upsampling_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.signal import find_peaks\n",
    "from itertools import chain\n",
    "from sys import platform\n",
    "from copy import deepcopy as dc\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "vims_wave = np.loadtxt('vims_wave.txt')\n",
    "\n",
    "new_array = ['_0405','_0607','_0809','_1011','_1213','_1415','_1617']\n",
    "if platform == 'darwin' or platform == 'win32' or platform == 'linux':\n",
    "    for number in new_array:\n",
    "        new_string = 'df'+number+' = pd.read_pickle(\"c_data/data'+number+'.pkl\")'\n",
    "        exec(new_string)\n",
    "else:\n",
    "    df_list = []\n",
    "    for number in new_array:\n",
    "        new_string = 'df'+number+' = pd.read_csv(\"c_data/data'+number+'.csv\")'\n",
    "        exec(new_string)\n",
    "        df_list.append(locals()['df'+number])\n",
    "    for df in df_list:\n",
    "        for i in range(len(df['spectrum'])):\n",
    "            df['spectrum'][i] = np.array([float(x) for x in df['spectrum'][i][1:-1].split(',')])\n",
    "\n",
    "vims_list = []\n",
    "for number in new_array:\n",
    "    exec('vims'+number+' = np.average([np.load(\"vims_shift/vims_20'+number[1:3]+'.npy\"),np.load(\"vims_shift/vims_20'+number[3:5]+'.npy\")],axis=0)')\n",
    "    vims_list.append(locals()['vims'+number])\n",
    "\n",
    "def powerlaw(x,a,b):\n",
    "    return a*np.power(x,b)\n",
    "\n",
    "def gaussian(x,a,mu,sigma):\n",
    "    return a*np.exp(-(x-mu)**2/(2*sigma**2))\n",
    "\n",
    "def flatten(some_list):\n",
    "    flat_list = []\n",
    "    for element in some_list:\n",
    "        if type(element) is list or type(element) is tuple:\n",
    "            for item in element:\n",
    "                flat_list.append(item)\n",
    "        else:\n",
    "            flat_list.append(element)\n",
    "    return flat_list\n",
    "\n",
    "band_channels = list(chain(range(29,35),range(46,60),range(78,96),range(102,106)))\n",
    "\n",
    "window = list(range(56,83))\n",
    "\n",
    "def fit_line(dataframe):\n",
    "    spectra = np.average(dataframe)\n",
    "    my_fit,_ = curve_fit(powerlaw,vims_wave[band_channels],spectra[band_channels],p0=[.15,-12])\n",
    "    final_fit = powerlaw(vims_wave,*my_fit)\n",
    "    return final_fit\n",
    "\n",
    "def custom_fit(dataframe):\n",
    "    spectra = np.average(dataframe)\n",
    "    my_fit,_ = curve_fit(powerlaw,vims_wave[band_channels],spectra[band_channels],p0=[.15,-12])\n",
    "    final_spectra = spectra-powerlaw(vims_wave,*my_fit)\n",
    "    return final_spectra\n",
    "\n",
    "def single_fit(spectrum):\n",
    "    my_fit,_ = curve_fit(powerlaw,vims_wave[band_channels],spectrum[band_channels],p0=[.15,-12])\n",
    "    final_spectrum = spectrum-powerlaw(vims_wave,*my_fit)\n",
    "    return final_spectrum\n",
    "\n",
    "def shift_ret(spectrum):\n",
    "    my_fit,_ = curve_fit(gaussian,vims_wave[window],spectrum[window],p0=[.05,2.05])\n",
    "    return my_fit[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (2507,256) (1,2507) (2507,256) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_28864/377538347.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0maveraged_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnew_array\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m     \u001b[0mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'spec'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' /= (np.cos(inc'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'*sf))/(np.cos(inc'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'*sf)+np.cos(em'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'*sf))[None,:]'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m     \u001b[0mmy_string\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'averaged'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' = np.average(spec'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m',axis=0)'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[0mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_string\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<string>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2507,256) (1,2507) (2507,256) "
     ]
    }
   ],
   "source": [
    "scatter_list = []\n",
    "for number in new_array:\n",
    "    my_string = 'scatter'+number+\" = df\"+number\n",
    "    exec(my_string)\n",
    "    scatter_list.append(locals()['scatter'+number])\n",
    "\n",
    "total_scatter = pd.concat(scatter_list)\n",
    "\n",
    "all_spectra = total_scatter['spectrum']\n",
    "\n",
    "averaged_total = np.average(all_spectra)\n",
    "\n",
    "spec_list = []\n",
    "for number in new_array:\n",
    "    my_string = 'old_spec'+number+' = df'+number+\"['spectrum']\"\n",
    "    exec(my_string)\n",
    "    exec('inc'+number+' = np.asarray(df'+number+\"['incidence'])\")\n",
    "    exec('em'+number+' = np.asarray(df'+number+\"['emission'])\")\n",
    "    new_string = 'spec'+number+' = np.array([np.abs(x) for x in old_spec'+number+'])'\n",
    "    exec(new_string)\n",
    "    spec = 'spec'+number\n",
    "    spec_list.append(locals()[spec])\n",
    "\n",
    "sf = np.pi/180\n",
    "averaged_list = []\n",
    "for number in new_array:\n",
    "    exec('spec'+number+' /= (np.cos(inc'+number+'*sf))/(np.cos(inc'+number+'*sf)+np.cos(em'+number+'*sf))[None,:]')\n",
    "    my_string = 'averaged'+number+' = np.average(spec'+number+',axis=0)'\n",
    "    exec(my_string)\n",
    "    averaged_list.append(locals()['averaged'+number])\n",
    "\n",
    "copy_list = []\n",
    "for number in new_array:\n",
    "    my_string = 'copy_spec'+number+' = dc(spec'+number+')'\n",
    "    exec(my_string)\n",
    "    copy_spec = 'copy_spec'+number\n",
    "    copy_list.append(locals()[copy_spec])\n",
    "\n",
    "for (spec,copy_spec) in zip(spec_list,copy_list):\n",
    "    for i in range(256):\n",
    "        filtered = False\n",
    "        k = 0\n",
    "        while filtered == False:\n",
    "            mean_1 = np.mean(copy_spec[:,i])\n",
    "            median = np.median(copy_spec[:,i])\n",
    "            std = np.std(copy_spec[:,i])\n",
    "            vals = copy_spec[:,i]\n",
    "            ind = np.where(np.abs(vals-mean_1)>2*std)\n",
    "            copy_spec[ind,i] = median\n",
    "            mean_2 = np.mean(copy_spec[:,i])\n",
    "            k += 1\n",
    "            if np.abs(mean_1-mean_2) <=.05*mean_1:\n",
    "                filtered = True\n",
    "\n",
    "pList_total = []\n",
    "for (copy_spec,number) in zip(copy_list,new_array):\n",
    "    exec('pList'+number+' = []')\n",
    "    for spec in copy_spec:\n",
    "        new_spec = single_fit(spec)\n",
    "        final_spec = np.abs(new_spec)\n",
    "        locals()['pList'+number].append(final_spec)\n",
    "    pList_total.append(locals()['pList'+number])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fileid</th>\n",
       "      <th>lineres</th>\n",
       "      <th>emission</th>\n",
       "      <th>incidence</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>spectrum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19779</td>\n",
       "      <td>62097.681413</td>\n",
       "      <td>30.881187</td>\n",
       "      <td>39.996733</td>\n",
       "      <td>10.408238</td>\n",
       "      <td>309.431710</td>\n",
       "      <td>[0.0627468, 0.0772501, 0.115962, 0.158278, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73581</td>\n",
       "      <td>15042.961556</td>\n",
       "      <td>30.017625</td>\n",
       "      <td>39.995293</td>\n",
       "      <td>10.798510</td>\n",
       "      <td>305.135524</td>\n",
       "      <td>[0.0649121, 0.0794369, 0.119561, 0.161287, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73581</td>\n",
       "      <td>15275.300926</td>\n",
       "      <td>33.924880</td>\n",
       "      <td>39.988389</td>\n",
       "      <td>13.097070</td>\n",
       "      <td>301.397352</td>\n",
       "      <td>[0.0686598, 0.0839468, 0.126895, 0.169983, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73581</td>\n",
       "      <td>15519.941008</td>\n",
       "      <td>34.936784</td>\n",
       "      <td>39.987750</td>\n",
       "      <td>13.601726</td>\n",
       "      <td>300.441459</td>\n",
       "      <td>[0.0683702, 0.0855701, 0.127764, 0.171474, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73581</td>\n",
       "      <td>13934.837773</td>\n",
       "      <td>31.017054</td>\n",
       "      <td>39.983581</td>\n",
       "      <td>11.435282</td>\n",
       "      <td>304.167414</td>\n",
       "      <td>[0.0649137, 0.0816108, 0.120783, 0.164939, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>27343</td>\n",
       "      <td>38414.199284</td>\n",
       "      <td>31.189751</td>\n",
       "      <td>20.247050</td>\n",
       "      <td>12.489629</td>\n",
       "      <td>310.872401</td>\n",
       "      <td>[0.0603998, 0.103166, 0.163553, 0.156522, 0.09...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>27864</td>\n",
       "      <td>37707.637871</td>\n",
       "      <td>25.783813</td>\n",
       "      <td>20.176448</td>\n",
       "      <td>9.217981</td>\n",
       "      <td>315.052787</td>\n",
       "      <td>[0.0590715, 0.100256, 0.157004, 0.149544, 0.09...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>27650</td>\n",
       "      <td>37434.752350</td>\n",
       "      <td>25.709792</td>\n",
       "      <td>20.136072</td>\n",
       "      <td>9.204018</td>\n",
       "      <td>315.129655</td>\n",
       "      <td>[0.059886, 0.100492, 0.15705, 0.1499, 0.096029...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>59077</td>\n",
       "      <td>49468.523178</td>\n",
       "      <td>32.391633</td>\n",
       "      <td>20.104393</td>\n",
       "      <td>10.177082</td>\n",
       "      <td>315.082358</td>\n",
       "      <td>[0.0598412, 0.10206, 0.160232, 0.15152, 0.0967...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>27146</td>\n",
       "      <td>79344.557851</td>\n",
       "      <td>24.638132</td>\n",
       "      <td>20.004499</td>\n",
       "      <td>11.368394</td>\n",
       "      <td>315.702389</td>\n",
       "      <td>[0.0577261, 0.0987031, 0.161035, 0.15991, 0.09...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12655 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fileid       lineres   emission  incidence   latitude   longitude  \\\n",
       "0      19779  62097.681413  30.881187  39.996733  10.408238  309.431710   \n",
       "1      73581  15042.961556  30.017625  39.995293  10.798510  305.135524   \n",
       "2      73581  15275.300926  33.924880  39.988389  13.097070  301.397352   \n",
       "3      73581  15519.941008  34.936784  39.987750  13.601726  300.441459   \n",
       "4      73581  13934.837773  31.017054  39.983581  11.435282  304.167414   \n",
       "...      ...           ...        ...        ...        ...         ...   \n",
       "1270   27343  38414.199284  31.189751  20.247050  12.489629  310.872401   \n",
       "1271   27864  37707.637871  25.783813  20.176448   9.217981  315.052787   \n",
       "1272   27650  37434.752350  25.709792  20.136072   9.204018  315.129655   \n",
       "1273   59077  49468.523178  32.391633  20.104393  10.177082  315.082358   \n",
       "1274   27146  79344.557851  24.638132  20.004499  11.368394  315.702389   \n",
       "\n",
       "                                               spectrum  \n",
       "0     [0.0627468, 0.0772501, 0.115962, 0.158278, 0.1...  \n",
       "1     [0.0649121, 0.0794369, 0.119561, 0.161287, 0.1...  \n",
       "2     [0.0686598, 0.0839468, 0.126895, 0.169983, 0.1...  \n",
       "3     [0.0683702, 0.0855701, 0.127764, 0.171474, 0.1...  \n",
       "4     [0.0649137, 0.0816108, 0.120783, 0.164939, 0.1...  \n",
       "...                                                 ...  \n",
       "1270  [0.0603998, 0.103166, 0.163553, 0.156522, 0.09...  \n",
       "1271  [0.0590715, 0.100256, 0.157004, 0.149544, 0.09...  \n",
       "1272  [0.059886, 0.100492, 0.15705, 0.1499, 0.096029...  \n",
       "1273  [0.0598412, 0.10206, 0.160232, 0.15152, 0.0967...  \n",
       "1274  [0.0577261, 0.0987031, 0.161035, 0.15991, 0.09...  \n",
       "\n",
       "[12655 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(facecolor='white',figsize=(12,8))\n",
    "for vims,spectrum in zip(vims_list,averaged_list):\n",
    "    my_fit,_ = curve_fit(gaussian,vims[window],spectrum[window],p0=[.05,2.025,.05])\n",
    "    plt.axvline(x=my_fit[1],linestyle='--')\n",
    "    plt.plot(vims,spectrum)\n",
    "plt.xlim([1.8,2.2])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_0405 /= np.cos(np.asarray(inc_0405)*(np.pi/180))[:,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf = np.pi/180\n",
    "spec_0405 = (np.cos(inc_0405*sf))/(np.cos(inc_0405*sf)+np.cos(em_0405*sf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for spectrum in copy_spec_0405:\n",
    "    plt.plot(spectrum,spectrum)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "665796ea3363072d3a6057ac2fdbe3c4fcb0d17a4b92295d9707f78e9c46c0af"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}