noise_modelselection.ipynb

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   "source": [
    "# Noise model selection on NANOGrav pulsars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import glob, os, json, string, pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import logging, inspect, copy\n",
    "logging.basicConfig(level=logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You'll need sksparse for get_coefficients() with common signals!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:enterprise.signals.signal_base:No sksparse library. Using scipy instead!\n"
     ]
    }
   ],
   "source": [
    "import enterprise\n",
    "from enterprise.pulsar import Pulsar\n",
    "\n",
    "import enterprise_extensions\n",
    "from enterprise_extensions import models, model_utils, blocks\n",
    "from enterprise_extensions.models import model_singlepsr_noise\n",
    "from enterprise_extensions.chromatic import solar_wind, chromatic\n",
    "from enterprise_extensions.hypermodel import HyperModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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       "report_default": {
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     }
    }
   },
   "source": [
    "# Red-noise model selection on 12.5yr Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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      }
     }
    }
   },
   "source": [
    "## Get par, tim, and noise files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "end_time": "2018-05-16T20:59:06.822343Z",
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   "outputs": [],
   "source": [
    "# psr = Pulsar('./partim_no_noise/J0613-0200_NANOGrav_11yv0.gls.strip.par',\n",
    "#              './partim_no_noise/J0613-0200_NANOGrav_11yv0.tim',\n",
    "#               ephem='DE436')\n",
    "\n",
    "# noisefiles = sorted(glob.glob('../11yr_stochastic_analysis/nano11y_data/noisefiles/*.json'))\n",
    "\n",
    "# params = {}\n",
    "# for noisefil in noisefiles:\n",
    "#     with open(noisefil, 'r') as fp:\n",
    "#         params.update(json.load(fp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Pickle File "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "psrname = 'J1911+1347'\n",
    "filepath = './no_dmx_pickles/'\n",
    "filepath += '{0}_ng12p5yr_v3_nodmx_ePSR.pkl'.format(psrname)\n",
    "with open(filepath,'rb') as fin:\n",
    "    psr=pickle.load(fin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing models with GP DM variations\n",
    "\n",
    "__Very Import:__ What follows is an __example__ of noise model selection. For *most* pulsars the choice of noise models used in any given model selection analysis will be different than the ones chosen here. Those working on pulsars highlighted in the 11-year noise model analysis should include those models in their analyses and also use the best combination of models from that work in any final model selection that is done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup GP model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T17:43:56.600849Z",
     "start_time": "2018-05-16T17:43:56.572218Z"
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   "outputs": [],
   "source": [
    "red_psd = ['powerlaw']\n",
    "dm_nondiag_kernel = ['None','sq_exp', 'periodic']\n",
    "dm_sw_gp = [True, False]\n",
    "white_vary = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the inspect package to pull the arguments from `model_singlepsr_noise` and make a template for the keyword arguments (kwargs) dictionary we will be using to keep track of these various models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tm_var': False,\n",
       " 'tm_linear': False,\n",
       " 'tmparam_list': None,\n",
       " 'red_var': True,\n",
       " 'psd': 'powerlaw',\n",
       " 'red_select': None,\n",
       " 'noisedict': None,\n",
       " 'tm_svd': False,\n",
       " 'tm_norm': True,\n",
       " 'white_vary': True,\n",
       " 'components': 30,\n",
       " 'upper_limit': False,\n",
       " 'wideband': False,\n",
       " 'gamma_val': None,\n",
       " 'dm_var': False,\n",
       " 'dm_type': 'gp',\n",
       " 'dmgp_kernel': 'diag',\n",
       " 'dm_psd': 'powerlaw',\n",
       " 'dm_nondiag_kernel': 'periodic',\n",
       " 'dm_dt': 15,\n",
       " 'dmx_data': None,\n",
       " 'dm_annual': False,\n",
       " 'gamma_dm_val': None,\n",
       " 'chrom_gp': False,\n",
       " 'chrom_gp_kernel': 'nondiag',\n",
       " 'chrom_psd': 'powerlaw',\n",
       " 'chrom_idx': 4,\n",
       " 'chrom_kernel': 'periodic',\n",
       " 'chrom_dt': 15,\n",
       " 'dm_expdip': False,\n",
       " 'dmexp_sign': 'negative',\n",
       " 'dm_expdip_idx': 2,\n",
       " 'dm_expdip_tmin': None,\n",
       " 'dm_expdip_tmax': None,\n",
       " 'num_dmdips': 1,\n",
       " 'dmdip_seqname': None,\n",
       " 'dm_cusp': False,\n",
       " 'dm_cusp_sign': 'negative',\n",
       " 'dm_cusp_idx': 2,\n",
       " 'dm_cusp_sym': False,\n",
       " 'dm_cusp_tmin': None,\n",
       " 'dm_cusp_tmax': None,\n",
       " 'num_dm_cusps': 1,\n",
       " 'dm_cusp_seqname': None,\n",
       " 'dm_dual_cusp': False,\n",
       " 'dm_dual_cusp_tmin': None,\n",
       " 'dm_dual_cusp_tmax': None,\n",
       " 'dm_dual_cusp_sym': False,\n",
       " 'dm_dual_cusp_idx1': 2,\n",
       " 'dm_dual_cusp_idx2': 4,\n",
       " 'dm_dual_cusp_sign': 'negative',\n",
       " 'num_dm_dual_cusps': 1,\n",
       " 'dm_dual_cusp_seqname': None,\n",
       " 'dm_sw_deter': False,\n",
       " 'dm_sw_gp': False,\n",
       " 'swgp_prior': None,\n",
       " 'swgp_basis': None,\n",
       " 'coefficients': False,\n",
       " 'extra_sigs': None}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args = inspect.getfullargspec(model_singlepsr_noise)\n",
    "keys = args[0][1:]\n",
    "vals = args[3]\n",
    "model_template = dict(zip(keys,vals))\n",
    "model_template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show one work flow where we set up a `for` loop to go through the various models. Make sure to save the `model_labels` and `model_kwargs`. The former will be useful for making noise flower plots, while the latter will be the final product for a pulsar in this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T17:44:51.155779Z",
     "start_time": "2018-05-16T17:43:59.412696Z"
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   },
   "outputs": [],
   "source": [
    "# Create list of pta models for our model selection\n",
    "nmodels = len(red_psd) * len(dm_nondiag_kernel) * len(dm_sw_gp)\n",
    "mod_index = np.arange(nmodels)\n",
    "\n",
    "ptas = dict.fromkeys(mod_index)\n",
    "model_dict = {}\n",
    "model_labels = []\n",
    "ct = 0\n",
    "for red in red_psd:\n",
    "    for dm in dm_nondiag_kernel:\n",
    "        for sw_gp in dm_sw_gp:\n",
    "            if dm == 'None':\n",
    "                dm_var = False\n",
    "            else:\n",
    "                dm_var = True\n",
    "            # Copy template kwargs dict and replace values we are changing. \n",
    "            kwargs = copy.deepcopy(model_template)\n",
    "            \n",
    "            kwargs.update({'dm_var':dm_var,\n",
    "                           'dmgp_kernel':'nondiag',\n",
    "                           'psd':red,\n",
    "                           'white_vary':white_vary,\n",
    "                           'dm_nondiag_kernel':dm,\n",
    "                           'dm_sw_deter':True,\n",
    "                           'dm_sw_gp':sw_gp,\n",
    "                           'swgp_basis': 'powerlaw'})\n",
    "            \n",
    "            # Instantiate single pulsar noise model\n",
    "            ptas[ct] = model_singlepsr_noise(psr, **kwargs)\n",
    "            \n",
    "            # Add labels and kwargs to save for posterity and plotting.\n",
    "            model_labels.append([string.ascii_uppercase[ct],red, dm, sw_gp])\n",
    "            model_dict.update({str(ct):kwargs})\n",
    "            ct += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T17:45:01.931206Z",
     "start_time": "2018-05-16T17:45:01.899033Z"
    }
   },
   "outputs": [],
   "source": [
    "# Instantiate a collection of models\n",
    "super_model = HyperModel(ptas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T17:45:02.370404Z",
     "start_time": "2018-05-16T17:45:02.336038Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[J1911+1347_430_PUPPI_efac:Uniform(pmin=0.01, pmax=10.0),\n",
       " J1911+1347_430_PUPPI_log10_ecorr:Uniform(pmin=-8.5, pmax=-5),\n",
       " J1911+1347_430_PUPPI_log10_equad:Uniform(pmin=-8.5, pmax=-5),\n",
       " J1911+1347_L-wide_PUPPI_efac:Uniform(pmin=0.01, pmax=10.0),\n",
       " J1911+1347_L-wide_PUPPI_log10_ecorr:Uniform(pmin=-8.5, pmax=-5),\n",
       " J1911+1347_L-wide_PUPPI_log10_equad:Uniform(pmin=-8.5, pmax=-5),\n",
       " J1911+1347_red_noise_gamma:Uniform(pmin=0, pmax=7),\n",
       " J1911+1347_red_noise_log10_A:Uniform(pmin=-20, pmax=-11),\n",
       " J1911+1347_dm_gp_log10_ell:Uniform(pmin=1, pmax=4),\n",
       " J1911+1347_dm_gp_log10_sigma:Uniform(pmin=-10, pmax=-4),\n",
       " J1911+1347_gp_sw_gamma:Uniform(pmin=-2, pmax=1),\n",
       " J1911+1347_gp_sw_log10_A:Uniform(pmin=-10, pmax=1),\n",
       " n_earth:ACE_SWEPAM(),\n",
       " J1911+1347_dm_gp_log10_gam_p:Uniform(pmin=-3, pmax=2),\n",
       " J1911+1347_dm_gp_log10_p:Uniform(pmin=-4, pmax=1)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "super_model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['A', 'powerlaw', 'None', True],\n",
       " ['B', 'powerlaw', 'None', False],\n",
       " ['C', 'powerlaw', 'sq_exp', True],\n",
       " ['D', 'powerlaw', 'sq_exp', False],\n",
       " ['E', 'powerlaw', 'periodic', True],\n",
       " ['F', 'powerlaw', 'periodic', False]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the out directory for you chains and other sampler setup\n",
    "### !!! Important !!! Please set the chain directory outside of the git repository (easier) or at least do not try and commit your chains to the repo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T17:45:29.836670Z",
     "start_time": "2018-05-16T17:45:29.789241Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding empirical proposals...\n",
      "\n",
      "Adding red noise prior draws...\n",
      "\n",
      "Adding DM GP noise prior draws...\n",
      "\n",
      "Adding Solar Wind DM GP prior draws...\n",
      "\n",
      "Adding nmodel uniform distribution draws...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outdir = '/Users/hazboun/nanograv_detection/ent_ext_testing/{}/nondiag_dmgp/'.format(psr.name)\n",
    "emp_distr_path = './wn_emp_dists/{0}_ng12p5yr_v3_std_plaw_emp_dist.pkl'.format(psr.name)\n",
    "sampler = super_model.setup_sampler(resume=True, outdir=outdir,\n",
    "                                    empirical_distr=emp_distr_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_params = {}\n",
    "for ky, pta in ptas.items():\n",
    "    model_params.update({str(ky) : pta.param_names})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(outdir+'/model_params.json' , 'w') as fout:\n",
    "    json.dump(model_params, fout, sort_keys=True,\n",
    "              indent=4, separators=(',', ': '))\n",
    "    \n",
    "with open(outdir+'/model_kwargs.json' , 'w') as fout:\n",
    "    json.dump(model_dict, fout, sort_keys=True,\n",
    "              indent=4, separators=(',', ': '))\n",
    "    \n",
    "with open(outdir+'/model_labels.json' , 'w') as fout:\n",
    "    json.dump(model_labels, fout, sort_keys=True,\n",
    "              indent=4, separators=(',', ': '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T17:45:31.805754Z",
     "start_time": "2018-05-16T17:45:31.774319Z"
    }
   },
   "outputs": [],
   "source": [
    "# sampler for N steps\n",
    "N = int(5e6)\n",
    "x0 = super_model.initial_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T18:15:35.402822Z",
     "start_time": "2018-05-16T17:45:31.938466Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hazboun/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/parameter.py:64: RuntimeWarning: divide by zero encountered in log\n",
      "  logpdf = np.log(self.prior(value, **kwargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 1.94 percent in 5902.905363 s Acceptance rate = 0.380546"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-8b4d392a80bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSCAMweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAMweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDEweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mburn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/PTMCMCSampler/PTMCMCSampler.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, p0, Niter, ladder, Tmin, Tmax, Tskip, isave, covUpdate, SCAMweight, AMweight, DEweight, NUTSweight, MALAweight, HMCweight, burn, HMCstepsize, HMCsteps, maxIter, thin, i0, neff, writeHotChains, hotChain)\u001b[0m\n\u001b[1;32m    360\u001b[0m             \u001b[0;31m# call PTMCMCOneStep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m             p0, lnlike0, lnprob0 = self.PTMCMCOneStep(\n\u001b[0;32m--> 362\u001b[0;31m                 p0, lnlike0, lnprob0, iter)\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m             \u001b[0;31m# compute effective number of samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/PTMCMCSampler/PTMCMCSampler.py\u001b[0m in \u001b[0;36mPTMCMCOneStep\u001b[0;34m(self, p0, lnlike0, lnprob0, iter)\u001b[0m\n\u001b[1;32m    488\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m                 \u001b[0mnewlnlike\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m                 \u001b[0mnewlnprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemp\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnewlnlike\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/PTMCMCSampler/PTMCMCSampler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise_extensions/hypermodel.py\u001b[0m in \u001b[0;36mget_lnlikelihood\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;31m# only active parameters enter likelihood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0mactive_lnlike\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_lnlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_weights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mget_lnlikelihood\u001b[0;34m(self, params, **kwargs)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_lnlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lnlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, xs, phiinv_method)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0;31m# get -0.5 * (rNr + logdet_N) piece of likelihood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0mloglike\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# red noise piece\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mget_rNr_logdet\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msignalcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msignalcollection\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_signalcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_residuals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msignalcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msignalcollection\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_signalcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_residuals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    898\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m                 \u001b[0mcache_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m                     \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# noqa: F841\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mget_rNr_logdet\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    846\u001b[0m             \u001b[0mNvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ndiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_detres\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mNvec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogdet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mSignalCollection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, other, left_array, logdet)\u001b[0m\n\u001b[1;32m   1146\u001b[0m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_D1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mleft_array\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_1D1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1149\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mleft_array\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_D1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36m_solve_1D1\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m   1102\u001b[0m         \u001b[0mNx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nvec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0myNx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mslc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jvec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mslc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mslc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m                 \u001b[0mxblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mslc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sampler.sample(x0, N, SCAMweight=30, AMweight=15, DEweight=50, burn=100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example with chromatic models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis above seems to indicate that `J1600-3053` wants an annual-DM term whenever a Fourier basis representation of the DM variations is used. We use that informatio in the following. We also fix the red-noise PSD to be a `power-law`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "psrname = 'J1600-3053'\n",
    "filepath = './no_dmx_pickles/'\n",
    "filepath += '{0}_ng12p5yr_v3_nodmx_ePSR.pkl'.format(psrname)\n",
    "with open(filepath,'rb') as fin:\n",
    "    psr=pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T20:59:52.925788Z",
     "start_time": "2018-05-16T20:59:26.185526Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create list of pta models for our model selection\n",
    "ptas = {}\n",
    "model_labels = []\n",
    "model_dict = {}\n",
    "\n",
    "kwargs = copy.deepcopy(model_template)            \n",
    "kwargs.update({'dm_var':True,\n",
    "               'dmgp_kernel':'nondiag',\n",
    "               'white_vary':True,\n",
    "               'dm_nondiag_kernel':'sq_exp_rfband',\n",
    "               'dm_sw_deter':True,\n",
    "               })\n",
    "            \n",
    "# Instantiate single pulsar noise model\n",
    "ptas[0] = model_singlepsr_noise(psr, **kwargs)\n",
    "\n",
    "# Add labels and kwargs to save for posterity and plotting.\n",
    "# Model, DM Kernel, Chromatic, Chrom Idx\n",
    "model_labels.append(['A','sq_exp_rfband', False, None])\n",
    "model_dict.update({str(0):kwargs})\n",
    "\n",
    "kwargs = copy.deepcopy(model_template)            \n",
    "kwargs.update({'dm_var':True,\n",
    "               'dmgp_kernel':'nondiag',\n",
    "               'white_vary':True,\n",
    "               'dm_nondiag_kernel':'periodic_rfband',\n",
    "               'dm_sw_deter':True,\n",
    "               })\n",
    "            \n",
    "# Instantiate single pulsar noise model\n",
    "ptas[1] = model_singlepsr_noise(psr, **kwargs)\n",
    "\n",
    "# Add labels and kwargs to save for posterity and plotting.\n",
    "# Model, DM Kernel, Chromatic, Chrom Idx\n",
    "model_labels.append(['B','periodic_rfband', False, None])\n",
    "model_dict.update({str(1):kwargs})\n",
    "\n",
    "kwargs = copy.deepcopy(model_template)            \n",
    "kwargs.update({'dm_var':True,\n",
    "               'dmgp_kernel':'nondiag',\n",
    "               'white_vary':True,\n",
    "               'dm_nondiag_kernel':'sq_exp_rfband',\n",
    "               'chrom_gp':True,\n",
    "               'chrom_gp_kernel':'nondiag',\n",
    "               'chrom_kernel':'sq_exp',\n",
    "               'dm_sw_deter':True,\n",
    "               })\n",
    "# Instantiate single pulsar noise model\n",
    "ptas[2] = model_singlepsr_noise(psr, **kwargs)\n",
    "\n",
    "# Add labels and kwargs to save for posterity and plotting.\n",
    "# Model, DM Kernel, Chromatic, Chrom Idx\n",
    "model_labels.append(['C','sq_exp_rfband', True, 'sq_exp'])\n",
    "model_dict.update({str(2):kwargs})\n",
    "\n",
    "\n",
    "kwargs = copy.deepcopy(model_template)            \n",
    "kwargs.update({'dm_var':True,\n",
    "               'dmgp_kernel':'nondiag',\n",
    "               'white_vary':True,\n",
    "               'dm_nondiag_kernel':'sq_exp_rfband',\n",
    "               'chrom_gp':True,\n",
    "               'chrom_gp_kernel':'nondiag',\n",
    "               'chrom_kernel':'sq_exp',\n",
    "               'dm_sw_deter':True,\n",
    "               })\n",
    "\n",
    "# Instantiate single pulsar noise model\n",
    "ptas[3] = model_singlepsr_noise(psr, **kwargs)\n",
    "\n",
    "# Add labels and kwargs to save for posterity and plotting.\n",
    "# Model, DM Kernel, Chromatic, Chrom Idx\n",
    "model_labels.append(['D','sq_exp_rfband', True, 'periodic'])\n",
    "model_dict.update({str(3):kwargs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T21:02:02.034726Z",
     "start_time": "2018-05-16T21:02:02.006946Z"
    }
   },
   "outputs": [],
   "source": [
    "# Instanciate a collection of models\n",
    "super_model = HyperModel(ptas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T21:02:35.979921Z",
     "start_time": "2018-05-16T21:02:35.951261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding empirical proposals...\n",
      "\n",
      "Adding red noise prior draws...\n",
      "\n",
      "Adding DM GP noise prior draws...\n",
      "\n",
      "Adding nmodel uniform distribution draws...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "outdir = '/Users/hazboun/nanograv_detection/ent_ext_testing/{}/chrom_dmgp/'.format(psr.name)\n",
    "emp_distr_path = './wn_emp_dists/{0}_ng12p5yr_v3_std_plaw_emp_dist.pkl'.format(psr.name)\n",
    "sampler = super_model.setup_sampler(resume=True, outdir=outdir,\n",
    "                                    empirical_distr=emp_distr_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_params = {}\n",
    "for ky, pta in ptas.items():\n",
    "    model_params.update({str(ky) : pta.param_names})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(outdir+'/model_params.json' , 'w') as fout:\n",
    "    json.dump(model_params, fout, sort_keys=True,\n",
    "              indent=4, separators=(',', ': '))\n",
    "    \n",
    "with open(outdir+'/model_kwargs.json' , 'w') as fout:\n",
    "    json.dump(model_dict, fout, sort_keys=True,\n",
    "              indent=4, separators=(',', ': '))\n",
    "    \n",
    "with open(outdir+'/model_labels.json' , 'w') as fout:\n",
    "    json.dump(model_labels, fout, sort_keys=True,\n",
    "              indent=4, separators=(',', ': '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T21:02:36.575939Z",
     "start_time": "2018-05-16T21:02:36.546253Z"
    }
   },
   "outputs": [],
   "source": [
    "# sampler for N steps\n",
    "N = int(5e6)\n",
    "x0 = super_model.initial_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-16T22:08:15.060176Z",
     "start_time": "2018-05-16T21:02:37.034858Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 0.68 percent in 13527.782160 s Acceptance rate = 0.501294"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-b8b028b05058>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSCAMweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAMweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDEweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mburn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/PTMCMCSampler/PTMCMCSampler.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, p0, Niter, ladder, Tmin, Tmax, Tskip, isave, covUpdate, SCAMweight, AMweight, DEweight, NUTSweight, MALAweight, HMCweight, burn, HMCstepsize, HMCsteps, maxIter, thin, i0, neff, writeHotChains, hotChain)\u001b[0m\n\u001b[1;32m    360\u001b[0m             \u001b[0;31m# call PTMCMCOneStep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m             p0, lnlike0, lnprob0 = self.PTMCMCOneStep(\n\u001b[0;32m--> 362\u001b[0;31m                 p0, lnlike0, lnprob0, iter)\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m             \u001b[0;31m# compute effective number of samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/PTMCMCSampler/PTMCMCSampler.py\u001b[0m in \u001b[0;36mPTMCMCOneStep\u001b[0;34m(self, p0, lnlike0, lnprob0, iter)\u001b[0m\n\u001b[1;32m    488\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m                 \u001b[0mnewlnlike\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m                 \u001b[0mnewlnprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemp\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnewlnlike\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/PTMCMCSampler/PTMCMCSampler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise_extensions/hypermodel.py\u001b[0m in \u001b[0;36mget_lnlikelihood\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;31m# only active parameters enter likelihood\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0mactive_lnlike\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_lnlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_weights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mget_lnlikelihood\u001b[0;34m(self, params, **kwargs)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_lnlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lnlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, xs, phiinv_method)\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;31m# correlated signals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0mTNrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_TNr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m         \u001b[0mTNTs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_TNT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m         \u001b[0mphiinvs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_phiinv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogdet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mphiinv_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mget_TNT\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_TNT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msignalcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_TNT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msignalcollection\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_signalcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_TNT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msignalcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_TNT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msignalcollection\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_signalcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_rNr_logdet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    898\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m                 \u001b[0mcache_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m                 \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_list\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m                     \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# noqa: F841\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36mget_TNT\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    840\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    841\u001b[0m             \u001b[0mNvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ndiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 842\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mNvec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mcache_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"white_params\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"delay_params\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, other, left_array, logdet)\u001b[0m\n\u001b[1;32m   1155\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1156\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mleft_array\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1157\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_2D2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1158\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mleft_array\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mleft_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1159\u001b[0m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve_D1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pint/lib/python3.6/site-packages/enterprise/signals/signal_base.py\u001b[0m in \u001b[0;36m_solve_2D2\u001b[0;34m(self, X, Z)\u001b[0m\n\u001b[1;32m   1125\u001b[0m                 \u001b[0mzn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mniblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZblock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m                 \u001b[0mxn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mniblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXblock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1127\u001b[0;31m                 \u001b[0mZNX\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mbeta\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mouter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1128\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mZNX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
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