check_absrot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, prjlib, binning as bins, plottools as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix\n",
    "p, f = prjlib.filename_init(doreal='True',stype='lcmb',dearot='False')\n",
    "mb = bins.multipole_binning(50,lmin=200,lmax=3000)\n",
    "for sn in [100,300,500]:\n",
    "    mb = bins.multipole_binning(10,lmin=200,lmax=2048)\n",
    "    sEE = prjlib.binned_cl_rlz(f.cli,1,sn,mb,cn=2)\n",
    "    sBB = prjlib.binned_cl_rlz(f.cli,1,sn,mb,cn=3)\n",
    "    sEB = prjlib.binned_cl_rlz(f.cli,1,sn,mb,cn=6)\n",
    "    amp = sEB/(sEE-sBB)\n",
    "    pl.plot_corr(amp,mb.bp,clab='corr. coeff.',zmin=-.5,zmax=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TB 30 200 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.364 [deg] std 0.226 [deg] PTE 0.11\n",
      "full cov, w/ BB, obs: 0.361 [deg] std 0.207 [deg] PTE 0.07\n",
      "TB 30 250 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.356 [deg] std 0.229 [deg] PTE 0.136\n",
      "full cov, w/ BB, obs: 0.338 [deg] std 0.215 [deg] PTE 0.144\n",
      "TB 30 300 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.348 [deg] std 0.225 [deg] PTE 0.127\n",
      "full cov, w/ BB, obs: 0.305 [deg] std 0.216 [deg] PTE 0.193\n",
      "TB 30 200 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.313 [deg] std 0.23 [deg] PTE 0.18\n",
      "full cov, w/ BB, obs: 0.41 [deg] std 0.204 [deg] PTE 0.04\n",
      "TB 30 250 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.31 [deg] std 0.235 [deg] PTE 0.2\n",
      "full cov, w/ BB, obs: 0.396 [deg] std 0.214 [deg] PTE 0.064\n",
      "TB 30 300 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.311 [deg] std 0.229 [deg] PTE 0.173\n",
      "full cov, w/ BB, obs: 0.372 [deg] std 0.215 [deg] PTE 0.08\n",
      "TB 50 200 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.269 [deg] std 0.217 [deg] PTE 0.24\n",
      "full cov, w/ BB, obs: 0.417 [deg] std 0.19 [deg] PTE 0.03\n",
      "TB 50 250 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.263 [deg] std 0.223 [deg] PTE 0.288\n",
      "full cov, w/ BB, obs: 0.37 [deg] std 0.198 [deg] PTE 0.064\n",
      "TB 50 300 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.262 [deg] std 0.219 [deg] PTE 0.267\n",
      "full cov, w/ BB, obs: 0.302 [deg] std 0.2 [deg] PTE 0.127\n",
      "TB 50 200 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.339 [deg] std 0.227 [deg] PTE 0.14\n",
      "full cov, w/ BB, obs: 0.622 [deg] std 0.195 [deg] PTE 0.0\n",
      "TB 50 250 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.331 [deg] std 0.23 [deg] PTE 0.168\n",
      "full cov, w/ BB, obs: 0.544 [deg] std 0.204 [deg] PTE 0.0\n",
      "TB 50 300 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.333 [deg] std 0.224 [deg] PTE 0.16\n",
      "full cov, w/ BB, obs: 0.477 [deg] std 0.206 [deg] PTE 0.007\n",
      "TB 30 200 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.031 [deg] std 0.271 [deg] PTE 0.94\n",
      "full cov, w/ BB, obs: -0.025 [deg] std 0.258 [deg] PTE 0.83\n",
      "TB 30 250 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.03 [deg] std 0.271 [deg] PTE 0.824\n",
      "full cov, w/ BB, obs: 0.028 [deg] std 0.256 [deg] PTE 0.904\n",
      "TB 30 300 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.007 [deg] std 0.271 [deg] PTE 0.78\n",
      "full cov, w/ BB, obs: -0.005 [deg] std 0.259 [deg] PTE 0.773\n",
      "TB 30 200 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.05 [deg] std 0.275 [deg] PTE 0.88\n",
      "full cov, w/ BB, obs: 0.048 [deg] std 0.261 [deg] PTE 0.93\n",
      "TB 30 250 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.055 [deg] std 0.276 [deg] PTE 0.824\n",
      "full cov, w/ BB, obs: 0.056 [deg] std 0.259 [deg] PTE 0.92\n",
      "TB 30 300 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.04 [deg] std 0.277 [deg] PTE 0.793\n",
      "full cov, w/ BB, obs: 0.006 [deg] std 0.26 [deg] PTE 0.76\n",
      "TB 50 200 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.032 [deg] std 0.257 [deg] PTE 0.73\n",
      "full cov, w/ BB, obs: 0.178 [deg] std 0.224 [deg] PTE 0.45\n",
      "TB 50 250 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.029 [deg] std 0.263 [deg] PTE 0.704\n",
      "full cov, w/ BB, obs: 0.069 [deg] std 0.238 [deg] PTE 0.976\n",
      "TB 50 300 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.037 [deg] std 0.261 [deg] PTE 0.713\n",
      "full cov, w/ BB, obs: 0.034 [deg] std 0.241 [deg] PTE 0.9\n",
      "TB 50 200 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.021 [deg] std 0.269 [deg] PTE 0.72\n",
      "full cov, w/ BB, obs: 0.021 [deg] std 0.243 [deg] PTE 0.86\n",
      "TB 50 250 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.012 [deg] std 0.272 [deg] PTE 0.712\n",
      "full cov, w/ BB, obs: -0.049 [deg] std 0.254 [deg] PTE 0.672\n",
      "TB 50 300 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.019 [deg] std 0.275 [deg] PTE 0.7\n",
      "full cov, w/ BB, obs: -0.136 [deg] std 0.258 [deg] PTE 0.433\n",
      "EB 30 200 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.145 [deg] std 0.071 [deg] PTE 0.04\n",
      "full cov, w/ BB, obs: 0.143 [deg] std 0.067 [deg] PTE 0.03\n",
      "EB 30 250 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.144 [deg] std 0.07 [deg] PTE 0.048\n",
      "full cov, w/ BB, obs: 0.133 [deg] std 0.067 [deg] PTE 0.056\n",
      "EB 30 300 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.147 [deg] std 0.07 [deg] PTE 0.04\n",
      "full cov, w/ BB, obs: 0.142 [deg] std 0.067 [deg] PTE 0.027\n",
      "EB 30 200 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.14 [deg] std 0.072 [deg] PTE 0.05\n",
      "full cov, w/ BB, obs: 0.111 [deg] std 0.067 [deg] PTE 0.08\n",
      "EB 30 250 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.141 [deg] std 0.071 [deg] PTE 0.048\n",
      "full cov, w/ BB, obs: 0.121 [deg] std 0.068 [deg] PTE 0.08\n",
      "EB 30 300 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.14 [deg] std 0.07 [deg] PTE 0.04\n",
      "full cov, w/ BB, obs: 0.137 [deg] std 0.067 [deg] PTE 0.047\n",
      "EB 50 200 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.146 [deg] std 0.072 [deg] PTE 0.04\n",
      "full cov, w/ BB, obs: 0.141 [deg] std 0.06 [deg] PTE 0.0\n",
      "EB 50 250 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.146 [deg] std 0.07 [deg] PTE 0.04\n",
      "full cov, w/ BB, obs: 0.12 [deg] std 0.063 [deg] PTE 0.016\n",
      "EB 50 300 deep56 200<l<2048\n",
      "diag cov, w/ BB, obs: 0.148 [deg] std 0.07 [deg] PTE 0.027\n",
      "full cov, w/ BB, obs: 0.123 [deg] std 0.063 [deg] PTE 0.04\n",
      "EB 50 200 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.145 [deg] std 0.071 [deg] PTE 0.03\n",
      "full cov, w/ BB, obs: 0.139 [deg] std 0.06 [deg] PTE 0.01\n",
      "EB 50 250 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.144 [deg] std 0.07 [deg] PTE 0.048\n",
      "full cov, w/ BB, obs: 0.127 [deg] std 0.063 [deg] PTE 0.024\n",
      "EB 50 300 deep56 350<l<2048\n",
      "diag cov, w/ BB, obs: 0.143 [deg] std 0.07 [deg] PTE 0.04\n",
      "full cov, w/ BB, obs: 0.141 [deg] std 0.064 [deg] PTE 0.027\n",
      "EB 30 200 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.264 [deg] std 0.092 [deg] PTE 0.0\n",
      "full cov, w/ BB, obs: -0.272 [deg] std 0.088 [deg] PTE 0.0\n",
      "EB 30 250 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.265 [deg] std 0.093 [deg] PTE 0.0\n",
      "full cov, w/ BB, obs: -0.253 [deg] std 0.09 [deg] PTE 0.0\n",
      "EB 30 300 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.258 [deg] std 0.092 [deg] PTE 0.007\n",
      "full cov, w/ BB, obs: -0.24 [deg] std 0.089 [deg] PTE 0.0\n",
      "EB 30 200 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.271 [deg] std 0.093 [deg] PTE 0.0\n",
      "full cov, w/ BB, obs: -0.258 [deg] std 0.088 [deg] PTE 0.0\n",
      "EB 30 250 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.269 [deg] std 0.093 [deg] PTE 0.0\n",
      "full cov, w/ BB, obs: -0.267 [deg] std 0.089 [deg] PTE 0.0\n",
      "EB 30 300 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.266 [deg] std 0.093 [deg] PTE 0.0\n",
      "full cov, w/ BB, obs: -0.284 [deg] std 0.09 [deg] PTE 0.0\n",
      "EB 50 200 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.218 [deg] std 0.091 [deg] PTE 0.02\n",
      "full cov, w/ BB, obs: -0.158 [deg] std 0.082 [deg] PTE 0.07\n",
      "EB 50 250 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.215 [deg] std 0.092 [deg] PTE 0.024\n",
      "full cov, w/ BB, obs: -0.231 [deg] std 0.085 [deg] PTE 0.0\n",
      "EB 50 300 boss 200<l<2048\n",
      "diag cov, w/ BB, obs: -0.212 [deg] std 0.091 [deg] PTE 0.033\n",
      "full cov, w/ BB, obs: -0.207 [deg] std 0.086 [deg] PTE 0.06\n",
      "EB 50 200 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.254 [deg] std 0.093 [deg] PTE 0.01\n",
      "full cov, w/ BB, obs: -0.275 [deg] std 0.081 [deg] PTE 0.0\n",
      "EB 50 250 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.258 [deg] std 0.094 [deg] PTE 0.0\n",
      "full cov, w/ BB, obs: -0.302 [deg] std 0.084 [deg] PTE 0.0\n",
      "EB 50 300 boss 350<l<2048\n",
      "diag cov, w/ BB, obs: -0.254 [deg] std 0.093 [deg] PTE 0.007\n",
      "full cov, w/ BB, obs: -0.293 [deg] std 0.087 [deg] PTE 0.0\n"
     ]
    }
   ],
   "source": [
    "patches = ['deep56','boss']\n",
    "brange = [(200,2048),(350,2048)]\n",
    "spec = 'EB'\n",
    "for spec in ['TB','EB']:\n",
    "    for patch in patches:\n",
    "        for bn in [30,50]:\n",
    "            for lmin, lmax in brange:\n",
    "                for sn in [200,250,300]:\n",
    "                    print(spec,bn,sn,patch,str(lmin)+'<l<'+str(lmax))\n",
    "                    prjlib.est_angles(patch,spec,sn=sn,bn=bn,lmin=lmin,lmax=lmax,diag=True,disp='diag cov, w/ BB')\n",
    "                    prjlib.est_angles(patch,spec,sn=sn,bn=bn,lmin=lmin,lmax=lmax,disp='full cov, w/ BB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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