check_null_dmap_iso15.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, plottools as pl, local, tools_cmb, quad_func, binning as bn, analysis as ana\n",
    "from matplotlib.pyplot import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spec(qobj,w=np.ones(5),rlz=None,cn=1,lmin=40,lmax=2048,bnum=10,scale=1.):\n",
    "    mb = bn.multipole_binning(bnum,lmin=lmin,lmax=lmax)\n",
    "    Skk, Okk = local.load_spec(qobj,mb,rlz=rlz,cn=cn)\n",
    "    Mkk = np.mean(Skk,axis=0)\n",
    "    Vkk = np.std(Skk,axis=0)\n",
    "    if rlz is not None:\n",
    "        skk, okk = local.load_spec(qobj,mb,rlz=None,cn=cn)\n",
    "        vkk = np.std(skk,axis=0)\n",
    "        print(Vkk/vkk)\n",
    "    st0 = ana.PTEs(Okk,Skk,x1pte=True)\n",
    "    st1 = ana.PTEs(Okk,Skk,x1pte=True,diag=True)\n",
    "    # plot binned spectrum lin-scale\n",
    "    pl.plot_1dstyle(fsize=[10,4],xmin=mb.lmin,xmax=lmax,ymin=-1.,ymax=1.6,ylab='$10^2L^{0.5}C_L^{\\kappa\\kappa}$')\n",
    "    errorbar(mb.bc-5,mb.bc**0.5*(Okk-Mkk)/w[4]*1e6*scale,yerr=mb.bc**0.5*Vkk/w[4]*1e6*scale,fmt='o')\n",
    "    plot(aobj.l,aobj.l**0.5*aobj.ckk*1e6*scale,color='k',ls='--')\n",
    "    #figtext(.7,.80,r'$\\chi$-PTE:'+str(np.round(st0.px1,2)))\n",
    "    figtext(.7,.75,r'$\\chi^2$-PTE:'+str(np.round(st0.px2,2)))\n",
    "    figtext(.7,.70,r'$\\chi^2$-PTE (diag):'+str(np.round(st1.px2,2)))\n",
    "    axhline(0,color='k')\n",
    "    show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "qids = ['boss_01','boss_02','boss_03','boss_04']\n",
    "#qids = ['boss_02','boss_03','boss_04']\n",
    "dirs = local.data_directory()\n",
    "qobj, Qobj = {}, {}\n",
    "for q in qids:\n",
    "    aobj = local.init_analysis_params(qid='diff_'+q,ascale=3.,wtype='iso15v3PT')\n",
    "    qobj[q] = quad_func.reconstruction(dirs['local'],aobj.ids,stag=aobj.stag,rlmin=500,qlist=['TT'],bhe=['src'])\n",
    "    Qobj[q] = quad_func.reconstruction(dirs['local'],aobj.ids,stag=aobj.stag,rlmin=500,qlist=['TT'])\n",
    "# combined\n",
    "for wtype in ['iso15vcpt','iso15v3pt','iso15v3PT','iso15V3PT']:\n",
    "    aobj = local.init_analysis_params(qid='diff_dn',ascale=3.,wtype=wtype)\n",
    "    qobj[wtype] = quad_func.reconstruction(dirs['local'],aobj.ids,stag=aobj.stag,rlmin=500,qlist=['TT'],bhe=['src'])\n",
    "    Qobj[wtype] = quad_func.reconstruction(dirs['local'],aobj.ids,stag=aobj.stag,rlmin=500,qlist=['TT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wfactors: [2.64551209e-02 4.43198474e-08 1.08375184e-13 2.89033159e-19\n",
      " 8.10469432e-25]\n",
      "wfactors: [2.64897241e-02 7.90728095e-07 3.63343224e-11 1.90522948e-15\n",
      " 1.09009040e-19]\n",
      "wfactors: [2.47789282e-02 6.91236469e-07 3.10845873e-11 1.60908806e-15\n",
      " 9.12069538e-20]\n"
     ]
    }
   ],
   "source": [
    "w = {wtype: tools_cmb.get_wfactors(['diff_dn'],3.,wtype=wtype)['diff_dn'] for wtype in ['iso15vcpt','iso15v3pt','iso15v3PT']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi: 58.6 , chi (sim mean): 0.0 , chi (sim std): 5.1 , PTE: 0.00 \n",
      "chi^2: 160.3 , chi^2 (sim): 11.2 , PTE: 0.00 \n",
      "chi: 58.6 , chi (sim mean): 0.0 , chi (sim std): 5.1 , PTE: 0.00 \n",
      "chi^2: 352.3 , chi^2 (sim): 11.2 , PTE: 0.00 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spec(qobj['iso15v3pt'],scale=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi: 5.2 , chi (sim mean): 0.0 , chi (sim std): 5.2 , PTE: 0.28 \n",
      "chi^2: 29.9 , chi^2 (sim): 11.1 , PTE: 0.01 \n",
      "chi: 5.2 , chi (sim mean): 0.0 , chi (sim std): 5.2 , PTE: 0.28 \n",
      "chi^2: 19.4 , chi^2 (sim): 11.1 , PTE: 0.07 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spec(Qobj['iso15V3PT'],scale=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi: 4.7 , chi (sim mean): 0.0 , chi (sim std): 5.7 , PTE: 0.38 \n",
      "chi^2: 15.3 , chi^2 (sim): 11.1 , PTE: 0.18 \n",
      "chi: 4.7 , chi (sim mean): 0.0 , chi (sim std): 5.7 , PTE: 0.38 \n",
      "chi^2: 10.6 , chi^2 (sim): 11.1 , PTE: 0.52 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi: 8.1 , chi (sim mean): 0.0 , chi (sim std): 5.3 , PTE: 0.10 \n",
      "chi^2: 22.1 , chi^2 (sim): 11.1 , PTE: 0.01 \n",
      "chi: 8.1 , chi (sim mean): 0.0 , chi (sim std): 5.3 , PTE: 0.10 \n",
      "chi^2: 22.5 , chi^2 (sim): 11.1 , PTE: 0.01 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spec(qobj['boss_03'],bnum=10,scale=.2)\n",
    "plot_spec(Qobj['boss_03'],bnum=10,scale=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi: 19.7 , chi (sim mean): -0.0 , chi (sim std): 5.3 , PTE: 0.00 \n",
      "chi^2: 30.4 , chi^2 (sim): 11.1 , PTE: 0.00 \n",
      "chi: 19.7 , chi (sim mean): -0.0 , chi (sim std): 5.3 , PTE: 0.00 \n",
      "chi^2: 50.0 , chi^2 (sim): 11.1 , PTE: 0.00 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi: 10.2 , chi (sim mean): 0.0 , chi (sim std): 4.8 , PTE: 0.04 \n",
      "chi^2: 24.1 , chi^2 (sim): 11.1 , PTE: 0.02 \n",
      "chi: 10.2 , chi (sim mean): 0.0 , chi (sim std): 4.8 , PTE: 0.04 \n",
      "chi^2: 21.3 , chi^2 (sim): 11.1 , PTE: 0.02 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_spec(qobj['boss_01'],bnum=10,scale=.001)\n",
    "plot_spec(Qobj['boss_02'],bnum=10,scale=.02)\n",
    "plot_spec(Qobj['boss_04'],bnum=10,scale=.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py4so",
   "language": "python",
   "name": "py4so"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}