{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pysm3\n",
    "from fgbuster import get_instrument, get_sky, get_observation\n",
    "import healpy as hp\n",
    "import numpy as np\n",
    "from fgbuster import xForecast, CMB, Dust, Synchrotron\n",
    "\n",
    "nside = 64\n",
    "# define sky and foregrounds simulations\n",
    "sky = get_sky(nside, 'd0s0')\n",
    "# define instrument\n",
    "instrument = get_instrument('SO_SAT')\n",
    "# get noiseless frequency maps\n",
    "freq_maps = get_observation(instrument, sky)\n",
    "# take only the Q and U maps\n",
    "freq_maps = freq_maps[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create 3% circular sky mask\n",
    "RA = 2*np.pi-70.*np.pi/180\n",
    "DEC = np.pi/2+70.*np.pi/180\n",
    "radius = 34*np.pi/180\n",
    "mask_circular = np.zeros(12*nside**2)\n",
    "for ipix in range(12*nside**2):\n",
    "    theta, phi = hp.pix2ang(nside, ipix)\n",
    "    if (((phi - RA)**2 + (theta - DEC)**2 <= radius**2)):\n",
    "        mask_circular[ipix] = 1.0\n",
    "    if (((phi - RA+2*np.pi)**2 + (theta - DEC)**2 <= radius**2)):\n",
    "        mask_circular[ipix] = 1.0\n",
    "# applying mask to observed frequency maps\n",
    "freq_maps[...,mask_circular==0] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define components used in the modeling\n",
    "components = [CMB(), Dust(150.), Synchrotron(150.)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# call for xForecast\n",
    "# with lmin=2, lmax=2*nside-1, and Alens=0.1\n",
    "# you can try with make_figure=True if you want to output angular power spectra and profile likelihood on r\n",
    "res = xForecast(components, instrument, freq_maps, 2, 2*nside-1, Alens=0.1, r=0.001, make_figure=True)"
   ]
  }
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