exercicio2-solucao.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1774d58",
   "metadata": {},
   "source": [
    "# Exercício 2 - CBPF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87b6b9d",
   "metadata": {},
   "source": [
    "## Explorando o Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd83b61",
   "metadata": {},
   "source": [
    "Primeiramente, vamos importar os pacotes necessários."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95b367c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "from astropy.table import Table\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac52c9a9",
   "metadata": {},
   "source": [
    "A seguir, gera-se uma lista HDU a partir do fits e a tabela a partir da segunda extensão da lista."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61748db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ra                dec         ...        z_spec       training\n",
      "------------------ ------------------ ... ------------------- --------\n",
      "219.08289517954503  34.62503563674176 ...               -99.0      0.0\n",
      "  219.018686653907  34.62537369835652 ...               -99.0      0.0\n",
      " 218.9763831277981 34.626465741964694 ... 0.20600000023841858      1.0\n",
      "218.92461988134852  34.62559613136096 ...               -99.0      0.0\n",
      "219.10422528282172  34.62584794653823 ...               -99.0      0.0\n",
      " 219.1401551191026  34.63060062779776 ...               -99.0      0.0\n",
      "219.07940419061788   34.6265108832743 ...               -99.0      0.0\n",
      "               ...                ... ...                 ...      ...\n",
      "221.30998851742496  35.03107476388605 ...               -99.0      0.0\n",
      "221.30899510953466  35.02957290846069 ...               -99.0      0.0\n",
      " 221.3846269372925  35.03130360684637 ...               -99.0      0.0\n",
      "221.26710586697644  35.03265425222566 ...               -99.0      0.0\n",
      "221.35793083937708   35.0319557953791 ...               -99.0      0.0\n",
      "221.14003466677406 35.031834870454304 ...               -99.0      0.0\n",
      "221.30611517880223 35.032600651235455 ...               -99.0      0.0\n",
      "Length = 203484 rows\n",
      "<TableColumns names=('ra','dec','MAG_R','MAG_G','MAG_Z','MAG_W1','MAG_W2','MAG_W3','MAG_W4','MAGErr_R','MAGErr_G','MAGErr_Z','MAGErr_W1','MAGErr_W2','MAGErr_W3','MAGErr_W4','z','zErr','zmin','zmax','z_spec','training')>\n"
     ]
    }
   ],
   "source": [
    "hdul = fits.open(\"legacy_dr8_class.fits\", ignore_missing_end=True) \n",
    "data = hdul[1].data # table extensions can't be the first extension, so there's a dummy image extension at 1\n",
    "cat_table = Table(data)\n",
    "cols=cat_table.columns\n",
    "print(cat_table)\n",
    "print(cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c082c7",
   "metadata": {},
   "source": [
    "Em seguida, constrói-se o data frame do pandas a partir da tabela anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3096681f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                ra        dec      MAG_R      MAG_G      MAG_Z     MAG_W1  \\\n",
      "0       219.082895  34.625036  22.280201  23.840595  20.669277  19.442677   \n",
      "1       219.018687  34.625374  20.950979  22.239498  20.180450  19.764698   \n",
      "2       218.976383  34.626466  18.890739  20.205397  18.109526  18.055065   \n",
      "3       218.924620  34.625596  20.606371  21.781631  19.691599  19.006557   \n",
      "4       219.104225  34.625848  20.372391  21.050526  20.042389  20.373194   \n",
      "...            ...        ...        ...        ...        ...        ...   \n",
      "203479  221.384627  35.031304  20.289848  22.006527  19.473064  18.800829   \n",
      "203480  221.267106  35.032654  22.278854  24.686527  21.332008  20.327465   \n",
      "203481  221.357931  35.031956  21.256090  22.794085  20.244637  18.834969   \n",
      "203482  221.140035  35.031835  20.928772  22.810913  19.648155  18.766457   \n",
      "203483  221.306115  35.032601  22.024387  23.518301  20.663643  19.733841   \n",
      "\n",
      "           MAG_W2     MAG_W3     MAG_W4  MAGErr_R  ...  MAGErr_W1  MAGErr_W2  \\\n",
      "0       19.876930  16.879316        NaN  0.068069  ...   0.030203   0.102416   \n",
      "1       20.027622  18.840683        NaN  0.020584  ...   0.040873   0.118477   \n",
      "2       18.447739        NaN        NaN  0.005011  ...   0.009083   0.029333   \n",
      "3       19.443335  20.748812        NaN  0.020479  ...   0.021253   0.071826   \n",
      "4       20.973274  19.442488  17.469923  0.018710  ...   0.078672   0.304236   \n",
      "...           ...        ...        ...       ...  ...        ...        ...   \n",
      "203479  19.199001  22.347729        NaN  0.023790  ...   0.017453   0.056976   \n",
      "203480  21.582365        NaN  15.524459  0.155021  ...   0.070509   0.506848   \n",
      "203481  19.022402  17.893970  16.110783  0.057986  ...   0.018522   0.049890   \n",
      "203482  19.179871        NaN        NaN  0.033461  ...   0.016835   0.056150   \n",
      "203483  20.518625        NaN        NaN  0.079692  ...   0.040753   0.190262   \n",
      "\n",
      "        MAGErr_W3  MAGErr_W4         z      zErr      zmin      zmax  z_spec  \\\n",
      "0        0.114776  -0.075900  0.778901  0.080700  0.702562  0.863962 -99.000   \n",
      "1        0.706567  -0.799649  0.335511  0.054689  0.288449  0.397827 -99.000   \n",
      "2       -9.551555  -0.682852  0.219682  0.010735  0.209061  0.230530   0.206   \n",
      "3        4.317370  -0.843075  0.542923  0.051388  0.485855  0.588631 -99.000   \n",
      "4        1.281699   1.788509  0.227206  0.033312  0.191055  0.257679 -99.000   \n",
      "...           ...        ...       ...       ...       ...       ...     ...   \n",
      "203479  18.655874  -0.504265  0.399513  0.029692  0.371137  0.430521 -99.000   \n",
      "203480  -0.115952   0.318850  0.525794  0.076385  0.445291  0.598062 -99.000   \n",
      "203481   0.324572   0.561515  0.497148  0.061960  0.444781  0.568702 -99.000   \n",
      "203482  -1.286056  -0.460461  0.561410  0.034761  0.527032  0.596554 -99.000   \n",
      "203483  -0.767880  -0.541523  0.670019  0.082765  0.608144  0.773673 -99.000   \n",
      "\n",
      "        training  \n",
      "0            0.0  \n",
      "1            0.0  \n",
      "2            1.0  \n",
      "3            0.0  \n",
      "4            0.0  \n",
      "...          ...  \n",
      "203479       0.0  \n",
      "203480       0.0  \n",
      "203481       0.0  \n",
      "203482       0.0  \n",
      "203483       0.0  \n",
      "\n",
      "[203484 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "df = cat_table.to_pandas()\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35483299",
   "metadata": {},
   "source": [
    "### Adicionar uma coluna ao Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4aaba08c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        ra                 dec      MAG_R      MAG_G  \\\n",
      "0       219.08289517954503   34.62503563674176  22.280201  23.840595   \n",
      "1         219.018686653907   34.62537369835652  20.950979  22.239498   \n",
      "2        218.9763831277981  34.626465741964694  18.890739  20.205397   \n",
      "3       218.92461988134852   34.62559613136096  20.606371  21.781631   \n",
      "4       219.10422528282172   34.62584794653823  20.372391  21.050526   \n",
      "...                    ...                 ...        ...        ...   \n",
      "203479   221.3846269372925   35.03130360684637  20.289848  22.006527   \n",
      "203480  221.26710586697644   35.03265425222566  22.278854  24.686527   \n",
      "203481  221.35793083937708    35.0319557953791  21.256090  22.794085   \n",
      "203482  221.14003466677406  35.031834870454304  20.928772  22.810913   \n",
      "203483  221.30611517880223  35.032600651235455  22.024387  23.518301   \n",
      "\n",
      "            MAG_Z     MAG_W1     MAG_W2     MAG_W3     MAG_W4  MAGErr_R  ...  \\\n",
      "0       20.669277  19.442677  19.876930  16.879316        NaN  0.068069  ...   \n",
      "1       20.180450  19.764698  20.027622  18.840683        NaN  0.020584  ...   \n",
      "2       18.109526  18.055065  18.447739        NaN        NaN  0.005011  ...   \n",
      "3       19.691599  19.006557  19.443335  20.748812        NaN  0.020479  ...   \n",
      "4       20.042389  20.373194  20.973274  19.442488  17.469923  0.018710  ...   \n",
      "...           ...        ...        ...        ...        ...       ...  ...   \n",
      "203479  19.473064  18.800829  19.199001  22.347729        NaN  0.023790  ...   \n",
      "203480  21.332008  20.327465  21.582365        NaN  15.524459  0.155021  ...   \n",
      "203481  20.244637  18.834969  19.022402  17.893970  16.110783  0.057986  ...   \n",
      "203482  19.648155  18.766457  19.179871        NaN        NaN  0.033461  ...   \n",
      "203483  20.663643  19.733841  20.518625        NaN        NaN  0.079692  ...   \n",
      "\n",
      "        MAGErr_W2  MAGErr_W3  MAGErr_W4         z      zErr      zmin  \\\n",
      "0        0.102416   0.114776  -0.075900  0.778901  0.080700  0.702562   \n",
      "1        0.118477   0.706567  -0.799649  0.335511  0.054689  0.288449   \n",
      "2        0.029333  -9.551555  -0.682852  0.219682  0.010735  0.209061   \n",
      "3        0.071826   4.317370  -0.843075  0.542923  0.051388  0.485855   \n",
      "4        0.304236   1.281699   1.788509  0.227206  0.033312  0.191055   \n",
      "...           ...        ...        ...       ...       ...       ...   \n",
      "203479   0.056976  18.655874  -0.504265  0.399513  0.029692  0.371137   \n",
      "203480   0.506848  -0.115952   0.318850  0.525794  0.076385  0.445291   \n",
      "203481   0.049890   0.324572   0.561515  0.497148  0.061960  0.444781   \n",
      "203482   0.056150  -1.286056  -0.460461  0.561410  0.034761  0.527032   \n",
      "203483   0.190262  -0.767880  -0.541523  0.670019  0.082765  0.608144   \n",
      "\n",
      "            zmax  z_spec  training                                   _id  \n",
      "0       0.863962 -99.000       0.0   219.0828951795450334.62503563674176  \n",
      "1       0.397827 -99.000       0.0     219.01868665390734.62537369835652  \n",
      "2       0.230530   0.206       1.0   218.976383127798134.626465741964694  \n",
      "3       0.588631 -99.000       0.0   218.9246198813485234.62559613136096  \n",
      "4       0.257679 -99.000       0.0   219.1042252828217234.62584794653823  \n",
      "...          ...     ...       ...                                   ...  \n",
      "203479  0.430521 -99.000       0.0    221.384626937292535.03130360684637  \n",
      "203480  0.598062 -99.000       0.0   221.2671058669764435.03265425222566  \n",
      "203481  0.568702 -99.000       0.0    221.3579308393770835.0319557953791  \n",
      "203482  0.596554 -99.000       0.0  221.1400346667740635.031834870454304  \n",
      "203483  0.773673 -99.000       0.0  221.3061151788022335.032600651235455  \n",
      "\n",
      "[203484 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "new_col = \"_id\" # name new column\n",
    "df['ra'] = df['ra'].astype(str) # change numbers to string\n",
    "df['dec'] = df['dec'].astype(str) # change numbers to string\n",
    "df[new_col] = df['ra'] + df['dec'] # join two columns\n",
    "print(df)\n",
    "\n",
    "#Há uma concatenação das strings das duas colunas, em cada linha."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75b8899",
   "metadata": {},
   "source": [
    "### Separando o dataframe em dois"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "409dac76",
   "metadata": {},
   "source": [
    "Para o primeiro \"pedaço\", temos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd1e7bf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daframe 1\n",
      "            MAG_R      MAG_G      MAG_Z     MAG_W1     MAG_W2     MAG_W3  \\\n",
      "0       22.280201  23.840595  20.669277  19.442677  19.876930  16.879316   \n",
      "1       20.950979  22.239498  20.180450  19.764698  20.027622  18.840683   \n",
      "2       18.890739  20.205397  18.109526  18.055065  18.447739        NaN   \n",
      "3       20.606371  21.781631  19.691599  19.006557  19.443335  20.748812   \n",
      "4       20.372391  21.050526  20.042389  20.373194  20.973274  19.442488   \n",
      "...           ...        ...        ...        ...        ...        ...   \n",
      "203479  20.289848  22.006527  19.473064  18.800829  19.199001  22.347729   \n",
      "203480  22.278854  24.686527  21.332008  20.327465  21.582365        NaN   \n",
      "203481  21.256090  22.794085  20.244637  18.834969  19.022402  17.893970   \n",
      "203482  20.928772  22.810913  19.648155  18.766457  19.179871        NaN   \n",
      "203483  22.024387  23.518301  20.663643  19.733841  20.518625        NaN   \n",
      "\n",
      "           MAG_W4  MAGErr_R  MAGErr_G  MAGErr_Z  MAGErr_W1  MAGErr_W2  \\\n",
      "0             NaN  0.068069  0.186609  0.019647   0.030203   0.102416   \n",
      "1             NaN  0.020584  0.050908  0.015757   0.040873   0.118477   \n",
      "2             NaN  0.005011  0.011483  0.003473   0.009083   0.029333   \n",
      "3             NaN  0.020479  0.043270  0.017061   0.021253   0.071826   \n",
      "4       17.469923  0.018710  0.025214  0.022851   0.078672   0.304236   \n",
      "...           ...       ...       ...       ...        ...        ...   \n",
      "203479        NaN  0.023790  0.058753  0.023217   0.017453   0.056976   \n",
      "203480  15.524459  0.155021  0.703358  0.151881   0.070509   0.506848   \n",
      "203481  16.110783  0.057986  0.140764  0.046815   0.018522   0.049890   \n",
      "203482        NaN  0.033461  0.111600  0.021100   0.016835   0.056150   \n",
      "203483        NaN  0.079692  0.186413  0.058294   0.040753   0.190262   \n",
      "\n",
      "        MAGErr_W3  MAGErr_W4                                   _id  \n",
      "0        0.114776  -0.075900   219.0828951795450334.62503563674176  \n",
      "1        0.706567  -0.799649     219.01868665390734.62537369835652  \n",
      "2       -9.551555  -0.682852   218.976383127798134.626465741964694  \n",
      "3        4.317370  -0.843075   218.9246198813485234.62559613136096  \n",
      "4        1.281699   1.788509   219.1042252828217234.62584794653823  \n",
      "...           ...        ...                                   ...  \n",
      "203479  18.655874  -0.504265    221.384626937292535.03130360684637  \n",
      "203480  -0.115952   0.318850   221.2671058669764435.03265425222566  \n",
      "203481   0.324572   0.561515    221.3579308393770835.0319557953791  \n",
      "203482  -1.286056  -0.460461  221.1400346667740635.031834870454304  \n",
      "203483  -0.767880  -0.541523  221.3061151788022335.032600651235455  \n",
      "\n",
      "[203484 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Daframe 1\")\n",
    "df_1 = df.iloc[:,2:-7] # Aqui, apenas seleciona-se as colunas referentes às magnitudes.\n",
    "df_1[new_col] = df[new_col] # Aqui, insere-se a coluna ID, definida anteriormente.\n",
    "print(df_1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc9e04b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MAG_R', 'MAG_G', 'MAG_Z', 'MAG_W1', 'MAG_W2', 'MAG_W3', 'MAG_W4', 'MAGErr_R', 'MAGErr_G', 'MAGErr_Z', 'MAGErr_W1', 'MAGErr_W2', 'MAGErr_W3', 'MAGErr_W4', '_id']\n"
     ]
    }
   ],
   "source": [
    "cols1 = df_1.columns.tolist() #Listando as colunas do dataframe.\n",
    "print(cols1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5211d395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['_id', 'MAG_R', 'MAG_G', 'MAG_Z', 'MAG_W1', 'MAG_W2', 'MAG_W3', 'MAG_W4', 'MAGErr_R', 'MAGErr_G', 'MAGErr_Z', 'MAGErr_W1', 'MAGErr_W2', 'MAGErr_W3', 'MAGErr_W4']\n"
     ]
    }
   ],
   "source": [
    "cols1 = cols1[-1:] + cols1[:-1] #Selecionando a primeira coluna como sendo ID.\n",
    "print(cols1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "079431c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         _id      MAG_R      MAG_G      MAG_Z  \\\n",
      "0        219.0828951795450334.62503563674176  22.280201  23.840595  20.669277   \n",
      "1          219.01868665390734.62537369835652  20.950979  22.239498  20.180450   \n",
      "2        218.976383127798134.626465741964694  18.890739  20.205397  18.109526   \n",
      "3        218.9246198813485234.62559613136096  20.606371  21.781631  19.691599   \n",
      "4        219.1042252828217234.62584794653823  20.372391  21.050526  20.042389   \n",
      "...                                      ...        ...        ...        ...   \n",
      "203479    221.384626937292535.03130360684637  20.289848  22.006527  19.473064   \n",
      "203480   221.2671058669764435.03265425222566  22.278854  24.686527  21.332008   \n",
      "203481    221.3579308393770835.0319557953791  21.256090  22.794085  20.244637   \n",
      "203482  221.1400346667740635.031834870454304  20.928772  22.810913  19.648155   \n",
      "203483  221.3061151788022335.032600651235455  22.024387  23.518301  20.663643   \n",
      "\n",
      "           MAG_W1     MAG_W2     MAG_W3     MAG_W4  MAGErr_R  MAGErr_G  \\\n",
      "0       19.442677  19.876930  16.879316        NaN  0.068069  0.186609   \n",
      "1       19.764698  20.027622  18.840683        NaN  0.020584  0.050908   \n",
      "2       18.055065  18.447739        NaN        NaN  0.005011  0.011483   \n",
      "3       19.006557  19.443335  20.748812        NaN  0.020479  0.043270   \n",
      "4       20.373194  20.973274  19.442488  17.469923  0.018710  0.025214   \n",
      "...           ...        ...        ...        ...       ...       ...   \n",
      "203479  18.800829  19.199001  22.347729        NaN  0.023790  0.058753   \n",
      "203480  20.327465  21.582365        NaN  15.524459  0.155021  0.703358   \n",
      "203481  18.834969  19.022402  17.893970  16.110783  0.057986  0.140764   \n",
      "203482  18.766457  19.179871        NaN        NaN  0.033461  0.111600   \n",
      "203483  19.733841  20.518625        NaN        NaN  0.079692  0.186413   \n",
      "\n",
      "        MAGErr_Z  MAGErr_W1  MAGErr_W2  MAGErr_W3  MAGErr_W4  \n",
      "0       0.019647   0.030203   0.102416   0.114776  -0.075900  \n",
      "1       0.015757   0.040873   0.118477   0.706567  -0.799649  \n",
      "2       0.003473   0.009083   0.029333  -9.551555  -0.682852  \n",
      "3       0.017061   0.021253   0.071826   4.317370  -0.843075  \n",
      "4       0.022851   0.078672   0.304236   1.281699   1.788509  \n",
      "...          ...        ...        ...        ...        ...  \n",
      "203479  0.023217   0.017453   0.056976  18.655874  -0.504265  \n",
      "203480  0.151881   0.070509   0.506848  -0.115952   0.318850  \n",
      "203481  0.046815   0.018522   0.049890   0.324572   0.561515  \n",
      "203482  0.021100   0.016835   0.056150  -1.286056  -0.460461  \n",
      "203483  0.058294   0.040753   0.190262  -0.767880  -0.541523  \n",
      "\n",
      "[203484 rows x 15 columns]\n",
      "['_id', 'MAG_R', 'MAG_G', 'MAG_Z', 'MAG_W1', 'MAG_W2', 'MAG_W3', 'MAG_W4', 'MAGErr_R', 'MAGErr_G', 'MAGErr_Z', 'MAGErr_W1', 'MAGErr_W2', 'MAGErr_W3', 'MAGErr_W4']\n"
     ]
    }
   ],
   "source": [
    "df_1 = df_1[cols1] #Rearranjando as colunas do data frame, para que ID seja a primeira.\n",
    "print(df_1)\n",
    "print(df_1.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb4bed9",
   "metadata": {},
   "source": [
    "Para o segundo \"pedaço\", temos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65368b20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************\n",
      "Daframe 2\n",
      "                                         _id         z      zErr      zmin  \\\n",
      "0        219.0828951795450334.62503563674176  0.778901  0.080700  0.702562   \n",
      "1          219.01868665390734.62537369835652  0.335511  0.054689  0.288449   \n",
      "2        218.976383127798134.626465741964694  0.219682  0.010735  0.209061   \n",
      "3        218.9246198813485234.62559613136096  0.542923  0.051388  0.485855   \n",
      "4        219.1042252828217234.62584794653823  0.227206  0.033312  0.191055   \n",
      "...                                      ...       ...       ...       ...   \n",
      "203479    221.384626937292535.03130360684637  0.399513  0.029692  0.371137   \n",
      "203480   221.2671058669764435.03265425222566  0.525794  0.076385  0.445291   \n",
      "203481    221.3579308393770835.0319557953791  0.497148  0.061960  0.444781   \n",
      "203482  221.1400346667740635.031834870454304  0.561410  0.034761  0.527032   \n",
      "203483  221.3061151788022335.032600651235455  0.670019  0.082765  0.608144   \n",
      "\n",
      "            zmax  z_spec  \n",
      "0       0.863962 -99.000  \n",
      "1       0.397827 -99.000  \n",
      "2       0.230530   0.206  \n",
      "3       0.588631 -99.000  \n",
      "4       0.257679 -99.000  \n",
      "...          ...     ...  \n",
      "203479  0.430521 -99.000  \n",
      "203480  0.598062 -99.000  \n",
      "203481  0.568702 -99.000  \n",
      "203482  0.596554 -99.000  \n",
      "203483  0.773673 -99.000  \n",
      "\n",
      "[203484 rows x 6 columns]\n",
      "['_id', 'z', 'zErr', 'zmin', 'zmax', 'z_spec']\n"
     ]
    }
   ],
   "source": [
    "print(\"********************************\")\n",
    "print(\"Daframe 2\")\n",
    "df_2 = df.iloc[:,-7:-2] # set photoz columns dataframe 1 from dataframe\n",
    "df_2[new_col] = df[new_col] # insert ID column\n",
    "cols2 = df_2.columns.tolist()\n",
    "cols2 = cols2[-1:] + cols2[:-1] # set first column _id\n",
    "df_2 = df_2[cols2] # set first column _id\n",
    "print(df_2)\n",
    "print(df_2.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33495323",
   "metadata": {},
   "source": [
    "### Usando a operação join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5329408",
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_mag = df_1.set_index(new_col) #Seleciona o index do dataframe (substituindo o padrão 0,1,2...) pela coluna ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79996330",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "      <th>MAG_R</th>\n",
       "      <th>MAG_G</th>\n",
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       "      <th>MAG_W2</th>\n",
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       "      <th>MAG_W4</th>\n",
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       "      <th>MAGErr_G</th>\n",
       "      <th>MAGErr_Z</th>\n",
       "      <th>MAGErr_W1</th>\n",
       "      <th>MAGErr_W2</th>\n",
       "      <th>MAGErr_W3</th>\n",
       "      <th>MAGErr_W4</th>\n",
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       "    <tr>\n",
       "      <th>_id</th>\n",
       "      <th></th>\n",
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       "<p>203484 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          MAG_R      MAG_G      MAG_Z  \\\n",
       "_id                                                                     \n",
       "219.0828951795450334.62503563674176   22.280201  23.840595  20.669277   \n",
       "219.01868665390734.62537369835652     20.950979  22.239498  20.180450   \n",
       "218.976383127798134.626465741964694   18.890739  20.205397  18.109526   \n",
       "218.9246198813485234.62559613136096   20.606371  21.781631  19.691599   \n",
       "219.1042252828217234.62584794653823   20.372391  21.050526  20.042389   \n",
       "...                                         ...        ...        ...   \n",
       "221.384626937292535.03130360684637    20.289848  22.006527  19.473064   \n",
       "221.2671058669764435.03265425222566   22.278854  24.686527  21.332008   \n",
       "221.3579308393770835.0319557953791    21.256090  22.794085  20.244637   \n",
       "221.1400346667740635.031834870454304  20.928772  22.810913  19.648155   \n",
       "221.3061151788022335.032600651235455  22.024387  23.518301  20.663643   \n",
       "\n",
       "                                         MAG_W1     MAG_W2     MAG_W3  \\\n",
       "_id                                                                     \n",
       "219.0828951795450334.62503563674176   19.442677  19.876930  16.879316   \n",
       "219.01868665390734.62537369835652     19.764698  20.027622  18.840683   \n",
       "218.976383127798134.626465741964694   18.055065  18.447739        NaN   \n",
       "218.9246198813485234.62559613136096   19.006557  19.443335  20.748812   \n",
       "219.1042252828217234.62584794653823   20.373194  20.973274  19.442488   \n",
       "...                                         ...        ...        ...   \n",
       "221.384626937292535.03130360684637    18.800829  19.199001  22.347729   \n",
       "221.2671058669764435.03265425222566   20.327465  21.582365        NaN   \n",
       "221.3579308393770835.0319557953791    18.834969  19.022402  17.893970   \n",
       "221.1400346667740635.031834870454304  18.766457  19.179871        NaN   \n",
       "221.3061151788022335.032600651235455  19.733841  20.518625        NaN   \n",
       "\n",
       "                                         MAG_W4  MAGErr_R  MAGErr_G  MAGErr_Z  \\\n",
       "_id                                                                             \n",
       "219.0828951795450334.62503563674176         NaN  0.068069  0.186609  0.019647   \n",
       "219.01868665390734.62537369835652           NaN  0.020584  0.050908  0.015757   \n",
       "218.976383127798134.626465741964694         NaN  0.005011  0.011483  0.003473   \n",
       "218.9246198813485234.62559613136096         NaN  0.020479  0.043270  0.017061   \n",
       "219.1042252828217234.62584794653823   17.469923  0.018710  0.025214  0.022851   \n",
       "...                                         ...       ...       ...       ...   \n",
       "221.384626937292535.03130360684637          NaN  0.023790  0.058753  0.023217   \n",
       "221.2671058669764435.03265425222566   15.524459  0.155021  0.703358  0.151881   \n",
       "221.3579308393770835.0319557953791    16.110783  0.057986  0.140764  0.046815   \n",
       "221.1400346667740635.031834870454304        NaN  0.033461  0.111600  0.021100   \n",
       "221.3061151788022335.032600651235455        NaN  0.079692  0.186413  0.058294   \n",
       "\n",
       "                                      MAGErr_W1  MAGErr_W2  MAGErr_W3  \\\n",
       "_id                                                                     \n",
       "219.0828951795450334.62503563674176    0.030203   0.102416   0.114776   \n",
       "219.01868665390734.62537369835652      0.040873   0.118477   0.706567   \n",
       "218.976383127798134.626465741964694    0.009083   0.029333  -9.551555   \n",
       "218.9246198813485234.62559613136096    0.021253   0.071826   4.317370   \n",
       "219.1042252828217234.62584794653823    0.078672   0.304236   1.281699   \n",
       "...                                         ...        ...        ...   \n",
       "221.384626937292535.03130360684637     0.017453   0.056976  18.655874   \n",
       "221.2671058669764435.03265425222566    0.070509   0.506848  -0.115952   \n",
       "221.3579308393770835.0319557953791     0.018522   0.049890   0.324572   \n",
       "221.1400346667740635.031834870454304   0.016835   0.056150  -1.286056   \n",
       "221.3061151788022335.032600651235455   0.040753   0.190262  -0.767880   \n",
       "\n",
       "                                      MAGErr_W4  \n",
       "_id                                              \n",
       "219.0828951795450334.62503563674176   -0.075900  \n",
       "219.01868665390734.62537369835652     -0.799649  \n",
       "218.976383127798134.626465741964694   -0.682852  \n",
       "218.9246198813485234.62559613136096   -0.843075  \n",
       "219.1042252828217234.62584794653823    1.788509  \n",
       "...                                         ...  \n",
       "221.384626937292535.03130360684637    -0.504265  \n",
       "221.2671058669764435.03265425222566    0.318850  \n",
       "221.3579308393770835.0319557953791     0.561515  \n",
       "221.1400346667740635.031834870454304  -0.460461  \n",
       "221.3061151788022335.032600651235455  -0.541523  \n",
       "\n",
       "[203484 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_mag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d746b5ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         _id         z      zErr      zmin  \\\n",
      "0        219.0828951795450334.62503563674176  0.778901  0.080700  0.702562   \n",
      "1          219.01868665390734.62537369835652  0.335511  0.054689  0.288449   \n",
      "2        218.976383127798134.626465741964694  0.219682  0.010735  0.209061   \n",
      "3        218.9246198813485234.62559613136096  0.542923  0.051388  0.485855   \n",
      "4        219.1042252828217234.62584794653823  0.227206  0.033312  0.191055   \n",
      "...                                      ...       ...       ...       ...   \n",
      "203479    221.384626937292535.03130360684637  0.399513  0.029692  0.371137   \n",
      "203480   221.2671058669764435.03265425222566  0.525794  0.076385  0.445291   \n",
      "203481    221.3579308393770835.0319557953791  0.497148  0.061960  0.444781   \n",
      "203482  221.1400346667740635.031834870454304  0.561410  0.034761  0.527032   \n",
      "203483  221.3061151788022335.032600651235455  0.670019  0.082765  0.608144   \n",
      "\n",
      "            zmax  z_spec      MAG_R      MAG_G      MAG_Z     MAG_W1  \\\n",
      "0       0.863962 -99.000  22.280201  23.840595  20.669277  19.442677   \n",
      "1       0.397827 -99.000  20.950979  22.239498  20.180450  19.764698   \n",
      "2       0.230530   0.206  18.890739  20.205397  18.109526  18.055065   \n",
      "3       0.588631 -99.000  20.606371  21.781631  19.691599  19.006557   \n",
      "4       0.257679 -99.000  20.372391  21.050526  20.042389  20.373194   \n",
      "...          ...     ...        ...        ...        ...        ...   \n",
      "203479  0.430521 -99.000  20.289848  22.006527  19.473064  18.800829   \n",
      "203480  0.598062 -99.000  22.278854  24.686527  21.332008  20.327465   \n",
      "203481  0.568702 -99.000  21.256090  22.794085  20.244637  18.834969   \n",
      "203482  0.596554 -99.000  20.928772  22.810913  19.648155  18.766457   \n",
      "203483  0.773673 -99.000  22.024387  23.518301  20.663643  19.733841   \n",
      "\n",
      "           MAG_W2     MAG_W3     MAG_W4  MAGErr_R  MAGErr_G  MAGErr_Z  \\\n",
      "0       19.876930  16.879316        NaN  0.068069  0.186609  0.019647   \n",
      "1       20.027622  18.840683        NaN  0.020584  0.050908  0.015757   \n",
      "2       18.447739        NaN        NaN  0.005011  0.011483  0.003473   \n",
      "3       19.443335  20.748812        NaN  0.020479  0.043270  0.017061   \n",
      "4       20.973274  19.442488  17.469923  0.018710  0.025214  0.022851   \n",
      "...           ...        ...        ...       ...       ...       ...   \n",
      "203479  19.199001  22.347729        NaN  0.023790  0.058753  0.023217   \n",
      "203480  21.582365        NaN  15.524459  0.155021  0.703358  0.151881   \n",
      "203481  19.022402  17.893970  16.110783  0.057986  0.140764  0.046815   \n",
      "203482  19.179871        NaN        NaN  0.033461  0.111600  0.021100   \n",
      "203483  20.518625        NaN        NaN  0.079692  0.186413  0.058294   \n",
      "\n",
      "        MAGErr_W1  MAGErr_W2  MAGErr_W3  MAGErr_W4  \n",
      "0        0.030203   0.102416   0.114776  -0.075900  \n",
      "1        0.040873   0.118477   0.706567  -0.799649  \n",
      "2        0.009083   0.029333  -9.551555  -0.682852  \n",
      "3        0.021253   0.071826   4.317370  -0.843075  \n",
      "4        0.078672   0.304236   1.281699   1.788509  \n",
      "...           ...        ...        ...        ...  \n",
      "203479   0.017453   0.056976  18.655874  -0.504265  \n",
      "203480   0.070509   0.506848  -0.115952   0.318850  \n",
      "203481   0.018522   0.049890   0.324572   0.561515  \n",
      "203482   0.016835   0.056150  -1.286056  -0.460461  \n",
      "203483   0.040753   0.190262  -0.767880  -0.541523  \n",
      "\n",
      "[203622 rows x 20 columns]\n",
      "['_id', 'z', 'zErr', 'zmin', 'zmax', 'z_spec', 'MAG_R', 'MAG_G', 'MAG_Z', 'MAG_W1', 'MAG_W2', 'MAG_W3', 'MAG_W4', 'MAGErr_R', 'MAGErr_G', 'MAGErr_Z', 'MAGErr_W1', 'MAGErr_W2', 'MAGErr_W3', 'MAGErr_W4']\n"
     ]
    }
   ],
   "source": [
    "DF = df_2.join(DF_mag, on=new_col) \n",
    "print(DF)\n",
    "print(DF.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b01c3b1",
   "metadata": {},
   "source": [
    "## Solução do Exercício"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa274861",
   "metadata": {},
   "source": [
    "Primeiramente, irei \"binar\" os dados, com bins de mesmo tamanho."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4dcb200",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nbins = 8 #Número de bins.\n",
    "\n",
    "DF['BINS'] = pd.cut(DF[\"z\"], nbins)\n",
    "\n",
    "#Caso queira o meio de cada bin, é só usar DF['MIDBINS'] = [(a.left + a.right)/2 for a in DF['BINS']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32053ae7",
   "metadata": {},
   "source": [
    "Agora, irei agrupar os dados de cada bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61a31765",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Interval(0.00182, 0.155, closed='right'),\n",
       " Interval(0.155, 0.307, closed='right'),\n",
       " Interval(0.307, 0.459, closed='right'),\n",
       " Interval(0.459, 0.612, closed='right'),\n",
       " Interval(0.612, 0.764, closed='right'),\n",
       " Interval(0.764, 0.916, closed='right'),\n",
       " Interval(0.916, 1.068, closed='right'),\n",
       " Interval(1.068, 1.22, closed='right')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupos = DF.groupby(DF['BINS'])\n",
    "list(grupos.groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d538654",
   "metadata": {},
   "source": [
    "Define-se, abaixo, a média de MAG_R, a média de MAGErr_R, e os respectivos erros em cada bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ad0a9505",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = grupos['MAG_R'].mean()\n",
    "x_std = grupos['MAG_R'].std()\n",
    "\n",
    "x_err = grupos['MAGErr_R'].mean()\n",
    "x_err_std = grupos['MAGErr_R'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77de7f1e",
   "metadata": {},
   "source": [
    "Agora, faço o plot e dou uma estimativa visual, com base no plot, da magnitude limite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd74591c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0ca30359d0>]"
      ]
     },
     "execution_count": 16,
     "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": [
    "maglim = 22.37 #Estimativa visual da magnitude limite.\n",
    "\n",
    "##Plot\n",
    "#plt.errorbar(x, x_err, yerr = x_err_std, xerr = x_std, ecolor='red') #Para plotar os erros em x também.\n",
    "plt.errorbar(x, x_err, yerr = x_err_std, ecolor='red')\n",
    "plt.stem(maglim, 0.1, linefmt='--')\n",
    "plt.plot(x, x_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6495810",
   "metadata": {},
   "source": [
    "Abaixo, faz-se os histogramas com os cortes referentes à magnitude limite encontrada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1fdc3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DFMAGR_lim = DF[DF['MAG_R'] < maglim]\n",
    "\n",
    "hor = 3\n",
    "ver = 1\n",
    "\n",
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(14, 6), constrained_layout=True)\n",
    "gs = fig.add_gridspec(ver, hor, left=0.1, right=0.5, wspace=0.1)\n",
    "\n",
    "\n",
    "binsize=0.2\n",
    "def binarange(arr, binsize):\n",
    "    return np.arange(np.nanmin(arr), np.nanmax(arr)+binsize, binsize)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 0])\n",
    "bins0 = binarange(DF[\"MAG_R\"], binsize)\n",
    "ax = DF[\"MAG_R\"].plot.hist(bins=bins0, color='blue', alpha=0.7, label=\"MAG_R\")\n",
    "ax.set_title(\"MAG_R\")\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 1])\n",
    "bins1 = binarange(DFMAGR_lim[\"MAG_R\"], binsize)\n",
    "ax = DFMAGR_lim[\"MAG_R\"].plot.hist(bins=bins1, color='green', alpha=0.7, label=\"MAG_R < %.1f\" %maglim)\n",
    "ax.set_title(\"MAG_R < %.1f\" %maglim)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "ax = fig.add_subplot(gs[0, 2])\n",
    "ax = DF[\"MAG_R\"].plot.hist(bins=bins0, color='blue', alpha=0.5, label=\"MAG_R\")\n",
    "ax = DFMAGR_lim[\"MAG_R\"].plot.hist(bins=bins0, color='green', alpha=0.5, label=\"MAG_R < %.1f\" %maglim) # equal bins all mag r\n",
    "ax.set_title(\"Compare MAG_R and MAG_R < %.1f\" %maglim)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show"
   ]
  }
 ],
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