SkyCalibration-SVD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "third-chart",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import ephem as ep\n",
    "import statistics as stat\n",
    "import healpy as hp\n",
    "from pygdsm import GlobalSkyModel2016\n",
    "from pygdsm import GlobalSkyModel\n",
    "import scipy as sci\n",
    "#import read_vna_csv as cs\n",
    "from matplotlib import pyplot as plt\n",
    "import PrizmCalibration as cl\n",
    "import prizmatoid as pzt\n",
    "try:\n",
    "    reload(pzt)\n",
    "    reload(cl)\n",
    "except:\n",
    "    from importlib import reload\n",
    "    reload(pzt)\n",
    "    reload(cl)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supposed-ranking",
   "metadata": {},
   "source": [
    "# LOADING ARRAYS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "stunning-officer",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('100MHz_EW_antenna_average.npy', 'rb') as f:\n",
    "    alldataavg = np.load(f)\n",
    "with open('100MHz_EW_GSM_average.npy', 'rb') as g:\n",
    "    allgsmavg = np.load(g)\n",
    "krat = allgsmavg/alldataavg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "innovative-story",
   "metadata": {},
   "outputs": [],
   "source": [
    "flow = 60\n",
    "fhigh = 120"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "absolute-thesaurus",
   "metadata": {},
   "source": [
    "# SVD on K (ratio) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "reported-railway",
   "metadata": {},
   "outputs": [],
   "source": [
    "bone = np.ones((krat.shape[0],krat.shape[1]))\n",
    "n = krat.shape[0]\n",
    "r = np.linalg.matrix_rank(krat)\n",
    "U, σ, VH = np.linalg.svd(krat, full_matrices=True)\n",
    "V = VH.T\n",
    "\n",
    "sigma_inv = np.vstack([np.diag(1/σ), np.zeros((n-r,r))])\n",
    "sigma = np.vstack([np.diag(σ), np.zeros((n-r,r))])\n",
    "V1=V[:,0:r] \n",
    "Sr=sigma_inv[0:r,0:r]\n",
    "U1=U[:,0:r]\n",
    "\n",
    "xk = V1 @ Sr @ U1.T @ bone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "beneficial-blank",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1196161d0>"
      ]
     },
     "execution_count": 5,
     "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.scatter([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],np.log10(σ[0:16]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "tamil-poker",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get first 5 modes of K\n",
    "mode1= np.reshape(U[:,0],(krat.shape[0],1))*sigma[0,0]* np.reshape(VH[0,:], (1,krat.shape[1]))\n",
    "mode2= np.reshape(U[:,1],(krat.shape[0],1))*sigma[1,1]* np.reshape(VH[1,:], (1,krat.shape[1]))\n",
    "mode3= np.reshape(U[:,2],(krat.shape[0],1))*sigma[2,2]* np.reshape(VH[2,:], (1,krat.shape[1]))\n",
    "mode4= np.reshape(U[:,3],(krat.shape[0],1))*sigma[3,3]* np.reshape(VH[3,:], (1,krat.shape[1]))\n",
    "mode5= np.reshape(U[:,4],(krat.shape[0],1))*sigma[4,4]* np.reshape(VH[4,:], (1,krat.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "joined-blowing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1198033d0>,\n",
       "  <matplotlib.axis.YTick at 0x1197f7ed0>,\n",
       "  <matplotlib.axis.YTick at 0x1197fa850>,\n",
       "  <matplotlib.axis.YTick at 0x119792890>,\n",
       "  <matplotlib.axis.YTick at 0x119792dd0>,\n",
       "  <matplotlib.axis.YTick at 0x11979b390>,\n",
       "  <matplotlib.axis.YTick at 0x11979b8d0>],\n",
       " [Text(0, 0, '0'),\n",
       "  Text(0, 20, '200'),\n",
       "  Text(0, 40, '400'),\n",
       "  Text(0, 60, '600'),\n",
       "  Text(0, 80, '800'),\n",
       "  Text(0, 100, '1000'),\n",
       "  Text(0, 120, '1200')])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 7200x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(100, 30))\n",
    "plt.title('Mode1')\n",
    "plt.imshow(np.log10(mode1), vmin=-5.4, vmax=-5.1)\n",
    "plt.xlabel('Frequency')\n",
    "xlabels=([\"60\", \"80\", \"100\", \"120\"])\n",
    "plt.xticks([0, 10, 20, 30], xlabels)\n",
    "plt.ylabel('Time (Minutes)')\n",
    "ylabels=([\"0\", \"200\", \"400\", \"600\", \"800\", \"1000\", \"1200\"])\n",
    "plt.yticks([0, 20, 40, 60, 80, 100, 120], ylabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "reserved-chase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mode 1 of Calibration Parameter K')"
      ]
     },
     "execution_count": 11,
     "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.plot(np.mean(mode1, axis=0),lw = 2, ls=':')\n",
    "plt.plot(mode1[0,:])\n",
    "plt.plot(mode1[20,:])\n",
    "plt.plot(mode1[40,:])\n",
    "plt.plot(mode1[60,:])\n",
    "plt.plot(mode1[80,:])\n",
    "plt.plot(mode1[100,:])\n",
    "plt.plot(mode1[120,:])\n",
    "labels=([\"60\", \"80\", \"100\", \"120\"])\n",
    "plt.xticks([0, 10, 20, 30], labels)\n",
    "plt.xlabel('Frequency (MHz)')\n",
    "plt.ylabel('Mode 1 of Calibration Parameter K')\n",
    "#plt.legend(loc = 'upper right', prop ={'size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "alike-airplane",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mode 1 of Calibration Parameter K')"
      ]
     },
     "execution_count": 14,
     "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.plot(np.mean(mode1, axis=1),lw = 2)\n",
    "#plt.plot(mode1[:,10],lw = 2, ls = \":\")\n",
    "#plt.plot(mode1[:,20],lw = 2, ls = \":\")\n",
    "labels=([\"0\", \"250\",\"500\", \"750\",\"1000\", \"1250\"])\n",
    "plt.xticks([0, 25, 50, 75, 100, 125], labels)\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('Mode 1 of Calibration Parameter K')\n",
    "#plt.legend(loc = 'upper right', prop ={'size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "obvious-behalf",
   "metadata": {},
   "outputs": [],
   "source": [
    "observed = mode1*alldataavg\n",
    "poly=3\n",
    "obs=np.nanmean(observed, axis=0)\n",
    "v = []\n",
    "for j in range(flow, fhigh+2, 2):\n",
    "    v.append(j)\n",
    "v=np.array(v)\n",
    "fit = np.polyfit(np.log10(v/100), np.log10(np.abs(obs)), poly)    \n",
    "power = np.zeros(int((fhigh-flow)/2)+1)\n",
    "i = 0\n",
    "while i <= poly:\n",
    "    power = power + fit[poly-i]*(np.log10(v/100))**i\n",
    "    i += 1\n",
    "y = 10**(power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "developmental-disease",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frequency (MHz)')"
      ]
     },
     "execution_count": 23,
     "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.plot(y, label = 'Sky Model')\n",
    "#plt.plot(obs,lw = 3, ls = \":\", label = 'Observed Data')\n",
    "plt.plot(abs(y - obs))\n",
    "labels=([\"60\", \"80\", \"100\", \"120\"])\n",
    "plt.xticks([0, 10, 20, 30], labels)\n",
    "plt.ylabel('Temperature (K)')\n",
    "plt.xlabel('Frequency (MHz)')\n",
    "#plt.legend(loc = 'upper right', prop ={'size': 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "chronic-surveillance",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}