False)\n",
    "pta = pta_setup(psr_reload,datarelease=\"9yr\",psr_name=psr_name,\n",
    "                tm_var=tm_var,red_var=False,\n",
    "                white_var=True,fit_remaining_pars=True,\n",
    "                lin_dmx_jump_fd=lin_dmx_jump_fd,\n",
    "                tm_linear=False,\n",
    "                incTimingModel=incTimingModel,Ecorr_gp_basis=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ducks 2!\n",
      "/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/ent_t2_desmat.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Could not find pulsar distance for PSR J0340+4130. Setting value to 1 with 20% uncertainty.\n"
     ]
    }
   ],
   "source": [
    "newparfile = top_dir + f\"/enterprise_timing/{psr_name}/ark_PAL2/{psr_name}_NANOGrav_9yv1.gls.par\"\n",
    "newtimfile = top_dir + f\"/enterprise_timing/{psr_name}/ark_PAL2/{psr_name}_NANOGrav_9yv1.tim\"\n",
    "t2_psr = Pulsar(newparfile, newtimfile, ephem=ephem, clk=None, drop_t2pulsar=False)\n",
    "#pint_psr = Pulsar(newparfile, newtimfile, ephem=ephem, clk=None, drop_pintpsr=False,timing_package='pint')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PAL2 Pulsar init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[] []\n",
      "WARNING: Cannot find sky location coordinates.Setting to 0.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andrew/Documents/Research/nanograv/PAL2/PAL2/PALdatafile.py:142: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  data = datGroup[field].value\n"
     ]
    }
   ],
   "source": [
    "pal2_path = top_dir + \"/PAL2/\"\n",
    "sys.path.insert(0, pal2_path)\n",
    "import PAL2\n",
    "from PAL2 import PALpsr\n",
    "from PAL2 import PALdatafile\n",
    "import h5py as h5\n",
    "\n",
    "h5File=f\"/home/andrew/Documents/Research/nanograv/enterprise_timing/{psr_name}/ark_PAL2/{psr_name}_h5file.hdf5\"\n",
    "h5df = PALdatafile.DataFile(h5File)\n",
    "\n",
    "pal2psr = PALpsr.Pulsar()\n",
    "pal2psr.readFromH5(h5df, psr_name, None, None)\n",
    "#pal2psr.initLibsTempoObject(ephem='DE421')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section to compare residuals for a grid of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('PMELONG', [-2.4185, 0.8314000000000000279, 'normalized']), ('PMELAT', [-4.2499, 1.3380000000000000782, 'normalized']), ('PX', [0.6837, 0.69620000000000004103, 'normalized'])]\n",
      "\n",
      "J0340+4130_timing_model_PMELONG -2.75470752903496\n",
      "J0340+4130_timing_model_PMELAT -4.546469243634412\n",
      "{'PMELONG': -0.40438721317652161073, 'PMELAT': -0.22165115368790155187}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>map_dict_e_e</th>\n",
       "      <th>map_dict_PAL2</th>\n",
       "      <th>pert_map_dict_PAL2</th>\n",
       "      <th>map_dict_PAL2_4_e_e_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>J0340+4130_Rcvr1_2_GUPPI_efac</th>\n",
       "      <td>1.165308</td>\n",
       "      <td>1.158785</td>\n",
       "      <td>1.274664</td>\n",
       "      <td>1.274664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_Rcvr1_2_GUPPI_log10_ecorr</th>\n",
       "      <td>-7.933831</td>\n",
       "      <td>-7.714524</td>\n",
       "      <td>-8.485976</td>\n",
       "      <td>-8.485976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_Rcvr1_2_GUPPI_log10_equad</th>\n",
       "      <td>-9.975840</td>\n",
       "      <td>-9.662002</td>\n",
       "      <td>-10.628202</td>\n",
       "      <td>-10.628202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_Rcvr_800_GUPPI_efac</th>\n",
       "      <td>1.091804</td>\n",
       "      <td>1.093972</td>\n",
       "      <td>1.203370</td>\n",
       "      <td>1.203370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_Rcvr_800_GUPPI_log10_ecorr</th>\n",
       "      <td>-8.314049</td>\n",
       "      <td>-7.998354</td>\n",
       "      <td>-8.798190</td>\n",
       "      <td>-8.798190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_Rcvr_800_GUPPI_log10_equad</th>\n",
       "      <td>-7.377330</td>\n",
       "      <td>-8.041009</td>\n",
       "      <td>-8.845110</td>\n",
       "      <td>-8.845110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_timing_model_PMELAT</th>\n",
       "      <td>-4.178652</td>\n",
       "      <td>-4.133154</td>\n",
       "      <td>-4.546469</td>\n",
       "      <td>-0.221651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_timing_model_PMELONG</th>\n",
       "      <td>-2.339030</td>\n",
       "      <td>-2.504280</td>\n",
       "      <td>-2.754708</td>\n",
       "      <td>-0.404387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J0340+4130_timing_model_PX</th>\n",
       "      <td>0.647544</td>\n",
       "      <td>0.806028</td>\n",
       "      <td>0.886631</td>\n",
       "      <td>0.886631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       map_dict_e_e  map_dict_PAL2  \\\n",
       "J0340+4130_Rcvr1_2_GUPPI_efac              1.165308       1.158785   \n",
       "J0340+4130_Rcvr1_2_GUPPI_log10_ecorr      -7.933831      -7.714524   \n",
       "J0340+4130_Rcvr1_2_GUPPI_log10_equad      -9.975840      -9.662002   \n",
       "J0340+4130_Rcvr_800_GUPPI_efac             1.091804       1.093972   \n",
       "J0340+4130_Rcvr_800_GUPPI_log10_ecorr     -8.314049      -7.998354   \n",
       "J0340+4130_Rcvr_800_GUPPI_log10_equad     -7.377330      -8.041009   \n",
       "J0340+4130_timing_model_PMELAT            -4.178652      -4.133154   \n",
       "J0340+4130_timing_model_PMELONG           -2.339030      -2.504280   \n",
       "J0340+4130_timing_model_PX                 0.647544       0.806028   \n",
       "\n",
       "                                       pert_map_dict_PAL2  \\\n",
       "J0340+4130_Rcvr1_2_GUPPI_efac                    1.274664   \n",
       "J0340+4130_Rcvr1_2_GUPPI_log10_ecorr            -8.485976   \n",
       "J0340+4130_Rcvr1_2_GUPPI_log10_equad           -10.628202   \n",
       "J0340+4130_Rcvr_800_GUPPI_efac                   1.203370   \n",
       "J0340+4130_Rcvr_800_GUPPI_log10_ecorr           -8.798190   \n",
       "J0340+4130_Rcvr_800_GUPPI_log10_equad           -8.845110   \n",
       "J0340+4130_timing_model_PMELAT                  -4.546469   \n",
       "J0340+4130_timing_model_PMELONG                 -2.754708   \n",
       "J0340+4130_timing_model_PX                       0.886631   \n",
       "\n",
       "                                       map_dict_PAL2_4_e_e_scaled  \n",
       "J0340+4130_Rcvr1_2_GUPPI_efac                            1.274664  \n",
       "J0340+4130_Rcvr1_2_GUPPI_log10_ecorr                    -8.485976  \n",
       "J0340+4130_Rcvr1_2_GUPPI_log10_equad                   -10.628202  \n",
       "J0340+4130_Rcvr_800_GUPPI_efac                           1.203370  \n",
       "J0340+4130_Rcvr_800_GUPPI_log10_ecorr                   -8.798190  \n",
       "J0340+4130_Rcvr_800_GUPPI_log10_equad                   -8.845110  \n",
       "J0340+4130_timing_model_PMELAT                          -0.221651  \n",
       "J0340+4130_timing_model_PMELONG                         -0.404387  \n",
       "J0340+4130_timing_model_PX                               0.886631  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_df = pd.DataFrame.from_dict({k:v for k,v in map_dict_e_e.items() if k not in exclude_params},orient='index',columns=['map_dict_e_e'])\n",
    "print_df = print_df.join(pd.DataFrame.from_dict({k:v for k,v in map_dict_PAL2.items() if k not in exclude_params},orient='index',columns=['map_dict_PAL2']))\n",
    "print_df = print_df.join(pd.DataFrame.from_dict({k:v for k,v in pert_map_dict_PAL2.items() if k not in exclude_params},orient='index',columns=['pert_map_dict_PAL2']))\n",
    "print([(param.split('_')[-1],tm_params_orig[param.split('_')[-1]]) for param in pert_map_dict_PAL2 if param.split('_')[-1] in tm_params_orig.keys()])\n",
    "print('')\n",
    "\n",
    "#copy_map_dict = deepcopy(map_dict_PAL2)\n",
    "copy_map_dict = deepcopy(pert_map_dict_PAL2)\n",
    "\n",
    "\n",
    "map_dict_PAL2_4_e_e_scaled = OrderedDict()\n",
    "scaled_vals = {}\n",
    "for pparam,pval in copy_map_dict.items():\n",
    "    if pparam.split('_')[-1] in tm_params_orig.keys() and pparam.split('_')[-1] in ['PMELONG','PMELAT']:\n",
    "        print(pparam,pval)\n",
    "        scaled_vals[pparam.split('_')[-1]] = (pval-tm_params_orig[pparam.split('_')[-1]][0])/tm_params_orig[pparam.split('_')[-1]][1]\n",
    "print(scaled_vals)\n",
    "for e_eparam,e_eval in map_dict_e_e_scaled.items():\n",
    "    if e_eparam not in exclude_params:\n",
    "        if e_eparam.split('_')[-1] in scaled_vals.keys():\n",
    "            map_dict_PAL2_4_e_e_scaled[e_eparam] = scaled_vals[e_eparam.split('_')[-1]]\n",
    "        elif e_eparam in copy_map_dict.keys():\n",
    "            map_dict_PAL2_4_e_e_scaled[e_eparam] = copy_map_dict[e_eparam]\n",
    "        else:\n",
    "            print('uh oh')\n",
    "print('')\n",
    "print_df = print_df.join(pd.DataFrame.from_dict({k:v for k,v in map_dict_PAL2_4_e_e_scaled.items() if k not in exclude_params},\n",
    "                                                orient='index',columns=['map_dict_PAL2_4_e_e_scaled']))\n",
    "print_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Design Matrix Checks\n",
    "- In `Enterprise` the `PINT` and `TEMPO2` `Mmat`s and `designmatrix`es are different because `Mmats` are sorted by toa dates (I think)\n",
    "- `PAL2` doesn't sort `Mmat` so it is the same as the `designmatrix`\n",
    "- When sorted the same, `TEMPO2 enterprise` and `PAL2` `Mmat`s are the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(len(psr.t2pulsar.pars()))\n",
    "print(len(psr_reload.t2pulsar.pars()))\n",
    "print(len(t2_psr.t2pulsar.pars()))\n",
    "design_idxs = [\"Offset\"] + [p for p in t2_psr.t2pulsar.pars()]\n",
    "print(design_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PAL2_t2_desmat_file = f'/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/PAL2_t2_desmat.npz'\n",
    "PAL2_t2_desmat = np.load(PAL2_t2_desmat_file,allow_pickle=True)\n",
    "PAL2_t2_desmat_df = pd.DataFrame(PAL2_t2_desmat['desmat'],columns=design_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PAL2_t2_desmat_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ent_t2_desmat_file = f'/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/ent_t2_desmat.npz'\n",
    "ent_t2_desmat = np.load(ent_t2_desmat_file,allow_pickle=True)\n",
    "ent_t2_desmat_df = pd.DataFrame(ent_t2_desmat['desmat'],columns=design_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ent_t2_desmat_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PAL2_isort = np.argsort(pal2psr.toas, kind=\"mergesort\")\n",
    "PAL2_isort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Mmat_df_PAL2 = pd.DataFrame(PAL2_lnlike['Mmat'])\n",
    "Mmat_df_PAL2 = pd.DataFrame(pal2psr.Mmat[PAL2_isort,:],columns=design_idxs)\n",
    "\n",
    "Mmat_df_PAL2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mmat_df_t2_psr = pd.DataFrame(t2_psr.Mmat,columns=design_idxs)\n",
    "desmat_df_t2_psr = pd.DataFrame(t2_psr._designmatrix,columns=design_idxs)\n",
    "Mmat_df_t2_psr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mmat_df_pint_psr = pd.DataFrame(pint_psr.Mmat,columns=design_idxs)\n",
    "desmat_df_pint_psr = pd.DataFrame(pint_psr._designmatrix,columns=design_idxs)\n",
    "Mmat_df_pint_psr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "desmat_df_pint_psr.compare(Mmat_df_pint_psr, align_axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "Mmat_df_t2_psr.compare(Mmat_df_pint_psr, align_axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mmat_df_t2_psr.compare(Mmat_df_PAL2, align_axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mmat_df_pint_psr.compare(Mmat_df_PAL2, align_axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Likelihood Chunks\n",
    "- The deterministic residuals in `Enterprise` are calculated inside `SignalCollection`. More importantly, they are calculated by `self._residuals - self.get_delay(params)`. \n",
    "    - This means the signal delay from `tm_delay` is subtracted from the _*sorted*_ residuals.\n",
    "    - Furthermore, the delay is already subtracted, so the signs inside `tm_delay` needed to be flipped to be the same as the `PAL2` version.\n",
    "- With these corrections, both `logdet_N` and `rNr` are the same between `enterprise` and `PAL2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unsorted vs sorted residuals depending on which pulsar is used.\n",
    "plt.plot(range(len(psr_reload.residuals)),psr_reload.residuals/1e6)\n",
    "plt.plot(range(len(psr_reload.t2pulsar.residuals())),psr_reload.t2pulsar.residuals()/1e6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "-72586.42163923202, 3749.612420014312"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_zeros:\n",
    "    for p in e_e_dict.keys():\n",
    "        if 'timing' in p:\n",
    "            if p.split('_')[-1] in psr_reload.tm_params_orig:\n",
    "                if psr_reload.tm_params_orig[p.split('_')[-1]][-1] == 'normalized':\n",
    "                    e_e_dict.update({p:0.0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(e_e_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_params = ['PMELONG', 'PMELAT', 'PX']\n",
    "#grid_params = ['PMELONG']\n",
    "scale = 10.\n",
    "for i,grid_param in enumerate(grid_params):\n",
    "    if c_e_e.tm_pars_orig[grid_param][-1] == 'physical':\n",
    "        grid_point = np.float(tm_params_orig[grid_param][0])\n",
    "        #grid_point = tm_params_orig[grid_param][0]-scale*tm_params_orig[grid_param][1]\n",
    "        #grid_point = 0.0001\n",
    "    else:\n",
    "        grid_point = 0.\n",
    "        #grid_point = -scale\n",
    "\n",
    "    #grid_map_dict = deepcopy(map_dict_PAL2_4_e_e_scaled)\n",
    "    #grid_map_dict = deepcopy(map_dict_e_e)\n",
    "    grid_map_dict = deepcopy(e_e_dict)\n",
    "    print(grid_map_dict)\n",
    "    print(grid_param,grid_point)\n",
    "    x0 = []\n",
    "    for key,val in grid_map_dict.items():\n",
    "        if key not in exclude_params:\n",
    "            if key.split('_')[-1] == grid_param:\n",
    "                x0.append(grid_point)\n",
    "            else:\n",
    "                x0.append(val)\n",
    "    print('x0:',x0)\n",
    "    params = {nm:p for nm, p in zip(grid_map_dict.keys(),x0)}\n",
    "    print(params)\n",
    "    \"\"\"\n",
    "    for signalcollection in pta._signalcollections:\n",
    "        print(signalcollection)\n",
    "        for signal in signalcollection._signals:\n",
    "            print(signal)\n",
    "            print(signal.get_ndiag(params))\n",
    "            if 'Deterministic' in str(type(signal)):\n",
    "                print('delay pars',signal.delay_params)\n",
    "                print('psr residuals',psr_reload.residuals)\n",
    "                print('sigcollect residuals',signalcollection._residuals)\n",
    "                print('delay',signal.get_delay(params))\n",
    "                print('detres',signalcollection.get_detres(params))\n",
    "            elif 'TimingModel' in str(type(signal)):\n",
    "                #print(dir(signal))\n",
    "                print(signal.basis_params)\n",
    "                print(signal.delay_params)\n",
    "                print(signal.param_names)\n",
    "                print(signal.get_logsignalprior(params))\n",
    "                print(signal.get_delay(params))\n",
    "                print(signal.get_phi(params))\n",
    "                print(signal.get_phiinv(params))\n",
    "                print(signal.get_basis(params))\n",
    "            elif 'EcorrKernelNoise' in str(type(signal)):\n",
    "                #print('')\n",
    "                Nvec = signalcollection.get_ndiag(params)\n",
    "                res = signalcollection.get_detres(params)\n",
    "                e_e_rNr,e_e_logdet_N = Nvec.solve(res, left_array=res, logdet=True)\n",
    "                print(e_e_rNr,e_e_logdet_N)\n",
    "            print('----------------')\n",
    "        e_e_comp_param = signalcollection.get_detres(params)\n",
    "        print(signalcollection.get_detres(params))\n",
    "    #print(pta.get_rNr_logdet(params))\n",
    "    phiinvs = pta.get_phiinv(params, logdet=True, method='cliques')\n",
    "    TNrs = pta.get_TNr(params)\n",
    "    TNTs = pta.get_TNT(params)\n",
    "    for (k,TNr), TNT, pl in zip(enumerate(TNrs), TNTs, phiinvs):\n",
    "        print(k)\n",
    "        e_e_phiinv, e_e_logdet_phi = pl\n",
    "        e_e_Sigma = TNT + (np.diag(e_e_phiinv) if e_e_phiinv.ndim == 1 else e_e_phiinv)\n",
    "        cf = sl.cho_factor(e_e_Sigma)\n",
    "        expval = sl.cho_solve(cf, TNr)\n",
    "        e_e_logdet_sigma = np.sum(2 * np.log(np.diag(cf[0])))\n",
    "    \"\"\"\n",
    "    grid_lnlike = pta.get_lnlikelihood({nm:p for nm, p in zip(grid_map_dict.keys(),x0)})\n",
    "    print(grid_lnlike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PAL2_isort = np.argsort(pal2psr.toas, kind=\"mergesort\")\n",
    "PAL2_isort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lnlike_param = 'PMELONG'\n",
    "#lnlike_grouped_params = ['savelike_1','savelike_2','savelike_3']\n",
    "lnlike_grouped_params = ['savelike_1','savelike_2','savelike_3','logdet_Sigma','logdet_Phi','rNr']\n",
    "\n",
    "e_e_lnlike_save_file = f'/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/{lnlike_param}_grid_lnlike_in_lnlike.npz'\n",
    "e_e_lnlike = np.load(e_e_lnlike_save_file,allow_pickle=True)\n",
    "\n",
    "PAL2_lnlike_save_file = f'/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/PAL2_{lnlike_param}_grid_lnlike_in_lnlike.npz'\n",
    "PAL2_lnlike = np.load(PAL2_lnlike_save_file,allow_pickle=True)\n",
    "\n",
    "PAL2_res_save_file = f'{top_dir}/enterprise_timing/residual_checking/PAL2_{lnlike_param}_grid_lnlike.npz'\n",
    "PAL2_param_residual = np.load(PAL2_res_save_file)\n",
    "\n",
    "df_PAL2_lnlike = pd.DataFrame.from_dict({f'{lnlike_param}_{item}': PAL2_lnlike[item] for item in PAL2_lnlike.files if item in lnlike_grouped_params},\n",
    "                                        orient='index')\n",
    "df_PAL2_lnlike = df_PAL2_lnlike.sort_index()\n",
    "\n",
    "df_e_e_lnlike = pd.DataFrame.from_dict({f'{lnlike_param}_{item}': e_e_lnlike[item] for item in e_e_lnlike.files if item in lnlike_grouped_params},\n",
    "                                       orient='index')\n",
    "df_e_e_lnlike = df_e_e_lnlike.sort_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('PAL2 rNr:',PAL2_lnlike['rNr'])\n",
    "print('e_e rNr: ',e_e_rNr)\n",
    "print('--------')\n",
    "print('PAL2 logdet_N:',PAL2_lnlike['logdet_N'])\n",
    "print('e_e logdet_N: ',e_e_logdet_N)\n",
    "print('--------')\n",
    "print('PAL2 logdet_Phi:',PAL2_lnlike['logdet_Phi'])\n",
    "print('e_e logdet_Phi: ',e_e_logdet_phi)\n",
    "print('--------')\n",
    "print('PAL2 logdet_Sigma:',PAL2_lnlike['logdet_Sigma'])\n",
    "print('e_e logdet_Sigma: ',e_e_logdet_sigma)\n",
    "print('--------')\n",
    "print('PAL2 np.dot(dd,expval2):',np.dot(PAL2_lnlike['dd'],PAL2_lnlike['expval2']))\n",
    "print('e_e np.dot(TNr,expval): ',np.dot(TNr,expval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_param = 'detres'\n",
    "PAL2_tmp_df = pd.DataFrame(PAL2_lnlike[comp_param][PAL2_isort])\n",
    "PAL2_tmp_df_2 = pd.DataFrame(PAL2_param_residual['new_res_sans_offset'])\n",
    "e_e_tmp_df = pd.DataFrame(e_e_comp_param)\n",
    "e_e_tmp_df.compare(PAL2_tmp_df,align_axis=1,keep_equal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PAL2_lnlike['Nvec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#PAL2_tmp_df_3 = pd.DataFrame(PAL2_lnlike['Sigma'])#[PAL2_isort])\n",
    "#PAL2_tmp_df_3 = pd.DataFrame(PAL2_lnlike['TNT'])#[PAL2_isort])\n",
    "#PAL2_tmp_df_3 = pd.DataFrame(PAL2_lnlike['Phiinv'])#[PAL2_isort])\n",
    "PAL2_tmp_df_3 = pd.DataFrame(PAL2_lnlike['dd'])#[PAL2_isort])\n",
    "#PAL2_tmp_df_3 = pd.DataFrame(PAL2_lnlike['cf'])#[PAL2_isort])\n",
    "#PAL2_tmp_df_3 = pd.DataFrame(PAL2_lnlike['expval2'])#[PAL2_isort])\n",
    "\n",
    "\n",
    "#e_e_tmp_df_3 = pd.DataFrame(e_e_Sigma)\n",
    "#e_e_tmp_df_3 = pd.DataFrame(TNT)\n",
    "#e_e_tmp_df_3 = pd.DataFrame(e_e_phiinv)\n",
    "#e_e_tmp_df_3 = pd.DataFrame(cf)\n",
    "e_e_tmp_df_3 = pd.DataFrame(TNr)\n",
    "e_e_tmp_df_3.compare(PAL2_tmp_df_3.loc[0:e_e_tmp_df_3.shape[0]-1,0:e_e_tmp_df_3.shape[1]-1],\n",
    "                     align_axis=1,keep_equal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#incr = 33\n",
    "#PAL2_tmp_df_3.loc[0+incr:33+incr,0+incr:33+incr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(len(PAL2_lnlike[comp_param])),PAL2_lnlike[comp_param]/1e-6)\n",
    "plt.plot(range(len(pal2psr.residuals)),pal2psr.residuals/1e-6)\n",
    "plt.plot(range(len(e_e_comp_param)),e_e_comp_param/1e-6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_PAL2_lnlike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(e_e_lnlike['Nvec'][0])\n",
    "print(PAL2_lnlike['Nvec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_e_e_lnlike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_e_e_lnlike.compare(df_PAL2_lnlike, align_axis=1,keep_equal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_plot_res(psr,new_res,ax,pal2=False,label='',**kwargs):\n",
    "    \n",
    "    meannewres = np.sqrt(np.mean(new_res ** 2)) / 1e-6\n",
    "    if pal2:\n",
    "        t = psr.toas/u.d.to('s')\n",
    "        #times = Time(sec_times, format='J2000')\n",
    "        errs = psr.toaerrs\n",
    "        meanoldres = np.sqrt(np.mean(psr.residuals ** 2)) / 1e-6\n",
    "        ax.scatter(\n",
    "            t,\n",
    "            new_res / 1e-6,\n",
    "            #yerr=errs[i],\n",
    "            #marker=\"x\",\n",
    "            s=100,\n",
    "            label = f\"{label}, res {meannewres:.3f} us\",\n",
    "            **kwargs\n",
    "        )\n",
    "    else:\n",
    "        t, errs = psr.toas(), psr.toaerrs\n",
    "        meanoldres = np.sqrt(np.mean(psr.residuals() ** 2)) / 1e-6\n",
    "            \n",
    "        i = np.argsort(t)\n",
    "        ax.scatter(\n",
    "            t[i],\n",
    "            new_res[i] / 1e-6,\n",
    "            #yerr=errs[i],\n",
    "            #marker=\"x\",\n",
    "            s=100,\n",
    "            label = f\"{label}, res {meannewres:.3f} us\",\n",
    "            **kwargs\n",
    "        )\n",
    "    ax.set_ylabel(\"res [us]\")\n",
    "    ax.set_title(f\"{psr.name} - rms old res {meanoldres:.3f} us\")\n",
    "    #plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_plot_res_redux(toas,errs,old_res,new_res,ax,pal2=False,label='',**kwargs):\n",
    "    meannewres = np.sqrt(np.mean(new_res ** 2)) / 1e-6\n",
    "    t = toas/u.d.to('s')\n",
    "    #times = Time(sec_times, format='J2000')\n",
    "    #errs = psr.toaerrs\n",
    "    meanoldres = np.sqrt(np.mean(old_res ** 2)) / 1e-6\n",
    "    ax.scatter(\n",
    "        t,\n",
    "        new_res / 1e-6,\n",
    "        #yerr=errs[i],\n",
    "        #marker=\"x\",\n",
    "        s=100,\n",
    "        label = f\"{label}, res {meannewres:.3f} us\",\n",
    "        **kwargs\n",
    "    )\n",
    "\n",
    "    ax.set_ylabel(\"res [us]\")\n",
    "    ax.set_title(f\"{psr.name} - rms old res {meanoldres:.3f} us\")\n",
    "    plt.suptitle(f\"{psr.name} - rms res {meanoldres:.3f} us\",fontsize=25,y=.95)\n",
    "    #plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "-2.4185,-4.2499,0.6837"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_param = 'PX'\n",
    "#MAP\n",
    "#e_e_save_file = f'{top_dir}/enterprise_timing/residual_checking/{grid_param}_grid_lnlike.npz'\n",
    "#perturbed\n",
    "e_e_save_file = f'{top_dir}/enterprise_timing/residual_checking/{grid_param}_grid_lnlike_pert.npz'\n",
    "e_e_param_residual = np.load(e_e_save_file)\n",
    "fig,ax = plt.subplots(figsize=(15,10))\n",
    "simple_plot_res(t2_psr.t2pulsar, t2_psr.t2pulsar.residuals(),ax,label='Original',marker='x')\n",
    "simple_plot_res(t2_psr.t2pulsar, e_e_param_residual['new_res'],ax,label=f'e_e new {grid_param}',marker='+')\n",
    "simple_plot_res(t2_psr.t2pulsar, e_e_param_residual['old_res'],ax,label=f'e_e old {grid_param}',marker='1')\n",
    "simple_plot_res(pal2psr, pal2psr.residuals,ax,pal2=True,label=f'PAL2 old {grid_param}',marker='3')\n",
    "ax.legend(bbox_to_anchor=(1.,.9))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,10))\n",
    "grid_param = 'PX'\n",
    "#MAP\n",
    "#PAL2_save_file = f'{top_dir}/enterprise_timing/residual_checking/PAL2_{grid_param}_grid_lnlike.npz'\n",
    "#perturbed\n",
    "PAL2_save_file = f'{top_dir}/enterprise_timing/residual_checking/PAL2_{grid_param}_grid_lnlike_pert.npz'\n",
    "PAL2_param_residual = np.load(PAL2_save_file)\n",
    "simple_plot_res_redux(PAL2_param_residual['toas'],PAL2_param_residual['errs'],PAL2_param_residual['old_res'],\n",
    "                     PAL2_param_residual['new_res'],ax,pal2=True,label=f'PAL2 new {grid_param}',marker='*')\n",
    "simple_plot_res_redux(PAL2_param_residual['toas'],PAL2_param_residual['errs'],PAL2_param_residual['old_res'],\n",
    "                     PAL2_param_residual['old_res'],ax,pal2=True,label=f'PAL2 old {grid_param}',marker='3')\n",
    "simple_plot_res(pal2psr, pal2psr.residuals,ax,pal2=True,label=f'PAL2 old {grid_param}',marker='3')\n",
    "ax.legend(bbox_to_anchor=(1.,.9))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "grid_param_dict = {'0':{'PMELONG': 0.0, 'PMELAT': -0.1518348065271708, 'PX': 0.5223205378367722},\n",
    "                   '1':{'PMELONG': -0.30563301210741267, 'PMELAT': 0.0, 'PX': 0.5223205378367722},\n",
    "                   '2':{'PMELONG': -0.30563301210741267, 'PMELAT': -0.1518348065271708, 'PX': 0.6837}}\n",
    "grid_params = ['PMELONG', 'PMELAT', 'PX']\n",
    "fig,axes = plt.subplots(len(grid_params),1,figsize=(15,15),sharex=True)\n",
    "for (i,grid_param),ax in zip(enumerate(grid_params),axes):\n",
    "    e_e_save_file = f'{top_dir}/enterprise_timing/residual_checking/{grid_param}_grid_lnlike.npz'\n",
    "    e_e_param_residual = np.load(e_e_save_file)\n",
    "    PAL2_save_file = f'{top_dir}/enterprise_timing/residual_checking/PAL2_{grid_param}_grid_lnlike.npz'\n",
    "    PAL2_param_residual = np.load(PAL2_save_file)\n",
    "    #nltm_plt.plotres(psr_reload.t2pulsar, e_e_param_residual['new_res'], psr_reload.t2pulsar.residuals(), grid_param_dict[f'{i}'],ms=10)\n",
    "    #if i == 0:\n",
    "    simple_plot_res(psr_reload.t2pulsar, psr_reload.t2pulsar.residuals(),ax,label='Original',marker='x')\n",
    "    simple_plot_res(psr_reload.t2pulsar, e_e_param_residual['new_res'],ax,label=f'e_e new {grid_param}',marker='+')\n",
    "    simple_plot_res_redux(PAL2_param_residual['toas'],PAL2_param_residual['errs'],PAL2_param_residual['old_res'],\n",
    "                     PAL2_param_residual['new_res'],ax,pal2=True,label=f'PAL2 new {grid_param}',marker='*')\n",
    "    simple_plot_res_redux(PAL2_param_residual['toas'],PAL2_param_residual['errs'],PAL2_param_residual['old_res'],\n",
    "                         PAL2_param_residual['old_res'],ax,pal2=True,label=f'PAL2 old {grid_param}',marker='3')\n",
    "    ax.legend(bbox_to_anchor=(1.,.9))\n",
    "    ax.set_title(fr'{grid_param}=val-10*err')\n",
    "    if i == len(grid_params)-1:\n",
    "        ax.set_title(fr'{grid_param}$=0.0001$')# without refixing t2psr after fit')\n",
    "        ax.set_xlabel(\"MJD\")\n",
    "        #plt.suptitle(f\"{psr.name} - rms old res {meanoldres:.3f} us\")\n",
    "        filename = f'{psr_name}_9yr_res_comp_2'\n",
    "        savefile = f'{top_dir}/enterprise_timing/Figures/{filename}.pdf'\n",
    "        if not os.path.isfile(savefile):\n",
    "            print('saving to',savefile)\n",
    "            plt.savefig(savefile,bbox_inches='tight')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "grouped_params = ['t2psr_vals','t2psr_errs']\n",
    "grouped_params = ['t2psr_vals']\n",
    "\n",
    "grid_params = ['PMELONG', 'PMELAT', 'PX']\n",
    "for i,grid_param in enumerate(grid_params):\n",
    "    e_e_save_file = f'{top_dir}/enterprise_timing/residual_checking/{grid_param}_grid_lnlike.npz'\n",
    "    e_e_param_residual = np.load(e_e_save_file)\n",
    "    PAL2_save_file = f'{top_dir}/enterprise_timing/residual_checking/PAL2_{grid_param}_grid_lnlike.npz'\n",
    "    PAL2_param_residual = np.load(PAL2_save_file)\n",
    "    \n",
    "    if i == 0:\n",
    "        PAL2_df= pd.DataFrame.from_dict({f'{grid_param}_{item}': PAL2_param_residual[item] for item in PAL2_param_residual.files if item in grouped_params},\n",
    "                                        columns=[str(label).split(\"'\")[1] for label in PAL2_param_residual['t2psr_pars']],\n",
    "                                        orient='index')\n",
    "        df = PAL2_df.copy()\n",
    "        e_e_df = pd.DataFrame.from_dict({f'{grid_param}_{item}': e_e_param_residual[item] for item in e_e_param_residual.files if item in grouped_params},\n",
    "                                        columns=e_e_param_residual['t2psr_pars'],orient='index')\n",
    "        df = df.append(e_e_df)\n",
    "    else:\n",
    "        tmp_PAL2_df= pd.DataFrame.from_dict({f'{grid_param}_{item}': PAL2_param_residual[item] for item in PAL2_param_residual.files if item in grouped_params},\n",
    "                                            columns=[str(label).split(\"'\")[1] for label in PAL2_param_residual['t2psr_pars']],\n",
    "                                            orient='index')\n",
    "        tmp_e_e_df = pd.DataFrame.from_dict({f'{grid_param}_{item}': e_e_param_residual[item] for item in e_e_param_residual.files if item in grouped_params},\n",
    "                                        columns=e_e_param_residual['t2psr_pars'],orient='index')\n",
    "        PAL2_df = PAL2_df.append(tmp_PAL2_df)\n",
    "        e_e_df = e_e_df.append(tmp_e_e_df)\n",
    "        df = df.append(e_e_df)\n",
    "        df = df.append(PAL2_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "e_e_df.compare(PAL2_df, align_axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nltm_timing_run_name = 'PAL2_nltm_ltm_noRN_varyWN_lin_DMX_FD_JUMP_etc_mark9_like_test_1'\n",
    "nltm_timing_outDir=\"/home/andrew/Documents/Research/nanograv/enterprise_timing/{}/ark_PAL2/{}\".format(psr_name,nltm_timing_run_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grid_params = ['PMELONG', 'PMELAT', 'PX']\n",
    "scale = 10\n",
    "num = 100\n",
    "fig,ax = plt.subplots(len(grid_params),2,figsize=(15,4*len(grid_params)))\n",
    "for i,grid_param in enumerate(grid_params):\n",
    "    if c_e_e.tm_pars_orig[grid_param][-1] == 'physical':\n",
    "        #grid_array = np.linspace(tm_params_orig[grid_param][0]-scale*tm_params_orig[grid_param][1],\n",
    "        #                        tm_params_orig[grid_param][0]+scale*tm_params_orig[grid_param][1],\n",
    "        #                        num=num)\n",
    "        grid_array = np.linspace(0.0001,10.,num=num)\n",
    "        shift = False\n",
    "    else:\n",
    "        grid_array = np.linspace(-scale,scale,num=num)\n",
    "        shift = False\n",
    "    #print(grid_array)\n",
    "    grid_lnlike_array = []\n",
    "    #grid_map_dict = deepcopy(map_dict_e_e_scaled)\n",
    "    grid_map_dict = deepcopy(map_dict_PAL2_4_e_e_scaled)\n",
    "\n",
    "    for grid_point in grid_array:\n",
    "        x0 = []\n",
    "        for key,val in grid_map_dict.items():\n",
    "            if key not in exclude_params:\n",
    "                if key.split('_')[-1] == grid_param:\n",
    "                    x0.append(grid_point)\n",
    "                else:\n",
    "                    x0.append(val)\n",
    "        #for nm, p in zip(grid_map_dict.keys(),x0):\n",
    "        #    print(nm,p)\n",
    "        grid_lnlike = pta.get_lnlikelihood({nm:p for nm, p in zip(grid_map_dict.keys(),x0)})\n",
    "        grid_lnlike_array.append(grid_lnlike)\n",
    "\n",
    "    PAL2_grid_lnlike = np.load(nltm_timing_outDir+f'/{grid_param}_grid_lnlike.npz')\n",
    "\n",
    "    if shift:\n",
    "        shifted_grid_array = tm_params_orig[grid_param][0] + grid_array*tm_params_orig[grid_param][1]\n",
    "        ax[i][0].plot(shifted_grid_array,grid_lnlike_array,linewidth=2)\n",
    "        ax[i][0].axvline(x=shifted_grid_array[np.argmax(grid_lnlike_array)],linestyle=':',linewidth=2,color='C2',label='Max Likelihood')\n",
    "    else:\n",
    "        ax[i][0].plot(grid_array,grid_lnlike_array)\n",
    "        ax[i][0].axvline(x=grid_array[np.argmax(grid_lnlike_array)],linestyle=':',linewidth=2,color='C2',label='Max Likelihood')\n",
    "    ax[i][0].axvline(x=tm_params_orig[grid_param][0],linestyle='--',linewidth=2,color='C1',label='parfile value')\n",
    "    ax[i][0].set_title('Enterprise likelihood',fontsize=16)\n",
    "    ax[i][0].set_xlabel(f'{grid_param}',fontsize=14)\n",
    "    ax[i][0].set_ylabel('lnlike',fontsize=16)\n",
    "    ax[i][0].legend()\n",
    "    \n",
    "    ax[i][1].plot(PAL2_grid_lnlike['grid_array'],PAL2_grid_lnlike['grid_lnlike_array'],linewidth=2)\n",
    "    ax[i][1].axvline(x=tm_params_orig[grid_param][0],linestyle='--',linewidth=2,color='C1',label='parfile value')\n",
    "    ax[i][1].axvline(x=PAL2_grid_lnlike['grid_array'][np.argmax(PAL2_grid_lnlike['grid_lnlike_array'])],\n",
    "                  linestyle=':',linewidth=2,color='C2',label='Max Likelihood')\n",
    "    ax[i][1].set_title(f'PAL2 likelihood',fontsize=16)\n",
    "    ax[i][1].set_xlabel(f'{grid_param}',fontsize=14)\n",
    "    ax[i][1].set_ylabel('lnlike',fontsize=16)\n",
    "    ax[i][1].legend()\n",
    "\n",
    "plt.subplots_adjust(wspace=0.35,hspace=0.3)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_params = ['PMELONG', 'PMELAT', 'PX']\n",
    "scale = 10\n",
    "num = 100\n",
    "fig,ax = plt.subplots(len(grid_params),1,figsize=(15,4*len(grid_params)))\n",
    "for i,grid_param in enumerate(grid_params):\n",
    "    if c_e_e.tm_pars_orig[grid_param][-1] == 'physical':\n",
    "        #grid_array = np.linspace(tm_params_orig[grid_param][0]-scale*tm_params_orig[grid_param][1],\n",
    "        #                        tm_params_orig[grid_param][0]+scale*tm_params_orig[grid_param][1],\n",
    "        #                        num=num)\n",
    "        grid_array = np.linspace(0.0001,10.,num=num)\n",
    "        shift = False\n",
    "    else:\n",
    "        grid_array = np.linspace(-scale,scale,num=num)\n",
    "        shift = True\n",
    "    #print(grid_array)\n",
    "    grid_lnlike_array = []\n",
    "    #grid_map_dict = deepcopy(map_dict_e_e_scaled)\n",
    "    grid_map_dict = deepcopy(map_dict_PAL2_4_e_e_scaled)\n",
    "\n",
    "    for grid_point in grid_array:\n",
    "        x0 = []\n",
    "        for key,val in grid_map_dict.items():\n",
    "            if key not in exclude_params:\n",
    "                if key.split('_')[-1] == grid_param:\n",
    "                    x0.append(grid_point)\n",
    "                else:\n",
    "                    x0.append(val)\n",
    "        #for nm, p in zip(grid_map_dict.keys(),x0):\n",
    "        #    print(nm,p)\n",
    "        grid_lnlike = pta.get_lnlikelihood({nm:p for nm, p in zip(grid_map_dict.keys(),x0)})\n",
    "        grid_lnlike_array.append(grid_lnlike)\n",
    "\n",
    "    PAL2_grid_lnlike = np.load(nltm_timing_outDir+f'/{grid_param}_grid_lnlike.npz')\n",
    "\n",
    "    vert_shift = np.unique(np.max(PAL2_grid_lnlike['grid_lnlike_array']))-np.unique(np.max(grid_lnlike_array))\n",
    "    #print(vert_shift)\n",
    "    if shift:\n",
    "        shifted_grid_array = tm_params_orig[grid_param][0] + grid_array*tm_params_orig[grid_param][1]\n",
    "        ax[i].plot(shifted_grid_array,grid_lnlike_array+vert_shift,linewidth=2,label='Enterprise likelihood')\n",
    "        ax[i].axvline(x=shifted_grid_array[np.argmax(grid_lnlike_array)],linestyle=':',color='C0',label='Enterprise Max Likelihood')\n",
    "    else:\n",
    "        ax[i].plot(grid_array,grid_lnlike_array+vert_shift,label='Enterprise likelihood')\n",
    "        ax[i].axvline(x=grid_array[np.argmax(grid_lnlike_array)],linestyle=':',color='C0',label='Max Likelihood')\n",
    "    \n",
    "    ax[i].plot(PAL2_grid_lnlike['grid_array'],PAL2_grid_lnlike['grid_lnlike_array'],label='PAL2 likelihood')\n",
    "    ax[i].axvline(x=PAL2_grid_lnlike['grid_array'][np.argmax(PAL2_grid_lnlike['grid_lnlike_array'])],\n",
    "                  linestyle=':',color='C1',label='PAL2 Max Likelihood')\n",
    "    ax[i].axvline(x=tm_params_orig[grid_param][0],linestyle='--',color='C3',label='parfile value')\n",
    "    ax[i].set_xlabel(f'{grid_param}')\n",
    "    ax[i].set_ylabel('lnlike')\n",
    "    ax[i].legend(loc=(.2,.1),fontsize=12)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "filename = f'{psr_name}_9yr_like_comp_6'\n",
    "savefile = f'{top_dir}/enterprise_timing/Figures/{filename}.pdf'\n",
    "if not os.path.isfile(savefile):\n",
    "    print('saving to',savefile)\n",
    "    plt.savefig(savefile)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "PAL2_grid_lnlike = np.load(nltm_timing_outDir+f'/{grid_param}_grid_lnlike.npz')\n",
    "fig,ax = plt.subplots(1,2,figsize=(10,4))\n",
    "ax[0].plot(grid_array+shift,grid_lnlike_array)\n",
    "ax[0].axvline(x=truth+shift,linestyle='--',color='C1')\n",
    "ax[0].axvline(x=grid_array[np.argmax(grid_lnlike_array)]+shift,linestyle=':',color='C2')\n",
    "ax[0].set_title('Enterprise likelihood')\n",
    "ax[0].set_xlabel(f'{grid_param}')\n",
    "ax[0].set_ylabel('lnlike')\n",
    "\n",
    "ax[1].plot(PAL2_grid_lnlike['grid_array'],PAL2_grid_lnlike['grid_lnlike_array'])\n",
    "ax[1].axvline(x=tm_params_orig[grid_param][0],linestyle='--',color='C1')\n",
    "ax[1].axvline(x=PAL2_grid_lnlike['grid_array'][np.argmax(PAL2_grid_lnlike['grid_lnlike_array'])],\n",
    "              linestyle=':',color='C2')\n",
    "ax[1].set_title(f'PAL2 likelihood')\n",
    "ax[1].set_xlabel(f'{grid_param}')\n",
    "ax[1].set_ylabel('lnlike')\n",
    "plt.suptitle(f'{grid_param}')\n",
    "fig.subplots_adjust(wspace=0.5)\n",
    "plt.show()\n",
    "plt.close()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "for par,val in map_dict_e_e.items():\n",
    "    print(par,val)\n",
    "for par,val in map_dict_PAL2.items():\n",
    "    print(par,val)\n",
    "\"\"\"\n",
    "updated_map_dict_PAL2 = OrderedDict()\n",
    "for i,(par,x) in enumerate(map_dict_PAL2.items()):\n",
    "    for normed_pars in c_e_e._norm_tm_par_idxs:\n",
    "        if i == normed_pars:\n",
    "            val, err, _ = c_e_e.tm_pars_orig[c_e_e._get_real_tm_par_name(par)]\n",
    "            print(par,val,err)\n",
    "            updated_map_dict_PAL2[par] = (x - val)/err\n",
    "    if par not in updated_map_dict_PAL2.keys():\n",
    "        updated_map_dict_PAL2[par] = x\n",
    "print(updated_map_dict_PAL2)\n",
    "max_lnlike = pta.get_lnlikelihood({p:v for p, v in updated_map_dict_PAL2.items() if p not in exclude_params})\n",
    "max_lnprior = pta.get_lnprior({p:v for p, v in updated_map_dict_PAL2.items() if p not in exclude_params})\n",
    "\n",
    "print(map_dict_e_e['lnlike'],map_dict_e_e['lnpost'])\n",
    "print(map_dict_PAL2['lnlike'],map_dict_PAL2['lnpost'])\n",
    "print(max_lnlike,max_lnlike+max_lnprior)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we take n_samples of chain slices from the PAL2 chain to get the parameter values at each slice. Then for that set of parameters/values, we calculate the likelihood and prior in Enterprise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "PAL2_lnlike_array = []\n",
    "PAL2_lnprior_array = []\n",
    "n_samples = 1000\n",
    "for idx in np.random.randint(c_PAL2.burn, c_PAL2.chain.shape[0],n_samples):\n",
    "    x0 = c_PAL2.chain[idx,:-5]\n",
    "    for (i,x),par in zip(enumerate(x0),updated_map_dict_PAL2.keys()):\n",
    "        for normed_pars in c_e_e._norm_tm_par_idxs:\n",
    "            if par == c_e_e.params[normed_pars]:\n",
    "                val, err, _ = c_e_e.tm_pars_orig[c_e_e._get_real_tm_par_name(par)]\n",
    "                x0[i] = (c_PAL2.chain[idx,i] - val)/err\n",
    "    #[print(x,': ',y) for x,y in zip(map_dict_PAL2.keys(),x0)]\n",
    "    #print('-----')\n",
    "    PAL2_lnlike = pta.get_lnlikelihood({nm:p for nm, p in zip(updated_map_dict_PAL2.keys(),x0)})\n",
    "    PAL2_lnprior = pta.get_lnprior({nm:p for nm, p in zip(updated_map_dict_PAL2.keys(),x0)})\n",
    "    PAL2_lnlike_array.append(PAL2_lnlike)\n",
    "    PAL2_lnprior_array.append(PAL2_lnprior)\n",
    "    #print(PAL2_lnprior)\n",
    "    #print(PAL2_lnlike,c_PAL2.chain[idx,-4])\n",
    "    #print(c_PAL2.chain[idx,-4]-PAL2_lnlike)\n",
    "    #print(c_PAL2.chain[idx,-5]-c_PAL2.chain[idx,-4])\n",
    "    #print(PAL2_lnlike+PAL2_lnprior,c_PAL2.chain[idx,-5])\n",
    "    #print('-----')\n",
    "\n",
    "\"\"\"\n",
    "for idx in np.random.randint(nihan.burn, nihan.chain.shape[0],20):\n",
    "    x0 = nihan.chain[idx,:-4]\n",
    "#     print(x0)\n",
    "    lnlike = pta.get_lnlikelihood({nm:p for nm, p in zip(pta.param_names,x0)})\n",
    "    lnprior = pta.get_lnprior({nm:p for nm, p in zip(pta.param_names,x0)})\n",
    "    print(lnprior)\n",
    "    print(lnlike,nihan.chain[idx,-3])\n",
    "    print(lnlike+lnprior,nihan.chain[idx,-4])\n",
    "    print('-----')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(PAL2_lnlike_array,bins=30,density=True,log=True,label='PAL2 in Enterprise',color='C1',alpha=.5)\n",
    "plt.hist(c_e_e.get_param('lnlike'),bins=30,density=True,log=True,label='Enterprise Run',color='C2',alpha=.5)\n",
    "plt.hist(c_PAL2.get_param('lnlike'),bins=30,density=True,log=True,label='PAL2 Run',color='C3')\n",
    "plt.axvline(x=max_lnlike,color='C4',label='PAL2 max likelihood slice',linestyle='--')\n",
    "plt.xlabel('lnlike')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(PAL2_lnlike_array,bins=30,density=True,log=True,label='PAL2 in Enterprise',color='C1',alpha=.5)\n",
    "plt.hist(c_e_e.get_param('lnlike'),bins=30,density=True,log=True,label='Enterprise Run',color='C2',alpha=.5)\n",
    "#plt.hist(c_PAL2.get_param('lnlike'),bins=30,density=True,log=True,label='PAL2 Run',color='C3')\n",
    "plt.axvline(x=max_lnlike,color='C4',label='PAL2 max likelihood slice',linestyle='--')\n",
    "plt.xlabel('lnlike')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(np.asarray(PAL2_lnlike_array)+np.asarray(PAL2_lnprior_array),bins=30,density=True,log=True,label='PAL2 in Enterprise',alpha=.5)\n",
    "plt.hist(c_e_e.get_param('lnpost'),bins=30,density=True,log=True,label='Enterprise Run',alpha=.5)\n",
    "plt.axvline(x=max_lnlike+max_lnprior,color='C2',label='PAL2 max likelihood slice')\n",
    "plt.xlabel('lnpost')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pta.param_names,x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Parameter \\t\\t NLTM MAP \\t\\t PAL2 PAL2 MAP \\t\\t Rel Diff abs(PAL2-NLTM)/PAL2')\n",
    "for par,val in chain_tm_delay_kwargs.items():\n",
    "    if par in PAL2_tm_delay_kwargs:\n",
    "        print(f'{par}: \\t\\t',val,'\\t\\t',PAL2_tm_delay_kwargs[par],'\\t\\t',\n",
    "              np.abs(PAL2_tm_delay_kwargs[par]-val)/PAL2_tm_delay_kwargs[par])\n",
    "    elif par == 'COSI':\n",
    "        print(f'{par}: \\t\\t',val,'\\t\\t',np.sqrt(1-PAL2_tm_delay_kwargs['SINI']**2),'\\t\\t',\n",
    "              np.abs(np.sqrt(1-PAL2_tm_delay_kwargs['SINI']**2)-val)/np.sqrt(1-PAL2_tm_delay_kwargs['SINI']**2))\n",
    "    else:\n",
    "        print(f'{par}: \\t\\t',val,'\\t\\t','--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stripped Out from Timing for push"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print('2:',new_res[isort])\n",
    "#print('')\n",
    "\"\"\"\n",
    "##################################3\n",
    "#Start of edits\n",
    "print('ducks!')\n",
    "print(kwargs)\n",
    "grid_param = 'PMELAT'#'PMELONG'#'PMELAT'#'PX'\n",
    "save_file = f'/home/andrew/Documents/Research/nanograv/enterprise_timing/residual_checking/{grid_param}_grid_lnlike.npz'\n",
    "print(save_file)\n",
    "print(\"tm_params_orig:\",[(key,tm_params_orig[key]) for key in kwargs.keys()])\n",
    "print(\"tm_params_rescaled:\",tm_params_rescaled)\n",
    "print(\"orig_params:\",orig_params)\n",
    "#np.savez(save_file,new_res=new_res,old_res=residuals,t2psr_pars=t2pulsar.pars(),t2psr_vals=t2pulsar.vals(),t2psr_errs=t2pulsar.errs())\n",
    "\n",
    "#End of edits\n",
    "######################################################\n",
    "\"\"\""
   ]
  }
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