{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a short tutorial for modeling polarized X-ray flux from a rapidly rotating neutron star. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us first make the initial setup (see the Modeling tutorial for more detailed explanations):"
   ]
  },
  {
   "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: 2.2.0               |\n",
      "|---------------------------------------------|\n",
      "|      https://xpsi-group.github.io/xpsi      |\n",
      "\\=============================================/\n",
      "\n",
      "Imported GetDist version: 1.4.7\n",
      "Imported nestcheck version: 0.2.1\n",
      "Creating parameter:\n",
      "    > Named \"frequency\" with fixed value 6.000e+02.\n",
      "    > Spin frequency [Hz].\n",
      "Creating parameter:\n",
      "    > Named \"mass\" with bounds [1.000e+00, 3.000e+00].\n",
      "    > Gravitational mass [solar masses].\n",
      "Creating parameter:\n",
      "    > Named \"radius\" with bounds [4.430e+00, 1.600e+01].\n",
      "    > Coordinate equatorial radius [km].\n",
      "Creating parameter:\n",
      "    > Named \"distance\" with bounds [1.000e-01, 1.000e+00].\n",
      "    > Earth distance [kpc].\n",
      "Creating parameter:\n",
      "    > Named \"cos_inclination\" with bounds [0.000e+00, 1.000e+00].\n",
      "    > Cosine of Earth inclination to rotation axis.\n",
      "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 [0.000e+00, 1.000e-01].\n",
      "    > The phase of the hot region, a periodic parameter [cycles].\n",
      "Creating parameter:\n",
      "    > Named \"super_temperature\" with bounds [3.000e+00, 7.600e+00].\n",
      "    > log10(superseding region effective temperature [K]).\n",
      "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 [0.000e+00, 1.000e-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",
      "Creating parameter:\n",
      "    > Named \"mode_frequency\" with fixed value 6.000e+02.\n",
      "    > Coordinate frequency of the mode of radiative asymmetry in the\n",
      "photosphere that is assumed to generate the pulsed signal [Hz].\n",
      "True\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\n",
    "\n",
    "\n",
    "freq = 600.0 \n",
    "\n",
    "bounds = dict(distance = (0.1, 1.0),                     # (Earth) distance\n",
    "                mass = (1.0, 3.0),                       # mass\n",
    "                radius = (3.0 * gravradius(1.0), 16.0),  # equatorial radius\n",
    "                cos_inclination = (0.0, 1.0))            # (Earth) inclination to rotation axis\n",
    "\n",
    "spacetime = xpsi.Spacetime(bounds=bounds, values=dict(frequency=freq))\n",
    "\n",
    "\n",
    "ceding = False #ceding=True might not work as intended. The option is left here for testing.\n",
    "\n",
    "bounds = dict(super_colatitude = (None, None),\n",
    "              super_radius = (None, None),\n",
    "              phase_shift = (0.0, 0.1),\n",
    "              super_temperature = (None, None))\n",
    "\n",
    "if ceding:\n",
    "\n",
    "\tbounds = dict(super_colatitude=(None,None),\n",
    "\t\t      super_radius = (None, None),\n",
    "\t\t      phase_shift = (0.0, 1.0),\n",
    "\t\t      super_temperature = (None, None),\n",
    "\t\t      cede_colatitude = (None, None),\n",
    "\t\t      cede_radius = (None, None),\n",
    "\t\t      cede_azimuth = (None, None),\n",
    "\t\t      cede_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",
    "                            #symmetry=False, \n",
    "                            omit=False,\n",
    "                            cede=ceding, \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_leaves=121,\n",
    "                            num_rays=200,\n",
    "                            do_fast=False,\n",
    "                            atm_ext=\"Pol_BB_Burst\",\n",
    "                            prefix='p')\n",
    "\n",
    "\n",
    "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=ceding, \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",
    "                              atm_ext=\"Pol_BB_Burst\", #the simplest polarized atmosphere extension                       \n",
    "                              is_antiphased=True,\n",
    "                              prefix='s') # unique prefix needed because >1 instance\n",
    "\n",
    "from xpsi import HotRegions\n",
    "\n",
    "hot = HotRegions((primary, secondary))\n",
    "\n",
    "h = hot.objects[0]\n",
    "\n",
    "hot['p__super_temperature'] = 6.0 # equivalent to ``primary['super_temperature'] = 6.0``\n",
    "\n",
    "\n",
    "class CustomPhotosphere(xpsi.Photosphere):\n",
    "    \"\"\" Implement method for imaging.\"\"\"\n",
    "\n",
    "    @property\n",
    "    def global_variables(self):\n",
    "        \"\"\" This method is needed if we also want to ivoke the image-plane signal simulator. \"\"\"\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']])\n",
    "\n",
    "#Set here stokes=True to calculate the polarized X-ray signal.\n",
    "photosphere = CustomPhotosphere(hot = hot, elsewhere = None, stokes=True,\n",
    "                                values=dict(mode_frequency = spacetime['frequency']))\n",
    "\n",
    "\n",
    "print(photosphere['mode_frequency'] == spacetime['frequency'])\n",
    "\n",
    "star = xpsi.Star(spacetime = spacetime, photospheres = photosphere)\n",
    "\n",
    "#Let us first initialize the star with these values but change them later:\n",
    "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",
    "if ceding:\n",
    "\tp = [1.4, #mass\n",
    "\t     12.0, #radius\n",
    "\t     0.2, #distance\n",
    "\t     math.cos(1.25), #cos_inclination\n",
    "\t     0.0, #p__phase_shift\n",
    "\t     1.0, #p__super_colatitude\n",
    "\t     0.075, #p__super_radius\n",
    "\t     6.2, #p__super_temperature\n",
    "\t     0.1, #p__cede_colatitude\n",
    "\t     0.1, #p__cede_radius\n",
    "\t     0.0, #p__cede_azimuth\n",
    "\t     6.2, #p__cede_temperature\n",
    "\t     0.025, #s__phase_shift\n",
    "\t     math.pi - 1.0, #s__super_colatitude\n",
    "\t     0.2, #s__super_radius\n",
    "\t     math.pi-1.0, #s__cede_colatitude ..\n",
    "\t     0.3, #s__cede_radius\n",
    "\t     0.0, #s__cede_azimuth\n",
    "\t     6.2] #s__cede_temperature\n",
    "\n",
    "star(p)\n",
    "\n",
    "\n",
    "#These are the values used in the light curve computation:\n",
    "star['mass'] = 1.4 #2.7088795 \n",
    "star['radius'] = 12.0 #20.0 #12.0\n",
    "star['distance'] = 0.2\n",
    "incl_deg = 40.0 #90.0 #40.0\n",
    "star['cos_inclination'] = math.cos(math.pi*incl_deg/180.0)#math.cos(2.0)\n",
    "theta_deg = 60.0\n",
    "star['p__super_colatitude'] = math.pi*theta_deg/180.0 #0.0 #2.0\n",
    "rho_deg = 10.0 #10.0 #0.001 #10.0\n",
    "star['p__super_radius'] = math.pi*rho_deg/180.0\n",
    "tplanck = 1.0219978 #1.0 #in keV #1 keV -> log10(T[K]) = 7.06 (out of bounds originally)\n",
    "star['p__super_temperature'] = np.log10(tplanck*11604525.0061657)\n",
    "\n",
    "\n",
    "if ceding:\n",
    "\tstar['p__cede_colatitude'] = math.pi*theta_deg/180.0 \n",
    "\tstar['p__cede_azimuth'] = 0.0 #0.0\n",
    "\tstar['p__cede_radius'] = math.pi*rho_deg/180.0+0.02\n",
    "\tstar['p__cede_temperature'] = np.log10(tplanck*11604525.0061657)\n",
    "\tstar['s__super_radius'] = math.pi*rho_deg/180.0\n",
    "\tstar['s__super_colatitude'] = math.pi-math.pi*theta_deg/180.0\n",
    "\tstar['s__cede_colatitude'] = math.pi-math.pi*theta_deg/180.0 \n",
    "\tstar['s__cede_azimuth'] = 0.1 #math.pi/2.0\n",
    "\tstar['s__cede_radius'] = math.pi*rho_deg/180.0+0.02\n",
    "\tstar['s__cede_temperature'] = np.log10(tplanck*11604525.0061657)\n",
    "\n",
    "\n",
    "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_pulse(stokes=\"I\"):\n",
    "    \"\"\" Plot hot region signals before telescope operation. \"\"\"\n",
    "    fig = plt.figure(figsize=(7,7))\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    ax.set_ylabel('Signal [arbitrary normalisation]')\n",
    "    ax.set_xlabel('Phase [cycles]')\n",
    "    \n",
    "    if stokes==\"I\":    \n",
    "        temp1 = np.sum(photosphere.signal[0][0], axis=0)\n",
    "        temp2 = np.sum(photosphere.signal[1][0], axis=0) \n",
    "    elif stokes==\"Q\":    \n",
    "        temp1 = np.sum(photosphere.signalQ[0][0], axis=0)\n",
    "        temp2 = np.sum(photosphere.signalQ[1][0], axis=0) \n",
    "    elif stokes==\"U\":    \n",
    "        temp1 = np.sum(photosphere.signalU[0][0], axis=0)\n",
    "        temp2 = np.sum(photosphere.signalU[1][0], axis=0) \n",
    "    else:\n",
    "         print(\"ERROR: Stokes option must be 'I', 'Q', or 'U'\")\n",
    "         exit()              \n",
    "                \n",
    "    ax.plot(hot.phases_in_cycles[0], temp1/np.max(temp1), 'o-', color='k', lw=0.5, markersize=2)\n",
    "    ax.plot(hot.phases_in_cycles[1], temp2/np.max(temp2), 'o-', color='r', lw=0.5, markersize=2)       \n",
    "        \n",
    "\n",
    "    veneer((0.05,0.2), (0.05,0.2), ax)\n",
    "    #fig.savefig(\"figs/pulse_profile\"+stokes+\"X.pdf\")\n",
    "\n",
    "nene = 281 #128\n",
    "\n",
    "from numpy import logspace\n",
    "from numpy import log\n",
    "evere=0.5109989e6 # electron volts in elecron rest energy \n",
    "x_l, x_u = -3.7 , .3 # lower and upper bounds of the log_10 energy span\n",
    "IntEnergy = logspace(x_l,x_u,nene), log(1e1)*(x_u-x_l)/(nene-1.) \n",
    "x, x_weight = IntEnergy\n",
    "energies = (x*evere)/1e3\n",
    "\n",
    "star.update() #Calculating the space-time integrals etc. \n",
    "\n",
    "\n",
    "import time\n",
    "\n",
    "primary.image_order_limit = 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then let us make the final integration and plot the results for all Stokes parameters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first check Stokes I profiles (integrated over all energies) for the primary and secondary hot spots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time spent in integration (unpolarized): 4.392322540283203\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = time.time()\n",
    "photosphere.integrate(energies, threads=1) \n",
    "end = time.time()\n",
    "print(\"Time spent in integration (unpolarized):\",end - start)\n",
    "\n",
    "plot_pulse(stokes=\"I\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also check Stokes Q and U profiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pulse(stokes=\"Q\") \n",
    "plot_pulse(stokes=\"U\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating synthetic data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To produce polarized synthetic data, we can couple X-PSI to ixpeobssim as an input model. The input model file needs to be placed in the `config` folder of the [ixpeobssim repository](https://github.com/lucabaldini/ixpeobssim) . An example of the input model file is provided [here](https://github.com/xpsi-group/xpsi/tree/main/examples/examples_modeling_tutorial/ixpeobssim/config/model_amsp_xpsi.py). To prodcue the synthetic Stokes profiles and their associated errors one should execute `source setup.sh` in the main directory of ixpeobssim and then run a script like `simulate_amsp_xpsi.py` found [here](https://github.com/xpsi-group/xpsi/tree/main/examples/examples_modeling_tutorial/ixpeobssim/simulate_amsp_xpsi.py). For more details, check the ixpeobssim documentation [pages](https://ixpeobssim.readthedocs.io/en/latest/?badge=latest)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modeling X-ray polarized data using Bayesian inference is not yet part of this tutorial. However, a preliminary example of a such procedure can be found in this example script: [TestRun_PolNum_split_inference.py](https://github.com/xpsi-group/xpsi/blob/polarimetry/examples/examples_modeling_tutorial/TestRun_PolNum_split_inference.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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