corrections_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
    "from astropy.time import Time\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(\"total_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(\"total_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 i in range(4,18):\n",
    "    if i<10:\n",
    "        exec('vims_200'+str(i)+' = np.load(\"vims_shift/vims_200'+str(i)+'.npy\")')\n",
    "        vims_list.append(locals()['vims_200'+str(i)])\n",
    "    else:\n",
    "        exec('vims_20'+str(i)+' = np.load(\"vims_shift/vims_20'+str(i)+'.npy\")')\n",
    "        vims_list.append(locals()['vims_20'+str(i)])\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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lucas\\AppData\\Local\\Temp/ipykernel_17420/3357091760.py:61: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_scatter['spectrum'][i] = pList_total[i]\n"
     ]
    }
   ],
   "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,ignore_index=True)\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",
    "    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",
    "averaged_list = []\n",
    "for number in new_array:\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 = pList_total + locals()['pList'+number]\n",
    "\n",
    "for i in range(len(total_scatter['spectrum'])):\n",
    "    total_scatter['spectrum'][i] = pList_total[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "shift_dates = [\n",
    "'2004-01-01T00:00:00.00',\n",
    "'2005-01-01T00:00:00.00',\n",
    "'2005-07-01T00:00:00.00',\n",
    "'2006-01-01T00:00:00.00',\n",
    "'2006-07-01T00:00:00.00',\n",
    "'2007-01-01T00:00:00.00',\n",
    "'2007-07-01T00:00:00.00',\n",
    "'2008-01-01T00:00:00.00',\n",
    "'2008-07-01T00:00:00.00',\n",
    "'2009-01-01T00:00:00.00',\n",
    "'2009-07-01T00:00:00.00',\n",
    "'2010-01-01T00:00:00.00',\n",
    "'2010-07-01T00:00:00.00',\n",
    "'2011-01-01T00:00:00.00',\n",
    "'2011-07-01T00:00:00.00',\n",
    "'2012-01-01T00:00:00.00',\n",
    "'2012-07-01T00:00:00.00',\n",
    "'2013-01-01T00:00:00.00',\n",
    "'2013-07-01T00:00:00.00',\n",
    "'2014-01-01T00:00:00.00',\n",
    "'2014-04-19T00:00:00.00',\n",
    "'2014-07-01T00:00:00.00',\n",
    "'2015-01-01T00:00:00.00',\n",
    "'2015-07-01T00:00:00.00',\n",
    "'2016-01-01T00:00:00.00',\n",
    "'2016-07-01T00:00:00.00',\n",
    "'2017-01-01T00:00:00.00',\n",
    "'2017-07-01T00:00:00.00',\n",
    "'2017-10-19T00:00:00.00',\n",
    "]\n",
    "\n",
    "shift_vals = [0.0,0.0,0.4,1.1,1.9,2.6,3.4,4.1,4.9,5.6,6.0,6.0,6.0,6.0,7.0,7.3,7.7,8.0,8.4,8.7,9.1,9.1,9.2,9.3,9.4,9.5,9.6,9.7,9.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_vector = Time(shift_dates,format='isot').jd\n",
    "\n",
    "date_interp = interp1d(date_vector,shift_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_shifts = date_interp(list(total_scatter['starttime']))\n",
    "all_shifts = all_shifts/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lucas\\AppData\\Local\\Temp/ipykernel_17420/2275289876.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_scatter['spectrum'][i] = final_spectrum\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(total_scatter['spectrum'])):\n",
    "    f1 = interp1d(vims_2004+all_shifts[i],total_scatter['spectrum'][i],kind='linear',fill_value='extrapolate')\n",
    "    xnew = np.linspace(vims_2004[0]+all_shifts[i],vims_2004[255]+all_shifts[i],num=25600,endpoint=True)\n",
    "    final_spectrum = f1(xnew)\n",
    "    total_scatter['spectrum'][i] = final_spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "average = np.average(total_scatter['spectrum'],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethane = np.load('ethane.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2495, 44)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(ethane)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([93.95927, 93.87991, 93.96009, ..., 99.67151, 99.60638, 99.7215 ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethane[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
    "\n",
    "plt.plot(xnew,average)\n",
    "plt.plot(vims_2004,spec_0405[1])\n",
    "plt.plot(ethane[:,0],ethane[:,14]/1000)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2495,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethane[:,14].shape"
   ]
  },
  {
   "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": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}