arm_marvin_Metallicity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use the fits file downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from os.path import join\n",
    "import numpy.matlib \n",
    "path_notebooks = os.path.abspath('.')\n",
    "path_data = join(os.path.split(path_notebooks)[0], '/root/cosmos/galaxies-mzr/data')\n",
    "from marvin.tools import Maps \n",
    "filename = join(path_data, 'manga-8077-6104-MAPS-HYB10-GAU-MILESHC.fits.gz')\n",
    "maps = Maps(filename=filename)\n",
    "nii = maps.emline_gflux_nii_6585\n",
    "ha = maps.emline_gflux_ha_6564\n",
    "#nii_la=nii.value\n",
    "#ha_la=ha.value\n",
    "#n2_la=nii_la(nii_la!=0)/ha_la(ha_la!=0)\n",
    "#print(n2_la)\n",
    "#lala=np.matlib.zeros(ha.shape, dtype =  float)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use data from remote server "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from marvin.tools import Maps \n",
    "maps = Maps('8077-6104')\n",
    "nii = maps.emline_gflux_nii_6585\n",
    "ha = maps.emline_gflux_ha_6564"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:424: RuntimeWarning: divide by zero encountered in power\n",
      "  return np.log10(np.e) * ivar**-0.5 / value\n"
     ]
    }
   ],
   "source": [
    "n2 = nii / ha\n",
    "logn2 = np.log10(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test the bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'marvin.tools.quantities.map.EnhancedMap'>\n"
     ]
    }
   ],
   "source": [
    "print(type(logn2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = logn2 * 0.57\n",
    "z=   y+8.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'marvin.tools.quantities.map.EnhancedMap'>\n",
      "<class 'marvin.tools.quantities.map.EnhancedMap'>\n"
     ]
    }
   ],
   "source": [
    "print(type(y))\n",
    "print(type(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "marvin.tools.quantities.map.EnhancedMap?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## here is the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "np.add is not implemented for EnhancedMap.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-091fef445533>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlala\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#oh=0.57*logn2+lala*8.90\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8.90\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlala\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.57\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlogn2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py\u001b[0m in \u001b[0;36m__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array_ufunc__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0mivar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ivar_ufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         return EnhancedMap(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py\u001b[0m in \u001b[0;36m_ivar_ufunc\u001b[0;34m(self, ufunc, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m             raise NotImplementedError('np.{0} is not implemented for {1}.'\n\u001b[0;32m--> 183\u001b[0;31m                                       .format(ufunc.__name__, self.__class__.__name__))\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mivar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: np.add is not implemented for EnhancedMap."
     ]
    }
   ],
   "source": [
    "lala=np.ones(ha.shape, dtype =  float)\n",
    "#oh=0.57*logn2+lala*8.90\n",
    "oh=(8.90*lala)+(0.57*logn2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## if switch the two parts, it will success"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lala=np.ones(ha.value.shape, dtype =  float)\n",
    "oh =  (0.57*logn2)+(8.90*lala)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "oh=(8.90*lala)+(0.57*logn2.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in greater\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11295.004199765563"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(oh[np.where(oh>0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "oh =  (0.57*logn2)+8.90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "masks_bpt, __, __ = maps.get_bpt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks_bpt['sf']['global']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bit</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NOCOV</td>\n",
       "      <td>No coverage in this spaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LOWCOV</td>\n",
       "      <td>Low coverage in this spaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>DEADFIBER</td>\n",
       "      <td>Major contributing fiber is dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>FORESTAR</td>\n",
       "      <td>Foreground star</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NOVALUE</td>\n",
       "      <td>Spaxel was not fit because it did not meet sel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>UNRELIABLE</td>\n",
       "      <td>Value is deemed unreliable; see TRM for defini...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>MATHERROR</td>\n",
       "      <td>Mathematical error in computing value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>FITFAILED</td>\n",
       "      <td>Attempted fit for property failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>NEARBOUND</td>\n",
       "      <td>Fitted value is too near an imposed boundary; ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>NOCORRECTION</td>\n",
       "      <td>Appropriate correction not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>MULTICOMP</td>\n",
       "      <td>Multi-component velocity features present</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>DONOTUSE</td>\n",
       "      <td>Do not use this spaxel for science</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bit         label                                        description\n",
       "0     0         NOCOV                         No coverage in this spaxel\n",
       "1     1        LOWCOV                        Low coverage in this spaxel\n",
       "2     2     DEADFIBER                   Major contributing fiber is dead\n",
       "3     3      FORESTAR                                    Foreground star\n",
       "4     4       NOVALUE  Spaxel was not fit because it did not meet sel...\n",
       "5     5    UNRELIABLE  Value is deemed unreliable; see TRM for defini...\n",
       "6     6     MATHERROR              Mathematical error in computing value\n",
       "7     7     FITFAILED                  Attempted fit for property failed\n",
       "8     8     NEARBOUND  Fitted value is too near an imposed boundary; ...\n",
       "9     9  NOCORRECTION               Appropriate correction not available\n",
       "10   10     MULTICOMP          Multi-component velocity features present\n",
       "11   30      DONOTUSE                 Do not use this spaxel for science"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2.pixmask.schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_snr = 3.\n",
    "mask_nii_low_snr = (np.abs(nii.value * np.sqrt(nii.ivar)) < min_snr)\n",
    "mask_ha_low_snr = (np.abs(ha.value * np.sqrt(ha.ivar)) < min_snr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = mask_non_sf | mask_bad_data | mask_nii_low_snr | mask_ha_low_snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/utils/plot/map.py:111: RuntimeWarning: invalid value encountered in less\n",
      "  low_snr[np.abs(value * np.sqrt(ivar)) < snr_min] = True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = oh.plot(mask=mask, cblabel='12+log(O/H)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mstar = pd.read_csv(join(path_data, 'manga-{}_mstar.csv'.format(maps.plateifu)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(mstar, origin='lower')\n",
    "ax.set_xlabel('spaxel')\n",
    "ax.set_ylabel('spaxel')\n",
    "cb = fig.colorbar(p)\n",
    "cb.set_label('log(Mstar) [M$_\\odot$]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "spaxel_size = 0.5  # [arcsec]\n",
    "\n",
    "# or programmatically:\n",
    "# spaxel_size = float(maps.getCube().header['CD2_2']) * 3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-cf51d1415a59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mredshift\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "redshift = maps.nsa['z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'redshift' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-bdbe6bef4a4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m299792\u001b[0m  \u001b[0;31m# speed of light [km/s]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mH0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m70\u001b[0m  \u001b[0;31m# [km s^-1 Mpc^-1]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mredshift\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mH0\u001b[0m  \u001b[0;31m# approx. distance to galaxy [Mpc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'redshift' is not defined"
     ]
    }
   ],
   "source": [
    "c = 299792  # speed of light [km/s]\n",
    "H0 = 70  # [km s^-1 Mpc^-1]\n",
    "D = c * redshift / H0  # approx. distance to galaxy [Mpc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = 1 / 206265 * D * 1e6  # 1 radian = 206265 arcsec [pc / arcsec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "spaxel_area = (scale * spaxel_size)**2  # [pc^2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma_star = np.log10(10**mstar / spaxel_area)  # [Msun / pc^2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sigma_star' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-79e179874acc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma_star\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log(Mstar) [M$_\\odot$]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'12+log(O/H)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sigma_star' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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qenuSX01yoru/fkCzHbSd9uJVSd6Q5O+r6otZv0Z5+ip94Xbp35G/7O7/6u5/SfJU1v8BuNos2Yt7kjyUJN39qSTfkvXP2ZlmUU+22u/g+1iGi3bci43LGB/Neuyv1uu0yQ570d3Pd/d13X1zd9+c9dczTnT3rj9D5Aq25O/IX2T9Bf1U1XVZv8Tz9IFOeTCW7MWXkrwtSarq+7Me/AsHOuWV4XSSd238ts6bkzzf3f+20xft6yWd9rEM37BwLz6c5NuS/NnG69Zf6u4Thzb0Plm4FyMs3IuHk/xEVT2Z5L+T/HJ3f/nwpt4fC/fifUl+r6p+MeuXMN59NZ4gVtUnsn4J77qN1ys+kOSbkqS7P5L11y/uSHIuyVeTvGfR816FewXANrzTFmAIwQcYQvABhhB8gCEEH2AIwQcYQvABhhB8gCH+F+0NcgsBcfcTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n",
    "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n",
    "ax.set_ylabel('12+log(O/H)')\n",
    "ax.axis([0, 4, 8.0, 8.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting formula\n",
    "aa = 8.55\n",
    "bb = 0.014\n",
    "cc = 3.14\n",
    "xx = np.linspace(1, 3, 1000)\n",
    "yy = aa + bb * (xx - cc) * np.exp(-(xx - cc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 4, 8.0, 8.8]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n",
    "ax.plot(xx, yy)\n",
    "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n",
    "ax.set_ylabel('12+log(O/H)')\n",
    "ax.axis([0, 4, 8.0, 8.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用论文中的 [O II]/[N II] ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "from os.path import join\n",
    "path_notebooks = os.path.abspath('.')\n",
    "path_data = join(os.path.split(path_notebooks)[0], '/root/cosmos/galaxies-mzr/data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from marvin.tools.maps import Maps\n",
    "filename = join(path_data, 'manga-8077-6104-MAPS-HYB10-GAU-MILESHC.fits.gz')\n",
    "maps = Maps(filename=filename)\n",
    "#maps = Maps('8252-9102')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: No release version set. Setting default to DR15\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"Keyword 'MANGAID' not found.\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-fb120662ccae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmarvin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMaps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'manga_firefly-v2_4_3-STELLARPOP.fits'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmaps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMaps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m#maps = Maps('8252-9102')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/marvin/tools/maps.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input, filename, mangaid, plateifu, mode, data, release, drpall, download, nsa_source, bintype, template, template_kin)\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_origin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'file'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_maps_from_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_origin\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'db'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_maps_from_db\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/marvin/tools/maps.py\u001b[0m in \u001b[0;36m_load_maps_from_file\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmangaid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MANGAID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplateifu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PLATEIFU'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/astropy/io/fits/header.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0mkeyword\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m         \u001b[0mcard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cards\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cardindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfield_specifier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrawkeyword\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;31m# This is RVKC; if only the top-level keyword was specified return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/astropy/io/fits/header.py\u001b[0m in \u001b[0;36m_cardindex\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1653\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1654\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Keyword {!r} not found.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1656\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Keyword 'MANGAID' not found.\""
     ]
    }
   ],
   "source": [
    "#失败失败失败\n",
    "#直接使用Marvin读取fits文件\n",
    "#测试导入fits格式文件，提取各个元素的flux\n",
    "from marvin.tools.maps import Maps\n",
    "filename = join(path_data, 'manga_firefly-v2_4_3-STELLARPOP.fits')\n",
    "maps = Maps(filename=filename)\n",
    "#maps = Maps('8252-9102')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maps.emline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "nii = maps.emline_gflux_nii_6585\n",
    "ha = maps.emline_gflux_ha_6564\n",
    "oii7=maps.emline_gflux_oii_3727\n",
    "oii9=maps.emline_gflux_oii_3729"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:400: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return 1. / ((1. / ivar1 + 1. / ivar2))\n",
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:424: RuntimeWarning: divide by zero encountered in power\n",
      "  return np.log10(np.e) * ivar**-0.5 / value\n",
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:166: RuntimeWarning: invalid value encountered in log10\n",
      "  value=ufunc(self.value),\n"
     ]
    }
   ],
   "source": [
    "n2 = (oii7+oii9) / (2*ha)\n",
    "logn2 = np.log10(n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "oh = 0.57 * logn2 + 8.90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bit</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NOCOV</td>\n",
       "      <td>No coverage in this spaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LOWCOV</td>\n",
       "      <td>Low coverage in this spaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>DEADFIBER</td>\n",
       "      <td>Major contributing fiber is dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>FORESTAR</td>\n",
       "      <td>Foreground star</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NOVALUE</td>\n",
       "      <td>Spaxel was not fit because it did not meet sel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>UNRELIABLE</td>\n",
       "      <td>Value is deemed unreliable; see TRM for defini...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>MATHERROR</td>\n",
       "      <td>Mathematical error in computing value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>FITFAILED</td>\n",
       "      <td>Attempted fit for property failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>NEARBOUND</td>\n",
       "      <td>Fitted value is too near an imposed boundary; ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>NOCORRECTION</td>\n",
       "      <td>Appropriate correction not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>MULTICOMP</td>\n",
       "      <td>Multi-component velocity features present</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>DONOTUSE</td>\n",
       "      <td>Do not use this spaxel for science</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bit         label                                        description\n",
       "0     0         NOCOV                         No coverage in this spaxel\n",
       "1     1        LOWCOV                        Low coverage in this spaxel\n",
       "2     2     DEADFIBER                   Major contributing fiber is dead\n",
       "3     3      FORESTAR                                    Foreground star\n",
       "4     4       NOVALUE  Spaxel was not fit because it did not meet sel...\n",
       "5     5    UNRELIABLE  Value is deemed unreliable; see TRM for defini...\n",
       "6     6     MATHERROR              Mathematical error in computing value\n",
       "7     7     FITFAILED                  Attempted fit for property failed\n",
       "8     8     NEARBOUND  Fitted value is too near an imposed boundary; ...\n",
       "9     9  NOCORRECTION               Appropriate correction not available\n",
       "10   10     MULTICOMP          Multi-component velocity features present\n",
       "11   30      DONOTUSE                 Do not use this spaxel for science"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks_bpt, __, __ = maps.get_bpt()\n",
    "masks_bpt['sf']['global']\n",
    "n2.pixmask.schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')\n",
    "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])\n",
    "mask = mask_non_sf | mask_bad_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1073741825\n",
      "0\n",
      "(1, 35)\n"
     ]
    }
   ],
   "source": [
    "print(np.max(mask))\n",
    "print(np.min(mask))\n",
    "print(mask[np.where(mask[:,30]<10),0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1073741825, 1073741824, 1073741824, 1073741824, 1073741824],\n",
       "       [1073741824, 1073741824, 1073741824, 1073741824, 1073741824],\n",
       "       [1073741824, 1073741824, 1073741824, 1073741824, 1073741824]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask[22:25,3:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/utils/plot/map.py:111: RuntimeWarning: invalid value encountered in less\n",
      "  low_snr[np.abs(value * np.sqrt(ivar)) < snr_min] = True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = oh.plot(cmap='jet',mask=mask, cblabel='12+log(O/H)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax, cb=ha.plot(cmap='jet',cblabel='Flux( $10^{-17}$ erg s$^{-1}$) spaxel$^{-1}$ cm$^{-2}$',log_cb=True,cbrange=(0.1,100),return_cb=True)\n",
    "cb.set_ticks([0., 0.1, 1, 10, 100])\n",
    "cb.set_ticklabels(['$10^{-2}$','$10^{-1}$', '$10^{0}$', '$10^{1}$', '$10^{2}$'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 金属丰度和表面质量密度关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: No release version set. Setting default to DR15\n"
     ]
    }
   ],
   "source": [
    "import marvin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "marvin.tools?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/root/cosmos/galaxies-mzr/data'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'plateifu'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-385cce353e7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#使用pandas读取csv文件中的质量\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmstar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'manga-{}_mstar.csv'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplateifu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#mstar = pd.read_csv(join(path_data, 'sqlMastar-goodstars-v2_4_3.csv'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'plateifu'"
     ]
    }
   ],
   "source": [
    "#使用pandas读取csv文件中的质量\n",
    "import pandas as pd\n",
    "mstar = pd.read_csv(join(path_data, 'manga-{}_mstar.csv'.format(maps.plateifu)))\n",
    "\n",
    "#mstar = pd.read_csv(join(path_data, 'sqlMastar-goodstars-v2_4_3.csv'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mstar' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-100-9661f2868955>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#计算画出表面质量密度\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmstar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lower'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'spaxel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'spaxel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mstar' is not defined"
     ]
    },
    {
     "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": [
    "#计算画出表面质量密度\n",
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(mstar, origin='lower')\n",
    "ax.set_xlabel('spaxel')\n",
    "ax.set_ylabel('spaxel')\n",
    "cb = fig.colorbar(p)\n",
    "cb.set_label('log(Mstar) [M$_\\odot$]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算像素实际大小\n",
    "spaxel_size = 0.5  # [arcsec]\n",
    "redshift = maps.nsa['z']\n",
    "c = 299792  # speed of light [km/s]\n",
    "H0 = 70  # [km s^-1 Mpc^-1]\n",
    "D = c * redshift / H0  # approx. distance to galaxy [Mpc]\n",
    "scale = 1 / 206265 * D * 1e6  # 1 radian = 206265 arcsec [pc / arcsec]\n",
    "spaxel_area = (scale * spaxel_size)**2  # [pc^2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 4, 8.0, 8.8]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#计算质量密度\n",
    "sigma_star = np.log10(10**mstar / spaxel_area)  # [Msun / pc^2]\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n",
    "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n",
    "ax.set_ylabel('12+log(O/H)')\n",
    "ax.axis([0, 4, 8.0, 8.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8077-6104'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maps.plateifu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用一般方式导入读取fits文件数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Could not memory map array with mode='readonly', falling back to mode='denywrite', which means that the array will be read-only [astropy.io.fits.file]\n"
     ]
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "data1 = fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits',header=True)\n",
    "data2 = fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_1_2-STELLARPOP.fits',header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  138 / length of dimension 1                          \n",
       "NAXIS2  =                 4675 / length of dimension 2                          \n",
       "PCOUNT  =                    0 / number of group parameters                     \n",
       "GCOUNT  =                    1 / number of groups                               \n",
       "TFIELDS =                   11 / number of table fields                         \n",
       "TTYPE1  = 'MANGAID '                                                            \n",
       "TFORM1  = '20A     '                                                            \n",
       "TTYPE2  = 'PLATEIFU'                                                            \n",
       "TFORM2  = '20A     '                                                            \n",
       "TTYPE3  = 'PLATE   '                                                            \n",
       "TFORM3  = 'I       '                                                            \n",
       "TTYPE4  = 'IFUDSGN '                                                            \n",
       "TFORM4  = '20A     '                                                            \n",
       "TTYPE5  = 'OBJRA   '                                                            \n",
       "TFORM5  = 'E       '                                                            \n",
       "TTYPE6  = 'OBJDEC  '                                                            \n",
       "TFORM6  = 'E       '                                                            \n",
       "TTYPE7  = 'REDSHIFT'                                                            \n",
       "TFORM7  = 'E       '                                                            \n",
       "TTYPE8  = 'PHOTOMETRIC_MASS'                                                    \n",
       "TFORM8  = 'E       '                                                            \n",
       "TTYPE9  = 'MANGADRP_ver'                                                        \n",
       "TFORM9  = '20A     '                                                            \n",
       "TTYPE10 = 'MANGADAP_ver'                                                        \n",
       "TFORM10 = '20A     '                                                            \n",
       "TTYPE11 = 'FIREFLY_ver'                                                         \n",
       "TFORM11 = '20A     '                                                            \n",
       "EXTNAME = 'GALAXY_INFO'        / extension name                                 "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  138 / length of dimension 1                          \n",
       "NAXIS2  =                 2777 / length of dimension 2                          \n",
       "PCOUNT  =                    0 / number of group parameters                     \n",
       "GCOUNT  =                    1 / number of groups                               \n",
       "TFIELDS =                   11 / number of table fields                         \n",
       "TTYPE1  = 'MANGAID '                                                            \n",
       "TFORM1  = '20A     '                                                            \n",
       "TTYPE2  = 'PLATEIFU'                                                            \n",
       "TFORM2  = '20A     '                                                            \n",
       "TTYPE3  = 'PLATE   '                                                            \n",
       "TFORM3  = 'INT16   '                                                            \n",
       "TTYPE4  = 'IFUDSGN '                                                            \n",
       "TFORM4  = '20A     '                                                            \n",
       "TTYPE5  = 'OBJRA   '                                                            \n",
       "TFORM5  = 'E       '                                                            \n",
       "TTYPE6  = 'OBJDEC  '                                                            \n",
       "TFORM6  = 'E       '                                                            \n",
       "TTYPE7  = 'REDSHIFT'                                                            \n",
       "TFORM7  = 'E       '                                                            \n",
       "TTYPE8  = 'PHOTOMETRIC_MASS'                                                    \n",
       "TFORM8  = 'E       '                                                            \n",
       "TTYPE9  = 'MANGADRP_ver'                                                        \n",
       "TFORM9  = '20A     '                                                            \n",
       "TTYPE10 = 'MANGADAP_ver'                                                        \n",
       "TFORM10 = '20A     '                                                            \n",
       "TTYPE11 = 'FIREFLY_ver'                                                         \n",
       "TFORM11 = '20A     '                                                            \n",
       "EXTNAME = 'GALAXY_INFO'        / extension name                                 "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `choose` not found.\n"
     ]
    }
   ],
   "source": [
    "data1[0].choose?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-6d146e74e956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoose\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data1' is not defined"
     ]
    }
   ],
   "source": [
    "data1[0].choose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "lalala = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits')\n",
    "lala=fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits',header=True)\n",
    "data2 = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_1_2-STELLARPOP.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /root/cosmos/galaxies-mzr/data/manga_firefly-v2_1_2-STELLARPOP.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU       4   ()      \n",
      "  1  GALAXY_INFO    1 BinTableHDU     31   2777R x 11C   [20A, 20A, INT16, 20A, E, E, E, E, 20A, 20A, 20A]   \n",
      "  2  GLOBAL_PARAMETERS    1 BinTableHDU     41   2777R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  3  GRADIENT_PARAMETERS    1 BinTableHDU     41   2777R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  4  SPATIAL_INFO    1 ImageHDU         9   (4, 2800, 2777)   float32   \n",
      "  5  SPAXEL_BINID    1 ImageHDU        10   (6, 100, 100, 2777)   float64   \n",
      "  6  LW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 2777)   float32   \n",
      "  7  MW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 2777)   float32   \n",
      "  8  LW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 2777)   float32   \n",
      "  9  MW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 2777)   float32   \n",
      " 10  E(B_V)_VORONOI    1 ImageHDU         8   (2800, 2777)   float32   \n",
      " 11  STELLAR_MASS_VORONOI    1 ImageHDU         9   (2, 2800, 2777)   float32   \n",
      " 12  SURFACE_MASS_DENSITY_VORONOI    1 ImageHDU         9   (2, 2800, 2777)   float32   \n",
      " 13  SIGNAL_NOISE_VORONOI    1 ImageHDU         8   (2800, 2777)   float32   \n"
     ]
    }
   ],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.40308e+09 4.32497e+10 6.19020e+09 ... 4.59672e+09 1.08103e+11\n",
      " 1.60425e+09]\n"
     ]
    }
   ],
   "source": [
    "sm = lalala[1].data['PHOTOMETRIC_MASS']\n",
    "print(sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4675,)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  138 / length of dimension 1                          \n",
       "NAXIS2  =                 4675 / length of dimension 2                          \n",
       "PCOUNT  =                    0 / number of group parameters                     \n",
       "GCOUNT  =                    1 / number of groups                               \n",
       "TFIELDS =                   11 / number of table fields                         \n",
       "TTYPE1  = 'MANGAID '                                                            \n",
       "TFORM1  = '20A     '                                                            \n",
       "TTYPE2  = 'PLATEIFU'                                                            \n",
       "TFORM2  = '20A     '                                                            \n",
       "TTYPE3  = 'PLATE   '                                                            \n",
       "TFORM3  = 'I       '                                                            \n",
       "TTYPE4  = 'IFUDSGN '                                                            \n",
       "TFORM4  = '20A     '                                                            \n",
       "TTYPE5  = 'OBJRA   '                                                            \n",
       "TFORM5  = 'E       '                                                            \n",
       "TTYPE6  = 'OBJDEC  '                                                            \n",
       "TFORM6  = 'E       '                                                            \n",
       "TTYPE7  = 'REDSHIFT'                                                            \n",
       "TFORM7  = 'E       '                                                            \n",
       "TTYPE8  = 'PHOTOMETRIC_MASS'                                                    \n",
       "TFORM8  = 'E       '                                                            \n",
       "TTYPE9  = 'MANGADRP_ver'                                                        \n",
       "TFORM9  = '20A     '                                                            \n",
       "TTYPE10 = 'MANGADAP_ver'                                                        \n",
       "TFORM10 = '20A     '                                                            \n",
       "TTYPE11 = 'FIREFLY_ver'                                                         \n",
       "TFORM11 = '20A     '                                                            \n",
       "EXTNAME = 'GALAXY_INFO'        / extension name                                 "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lalala[1].header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / binary table extension                         \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  138 / length of dimension 1                          \n",
       "NAXIS2  =                 4675 / length of dimension 2                          \n",
       "PCOUNT  =                    0 / number of group parameters                     \n",
       "GCOUNT  =                    1 / number of groups                               \n",
       "TFIELDS =                   11 / number of table fields                         \n",
       "TTYPE1  = 'MANGAID '                                                            \n",
       "TFORM1  = '20A     '                                                            \n",
       "TTYPE2  = 'PLATEIFU'                                                            \n",
       "TFORM2  = '20A     '                                                            \n",
       "TTYPE3  = 'PLATE   '                                                            \n",
       "TFORM3  = 'I       '                                                            \n",
       "TTYPE4  = 'IFUDSGN '                                                            \n",
       "TFORM4  = '20A     '                                                            \n",
       "TTYPE5  = 'OBJRA   '                                                            \n",
       "TFORM5  = 'E       '                                                            \n",
       "TTYPE6  = 'OBJDEC  '                                                            \n",
       "TFORM6  = 'E       '                                                            \n",
       "TTYPE7  = 'REDSHIFT'                                                            \n",
       "TFORM7  = 'E       '                                                            \n",
       "TTYPE8  = 'PHOTOMETRIC_MASS'                                                    \n",
       "TFORM8  = 'E       '                                                            \n",
       "TTYPE9  = 'MANGADRP_ver'                                                        \n",
       "TFORM9  = '20A     '                                                            \n",
       "TTYPE10 = 'MANGADAP_ver'                                                        \n",
       "TFORM10 = '20A     '                                                            \n",
       "TTYPE11 = 'FIREFLY_ver'                                                         \n",
       "TFORM11 = '20A     '                                                            \n",
       "EXTNAME = 'GALAXY_INFO'        / extension name                                 "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lala[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU       4   ()      \n",
      "  1  GALAXY_INFO    1 BinTableHDU     31   4675R x 11C   [20A, 20A, I, 20A, E, E, E, E, 20A, 20A, 20A]   \n",
      "  2  GLOBAL_PARAMETERS    1 BinTableHDU     41   4675R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  3  GRADIENT_PARAMETERS    1 BinTableHDU     41   4675R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  4  SPATIAL_INFO    1 ImageHDU         9   (4, 2800, 4675)   float32   \n",
      "  5  SPATIAL_BINID    1 ImageHDU         9   (76, 76, 4675)   int16   \n",
      "  6  LW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  7  MW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  8  LW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  9  MW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 10  E(B_V)_VORONOI    1 ImageHDU         8   (2800, 4675)   float32   \n",
      " 11  STELLAR_MASS_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 12  STELLAR_MASS_REMNANT    1 ImageHDU         9   (4, 2800, 4675)   float32   \n",
      " 13  SURFACE_MASS_DENSITY_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 14  STAR_FORMATION_HISTORY    1 ImageHDU        10   (3, 8, 2800, 4675)   float32   \n",
      " 15  SIGNAL_NOISE_VORONOI    1 ImageHDU         8   (2800, 4675)   float32   \n"
     ]
    }
   ],
   "source": [
    "lalala.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot1=lalala[11].data[1]\n",
    "plot2=lalala[11].data[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lalala[11].data[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2800, 2)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(plot1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.8200140e+00,  6.5209389e-02],\n",
       "       [ 8.6450930e+00,  1.9699287e-01],\n",
       "       [ 8.7641287e+00,  7.0210457e-02],\n",
       "       ...,\n",
       "       [-9.9990000e+03, -9.9990000e+03],\n",
       "       [-9.9990000e+03, -9.9990000e+03],\n",
       "       [-9.9990000e+03, -9.9990000e+03]], dtype=float32)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[[0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " \\dots,~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0],~\n",
       " [0,~0,~0,~\\dots,~0,~0,~0]] \\; \\mathrm{1 \\times 10^{-17}\\,\\frac{erg}{s\\,spaxel\\,cm^{2}}}$"
      ],
      "text/plain": [
       "<Marvin Map (property='emline_gflux_nii_6585')>\n",
       "[[0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " ...\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]\n",
       " [0. 0. 0. ... 0. 0. 0.]] 1e-17 erg / (cm2 s spaxel)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "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.imshow(plot1, cmap= 'gray');\n",
    "#plt.colorbar();\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从Marvin获取stellar mass数据及surface mass density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division, absolute_import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from marvin import config\n",
    "config.setRelease('DR15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: No release version set. Setting default to DR15\n"
     ]
    }
   ],
   "source": [
    "from marvin.tools.query import Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/query.py:236: MarvinUserWarning: No local database found. Cannot perform queries.\n",
      "  warnings.warn('No local database found. Cannot perform queries.', MarvinUserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=1.0/1, index=0:22, count_in_set=22, total=22)>\n",
       "[ResultRow(mangaid='1-134760', plateifu='8555-12701', sersic_mass=561190000000.0),\n",
       " ResultRow(mangaid='1-134760', plateifu='8600-9102', sersic_mass=561190000000.0),\n",
       " ResultRow(mangaid='1-146420', plateifu='8938-3704', sersic_mass=440193000000.0),\n",
       " ResultRow(mangaid='1-176803', plateifu='8613-12705', sersic_mass=498718000000.0),\n",
       " ResultRow(mangaid='1-177135', plateifu='8613-6102', sersic_mass=339921000000.0),\n",
       " ResultRow(mangaid='1-180153', plateifu='8616-12703', sersic_mass=366874000000.0),\n",
       " ResultRow(mangaid='1-217041', plateifu='8247-9102', sersic_mass=328593000000.0),\n",
       " ResultRow(mangaid='1-254532', plateifu='8454-1902', sersic_mass=1355340000000.0),\n",
       " ResultRow(mangaid='1-256301', plateifu='8258-9102', sersic_mass=7116800000000.0),\n",
       " ResultRow(mangaid='1-265907', plateifu='8333-12702', sersic_mass=711371000000.0),\n",
       " ResultRow(mangaid='1-278075', plateifu='8257-6104', sersic_mass=369127000000.0),\n",
       " ResultRow(mangaid='1-283711', plateifu='8317-1901', sersic_mass=2579810000000.0),\n",
       " ResultRow(mangaid='1-295303', plateifu='9048-3703', sersic_mass=314463000000.0),\n",
       " ResultRow(mangaid='1-314409', plateifu='9024-12705', sersic_mass=506441000000.0),\n",
       " ResultRow(mangaid='1-317388', plateifu='9888-6104', sersic_mass=349033000000.0),\n",
       " ResultRow(mangaid='1-338090', plateifu='8131-3701', sersic_mass=1150580000000.0),\n",
       " ResultRow(mangaid='1-374107', plateifu='9045-12705', sersic_mass=326909000000.0),\n",
       " ResultRow(mangaid='1-377378', plateifu='8132-6102', sersic_mass=513575000000.0),\n",
       " ResultRow(mangaid='1-377841', plateifu='8133-12705', sersic_mass=317071000000.0),\n",
       " ResultRow(mangaid='1-45787', plateifu='8725-12704', sersic_mass=915778000000.0),\n",
       " ResultRow(mangaid='1-550913', plateifu='8615-12704', sersic_mass=452247000000.0),\n",
       " ResultRow(mangaid='1-71481', plateifu='8717-1901', sersic_mass=321271000000.0)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最简单的查询\n",
    "myquery1 = 'nsa.sersic_mass > 3e11'\n",
    "q1 = Query(search_filter=myquery1)\n",
    "r1 = q1.run()\n",
    "r1.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=1.0/1, index=0:7, count_in_set=7, total=7)>\n",
       "[ResultRow(mangaid='1-176803', plateifu='8613-12705', sersic_mass=498718000000.0, z=0.0992977),\n",
       " ResultRow(mangaid='1-177135', plateifu='8613-6102', sersic_mass=339921000000.0, z=0.0863756),\n",
       " ResultRow(mangaid='1-256301', plateifu='8258-9102', sersic_mass=7116800000000.0, z=0.0961407),\n",
       " ResultRow(mangaid='1-283711', plateifu='8317-1901', sersic_mass=2579810000000.0, z=0.094444),\n",
       " ResultRow(mangaid='1-314409', plateifu='9024-12705', sersic_mass=506441000000.0, z=0.0601609),\n",
       " ResultRow(mangaid='1-338090', plateifu='8131-3701', sersic_mass=1150580000000.0, z=0.0806724),\n",
       " ResultRow(mangaid='1-377378', plateifu='8132-6102', sersic_mass=513575000000.0, z=0.0265521)]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 两个条件限定查询\n",
    "myquery2 = 'nsa.sersic_mass > 3e11 AND nsa.z < 0.1'\n",
    "q2 = Query(search_filter=myquery2)\n",
    "r2 = q2.run()\n",
    "r2.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=1.0/1, index=0:36, count_in_set=36, total=36)>\n",
       "[ResultRow(mangaid='1-145922', plateifu='8149-12704', sersic_mass=42171500000.0, elpetro_ba=0.986297, ifudesign_name='12704', z=0.026607),\n",
       " ResultRow(mangaid='1-149507', plateifu='8997-12705', sersic_mass=28400300000.0, elpetro_ba=0.953317, ifudesign_name='12705', z=0.0322763),\n",
       " ResultRow(mangaid='1-176803', plateifu='8613-12705', sersic_mass=498718000000.0, elpetro_ba=0.789834, ifudesign_name='12705', z=0.0992977),\n",
       " ResultRow(mangaid='1-177135', plateifu='8613-6102', sersic_mass=339921000000.0, elpetro_ba=0.94393, ifudesign_name='6102', z=0.0863756),\n",
       " ResultRow(mangaid='1-197186', plateifu='9864-12701', sersic_mass=119911000000.0, elpetro_ba=0.970726, ifudesign_name='12701', z=0.074555),\n",
       " ResultRow(mangaid='1-198212', plateifu='9034-12704', sersic_mass=87799700000.0, elpetro_ba=0.976644, ifudesign_name='12704', z=0.037839),\n",
       " ResultRow(mangaid='1-210881', plateifu='8604-12701', sersic_mass=113843000000.0, elpetro_ba=0.980852, ifudesign_name='12701', z=0.0350385),\n",
       " ResultRow(mangaid='1-211017', plateifu='8312-12703', sersic_mass=25590900000.0, elpetro_ba=0.961728, ifudesign_name='12703', z=0.0358501),\n",
       " ResultRow(mangaid='1-218295', plateifu='8942-12704', sersic_mass=2184700000.0, elpetro_ba=0.955902, ifudesign_name='12704', z=0.0376719),\n",
       " ResultRow(mangaid='1-251764', plateifu='8335-12702', sersic_mass=1883260000.0, elpetro_ba=0.956245, ifudesign_name='12702', z=0.0188389),\n",
       " ResultRow(mangaid='1-256301', plateifu='8258-9102', sersic_mass=7116800000000.0, elpetro_ba=0.969656, ifudesign_name='9102', z=0.0961407),\n",
       " ResultRow(mangaid='1-258425', plateifu='8261-12702', sersic_mass=84189200000.0, elpetro_ba=0.989192, ifudesign_name='12702', z=0.112247),\n",
       " ResultRow(mangaid='1-260733', plateifu='8447-12701', sersic_mass=15064300000.0, elpetro_ba=0.950636, ifudesign_name='12701', z=0.0507247),\n",
       " ResultRow(mangaid='1-265863', plateifu='8333-12705', sersic_mass=8705150000.0, elpetro_ba=0.950169, ifudesign_name='12705', z=0.0259548),\n",
       " ResultRow(mangaid='1-272361', plateifu='9027-12703', sersic_mass=38981600000.0, elpetro_ba=0.953689, ifudesign_name='12703', z=0.0460632),\n",
       " ResultRow(mangaid='1-283711', plateifu='8317-1901', sersic_mass=2579810000000.0, elpetro_ba=0.60888, ifudesign_name='1901', z=0.094444),\n",
       " ResultRow(mangaid='12-84674', plateifu='7443-12702', sersic_mass=25869000000.0, elpetro_ba=0.96167, ifudesign_name='12702', z=0.0575241),\n",
       " ResultRow(mangaid='1-285095', plateifu='8319-12701', sersic_mass=132829000000.0, elpetro_ba=0.961789, ifudesign_name='12701', z=0.0571118),\n",
       " ResultRow(mangaid='1-314013', plateifu='9024-12703', sersic_mass=86073400000.0, elpetro_ba=0.96122, ifudesign_name='12703', z=0.086961),\n",
       " ResultRow(mangaid='1-314409', plateifu='9024-12705', sersic_mass=506441000000.0, elpetro_ba=0.826747, ifudesign_name='12705', z=0.0601609),\n",
       " ResultRow(mangaid='1-322277', plateifu='9871-12704', sersic_mass=2458230000.0, elpetro_ba=0.973166, ifudesign_name='12704', z=0.0180004),\n",
       " ResultRow(mangaid='1-338090', plateifu='8131-3701', sersic_mass=1150580000000.0, elpetro_ba=0.744847, ifudesign_name='3701', z=0.0806724),\n",
       " ResultRow(mangaid='1-338629', plateifu='8132-12703', sersic_mass=157488000000.0, elpetro_ba=0.959828, ifudesign_name='12703', z=0.116056),\n",
       " ResultRow(mangaid='1-37695', plateifu='8080-12705', sersic_mass=12024000000.0, elpetro_ba=0.962273, ifudesign_name='12705', z=0.0282305),\n",
       " ResultRow(mangaid='1-377378', plateifu='8132-6102', sersic_mass=513575000000.0, elpetro_ba=0.674425, ifudesign_name='6102', z=0.0265521),\n",
       " ResultRow(mangaid='1-385222', plateifu='9501-12703', sersic_mass=38873200000.0, elpetro_ba=0.993161, ifudesign_name='12703', z=0.0579295),\n",
       " ResultRow(mangaid='1-386272', plateifu='8987-12701', sersic_mass=202142000000.0, elpetro_ba=0.975283, ifudesign_name='12701', z=0.143701),\n",
       " ResultRow(mangaid='1-38894', plateifu='8156-12703', sersic_mass=3576410000.0, elpetro_ba=0.975828, ifudesign_name='12703', z=0.0417053),\n",
       " ResultRow(mangaid='1-402409', plateifu='8330-12705', sersic_mass=148245000000.0, elpetro_ba=0.959511, ifudesign_name='12705', z=0.117655),\n",
       " ResultRow(mangaid='1-45151', plateifu='9487-12702', sersic_mass=2684040000.0, elpetro_ba=0.973668, ifudesign_name='12702', z=0.0229193),\n",
       " ResultRow(mangaid='1-456894', plateifu='9863-12703', sersic_mass=4969600000.0, elpetro_ba=0.953238, ifudesign_name='12703', z=0.0248898),\n",
       " ResultRow(mangaid='1-586585', plateifu='8454-12705', sersic_mass=51579100000.0, elpetro_ba=0.968421, ifudesign_name='12705', z=0.0234362),\n",
       " ResultRow(mangaid='1-593748', plateifu='9891-12703', sersic_mass=931687000.0, elpetro_ba=0.968276, ifudesign_name='12703', z=0.0175355),\n",
       " ResultRow(mangaid='1-633196', plateifu='8485-12703', sersic_mass=84212200000.0, elpetro_ba=0.95488, ifudesign_name='12703', z=0.0380864),\n",
       " ResultRow(mangaid='1-90849', plateifu='8481-12703', sersic_mass=49861900000.0, elpetro_ba=0.973022, ifudesign_name='12703', z=0.0661473),\n",
       " ResultRow(mangaid='1-95112', plateifu='8588-12703', sersic_mass=26217600000.0, elpetro_ba=0.965881, ifudesign_name='12703', z=0.0327634)]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 复合搜索\n",
    "myquery3 = '(nsa.sersic_mass > 3e11 AND nsa.z < 0.1) OR (ifu.name=127* AND nsa.elpetro_ba >= 0.95)'\n",
    "q3 = Query(search_filter=myquery3)\n",
    "r3 = q3.run()\n",
    "r3.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<ParameterGroup name=Metadata, n_parameters=7>,\n",
       " <ParameterGroup name=NSA Catalog, n_parameters=11>]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询可以用的参数有哪些\n",
    "q = Query()\n",
    "q.get_available_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 好像是？？？输出csv文件\n",
    "q.datamodel.write_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "Query?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过一定条件限定，返回相应的其他参数的值\n",
    "myquery5 = 'nsa.z > 0.1'\n",
    "bonusparams5 = ['cube.ra', 'cube.dec']\n",
    "# bonusparams5 = 'cube.ra'  # This works too\n",
    "\n",
    "q5 = Query(search_filter=myquery5, return_params=bonusparams5)\n",
    "r5 = q5.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=1.0/1, index=0:332, count_in_set=332, total=332)>\n",
       "[ResultRow(mangaid='1-109308', plateifu='8082-12704', ra=49.9495621925, dec=-0.22114540619, z=0.131681),\n",
       " ResultRow(mangaid='1-109419', plateifu='8085-3702', ra=51.22895826, dec=0.605956492656, z=0.103253),\n",
       " ResultRow(mangaid='1-109493', plateifu='8156-9101', ra=56.4251416467, dec=-0.378460418812, z=0.10932),\n",
       " ResultRow(mangaid='1-113292', plateifu='7972-9101', ra=316.043643235, dec=8.99511499267, z=0.102271),\n",
       " ResultRow(mangaid='1-113468', plateifu='7815-3701', ra=317.918280237, dec=9.34565198917, z=0.136342),\n",
       " ResultRow(mangaid='1-113476', plateifu='8618-12705', ra=317.568825378, dec=10.4088108392, z=0.118158),\n",
       " ResultRow(mangaid='1-113521', plateifu='7815-6103', ra=317.388736588, dec=11.5029226993, z=0.135043),\n",
       " ResultRow(mangaid='1-113668', plateifu='7815-6102', ra=318.827736002, dec=10.4968107833, z=0.129233),\n",
       " ResultRow(mangaid='1-113675', plateifu='8618-3702', ra=318.775640877, dec=10.0984558649, z=0.129881),\n",
       " ResultRow(mangaid='1-114973', plateifu='7977-12702', ra=332.238573881, dec=11.8776065621, z=0.12046),\n",
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       " ResultRow(mangaid='1-576172', plateifu='8989-6103', ra=174.993504966, dec=51.1513445994, z=0.128867),\n",
       " ResultRow(mangaid='1-576188', plateifu='8947-3704', ra=169.475782191, dec=49.5933752164, z=0.128062),\n",
       " ResultRow(mangaid='1-576235', plateifu='8999-3702', ra=163.433876181, dec=49.4988684961, z=0.140443),\n",
       " ResultRow(mangaid='1-576269', plateifu='8481-6104', ra=240.367914805, dec=53.9364085499, z=0.106607),\n",
       " ResultRow(mangaid='1-576333', plateifu='8592-12705', ra=222.146717119, dec=51.3855754913, z=0.131503),\n",
       " ResultRow(mangaid='1-609414', plateifu='8943-12705', ra=157.93471, dec=35.04137, z=0.119496),\n",
       " ResultRow(mangaid='1-631872', plateifu='9891-3702', ra=226.943577224, dec=28.8199794804, z=0.110593),\n",
       " ResultRow(mangaid='1-632641', plateifu='9043-12703', ra=231.27392, dec=29.67017, z=0.111541),\n",
       " ResultRow(mangaid='1-634093', plateifu='8978-12702', ra=249.587768862, dec=40.8172085562, z=0.133016),\n",
       " ResultRow(mangaid='1-639152', plateifu='8728-1902', ra=58.2919930394, dec=-5.49709736449, z=0.120884),\n",
       " ResultRow(mangaid='1-71481', plateifu='8717-1901', ra=117.456001215, dec=34.8839120986, z=0.131157),\n",
       " ResultRow(mangaid='1-71736', plateifu='9181-3702', ra=118.449505659, dec=37.7025165735, z=0.107061),\n",
       " ResultRow(mangaid='1-71811', plateifu='9485-6103', ra=120.132625014, dec=37.8628713704, z=0.149622),\n",
       " ResultRow(mangaid='1-71820', plateifu='9182-1902', ra=120.647559412, dec=38.7411877612, z=0.116392),\n",
       " ResultRow(mangaid='1-71950', plateifu='9181-12701', ra=118.5709466, dec=38.220893904, z=0.117955),\n",
       " ResultRow(mangaid='1-72322', plateifu='8143-6101', ra=121.014200537, dec=40.8026129505, z=0.126168),\n",
       " ResultRow(mangaid='1-72928', plateifu='8725-6101', ra=127.256488342, dec=45.0167725695, z=0.127041),\n",
       " ResultRow(mangaid='1-73184', plateifu='8725-6104', ra=127.528168009, dec=45.3517505123, z=0.148244),\n",
       " ResultRow(mangaid='1-73584', plateifu='8244-3704', ra=132.627858985, dec=50.2789266122, z=0.12215),\n",
       " ResultRow(mangaid='1-73638', plateifu='8244-12704', ra=133.035323408, dec=51.3127390765, z=0.115137),\n",
       " ResultRow(mangaid='1-73667', plateifu='8244-12701', ra=133.06809411, dec=50.627237501, z=0.112362),\n",
       " ResultRow(mangaid='1-91550', plateifu='8481-3704', ra=240.186697059, dec=53.7755312455, z=0.110132),\n",
       " ResultRow(mangaid='1-93471', plateifu='8483-6104', ra=247.694974246, dec=47.7947656349, z=0.127959),\n",
       " ResultRow(mangaid='1-93872', plateifu='8555-6103', ra=247.097142285, dec=43.9576126147, z=0.139743),\n",
       " ResultRow(mangaid='1-93876', plateifu='8484-9101', ra=246.942944115, dec=44.177521384, z=0.139404),\n",
       " ResultRow(mangaid='1-93876', plateifu='8555-3704', ra=246.942944115, dec=44.177521384, z=0.139404)]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r5.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/query.py:236: MarvinUserWarning: No local database found. Cannot perform queries.\n",
      "  warnings.warn('No local database found. Cannot perform queries.', MarvinUserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4275"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用Marvin中的convertToTool. convertToTool来转换数据为fits格式或者csv格式等\n",
    "from marvin.tools.query import Query\n",
    "q = Query(search_filter='nsa.z < 0.1', return_params=['absmag_g_r'])\n",
    "r = q.run()\n",
    "\n",
    "# number of results\n",
    "r.totalcount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=1.0/43, index=0:100, count_in_set=100, total=4275)>\n",
       "[ResultRow(mangaid='1-109056', plateifu='8077-6103', elpetro_absmag_g_r=1.11703872680664, z=0.0473019),\n",
       " ResultRow(mangaid='1-109073', plateifu='8078-12704', elpetro_absmag_g_r=1.05978775024414, z=0.0435882),\n",
       " ResultRow(mangaid='1-109081', plateifu='8077-12705', elpetro_absmag_g_r=1.23031806945801, z=0.0438592),\n",
       " ResultRow(mangaid='1-109112', plateifu='8078-1901', elpetro_absmag_g_r=1.5319881439209, z=0.0249702),\n",
       " ResultRow(mangaid='1-109152', plateifu='8154-12702', elpetro_absmag_g_r=1.5439453125, z=0.0281736),\n",
       " ResultRow(mangaid='1-109167', plateifu='8078-1902', elpetro_absmag_g_r=1.65657424926758, z=0.0246344),\n",
       " ResultRow(mangaid='1-109234', plateifu='9193-1901', elpetro_absmag_g_r=1.52570343017578, z=0.0409309),\n",
       " ResultRow(mangaid='1-109244', plateifu='8080-12702', elpetro_absmag_g_r=1.52271270751953, z=0.0267911),\n",
       " ResultRow(mangaid='1-109250', plateifu='8080-1902', elpetro_absmag_g_r=0.780342102050781, z=0.0383731),\n",
       " ResultRow(mangaid='1-109270', plateifu='9193-12703', elpetro_absmag_g_r=1.28193092346191, z=0.0252266),\n",
       " ResultRow(mangaid='1-109284', plateifu='8080-6103', elpetro_absmag_g_r=1.13128662109375, z=0.0686101),\n",
       " ResultRow(mangaid='1-109356', plateifu='8081-3703', elpetro_absmag_g_r=1.01610565185547, z=0.0241782),\n",
       " ResultRow(mangaid='1-109378', plateifu='8084-12703', elpetro_absmag_g_r=1.15368461608887, z=0.0391104),\n",
       " ResultRow(mangaid='1-109392', plateifu='8085-6101', elpetro_absmag_g_r=1.57899284362793, z=0.0514803),\n",
       " ResultRow(mangaid='1-109394', plateifu='8082-9102', elpetro_absmag_g_r=1.26038932800293, z=0.0361073),\n",
       " ResultRow(mangaid='1-109428', plateifu='8155-3704', elpetro_absmag_g_r=1.65565872192383, z=0.0476594),\n",
       " ResultRow(mangaid='1-109521', plateifu='8156-6101', elpetro_absmag_g_r=1.79952621459961, z=0.0490484),\n",
       " ResultRow(mangaid='1-113208', plateifu='8618-3701', elpetro_absmag_g_r=1.48788070678711, z=0.0699044),\n",
       " ResultRow(mangaid='1-113219', plateifu='7815-9102', elpetro_absmag_g_r=0.543312072753906, z=0.0408897),\n",
       " ResultRow(mangaid='1-113242', plateifu='7972-6101', elpetro_absmag_g_r=1.90381813049316, z=0.0437197),\n",
       " ResultRow(mangaid='1-113265', plateifu='7972-12703', elpetro_absmag_g_r=1.04117393493652, z=0.0437398),\n",
       " ResultRow(mangaid='1-113315', plateifu='7972-3702', elpetro_absmag_g_r=1.66540908813477, z=0.0622162),\n",
       " ResultRow(mangaid='1-113322', plateifu='7972-12701', elpetro_absmag_g_r=2.22256088256836, z=0.0431628),\n",
       " ResultRow(mangaid='1-113343', plateifu='7972-1901', elpetro_absmag_g_r=1.6228084564209, z=0.0433949),\n",
       " ResultRow(mangaid='1-113346', plateifu='7972-12702', elpetro_absmag_g_r=0.943971633911133, z=0.0431389),\n",
       " ResultRow(mangaid='1-113368', plateifu='7972-6103', elpetro_absmag_g_r=1.61076927185059, z=0.0431534),\n",
       " ResultRow(mangaid='1-113375', plateifu='7972-12704', elpetro_absmag_g_r=0.757579803466797, z=0.028215),\n",
       " ResultRow(mangaid='1-113375', plateifu='7815-9101', elpetro_absmag_g_r=0.757579803466797, z=0.028215),\n",
       " ResultRow(mangaid='1-113379', plateifu='7972-3701', elpetro_absmag_g_r=1.09770011901855, z=0.0171611),\n",
       " ResultRow(mangaid='1-113379', plateifu='7815-6101', elpetro_absmag_g_r=1.09770011901855, z=0.0171611),\n",
       " ResultRow(mangaid='1-113403', plateifu='7815-12703', elpetro_absmag_g_r=0.745466232299805, z=0.0715126),\n",
       " ResultRow(mangaid='1-113404', plateifu='7972-6104', elpetro_absmag_g_r=1.0419807434082, z=0.0422713),\n",
       " ResultRow(mangaid='1-113405', plateifu='7972-3704', elpetro_absmag_g_r=1.27382469177246, z=0.0424723),\n",
       " ResultRow(mangaid='1-113418', plateifu='7815-12704', elpetro_absmag_g_r=1.44098854064941, z=0.0430806),\n",
       " ResultRow(mangaid='1-113469', plateifu='7972-12705', elpetro_absmag_g_r=0.847789764404297, z=0.0394617),\n",
       " ResultRow(mangaid='1-113469', plateifu='7815-12702', elpetro_absmag_g_r=0.847789764404297, z=0.0394617),\n",
       " ResultRow(mangaid='1-113518', plateifu='7972-6102', elpetro_absmag_g_r=0.380338668823242, z=0.0299975),\n",
       " ResultRow(mangaid='1-113520', plateifu='7815-1901', elpetro_absmag_g_r=1.7510347366333, z=0.0167652),\n",
       " ResultRow(mangaid='1-113525', plateifu='8618-6103', elpetro_absmag_g_r=1.57906627655029, z=0.0169457),\n",
       " ResultRow(mangaid='1-113525', plateifu='7815-1902', elpetro_absmag_g_r=1.57906627655029, z=0.0169457),\n",
       " ResultRow(mangaid='1-113525', plateifu='7972-9102', elpetro_absmag_g_r=1.57906627655029, z=0.0169457),\n",
       " ResultRow(mangaid='1-113539', plateifu='8618-12701', elpetro_absmag_g_r=1.26716613769531, z=0.0177002),\n",
       " ResultRow(mangaid='1-113540', plateifu='7815-3702', elpetro_absmag_g_r=0.952407836914062, z=0.0293823),\n",
       " ResultRow(mangaid='1-113551', plateifu='7972-3703', elpetro_absmag_g_r=1.46421432495117, z=0.0588507),\n",
       " ResultRow(mangaid='1-113567', plateifu='7815-12701', elpetro_absmag_g_r=1.41732978820801, z=0.0167432),\n",
       " ResultRow(mangaid='1-113567', plateifu='8618-1902', elpetro_absmag_g_r=1.41732978820801, z=0.0167432),\n",
       " ResultRow(mangaid='1-113585', plateifu='7815-3703', elpetro_absmag_g_r=1.68158912658691, z=0.070276),\n",
       " ResultRow(mangaid='1-113587', plateifu='8618-12704', elpetro_absmag_g_r=1.02355575561523, z=0.0704926),\n",
       " ResultRow(mangaid='1-113647', plateifu='8618-6104', elpetro_absmag_g_r=1.78754997253418, z=0.0738563),\n",
       " ResultRow(mangaid='1-113651', plateifu='7815-3704', elpetro_absmag_g_r=1.4986743927002, z=0.0708847),\n",
       " ResultRow(mangaid='1-113654', plateifu='8618-9102', elpetro_absmag_g_r=1.10831832885742, z=0.0430694),\n",
       " ResultRow(mangaid='1-113663', plateifu='8618-3703', elpetro_absmag_g_r=2.80322933197021, z=0.0316328),\n",
       " ResultRow(mangaid='1-113672', plateifu='8618-3704', elpetro_absmag_g_r=1.25676536560059, z=0.0702278),\n",
       " ResultRow(mangaid='1-113698', plateifu='8618-1901', elpetro_absmag_g_r=0.995195388793945, z=0.0167445),\n",
       " ResultRow(mangaid='1-113700', plateifu='8618-12703', elpetro_absmag_g_r=0.61408805847168, z=0.0378372),\n",
       " ResultRow(mangaid='1-113712', plateifu='7815-6104', elpetro_absmag_g_r=0.69244384765625, z=0.0806967),\n",
       " ResultRow(mangaid='1-114073', plateifu='7975-12705', elpetro_absmag_g_r=0.751516342163086, z=0.0402895),\n",
       " ResultRow(mangaid='1-114082', plateifu='7975-3701', elpetro_absmag_g_r=1.44381332397461, z=0.0402683),\n",
       " ResultRow(mangaid='1-114121', plateifu='7975-12701', elpetro_absmag_g_r=1.43171119689941, z=0.0879313),\n",
       " ResultRow(mangaid='1-114128', plateifu='7975-6101', elpetro_absmag_g_r=1.86342239379883, z=0.077875),\n",
       " ResultRow(mangaid='1-114129', plateifu='7975-12702', elpetro_absmag_g_r=2.19032287597656, z=0.0774097),\n",
       " ResultRow(mangaid='1-114145', plateifu='7975-6102', elpetro_absmag_g_r=1.41496467590332, z=0.0341885),\n",
       " ResultRow(mangaid='1-114171', plateifu='7975-3702', elpetro_absmag_g_r=1.70641708374023, z=0.0881405),\n",
       " ResultRow(mangaid='1-114303', plateifu='7975-1901', elpetro_absmag_g_r=0.658689498901367, z=0.0220107),\n",
       " ResultRow(mangaid='1-114306', plateifu='7975-9101', elpetro_absmag_g_r=0.99525260925293, z=0.0636505),\n",
       " ResultRow(mangaid='1-114325', plateifu='7975-12703', elpetro_absmag_g_r=1.34337997436523, z=0.0288791),\n",
       " ResultRow(mangaid='1-114334', plateifu='7975-1902', elpetro_absmag_g_r=1.43183898925781, z=0.0222473),\n",
       " ResultRow(mangaid='1-114454', plateifu='7975-12704', elpetro_absmag_g_r=1.29723358154297, z=0.0888606),\n",
       " ResultRow(mangaid='1-114465', plateifu='7975-6104', elpetro_absmag_g_r=1.21394157409668, z=0.0788547),\n",
       " ResultRow(mangaid='1-114500', plateifu='7975-9102', elpetro_absmag_g_r=1.14164924621582, z=0.0220849),\n",
       " ResultRow(mangaid='1-114502', plateifu='7975-6103', elpetro_absmag_g_r=1.4673023223877, z=0.0798058),\n",
       " ResultRow(mangaid='1-114532', plateifu='7975-3703', elpetro_absmag_g_r=1.73165702819824, z=0.0902261),\n",
       " ResultRow(mangaid='1-114928', plateifu='7977-3702', elpetro_absmag_g_r=1.65719413757324, z=0.0273478),\n",
       " ResultRow(mangaid='1-114955', plateifu='7977-12701', elpetro_absmag_g_r=1.01249313354492, z=0.0922799),\n",
       " ResultRow(mangaid='1-114956', plateifu='7977-3704', elpetro_absmag_g_r=1.3456974029541, z=0.0270248),\n",
       " ResultRow(mangaid='1-114980', plateifu='7977-9102', elpetro_absmag_g_r=1.14808464050293, z=0.0630915),\n",
       " ResultRow(mangaid='1-114998', plateifu='7977-6102', elpetro_absmag_g_r=2.77035713195801, z=0.0614042),\n",
       " ResultRow(mangaid='1-115062', plateifu='7977-1901', elpetro_absmag_g_r=1.65952682495117, z=0.0260569),\n",
       " ResultRow(mangaid='1-115085', plateifu='7977-6103', elpetro_absmag_g_r=0.912630081176758, z=0.0349304),\n",
       " ResultRow(mangaid='1-115097', plateifu='7977-3701', elpetro_absmag_g_r=1.49947357177734, z=0.0274473),\n",
       " ResultRow(mangaid='1-115128', plateifu='7977-1902', elpetro_absmag_g_r=1.1044979095459, z=0.0358116),\n",
       " ResultRow(mangaid='1-115162', plateifu='7977-12703', elpetro_absmag_g_r=1.13131713867188, z=0.0738627),\n",
       " ResultRow(mangaid='1-115320', plateifu='7977-3703', elpetro_absmag_g_r=0.99519157409668, z=0.0275274),\n",
       " ResultRow(mangaid='1-120935', plateifu='8144-12703', elpetro_absmag_g_r=1.19215965270996, z=0.0389089),\n",
       " ResultRow(mangaid='1-120941', plateifu='8144-9101', elpetro_absmag_g_r=1.37554359436035, z=0.0157875),\n",
       " ResultRow(mangaid='1-120943', plateifu='8144-6102', elpetro_absmag_g_r=1.49009323120117, z=0.0393096),\n",
       " ResultRow(mangaid='1-120955', plateifu='8144-12702', elpetro_absmag_g_r=1.05715751647949, z=0.0512278),\n",
       " ResultRow(mangaid='1-120967', plateifu='8144-12704', elpetro_absmag_g_r=1.39360427856445, z=0.0889617),\n",
       " ResultRow(mangaid='1-121023', plateifu='8144-6103', elpetro_absmag_g_r=1.77910804748535, z=0.0617402),\n",
       " ResultRow(mangaid='1-121035', plateifu='8144-1901', elpetro_absmag_g_r=1.74846458435059, z=0.0268051),\n",
       " ResultRow(mangaid='1-121075', plateifu='8144-3702', elpetro_absmag_g_r=1.42728805541992, z=0.0978535),\n",
       " ResultRow(mangaid='1-121151', plateifu='8139-12701', elpetro_absmag_g_r=0.917177200317383, z=0.0155876),\n",
       " ResultRow(mangaid='1-121202', plateifu='8139-1902', elpetro_absmag_g_r=1.09575843811035, z=0.0406385),\n",
       " ResultRow(mangaid='1-121206', plateifu='8139-9101', elpetro_absmag_g_r=0.632560729980469, z=0.0191816),\n",
       " ResultRow(mangaid='1-121209', plateifu='8139-6101', elpetro_absmag_g_r=1.92057991027832, z=0.027711),\n",
       " ResultRow(mangaid='1-121218', plateifu='8139-3703', elpetro_absmag_g_r=0.984956741333008, z=0.0440361),\n",
       " ResultRow(mangaid='1-121235', plateifu='8936-3702', elpetro_absmag_g_r=1.61528015136719, z=0.0609332),\n",
       " ResultRow(mangaid='1-121236', plateifu='8139-6104', elpetro_absmag_g_r=1.68639945983887, z=0.0607858),\n",
       " ResultRow(mangaid='1-121277', plateifu='8977-6101', elpetro_absmag_g_r=2.23527336120605, z=0.0541615),\n",
       " ResultRow(mangaid='1-121330', plateifu='8977-12702', elpetro_absmag_g_r=0.697284698486328, z=0.0174976)]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.run().results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# view the results\n",
    "r.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r.datamodel\n",
    "<QueryDataModel release='MPL-4', n_groups=7, n_parameters=565, n_total=0>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = q.run()\n",
    "r.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert results to Marvin Cube tools\n",
    "r.convertToTool('cube')\n",
    "r.objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "To Pandas Dataframe\n",
    "r.toDF()  # r.toDataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "To Astropy Tables\n",
    "r.toTable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "To FITS\n",
    "r.toFits(filename='myresults.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing new CSV file myresults.csv\n"
     ]
    }
   ],
   "source": [
    "#To CSV\n",
    "r.toCSV(filename='myresults.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyds9 import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "import copy as copy\n",
    "lalala0 = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits')\n",
    "lalala=copy.copy(lalala0)\n",
    "lala=fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits',header=True)\n",
    "# 调用pyds9.DS9\n",
    "d=pyds9.DS9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU       4   ()      \n",
      "  1  GALAXY_INFO    1 BinTableHDU     31   4675R x 11C   [20A, 20A, I, 20A, E, E, E, E, 20A, 20A, 20A]   \n",
      "  2  GLOBAL_PARAMETERS    1 BinTableHDU     41   4675R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  3  GRADIENT_PARAMETERS    1 BinTableHDU     41   4675R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  4  SPATIAL_INFO    1 ImageHDU         9   (4, 2800, 4675)   float32   \n",
      "  5  SPATIAL_BINID    1 ImageHDU         9   (76, 76, 4675)   int16   \n",
      "  6  LW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  7  MW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  8  LW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  9  MW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 10  E(B_V)_VORONOI    1 ImageHDU         8   (2800, 4675)   float32   \n",
      " 11  STELLAR_MASS_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 12  STELLAR_MASS_REMNANT    1 ImageHDU         9   (4, 2800, 4675)   float32   \n",
      " 13  SURFACE_MASS_DENSITY_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 14  STAR_FORMATION_HISTORY    1 ImageHDU        10   (3, 8, 2800, 4675)   float32   \n",
      " 15  SIGNAL_NOISE_VORONOI    1 ImageHDU         8   (2800, 4675)   float32   \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 8.8200140e+00,  6.5209389e-02],\n",
       "       [ 8.6450930e+00,  1.9699287e-01],\n",
       "       [ 8.7641287e+00,  7.0210457e-02],\n",
       "       ...,\n",
       "       [-9.9990000e+03, -9.9990000e+03],\n",
       "       [-9.9990000e+03, -9.9990000e+03],\n",
       "       [-9.9990000e+03, -9.9990000e+03]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lalala[1].header\n",
    "lalala.info()\n",
    "lalala[11].data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lalala = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU       4   ()      \n",
      "  1  GALAXY_INFO    1 BinTableHDU     31   4675R x 11C   [20A, 20A, I, 20A, E, E, E, E, 20A, 20A, 20A]   \n",
      "  2  GLOBAL_PARAMETERS    1 BinTableHDU     41   4675R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  3  GRADIENT_PARAMETERS    1 BinTableHDU     41   4675R x 16C   [E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]   \n",
      "  4  SPATIAL_INFO    1 ImageHDU         9   (4, 2800, 4675)   float32   \n",
      "  5  SPATIAL_BINID    1 ImageHDU         9   (76, 76, 4675)   int16   \n",
      "  6  LW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  7  MW_AGE_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  8  LW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      "  9  MW_Z_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 10  E(B_V)_VORONOI    1 ImageHDU         8   (2800, 4675)   float32   \n",
      " 11  STELLAR_MASS_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 12  STELLAR_MASS_REMNANT    1 ImageHDU         9   (4, 2800, 4675)   float32   \n",
      " 13  SURFACE_MASS_DENSITY_VORONOI    1 ImageHDU         9   (2, 2800, 4675)   float32   \n",
      " 14  STAR_FORMATION_HISTORY    1 ImageHDU        10   (3, 8, 2800, 4675)   float32   \n",
      " 15  SIGNAL_NOISE_VORONOI    1 ImageHDU         8   (2800, 4675)   float32   \n"
     ]
    }
   ],
   "source": [
    "lalala.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "质量信息:\n",
      " [7.0619     0.16655016] \n",
      "\n",
      "\n",
      " 坐标信息:\n",
      " -0.0067892107\n"
     ]
    }
   ],
   "source": [
    "la_mass=lalala[11].data[785][0]\n",
    "la_space=lalala[4].data[5][0][1]\n",
    "print('质量信息:\\n',la_mass,'\\n\\n\\n','坐标信息:\\n',la_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lalala[11].data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lalala[4].data[4674,:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2,)\n",
      "[8.249257   0.17722225]\n"
     ]
    }
   ],
   "source": [
    "la_test_mass=lalala[13].data[4674,1,:]\n",
    "print(np.shape(la_test_mass))\n",
    "print(la_test_mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.0000000e+00, -4.9445590e-01,  1.5865494e-03,  7.4015744e-02],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lalala7=lalala[4].data[4674,1,:]\n",
    "lalala7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "la1=lalala[11].data[1]\n",
    "xx, yy = np.meshgrid(la1, la1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, nan, nan, nan, nan]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用numpy生成一个nan的数组\n",
    "np.full([1,5], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "la2=copy.copy(la1)\n",
    "la2 += 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 尝试用一个星系画出其质量分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-9999.0\n"
     ]
    }
   ],
   "source": [
    "#lalala_mass=np.zeros([2800,2],dtype=float)\n",
    "print(lalala_mass)"
   ]
  },
  {
   "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"
    }
   ],
   "source": [
    "################################################\n",
    "# 画出编号为'8077-6104'，数组位置为249的星系的surface mass density\n",
    "# 使用粗略的四舍五入进行分bin操作画图\n",
    "################################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "import copy as copy\n",
    "\n",
    "# 读取fits文件\n",
    "lalala0 = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits')\n",
    "lala=fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits',header=True)\n",
    "lalala=copy.copy(lalala0)    # copy一下，去除只读属性\n",
    "# plate_ifu 星系编号\n",
    "plate_ifu_num = '8077-6104'\n",
    "\n",
    "# 星系的数组位置【共4675个星系】\n",
    "galaxy_arr_num = 249\n",
    "\n",
    "# 表面质量密度\n",
    "lalala_mass=np.zeros([2800,1],dtype=float)\n",
    "lalala_mass[:,0]=lalala[13].data[galaxy_arr_num,:,0]\n",
    "\n",
    "# 坐标信息\n",
    "lalala_x_y=np.zeros([2800,2],dtype=float)\n",
    "lalala_x_y[:,0]=lalala[4].data[galaxy_arr_num,:,1]\n",
    "lalala_x_y[:,1]=lalala[4].data[galaxy_arr_num,:,2]\n",
    "\n",
    "# 异常数据重置为0\n",
    "# 异常数据 == -9999,且所有的异常数据在数组位置中都为-9999\n",
    "lalala_mass[np.where(lalala_mass==-9999)]=0\n",
    "lalala_x_y[np.where(lalala_x_y==-9999)]=0\n",
    "\n",
    "# 平移缩放坐标\n",
    "lalala_x_y+=15\n",
    "lalala_x_y*=1\n",
    "\n",
    "# 四舍五入\n",
    "lalala_x_y=np.around(lalala_x_y)\n",
    "\n",
    "# 生成目标画图数组\n",
    "lalala_plot_mass_density=np.zeros([300,300],dtype=float)\n",
    "for i in range(0,2800):\n",
    "    ii=int(lalala_x_y[i,0])-1\n",
    "    jj=int(lalala_x_y[i,1])-1\n",
    "    lalala_plot_mass_density[ii,jj]+=lalala_mass[i]\n",
    "\n",
    "# 画图\n",
    "lalala_plot_mass_density[np.where(lalala_plot_mass_density==-0)]=np.nan\n",
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(lalala_plot_mass_density)\n",
    "ax.set_xlabel('spaxel')\n",
    "ax.set_ylabel('spaxel')\n",
    "plt.xlim((0,25))    # x轴坐标范围\n",
    "plt.ylim((0,25))    # y轴坐标范围\n",
    "cb = fig.colorbar(p)\n",
    "cb.set_label('log(Mstar) [M$_\\odot$]')\n",
    "cb.range=(5.5,8.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(lalala_mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1,  -1,  -1,  -1,  -1, 756, 756, 756, 756, 751, 751, 758, 757,\n",
       "        753, 752, 739, 750, 750, 717, 717],\n",
       "       [ -1,  -1,  -1, 716, 716, 727, 727, 756, 756, 743, 751, 747, 746,\n",
       "        741, 739, 739, 738, 714, 717, 717],\n",
       "       [ -1,  -1,  -1, 716, 716, 716, 727, 727, 743, 743, 730, 724, 718,\n",
       "        715, 711, 708, 710, 714, 693, 696],\n",
       "       [ -1,  -1,  -1, 716, 716, 700, 712, 712, 692, 692, 702, 697, 695,\n",
       "        691, 688, 685, 687, 690, 693, 696],\n",
       "       [ -1,  -1, 694, 716, 716, 700, 712, 712, 692, 692, 684, 680, 674,\n",
       "        668, 665, 663, 661, 664, 667, 672],\n",
       "       [ -1,  -1, 694, 694, 694, 681, 675, 675, 682, 673, 662, 657, 648,\n",
       "        644, 636, 633, 632, 634, 638, 643],\n",
       "       [694, 694, 694, 694, 681, 681, 675, 675, 659, 651, 637, 627, 618,\n",
       "        601, 596, 593, 594, 599, 603, 616],\n",
       "       [ -1, 694, 694, 694, 681, 681, 666, 655, 635, 619, 592, 585, 575,\n",
       "        569, 564, 562, 563, 566, 573, 579],\n",
       "       [646, 646, 646, 646, 660, 660, 647, 626, 590, 574, 554, 545, 541,\n",
       "        536, 531, 528, 529, 534, 540, 544],\n",
       "       [646, 646, 646, 646, 591, 591, 595, 576, 547, 539, 525, 501, 491,\n",
       "        482, 480, 481, 490, 495, 502, 518],\n",
       "       [646, 646, 646, 646, 591, 591, 567, 543, 522, 479, 471, 465, 458,\n",
       "        455, 453, 454, 457, 462, 473, 478],\n",
       "       [469, 535, 535, 535, 553, 553, 537, 474, 466, 451, 446, 442, 438,\n",
       "        432, 431, 430, 437, 440, 445, 450],\n",
       "       [469, 535, 535, 535, 468, 468, 470, 452, 443, 436, 429, 417, 408,\n",
       "        402, 398, 396, 400, 404, 410, 419],\n",
       "       [469, 469, 535, 467, 467, 468, 448, 439, 428, 411, 395, 384, 375,\n",
       "        369, 367, 366, 368, 373, 380, 389],\n",
       "       [469, 469, 467, 467, 467, 447, 433, 421, 399, 378, 341, 329, 324,\n",
       "        319, 318, 323, 328, 335, 349, 363],\n",
       "       [469, 469, 435, 435, 435, 420, 418, 390, 331, 317, 306, 291, 287,\n",
       "        285, 286, 290, 301, 308, 316, 330],\n",
       "       [434, 427, 427, 435, 435, 420, 388, 325, 304, 249, 244, 240, 238,\n",
       "        239, 241, 246, 272, 281, 289, 305],\n",
       "       [434, 427, 427, 427, 391, 391, 314, 248, 237, 230, 224, 202, 179,\n",
       "        150, 146, 149, 154, 245, 269, 282],\n",
       "       [434, 425, 425, 425, 407, 315, 243, 234, 223, 201, 178, 129,  77,\n",
       "         35,  15,   5,   0,   1, 152, 264],\n",
       "       [434, 425, 425, 425, 327, 242, 231, 220, 198, 175, 101,  76,  37,\n",
       "         17,   9,   4,   3,   2,  25, 123]], dtype=int16)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=lalala[5].data\n",
    "test[249,10:30,10:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "test=lalala[4].data\n",
    "plt.hist2d(test[2,1,:],test[2,1,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ################################################\n",
    "# 画出编号为'8077-6104'，数组位置为249的星系的surface mass density\n",
    "# 使用分bin操作重现Marvin网站示例图\n",
    "################################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "import copy as copy\n",
    "\n",
    "# 读取fits文件\n",
    "lalala0 = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits')\n",
    "lala=fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits',header=True)\n",
    "lalala=copy.copy(lalala0)    # copy一下，去除只读属性\n",
    "# plate_ifu 星系编号\n",
    "plate_ifu_num = '8077-6104'\n",
    "\n",
    "# 星系的数组位置【共4675个星系】\n",
    "galaxy_arr_num = 249\n",
    "\n",
    "# 表面质量密度\n",
    "lalala_mass=np.zeros([2800,1],dtype=float)\n",
    "lalala_mass[:,0]=lalala[11].data[galaxy_arr_num,:,0]\n",
    "\n",
    "# 坐标信息\n",
    "lalala_x_y=np.zeros([2800,2],dtype=float)\n",
    "lalala_x_y[:,0]=lalala[4].data[galaxy_arr_num,:,1]\n",
    "lalala_x_y[:,1]=lalala[4].data[galaxy_arr_num,:,2]\n",
    "\n",
    "# 异常数据重置为0\n",
    "# 异常数据 == -9999,且所有的异常数据在数组位置中都为-9999\n",
    "lalala_mass[np.where(lalala_mass==-9999)]=0\n",
    "lalala_x_y[np.where(lalala_x_y==-9999)]=0\n",
    "\n",
    "# 平移缩放坐标\n",
    "lalala_x_y[np.where(lalala_x_y!=0)]+=15\n",
    "lalala_x_y[np.where(lalala_x_y!=0)]*=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(lalala_x_y[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          2.6000514   5.20010281  7.80015421 10.40020561 13.00025702\n",
      " 15.60030842 18.20035982 20.80041122 23.40046263 26.00051403]\n",
      "[ 0.          2.40000734  4.80001469  7.20002203  9.60002937 12.00003672\n",
      " 14.40004406 16.8000514  19.20005875 21.60006609 24.00007343]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.6000514030456543"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BsAAYQbAAGOH/AH4Buen33oOhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "hist, xbins, ybins, cax = plt.hist2d(lalala_x_y[:,0],lalala_x_y[:,1],bins=(10,10));\n",
    "print(xbins)\n",
    "print(ybins)\n",
    "xbins[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成目标画图数组\n",
    "plot_size = 30\n",
    "plot_delta = 0.1    # 一就是默认分辨率\n",
    "plot_mesh_size = int(plot_size/plot_delta)\n",
    "lalala_plot_mass_density=np.zeros([plot_mesh_size,plot_mesh_size],dtype=float)\n",
    "xx = lalala_x_y[:,0]\n",
    "yy = lalala_x_y[:,1]\n",
    "for ii in range(0,2800):\n",
    "    for i in range(0,ybins.size-1):    # 先检索y方向\n",
    "        if (lalala_x_y[ii,1]>=ybins[i]) & (lalala_x_y[ii,1]<ybins[i+1]):\n",
    "            for j in range(0,xbins.size-1):    # 再检索x方向\n",
    "                if (lalala_x_y[ii,0]>=xbins[j]) & (lalala_x_y[ii,0]<xbins[j+1]):\n",
    "                    plot_x = int(i / (ybins.size) * (plot_size/plot_delta))\n",
    "                    plot_y = int(j / (xbins.size) * (plot_size/plot_delta))\n",
    "                    lalala_plot_mass_density[plot_x,plot_y] += lalala_mass[ii,0]\n",
    "                \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.95756292,  13.02053738,  48.44445896,  92.69807816,\n",
       "        35.32560301,  20.20006704,  13.78909588,  93.61393166,\n",
       "       185.31004286, 182.11585665,  86.24430799,  20.52170753,\n",
       "        36.16880274,  49.42043829, 147.69694996, 182.05509567,\n",
       "       150.49332905, 147.01146603,  14.37503242,  13.59425592,\n",
       "         6.05439186, 113.18422508, 144.13497639, 190.56240129,\n",
       "       233.84904194, 192.66244316, 185.24033451,  50.56703043,\n",
       "        49.49606562, 176.35332537, 191.35286045, 237.5599637 ,\n",
       "       195.21956348, 186.09197283,  78.99895287,  14.28400946,\n",
       "       100.93209934, 188.83206511, 230.61180401, 192.06030941,\n",
       "       185.89467049,  77.91014242,   6.86451387,  48.43406153,\n",
       "       145.2576232 , 222.55284929, 177.65558243, 128.60310793,\n",
       "        34.28562832,  13.65456438,  56.51612282,  84.42494726,\n",
       "        70.84806681,  20.36647892])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lalala_plot_mass_density[np.where(lalala_plot_mass_density!=0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "import copy as copy\n",
    "\n",
    "# 读取fits文件\n",
    "lalala0 = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits')\n",
    "lala=fits.getdata('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits',header=True)\n",
    "lalala=copy.copy(lalala0)    # copy一下，去除只读属性\n",
    "# plate_ifu 星系编号\n",
    "\n",
    "\n",
    "# 星系的数组位置【共4675个星系】\n",
    "galaxy_arr_num = 249\n",
    "\n",
    "# 表面质量密度\n",
    "lalala_mass=np.zeros([2800,1],dtype=float)\n",
    "lalala_mass[:,0]=lalala[11].data[galaxy_arr_num,:,0]\n",
    "\n",
    "# 坐标信息\n",
    "lalala_x_y=np.zeros([2800,2],dtype=float)\n",
    "lalala_x_y[:,0]=lalala[4].data[galaxy_arr_num,:,1]\n",
    "lalala_x_y[:,1]=lalala[4].data[galaxy_arr_num,:,2]\n",
    "\n",
    "# 异常数据重置为0\n",
    "# 异常数据 == -9999,且所有的异常数据在数组位置中都为-9999\n",
    "lalala_mass[np.where(lalala_mass==-9999)]=-2333\n",
    "lalala_x_y[np.where(lalala_x_y==-9999)]=-2333\n",
    "\n",
    "# 平移缩放坐标\n",
    "lalala_x_y+=15\n",
    "lalala_x_y*=10\n",
    "\n",
    "# 四舍五入\n",
    "#lalala_x_y=np.around(lalala_x_y)\n",
    "\n",
    "# 生成目标画图数组\n",
    "#lalala_plot_mass_density=np.zeros([300,300],dtype=float)\n",
    "#for i in range(0,2800):\n",
    "#    ii=int(lalala_x_y[i,0])-1\n",
    "#    jj=int(lalala_x_y[i,1])-1\n",
    "#    lalala_plot_mass_density[ii,jj]+=lalala_mass[i]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['8252-12701']\n",
      " ['8979-12701']\n",
      " ['8933-6102']\n",
      " ...\n",
      " ['8084-3702']\n",
      " ['8082-12704']\n",
      " ['8485-12704']]\n",
      "249\n"
     ]
    }
   ],
   "source": [
    "# 读取星系编号\n",
    "plate_ifu0=lalala[1].data\n",
    "plate_ifu=np.zeros((plate_ifu0.size,1))\n",
    "plate_ifu=plate_ifu.astype(np.str)\n",
    "#plate_ifu=plate_ifu0[0][1]\n",
    "for i in range(0,plate_ifu0.size):\n",
    "    plate_ifu[i,0]=plate_ifu0[i][1]\n",
    "    #plate_ifu = np.char.add( plate_ifu, plate_ifu0[i][1] )\n",
    "print( plate_ifu)\n",
    "\n",
    "# 根据plateifu编号来寻找数组中的位置\n",
    "plate_ifu_search = '8077-6104'\n",
    "num_array = np.where(plate_ifu==plate_ifu_search)\n",
    "print(num_array[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249\n"
     ]
    }
   ],
   "source": [
    "# 根据plateifu编号来寻找数组中的位置\n",
    "plate_ifu_search = '8077-6104'\n",
    "num_array = np.where(plate_ifu==plate_ifu_search)\n",
    "print(num_array[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "hist, xbins, ybins, cax = plt.hist2d(lalala_x_y[:,0],lalala_x_y[:,1],bins=(50,50));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2318.        , -2271.11998972, -2224.23997944, -2177.35996916,\n",
       "       -2130.47995888, -2083.5999486 , -2036.71993832, -1989.83992804,\n",
       "       -1942.95991776, -1896.07990747, -1849.19989719, -1802.31988691,\n",
       "       -1755.43987663, -1708.55986635, -1661.67985607, -1614.79984579,\n",
       "       -1567.91983551, -1521.03982523, -1474.15981495, -1427.27980467,\n",
       "       -1380.39979439, -1333.51978411, -1286.63977383, -1239.75976355,\n",
       "       -1192.87975327, -1145.99974298, -1099.1197327 , -1052.23972242,\n",
       "       -1005.35971214,  -958.47970186,  -911.59969158,  -864.7196813 ,\n",
       "        -817.83967102,  -770.95966074,  -724.07965046,  -677.19964018,\n",
       "        -630.3196299 ,  -583.43961962,  -536.55960934,  -489.67959906,\n",
       "        -442.79958878,  -395.9195785 ,  -349.03956821,  -302.15955793,\n",
       "        -255.27954765,  -208.39953737,  -161.51952709,  -114.63951681,\n",
       "         -67.75950653,   -20.87949625,    26.00051403])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xbins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid dimensions for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-162-b448df265754>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlalala_plot_mass_density\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'jet'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'low'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2699\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2700\u001b[0m         resample=resample, url=url, **({\"data\": data} if data is not\n\u001b[0;32m-> 2701\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2702\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2703\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5492\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5494\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5495\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    644\u001b[0m         if not (self._A.ndim == 2\n\u001b[1;32m    645\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[0;32m--> 646\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid dimensions for image data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid dimensions for image data"
     ]
    },
    {
     "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": [
    "# 直接利用坐标数组和密度数组画图，不使用四舍五入\n",
    "lalala_plot_mass_density=np.zeros([2800,3],dtype=float)\n",
    "lalala_plot_mass_density[:,0] = lalala_x_y[:,0]\n",
    "lalala_plot_mass_density[:,1] = lalala_x_y[:,1]\n",
    "lalala_plot_mass_density[:,2] = lalala_mass[:,0]\n",
    "xx = lalala_plot_mass_density[:,0]\n",
    "yy = lalala_plot_mass_density[:,1]\n",
    "z = lalala_plot_mass_density[:,2]\n",
    "x, y = np.meshgrid(xx, yy)\n",
    "plt.imshow(z, cmap='jet', origin='low')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: covariance is not symmetric positive-semidefinite.\n",
      "  \n"
     ]
    },
    {
     "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": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt \n",
    "# 高斯分布\n",
    "mean = [0,0]\n",
    "cov = [[0,1],[1,0]]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 100).T\n",
    "\n",
    " \n",
    "hist, xedges, yedges = np.histogram2d(x,y)\n",
    "X,Y = np.meshgrid(xedges,yedges)\n",
    "\n",
    "plt.imshow(hist, interpolation='nearest')\n",
    "plt.grid(True)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.55229954,  2.0505071 , -0.84151593, ..., -0.73836633,\n",
       "       -0.36120048, -0.0985296 ])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lalala_plot_mass_density' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-cf18e53422b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlalala_plot_mass_density\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlalala_plot_mass_density\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m233\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlalala_plot_mass_density\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'spaxel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'spaxel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'lalala_plot_mass_density' is not defined"
     ]
    }
   ],
   "source": [
    "lalala_plot_mass_density[np.where(lalala_plot_mass_density==-233)]=np.nan\n",
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(lalala_plot_mass_density)\n",
    "ax.set_xlabel('spaxel')\n",
    "ax.set_ylabel('spaxel')\n",
    "plt.xlim((-500,250))\n",
    "plt.ylim((-500,250))\n",
    "cb = fig.colorbar(p)\n",
    "#cb.set_label('log(Mstar) [M$_\\odot$]')\n",
    "cb.set_label('log(Mstar) [M$_\\odot$]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(np.max(lalala_x_y))\n",
    "print(np.min(lalala_x_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 102,  103,  104, ..., 2797, 2798, 2799]),)\n",
      "(array([ 102,  103,  104, ..., 2797, 2798, 2799]),)\n",
      "(array([ 102,  103,  104, ..., 2797, 2798, 2799]),)\n",
      "(array([ 102,  103,  104, ..., 2797, 2798, 2799]),)\n"
     ]
    }
   ],
   "source": [
    "# 确认异常数据中，坐标x，y，质量密度是否一致-----> 已确认一致\n",
    "te1_x=lalala[4].data[4674,:,1]\n",
    "te1_y=lalala[4].data[4674,:,2]\n",
    "te2=lalala[11].data[4674,:,0]\n",
    "print(np.where(te1_x==-9999))\n",
    "print(np.where(te1_y==-9999))\n",
    "print(np.where(te2==-9999))\n",
    "print(np.where(te1_x==te2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
      "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
      "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
      "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
      "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
      "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
      "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
      "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]),)\n",
      "[ 5.5444990e-03 -4.9445590e-01  5.5444990e-03  5.5444990e-03\n",
      "  5.0554490e-01 -4.9445453e-01 -4.9445727e-01  5.0554353e-01\n",
      "  5.0554627e-01 -9.9445629e-01  5.5444990e-03  5.5444990e-03\n",
      "  1.0055453e+00 -9.9445355e-01 -9.9445909e-01 -4.9445313e-01\n",
      " -4.9445868e-01  5.0554216e-01  5.0554764e-01  1.0055425e+00\n",
      "  1.0055481e+00 -9.9445081e-01 -9.9446183e-01  1.0055398e+00\n",
      "  1.0055509e+00 -1.4944568e+00  5.5444990e-03  5.5444990e-03\n",
      " -1.4944526e+00 -1.4944608e+00 -4.9445176e-01 -4.9446005e-01\n",
      "  5.0554079e-01  5.0554907e-01 -1.4944484e+00 -9.9444801e-01\n",
      " -1.4944650e+00 -9.9446458e-01  1.0055536e+00  5.5444990e-03\n",
      "  5.5444990e-03  2.0055461e+00 -1.9944516e+00 -4.9445039e-01\n",
      " -4.9446142e-01  5.0555044e-01  2.0055406e+00  2.0055516e+00\n",
      " -1.4944443e+00 -1.9944460e+00 -9.9444526e-01  1.0055563e+00\n",
      "  2.0055571e+00 -1.4944402e+00 -1.4944733e+00  5.5444990e-03\n",
      "  1.5055292e+00  2.0055628e+00 -4.9444899e-01  5.0555182e-01\n",
      " -9.9444252e-01  1.0055315e+00  1.0055591e+00 -1.9944351e+00\n",
      " -1.9944792e+00  2.0055683e+00 -1.4944360e+00  1.5055665e+00\n",
      "  5.5444990e-03 -2.9944496e+00  5.0553662e-01 -4.9446419e-01\n",
      "  5.0555319e-01 -2.4944298e+00 -1.9944295e+00  2.5055189e+00\n",
      " -1.4944319e+00  1.5055705e+00 -4.9444625e-01  5.0555456e-01\n",
      "  3.5055377e+00 -3.9944477e+00  2.0055075e+00  3.5055859e+00\n",
      "  1.0055702e+00 -2.4944022e+00  3.0056133e+00  5.0553107e-01\n",
      " -9.9442869e-01 -3.9943705e+00  3.5056536e+00 -2.9943502e+00\n",
      "  7.0054150e+00  4.0053821e+00  8.5055752e+00  1.5054669e+00\n",
      "  9.0057259e+00  9.0058498e+00 -1.4943656e+00 -2.9942756e+00\n",
      " -7.9940419e+00 -1.1994101e+01]\n",
      "\n",
      "最大值为  9.00585 \n",
      " \n",
      "最小值为  -11.994101 \n",
      "个数为  (102,)\n"
     ]
    }
   ],
   "source": [
    "# 找出数组中某一范围中的最大值最小值\n",
    "te=lalala[4].data[4674,:,1]\n",
    "num_max=1000\n",
    "num_min=-9999\n",
    "print(np.where((te<num_max)&(te>num_min)))\n",
    "print(te[np.where((te<num_max)&(te>num_min))])\n",
    "te_temp=te[np.where((te<num_max)&(te>num_min))]\n",
    "te_min=np.min(te_temp)\n",
    "te_max=np.max(te_temp)\n",
    "print('\\n最大值为 ',te_max,'\\n','\\n最小值为 ',te_min,'\\n个数为 ',te_temp.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2800,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot1 = stats.norm.pdf(xx) * stats.norm.pdf(yy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute '_selftest'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-ea0135f40cc0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_arr2np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# Zoom to fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'zoom to fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Change the colormap and scaling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyds9-1.9.dev211-py3.7-linux-x86_64.egg/pyds9/pyds9.py\u001b[0m in \u001b[0;36mget_arr2np\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m         \"\"\"\n\u001b[0;32m--> 863\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selftest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    864\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fits width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fits height'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute '_selftest'"
     ]
    }
   ],
   "source": [
    "d.get_arr2np(plot1)\n",
    "# Zoom to fit\n",
    "d.set('zoom to fit')\n",
    " \n",
    "# Change the colormap and scaling\n",
    "d.set('cmap bb')\n",
    "d.set('scale log')\n",
    " \n",
    "# Add a label\n",
    "d.set('regions command {text 30 20 #text=\"Fun with pyds9\" font=\"times 18 bold\"}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute '_selftest'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-3d0f0cded87a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mla1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlalala\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_arr2np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mla1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pyds9-1.9.dev211-py3.7-linux-x86_64.egg/pyds9/pyds9.py\u001b[0m in \u001b[0;36mget_arr2np\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m         \"\"\"\n\u001b[0;32m--> 863\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selftest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    864\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fits width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fits height'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute '_selftest'"
     ]
    }
   ],
   "source": [
    "la1=lalala[11].data[1]\n",
    "d.get_arr2np(la1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用提供的脚本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "\n",
    "#read in the vac file\n",
    "vac = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits') \n",
    "\n",
    "#plate and ifu ID of the galaxy\n",
    "plate = 8077\n",
    "ifu = 6104\n",
    "\n",
    "#choose the quantity to make the map, could be changed to other properties for which the extension names are listed in the data model  \n",
    "prop = 'STELLAR_MASS_VORONOI'    # property 特性\n",
    "#SURFACE_MASS_DENSITY_VORONOI\n",
    "\n",
    "binid = vac[5].data\n",
    "basic = vac[1].data\n",
    "galid = (basic['plate']==plate)&(basic['ifudsgn']==str(ifu))    # 星系编号 galaxy ID\n",
    "mass = vac[prop].data[galid,:,0][0]    # 获取质量密度数据\n",
    "bin1d = vac[4].data[galid,:,0][0]    # 获取一维的bin数据\n",
    "image_sz = 76\n",
    "maps = np.zeros((image_sz,image_sz))-99    # 初始化画图数组\n",
    "for i in range(image_sz):\n",
    "    for j in range(image_sz):\n",
    "        idbin = (bin1d==binid[galid,i,j])    # 搜索二维bin的值在一维bin数组中的 位置\n",
    "        if len(bin1d[idbin])==1:    # 挑选出 一维2800的bin和二维76*76的bin相同的数值\n",
    "            maps[i,j] = mass[idbin]\n",
    "\n",
    "#only show the spaxels with non-empty values\n",
    "masked_array = np.ma.array(maps,mask=(maps<-10))\n",
    "\n",
    "#colour scheme for the colour bar\n",
    "cmap = matplotlib.cm.jet\n",
    "#cmap.set_bad('white',1.)\n",
    "\n",
    "#plot the map \n",
    "f = plt.imshow(masked_array,interpolation='nearest',cmap='YlGnBu_r',origin='lower')\n",
    "\n",
    "plt.minorticks_on()\n",
    "plt.tick_params(length=10, width=1, which='major')\n",
    "plt.tick_params(length=5, width=1, which='minor')\n",
    "#plt.xlabel(r\"X-offset $('')$\",fontsize=24)\n",
    "#plt.ylabel(r\"Y-offset $('')$\",fontsize=24)\n",
    "plt.xlabel(r\"spaxel\",fontsize=16)\n",
    "plt.ylabel(r\"spaxel\",fontsize=16)\n",
    "#plot the colour bar\n",
    "cbar = plt.colorbar(f,fraction=0.15,shrink=1.0)\n",
    "cbar.set_label('log(Mstar) [M$_\\odot$]',fontsize=18, rotation=90, labelpad=20)\n",
    "cbar.ax.tick_params(labelsize=16)\n",
    "\n",
    "#plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: ResourceWarning: unclosed file <_io.FileIO name='/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits' mode='rb' closefd=True>\n",
      "  import sys\n",
      "WARNING: Could not memory map array with mode='readonly', falling back to mode='denywrite', which means that the array will be read-only [astropy.io.fits.file]\n",
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:424: RuntimeWarning: divide by zero encountered in power\n",
      "  return np.log10(np.e) * ivar**-0.5 / value\n",
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:166: RuntimeWarning: divide by zero encountered in log10\n",
      "  value=ufunc(self.value),\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "MaskError",
     "evalue": "Mask and data not compatible: data size is 2916, mask size is 4096.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMaskError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-43249e42c836>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;31m#only show the spaxels with non-empty values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0mmaps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m54\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m54\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mmasked_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36marray\u001b[0;34m(data, dtype, copy, order, mask, fill_value, keep_mask, hard_mask, shrink, subok, ndmin)\u001b[0m\n\u001b[1;32m   6376\u001b[0m                        \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6377\u001b[0m                        \u001b[0mhard_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhard_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6378\u001b[0;31m                        ndmin=ndmin, shrink=shrink, order=order)\n\u001b[0m\u001b[1;32m   6379\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmasked_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, mask, dtype, copy, subok, ndmin, fill_value, keep_mask, hard_mask, shrink, order, **options)\u001b[0m\n\u001b[1;32m   2863\u001b[0m                     \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Mask and data not compatible: data size is %i, \"\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2864\u001b[0m                           \u001b[0;34m\"mask size is %i.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2865\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mMaskError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2866\u001b[0m                 \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2867\u001b[0m             \u001b[0;31m# Set the mask to the new value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMaskError\u001b[0m: Mask and data not compatible: data size is 2916, mask size is 4096."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "\n",
    "#read in the vac file\n",
    "vac = fits.open('/root/cosmos/galaxies-mzr/data/manga_firefly-v2_4_3-STELLARPOP.fits') \n",
    "\n",
    "#plate and ifu ID of the galaxy\n",
    "plate = 8252\n",
    "ifu = 9102\n",
    "\n",
    "#choose the quantity to make the map, could be changed to other properties for which the extension names are listed in the data model  \n",
    "prop = 'STELLAR_MASS_VORONOI'    # property 特性\n",
    "#SURFACE_MASS_DENSITY_VORONOI\n",
    "\n",
    "binid = vac[5].data\n",
    "basic = vac[1].data\n",
    "galid = (basic['plate']==plate)&(basic['ifudsgn']==str(ifu))    # 星系编号 galaxy ID\n",
    "mass = vac[prop].data[galid,:,0][0]    # 获取质量密度数据\n",
    "bin1d = vac[4].data[galid,:,0][0]    # 获取一维的bin数据\n",
    "\n",
    "# 导入Marvin的mask\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "from marvin.tools import Maps\n",
    "import marvin.utils.plot.map as mapplot\n",
    "\n",
    "new_maps = Maps('8252-9102')\n",
    "nii = new_maps.emline_gflux_nii_6585\n",
    "ha = new_maps.emline_gflux_ha_6564\n",
    "n2 = nii / ha\n",
    "logn2 = np.log10(n2)\n",
    "oh =  0.57 * logn2+8.90 \n",
    "masks_bpt, __, __ = new_maps.get_bpt()\n",
    "masks_bpt['sf']['global']\n",
    "n2.pixmask.schema\n",
    "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')\n",
    "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])\n",
    "min_snr = 3.\n",
    "mask_nii_low_snr = (np.abs(nii.value * np.sqrt(nii.ivar)) < min_snr)\n",
    "mask_ha_low_snr = (np.abs(ha.value * np.sqrt(ha.ivar)) < min_snr)\n",
    "new_mask = mask_non_sf | mask_bad_data | mask_nii_low_snr | mask_ha_low_snr\n",
    "\n",
    "# 画图\n",
    "image_sz = 76    # 图像边长\n",
    "maps = np.zeros((image_sz,image_sz))-99    # 初始化画图数组\n",
    "for i in range(image_sz):\n",
    "    for j in range(image_sz):\n",
    "        idbin = (bin1d==binid[galid,i,j])    # 搜索二维bin的值在一维bin数组中的 位置\n",
    "        if len(bin1d[idbin])==1:    # 挑选出 一维2800的bin和二维76*76的bin相同的数值\n",
    "            maps[i,j] = mass[idbin]\n",
    "\n",
    "#only show the spaxels with non-empty values\n",
    "maps = maps[0:54,0:54]\n",
    "masked_array = np.ma.array(maps,mask=new_mask)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(masked_array , origin='lower',vmin=5.5,vmax=8.5)\n",
    "ax.set_xlabel('spaxel',fontsize=16)\n",
    "ax.set_ylabel('spaxel',fontsize=16)\n",
    "cb = fig.colorbar(p)\n",
    "cb.set_label('surface mass density ',fontsize=18,labelpad=10)\n",
    "plt.xlim((0,53))    # x轴坐标范围\n",
    "plt.ylim((0,53))    # y轴坐标范围\n",
    "\n",
    "cb.range=(10,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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"
    }
   ],
   "source": [
    "# 画图\n",
    "image_sz = 76    # 图像边长\n",
    "maps = np.zeros((image_sz,image_sz))-99    # 初始化画图数组\n",
    "for i in range(image_sz):\n",
    "    for j in range(image_sz):\n",
    "        idbin = (bin1d==binid[galid,i,j])    # 搜索二维bin的值在一维bin数组中的 位置\n",
    "        if len(bin1d[idbin])==1:    # 挑选出 一维2800的bin和二维76*76的bin相同的数值\n",
    "            maps[i,j] = mass[idbin]\n",
    "\n",
    "#only show the spaxels with non-empty values\n",
    "masked_array = np.ma.array(maps,mask=(maps<-10))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#p = ax.imshow(masked_array , origin='lower',vmin=6.0,vmax=8.9,norm=mpl.colors.LogNorm())\n",
    "p = ax.imshow(masked_array , origin='lower',vmin=5.5,vmax=8.5)\n",
    "ax.set_xlabel('spaxel',fontsize=16)\n",
    "ax.set_ylabel('spaxel',fontsize=16)\n",
    "cb = fig.colorbar(p)\n",
    "cb.set_label('surface mass density ',fontsize=18,labelpad=10)\n",
    "plt.xlim((0,60))    # x轴坐标范围\n",
    "plt.ylim((0,60))    # y轴坐标范围\n",
    "\n",
    "cb.range=(10,30)\n",
    "#cb.set_ticks([5.5, 6.0, 6.5, 7.0, 7.5,8.0,8.5])\n",
    "#cb.set_ticklabels(['5.5','6.0', '6.5', '7.0', '7.5', '8.0', '8.5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76, 76)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(maps<-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.7/site-packages/marvin/tools/quantities/map.py:424: RuntimeWarning: divide by zero encountered in power\n",
      "  return np.log10(np.e) * ivar**-0.5 / value\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from marvin.tools import Maps\n",
    "import marvin.utils.plot.map as mapplot\n",
    "\n",
    "new_maps = Maps('8077-6104')\n",
    "nii = new_maps.emline_gflux_nii_6585\n",
    "ha = new_maps.emline_gflux_ha_6564\n",
    "n2 = nii / ha\n",
    "logn2 = np.log10(n2)\n",
    "oh =  0.57 * logn2+8.90 \n",
    "masks_bpt, __, __ = new_maps.get_bpt()\n",
    "masks_bpt['sf']['global']\n",
    "n2.pixmask.schema\n",
    "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')\n",
    "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])\n",
    "min_snr = 3.\n",
    "mask_nii_low_snr = (np.abs(nii.value * np.sqrt(nii.ivar)) < min_snr)\n",
    "mask_ha_low_snr = (np.abs(ha.value * np.sqrt(ha.ivar)) < min_snr)\n",
    "new_mask = mask_non_sf | mask_bad_data | mask_nii_low_snr | mask_ha_low_snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "maps.get_bpt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "masks_bpt, __, __ = maps.get_bpt()\n",
    "masks_bpt['sf']['global']\n",
    "n2.pixmask.schema\n",
    "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')\n",
    "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])\n",
    "min_snr = 3.\n",
    "mask_nii_low_snr = (np.abs(nii.value * np.sqrt(nii.ivar)) < min_snr)\n",
    "mask_ha_low_snr = (np.abs(ha.value * np.sqrt(ha.ivar)) < min_snr)\n",
    "new_mask = mask_non_sf | mask_bad_data | mask_nii_low_snr | mask_ha_low_snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 54)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(new_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test[test1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - ###  做出一个星系的 metallicity and stellar surface mass density\n",
    "> - ###  筛选星系\n",
    ">> - ###  做出筛选出的星系的所有的 metallicity and stellar surface mass density\n",
    "> - ###  根据stellar mass 分组，分别作出 每一组的 metallicity 分布\n",
    ">> - ###  找出每个的peak，然后重叠在原图中，即可完全复原论文的图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.pi"
   ]
  }
 ],
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