{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instrument synergy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to prototype support for multiple instruments operating on the same signal incident at Earth. The instruments may have different wavebands and the data may or may not be phase resolved for each instrument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this tutorial, you should install X-PSI with the default (blackbody) atmosphere extension module, which is compiled when installing X-PSI with default settings. Some data files are needed and they can be found in the [Zenodo](https://zenodo.org/record/7094144)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/=============================================\\\n",
      "| X-PSI: X-ray Pulse Simulation and Inference |\n",
      "|---------------------------------------------|\n",
      "|                Version: 1.0.0               |\n",
      "|---------------------------------------------|\n",
      "|      https://xpsi-group.github.io/xpsi      |\n",
      "\\=============================================/\n",
      "\n",
      "Imported GetDist version: 0.3.1\n",
      "Imported nestcheck version: 0.2.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.ticker import MultipleLocator, AutoLocator, AutoMinorLocator\n",
    "from matplotlib import gridspec\n",
    "from matplotlib import cm\n",
    "\n",
    "import xpsi\n",
    "\n",
    "from xpsi.global_imports import _c, _G, _dpr, gravradius, _csq, _km, _2pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Telescope(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NICER = Telescope()\n",
    "XMM = Telescope() # fabricated toy that we'll just pretend is XMM as a placeholder!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load a synthetic data set that we generated in advance, and know the fictitious exposure time for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "obs_settings = dict(counts=np.loadtxt('../../examples/examples_modeling_tutorial/model_data/example_synthetic_realisation.dat', dtype=np.double),\n",
    "                    channels=np.arange(20, 201),\n",
    "                    phases=np.linspace(0.0, 1.0, 33),\n",
    "                    first=0,last=180,\n",
    "                    exposure_time=984307.6661)\n",
    "\n",
    "NICER.data = xpsi.Data(**obs_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "obs_settings = dict(counts=np.loadtxt('../../examples/examples_modeling_tutorial/model_data/XMM_realisation.dat', dtype=np.double).reshape(-1,1),\n",
    "                    channels=np.arange(20, 201),\n",
    "                    phases=np.array([0.0, 1.0]),\n",
    "                    first=0,last=180,\n",
    "                    exposure_time=1818434.247359)\n",
    "\n",
    "XMM.data = xpsi.Data(**obs_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['text.usetex'] = False\n",
    "rcParams['font.size'] = 14.0\n",
    "\n",
    "def veneer(x, y, axes, lw=1.0, length=8):\n",
    "    \"\"\" Make the plots a little more aesthetically pleasing. \"\"\"\n",
    "    if x is not None:\n",
    "        if x[1] is not None:\n",
    "            axes.xaxis.set_major_locator(MultipleLocator(x[1]))\n",
    "        if x[0] is not None:\n",
    "            axes.xaxis.set_minor_locator(MultipleLocator(x[0]))\n",
    "    else:\n",
    "        axes.xaxis.set_major_locator(AutoLocator())\n",
    "        axes.xaxis.set_minor_locator(AutoMinorLocator())\n",
    "        \n",
    "    if y is not None:\n",
    "        if y[1] is not None:\n",
    "            axes.yaxis.set_major_locator(MultipleLocator(y[1]))\n",
    "        if y[0] is not None:\n",
    "            axes.yaxis.set_minor_locator(MultipleLocator(y[0]))\n",
    "    else:\n",
    "        axes.yaxis.set_major_locator(AutoLocator())\n",
    "        axes.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "        \n",
    "    axes.tick_params(which='major', colors='black', length=length, width=lw)\n",
    "    axes.tick_params(which='minor', colors='black', length=int(length/2), width=lw)\n",
    "    plt.setp(axes.spines.values(), linewidth=lw, color='black')\n",
    "\n",
    "def plot_one_pulse(pulse, x, data, label=r'Counts',\n",
    "                   cmap=cm.magma, vmin=None, vmax=None):\n",
    "    \"\"\" Plot a pulse resolved over a single rotational cycle. \"\"\"\n",
    "    \n",
    "    fig = plt.figure(figsize = (7,7))\n",
    "\n",
    "    gs = gridspec.GridSpec(1, 2, width_ratios=[50,1])\n",
    "    ax = plt.subplot(gs[0])\n",
    "    ax_cb = plt.subplot(gs[1])\n",
    "\n",
    "    profile = ax.pcolormesh(x,\n",
    "                             data.channels,\n",
    "                             pulse,\n",
    "                             cmap = cmap,\n",
    "                             vmin=vmin,\n",
    "                             vmax=vmax,\n",
    "                             linewidth = 0,\n",
    "                             rasterized = True)\n",
    "\n",
    "    profile.set_edgecolor('face')\n",
    "\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylabel(r'Channel')\n",
    "    ax.set_xlabel(r'Phase')\n",
    "\n",
    "    cb = plt.colorbar(profile,\n",
    "                      cax = ax_cb)\n",
    "\n",
    "    cb.set_label(label=label, labelpad=25)\n",
    "    cb.solids.set_edgecolor('face')\n",
    "\n",
    "    veneer((0.05, 0.2), (None, None), ax)\n",
    "\n",
    "    plt.subplots_adjust(wspace = 0.025)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(NICER.data.counts, NICER.data.phases, NICER.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "veneer((5, 25), (None,None), ax)\n",
    "\n",
    "ax.plot(XMM.data.counts, 'k-', ls='steps', label='XMM')\n",
    "ax.plot(np.sum(NICER.data.counts, axis=1), 'r-', ls='steps', label='NICER')\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylabel('Counts')\n",
    "_ = ax.set_xlabel('Channel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instrument"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We require a model instrument object to transform incident specific flux signals into a form which enters directly in the sampling distribution of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomInstrument(xpsi.Instrument):\n",
    "    \"\"\" A model of the NICER telescope response. \"\"\"\n",
    "    \n",
    "    def __call__(self, signal, *args):\n",
    "        \"\"\" Overwrite base just to show it is possible.\n",
    "        \n",
    "        We loaded only a submatrix of the total instrument response\n",
    "        matrix into memory, so here we can simplify the method in the\n",
    "        base class.\n",
    "        \n",
    "        \"\"\"\n",
    "\n",
    "        matrix = self.construct_matrix()\n",
    "\n",
    "        self._folded_signal = np.dot(matrix, signal)\n",
    "\n",
    "        return self._folded_signal\n",
    "\n",
    "    @classmethod\n",
    "    def from_response_files(cls, ARF, RMF, max_input, min_input=0,\n",
    "                            channel_edges=None, translate_edges=None, scaling=None):\n",
    "        \"\"\" Constructor which converts response files into :class:`numpy.ndarray`s.\n",
    "        :param str ARF: Path to ARF which is compatible with\n",
    "                                :func:`numpy.loadtxt`.\n",
    "        :param str RMF: Path to RMF which is compatible with\n",
    "                                :func:`numpy.loadtxt`.\n",
    "        :param str channel_edges: Optional path to edges which is compatible with\n",
    "                                  :func:`numpy.loadtxt`.\n",
    "        \"\"\"\n",
    "\n",
    "        if min_input != 0:\n",
    "            min_input = int(min_input)\n",
    "\n",
    "        max_input = int(max_input)\n",
    "\n",
    "        try:\n",
    "            ARF = np.loadtxt(ARF, dtype=np.double, skiprows=3)\n",
    "            RMF = np.loadtxt(RMF, dtype=np.double)\n",
    "            if channel_edges:\n",
    "                channel_edges = np.loadtxt(channel_edges, dtype=np.double, skiprows=3)[:,1:]\n",
    "        except:\n",
    "            print('A file could not be loaded.')\n",
    "            raise\n",
    "            \n",
    "        if scaling is not None:\n",
    "            ARF[:,3] *= scaling\n",
    "            \n",
    "        matrix = np.ascontiguousarray(RMF[min_input:max_input,20:201].T, dtype=np.double)\n",
    "\n",
    "        edges = np.zeros(ARF[min_input:max_input,3].shape[0]+1, dtype=np.double)\n",
    "\n",
    "        edges[0] = ARF[min_input,1]; edges[1:] = ARF[min_input:max_input,2]\n",
    "\n",
    "        for i in range(matrix.shape[0]):\n",
    "            matrix[i,:] *= ARF[min_input:max_input,3]\n",
    "\n",
    "        channels = np.arange(20, 201)\n",
    "        \n",
    "        if translate_edges is not None:\n",
    "            edges += translate_edges\n",
    "\n",
    "        return cls(matrix, edges, channels, channel_edges[20:202,-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's construct an instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for loaded instrument response (sub)matrix...\n",
      "Channels set.\n",
      "No parameters supplied... empty subspace created.\n"
     ]
    }
   ],
   "source": [
    "NICER.instrument = CustomInstrument.from_response_files(ARF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_arf.txt',\n",
    "                                         RMF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_matrix.txt',\n",
    "                                         max_input = 500,\n",
    "                                         min_input = 0,\n",
    "                                         channel_edges = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_energymap.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for loaded instrument response (sub)matrix...\n",
      "Channels set.\n",
      "No parameters supplied... empty subspace created.\n"
     ]
    }
   ],
   "source": [
    "XMM.instrument = CustomInstrument.from_response_files(ARF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_arf.txt',\n",
    "                                       scaling = 0.5,\n",
    "                                       RMF = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_matrix.txt',\n",
    "                                       max_input = 500,\n",
    "                                       min_input = 0,\n",
    "                                       channel_edges = '../../examples/examples_modeling_tutorial/model_data/nicer_v1.01_rmf_energymap.txt',\n",
    "                                       translate_edges = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NICER ``v1.01`` response matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (14,7))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "veneer((25, 100), (10, 50), ax)\n",
    "\n",
    "_ = ax.imshow(NICER.instrument.matrix,\n",
    "              cmap = cm.viridis,\n",
    "              rasterized = True)\n",
    "\n",
    "ax.set_ylabel('Channel $-\\;20$')\n",
    "_ = ax.set_xlabel('Energy interval')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summed over channel subset $[20,200]$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "veneer((0.1, 0.5), (50,250), ax)\n",
    "\n",
    "ax.plot((NICER.instrument.energy_edges[:-1] + NICER.instrument.energy_edges[1:])/2.0,\n",
    "        np.sum(NICER.instrument.matrix, axis=0), 'k-')\n",
    "\n",
    "ax.plot((XMM.instrument.energy_edges[:-1] + XMM.instrument.energy_edges[1:])/2.0,\n",
    "        np.sum(XMM.instrument.matrix, axis=0), 'r-')\n",
    "\n",
    "ax.set_ylabel('Effective area [cm$^{-2}$]')\n",
    "_ = ax.set_xlabel('Energy [keV]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xpsi.likelihoods.default_background_marginalisation import eval_marginal_likelihood\n",
    "from xpsi.likelihoods.default_background_marginalisation import precomputation\n",
    "\n",
    "class CustomSignal(xpsi.Signal):\n",
    "    \"\"\" A custom calculation of the logarithm of the likelihood.\n",
    "    We extend the :class:`xpsi.Signal.Signal` class to make it callable.\n",
    "    We overwrite the body of the __call__ method. The docstring for the\n",
    "    abstract method is copied.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, workspace_intervals = 1000, epsabs = 0, epsrel = 1.0e-8,\n",
    "                 epsilon = 1.0e-3, sigmas = 10.0, support = None, *args, **kwargs):\n",
    "        \"\"\" Perform precomputation. \"\"\"\n",
    "\n",
    "        super(CustomSignal, self).__init__(*args, **kwargs)\n",
    "\n",
    "        try:\n",
    "            self._precomp = precomputation(self._data.counts.astype(np.int32))\n",
    "        except AttributeError:\n",
    "            print('Warning: No data... can synthesise data but cannot evaluate a '\n",
    "                  'likelihood function.')\n",
    "        else:\n",
    "            self._workspace_intervals = workspace_intervals\n",
    "            self._epsabs = epsabs\n",
    "            self._epsrel = epsrel\n",
    "            self._epsilon = epsilon\n",
    "            self._sigmas = sigmas\n",
    "            if support is not None:\n",
    "                self._support = support\n",
    "            else:\n",
    "                self._support = -1.0 * np.ones((self._data.counts.shape[0],2))\n",
    "                self._support[:,0] = 0.0\n",
    "        \n",
    "    @property\n",
    "    def support(self):\n",
    "        return self._support\n",
    "\n",
    "    @support.setter\n",
    "    def support(self, obj):\n",
    "        self._support = obj\n",
    "\n",
    "    def __call__(self, **kwargs):\n",
    "        self.loglikelihood, self.expected_counts, self.background_signal, self.background_given_support = \\\n",
    "                eval_marginal_likelihood(self._data.exposure_time,\n",
    "                                          self._data.phases,\n",
    "                                          self._data.counts,\n",
    "                                          self._signals,\n",
    "                                          self._phases,\n",
    "                                          self._shifts,\n",
    "                                          self._precomp,\n",
    "                                          self._support,\n",
    "                                          self._workspace_intervals,\n",
    "                                          self._epsabs,\n",
    "                                          self._epsrel,\n",
    "                                          self._epsilon,\n",
    "                                          self._sigmas,\n",
    "                                          kwargs.get('llzero'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if we need an additional overall phase shift parameter for additional instruments whose recorded data are phase resolved, then it could be passed to the subclass above for the signal associated with a given telescope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n"
     ]
    }
   ],
   "source": [
    "NICER.signal = CustomSignal(data = NICER.data,\n",
    "                              instrument = NICER.instrument,\n",
    "                              background = None,\n",
    "                              interstellar = None,\n",
    "                              workspace_intervals = 1000,\n",
    "                              epsrel = 1.0e-8,\n",
    "                              epsilon = 1.0e-3,\n",
    "                              sigmas = 10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n"
     ]
    }
   ],
   "source": [
    "XMM.signal = CustomSignal(data = XMM.data,\n",
    "                               instrument = XMM.instrument,\n",
    "                               background = None,\n",
    "                               interstellar = None,\n",
    "                               support = None,\n",
    "                               workspace_intervals = 1000,\n",
    "                               epsrel = 1.0e-8,\n",
    "                               epsilon = 1.0e-3,\n",
    "                               sigmas = 10.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"frequency\" with fixed value 3.000e+02.\n",
      "    > Spin frequency [Hz].\n",
      "Creating parameter:\n",
      "    > Named \"mass\" with bounds [1.000e-03, 3.000e+00].\n",
      "    > Gravitational mass [solar masses].\n",
      "Creating parameter:\n",
      "    > Named \"radius\" with bounds [1.000e+00, 2.000e+01].\n",
      "    > Coordinate equatorial radius [km].\n",
      "Creating parameter:\n",
      "    > Named \"distance\" with bounds [1.000e-02, 3.000e+01].\n",
      "    > Earth distance [kpc].\n",
      "Creating parameter:\n",
      "    > Named \"cos_inclination\" with bounds [-1.000e+00, 1.000e+00].\n",
      "    > Cosine of Earth inclination to rotation axis.\n"
     ]
    }
   ],
   "source": [
    "bounds = dict(distance = (None, None),                   # (Earth) distance\n",
    "                mass = (None, None),                     # mass\n",
    "                radius = (None, None),                   # equatorial radius\n",
    "                cos_inclination = (None, None))          # (Earth) inclination to rotation axis\n",
    "\n",
    "values = dict(frequency=300.0 )                          # spin frequency\n",
    "\n",
    "spacetime = xpsi.Spacetime(bounds=bounds, values=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"super_colatitude\" with bounds [0.000e+00, 3.142e+00].\n",
      "    > The colatitude of the centre of the superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"super_radius\" with bounds [0.000e+00, 1.571e+00].\n",
      "    > The angular radius of the (circular) superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with bounds [-5.000e-01, 5.000e-01].\n",
      "    > The phase of the hot region, a periodic parameter [cycles].\n",
      "Creating parameter:\n",
      "    > Named \"super_temperature\" with bounds [3.000e+00, 7.000e+00].\n",
      "    > log10(superseding region effective temperature [K]).\n"
     ]
    }
   ],
   "source": [
    "bounds = dict(super_colatitude = (None, None),\n",
    "              super_radius = (None, None),\n",
    "              phase_shift = (-0.5, 0.5),\n",
    "              super_temperature = (None, None))\n",
    "\n",
    "# a simple circular, simply-connected spot\n",
    "primary = xpsi.HotRegion(bounds=bounds,\n",
    "                            values={}, # no initial values and no derived/fixed\n",
    "                            symmetry=True,\n",
    "                            omit=False,\n",
    "                            cede=False,\n",
    "                            concentric=False,\n",
    "                            sqrt_num_cells=32,\n",
    "                            min_sqrt_num_cells=10,\n",
    "                            max_sqrt_num_cells=64,\n",
    "                            num_leaves=100,\n",
    "                            num_rays=200,\n",
    "                            do_fast=False,\n",
    "                            prefix='p') # unique prefix needed because >1 instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"super_colatitude\" with bounds [0.000e+00, 3.142e+00].\n",
      "    > The colatitude of the centre of the superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"super_radius\" with bounds [0.000e+00, 1.571e+00].\n",
      "    > The angular radius of the (circular) superseding region [radians].\n",
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with bounds [-5.000e-01, 5.000e-01].\n",
      "    > The phase of the hot region, a periodic parameter [cycles].\n",
      "Creating parameter:\n",
      "    > Named \"super_temperature\" that is derived from ulterior variables.\n",
      "    > log10(superseding region effective temperature [K]).\n"
     ]
    }
   ],
   "source": [
    "class derive(xpsi.Derive):\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        We can pass a reference to the primary here instead\n",
    "        and store it as an attribute if there is risk of\n",
    "        the global variable changing.\n",
    "        \n",
    "        This callable can for this simple case also be\n",
    "        achieved merely with a function instead of a magic\n",
    "        method associated with a class.\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    def __call__(self, boundto, caller = None):\n",
    "        # one way to get the required reference\n",
    "        global primary # unnecessary, but for clarity\n",
    "        return primary['super_temperature'] - 0.2\n",
    "    \n",
    "bounds['super_temperature'] = None # declare fixed/derived variable\n",
    "\n",
    "secondary = xpsi.HotRegion(bounds=bounds, # can otherwise use same bounds\n",
    "                              values={'super_temperature': derive()}, # create a callable value\n",
    "                              symmetry=True,\n",
    "                              omit=False,\n",
    "                              cede=False,\n",
    "                              concentric=False,\n",
    "                              sqrt_num_cells=32,\n",
    "                              min_sqrt_num_cells=10,\n",
    "                              max_sqrt_num_cells=100,\n",
    "                              num_leaves=100,\n",
    "                              num_rays=200,\n",
    "                              do_fast=False,\n",
    "                              is_antiphased=True,\n",
    "                              prefix='s') # unique prefix needed because >1 instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xpsi import HotRegions\n",
    "\n",
    "hot = HotRegions((primary, secondary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomPhotosphere(xpsi.Photosphere):\n",
    "    \"\"\" Implement method for imaging.\"\"\"\n",
    "    \n",
    "    def _global_variables(self):\n",
    "    \n",
    "        return np.array([self['p__super_colatitude'],\n",
    "                          self['p__phase_shift'] * _2pi,\n",
    "                          self['p__super_radius'],\n",
    "                          self['p__super_temperature'],\n",
    "                          self['s__super_colatitude'],\n",
    "                          (self['s__phase_shift'] + 0.5) * _2pi,\n",
    "                          self['s__super_radius'],\n",
    "                          self.hot.objects[1]['s__super_temperature']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"mode_frequency\" with fixed value 3.000e+02.\n",
      "    > Coordinate frequency of the mode of radiative asymmetry in the\n",
      "photosphere that is assumed to generate the pulsed signal [Hz].\n"
     ]
    }
   ],
   "source": [
    "photosphere = CustomPhotosphere(hot = hot, elsewhere = None,\n",
    "                                values=dict(mode_frequency = spacetime['frequency']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "star = xpsi.Star(spacetime = spacetime, photospheres = photosphere)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood = xpsi.Likelihood(star = star, signals = [NICER.signal, XMM.signal],\n",
    "                             threads = 1, externally_updated = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The instrument wavebands exhibit a high degree of overlap. The energies at which we compute incident specific flux signals are based on the union of wavebands, distributed logarithmically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.plot(XMM.signal.energies, 'kx')\n",
    "ax = plt.gca()\n",
    "veneer((5,20),(0.2,1.0),ax)\n",
    "ax.set_xlabel('Index')\n",
    "_ = ax.set_ylabel('Energy [keV]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple, non-adaptive protocol to ensure that signals are not computed at very nearby energies for multiple telescopes, resulting in overhead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's call the ``likelihood`` object with the true model parameter values that we injected to generate the synthetic data rendered above, omitting background parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ll = -29322.44238519; time = 0.763\n"
     ]
    }
   ],
   "source": [
    "p = [1.4,\n",
    "     12.5,\n",
    "     0.2,\n",
    "     math.cos(1.25),\n",
    "     0.0,\n",
    "     1.0,\n",
    "     0.075,\n",
    "     6.2,\n",
    "     0.025,\n",
    "     math.pi - 1.0,\n",
    "     0.2]\n",
    "\n",
    "t = time.time()\n",
    "ll = likelihood(p, force=True) # force if you want to clear parameter value caches\n",
    "\n",
    "print('ll = %.8f; time = %.3f' % (ll, time.time() - t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-26713.601349482524"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NICER.signal.loglikelihood # check NICER ll ~ -26713.602 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2608.841035710227"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XMM.signal.loglikelihood # check XMM ll ~ -2608.841 ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fabricate some rough prior information as the constrained support of the background parameters for XMM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "support = np.zeros((181, 2))\n",
    "support[:,0] = XMM.signal.background_signal - 5.0 * np.sqrt(XMM.signal.background_signal)\n",
    "support[:,1] = XMM.signal.background_signal + 5.0 * np.sqrt(XMM.signal.background_signal)\n",
    "support /= XMM.data.exposure_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "XMM.signal.support = support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ll = likelihood(p, force=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's confirm that the XMM background-marginalised likelihood did indeed change:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2610.1216095310615"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XMM.signal.loglikelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The background-marginalised likelihood function has the following form. Subscripts N denote NICER, whilst subscripts X denote XMM.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "p(d_{\\rm X}, d_{\\rm N}, \\{b_{\\rm X}\\} \\,|\\, s)\n",
    "    \\propto\n",
    "    &\n",
    "    \\underbrace{\\mathop{\\int}_{\\{0\\}}^{\\{\\mathcal{U}_{\\rm N}\\}}\n",
    "    p(d_{\\rm N} \\,|\\, s, \\{\\mathbb{E}[b_{\\rm N}]\\}, \\texttt{NICER})\n",
    "    d\\{\\mathbb{E}[b_{\\rm N}]\\}}_{\\rm exp( \\texttt{NICER.signal.loglikelihood} )}\\\\\n",
    "    &\n",
    "    \\times\\underbrace{\\mathop{\\int}_{\\{0\\}}^{\\{\\mathcal{U}_{X}\\}}\n",
    "    p(d_{\\rm X} \\,|\\, s, \\{\\mathbb{E}[b_{\\rm X}]\\}, \\texttt{XMM})\n",
    "    p(\\{\\mathbb{E}[b_{\\rm X}]\\} \\,|\\, \\{b_{\\rm X}\\})d\\{\\mathbb{E}[b_{\\rm X}]\\}}_{\\rm exp( \\texttt{XMM.signal.loglikelihood} )}.\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "The term $p(\\{\\mathbb{E}[b_{\\rm X}]\\} \\,|\\, \\{b_{\\rm X}\\})$ truncates the integral over XMM channel-by-channel background count rate variables to an interval $[a,b]$ in each channel, where $a,b\\in\\mathbb{R}^{+}$. This is the joint prior support of the variables $\\{\\mathbb{E}[b_{\\rm X}]\\}$ rendered in the spectral plot below. The form of the prior density $p(\\{\\mathbb{E}[b_{\\rm X}]\\} \\,|\\, \\{b_{\\rm X}\\})$ is flat on this interval for each channel. This is a simplifying approximation to the probability of the background data $\\{b_{\\rm X}\\}$ given the variables $\\{\\mathbb{E}[b_{\\rm X}]\\}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_spectrum():\n",
    "\n",
    "    fig = plt.figure(figsize = (10,10))\n",
    "\n",
    "    ax = fig.add_subplot(111)\n",
    "    veneer((5, 25), (None,None), ax)\n",
    "\n",
    "    ax.fill_between(np.arange(support.shape[0]),\n",
    "                    support[:,0]*XMM.data.exposure_time,\n",
    "                    support[:,1]*XMM.data.exposure_time,\n",
    "                    step = 'pre',\n",
    "                    color = 'k',\n",
    "                    alpha = 0.5,\n",
    "                    label = 'background support')\n",
    "\n",
    "    ax.plot(XMM.signal.background_signal, 'b-', ls='steps', label='MCL background')\n",
    "    ax.plot(XMM.signal.expected_counts, 'k-', ls='steps', label='MCL counts given support', lw=5.0)\n",
    "    ax.plot(XMM.data.counts, 'r-', ls='steps', label='XMM data')\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_ylabel('Counts')\n",
    "    _ = ax.set_xlabel('Channel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spectrum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectrum labelled *MCL counts given support* means the expected signal from the pulsar, plus the background count vector that maximises the conditional likelihood function given that pulsar signal, subject to the background vector existing in the prior support. The spectrum labelled *MCL background*, on the other hand, is the background vector that maximises the conditional likelihood function, but *not* subject to the prior support."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood['p__super_temperature'] = 6.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood.externally_updated = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-391383.432561544"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-321117.0621271402"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XMM.signal.loglikelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spectrum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook thus far we have not generated sythetic data. However, we did condition on synthetic data. Below we outline how that data was generated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The background radiation field incident on the model instrument for the purpose of generating synthetic data was a time-invariant powerlaw spectrum, and was transformed into a count-rate in each output channel using the response matrix for synthetic data generation. We would reproduce this background here by writing a custom subclass as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomBackground(xpsi.Background):\n",
    "    \"\"\" The background injected to generate synthetic data. \"\"\"\n",
    "\n",
    "    def __init__(self, bounds=None, value=None):\n",
    "        \n",
    "        # first the parameters that are fundemental to this class\n",
    "        doc = \"\"\"\n",
    "        Powerlaw spectral index.\n",
    "        \"\"\"\n",
    "        index = xpsi.Parameter('powerlaw_index',\n",
    "                                strict_bounds = (-3.0, -1.01),\n",
    "                                bounds = bounds,\n",
    "                                doc = doc,\n",
    "                                symbol = r'$\\Gamma$',\n",
    "                                value = value,\n",
    "                                permit_prepend = False) # because to be shared by multiple objects\n",
    "        \n",
    "        super(CustomBackground, self).__init__(index)\n",
    "\n",
    "    def __call__(self, energy_edges, phases):\n",
    "        \"\"\" Evaluate the incident background field. \"\"\"\n",
    "        \n",
    "        G = self['powerlaw_index']\n",
    "\n",
    "        temp = np.zeros((energy_edges.shape[0] - 1, phases.shape[0]))\n",
    "\n",
    "        temp[:,0] = (energy_edges[1:]**(G + 1.0) - energy_edges[:-1]**(G + 1.0)) / (G + 1.0)\n",
    "\n",
    "        for i in range(phases.shape[0]):\n",
    "            temp[:,i] = temp[:,0]\n",
    "\n",
    "        self._incident_background = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"powerlaw_index\" with bounds [-3.000e+00, -1.010e+00].\n",
      "    > Powerlaw spectral index.\n"
     ]
    }
   ],
   "source": [
    "background = CustomBackground(bounds=(None, None)) # use strict bounds, but do not fix/derive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this same background signal, albeit with different normalisations, for both telescopes. This is simply to generate finite background contributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also in need of a simpler data object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SynthesiseData(xpsi.Data):\n",
    "    \"\"\" Custom data container to enable synthesis. \"\"\"\n",
    "    \n",
    "    def __init__(self, channels, phases, first, last):\n",
    "        self.channels = channels\n",
    "        self._phases = phases\n",
    "        \n",
    "        try:\n",
    "            self._first = int(first)\n",
    "            self._last = int(last)\n",
    "        except TypeError:\n",
    "            raise TypeError('The first and last channels must be integers.')\n",
    "        if self._first >= self._last:\n",
    "            raise ValueError('The first channel number must be lower than the '\n",
    "                             'the last channel number.')\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "NICER.synth = SynthesiseData(np.arange(20,201), np.linspace(0.0, 1.0, 33), 0, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting channels for event data...\n",
      "Channels set.\n"
     ]
    }
   ],
   "source": [
    "XMM.synth = SynthesiseData(np.arange(20,201), np.array([0.0,1.0]), 0, 180)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xpsi.tools.synthesise import synthesise_given_total_count_number as _synthesise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def synthesise(self,\n",
    "               require_source_counts,\n",
    "               require_background_counts,\n",
    "               name='synthetic',\n",
    "               directory='./data',\n",
    "               **kwargs):\n",
    "        \"\"\" Synthesise data set.\n",
    "\n",
    "        \"\"\"\n",
    "        self._expected_counts, synthetic, _, _ = _synthesise(self._data.phases,\n",
    "                                            require_source_counts,\n",
    "                                            self._signals,\n",
    "                                            self._phases,\n",
    "                                            self._shifts,\n",
    "                                            require_background_counts,\n",
    "                                            self._background.registered_background)\n",
    "        try:\n",
    "            if not os.path.isdir(directory):\n",
    "                os.mkdir(directory)\n",
    "        except OSError:\n",
    "            print('Cannot create write directory.')\n",
    "            raise\n",
    "\n",
    "        np.savetxt(os.path.join(directory, name+'_realisation.dat'),\n",
    "                   synthetic,\n",
    "                   fmt = '%u')\n",
    "\n",
    "        self._write(self.expected_counts,\n",
    "                    filename = os.path.join(directory, name+'_expected_hreadable.dat'),\n",
    "                    fmt = '%.8e')\n",
    "\n",
    "        self._write(synthetic,\n",
    "                    filename = os.path.join(directory, name+'_realisation_hreadable.dat'),\n",
    "                    fmt = '%u')\n",
    "\n",
    "def _write(self, counts, filename, fmt):\n",
    "    \"\"\" Write to file in human readable format. \"\"\"\n",
    "\n",
    "    rows = len(self._data.phases) - 1\n",
    "    rows *= len(self._data.channels)\n",
    "\n",
    "    phases = self._data.phases[:-1]\n",
    "    array = np.zeros((rows, 3))\n",
    "\n",
    "    for i in range(counts.shape[0]):\n",
    "        for j in range(counts.shape[1]):\n",
    "            array[i*len(phases) + j,:] = self._data.channels[i], phases[j], counts[i,j]\n",
    "\n",
    "    np.savetxt(filename, array, fmt=['%u', '%.6f'] + [fmt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomSignal.synthesise = synthesise\n",
    "CustomSignal._write = _write"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to instantiate, and reconfigure the likelihood object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n",
      "Warning: No data... can synthesise data but cannot evaluate a likelihood function.\n",
      "Creating parameter:\n",
      "    > Named \"phase_shift\" with fixed value 0.000e+00.\n",
      "    > The phase shift for the signal, a periodic parameter [cycles].\n",
      "Warning: No data... can synthesise data but cannot evaluate a likelihood function.\n"
     ]
    }
   ],
   "source": [
    "NICER.signal = CustomSignal(data = NICER.synth,\n",
    "                              instrument = NICER.instrument,\n",
    "                              background = background,\n",
    "                              interstellar = None,\n",
    "                              workspace_intervals = 1000,\n",
    "                              epsrel = 1.0e-8,\n",
    "                              epsilon = 1.0e-3,\n",
    "                              sigmas = 10.0,\n",
    "                              prefix='NICER')\n",
    "\n",
    "XMM.signal = CustomSignal(data = XMM.synth,\n",
    "                          instrument = XMM.instrument,\n",
    "                          background = background,\n",
    "                          interstellar = None,\n",
    "                          workspace_intervals = 1000,\n",
    "                          epsrel = 1.0e-8,\n",
    "                          epsilon = 1.0e-3,\n",
    "                          sigmas = 10.0,\n",
    "                          prefix='XMM')\n",
    "\n",
    "for h in hot.objects:\n",
    "    h.set_phases(num_leaves = 100)\n",
    "    \n",
    "likelihood = xpsi.Likelihood(star = star, signals = [NICER.signal, XMM.signal], threads=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthesise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We proceed to synthesise. First we set an environment variable to seed the random number generator being called:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: GSL_RNG_SEED=0\n"
     ]
    }
   ],
   "source": [
    "%env GSL_RNG_SEED=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check write path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/tuomo/xpsi/xpsi_group/xpsi/docs/source\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Free parameters\n",
       "---------------\n",
       "mass: Gravitational mass [solar masses].\n",
       "radius: Coordinate equatorial radius [km].\n",
       "distance: Earth distance [kpc].\n",
       "cos_inclination: Cosine of Earth inclination to rotation axis.\n",
       "p__phase_shift: The phase of the hot region, a periodic parameter [cycles].\n",
       "p__super_colatitude: The colatitude of the centre of the superseding region [radians].\n",
       "p__super_radius: The angular radius of the (circular) superseding region [radians].\n",
       "p__super_temperature: log10(superseding region effective temperature [K]).\n",
       "s__phase_shift: The phase of the hot region, a periodic parameter [cycles].\n",
       "s__super_colatitude: The colatitude of the centre of the superseding region [radians].\n",
       "s__super_radius: The angular radius of the (circular) superseding region [radians].\n",
       "powerlaw_index: Powerlaw spectral index."
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exposure time: 984282.620969 [s]\n",
      "Background normalisation: 1.89132784e-05\n",
      "Exposure time: 1818398.718453 [s]\n",
      "Background normalisation: 2.21412640e-04\n"
     ]
    }
   ],
   "source": [
    "p = [1.4,\n",
    "     12.5,\n",
    "     0.2,\n",
    "     math.cos(1.25),\n",
    "     0.0,\n",
    "     1.0,\n",
    "     0.075,\n",
    "     6.2,\n",
    "     0.025,\n",
    "     math.pi - 1.0,\n",
    "     0.2,\n",
    "     -2.0]\n",
    "\n",
    "NICER_kwargs = dict(require_source_counts = 2.0e6,\n",
    "                      require_background_counts = 2.0e6,\n",
    "                      name = 'new_NICER',\n",
    "                      directory = './data')\n",
    "\n",
    "XMM_kwargs = dict(require_source_counts = 1.0e6,\n",
    "                      require_background_counts = 5.0e5,\n",
    "                      name = 'new_XMM',\n",
    "                      directory = './data')\n",
    "\n",
    "likelihood.synthesise(p,\n",
    "                      force = True,\n",
    "                      NICER = NICER_kwargs,\n",
    "                      XMM = XMM_kwargs) # SEED=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the normalisations, with units photons/s/cm^2/keV, are different because we require so many background counts. This detail is unimportant for this notebook, wherein we simply want some finite background contributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(np.loadtxt('data/new_NICER_realisation.dat', dtype=np.double), NICER.data.phases, NICER.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check we have generated the same count numbers, given the same seed and resolution settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = XMM.data.counts - np.loadtxt('data/new_XMM_realisation.dat', dtype=np.double).reshape(-1,1)\n",
    "(diff != 0.0).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = NICER.data.counts - np.loadtxt('data/new_NICER_realisation.dat', dtype=np.double)\n",
    "(diff != 0.0).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed in the `Modeling` tutorial, with `xpsi.__version__` of `v0.6`, the same RNG seed does not yield the same ($\\pm1$) count numbers, despite the small fractional difference between Poisson random variable expectations, for reasons that are unclear at present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.loadtxt('../../examples/examples_modeling_tutorial/model_data/example_synthetic_expected_hreadable.dat')\n",
    "y = np.loadtxt('data/new_NICER_expected_hreadable.dat')\n",
    "\n",
    "xx = np.zeros(NICER.data.counts.shape)\n",
    "yy = np.zeros(NICER.data.counts.shape)\n",
    "\n",
    "for i in range(xx.shape[0]):\n",
    "    for j in range(xx.shape[1]):\n",
    "        xx[i,j] = x[i*32 + j,-1]\n",
    "        yy[i,j] = y[i*32 + j,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(yy-xx, NICER.data.phases, NICER.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_r = (yy-xx)/np.sqrt(yy)\n",
    "plot_one_pulse(_r, NICER.data.phases, NICER.data,\n",
    "               'Standardised residuals',\n",
    "               cmap=cm.RdBu,\n",
    "               vmin=-np.max(np.fabs(_r)),\n",
    "               vmax=np.max(np.fabs(_r)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_pulse(diff, NICER.data.phases, NICER.data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}