zonca/compare_so_noise.ipynb

## compare_so_noise.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import healpy as hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mapsims\n",
    "NSIDE = 16\n",
    "import pysm.units as u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from so_models_v3 import SO_Noise_Calculator_Public_v3_1_1 as so_models                                                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zonca/zonca/p/software/mapsims/mapsims/utils/__init__.py:73: UserWarning: Retrieve data for total_hits_LA_classical.fits.gz (if not cached already)\n",
      "  warnings.warn(f\"Retrieve data for {filename} (if not cached already)\")\n",
      "/home/zonca/anaconda/envs/pysm3/lib/python3.6/site-packages/healpy/fitsfunc.py:352: UserWarning: If you are not specifying the input dtype and using the default np.float64 dtype of read_map(), please consider that it will change in a future version to None as to keep the same dtype of the input file: please explicitly set the dtype if it is important to you.\n",
      "  \"If you are not specifying the input dtype and using the default \"\n",
      "/home/zonca/zonca/p/software/mapsims/mapsims/utils/__init__.py:73: UserWarning: Retrieve data for total_hits_SA_classical.fits.gz (if not cached already)\n",
      "  warnings.warn(f\"Retrieve data for {filename} (if not cached already)\")\n",
      "/home/zonca/anaconda/envs/pysm3/lib/python3.6/site-packages/healpy/fitsfunc.py:352: UserWarning: If you are not specifying the input dtype and using the default np.float64 dtype of read_map(), please consider that it will change in a future version to None as to keep the same dtype of the input file: please explicitly set the dtype if it is important to you.\n",
      "  \"If you are not specifying the input dtype and using the default \"\n"
     ]
    }
   ],
   "source": [
    "    telescope = \"SA\"\n",
    "    seed = 1234 - 200                                                                                                         \n",
    "    if telescope == \"SA\":                                                                                                     \n",
    "        seed -= 1000                                                                                                          \n",
    "                                                                                                                              \n",
    "    simulator = mapsims.SONoiseSimulator(                                                                                     \n",
    "        telescopes=['LA','SA'],                                                                                               \n",
    "        nside=NSIDE,                                                                                                          \n",
    "        ell_max=500,                                                                                                          \n",
    "        return_uK_CMB=True,                                                                                                   \n",
    "        sensitivity_mode=\"baseline\",                                                                                          \n",
    "        apply_beam_correction=True,                                                                                           \n",
    "        apply_kludge_correction=True,                                                                                         \n",
    "        scanning_strategy=\"classical\",                                                                                        \n",
    "        LA_number_LF=1,                                                                                                       \n",
    "        LA_number_MF=4,                                                                                                       \n",
    "        LA_number_UHF=2,                                                                                                      \n",
    "        SA_years_LF=1,                                                                                                        \n",
    "        SA_one_over_f_mode=\"optimistic\",                                                                                      \n",
    "        seed=seed,                                                                                                            \n",
    "    )                                                                                                                         \n",
    "                                                                                                                              \n",
    "    output_map = simulator.simulate(mapsims.SOChannel(telescope, \"MFF1\")) * u.uK_CMB         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LA': 0.6165364583333334, 'SA': 0.3837890625}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator.sky_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "             survey = so_models.SOSatV3point1(                                                                                \n",
    "                sensitivity_mode=\"baseline\",                                                                       \n",
    "                survey_efficiency=1,                                                                                          \n",
    "                survey_years=1,                                                                                \n",
    "                N_tubes=None,  # FIXME expose to configuration                                                                \n",
    "                el=None,  # FIXME expose to configuration                                                                     \n",
    "                one_over_f_mode=1,                                                                      \n",
    "             )     \n",
    "             ell, noise_ell_T, noise_ell_P = survey.get_noise_curves(                                                         \n",
    "                simulator.sky_fraction[telescope],                                                                                 \n",
    "                500,                                                                                                 \n",
    "                delta_ell=1,                                                                                                  \n",
    "                full_covar=False,                                                                                             \n",
    "                deconv_beam=True,                                                                       \n",
    "             )                                                                                                                \n",
    "             # For SA, so_noise simulates only Polarization,                                                                   \n",
    "             # Assume that T is half                                                                                           \n",
    "             if noise_ell_T is None:                                                                                          \n",
    "                noise_ell_T = noise_ell_P / 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 27.,  39.,  93., 145., 225., 280.])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f76fffa1d68>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(simulator.ell[2:], simulator.noise_ell_T[\"SA\"][93][2:], label=\"old\")\n",
    "plt.loglog(ell, noise_ell_T[2], label=\"new\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f76ffe25be0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(simulator.ell[2:], simulator.noise_ell_T[\"SA\"][93][2:]-noise_ell_T[2], label=\"diff\")\n",
    "plt.loglog(simulator.ell[2:], simulator.noise_ell_T[\"SA\"][93][2:], label=\"old\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySM 3",
   "language": "python",
   "name": "pysm3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}