MaNGA_J0749.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import heapq\n",
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *\n",
    "from scipy import stats\n",
    "from scipy.stats import chisquare\n",
    "from astropy.io import fits\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib.patches import Circle\n",
    "from matplotlib import colors\n",
    "import matplotlib.image as mpimg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entire header SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =                    8 / array data type                                NAXIS   =                    0 / number of array dimensions                     EXTEND  =                    T                                                  AUTHOR  = 'K Westfall, B Andrews <kyle.westfall@port.co.uk, andrewsb@pitt.edu>' VERSDRP2= 'v2_0_1  '           / MaNGA DRP version (2d processing)              VERSDRP3= 'v2_0_1  '           / MaNGA DRP Version (3d processing)              VERSPLDS= 'v2_52   '           / Platedesign Version                            VERSFLAT= 'v1_26   '           / Specflat Version                               VERSCORE= 'v1_3_1  '           / MaNGAcore Version                              VERSPRIM= 'v2_2    '           / MaNGA Preimaging Version                       VERSUTIL= 'v5_5_25 '           / Version of idlutils                            VERSIDL = 'x86_64 linux unix linux 7.1.1 Aug 21 2009 64 64 ' / Version of IDL   MASKNAME= 'MANGA_DAPSPECMASK'  / Bits in sdssMaskbits.par used by mask extensionTELESCOP= 'SDSS 2.5-M'         / Sloan Digital Sky Survey                       INSTRUME= 'MaNGA   '           / SDSS-IV MaNGA IFU                              SRVYMODE= 'MaNGA dither'       / Survey leading this observation and its mode   PLATETYP= 'APOGEE-2&MaNGA'     / Type of plate (e.g. MaNGA, APOGEE-2&MANGA)     OBJSYS  = 'ICRS    '           / The TCC objSys                                 EQUINOX =              2000.00 /                                                RADESYS = 'FK5     '           /                                                LAMPLIST= 'lamphgcdne.dat'     /                                                NEXP    =                   15 / Total number of exposures                      EXPTIME =              13501.2 / Total exposure time (seconds)                  BLUESN2 =              35.6523 / Total SN2 in blue channel                      REDSN2  =              67.0262 / Total SN2 in red channel                       AIRMSMIN=              1.02449 / Minimum airmass                                AIRMSMED=              1.04356 / Median airmass                                 AIRMSMAX=              1.19242 / Maximum airmass                                SEEMIN  =              1.42677 / Best guider seeing                             SEEMED  =              1.68623 / Median guider seeing                           SEEMAX  =              2.18727 / Worst guider seeing                            TRANSMIN=             0.633201 / Worst guider transparency                      TRANSMED=             0.830151 / Median guider transparency                     TRANSMAX=             0.888651 / Best guider transparency                       MJDMIN  =                57008 / MJD of first exposure                          MJDMED  =                57011 / MJD of median exposure                         MJDMAX  =                57012 / MJD of last exposure                           DATE-OBS= '2014-12-20'         / Date of median exposure                        MJDRED  =                57538 / MJD of the reduction                           DATERED = '2016-05-30'         / Date of the reduction                          MNGTARG1=                 4288 / manga_target1 maskbit                          MNGTARG2=                    0 / manga_target2 maskbit                          MNGTARG3=                    4 / manga_target3 maskbit                          IFURA   =        117.472420000 / IFU R.A. (J2000 deg.)                          IFUDEC  =        45.2484830000 / IFU Dec. (J2000 deg.)                          OBJRA   =        117.472421015 / Object R.A. (J2000 deg.)                       OBJDEC  =        45.2484834965 / Object Dec. (J2000 deg.)                       CENRA   =        118.403910000 / Plate center R.A. (J2000 deg.)                 CENDEC  =        43.9157560000 / Plate center Dec. (J2000 deg.)                 PLATEID =                 8141 / Current plate                                  DESIGNID=                 8670 / Current design                                 IFUDSGN =                 1901 / ifuDesign                                      FRLPLUG =                   29 / Plugged ferrule                                PLATEIFU= '8141-1901'          / PLATEID-ifuDesign                              CARTID  = '3       '           / Cart(s) used                                   HARNAME = 'ma060   '           / Harness name(s)                                METFILE = 'ma060-56887-1.par'  / IFU metrology file(s)                          MANGAID = '1-339094'           / MaNGA ID number                                CATIDNUM= '1       '           / Primary target input catalog                   PLTTARG = 'plateTargets-1.par' / plateTarget reference file                     DRP3QUAL=                    0 / DRP-3d quality bitmask                         IFUGLON =        173.963743618 / IFU Galactic longitude (deg)                   IFUGLAT =        28.8098863814 / IFU Galactic latitude (deg)                    EBVGAL  =           0.0485885  / Galactic reddening E(B-V)                      GFWHM   =              2.72299 / Reconstructed FWHM in g-band (arcsec)          RFWHM   =              2.62156 / Reconstructed FWHM in r-band (arcsec)          IFWHM   =              2.60299 / Reconstructed FWHM in i-band (arcsec)          ZFWHM   =              2.60409 / Reconstructed FWHM in z-band (arcsec)          CHECKSUM= '6cjK8ZhK6bhK6ZhK'   / HDU checksum updated 2016-08-04T12:20:35       DATASUM = '0       '           / data unit checksum updated 2016-08-04T12:20:35 VERSPY  = '3.5.1   '           / Python version                                 VERSNP  = '1.11.0  '           / Numpy version                                  VERSSCI = '0.17.1  '           / Scipy version                                  VERSAST = '1.1.2   '           / Astropy version                                VERSPYDL= '0.5.0   '           / pydl version                                   VERSDAP = '2.0.2   '           / MaNGA DAP version                              RDXQAKEY= 'SNRR    '           / Method keyword                                 ECOOPA  =               167.58 / Position angle for ellip. coo                  ECOOELL =           0.12067801 / Ellipticity (1-b/a) for ellip. coo             COVWAVE =              6175.05 / Wavelength of S/N covariance channel           COVINDX =                 2317 / Index of S/N covariance channel                REFF    =              3.52613                                                  BINKEY  = 'VOR10   '           / Spectal binning method keyword                 BINMINSN=                  1.0 / Minimum S/N of spectrum to include             FSPCOV  =                  0.8 / Minimum allowed fraction of good pixels        NBINS   =                  399 / Number of unique spatial bins                  BINTYPE = 'voronoi '           / Binning method                                 BINSNR  =                 10.0 / Voronoi binning minimum S/N                    BINCOV  = 'full    '           / Voronoi binning S/N covariance type            STCKOP  = 'mean    '           / Stacking operation                             STCKVREG= 'False   '           / Spectra shifted in velocity before stacked     STCKCRMD= 'channels'           / Stacking treatment of covariance               STCKCRPR= '11      '           / Covariance parameter(s)                        GEXTLAW = 'ODonnell'           / Galactic extinction law                        RVGAL   =                  3.1 / Ratio of total to selective extinction, R(V)   VSTEP   =    69.02976447830537 / Velocity step per spectral channel.            SCKEY   = 'GAU-MILESHC'        / Stellar-continuum model keyword                SCMINSN =                  1.0 / Minimum S/N of spectrum to include             SCINPVEL=    9371.242700000001 / Initial guess velocity                         SCINPSIG=                100.0 / Initial guess velocity dispersion              NSCMOD  =                  399 / Number of unique stellar-continuum models      SCTYPE  = 'stellar_kinematics'                                                  SCMETH  = 'ppxf    '                                                            PPXFTPLK= 'MILESHC '           / Template library key used with pPXF            PPXFBIAS= 'None    '           / pPXF bias value                                PPXFCLN = 'False   '           / pPXF cleaned spectrum while fitting            PPXFMOM =                    2 / Number of fitted LOSVD moments in pPXF         PPXFDEG =                    8 / Order of additive polynomial in pPXF           PPXFMDEG=                   -1 / Order of multiplicative polynomial in pPXF     ELFKEY  = 'EFITF   '           / Emission-line modeling method keyword          ELFMINSN=                  0.0 / Minimum S/N of spectrum to include             ARTDB   = 'BADSKY  '           / Artifact database keyword                      EMLDB   = 'ELPFULL '           / Emission-line database keyword                 NELMOD  =                  399 / Number of unique emission-line models          ELTYPE  = 'emission_line'                                                       ELMETH  = 'elric   '                                                            LPBASEO =                    0 / Baseline Legendre polynomial order             LPWIN   =                 25.0 / Buffer for fitting window (ang)                DAPQUAL =                    0 / DAP quality bitmask                            END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
      "DAP VERSION 2.0.2\n",
      "REFF 3.52613\n",
      "SN min in fit 1.0\n",
      "Input Vel 9371.2427\n",
      "Input Sigma 100.0\n",
      "Number of Stellar Templates 399\n",
      "Type of Fitting stellar_kinematics\n",
      "PpXF?  ppxf\n",
      "Template lib MILESHC\n",
      "Bias value None\n",
      "Cleaned? False\n",
      "Moments 2\n",
      "Polynomial Degree 8\n",
      "Multiplicative Polynomial Degree -1\n"
     ]
    },
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KGSaljLA+Xg8YhRCuQABQKtGu7tZ1aaUzS0rpJaX0cnNze9xsKTkpRTWRKhkoSl732MFA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\nNyNYyVsspoqmD0QM8+yQ6tTt8TRNMGr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yAH4FnC3vhzHgjqXIIb9AjoiIvn+1NCIiBkCK\nQUREpBhERESKQUREkGIQERGkGEREBCkGERFBikFERAD/BYFw3zzXPcGWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f30160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vel_z 9227.7566181 9371.2427\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/scipy/optimize/minpack.py:715: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4563,)\n"
     ]
    }
   ],
   "source": [
    "plt.clf()\n",
    "path_spec='MaNGA_J0749/manga-8141-1901-LOGCUBE-VOR10-GAU-MILESHC.fits'\n",
    "#I need to get the VOR version: manga-8459-3702-LOGCUBE-VOR10-GAU-MILESHC.fits\n",
    "'''\n",
    "0\tPRIMARY\tEmpty extension with primary header information.\n",
    "1\tFLUX\tFlux of the binned spectra (10(-17) erg/s/cm2/angstrom/spaxel)\n",
    "2\tIVAR\tInverse variance in the binned spectra, accounting for covariance\n",
    "3\tMASK\tBitmask for the binned and model spectra\n",
    "4\tWAVE\tWavelength vector (angstroms)\n",
    "5\tREDCORR\tReddening correction applied during the fitting procedures; dereddened_flux = FLUX * REDCORR\n",
    "6\tMODEL\tThe best fitting model spectra (sum of the fitted continuum and emission-line models; 10(-17) erg/s/cm2/angstrom/spaxel)\n",
    "7\tEMLINE\tThe model spectrum with only the emission lines (10(-17) erg/s/cm2/angstrom/spaxel)\n",
    "8\tEMLINE_BASE\tThe model of the constant baseline fitted beneath the emission lines (10(-17) erg/s/cm2/angstrom/spaxel)\n",
    "9\tEMLINE_MASK\tThe bitmask that only applies to the emission-line modeling.\n",
    "'''\n",
    "logcube=pyfits.open(path_spec)\n",
    "print('entire header', logcube[0].header)\n",
    "\n",
    "print('DAP VERSION', logcube[0].header['VERSDAP'])\n",
    "\n",
    "print('REFF', logcube[0].header['REFF'])\n",
    "#print('Binning?', logcube[0].header['BINTYPE'])\n",
    "print('SN min in fit', logcube[0].header['SCMINSN'])\n",
    "print('Input Vel', logcube[0].header['SCINPVEL'])\n",
    "print('Input Sigma', logcube[0].header['SCINPSIG'])\n",
    "print('Number of Stellar Templates', logcube[0].header['NSCMOD'])\n",
    "print('Type of Fitting', logcube[0].header['SCTYPE'])\n",
    "print('PpXF? ', logcube[0].header['SCMETH'])\n",
    "print('Template lib', logcube[0].header['PPXFTPLK'])\n",
    "print('Bias value', logcube[0].header['PPXFBIAS'])\n",
    "print('Cleaned?', logcube[0].header['PPXFCLN'])\n",
    "print('Moments', logcube[0].header['PPXFMOM'])\n",
    "print('Polynomial Degree', logcube[0].header['PPXFDEG'])\n",
    "print('Multiplicative Polynomial Degree', logcube[0].header['PPXFMDEG'])\n",
    "\n",
    "stellar_cont=logcube[6].data-logcube[7].data-logcube[8].data\n",
    "plt.plot(logcube[4].data,stellar_cont[:,20,20],lw=3, label='Stellar Continuum')\n",
    "plt.plot(logcube[4].data,logcube[6].data[:,20,20],lw=2, label='Model')\n",
    "plt.legend()\n",
    "plt.xlim([5000,5500])\n",
    "plt.show()\n",
    "\n",
    "xs=logcube[4].data\n",
    "ys=logcube[1].data[:,20,20]\n",
    "noise=logcube[2].data[:,20,20]\n",
    "mask=logcube[3].data[:,20,20]\n",
    "\n",
    "tens=np.linspace(1,10,10)\n",
    "\n",
    "\n",
    "def gauss_2line(x, *p):\n",
    "    A, mu, sigma,A2, mu2, sigma2,a,  b = p\n",
    "    return a*x+b+A*numpy.exp(-(x-mu)**2/(2.*sigma**2))+A2*numpy.exp(-(x-mu2)**2/(2.*sigma2**2))\n",
    "\n",
    "def gauss_line(x, *p):\n",
    "    A, mu, sigma,a,  b = p\n",
    "    return a*x+b+A*numpy.exp(-(x-mu)**2/(2.*sigma**2))\n",
    "\n",
    "#first, redshift correct z = 0.0313\n",
    "#redshift_J0750 = 0.0315085915911\n",
    "\n",
    "input_z=0.0313\n",
    "input_z = 0.0315085915911\n",
    "input_z = 0.0312591\n",
    "c = 299792.458\n",
    "vel_z = c*np.log(1 + input_z) \n",
    "\n",
    "print('vel_z', vel_z, logcube[0].header['SCINPVEL'])\n",
    "length=len(logcube[1].data[1][0])\n",
    "plot='no'\n",
    "\n",
    "velocity_offset=np.zeros((length,length))\n",
    "aik_1=np.zeros((length,length))\n",
    "velocity_offset_2_1=np.zeros((length,length))\n",
    "velocity_offset_2_2=np.zeros((length,length))\n",
    "aik_2=np.zeros((length,length))\n",
    "\n",
    "\n",
    "\n",
    "for i in range(length):#len(tens)\n",
    "    for j in range(length):\n",
    "        xs_fit=xs[1520:1600]\n",
    "        ys=logcube[1].data[:,i,j]\n",
    "        ys_fit=ys[1520:1600]\n",
    "        \n",
    "        \n",
    "        p0=[max(ys_fit),5008.24+5008.24*(vel_z/c),5,0,0.2]#5008.24+5008.24*(vel/c)+5\n",
    "        coeff=curve_fit(gauss_line,xs_fit,ys_fit,p0=p0)[0]\n",
    "      \n",
    "        \n",
    "        '''\n",
    "        Do it the same as in the W_80 code:\n",
    "        vel_one.append(c*np.log(((vel_1[j]+wavelength_center-interval)*CDELT1+CRVAL1-lab_lambda)/lab_lambda+1))\n",
    "    \n",
    "        kilos.append((vel_one[j]+V_HELIO-vel_stars)/(1.+z_ossy))#relativistic velocity offset\n",
    "        '''\n",
    "        offset_v=(c*np.log(1+(coeff[1]-5008.24)/5008.24)-vel_z)/(1.+input_z)\n",
    "        #print('offset_v', offset_v)\n",
    "        velocity_offset[i][j]=offset_v\n",
    "        fit=gauss_line(xs_fit,*coeff)\n",
    "        \n",
    "        \n",
    "        params=5\n",
    "        length_pix=len(xs_fit)\n",
    "        dof=length_pix-1-params\n",
    "        ans_gauss=chisquare(ys_fit, fit, ddof=params)\n",
    "        \n",
    "        aik_1[i][j]=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)\n",
    "        aikk_1=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)\n",
    "        \n",
    "        if plot=='yes':\n",
    "            plt.plot(xs_fit, ys_fit, label='Data')\n",
    "            plt.plot(xs_fit, fit, label='1 Gauss')\n",
    "        '''Also, fit two gauss and see which is better according to akaike stats'''\n",
    "        try:\n",
    "        \n",
    "            p0=[coeff[0]/2,coeff[1]-3,coeff[2]/2,coeff[0]/2,coeff[1]+3,coeff[2]/2,coeff[3],coeff[4]]#5008.24+5008.24*(vel/c)+5\n",
    "            coeff_2=curve_fit(gauss_2line,xs_fit,ys_fit,p0=p0)[0]\n",
    "\n",
    "\n",
    "            '''\n",
    "            Do it the same as in the W_80 code:\n",
    "            vel_one.append(c*np.log(((vel_1[j]+wavelength_center-interval)*CDELT1+CRVAL1-lab_lambda)/lab_lambda+1))\n",
    "\n",
    "            kilos.append((vel_one[j]+V_HELIO-vel_stars)/(1.+z_ossy))#relativistic velocity offset\n",
    "            '''\n",
    "            offset_v_1=(c*np.log(1+(coeff_2[1]-5008.24)/5008.24)-vel_z)/(1.+input_z)\n",
    "            offset_v_2=(c*np.log(1+(coeff_2[4]-5008.24)/5008.24)-vel_z)/(1.+input_z)\n",
    "            #print('offset_v', offset_v)\n",
    "            if offset_v_1 < offset_v_2:\n",
    "                offset_blue=offset_v_1\n",
    "                offset_red=offset_v_2\n",
    "            else:\n",
    "                offset_blue=offset_v_2\n",
    "                offset_red=offset_v_1\n",
    "            velocity_offset_2_1[i][j]=offset_blue# this one is the bluer component\n",
    "            velocity_offset_2_2[i][j]=offset_red# this one is the redder component\n",
    "            fit=gauss_2line(xs_fit,*coeff_2)\n",
    "            \n",
    "            coeff_3=[coeff_2[0],coeff_2[1],coeff_2[2],coeff_2[6],coeff_2[7]]\n",
    "            fit_1=gauss_line(xs_fit,*coeff_3)\n",
    "            \n",
    "            coeff_4=[coeff_2[3],coeff_2[4],coeff_2[5],coeff_2[6],coeff_2[7]]\n",
    "            fit_2=gauss_line(xs_fit,*coeff_4)\n",
    "\n",
    "            '''Also, fit two gauss and see which is better according to akaike stats'''\n",
    "            params=8\n",
    "            length_pix=len(xs_fit)\n",
    "            dof=length_pix-1-params\n",
    "            ans_gauss=chisquare(ys_fit, fit, ddof=params)\n",
    "\n",
    "            aik_2[i][j]=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)\n",
    "            aikk_2=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)\n",
    "            \n",
    "            if plot=='yes':\n",
    "             \n",
    "                plt.plot(xs_fit, fit, label='2 Gauss')\n",
    "                plt.plot(xs_fit, fit_1, label='2 Gauss 1')\n",
    "                plt.plot(xs_fit, fit_2, label='2 Gauss 2')\n",
    "        except RuntimeError:\n",
    "            aik_2[i][j]=10000\n",
    "            aikk_2=10000\n",
    "            velocity_offset_2_1[i][j]=10000\n",
    "            velocity_offset_2_2[i][j]=10000\n",
    "        if coeff_2[0] < 0 or coeff_2[3] < 0:\n",
    "            #print('coeff_2', coeff_2)\n",
    "            aik_2[i][j]=10000\n",
    "            aikk_2=10000\n",
    "            velocity_offset_2_1[i][j]=10000\n",
    "            velocity_offset_2_2[i][j]=10000\n",
    "            \n",
    "        '''if aikk_1 < aikk_2:\n",
    "            velocity_offset_2_1[i][j]=10000\n",
    "            velocity_offset_2_2[i][j]=10000'''\n",
    "            \n",
    "        if plot=='yes':\n",
    "            plt.legend()\n",
    "            plt.show()\n",
    "        plt.clf()\n",
    "            \n",
    "        \n",
    "\n",
    "masked_vel_2_1=ma.masked_where(velocity_offset_2_1 > 9999 , velocity_offset_2_1)\n",
    "masked_vel_2_2=ma.masked_where(velocity_offset_2_2 > 9999 , velocity_offset_2_2)\n",
    "\n",
    "\n",
    "masked_velocity_offset=ma.masked_where(velocity_offset == velocity_offset[0][0] , velocity_offset)\n",
    "print(shape(mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =                    8 / array data type                                NAXIS   =                    0 / number of array dimensions                     EXTEND  =                    T                                                  AUTHOR  = 'K Westfall, B Andrews <kyle.westfall@port.co.uk, andrewsb@pitt.edu>' VERSDRP2= 'v2_0_1  '           / MaNGA DRP version (2d processing)              VERSDRP3= 'v2_0_1  '           / MaNGA DRP Version (3d processing)              VERSPLDS= 'v2_52   '           / Platedesign Version                            VERSFLAT= 'v1_26   '           / Specflat Version                               VERSCORE= 'v1_3_1  '           / MaNGAcore Version                              VERSPRIM= 'v2_2    '           / MaNGA Preimaging Version                       VERSUTIL= 'v5_5_25 '           / Version of idlutils                            VERSIDL = 'x86_64 linux unix linux 7.1.1 Aug 21 2009 64 64 ' / Version of IDL   MASKNAME= 'MANGA_DAPPIXMASK'   / Bits in sdssMaskbits.par used by mask extensionTELESCOP= 'SDSS 2.5-M'         / Sloan Digital Sky Survey                       INSTRUME= 'MaNGA   '           / SDSS-IV MaNGA IFU                              SRVYMODE= 'MaNGA dither'       / Survey leading this observation and its mode   PLATETYP= 'APOGEE-2&MaNGA'     / Type of plate (e.g. MaNGA, APOGEE-2&MANGA)     OBJSYS  = 'ICRS    '           / The TCC objSys                                 EQUINOX =              2000.00 /                                                RADESYS = 'FK5     '           /                                                LAMPLIST= 'lamphgcdne.dat'     /                                                NEXP    =                   15 / Total number of exposures                      EXPTIME =              13501.2 / Total exposure time (seconds)                  BLUESN2 =              35.6523 / Total SN2 in blue channel                      REDSN2  =              67.0262 / Total SN2 in red channel                       AIRMSMIN=              1.02449 / Minimum airmass                                AIRMSMED=              1.04356 / Median airmass                                 AIRMSMAX=              1.19242 / Maximum airmass                                SEEMIN  =              1.42677 / Best guider seeing                             SEEMED  =              1.68623 / Median guider seeing                           SEEMAX  =              2.18727 / Worst guider seeing                            TRANSMIN=             0.633201 / Worst guider transparency                      TRANSMED=             0.830151 / Median guider transparency                     TRANSMAX=             0.888651 / Best guider transparency                       MJDMIN  =                57008 / MJD of first exposure                          MJDMED  =                57011 / MJD of median exposure                         MJDMAX  =                57012 / MJD of last exposure                           DATE-OBS= '2014-12-20'         / Date of median exposure                        MJDRED  =                57538 / MJD of the reduction                           DATERED = '2016-05-30'         / Date of the reduction                          MNGTARG1=                 4288 / manga_target1 maskbit                          MNGTARG2=                    0 / manga_target2 maskbit                          MNGTARG3=                    4 / manga_target3 maskbit                          IFURA   =        117.472420000 / IFU R.A. (J2000 deg.)                          IFUDEC  =        45.2484830000 / IFU Dec. (J2000 deg.)                          OBJRA   =        117.472421015 / Object R.A. (J2000 deg.)                       OBJDEC  =        45.2484834965 / Object Dec. (J2000 deg.)                       CENRA   =        118.403910000 / Plate center R.A. (J2000 deg.)                 CENDEC  =        43.9157560000 / Plate center Dec. (J2000 deg.)                 PLATEID =                 8141 / Current plate                                  DESIGNID=                 8670 / Current design                                 IFUDSGN =                 1901 / ifuDesign                                      FRLPLUG =                   29 / Plugged ferrule                                PLATEIFU= '8141-1901'          / PLATEID-ifuDesign                              CARTID  = '3       '           / Cart(s) used                                   HARNAME = 'ma060   '           / Harness name(s)                                METFILE = 'ma060-56887-1.par'  / IFU metrology file(s)                          MANGAID = '1-339094'           / MaNGA ID number                                CATIDNUM= '1       '           / Primary target input catalog                   PLTTARG = 'plateTargets-1.par' / plateTarget reference file                     DRP3QUAL=                    0 / DRP-3d quality bitmask                         IFUGLON =        173.963743618 / IFU Galactic longitude (deg)                   IFUGLAT =        28.8098863814 / IFU Galactic latitude (deg)                    EBVGAL  =           0.0485885  / Galactic reddening E(B-V)                      GFWHM   =              2.72299 / Reconstructed FWHM in g-band (arcsec)          RFWHM   =              2.62156 / Reconstructed FWHM in r-band (arcsec)          IFWHM   =              2.60299 / Reconstructed FWHM in i-band (arcsec)          ZFWHM   =              2.60409 / Reconstructed FWHM in z-band (arcsec)          CHECKSUM= 'A2gAA1d9A1dAA1d7'   / HDU checksum updated 2016-08-04T12:20:33       DATASUM = '0       '           / data unit checksum updated 2016-08-04T12:20:33 VERSPY  = '3.5.1   '           / Python version                                 VERSNP  = '1.11.0  '           / Numpy version                                  VERSSCI = '0.17.1  '           / Scipy version                                  VERSAST = '1.1.2   '           / Astropy version                                VERSPYDL= '0.5.0   '           / pydl version                                   VERSDAP = '2.0.2   '           / MaNGA DAP version                              RDXQAKEY= 'SNRR    '           / Method keyword                                 ECOOPA  =               167.58 / Position angle for ellip. coo                  ECOOELL =           0.12067801 / Ellipticity (1-b/a) for ellip. coo             COVWAVE =              6175.05 / Wavelength of S/N covariance channel           COVINDX =                 2317 / Index of S/N covariance channel                REFF    =              3.52613                                                  BINKEY  = 'VOR10   '           / Spectal binning method keyword                 BINMINSN=                  1.0 / Minimum S/N of spectrum to include             FSPCOV  =                  0.8 / Minimum allowed fraction of good pixels        NBINS   =                  399 / Number of unique spatial bins                  BINTYPE = 'voronoi '           / Binning method                                 BINSNR  =                 10.0 / Voronoi binning minimum S/N                    BINCOV  = 'full    '           / Voronoi binning S/N covariance type            STCKOP  = 'mean    '           / Stacking operation                             STCKVREG= 'False   '           / Spectra shifted in velocity before stacked     STCKCRMD= 'channels'           / Stacking treatment of covariance               STCKCRPR= '11      '           / Covariance parameter(s)                        GEXTLAW = 'ODonnell'           / Galactic extinction law                        RVGAL   =                  3.1 / Ratio of total to selective extinction, R(V)   VSTEP   =    69.02976447830537 / Velocity step per spectral channel.            SCKEY   = 'GAU-MILESHC'        / Stellar-continuum model keyword                SCMINSN =                  1.0 / Minimum S/N of spectrum to include             SCINPVEL=    9371.242700000001 / Initial guess velocity                         SCINPSIG=                100.0 / Initial guess velocity dispersion              NSCMOD  =                  399 / Number of unique stellar-continuum models      SCTYPE  = 'stellar_kinematics'                                                  SCMETH  = 'ppxf    '                                                            PPXFTPLK= 'MILESHC '           / Template library key used with pPXF            PPXFBIAS= 'None    '           / pPXF bias value                                PPXFCLN = 'False   '           / pPXF cleaned spectrum while fitting            PPXFMOM =                    2 / Number of fitted LOSVD moments in pPXF         PPXFDEG =                    8 / Order of additive polynomial in pPXF           PPXFMDEG=                   -1 / Order of multiplicative polynomial in pPXF     ELMKEY  = 'EMOMF   '           / Emission-line moments database keyword         ELMMINSN=                  0.0 / Minimum S/N of spectrum to include             ARTDB   = 'BADSKY  '           / Artifact database keyword                      MOMDB   = 'ELBFULL '           / Emission-line moments database keyword         ELFKEY  = 'EFITF   '           / Emission-line modeling method keyword          ELFMINSN=                  0.0 / Minimum S/N of spectrum to include             EMLDB   = 'ELPFULL '           / Emission-line database keyword                 NELMOD  =                  399 / Number of unique emission-line models          ELTYPE  = 'emission_line'                                                       ELMETH  = 'elric   '                                                            LPBASEO =                    0 / Baseline Legendre polynomial order             LPWIN   =                 25.0 / Buffer for fitting window (ang)                SIKEY   = 'BHINDXN '           / Spectral-index database keyword                SIMINSN =                  0.0 / Minimum S/N of spectrum to include             SIFWHM  =                   -1 / FWHM of index system resolution (ang)          ABSDB   = '' / Absorption-index database keyword                                BHDDB   = 'BHD4000 '           / Bandhead-index database keyword                SICORR  =                    F / Indices corrected for velocity dispersion      DAPQUAL =                    0 / DAP quality bitmask                            END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n"
     ]
    }
   ],
   "source": [
    "path_spec='MaNGA_J0749/manga-8141-1901-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "#path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "b=pyfits.open(path_spec)\n",
    "\n",
    "print(b[0].header)\n",
    "data_stel_vel=b['STELLAR_VEL'].data\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#plt.title(r'H$\\beta$ Flux')\n",
    "im = ax.imshow(np.flipud(masked_velocity_offset)-np.flipud(data_stel_vel)+25.89, vmin=-150, vmax=150, interpolation='None', cmap='rainbow')\n",
    "ax.set_adjustable('box-forced')\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "middle=len(data_stel_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "\n",
    "cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=20,weight='normal', labelpad=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.xlim([0,len(data_stel_vel)])\n",
    "#plt.ylim([0,len(data_stel_vel)])\n",
    "plt.xlabel('Arcsec', size=20)\n",
    "plt.ylabel('Arcsec', size=20)\n",
    "\n",
    "ax.set_title(r'Residuals ([OIII]] - Stellar)', size=20)\n",
    "plt.tight_layout()\n",
    "savefig('diff_stel_vel_one_gauss.pdf')\n",
    "\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#plt.title(r'H$\\beta$ Flux')\n",
    "im = ax.imshow(np.flipud(masked_velocity_offset), vmin=-150, vmax=150, cmap='rainbow')\n",
    "ax.set_adjustable('box-forced')\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "middle=len(data_stel_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "\n",
    "cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=20,weight='normal', labelpad=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.xlim([0,len(data_stel_vel)])\n",
    "#plt.ylim([0,len(data_stel_vel)])\n",
    "plt.xlabel('Arcsec', size=20)\n",
    "plt.ylabel('Arcsec', size=20)\n",
    "\n",
    "\n",
    "ax.set_title('One Gaussian', size=20)\n",
    "plt.tight_layout()\n",
    "\n",
    "savefig('J0749_MaNGA_1_gauss.pdf')\n",
    "plt.clf()\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#plt.title(r'H$\\beta$ Flux')\n",
    "im = ax.imshow(np.flipud(masked_vel_2_1), vmin=-150, vmax=150, cmap='rainbow')\n",
    "ax.set_adjustable('box-forced')\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "middle=len(data_stel_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "\n",
    "cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=20,weight='normal', labelpad=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.xlim([0,len(data_stel_vel)])\n",
    "#plt.ylim([0,len(data_stel_vel)])\n",
    "plt.xlabel('Arcsec', size=20)\n",
    "plt.ylabel('Arcsec', size=20)\n",
    "\n",
    "\n",
    "ax.set_title('Two Gaussian 1 Bluer Component', size=20)\n",
    "plt.tight_layout()\n",
    "savefig('J0749_MaNGA_2_1_gauss.pdf')\n",
    "plt.clf()\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#plt.title(r'H$\\beta$ Flux')\n",
    "im = ax.imshow(np.flipud(masked_vel_2_2), vmin=-150, vmax=150, cmap='rainbow')\n",
    "ax.set_adjustable('box-forced')\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "middle=len(data_stel_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "\n",
    "cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=20,weight='normal', labelpad=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.xlim([0,len(data_stel_vel)])\n",
    "#plt.ylim([0,len(data_stel_vel)])\n",
    "plt.xlabel('Arcsec', size=20)\n",
    "plt.ylabel('Arcsec', size=20)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_title('Two Gaussian 2 Redder Component', size=20)\n",
    "plt.tight_layout()\n",
    "savefig('J0749_MaNGA_2_2_gauss.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a3bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be43978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11931f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11992b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kzfFltc83sxcz0kfhiy9da2YPtlq/IAhaSwsiJryc9uUv4CnAWGAdYHJ5ZkmD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\nEBQbSx5yXW3NRtIYSdbFdkU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X2EPSTmZ2b/dU7x2EEQqCoHD0ou64OcDHgAfNbIakU4FT\naix7jZmNrzHv8bgBugrYz8w6ACRdCVwDXCJpw5K8SER3XBAExSN5x1Xbmq6G2Twzu8HMZjSrDkkC\nSl10Xy83NGb2F+AOYANg+2bp0EyiJRQEQeEQvaY7rh42kfQ1YAngOWCCmT2bkW9NfHntqWb2ZEb6\nDcC2wE7AhGYp2yzCCAVBUDx6z5hQPRxb8f8CSRcBXzOzuWXyddN+as5xHkv7dRqpXKsII9QCJk+e\nPFPSU+3Wo0ZGAE2NmttLWB3I+qrsa/TW6/m+egrPYPKN31ugETVkXUJS+UD/BWZ2QT11N4AngWOA\nm4Bn8fV6Pgz8EDgCWAb4bFn+YWk/K+d4JfnwhmvaAsIItQAzG9luHWpF0iQzG9tuPZqNpLf6yXn2\nyetpZrs16liSptM9o3h5PYE+U4Tq8ijVc4A/SroHeAg4QNKPzeyhntZRJMIIBUHQ35kGzK2aayHP\nN0MJM3tG0t+AzwHb4QYJFrZ0hmUWXCh/vRl6NZswQkEQ9GvMbOd261DGy2k/tEw2Je3zxnzWTvu8\nMaNeTbhoB5W0u7+8VVzdbgVaRH+5nn2FLdP+iTLZNOBpYB1Jq2eU2T3tm7L8drMJIxQsQi8YtG0J\nZvb5duvQCvrL9SwSkjqN0UkaIOlbwNa4I8nfS2lmZsB56d+fSBpQVm5v3D37URYdZyoM8vMLgiAI\neoKkbwLrpX83ATYG7mah6/SdZnZRWX4DHsHHfJ7Dx3Q+BHwAd1L4pJndVFHHYLylsw0etucWfO7Q\nvsA8oLBhe8IIBUEQ1IGkiXQdreDXZnZwWf4zgA/iYznLAR14d9vNwJlm9kTWQSQtCXwTOAA3QG8A\nE4FTzOzRes+jXYQRCoIgCNpGjAkF7yFpfJUQ8+tVP0rvRdJYSVMlLUjnM1/SA5LGtFu3RiFpehfX\n74V26xcElYSLdpDFWWTPOeiNM+9rQtKOeHfHADzc/nQ8HMomwBRJ7zezx9unYUOZxcLlBcqZ3WpF\ngqAa0R0XvIek8cBBwOpmNr292jQWSTOB5YGrzGzfMvlkYDPgUTN7f7v0axRp9j9mNqa9mgRBbUR3\nXNDnSa2g5YH5wP4VyR9P+w0kFSa8UhD0FaI7Lshid0nLAAuAx4FbzeyNNutUDwel/WNmtkjs5bQQ\n2Su4kToIGNdq5ZrAYEkH4h5UbwEPA7dXnnsQ9AbCCAVZ/LLi/zclfcvMzm2LNvWzQdrnjfk8jxuh\nzVqjTtMZBVxWIXtS0iEpeGYQ9BqiOy4o53ZgPzyi8BB8Ma0TU9o5kg5vl2J1snTav5qTXmrlLdcC\nXZrNpcDOuCEaCmwInA+MAW6QtHH7VAuCzoRjQj9C0qkZ4vHVnBAkfRy4DveOG1W0bh1J/8VntC8y\nabAs/U58xvqNjVwioDchaRxwAnCNmX2y3foEQYnojutfnJIhm4i7K+diZtdLeg5YGe/a+nfDNWsu\nb6Z9XktnmbTPayn1Bc7DjdB27VYkCMoJI9SPMDPVUfxl3AgNrZaxF/IosAWwVk76Sml/f2vUaQtZ\nSwQEQduJMaGgKpKG4d1ZRjGXxP512q8taWB5gqTRuFNCeb6+yFZpnxmXLAjaRRihAABJoyStkiFf\nChgPLAHcbGYvtlq3ejGzCcAreMv/iork69P+UTN7mQIjaX1JnVo6KSzROenf37ZSpyCoRjgmBABI\n2gEPa/NPfIXGl/Dut11wT6sngB3N7Ol26VgPGWF7nsRbd8sC7wIbFD1sT3I8OQH3cnwKHwtbE9gD\n/4j4G75MwLx26RgElYQRCgCQtCpwEj52siowHF/bZArwF+BsM3sz/wi9H0kfBC4H1sCN0QLcyeKT\nfSFMkaTtgSOBTVnoov068CA+b+gyiwc+6GWEEQqCIAjaRowJBUEQBG0jjFAQBEHQNsIIBUEQBG0j\njFAQBEHQNsIIBUEQBG0jjFAQBEHQNsIIBUEQBG0jjFAQBEHQNsIIBW1D0nckWdrWbbc+QRC0njBC\nQVuQJOBLeGRugMPaqE4QBG0ijFDQLnbFl5z+NR5Q9CBJi7dVoyAIWk4YoaBdlFo+F+JBRUcAnZad\nlnRq6q7bQdJnJd0rabak6RX5PijpSknPSXpH0gxJN0n6TEW+vSTdktLfkfS8pNskHZ1R93KSfijp\nv5LeljQrld0176Qk7ZfyvCpprqTpkn4vaWwPfqMg6PNEANOg5UhaEXgGeNLM1pX0ATya9a1mtnNF\n3lPxZcmvx5eVuA6YBgwzs6NSnsOAX+FRsa8FHgNWAMYCr5vZDinf4cD5eMvrOmBmyrcR/ixsUVbv\n+/Clz8cAdwCT8ajUH8cjVB9hZheW5RdwKXBQOu5f8NVMVwF2BC4ys1Pr+NmCoE8Sy3sH7eAQYDF8\nsTzM7BFJk4EdJa2Vs67PTsDWZvZAuVDSBsAvgTeAbc3sPxXp5Qv1HQHMAzY2s5cq8o2oqO/XwPuA\nAyNHPdUAAAMRSURBVMzsirJ8w3HjdLaka8sW+TsMN0D3AbuY2ayyMgNxYxcEQQXRHRe0lDKHhA7g\nN2VJ4wGR76BwQaUBShyFf0x9v9IAAZjZsxWi+fgidpX5ZpbpuDGwPfCncgOU8r2Ot8yWAD5dlnRM\n2h9RboBSmQVmNiPnvIKgXxMtoaDV7ISv9nmjmT1XJv8d8FPgYEknmVmlofhXzvG2Svsbaqj78lTH\no5KuAG4D7spY1nvrtB+WugMrGZn26wOkJbU/ALyYYyiDIMghjFDQag5P+/HlQjN7VdJ1eOtib+Cq\ninIv5BxveNo/l5NeXseZkmYCRwNfBb4GmKTbgP9nZpNS1uXTfpe05bFUd3UIgmBRojsuaBmSRgKf\nSP/+vmyiqkkyFnZvHZ5RPM+D5vW0X7kWHczsN2a2FW5o9gAuBrYDbkz6AZS60441M3WxHdITHYIg\nWEi0hIJWchCwOO5p9mBOnr2Aj0ha3cyerOGY9+BecLsD/6tVkTS28zfgb5IGAF/EjdGf0jEBtgXO\nruFYb0l6BPiApE2jSy4IaidaQkErKTkdHG1mX8racBfqkvNCLfwKdzY4OXnKLUK5d5ykHZNjRCUl\nz7U5AKlb7g7gU5K+mFWppA0llXu8lYzV+ZKGVeQdIGl0jecTBP2KmCcUtARJOwATgH+b2UZd5BsD\nPIGPAa0GnIR7o+1oZhNzyhwGnIcbo7/g84SWB7YA3jCzHVO+14HZeEtnOm7stk35JuMu4O+mvKsA\ntwJrAw8B9+Ldbqvg84o+kPLfk/ILd+v+PD4/qDRPaCXcGeOSmCcUBJ2J7rigVZRaQRd1lcnMpku6\nGXcI2LOWA5vZhak77ERgB3zcaSbwcEV93wQ+CmwGfAyYCzwFfAP4VblHnpk9K2lz3PX608DngIG4\ncXwU+AU+wbaU34AvSLoRH9P6DDAYmIG3qq6t5VyCoL8RLaEgCIKgbcSYUBAEQdA2wggFQRAEbSOM\nUBAEQdA2wggFQRAEbSOMUBAEQdA2wggFQRAEbSOMUBAEQdA2wggFQRAEbSOMUBAEQdA2/j/30vMn\n9c7/rAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1191bc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D2gmKJ589knI9SSOqNabjOGVQWqrK4ZKm5l2HFzxlaFLOSRklVz+sFl/BKS/E\nxI2SjiTs1wFcZWZ/zt2XNBpYEri9ivIdnJQvFJ5PMLM3JL1LUBQHA/EpODDjnY/Y/6IHqyhWcWat\nsEpZ7dtiwcvaUxrPTalvSZmepdTHJtwf9ouvHj5KmfqlTQhjceo7U6bKqTHty5xttkdktJQocqkx\nclJcVttT6jvLOFCVtrvd03+bVaP06KKzzGxUbYVxKqHcEBMXE9JVxu5NJriSVpP1kjLNBvA6QRFs\nVnhD0pWEQHgM/tSqVRbLcRyo2sni3Ix/aMr9XP37VRnN6ULFJ4trTG5LfXbK/Zx9YtnCG2b2TeCb\nAKNGjbJrj+jZDJrbfu2BstpXw0ZAio2AMmwES/ViG0G/KtgIbE78tF6/OfHnDPigchvBtUfsF62v\nJddVyxGzOjaCXA6TtVLur5mUaTYEp0LKSV6/n6S7Ja2Ycn8lSXdJ+kr1xHMcp6GpTmKaSUm5q6TF\n3kmSliK4qs8DHqqKzE4XylkRfBcYZmavx26a2WuShibt/l4N4YAPk7LLjD9h6aRMWzHUjSnXHR+t\nX+fUeHKSzkgohLSQEZYywx8yPD6bX2Lgwnj7AV3rlxkYf8ag1rjBYnBrfKXQEtmvb0/LNJNCR8rU\nujPlOW993NUn6/2PB0fbftwa39P4OOXF1dYv/k+lY1DX1UxqCIyUUB+9FVnca6pczGy6pDsInkHf\nY/GcJqcBQ4CLmuQMQV0o51/mhsDUIm0eJWQpqxbPJuVnU+7nVif/ruKYjuOUSvVSVR5NOLB6rqTr\nJf1K0t2EU8XP06SZDyV9uifGKUcRLMviJ4tjvEtlcTIK+WNSrilpsamXpBUIhuL8do7j9CCy4lcp\nmNl0YBQwgZD7fCwhksA5wNZm9m5tvkHD84qkayXtVMtBytkamsUio00aa1JFy76ZTcpzEb2GxTOj\n3ZyUz5rZO9Ua03GcMqjibpeZ/ZcQdM5ZxPOE996+kqYDFwETqq0Yy1kRPADsKWmd2E1J6wJ7EeIN\nVZP9CKEs9pX0hqQpkmYTXEbbkjEdx+lpksQ0xS4nO2a2IfA54EpgJULE51clXSVph2qNU44iGE9Y\nQdwv6QeS1pI0JCl/SFAArXRzsCsLZjaJkBbzRUJ0wm0IRuLHgbU8zpDj1JHq2QicFMxsipkdQrCJ\n/pDwLjwQmCTpWUk/lFTRGa5yThY/KuloQnTAs5Irnw7gKDN7uBKBUsZ+hOLbUr2C1DhBEZXcOTg+\nnWodEvf2BraFAAAb/ElEQVTgWXLQgmj9ikvGT+6vOLhrVpXlB8bbtqT8i+7fEpelNdJ+YEvcw6h/\nSraeeSkZeGa1pUVsijy7Jf7suf0HRuvfSTl30JaSyKajX6Q+LV5R/743Pa5SrCGnBMxsDsGj6veS\ntgUOA74GnAn8UtJfCUlpijn1dKEsfz4zuwTYmBBjaBowPSnPBzY2s0vLFcBxnF6MrwjqxSzgPWA+\nIGAg8C3g4cTrKs3lPkrZJ4vN7D/AMeX2cxynj1GGV5BTOZL6A18FjiDkiRfBmPy/BG+rTYATgD0J\nk/MDS312OTmLZwC3mtn3Su3jOE4fxxVBzZH0WUKwz0MIHpSdhFzOF5hZfljuycBkSRMJeZ9LppwV\nwQjSw8Q6jtNkCPcKqjWS7iJkKxPwBmH2f3FahIeEacA+5YxTjiJ4hnDAw3EcJ+ArglozhhCL6QLg\n+sJw/CncRIjMXDLlKIJzgUslbWRmT5YziLOIzv7x+o5BXadWLUPiXjZLDJkfrR/cP6V9v3j9kH5d\nvYxWHhAP27R8v/hicKHFI4e2Wdc/rY6U3ADzOuMePAuI/7CWaInHThrW/+MudWk5EBZ0xP/0+/eP\ne0EpZTO8vSUS8bQt/jN56es/jdb3WtxG0BOsa2bPFW+2CDN7Gni6nD7lKIJXCbmDH5B0ESGu0JtE\n5gRmdm85QjiO00txRVBrtpI0sLvJt6QNgM3M7E9ZBylHEUwm/NoFHEf3fwLxKZHjOH0KtxHUnAnA\nOKC7XZi9gJ8DPaIIfo7rf8dx8vE3QiPQSoW/iXJOFo8r1iZJKvHlSgRyHKeX4AfGGoW1CIfLMlOV\nVJWSViUkpDkUWL5az3Ucp7HxraHqI+nygqq9JY2MNG0FVgG2B/5ZyZiZX9hJfoC9CAcdPk8IV2EE\ng7KTwvTjj4vWr3r5b7rUDVkyHjtoxJLxRE1p2cVi3kEArUQ8lSJ13TGsNT5mW8Sb6MPOeLaw+BPS\nvYwWRDyS0uhMeUa5tLTEp76x+s6UuESrX/3LaP2MA3uvN5F7DdWEQ/L+3wgnhjdJaWvAw4QEPpkp\nWxFIWp0Q7OgQQjRQCHEvLgIuM7OXKxHIcZxehCuCWrBaUgqYAZxNSNBTSAfwXjVSeJakCCT1I5xU\nO5xwwKEFWEjITfxV4AYzO7VSYRzH6T2Uk4HMKZ38ybSk04BJtZ5gd6sIJK1JmP0fTEhBKcLx5QnA\nX8zsPcl3CR2naXFFUFPM7LSeGKdYGOrnCLlDOwgxrzc0sy3M7Hwzq8hK7ThO76daOYsrkkEaKcm6\nua7ppu/Bkh6RNFfSHEmTJe1Re6kbi1K2hgy4FfibmT1TY3malpe//eMudbtMitt/lhsU3xJcdkDc\n7JqWEGZgJKlMmkF3SGc8rENaiInOyBzjo5RQEmn1C1LiccxPqW+LZfdJoSXlDTUoJcTEgrb4P5WY\nUXzGASeVLEevp7H2A54gROUsJBpuQdJ4wkT3VeASYABwAHCTpGPM7LxaCZpG4jFkwE/N7K2IB1Ea\nZmbfyTpuMUVwCvAdglvoIZKeI2wLXWlmb2Qd1HGcPkDj2QgeL+W8E0CS4WssIbnWFrkdDklnELa/\nx0u62cxm1kjWNA4hKILfAG+xuAdRdxjhXZ2JbqdQZna6ma0O7A78gxB99NfAK5L+KelrWQd2HKcP\n0HszlB2ZlKfnb3MnL/7zCRm/Dq2DXKsBqxO8hXKfS7lWr2TQkryGzOx24HZJnwK+TTg8tjsh+YEB\nm0ja3MymVSKM4zi9iwZbEawo6QhC8pZ3gQe7Cda2U1LeFrl3K2E3ZCfgZ1WXshsKvYN6yh2/3JzF\nb5vZr83ss8AuwESgDRgFPCLpMUmewcxxmgR1Fr+A4ZKm5l2H10icXYALgdOT8glJkyStspjM0hBg\nJWBuyhb3C0m5Vo3kbDjKUgT5mNldZrY/sDIhT+YLhMT251ZJNsdxGplStoXCimGWmY3Kuy6usiTz\nCJm7NgeWSa4dCQldRgN3JS//HEOTMi3jYq5+WJXlLBtJ+0m6W9KKKfdXknSXpK9UMk7FMYHMbBYw\nnmBcGU3YNnKqwL/GnBWt//6/D4rWp3nCxLyD0pjXOSBa//LC4dH6JVvjSXJiCWE6UuYd5XoHLeiM\n/9m2d5Ye/XxwSrKetv7xZ1hKgpun9vx5yWP2Saq0NSRpJrBqGV2uMrNvQNipAAoPtN4raVfgfmAr\nwnspdjq30fkuMCwtNaWZvSZpaNLu71kHqWpwODObTMhb4DhOH6fKOYunA/FZRZyiqRjNrF3SpQRF\nsAOLFEFuxj802nFR/ftlyFMrNgRuLtLmUSqM+uxRQh3HyYysOksCM9u5Kg/qyjtJ+cnWkJl9JOk1\nYCVJK0TsBGsm5fM1kqkclgXeLtLmXULkh8xkthE4jtPklG4jqCdbJ+WMgvq7k3K3SJ/dC9rUk1ks\nUkxprEmFqxdXBI7jZKZEr6HayiBtliTFKqzfmUXhmf9ccPvCpDxJ0jJ5fUYC3wMWAFdUXdjyeQDY\nU9I6sZuS1iWkA7ivkkF8a8hxnMw0yDmCM4E1JU0hhIsA2IhFZwVOMbMp+R3MbIqkMwn515+UNJEQ\nYmJ/wnbMMXU4VRxjPPAV4H5JPyece3iN4P66O+G8Q2vSLjOuCBqYHzx2YLR+6ZREMzFPHUj31onF\nyZnXEY/705oytUvz7CmHmByQ7sG0MMVrKJaEpiVlb2JAiifVoBRvons/f0a0vulpDEVwJSFM/haE\nl2N/QniG64DzzCw6WzazsZKeIqwADidETvo3cIaZFTPQ9ghm9qikowmnnc9Krnw6gKPM7OFKxnFF\n4DhONhok1pCZXQZclrHvBEL8tIbFzC6RdD9wNMEDahjBJvAQ8Acz+0+lY7gicBwnE1V2H3W6IXnZ\nH1Or57sicBwnO1VyH3VKQ9JShBXBHDP7oFrPda8hx3Ey0wiJafo6kvpJOlHSi4QtoZnAe5JeTOor\nntD7isBxnGwYqKPeQvRtJA0geArtSDDN/xd4A1gBGEkIsLebpF3NLJ49qgRcETQw5256dVntf/XM\nF6P1ad43MY+fD9oHlTVmGv0jb4i0WEhp3k5p3kQdKe2rwYDW+Jtt50nHRevvGnNmzWTpFfiMv9Yc\nRwicdzMw1sxykVGRtAbwO0J4ieMIuWIy4VtDjuNkxreGas5BhFSbe+crAQAzm044Y/AM8PVKBnFF\n4DhONgzUaUUvpyI+C9xqZlH/rKT+VkL2yMz41pDjONnx93ytWQgsWaTNEEKCsMz4isBxnEwI3xrq\nAZ4E9pU0InZT0nBgX+CJSgZxReA4Tjas+LaQbw1VzHnACEIq4O9IWl3SYEmrSToUeDi5f14lg/jW\nUB9imX4fRes72uL6fp519Sb6qD0ea+i9hYOj9WmeQINau8by6d8S98jpl3I8tSWlPi0TWVukfmFK\n27lt8e95x+izo/VOCv6erylmdp2kTYATgViKTwG/NbPrKhnHFYHjOJnxrZ/aY2Y/lXQj8B1gU0IG\ntTnAY8DlZvZgpWO4InAcJxsG+NZPj2BmDxGCzNWEhrYRSJogybq5oskaHMfpGRohMY1TOb1lRXAO\n8VRss3paEMdx8vCgc32C3qI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6626d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/numpy/ma/core.py:3158: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  dout = self.data[indx]\n",
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/numpy/ma/core.py:3213: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  dout._mask = _mask[indx]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6f26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/numpy/ma/core.py:4185: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6166d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119182f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3k2okBwH9wN+AEyPi0qEo+EAi4peSNgG+AOTN3RHw7Yj4ZTv5OKCYWem6pYYS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1191824e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Now I need to extract velocities according to the longslit data'''\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#plt.title(r'H$\\beta$ Flux')\n",
    "im = ax.imshow(np.flipud(masked_velocity_offset), vmin=-150, vmax=150)\n",
    "ax.set_adjustable('box-forced')\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "middle=len(data_stel_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "\n",
    "cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=20,weight='normal', labelpad=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.xlim([0,len(data_stel_vel)])\n",
    "#plt.ylim([0,len(data_stel_vel)])\n",
    "plt.xlabel('Arcsec', size=20)\n",
    "plt.ylabel('Arcsec', size=20)\n",
    "\n",
    "\n",
    "ax.set_title('One Gaussian', size=20)\n",
    "plt.tight_layout()\n",
    "plt.axhline(y=middle)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "'''You need to identify the spaxels by location'''\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "im = ax.imshow(np.flipud(masked_velocity_offset)[len(data_stel_vel)/2-10:len(data_stel_vel)/2+10,len(data_stel_vel)/2-10:len(data_stel_vel)/2+10], vmin=-150, vmax=150)\n",
    "plt.show()\n",
    "\n",
    "'''Maybe you need to make a new image composed of masks'''\n",
    "\n",
    "new_image=np.zeros([len(data_stel_vel), len(data_stel_vel)])\n",
    "\n",
    "for i in range(len(data_stel_vel)):\n",
    "    for j in range(len(data_stel_vel)):\n",
    "        if j > len(data_stel_vel)/2-3 and j < len(data_stel_vel)/2+3:\n",
    "            new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]\n",
    "plt.imshow(new_image)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "'''Now try with some slope'''\n",
    "\n",
    "new_image=np.zeros([len(data_stel_vel), len(data_stel_vel)])\n",
    "\n",
    "slope=np.tan(math.radians(90-87))\n",
    "offset=len(data_stel_vel)/2-slope*len(data_stel_vel)/2\n",
    "height=1.5/np.sin(math.radians(180-87))\n",
    "\n",
    "for i in range(len(data_stel_vel)):\n",
    "    for j in range(len(data_stel_vel)):\n",
    "        if i > slope*j+offset-height and i < slope*j+offset+height:\n",
    "            new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]\n",
    "            \n",
    "slope=np.tan(math.radians(90-177))\n",
    "offset=len(data_stel_vel)/2-slope*len(data_stel_vel)/2\n",
    "height=1.5/np.sin(math.radians(180-177))\n",
    "\n",
    "for i in range(len(data_stel_vel)):\n",
    "    for j in range(len(data_stel_vel)):\n",
    "        if i > slope*j+offset-height and i < slope*j+offset+height:\n",
    "            new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]\n",
    "\n",
    "            \n",
    "            \n",
    "plt.clf()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "masked_new_image=ma.masked_where(new_image == 0, new_image)\n",
    "im=ax.imshow(masked_new_image)\n",
    "#plt.axhline(y=(middle) , color='white')\n",
    "#plt.axvline(x=(middle), color='white')\n",
    "\n",
    "\n",
    "ax.set_adjustable('box-forced')\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "middle=len(data_stel_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "\n",
    "cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=20,weight='normal', labelpad=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.xlim([0,len(data_stel_vel)])\n",
    "#plt.ylim([0,len(data_stel_vel)])\n",
    "plt.xlabel('Arcsec', size=20)\n",
    "plt.ylabel('Arcsec', size=20)\n",
    "\n",
    "\n",
    "#plt.xlim([len(data_stel_vel)/2+10,len(data_stel_vel)/2-10])\n",
    "#plt.ylim([len(data_stel_vel)/2+10,len(data_stel_vel)/2-10])\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119847b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128e18dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a49c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Now, that weve made a swastika, now time to bin into the 0.5arcsec pixels (for PA 87)'''\n",
    "plt.clf()\n",
    "plt.imshow(masked_new_image)\n",
    "plt.show()\n",
    "\n",
    "new_image=np.zeros([len(data_stel_vel), len(data_stel_vel)])\n",
    "\n",
    "slope=np.tan(math.radians(90-87))\n",
    "offset=len(data_stel_vel)/2-slope*len(data_stel_vel)/2\n",
    "height=1.5/np.sin(math.radians(180-87))\n",
    "\n",
    "step=np.linspace(-4,4,9)#positive steps to right, negative to left\n",
    "#step=np.linspace(0,0,1)\n",
    "spatial_position=[]\n",
    "vel_mean=[]\n",
    "counter=0\n",
    "\n",
    "\n",
    "for k in range(len(step)):\n",
    "    vel=[]\n",
    "\n",
    "    for i in range(len(data_stel_vel)):\n",
    "        for j in range(len(data_stel_vel)):\n",
    "            \n",
    "            \n",
    "\n",
    "            if i > slope*j+offset-height and i < slope*j+offset+height and j < len(data_stel_vel)/2+1+step[k] and j > len(data_stel_vel)/2-1+step[k]:\n",
    "                spatial_position.append(0.5*step[k])\n",
    "                vel.append((np.flipud(masked_velocity_offset)[i,j]))\n",
    "                new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]\n",
    "                \n",
    "    vel_mean.append(np.mean(vel))\n",
    "    \n",
    "\n",
    "'''slope=np.tan(math.radians(90-177))\n",
    "offset=len(data_stel_vel)/2-slope*len(data_stel_vel)/2\n",
    "height=1.5/np.sin(math.radians(180-177))\n",
    "\n",
    "for i in range(len(data_stel_vel)):\n",
    "    for j in range(len(data_stel_vel)):\n",
    "        if i > slope*j+offset-height and i < slope*j+offset+height:\n",
    "            new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]'''\n",
    "masked_new_image=ma.masked_where(new_image == 0, new_image)\n",
    "plt.axhline(y=middle)\n",
    "plt.axvline(x=middle)\n",
    "plt.imshow(masked_new_image)\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "'''plt.scatter(spatial_position, vel)\n",
    "plt.show()\n",
    "print(spatial_position)'''\n",
    "\n",
    "'''step_arc=step*0.5\n",
    "vel_mean=[]\n",
    "\n",
    "for i in range(len(step_arc)):\n",
    "    vel_all=[]\n",
    "    for j in range(len(spatial_position)):\n",
    "        if spatial_position[j] == step_arc[i]:\n",
    "            vel_all.append(vel[j])\n",
    "    vel_mean.append(mean(vel_all))'''\n",
    "    \n",
    "plt.scatter(step,vel_mean)\n",
    "plt.xlabel('Step')\n",
    "plt.ylabel('Velocity')\n",
    "plt.show()\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a5c7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f46c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a040c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Now, that weve made a swastika, now time to bin into the 0.5arcsec pixels (for PA 87)'''\n",
    "\n",
    "plt.imshow(masked_new_image)\n",
    "plt.show()\n",
    "\n",
    "new_image=np.zeros([len(data_stel_vel), len(data_stel_vel)])\n",
    "\n",
    "slope=np.tan(math.radians(90-177))\n",
    "offset=len(data_stel_vel)/2-slope*len(data_stel_vel)/2\n",
    "height=1.5/np.sin(math.radians(180-177))\n",
    "\n",
    "step=np.linspace(-5,5,11)#positive steps to right, negative to left\n",
    "#step=np.linspace(0,0,1)\n",
    "spatial_position=[]\n",
    "vel_mean=[]\n",
    "counter=0\n",
    "\n",
    "\n",
    "for k in range(len(step)):\n",
    "    vel=[]\n",
    "\n",
    "    for i in range(len(data_stel_vel)):\n",
    "        for j in range(len(data_stel_vel)):\n",
    "            \n",
    "            \n",
    "\n",
    "            if i > slope*j+offset-height and i < slope*j+offset+height and i < len(data_stel_vel)/2+1+step[k] and i > len(data_stel_vel)/2-1+step[k]:\n",
    "                spatial_position.append(0.5*step[k])\n",
    "                vel.append((np.flipud(masked_velocity_offset)[i,j]))\n",
    "                new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]\n",
    "                \n",
    "    vel_mean.append(np.mean(vel))\n",
    "    \n",
    "\n",
    "'''slope=np.tan(math.radians(90-177))\n",
    "offset=len(data_stel_vel)/2-slope*len(data_stel_vel)/2\n",
    "height=1.5/np.sin(math.radians(180-177))\n",
    "\n",
    "for i in range(len(data_stel_vel)):\n",
    "    for j in range(len(data_stel_vel)):\n",
    "        if i > slope*j+offset-height and i < slope*j+offset+height:\n",
    "            new_image[i,j]=np.flipud(masked_velocity_offset)[i,j]'''\n",
    "masked_new_image=ma.masked_where(new_image == 0, new_image)\n",
    "plt.axhline(y=middle)\n",
    "plt.axvline(x=middle)\n",
    "plt.imshow(masked_new_image)\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "'''plt.scatter(spatial_position, vel)\n",
    "plt.show()\n",
    "print(spatial_position)'''\n",
    "\n",
    "'''step_arc=step*0.5\n",
    "vel_mean=[]\n",
    "\n",
    "for i in range(len(step_arc)):\n",
    "    vel_all=[]\n",
    "    for j in range(len(spatial_position)):\n",
    "        if spatial_position[j] == step_arc[i]:\n",
    "            vel_all.append(vel[j])\n",
    "    vel_mean.append(mean(vel_all))'''\n",
    "    \n",
    "plt.scatter(step,vel_mean)\n",
    "plt.xlabel('Step')\n",
    "plt.ylabel('Velocity')\n",
    "plt.show()\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:9: RuntimeWarning: divide by zero encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step [-4. -3. -2. -1.  0.  1.  2.  3.  4.]\n",
      "min max PA 2 33.3964952107 46.3741597356\n"
     ]
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "data_stel_vel=b['STELLAR_VEL'].data#'STELLAR_VEL'\n",
    "data_stel_vel_ivar=b['STELLAR_VEL_IVAR'].data\n",
    "data_stel_vel_mask=b['STELLAR_VEL_MASK'].data\n",
    "\n",
    "masked_stel_vel=ma.masked_where((1/np.sqrt(data_stel_vel_ivar)) > 100 , data_stel_vel)#(1/np.sqrt(data_stel_vel_ivar))/data_stel_vel > 1.0\n",
    "masked_stel_vel=ma.masked_where(data_stel_vel > 1000, masked_stel_vel)\n",
    "#plt.show()\n",
    "#plt.clf()\n",
    "\n",
    "ax.imshow((masked_stel_vel-25.89), vmin=-150, vmax=150, cmap='rainbow')#,vmin=-max(masked_stel_vel), vmax=max(masked_stel_vel))#, interpolation='None')#, vmin=-300, vmax=300)\n",
    "\n",
    "\n",
    "#step_arc and vel_mean\n",
    "print('step', step)\n",
    "arc_x_111=[-math.sin(math.radians(-87))*0.5*x for x in step]\n",
    "arc_y_111=[-math.cos(math.radians(-87))*0.5*x for x in step]\n",
    "    \n",
    "    \n",
    "ws = [1.5 for x in vel_mean]\n",
    "hs = [0.5 for x in vel_mean]\n",
    "\n",
    "mini=min(vel_mean)\n",
    "maxi=max(vel_mean)\n",
    "print('min max PA 2', mini,maxi)\n",
    "\n",
    "#mini=-300\n",
    "#maxi=100\n",
    "\n",
    "normal = [(x-mini)/(maxi-mini) for x in vel_mean]\n",
    "colors =normal#(vel_1_ortho[low_111:high_111])\n",
    "'''colors=pl.cm.jet(vel_2_1)\n",
    "print(arc_x_21[low_21:high_21])\n",
    "print(colors)'''\n",
    "\n",
    "patch=[]\n",
    "for x,y,w,h,c in zip(arc_x_111,arc_y_111,ws,hs,colors):\n",
    "    corr_x=-1.5/2\n",
    "    corr_y=-0.5/2\n",
    "    #corr_x=0\n",
    "    #corr_y=0\n",
    "    c=plt.cm.jet(c)\n",
    "    \n",
    "    rect = plt.Rectangle((x+corr_x,y+corr_y),w,h,color=c, alpha=0.5)\n",
    "    \n",
    "    '''t_start=ax.transData\n",
    "    t = mpl.transforms.Affine2D().rotate_deg(-45) + ax.transData\n",
    "    t_end = t_start + t\n",
    "    rect.set_transform(t_end)'''\n",
    "    \n",
    "  \n",
    "    tr = mpl.transforms.Affine2D().rotate_deg_around(x, y, 87)+ax.transData\n",
    "    rect.set_transform(tr)\n",
    "    patch.append(rect)\n",
    "\n",
    "    ax.add_patch(rect)\n",
    "\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.xlim([-5,5])\n",
    "plt.ylim([-5,5])\n",
    "\n",
    "from matplotlib.collections import PatchCollection\n",
    "p = PatchCollection(patch,alpha=0.5)\n",
    "    \n",
    "    \n",
    "p.set(array=normal, cmap=plt.cm.jet)\n",
    "#axs[counter].add_collection(p)\n",
    "\n",
    "tickz=[0,0.2,0.4,0.6,0.8,1.0]\n",
    "ticklbls=[int(x*(maxi-mini)+mini) for x in tickz]\n",
    "\n",
    "cbar=plt.colorbar(p,ax=ax,shrink=0.5,pad=-0.1, ticks=tickz)\n",
    "#cbar.set_clim(-1,1)\n",
    "\n",
    "\n",
    "cbar.ax.set_yticklabels(ticklbls)\n",
    "\n",
    "plt.scatter(0,0, marker='x')\n",
    "#how to set colorbar labels? (When the scale is normalized)\n",
    "'''mini=min(vel_1_ortho[low_111:high_111])\n",
    "maxi=max(vel_1_ortho[low_111:high_111])\n",
    "\n",
    "normal = [(x-mini)/(maxi-mini) for x in vel_1_ortho[low_111:high_111]]'''\n",
    "savefig('../my_pseudo_ifu_comp_1_J0749_m_1.pdf')\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "'''I would like to make the 3x3 profile maps for the OIII profiles (for PA 87 here)'''\n",
    "plt.clf()\n",
    "xs=logcube[4].data\n",
    "'''ys=logcube[1].data[:,20,20]\n",
    "noise=logcube[2].data[:,20,20]\n",
    "mask=logcube[3].data[:,20,20]'''\n",
    "\n",
    "\n",
    "\n",
    "#input_z=0.0313\n",
    "input_z = 0.0315085915911\n",
    "input_z = 0.0312591\n",
    "c = 299792.458\n",
    "vel_z = c*np.log(1 + input_z) \n",
    "length=len(logcube[1].data[1][0])\n",
    "plot='no'\n",
    "\n",
    "\n",
    "'''for i in range(length):#len(tens)\n",
    "    for j in range(length):\n",
    "        xs_fit=xs[1520:1600]\n",
    "        ys=logcube[1].data[:,i,j]\n",
    "        ys_fit=ys[1520:1600]'''\n",
    "        \n",
    "        \n",
    "        \n",
    "'''Transitioning from a swastika to a profile swastika'''\n",
    "\n",
    "\n",
    "\n",
    "slope=np.tan(math.radians(90-87))\n",
    "offset=length/2-slope*length/2\n",
    "height=1.5/np.sin(math.radians(180-87))\n",
    "\n",
    "step=np.linspace(-4,4,9)#positive steps to right, negative to left\n",
    "#step=np.linspace(0,0,1)\n",
    "spatial_position=[]\n",
    "\n",
    "counter=0\n",
    "\n",
    "fig, axs = plt.subplots(3,3, figsize=(20, 20), facecolor='w', edgecolor='r', sharex=True)\n",
    "fig.subplots_adjust(hspace = .10, wspace=.20)\n",
    "counter=0\n",
    "axs = axs.ravel()\n",
    "\n",
    "\n",
    "for k in range(len(step)):\n",
    "    \n",
    "    oiii_prof=[]\n",
    "\n",
    "    for i in range(length):\n",
    "        for j in range(length):\n",
    "            xs_fit=xs[1520:1600]\n",
    "            ys=logcube[1].data[:,i,j]\n",
    "            ys_fit=ys[1520:1600]\n",
    "\n",
    "            '''plt.plot(xs_fit,ys_fit)\n",
    "            plt.show()\n",
    "            plt.clf()'''\n",
    "\n",
    "            if i > slope*j+offset-height and i < slope*j+offset+height and j < length/2+1+step[k] and j > length/2-1+step[k]:\n",
    "                spatial_position.append(0.5*step[k])\n",
    "                oiii_prof.append(ys_fit)\n",
    "                #oiii_prof_lambdas.append(xs_fit)\n",
    "    summed_prof=np.sum(oiii_prof,axis=0)\n",
    "    offset_v=[(c*np.log(1+(x-5008.24)/5008.24)-vel_z)/(1.+input_z) for x in xs_fit]\n",
    "    \n",
    "    \n",
    "    \n",
    "    p0=[max(summed_prof),5008.24+5008.24*(vel_z/c),5,0,0.2]#5008.24+5008.24*(vel/c)+5\n",
    "    coeff=curve_fit(gauss_line,xs_fit,summed_prof,p0=p0)[0]\n",
    "\n",
    "    fit=gauss_line(xs_fit,*coeff)\n",
    "        \n",
    "        \n",
    "    '''params=5\n",
    "    length_pix=len(xs_fit)\n",
    "    dof=length_pix-1-params\n",
    "    ans_gauss=chisquare(summed_prof, fit, ddof=params)\n",
    "        \n",
    "    aik_1[i][j]=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)\n",
    "    aikk_1=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)'''\n",
    "    axs[counter].plot(offset_v, fit,color='cornflowerblue', linewidth=3)\n",
    "    \n",
    "    try:\n",
    "\n",
    "        p0=[coeff[0]/2,coeff[1]-3,coeff[2]/2,coeff[0]/2,coeff[1]+3,coeff[2]/2,coeff[3],coeff[4]]#5008.24+5008.24*(vel/c)+5\n",
    "        coeff_2=curve_fit(gauss_2line,xs_fit,summed_prof,p0=p0)[0]\n",
    "\n",
    "\n",
    "        \n",
    "        fit=gauss_2line(xs_fit,*coeff_2)\n",
    "\n",
    "        coeff_3=[coeff_2[0],coeff_2[1],coeff_2[2],coeff_2[6],coeff_2[7]]\n",
    "        fit_1=gauss_line(xs_fit,*coeff_3)\n",
    "\n",
    "        coeff_4=[coeff_2[3],coeff_2[4],coeff_2[5],coeff_2[6],coeff_2[7]]\n",
    "        fit_2=gauss_line(xs_fit,*coeff_4)\n",
    "\n",
    "        \n",
    "        \n",
    "        axs[counter].plot(offset_v, fit_1, ls='--', color='blue', lw=2)\n",
    "        axs[counter].plot(offset_v, fit_2, ls='--', color='red', lw=2)\n",
    "        axs[counter].plot(offset_v, fit, color='magenta', lw=4)\n",
    "    except RuntimeError:\n",
    "        aik_2[i][j]=10000\n",
    "        aikk_2=10000\n",
    "        velocity_offset_2_1[i][j]=10000\n",
    "        velocity_offset_2_2[i][j]=10000\n",
    "    if coeff_2[0] < 0 or coeff_2[3] < 0:\n",
    "        #print('coeff_2', coeff_2)\n",
    "        aik_2[i][j]=10000\n",
    "        aikk_2=10000\n",
    "        velocity_offset_2_1[i][j]=10000\n",
    "        velocity_offset_2_2[i][j]=10000\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    axs[counter].plot(offset_v,np.sum(oiii_prof,axis=0), lw=3, color='black')\n",
    "    axs[counter].annotate(str(0.5*step[k])+r'$^{\\prime\\prime}$', xy=(0.03,0.90), xycoords='axes fraction', size=20)\n",
    "    plt.xlim([-810,810])\n",
    "    axs[counter].axvline(x=0, color='black', ls='-.')\n",
    "    \n",
    "    axs[counter].set_ylim([0,max(np.sum(oiii_prof,axis=0))+2])\n",
    "    axs[counter].set_ylim([1,17])\n",
    "            #plt.get_yaxis().set_ticks([0, 20])\n",
    "    axs[counter].get_xaxis().set_ticks([-500,0,500])\n",
    "    fig.text(0.51, 0.05, r'Velocity [km s$^{-1}$]', ha='center', size=30)\n",
    "    fig.text(0.05, 0.5, 'Relative Intensity', va='center', rotation='vertical', size=30)\n",
    "    #fig.text(0.51, 0.93, str(tit), ha='center', size=30)\n",
    "    \n",
    "    \n",
    "    counter+=1\n",
    "    \n",
    "savefig('../fake_longslit_PA_87_Gaussfit.pdf')\n",
    "plt.clf()\n",
    "\n",
    "print('done')\n",
    "    \n",
    "    #vel_mean.append(np.mean(vel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "'''Now doing the same thing for PA 177, very exciting'''\n",
    "'''I would like to make the 3x3 profile maps for the OIII profiles (for PA 87 here)'''\n",
    "plt.clf()\n",
    "xs=logcube[4].data\n",
    "'''ys=logcube[1].data[:,20,20]\n",
    "noise=logcube[2].data[:,20,20]\n",
    "mask=logcube[3].data[:,20,20]'''\n",
    "\n",
    "\n",
    "\n",
    "#input_z=0.0313\n",
    "input_z = 0.0315085915911\n",
    "input_z = 0.0312591\n",
    "c = 299792.458\n",
    "vel_z = c*np.log(1 + input_z) \n",
    "length=len(logcube[1].data[1][0])\n",
    "plot='no'\n",
    "\n",
    "\n",
    "'''for i in range(length):#len(tens)\n",
    "    for j in range(length):\n",
    "        xs_fit=xs[1520:1600]\n",
    "        ys=logcube[1].data[:,i,j]\n",
    "        ys_fit=ys[1520:1600]'''\n",
    "        \n",
    "        \n",
    "        \n",
    "'''Transitioning from a swastika to a profile swastika'''\n",
    "\n",
    "\n",
    "\n",
    "slope=np.tan(math.radians(90-177))\n",
    "offset=length/2-slope*length/2\n",
    "height=1.5/np.sin(math.radians(180-177))\n",
    "\n",
    "step=np.linspace(-4,4,9)#positive steps to right, negative to left\n",
    "#step=np.linspace(0,0,1)\n",
    "spatial_position=[]\n",
    "\n",
    "counter=0\n",
    "\n",
    "fig, axs = plt.subplots(3,3, figsize=(20, 20), facecolor='w', edgecolor='r', sharex=True)\n",
    "fig.subplots_adjust(hspace = .10, wspace=.20)\n",
    "counter=0\n",
    "axs = axs.ravel()\n",
    "\n",
    "\n",
    "for k in range(len(step)):\n",
    "    \n",
    "    oiii_prof=[]\n",
    "\n",
    "    for i in range(length):\n",
    "        for j in range(length):\n",
    "            xs_fit=xs[1520:1600]\n",
    "            ys=logcube[1].data[:,i,j]\n",
    "            ys_fit=ys[1520:1600]\n",
    "\n",
    "            '''plt.plot(xs_fit,ys_fit)\n",
    "            plt.show()\n",
    "            plt.clf()'''\n",
    "\n",
    "            if i > slope*j+offset-height and i < slope*j+offset+height and i < len(data_stel_vel)/2+1+step[k] and i > len(data_stel_vel)/2-1+step[k]:\n",
    "                spatial_position.append(0.5*step[k])\n",
    "                oiii_prof.append(ys_fit)\n",
    "                #oiii_prof_lambdas.append(xs_fit)\n",
    "    summed_prof=np.sum(oiii_prof,axis=0)\n",
    "    offset_v=[(c*np.log(1+(x-5008.24)/5008.24)-vel_z)/(1.+input_z) for x in xs_fit]\n",
    "    \n",
    "    \n",
    "    \n",
    "    p0=[max(summed_prof),5008.24+5008.24*(vel_z/c),5,0,0.2]#5008.24+5008.24*(vel/c)+5\n",
    "    coeff=curve_fit(gauss_line,xs_fit,summed_prof,p0=p0)[0]\n",
    "\n",
    "    fit=gauss_line(xs_fit,*coeff)\n",
    "        \n",
    "        \n",
    "    '''params=5\n",
    "    length_pix=len(xs_fit)\n",
    "    dof=length_pix-1-params\n",
    "    ans_gauss=chisquare(summed_prof, fit, ddof=params)\n",
    "        \n",
    "    aik_1[i][j]=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)\n",
    "    aikk_1=ans_gauss[0]+2*params+(2*params*(params+1))/(dof)'''\n",
    "    axs[counter].plot(offset_v, fit,color='cornflowerblue', linewidth=3)\n",
    "    \n",
    "    try:\n",
    "\n",
    "        p0=[coeff[0]/2,coeff[1]-3,coeff[2]/2,coeff[0]/2,coeff[1]+3,coeff[2]/2,coeff[3],coeff[4]]#5008.24+5008.24*(vel/c)+5\n",
    "        coeff_2=curve_fit(gauss_2line,xs_fit,summed_prof,p0=p0)[0]\n",
    "\n",
    "\n",
    "        \n",
    "        fit=gauss_2line(xs_fit,*coeff_2)\n",
    "\n",
    "        coeff_3=[coeff_2[0],coeff_2[1],coeff_2[2],coeff_2[6],coeff_2[7]]\n",
    "        fit_1=gauss_line(xs_fit,*coeff_3)\n",
    "\n",
    "        coeff_4=[coeff_2[3],coeff_2[4],coeff_2[5],coeff_2[6],coeff_2[7]]\n",
    "        fit_2=gauss_line(xs_fit,*coeff_4)\n",
    "\n",
    "        \n",
    "        \n",
    "        axs[counter].plot(offset_v, fit_1, ls='--', color='blue', lw=2)\n",
    "        axs[counter].plot(offset_v, fit_2, ls='--', color='red', lw=2)\n",
    "        axs[counter].plot(offset_v, fit, color='magenta', lw=4)\n",
    "    except RuntimeError:\n",
    "        aik_2[i][j]=10000\n",
    "        aikk_2=10000\n",
    "        velocity_offset_2_1[i][j]=10000\n",
    "        velocity_offset_2_2[i][j]=10000\n",
    "    if coeff_2[0] < 0 or coeff_2[3] < 0:\n",
    "        #print('coeff_2', coeff_2)\n",
    "        aik_2[i][j]=10000\n",
    "        aikk_2=10000\n",
    "        velocity_offset_2_1[i][j]=10000\n",
    "        velocity_offset_2_2[i][j]=10000\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    axs[counter].plot(offset_v,np.sum(oiii_prof,axis=0), lw=3, color='black')\n",
    "    axs[counter].annotate(str(0.5*step[k])+r'$^{\\prime\\prime}$', xy=(0.03,0.90), xycoords='axes fraction', size=20)\n",
    "    plt.xlim([-810,810])\n",
    "    axs[counter].axvline(x=0, color='black', ls='-.')\n",
    "    \n",
    "    axs[counter].set_ylim([0,max(np.sum(oiii_prof,axis=0))+2])\n",
    "    axs[counter].set_ylim([1,17])\n",
    "            #plt.get_yaxis().set_ticks([0, 20])\n",
    "    axs[counter].get_xaxis().set_ticks([-500,0,500])\n",
    "    fig.text(0.51, 0.05, r'Velocity [km s$^{-1}$]', ha='center', size=30)\n",
    "    fig.text(0.05, 0.5, 'Relative Intensity', va='center', rotation='vertical', size=30)\n",
    "    #fig.text(0.51, 0.93, str(tit), ha='center', size=30)\n",
    "    \n",
    "    \n",
    "    counter+=1\n",
    "    \n",
    "savefig('../fake_longslit_PA_177_Gaussfit.pdf')\n",
    "plt.clf()\n",
    "\n",
    "print('done')\n",
    "    \n",
    "    #vel_mean.append(np.mean(vel))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
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    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[33]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:159: RuntimeWarning: invalid value encountered in sqrt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "counter 402\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:194: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:196: RuntimeWarning: divide by zero encountered in double_scalars\n"
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     "data": {
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sfu8L1ax04i3S8H8BDAI3F72S+1HvBRzvGT0E/G0v65Z0K3ApjVkhdgGfB75L\nY4jJucALwBURUfqN/0zdl9K4LAvgeeDjTfeByqz7rcA/Aj8Dpib/uo7Gva+efvYWdV9Ffz7779FI\nGAzSOHm4PSK+KOlM+vC923EeoWBmlbQQEwpmtgA4uJlZJTm4mVklObiZWSU5uJlZJTm4mVklObiZ\nWSU5uJlZJf1/gjMbq/ktb9oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119808898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'stop' 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-224-2ea55a390d4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mkinemetry_gas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'8141'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1901'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mstop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0mkinemetry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'8141'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'1901'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'stop' is not defined"
     ]
    }
   ],
   "source": [
    "#an alternate idea\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plate_list=['8141']\n",
    "fiber_list=['1901']\n",
    "\n",
    "\n",
    "#plate_list=['8086']\n",
    "#fiber_list=['12705']#46,54\n",
    "\n",
    "\n",
    "\n",
    "xcoord_list=[33 for x in plate_list]\n",
    "ycoord_list=[23 for x in plate_list]\n",
    "print(xcoord_list)\n",
    "\n",
    "kinemetry_gas('8141','1901')\n",
    "stop\n",
    "kinemetry('8141','1901')\n",
    "stop\n",
    "\n",
    "plot_BPTs(plate_list, fiber_list)\n",
    "\n",
    "\n",
    "\n",
    "y=plot_all(plate_list, fiber_list,'no','yes','no', xcoord_list,ycoord_list)\n",
    "oiii_vel=y[0]\n",
    "\n",
    "'''def plot_all(plate_list, fiber_list, Circle, Stellar, Write)\n",
    "Theres an option to turn stellar on or off (ie if you don't have any default files for that galaxy,\n",
    "Likewise, write creates an r-band image'''\n",
    "'''last two entries are xcoord and ycoord, we need to make these into a list'''\n",
    "\n",
    "print('Length check',len(plate_list), len(fiber_list))\n",
    "\n",
    "\n",
    "#file = open(\"../MaNGA/Table_BLR_pos.txt\", \"w\")\n",
    "#file.write('Plate_ID Fiber_ID AVG_sig_BLR'+'\\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def twoD_Gaussian(xdata_tuple, amplitude, xo, yo, sigma_x, sigma_y, theta, offset):\n",
    "    (x, y) = xdata_tuple\n",
    "    xo = float(xo)\n",
    "    yo = float(yo)    \n",
    "    a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2)\n",
    "    b = -(np.sin(2*theta))/(4*sigma_x**2) + (np.sin(2*theta))/(4*sigma_y**2)\n",
    "    c = (np.sin(theta)**2)/(2*sigma_x**2) + (np.cos(theta)**2)/(2*sigma_y**2)\n",
    "    g = offset + amplitude*np.exp( - (a*((x-xo)**2) + 2*b*(x-xo)*(y-yo) \n",
    "                            + c*((y-yo)**2)))\n",
    "    return g.ravel()\n",
    "\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "\n",
    "cmap = colors.ListedColormap(['k','b','y','g','r'])\n",
    "\n",
    "def LIER(x):\n",
    "    return 0.9*x+0.48\n",
    "    \n",
    "def kewley(x):\n",
    "    return (0.61/(x-0.47))+1.19\n",
    "\n",
    "def kauffman(x):\n",
    "    return (0.61/(x-0.05))+1.3\n",
    "\n",
    "def SII_AGN(x):\n",
    "    return (0.72/(x-0.32))+1.3\n",
    "\n",
    "def SII_LINER(x):\n",
    "    return 1.89*x + 0.76\n",
    "\n",
    "def OI_AGN(x):\n",
    "    return 0.73/(x + 0.59)+1.33\n",
    "\n",
    "def OI_LINER(x):\n",
    "    return 1.18*x+1.30\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_BPTs(plate_list, fiber_list):\n",
    "  \n",
    "    \n",
    "    plt.clf()\n",
    "    for i in range(len(plate_list)):\n",
    "        \n",
    "        correction=2\n",
    "        plate=plate_list[i]\n",
    "        fiber=fiber_list[i]\n",
    "        \n",
    "        \n",
    "        #opening the file for the r-band magnitude\n",
    "        path_spec='MaNGA_J0749/manga-'+plate+'-'+fiber+'-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "        b=pyfits.open(path_spec)\n",
    "        #print(b[0].header)\n",
    "        \n",
    "        '''Time to make some BPT diagrams'''\n",
    "        '''Sflux is the summed flux (no Gaussian fit)'''\n",
    "        data_elines = b['EMLINE_SFLUX'].data \n",
    "        data_elines_mask = b['EMLINE_SFLUX_MASK'].data\n",
    "        data_elines_ivar = b['EMLINE_SFLUX_IVAR'].data\n",
    "        \n",
    "        data_stel_vel= data_elines[1]\n",
    "        \n",
    "        \n",
    "        #Make all three plots = why not?\n",
    "        #1 = Hb\n",
    "        #3 = OIII\n",
    "        #4 = OI 6300\n",
    "        #7 = Ha 6564\n",
    "        #8 = NII 6583\n",
    "        #9 = SII 6718\n",
    "        #10 = SII 6732 (add these two for the SII diagnostic)\n",
    "        \n",
    "        error_index=[1,3,8,7,9,10,4]\n",
    "        name_errors=['H_beta','OIII','NII','H_alpha','SIIa','SIIb', 'OI']\n",
    "        emission_index=[1,3,8,7,9,10,4]\n",
    "        labels=[r'H$\\beta$ Flux / arcsec$^2$',r'[OIII] Flux / arcsec$^2$',r'[NII] Flux / arcsec$^2$',r'H$\\alpha$ Flux / arcsec$^2$',r'[SII]b Flux / arcsec$^2$',r'[SII]b Flux / arcsec$^2$',r'[OI] Flux / arcsec$^2$']\n",
    "        name_file=['hbeta','oiii','nii','halpha','siia','siib','oi']\n",
    "        \n",
    "        for k in range(len(name_errors)):\n",
    "            plt.clf()\n",
    "            fig = plt.figure()\n",
    "            ax = fig.add_subplot(1,1,1)\n",
    "            #plt.title(r'H$\\beta$ Flux')\n",
    "            im = ax.imshow(10*data_elines[emission_index[k]],norm=LogNorm(vmin=0.001, vmax=10*data_elines[emission_index[k]].max()), interpolation='None')\n",
    "            ax.set_adjustable('box-forced')\n",
    "                \n",
    "            ax.get_yaxis().set_ticks_position(\"left\")\n",
    "            ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "            ax.get_yaxis().set_tick_params(direction='out')\n",
    "            ax.get_xaxis().set_tick_params(direction='out')\n",
    "            #put ticks every 5\n",
    "            \n",
    "            x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "            middle=len(data_stel_vel)/2\n",
    "            #print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "                \n",
    "            x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "            x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "            x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "            \n",
    "            x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "            y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "            \n",
    "            ax.xaxis.set_ticks(x_tick_locs)\n",
    "            ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "            \n",
    "            ax.yaxis.set_ticks(x_tick_locs)\n",
    "            ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "            \n",
    "            cbar1=colorbar(im)\n",
    "            cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "    \n",
    "            cbar1.set_label(label=r'[10$^{-17}$ erg s$^{-1}$ cm$^{-2}$]',size=25,weight='normal', labelpad=10)\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            #plt.xlim([0,len(data_stel_vel)])\n",
    "            #plt.ylim([0,len(data_stel_vel)])\n",
    "            plt.xlabel('Arcsec', size=25)\n",
    "            plt.ylabel('Arcsec', size=25)\n",
    "            plt.tight_layout()\n",
    "            savefig('MaNGA_J0749/'+plate+'_'+fiber+'_'+name_file[k]+'_flux.pdf')\n",
    "        \n",
    "        \n",
    " \n",
    "        \n",
    "        \n",
    "        for k in range(len(name_errors)):\n",
    "        \n",
    "            fig = plt.figure()\n",
    "            ax = fig.add_subplot(1,1,1)\n",
    "            #plt.title(r'H$\\alpha$ Error')\n",
    "            im = ax.imshow(np.sqrt(1/data_elines_ivar[error_index[k]]),norm=LogNorm(vmin=0.001, vmax=np.sqrt(1/data_elines_ivar[error_index[k]].max())), interpolation='None')\n",
    "            \n",
    "            \n",
    "            ax.set_adjustable('box-forced')\n",
    "                \n",
    "            ax.get_yaxis().set_ticks_position(\"left\")\n",
    "            ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "            ax.get_yaxis().set_tick_params(direction='out')\n",
    "            ax.get_xaxis().set_tick_params(direction='out')\n",
    "            \n",
    "            x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "            x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "            x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "            \n",
    "            x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "            y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "            \n",
    "            ax.xaxis.set_ticks(x_tick_locs)\n",
    "            ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "            \n",
    "            ax.yaxis.set_ticks(x_tick_locs)\n",
    "            ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "            cbar1=colorbar(im)\n",
    "            cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "            #colorbar(im)\n",
    "            cbar1.set_label(label=r'Flux Error [10$^{-16}$ erg s$^{-1}$ cm$^{-2}$]',size=20,weight='normal', labelpad=10)\n",
    "            #plt.colorbar(im, label=)\n",
    "            #plt.xlim([0,len(data_stel_vel)])\n",
    "            #plt.ylim([0,len(data_stel_vel)])\n",
    "            plt.xlabel('Arcsec', size=25)\n",
    "            plt.ylabel('Arcsec', size=25)\n",
    "            plt.tight_layout()\n",
    "            savefig('MaNGA_J0749/'+plate+'_'+fiber+'_'+name_errors[k]+'_flux_error.pdf')\n",
    "            plt.clf()\n",
    "        \n",
    "        #\n",
    "        \n",
    "        \n",
    "        '''Time to make a BPT diagram yo'''\n",
    "        \n",
    "        ratio_OIII_Hbeta=[]\n",
    "        ratio_OIII_Hbeta_e=[]\n",
    "        ratio_OIII_Hbeta_e_log=[]\n",
    "        ratio_NII_Halpha=[]\n",
    "        ratio_NII_Halpha_e=[]\n",
    "        ratio_NII_Halpha_e_log=[]\n",
    "        colorcode=[]\n",
    "      \n",
    "        \n",
    "        #name_errors=['H_beta','OIII','NII','H_alpha']\n",
    "        #1,3,8,7\n",
    "        plt.clf()\n",
    "        plt.imshow(data_elines_mask[3])\n",
    "        \n",
    "        savefig('MaNGA_J0749/'+plate+'_'+fiber+'_OIII_mask.pdf')\n",
    "        \n",
    "        color_2d=np.zeros((shape(data_elines[1])[0],shape(data_elines[1])[0]))\n",
    "        mask_2d=np.zeros((shape(data_elines[1])[0],shape(data_elines[1])[0]))\n",
    "        \n",
    "        S_N_threshhold=1\n",
    "        \n",
    "        '''Step 1: Mask everything according to the individual emission line masks'''\n",
    "        \n",
    "        #1 = Hb\n",
    "        #3 = OIII\n",
    "        #4 = OI 6300\n",
    "        #7 = Ha 6564\n",
    "        #8 = NII 6583\n",
    "        #9 = SII 6718\n",
    "        #10 = SII 6732 (add these two for the SII diagnostic)\n",
    "        \n",
    "        for i in range(shape(data_elines[1])[0]):\n",
    "            for j in range(shape(data_elines[1])[0]):\n",
    "                percent_e_oiii=abs(np.sqrt(1/data_elines_ivar[3][i][j])/data_elines[3][i][j])\n",
    "                percent_e_nii=abs(np.sqrt(1/data_elines_ivar[8][i][j])/data_elines[8][i][j])\n",
    "                percent_e_hbeta=abs(np.sqrt(1/data_elines_ivar[1][i][j])/data_elines[1][i][j])\n",
    "                percent_e_halpha=abs(np.sqrt(1/data_elines_ivar[7][i][j])/data_elines[7][i][j])\n",
    "                percent_e_sii=abs(np.sqrt(1/(np.sqrt(data_elines_ivar[9][i][j]**2+data_elines_ivar[10][i][j]**2)))/(data_elines[9][i][j]+data_elines[10][i][j]))\n",
    "                percent_e_oi=abs(np.sqrt(1/data_elines_ivar[4][i][j])/data_elines[4][i][j])\n",
    "                \n",
    "                if data_elines_mask[1][i][j]==0 and data_elines_mask[3][i][j]==0 and data_elines_mask[7][i][j]==0 and data_elines_mask[8][i][j]==0:\n",
    "                    mask_2d[i][j]=0\n",
    "                    OIII_Hbeta=np.log10(data_elines[3][i][j]/data_elines[1][i][j])\n",
    "                    ratio_OIII_Hbeta.append(OIII_Hbeta)\n",
    "                    \n",
    "                    ratio_OIII_Hbeta_e.append((data_elines[3][i][j]/data_elines[1][i][j])*(np.sqrt(percent_e_oiii**2+percent_e_hbeta**2)))\n",
    "                    ratio_OIII_Hbeta_e_log.append(0.434*(np.sqrt(percent_e_oiii**2+percent_e_hbeta**2)))\n",
    "                    \n",
    "                    NII_Halpha=np.log10(data_elines[8][i][j]/data_elines[7][i][j])\n",
    "                    ratio_NII_Halpha.append(NII_Halpha)\n",
    "                    ratio_NII_Halpha_e.append((data_elines[8][i][j]/data_elines[7][i][j])*(np.sqrt(percent_e_nii**2+percent_e_halpha**2)))\n",
    "                    ratio_NII_Halpha_e_log.append(0.434*(np.sqrt(percent_e_nii**2+percent_e_halpha**2)))\n",
    "                    \n",
    "                    if kewley(NII_Halpha) < OIII_Hbeta or NII_Halpha > 0.5:\n",
    "                        colorcode.append('r')\n",
    "                        #print('red description', NII_Halpha, OIII_Hbeta)\n",
    "                        #print('kewley(NII_Halpha)', kewley(NII_Halpha), 'LIER(NII_Halpha)', LIER(NII_Halpha))\n",
    "                        color_2d[i][j]=10\n",
    "                        #color_2d[i][j]='red'\n",
    "                    else:\n",
    "                        if kauffman(NII_Halpha) > OIII_Hbeta and NII_Halpha < 0.0 :# and NII_Halpha < 0.47:\n",
    "                            colorcode.append('blue')\n",
    "                            color_2d[i][j]=20\n",
    "                        #color_2d[i][j]='blue'\n",
    "                        else:\n",
    "                            if OIII_Hbeta < kewley(NII_Halpha) and OIII_Hbeta > kauffman(NII_Halpha):#NII_Halpha > 0.47:\n",
    "                                colorcode.append('orange')\n",
    "                                color_2d[i][j]=13\n",
    "                            else:\n",
    "                                if OIII_Hbeta < kewley(NII_Halpha) and NII_Halpha > 0.05:\n",
    "                                    colorcode.append('orange')\n",
    "                                    color_2d[i][j]=13\n",
    "                                else:\n",
    "                                    colorcode.append('purple')\n",
    "                else:\n",
    "                    mask_2d[i][j]=1\n",
    "          \n",
    "        #log([OIII]/Hb) = 0.61 / (log([NII]/Ha) - 0.05) + 1.3     (Kauffmann+03 line)\n",
    "        \n",
    "        #log([OIII]/Hb) = 0.61 / (log([NII]/Ha) - 0.47) + 1.19    (Kewley+01 line)\n",
    "        xs_kauff=np.linspace(-2.5,0.04,1000)\n",
    "        ys_kauff=[(0.61/(y-0.05))+1.3 for y in xs_kauff]\n",
    "        xs_kewley=np.linspace(-2.5,0.46,1000)\n",
    "        ys_kewley=[(0.61/(y-0.47))+1.19 for y in xs_kewley]\n",
    "        xs_LINER=np.linspace(-0.209,1.0, 1000)\n",
    "        ys_LINER=[0.9*y+0.48 for y in xs_LINER]\n",
    "        print('color_2d before mask', color_2d)\n",
    "        \n",
    "        color_2d = ma.masked_array(color_2d, mask=mask_2d)\n",
    "        print('color_2d after mask', color_2d)\n",
    "        #cmap = matplotlib.cm.get_cmap('jet')\n",
    "        #norm = matplotlib.colors.Normalize(vmin=min(distance), vmax=max(distance))\n",
    "        #distance=[float(y) for y in distance]\n",
    "        #plt.plot(xs_kauff,ys_kauff, color='black')\n",
    "        plt.clf()\n",
    "        plt.plot(xs_kewley,ys_kewley, color='black', ls='--')\n",
    "        plt.plot(xs_LINER, ys_LINER, color='black', ls='--')\n",
    "        plt.plot(xs_kauff, ys_kauff, color='black', ls='--')\n",
    "        cmap = colors.ListedColormap(colorcode)\n",
    "        bounds=[0,1,2,3,4]\n",
    "        norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "        \n",
    "        #plt.title('Poor Mans BPT PA 0')\n",
    "        plt.xlabel(r'log [NII]$\\lambda$6584/H$\\alpha$',size=20)\n",
    "        plt.ylabel(r'log [OIII]$\\lambda$5007/H$\\beta$',size=20)\n",
    "        #plt.errorbar(ratio_NII_Halpha, ratio_OIII_Hbeta,yerr=ratio_OIII_Hbeta_e_log, xerr=ratio_NII_Halpha_e_log,ecolor='black', ls='None')# c=cmap(norm(distance)))#,edgecolors='none',s=100, marker='*')\n",
    "        plt.scatter(ratio_NII_Halpha, ratio_OIII_Hbeta, c=colorcode,edgecolors='none',s=30,zorder=100)\n",
    "        #cb1=plt.colorbar()\n",
    "        \n",
    "        #cb1.set_label('Distance [$^{\\prime \\prime}$] (Negative is S)', size=20)\n",
    "        plt.ylim([-1.5,1.25])\n",
    "        \n",
    "        plt.xlim([-1.0,0.5])\n",
    "        #plt.title('PA 0')\n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/'+plate+'_'+fiber+'_bpt.pdf')\n",
    "        plt.clf()\n",
    "        \n",
    "        plt.imshow(color_2d, vmin=10, vmax=20, cmap='RdYlBu', interpolation='none')\n",
    "        \n",
    "        #plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=15)\n",
    "        plt.ylabel('Arcsec', size=15)\n",
    "        savefig('MaNGA_J0749/'+plate+'_'+fiber+'_bpt_2d.pdf')\n",
    "        plt.clf()\n",
    "        \n",
    "        '''Now I actually have to make the plots onto a huge figure'''\n",
    "        img=mpimg.imread('MaNGA_J0749/img.png')\n",
    "        fig = plt.figure(figsize=(30,20))\n",
    "        fig.suptitle('Plate+Fiber'+' '+str(plate)+' '+str(fiber), fontsize=25)\n",
    "        ax0 = fig.add_subplot(2,4,1, aspect='equal')\n",
    "        im = ax0.imshow(np.flipud(img))\n",
    "        plt.axis('off')\n",
    "        \n",
    "        ax1 = fig.add_subplot(2,4,2, aspect='equal')\n",
    "        ax1.plot(xs_kewley,ys_kewley, color='black', ls='--')\n",
    "        #ax1.plot(xs_LINER, ys_LINER, color='black', ls='--')\n",
    "        ax1.plot(xs_kauff, ys_kauff, color='black', ls='--')\n",
    "        ax1.set_aspect('auto')\n",
    "        \n",
    "        cmap = colors.ListedColormap(colorcode)\n",
    "        bounds=[0,1,2,3,4]\n",
    "        norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "        \n",
    "        #plt.title('Poor Mans BPT PA 0')\n",
    "        plt.xlabel(r'log [NII]$\\lambda$6584/H$\\alpha$',size=25)\n",
    "        plt.ylabel(r'log [OIII]$\\lambda$5007/H$\\beta$',size=25)\n",
    "        #plt.errorbar(ratio_NII_Halpha, ratio_OIII_Hbeta,yerr=ratio_OIII_Hbeta_e_log, xerr=ratio_NII_Halpha_e_log,ecolor='black', ls='None')# c=cmap(norm(distance)))#,edgecolors='none',s=100, marker='*')\n",
    "        ax1.scatter(ratio_NII_Halpha, ratio_OIII_Hbeta, c=colorcode,edgecolors='none',s=30,zorder=100)\n",
    "        #cb1=plt.colorbar()\n",
    "        \n",
    "        #cb1.set_label('Distance [$^{\\prime \\prime}$] (Negative is S)', size=20)\n",
    "        plt.ylim([-1.5,1.25])\n",
    "        \n",
    "        plt.xlim([-1.0,0.5])\n",
    "    \n",
    "        '''Time to do SII and H alpha'''\n",
    "        ratio_OIII_Hbeta_SII=[]\n",
    "        ratio_OIII_Hbeta_e_SII=[]\n",
    "        ratio_OIII_Hbeta_e_log_SII=[]\n",
    "        ratio_SII_Halpha=[]\n",
    "        ratio_SII_Halpha_e=[]\n",
    "        ratio_SII_Halpha_e_log=[]\n",
    "        colorcode_SII=[]\n",
    "      \n",
    "        color_2d_SII=np.zeros((shape(data_elines[1])[0],shape(data_elines[1])[0]))\n",
    "        mask_2d_SII=np.zeros((shape(data_elines[1])[0],shape(data_elines[1])[0]))\n",
    "        for i in range(shape(data_elines[1])[0]):\n",
    "            for j in range(shape(data_elines[1])[0]):\n",
    "                percent_e_oiii=abs(np.sqrt(1/data_elines_ivar[3][i][j])/data_elines[3][i][j])\n",
    "                percent_e_hbeta=abs(np.sqrt(1/data_elines_ivar[1][i][j])/data_elines[1][i][j])\n",
    "                percent_e_halpha=abs(np.sqrt(1/data_elines_ivar[7][i][j])/data_elines[7][i][j])\n",
    "                percent_e_sii=abs(np.sqrt(1/(np.sqrt(data_elines_ivar[9][i][j]**2+data_elines_ivar[10][i][j]**2)))/(data_elines[9][i][j]+data_elines[10][i][j]))\n",
    "                \n",
    "                \n",
    "                if data_elines_mask[1][i][j]==0 and data_elines_mask[3][i][j]==0 and data_elines_mask[9][i][j]==0 and data_elines_mask[10][i][j]==0 and data_elines_mask[7][i][j]==0:\n",
    "                    mask_2d_SII[i][j]=0\n",
    "                    OIII_Hbeta=np.log10(data_elines[3][i][j]/data_elines[1][i][j])\n",
    "                    ratio_OIII_Hbeta_SII.append(OIII_Hbeta)\n",
    "                    \n",
    "                    ratio_OIII_Hbeta_e_SII.append((data_elines[3][i][j]/data_elines[1][i][j])*(np.sqrt(percent_e_oiii**2+percent_e_hbeta**2)))\n",
    "                    ratio_OIII_Hbeta_e_log_SII.append(0.434*(np.sqrt(percent_e_oiii**2+percent_e_hbeta**2)))\n",
    "                    \n",
    "                    SII_Halpha=np.log10((data_elines[9][i][j]+data_elines[10][i][j])/data_elines[7][i][j])\n",
    "                    ratio_SII_Halpha.append(SII_Halpha)\n",
    "                    ratio_SII_Halpha_e.append(((data_elines[9][i][j]+data_elines[10][i][j])/data_elines[7][i][j])*(np.sqrt(percent_e_sii**2+percent_e_halpha**2)))\n",
    "                    ratio_SII_Halpha_e_log.append(0.434*(np.sqrt(percent_e_sii**2+percent_e_halpha**2)))\n",
    "                    \n",
    "                    if SII_AGN(SII_Halpha) < OIII_Hbeta and OIII_Hbeta > SII_LINER(SII_Halpha):\n",
    "                        colorcode_SII.append('red')\n",
    "                        #print('red description', NII_Halpha, OIII_Hbeta)\n",
    "                        #print('kewley(NII_Halpha)', kewley(NII_Halpha), 'LIER(NII_Halpha)', LIER(NII_Halpha))\n",
    "                        color_2d_SII[i][j]=10\n",
    "                        #color_2d[i][j]='red'\n",
    "                    else:\n",
    "                        if SII_AGN(SII_Halpha) > OIII_Hbeta and SII_Halpha < 0.32:\n",
    "                            colorcode_SII.append('blue')\n",
    "                            color_2d_SII[i][j]=20\n",
    "                        #color_2d[i][j]='blue'\n",
    "                        else:\n",
    "                            if SII_Halpha > -0.314594 and OIII_Hbeta > SII_AGN(SII_Halpha) and OIII_Hbeta < SII_LINER(SII_Halpha):#NII_Halpha > 0.47:\n",
    "                                colorcode_SII.append('orange')\n",
    "                                color_2d_SII[i][j]=13\n",
    "                            else:\n",
    "                                if SII_Halpha > 0.32 and OIII_Hbeta < SII_LINER(SII_Halpha):\n",
    "                                    colorcode_SII.append('orange')\n",
    "                                    color_2d_SII[i][j]=13\n",
    "                                else:\n",
    "                                    colorcode_SII.append('purple')\n",
    "                else:\n",
    "                    mask_2d_SII[i][j]=1\n",
    "        \n",
    "        color_2d_SII = ma.masked_array(color_2d_SII, mask=mask_2d_SII)\n",
    "        \n",
    "        ax2 = fig.add_subplot(2,4,3, aspect='equal',adjustable='box-forced')\n",
    "        xs_SII_AGN=np.linspace(-2.5,0.31,1000)\n",
    "        ys_SII_AGN=SII_AGN(xs_SII_AGN)\n",
    "        \n",
    "        xs_SII_LINER=np.linspace(-0.314594,1.0, 1000)\n",
    "        ys_SII_LINER=SII_LINER(xs_SII_LINER)\n",
    "        \n",
    "        ax2.plot(xs_SII_AGN,ys_SII_AGN, color='black', ls='--')\n",
    "        ax2.plot(xs_SII_LINER, ys_SII_LINER, color='black', ls='--')\n",
    "        ax2.set_aspect('auto')\n",
    "        \n",
    "        #ax2.set_adjustable('box-forced')\n",
    "        cmap = colors.ListedColormap(colorcode)\n",
    "        bounds=[0,1,2,3,4]\n",
    "        norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "        \n",
    "        #plt.title('Poor Mans BPT PA 0')\n",
    "        plt.xlabel(r'log ([SII]$\\lambda$6716 + $\\lambda$6731)/H$\\alpha$',size=25)\n",
    "        plt.ylabel(r'log [OIII]$\\lambda$5007/H$\\beta$',size=25)\n",
    "        #plt.errorbar(ratio_NII_Halpha, ratio_OIII_Hbeta,yerr=ratio_OIII_Hbeta_e_log, xerr=ratio_NII_Halpha_e_log,ecolor='black', ls='None')# c=cmap(norm(distance)))#,edgecolors='none',s=100, marker='*')\n",
    "        ax2.scatter(ratio_SII_Halpha, ratio_OIII_Hbeta_SII, c=colorcode_SII,edgecolors='none',s=30,zorder=100)\n",
    "        \n",
    "        #plt.ylim([-1.0,1.5])\n",
    "        \n",
    "        #plt.xlim([-1.5,0.5])\n",
    "        \n",
    "        plt.ylim([-1.5,1.25])\n",
    "        \n",
    "        plt.xlim([-1.0,0.5])\n",
    "        \n",
    "        \n",
    "        '''Time to do OI and H alpha'''\n",
    "        ratio_OIII_Hbeta_OI=[]\n",
    "        ratio_OIII_Hbeta_e_OI=[]\n",
    "        ratio_OIII_Hbeta_e_log_OI=[]\n",
    "        ratio_OI_Halpha=[]\n",
    "        ratio_OI_Halpha_e=[]\n",
    "        ratio_OI_Halpha_e_log=[]\n",
    "        colorcode_OI=[]\n",
    "        \n",
    "          \n",
    "        #1 = Hb\n",
    "        #3 = OIII\n",
    "        #4 = OI 6300\n",
    "        #7 = Ha 6564\n",
    "        #8 = NII 6583\n",
    "        #9 = SII 6718\n",
    "        #10 = SII 6732 (add these two for the SII diagnostic)\n",
    "      \n",
    "        color_2d_OI=np.zeros((shape(data_elines[1])[0],shape(data_elines[1])[0]))\n",
    "        mask_2d_OI=np.zeros((shape(data_elines[1])[0],shape(data_elines[1])[0]))\n",
    "        for i in range(shape(data_elines[1])[0]):\n",
    "            for j in range(shape(data_elines[1])[0]):\n",
    "                percent_e_oiii=abs(np.sqrt(1/data_elines_ivar[3][i][j])/data_elines[3][i][j])\n",
    "                percent_e_hbeta=abs(np.sqrt(1/data_elines_ivar[1][i][j])/data_elines[1][i][j])\n",
    "                percent_e_halpha=abs(np.sqrt(1/data_elines_ivar[7][i][j])/data_elines[7][i][j])\n",
    "                percent_e_oi=abs(np.sqrt(1/data_elines_ivar[4][i][j])/data_elines[4][i][j])\n",
    "                \n",
    "                if data_elines_mask[1][i][j]==0 and data_elines_mask[3][i][j]==0 and data_elines_mask[4][i][j]==0 and data_elines_mask[7][i][j]==0:\n",
    "                    mask_2d_OI[i][j]=0\n",
    "                    OIII_Hbeta=np.log10(data_elines[3][i][j]/data_elines[1][i][j])\n",
    "                    ratio_OIII_Hbeta_OI.append(OIII_Hbeta)\n",
    "                    \n",
    "                    ratio_OIII_Hbeta_e_OI.append((data_elines[3][i][j]/data_elines[1][i][j])*(np.sqrt(percent_e_oiii**2+percent_e_hbeta**2)))\n",
    "                    ratio_OIII_Hbeta_e_log_OI.append(0.434*(np.sqrt(percent_e_oiii**2+percent_e_hbeta**2)))\n",
    "                    \n",
    "                    OI_Halpha=np.log10((data_elines[4][i][j])/data_elines[7][i][j])\n",
    "                    ratio_OI_Halpha.append(OI_Halpha)\n",
    "                    ratio_OI_Halpha_e.append(((data_elines[4][i][j])/data_elines[7][i][j])*(np.sqrt(percent_e_oi**2+percent_e_halpha**2)))\n",
    "                    ratio_OI_Halpha_e_log.append(0.434*(np.sqrt(percent_e_oi**2+percent_e_halpha**2)))\n",
    "                    \n",
    "                    if OI_AGN(OI_Halpha) < OIII_Hbeta and OIII_Hbeta > OI_LINER(OI_Halpha):\n",
    "                        colorcode_OI.append('red')#'PuOr'\n",
    "                        #print('red description', NII_Halpha, OIII_Hbeta)\n",
    "                        #print('kewley(NII_Halpha)', kewley(NII_Halpha), 'LIER(NII_Halpha)', LIER(NII_Halpha))\n",
    "                        color_2d_OI[i][j]=10\n",
    "                        #color_2d[i][j]='red'\n",
    "                    else:\n",
    "                        if OI_AGN(OI_Halpha) > OIII_Hbeta and OI_Halpha < -0.59:\n",
    "                            colorcode_OI.append('blue')\n",
    "                            color_2d_OI[i][j]=20\n",
    "                        #color_2d[i][j]='blue'\n",
    "                        else:\n",
    "                            if OI_Halpha > -1.12688 and OIII_Hbeta > OI_AGN(OI_Halpha) and OIII_Hbeta < OI_LINER(OI_Halpha):#NII_Halpha > 0.47:\n",
    "                                colorcode_OI.append('orange')\n",
    "                                color_2d_OI[i][j]=13\n",
    "                            else:\n",
    "                                #0.59\n",
    "                                if OI_Halpha > -0.59 and OIII_Hbeta > OI_LINER(OI_Halpha):\n",
    "                                    colorcode_OI.append('red')\n",
    "                                    color_2d_OI[i][j]=10\n",
    "                                else:\n",
    "                                    if OI_Halpha > -0.59 and OIII_Hbeta < OI_LINER(OI_Halpha):\n",
    "                                        colorcode_OI.append('orange')\n",
    "                                        color_2d_OI[i][j]=13 \n",
    "                                \n",
    "                                    else:\n",
    "                                        colorcode_OI.append('purple')\n",
    "                else:\n",
    "                    mask_2d_OI[i][j]=1\n",
    "        \n",
    "        color_2d_OI = ma.masked_array(color_2d_OI, mask=mask_2d_OI)\n",
    "        \n",
    "        ax3 = fig.add_subplot(2,4,4, aspect='equal')\n",
    "        xs_OI_AGN=np.linspace(-3.5,-0.7,1000)\n",
    "        ys_OI_AGN=OI_AGN(xs_OI_AGN)\n",
    "        \n",
    "        xs_OI_LINER=np.linspace(-1.12688,1.0, 1000)\n",
    "        ys_OI_LINER=OI_LINER(xs_OI_LINER)\n",
    "        \n",
    "        ax3.plot(xs_OI_AGN,ys_OI_AGN, color='black', ls='--')\n",
    "        ax3.plot(xs_OI_LINER, ys_OI_LINER, color='black', ls='--')\n",
    "        \n",
    "        #ax2.set_adjustable('box-forced')\n",
    "        cmap = colors.ListedColormap(colorcode)\n",
    "        bounds=[0,1,2,3,4]\n",
    "        norm = colors.BoundaryNorm(bounds, cmap.N)\n",
    "        \n",
    "        #plt.title('Poor Mans BPT PA 0')\n",
    "        plt.xlabel(r'log [OI]$\\lambda$6300/H$\\alpha$',size=25)\n",
    "        plt.ylabel(r'log [OIII]$\\lambda$5007/H$\\beta$',size=25)\n",
    "        #plt.errorbar(ratio_NII_Halpha, ratio_OIII_Hbeta,yerr=ratio_OIII_Hbeta_e_log, xerr=ratio_NII_Halpha_e_log,ecolor='black', ls='None')# c=cmap(norm(distance)))#,edgecolors='none',s=100, marker='*')\n",
    "        ax3.scatter(ratio_OI_Halpha, ratio_OIII_Hbeta_OI, c=colorcode_OI,edgecolors='none',s=30,zorder=100)\n",
    "        ax3.set_aspect('auto')\n",
    "        #plt.ylim([-1.0,1.5])\n",
    "        \n",
    "        #plt.xlim([-1.5,0.5])\n",
    "        \n",
    "        plt.ylim([-1.0,1.5])\n",
    "        \n",
    "        plt.xlim([-2.5,0])\n",
    "        \n",
    "        ax6 = fig.add_subplot(2,4,6)\n",
    "        #plt.title(r'BPT Results')\n",
    "        im = ax6.imshow(color_2d, vmin=10, vmax=20, cmap='RdYlBu', interpolation='none')\n",
    "        xs_legend=[len(data_stel_vel)-5,len(data_stel_vel)-5,len(data_stel_vel)-5]\n",
    "        ys_legend=[2,5,8]\n",
    "        colors_legend=[20,13,10]\n",
    "        '''This next part is to basically figure out the location of the legend'''\n",
    "        print('this is the length of the entire thing', len(data_stel_vel))\n",
    "        if len(data_stel_vel)==74 or len(data_stel_vel)==76:\n",
    "            minus=16\n",
    "            xsaxis=[4,8,12]\n",
    "        if len(data_stel_vel)==54:\n",
    "            minus=12\n",
    "            xsaxis=[3,6,9]\n",
    "        if len(data_stel_vel)==44:\n",
    "            minus=9.5\n",
    "            xsaxis=[2,4.5,7]\n",
    "        if len(data_stel_vel)==34:\n",
    "            minus=10\n",
    "            xsaxis=[2,4,6]\n",
    "        if len(data_stel_vel)==42:\n",
    "            minus=9.5\n",
    "            xsaxis=[2,4.5,7]\n",
    "        if len(data_stel_vel)==21:\n",
    "            minus=5\n",
    "            xsaxis=[1,2,3]\n",
    "        '''ax6.scatter(len(data_stel_vel)-minus, xsaxis[0],color='blue', marker='o', s=100)\n",
    "        ax6.annotate(r'SF', xy=(0.82,0.02), xycoords='axes fraction', size=25)\n",
    "        \n",
    "        ax6.scatter(len(data_stel_vel)-minus, xsaxis[1],color='orange', marker='o', s=100)\n",
    "        ax6.annotate(r'LIER', xy=(0.82,0.08), xycoords='axes fraction', size=25)\n",
    "        \n",
    "        ax6.scatter(len(data_stel_vel)-minus, xsaxis[2],color='red', marker='o', s=100)\n",
    "        ax6.annotate(r'AGN', xy=(0.82,0.14), xycoords='axes fraction', size=25)'''\n",
    "        \n",
    "        \n",
    "        #plt.Circle((x_coord,y_coord),.5,color='red',fill=True)\n",
    "        #plt.colorbar(im) \n",
    "        #ax6.set_adjustable('box-forced')\n",
    "            \n",
    "        ax6.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax6.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax6.get_yaxis().set_tick_params(direction='out')\n",
    "        ax6.get_xaxis().set_tick_params(direction='out')\n",
    "        \n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "        \n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "        \n",
    "        ax6.xaxis.set_ticks(x_tick_locs)\n",
    "        ax6.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "        \n",
    "        ax6.yaxis.set_ticks(x_tick_locs)\n",
    "        ax6.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "        #plt.legend()\n",
    "        #plt.xlim([0,len(data_stel_vel)])\n",
    "        #plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        #plt.tight_layout()\n",
    "        #plt.colorbar(im)\n",
    "    \n",
    "        '''SII 2d plot'''\n",
    "        ax7 = fig.add_subplot(2,4,7)\n",
    "        #plt.title(r'BPT Results')\n",
    "        im = ax7.imshow(color_2d_SII, vmin=10, vmax=20, cmap='RdYlBu', interpolation='none')\n",
    "        xs_legend=[len(data_stel_vel)-5,len(data_stel_vel)-5,len(data_stel_vel)-5]\n",
    "        ys_legend=[2,5,8]\n",
    "        colors_legend=[20,13,10]\n",
    "        \n",
    "        #ax7.set_adjustable('box-forced')\n",
    "            \n",
    "        ax7.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax7.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax7.get_yaxis().set_tick_params(direction='out')\n",
    "        ax7.get_xaxis().set_tick_params(direction='out')\n",
    "        \n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "        \n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "        \n",
    "        ax7.xaxis.set_ticks(x_tick_locs)\n",
    "        ax7.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "        \n",
    "        ax7.yaxis.set_ticks(x_tick_locs)\n",
    "        ax7.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "        #plt.legend()\n",
    "        #plt.xlim([0,len(data_stel_vel)])\n",
    "        #plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        \n",
    "        '''OI 2d plot'''\n",
    "        ax8 = fig.add_subplot(2,4,8)\n",
    "        #plt.title(r'BPT Results')\n",
    "        im = ax8.imshow(color_2d_OI, vmin=10, vmax=20, cmap='RdYlBu', interpolation='none')\n",
    "        xs_legend=[len(data_stel_vel)-5,len(data_stel_vel)-5,len(data_stel_vel)-5]\n",
    "        ys_legend=[2,5,8]\n",
    "        colors_legend=[20,13,10]\n",
    "        \n",
    "        #ax7.set_adjustable('box-forced')\n",
    "            \n",
    "        ax8.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax8.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax8.get_yaxis().set_tick_params(direction='out')\n",
    "        ax8.get_xaxis().set_tick_params(direction='out')\n",
    "        \n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "        \n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "        \n",
    "        ax8.xaxis.set_ticks(x_tick_locs)\n",
    "        ax8.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "        \n",
    "        ax8.yaxis.set_ticks(x_tick_locs)\n",
    "        ax8.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "        #plt.legend()\n",
    "        #plt.xlim([0,len(data_stel_vel)])\n",
    "        #plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        \n",
    "        \n",
    "        \n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/BPTs-'+plate+'-'+fiber+'.pdf')\n",
    "        plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_all(plate_list, fiber_list, Circle, Stellar, Write, xcoords, ycoords):\n",
    "    circle=Circle\n",
    "    stellar=Stellar\n",
    "    write=Write\n",
    "    \n",
    "    \n",
    "    for i in range(len(plate_list)):\n",
    "        '''The optional place to put in the center according to kinemetry:'''\n",
    "        x_coord=xcoords[i]\n",
    "        y_coord=ycoords[i]\n",
    "        \n",
    "        correction=2\n",
    "        plate=plate_list[i]\n",
    "        fiber=fiber_list[i]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        plt.clf()\n",
    "        #opening the file for the r-band magnitude\n",
    "        path_spec='MaNGA_J0749/manga-'+plate+'-'+fiber+'-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "        b=pyfits.open(path_spec)\n",
    "        #print(b[0].header)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        r_band=b['SPX_MFLUX'].data\n",
    "        if write=='yes':\n",
    "            hdu = fits.PrimaryHDU(r_band)\n",
    "            hdu.writeto('MaNGA_J0749/'+plate+'_'+fiber+'_r_band_image.fits')\n",
    "        \n",
    "        \n",
    "        if r_band.min() < 0 or r_band.min()==0.0:\n",
    "            print('less than zero', r_band.min())\n",
    "            min_val=0.001\n",
    "        else:\n",
    "            print('greater than zero', r_band.min())\n",
    "            min_val=r_band.min()\n",
    "        \n",
    "        '''This next part is to guess the center from the r-band image'''\n",
    "        square_size=len(r_band)\n",
    "        \n",
    "        \n",
    "        x = np.linspace(0, square_size-1, square_size)\n",
    "        y = np.linspace(0, square_size-1, square_size)\n",
    "        x, y = np.meshgrid(x, y)\n",
    "        \n",
    "        initial_guess = (max(r_band.flatten()),square_size/2,square_size/2,1,1,0,0)\n",
    "        \n",
    "        popt, pcov = curve_fit(twoD_Gaussian, (x, y), r_band.ravel(), p0=initial_guess)\n",
    "        \n",
    "        data_fitted = twoD_Gaussian((x, y), *popt)\n",
    "      \n",
    "        '''Now time to download the stellar velocities and create images of them'''\n",
    "        \n",
    "        #b_stellar=pyfits.open('../MaNGA/'+plate+'/mangadap-'+plate+'-'+fiber+'-default.fits')\n",
    "        data_stel_vel=b['STELLAR_VEL'].data#'STELLAR_VEL'\n",
    "        data_stel_vel_ivar=b['STELLAR_VEL_IVAR'].data\n",
    "        data_stel_vel_mask=b['STELLAR_VEL_MASK'].data\n",
    "        data_stel_sig=np.sqrt(b['STELLAR_SIGMA'].data**2-b['STELLAR_SIGMACORR'].data**2)\n",
    "        data_stel_sig_ivar=b['STELLAR_SIGMA_IVAR'].data\n",
    "        if write=='yes':\n",
    "            hdu = fits.PrimaryHDU(data_stel_vel)\n",
    "            hdu.writeto('MaNGA_J0749/'+plate+'_'+fiber+'_stel_vel.fits')\n",
    "\n",
    "            hdu = fits.PrimaryHDU(1/np.sqrt(data_stel_vel_ivar))\n",
    "            hdu.writeto('MaNGA_J0749/'+plate+'_'+fiber+'_stel_vel_sigma_error.fits')\n",
    "\n",
    "\n",
    "        plt.clf()\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        '''Masking really bad values'''\n",
    "        masked_stel_vel=ma.masked_where((1/np.sqrt(data_stel_vel_ivar)) > 100 , data_stel_vel)#(1/np.sqrt(data_stel_vel_ivar))/data_stel_vel > 1.0\n",
    "        masked_stel_vel=ma.masked_where(data_stel_vel > 1000, masked_stel_vel)\n",
    "        #masked_stel_vel=ma.masked_where(abs((1/np.sqrt(data_stel_vel_ivar))/data_stel_vel) > 1.0, abs(data_stel_vel) > 100, masked_stel_vel)\n",
    "        im=ax.imshow((masked_stel_vel-25.89), vmin=-150, vmax=150, cmap='rainbow')#,vmin=-max(masked_stel_vel), vmax=max(masked_stel_vel))#, interpolation='None')#, vmin=-300, vmax=300)\n",
    "        ax.set_adjustable('box-forced')\n",
    "\n",
    "        ax.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax.get_yaxis().set_tick_params(direction='out')\n",
    "        ax.get_xaxis().set_tick_params(direction='out')\n",
    "        #put ticks every 5\n",
    "\n",
    "        x_ticks=np.arange(0.25*len(data_stel_vel),0.75*len(data_stel_vel),10)\n",
    "        middle=len(data_stel_vel)/2\n",
    "        #print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "        ax.xaxis.set_ticks(x_tick_locs)\n",
    "        ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "        ax.yaxis.set_ticks(x_tick_locs)\n",
    "        ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "\n",
    "        ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "        if circle=='yes':\n",
    "            ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='black')\n",
    "            '''popt[1] and popt[2] are from the twoD gaussian fit'''\n",
    "\n",
    "        cbar1=colorbar(im)\n",
    "        cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "        cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "        #plt.xlim([0,len(data_stel_vel)])\n",
    "        #plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlim([0.25*len(data_stel_vel),0.75*len(data_stel_vel)])\n",
    "        plt.ylim([0.25*len(data_stel_vel),0.75*len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        #plt.annotate('Stellar Velocity', xy=(0.04,0.90),xycoords='axes fraction', size=20)\n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/stellar-vel-zoom-'+plate+'-'+fiber+'.pdf')\n",
    "        \n",
    "        \n",
    "        plt.clf()\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        '''Masking really bad values'''\n",
    "        masked_stel_vel=ma.masked_where((1/np.sqrt(data_stel_vel_ivar)) > 100 , data_stel_vel)#(1/np.sqrt(data_stel_vel_ivar))/data_stel_vel > 1.0\n",
    "        masked_stel_vel=ma.masked_where(data_stel_vel > 1000, masked_stel_vel)\n",
    "        #masked_stel_vel=ma.masked_where(abs((1/np.sqrt(data_stel_vel_ivar))/data_stel_vel) > 1.0, abs(data_stel_vel) > 100, masked_stel_vel)\n",
    "        im=ax.imshow((masked_stel_vel-25.89), vmin=-150, vmax=150, cmap='rainbow')#,vmin=-max(masked_stel_vel), vmax=max(masked_stel_vel))#, interpolation='None')#, vmin=-300, vmax=300)\n",
    "        ax.set_adjustable('box-forced')\n",
    "\n",
    "        ax.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax.get_yaxis().set_tick_params(direction='out')\n",
    "        ax.get_xaxis().set_tick_params(direction='out')\n",
    "        #put ticks every 5\n",
    "\n",
    "        x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "        middle=len(data_stel_vel)/2\n",
    "        #print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "        ax.xaxis.set_ticks(x_tick_locs)\n",
    "        ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "        ax.yaxis.set_ticks(x_tick_locs)\n",
    "        ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "\n",
    "        ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "        if circle=='yes':\n",
    "            ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='black')\n",
    "            '''popt[1] and popt[2] are from the twoD gaussian fit'''\n",
    "\n",
    "        cbar1=colorbar(im)\n",
    "        cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "        cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "        plt.xlim([0,len(data_stel_vel)])\n",
    "        plt.ylim([0,len(data_stel_vel)])\n",
    "        \n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        plt.annotate('Stellar Velocity', xy=(0.04,0.90),xycoords='axes fraction', size=20)\n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/stellar-vel-'+plate+'-'+fiber+'.pdf')\n",
    "\n",
    "        '''now to make the ivar image'''\n",
    "        plt.clf()\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        im=ax.imshow(1/np.sqrt(data_stel_vel_ivar))#, vmin=0, vmax=0.4)\n",
    "        ax.set_adjustable('box-forced')\n",
    "\n",
    "        ax.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax.get_yaxis().set_tick_params(direction='out')\n",
    "        ax.get_xaxis().set_tick_params(direction='out')\n",
    "        #put ticks every 5\n",
    "\n",
    "        ax.xaxis.set_ticks(x_tick_locs)\n",
    "        ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "        ax.yaxis.set_ticks(x_tick_locs)\n",
    "        ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "\n",
    "        ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "        if circle=='yes':\n",
    "            ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='black')\n",
    "        #plt.colorbar(im, label=r'Stellar Velocity [km s$^{-1}$]')\n",
    "\n",
    "        cbar1=colorbar(im)\n",
    "        cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "        #colorbar(im)\n",
    "        cbar1.set_label(label=r'Velocity Error [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "\n",
    "        plt.xlim([0,len(data_stel_vel)])\n",
    "        plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/stellar-vel-ivar-'+plate+'-'+fiber+'.pdf')\n",
    "        #plt.scatter(len(data_stel_vel)/2-cen_x[i],len(data_stel_vel)/2-cen_y[i], color='black', marker='*')\n",
    "\n",
    "        plt.clf()\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        '''Masking really bad values'''\n",
    "        masked_stel_sig=ma.masked_where((1/np.sqrt(data_stel_vel_ivar)) > 100 , data_stel_sig)#(1/np.sqrt(data_stel_vel_ivar))/data_stel_vel > 1.0\n",
    "        #masked_stel_sig=ma.masked_where(data_stel_vel > 1000, masked_stel_vel)\n",
    "        #masked_stel_vel=ma.masked_where(abs((1/np.sqrt(data_stel_vel_ivar))/data_stel_vel) > 1.0, abs(data_stel_vel) > 100, masked_stel_vel)\n",
    "        im=ax.imshow(masked_stel_sig)#,vmin=-max(masked_stel_vel), vmax=max(masked_stel_vel))#, interpolation='None')#, vmin=-300, vmax=300)\n",
    "        ax.set_adjustable('box-forced')\n",
    "        ax.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax.get_yaxis().set_tick_params(direction='out')\n",
    "        ax.get_xaxis().set_tick_params(direction='out')\n",
    "        #put ticks every 5\n",
    "\n",
    "        x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "        middle=len(data_stel_vel)/2\n",
    "        #print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "\n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "        ax.xaxis.set_ticks(x_tick_locs)\n",
    "        ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "        ax.yaxis.set_ticks(x_tick_locs)\n",
    "        ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "\n",
    "        ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "      \n",
    "        cbar1=colorbar(im)\n",
    "        cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "        cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "        plt.xlim([0,len(data_stel_vel)])\n",
    "        plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        plt.annotate('Stellar Dispersion', xy=(0.04,0.90),xycoords='axes fraction', size=20)\n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/stellar-sig-'+plate+'-'+fiber+'.pdf')\n",
    "   \n",
    "\n",
    "        '''now to make the ivar image'''\n",
    "        plt.clf()\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        im=ax.imshow(1/np.sqrt(data_stel_sig_ivar))#, vmin=0, vmax=0.4)\n",
    "        ax.set_adjustable('box-forced')\n",
    "\n",
    "        ax.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax.get_yaxis().set_tick_params(direction='out')\n",
    "        ax.get_xaxis().set_tick_params(direction='out')\n",
    "        #put ticks every 5\n",
    "\n",
    "        ax.xaxis.set_ticks(x_tick_locs)\n",
    "        ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "\n",
    "        ax.yaxis.set_ticks(x_tick_locs)\n",
    "        ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "\n",
    "\n",
    "        ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "        if circle=='yes':\n",
    "            ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='black')\n",
    "        #plt.colorbar(im, label=r'Stellar Velocity [km s$^{-1}$]')\n",
    "\n",
    "        cbar1=colorbar(im)\n",
    "        cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "        #colorbar(im)\n",
    "        cbar1.set_label(label=r'Velocity Error [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "\n",
    "        plt.xlim([0,len(data_stel_vel)])\n",
    "        plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/stellar-sig-ivar-'+plate+'-'+fiber+'.pdf')\n",
    "            \n",
    "        plt.clf()\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        '''r-band somehow adjusted to the proper magnitude scale'''\n",
    "        im=ax.imshow(22.5-2.5*np.log10(r_band),interpolation='None',cmap='gist_rainbow',vmax=22.5-2.5*np.log10(0.1),vmin=22.5-2.5*np.log10(r_band.max()))#, norm=LogNorm(vmin=min_val, vmax=r_band.max()), interpolation='None')\n",
    "        \n",
    "        ax.set_adjustable('box-forced')\n",
    "            \n",
    "        ax.get_yaxis().set_ticks_position(\"left\")\n",
    "        ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "        ax.get_yaxis().set_tick_params(direction='out')\n",
    "        ax.get_xaxis().set_tick_params(direction='out')\n",
    "        \n",
    "        x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "        x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "        x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "        \n",
    "        x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "        y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "        \n",
    "        ax.xaxis.set_ticks(x_tick_locs)\n",
    "        ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "        \n",
    "        ax.yaxis.set_ticks(x_tick_locs)\n",
    "        ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "        square_size=len(r_band)\n",
    "        \n",
    "        \n",
    "        x = np.linspace(0, square_size-1, square_size)\n",
    "        y = np.linspace(0, square_size-1, square_size)\n",
    "        x, y = np.meshgrid(x, y)\n",
    "        \n",
    "        initial_guess = (max(r_band.flatten()),square_size/2,square_size/2,1,1,0,0)\n",
    "        \n",
    "        popt, pcov = curve_fit(twoD_Gaussian, (x, y), r_band.ravel(), p0=initial_guess)\n",
    "        \n",
    "        \n",
    "        data_fitted = twoD_Gaussian((x, y), *popt)\n",
    "        \n",
    "        ax.scatter(popt[1], popt[2], color='white', marker='x', s=100)\n",
    "        \n",
    "        if circle=='yes':\n",
    "            ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='white')\n",
    "        \n",
    "        plt.xlim([0,len(data_stel_vel)])\n",
    "        plt.ylim([0,len(data_stel_vel)])\n",
    "        plt.xlabel('Arcsec', size=25)\n",
    "        plt.ylabel('Arcsec', size=25)\n",
    "        \n",
    "        min_colorbar=int(22.5-2.5*np.log10(r_band.max()))\n",
    "        max_colorbar=int(22.5-2.5*np.log10(min_val))\n",
    "        \n",
    "        diff=int((max_colorbar-min_colorbar)/4)# you wanna space by 4 magnitudes\n",
    "        tick_list=[0,1,2]#np.arange(0,diff,diff)\n",
    "        \n",
    "        m0=int(np.floor(22.5-2.5*np.log10(r_band.max())))            # colorbar min value\n",
    "        m4=int(np.ceil(22.5-2.5*np.log10(0.1)))             # colorbar max value\n",
    "        m1=int(1*(m4-m0)/5.0 + m0)               # colorbar mid value 1\n",
    "        m2=int(2*(m4-m0)/5.0 + m0)               # colorbar mid value 2\n",
    "        m3=int(3*(m4-m0)/5.0 + m0)               # colorbar mid value 3\n",
    "        m5=int(4*(m4-m0)/5.0 + m0)               # colorbar mid value 4\n",
    "        cbar1=colorbar(im)\n",
    "        \n",
    "        \n",
    "        cbar1.set_ticks([18,20,22,24,26,28,30,34])\n",
    "        cbar1.set_ticklabels([18,20,22,24,26,28,30,34])\n",
    "        \n",
    "        \n",
    "        colorbar_ticks=[str(4*x+min_colorbar) for x in tick_list]\n",
    "        \n",
    "        cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "        \n",
    "        \n",
    "        cbar1.set_label(label=r'Magnitude',size=25,weight='normal', labelpad=10)\n",
    "        \n",
    "        cbar1.ax.invert_yaxis() \n",
    "        \n",
    "        \n",
    "        plt.tight_layout()\n",
    "        savefig('MaNGA_J0749/kinemetry-center-guess-'+plate+'-'+fiber+'.pdf')\n",
    "        \n",
    "        \n",
    "        '''time to do the velocity dispersions'''\n",
    "        '''sigma_gas_corr = sqrt( EMLINE_GSIGMA2 - EMLINE_INSTSIGMA2 )'''\n",
    "        \n",
    "        '''elines_sig=np.sqrt(b['EMLINE_GSIGMA'].data**2-b['EMLINE_INSTSIGMA']**2)\n",
    "        \n",
    "        error_index=[1,3,8,7]\n",
    "        name_errors=['H_beta','OIII','NII','H_alpha']\n",
    "        emission_index=[1,3,8,7]\n",
    "        labels=[r'H$\\beta$ Flux / arcsec$^2$',r'[OIII] Flux / arcsec$^2$',r'[NII] Flux / arcsec$^2$',r'H$\\alpha$ Flux / arcsec$^2$']\n",
    "        name_file=['hbeta','oiii','nii','halpha']\n",
    "        \n",
    "        for k in range(len(name_errors)):\n",
    "            plt.clf()\n",
    "            fig = plt.figure()\n",
    "            ax = fig.add_subplot(1,1,1)\n",
    "            #plt.title(r'H$\\beta$ Flux')\n",
    "            im = ax.imshow(elines_sig[emission_index[k]],vmin=0, vmax=300, interpolation='None')\n",
    "            ax.set_adjustable('box-forced')\n",
    "                \n",
    "            ax.get_yaxis().set_ticks_position(\"left\")\n",
    "            ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "            ax.get_yaxis().set_tick_params(direction='out')\n",
    "            ax.get_xaxis().set_tick_params(direction='out')\n",
    "            #put ticks every 5\n",
    "            \n",
    "            x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "            middle=len(data_stel_vel)/2\n",
    "            #print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "                \n",
    "            x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "            x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "            x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "            \n",
    "            x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "            y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "            \n",
    "            ax.xaxis.set_ticks(x_tick_locs)\n",
    "            ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "            \n",
    "            ax.yaxis.set_ticks(x_tick_locs)\n",
    "            ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "            \n",
    "            cbar1=colorbar(im)\n",
    "            cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "    \n",
    "            cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "            \n",
    "            \n",
    "            ax.scatter(popt[1], popt[2], color='white', marker='x', s=100)\n",
    "            \n",
    "            if circle=='yes':\n",
    "                ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='white')\n",
    "            plt.xlim([0,len(data_stel_vel)])\n",
    "            plt.ylim([0,len(data_stel_vel)])\n",
    "            plt.xlabel('Arcsec', size=25)\n",
    "            plt.ylabel('Arcsec', size=25)\n",
    "            plt.tight_layout()\n",
    "            savefig('MaNGA_J0749/'+plate+'_'+fiber+'_'+name_file[k]+'_velocity_dispersion.pdf')\n",
    "            if k==1:\n",
    "                oiii_vel=elines_vel[emission_index[k]]'''\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        elines_vel=b['EMLINE_GVEL'].data\n",
    "        \n",
    "        error_index=[1,3,8,7]\n",
    "        name_errors=['H_beta','OIII','NII','H_alpha']\n",
    "        emission_index=[1,3,8,7]\n",
    "        labels=[r'H$\\beta$ Flux / arcsec$^2$',r'[OIII] Flux / arcsec$^2$',r'[NII] Flux / arcsec$^2$',r'H$\\alpha$ Flux / arcsec$^2$']\n",
    "        name_file=['hbeta','oiii','nii','halpha']\n",
    "        \n",
    "        for k in range(len(name_errors)):\n",
    "            plt.clf()\n",
    "            fig = plt.figure()\n",
    "            ax = fig.add_subplot(1,1,1)\n",
    "            #plt.title(r'H$\\beta$ Flux')\n",
    "            elines_vel_mask=ma.masked_where(elines_vel[emission_index[k]]<-1000,elines_vel[emission_index[k]])\n",
    "            im = ax.imshow(elines_vel_mask,vmin=-150, vmax=150, interpolation='None', cmap='rainbow')\n",
    "            ax.set_adjustable('box-forced')\n",
    "                \n",
    "            ax.get_yaxis().set_ticks_position(\"left\")\n",
    "            ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "            ax.get_yaxis().set_tick_params(direction='out')\n",
    "            ax.get_xaxis().set_tick_params(direction='out')\n",
    "            #put ticks every 5\n",
    "            \n",
    "            x_ticks=np.arange(0,len(data_stel_vel)/2,10)\n",
    "            middle=len(data_stel_vel)/2\n",
    "            #print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "                \n",
    "            x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "            x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "            x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "            \n",
    "            x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "            y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "            \n",
    "            ax.xaxis.set_ticks(x_tick_locs)\n",
    "            ax.xaxis.set_ticklabels(x_tick_lbls, size=20)\n",
    "            \n",
    "            ax.yaxis.set_ticks(x_tick_locs)\n",
    "            ax.yaxis.set_ticklabels(y_tick_lbls, size=20)\n",
    "            \n",
    "            cbar1=colorbar(im)\n",
    "            cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "    \n",
    "            cbar1.set_label(label=r'Velocity [km s$^{-1}$]',size=25,weight='normal', labelpad=10)\n",
    "            \n",
    "            \n",
    "            ax.scatter(popt[1], popt[2], color='white', marker='x', s=100)\n",
    "            \n",
    "            if circle=='yes':\n",
    "                ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='white')\n",
    "            plt.xlim([0,len(data_stel_vel)])\n",
    "            plt.ylim([0,len(data_stel_vel)])\n",
    "            plt.xlabel('Arcsec', size=25)\n",
    "            plt.ylabel('Arcsec', size=25)\n",
    "            plt.tight_layout()\n",
    "            savefig('MaNGA_J0749/'+plate+'_'+fiber+'_'+name_file[k]+'_velocity.pdf')\n",
    "            if k==1:\n",
    "                oiii_vel=elines_vel[emission_index[k]]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        #stop\n",
    "        \n",
    "        return oiii_vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def kinemetry(plate, fiber):\n",
    "    \n",
    "    plt.clf()\n",
    "    \n",
    "    \n",
    "    path_spec='MaNGA_J0749/manga-'+plate+'-'+fiber+'-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "    b_stellar=pyfits.open(path_spec)\n",
    "    #print(b[0].header)\n",
    "\n",
    "\n",
    "    '''Now time to download the stellar velocities and create images of them'''\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    file = open(\"../MaNGA/kinemetry_input_\"+plate+'-'+fiber+\".txt\",\"w\")\n",
    "    file.write('#'+'\\t'+'XBIN'+'\\t'+'YBIN'+'\\t'+'VEL'+'\\t'+'ER_VEL'+'\\t'+'SIG'+'\\t'+'ER_SIG'+'\\n')\n",
    "    \n",
    "    #print(b_stellar[0].header)\n",
    "    data_stel_vel=b_stellar['STELLAR_VEL'].data-25.89#'STELLAR_VEL'\n",
    "    data_stel_vel_ivar=b_stellar['STELLAR_VEL_IVAR'].data\n",
    "    print(plate, shape(data_stel_vel))\n",
    "    data_stel_sig=np.sqrt(b_stellar['STELLAR_SIGMA'].data**2-b_stellar['STELLAR_SIGMACORR'].data**2)\n",
    "    data_stel_sig_ivar=b_stellar['STELLAR_SIGMA_IVAR'].data\n",
    "    counter=1\n",
    "    plt.clf()\n",
    "    \n",
    "    fig=plt.figure()\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.set_title(r'Stellar Velocity [km s$^{-1}$]', y=1.08)\n",
    "    '''if cen_x[i] ==0 and cen_y[i]==0:\n",
    "        print('doin nothing')\n",
    "    else:\n",
    "        ax.scatter(len(data_stel_vel)/2-cen_x[i],len(data_stel_vel)/2-cen_y[i], color='black', marker='*')\n",
    "    '''\n",
    "    im=ax.imshow(data_stel_vel, vmin=-200, vmax=200)\n",
    "    cbar=plt.colorbar(im)\n",
    "    \n",
    "    '''ax1 = fig.add_subplot(2, 2, 3)\n",
    "    ax1.set_title(r'Stellar Velocity Error [km s$^{-1}$]', y=1.08)\n",
    "    im1=ax1.imshow(1./np.sqrt(data_stel_vel_ivar), vmin=0, vmax=100)\n",
    "    cbar=plt.colorbar(im1)\n",
    "    \n",
    "    ax2 = fig.add_subplot(2, 2, 2)\n",
    "    ax2.set_title(r'Stellar Velocity Dispersion [km s$^{-1}$]', y=1.08)\n",
    "    im2=ax2.imshow(data_stel_sig, vmin=0, vmax=500)\n",
    "    cbar2=plt.colorbar(im2)\n",
    "    \n",
    "    ax3 = fig.add_subplot(2, 2, 4)\n",
    "    ax3.set_title(r'Stellar Velocity Dispersion Error [km s$^{-1}$]', y=1.08)\n",
    "    im3=ax3.imshow(1./np.sqrt(data_stel_sig_ivar), vmin=0, vmax=100)\n",
    "    cbar3=plt.colorbar(im3)'''\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    savefig('../MaNGA/sigma_vel/kinemetry-maps-post-kinemetry-'+plate+'-'+fiber+'.pdf')\n",
    "    \n",
    "    \n",
    "    '''make another figure of the r band intensity which we use for the initial guess of the center'''\n",
    "    plt.clf()\n",
    "    #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "    \n",
    "    path_spec='MaNGA_J0749/manga-'+plate+'-'+fiber+'-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "    b=pyfits.open(path_spec)\n",
    "    #print(b[0].header)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    r_band=b['SPX_MFLUX'].data\n",
    "    \n",
    "    '''path_spec='MaNGA_J0749/manga-8141-1901-LOGCUBE-VOR10-GAU-MILESHC.fits'\n",
    "    b=pyfits.open(path_spec)\n",
    "    r_band=b[9].data'''\n",
    "    \n",
    "    plt.title(r'r-band Flux')\n",
    "    im=plt.imshow(r_band, vmin=0, vmax=r_band.max())\n",
    "    square_size=len(r_band)\n",
    "    \n",
    "    \n",
    "    x = np.linspace(0, square_size-1, square_size)\n",
    "    y = np.linspace(0, square_size-1, square_size)\n",
    "    x, y = np.meshgrid(x, y)\n",
    "    \n",
    "    initial_guess = (max(r_band.flatten()),square_size/2,square_size/2,1,1,0,0)\n",
    "    \n",
    "    popt, pcov = curve_fit(twoD_Gaussian, (x, y), r_band.ravel(), p0=initial_guess)\n",
    "    \n",
    "    \n",
    "    data_fitted = twoD_Gaussian((x, y), *popt)\n",
    "    \n",
    "    \n",
    "    \n",
    "    plt.contour(x, y, data_fitted.reshape(square_size, square_size), 10, colors='w', lw=2)\n",
    "    #plt.colorbar(im)\n",
    "    plt.annotate(r'x$_\\mathrm{cen}$ = '+str(int(round(popt[1],0)))+' $\\pm$ '+str(round(np.sqrt(pcov[1][1]),2)), xy=(0.03,0.90), xycoords='axes fraction', color='white', size=15)\n",
    "    plt.annotate(r'y$_\\mathrm{cen}$ = '+str(int(round(popt[2],0)))+' $\\pm$ '+str(round(np.sqrt(pcov[2][2]),2)), xy=(0.03,0.80), xycoords='axes fraction', color='white', size=15)\n",
    "    plt.annotate(str(plate)+' '+str(fiber), xy=(0.03, 0.70), xycoords='axes fraction', color='white', size=15)\n",
    "    \n",
    "    \n",
    "    plt.colorbar(im)\n",
    "    savefig('../MaNGA/sigma_vel/kinemetry-center-guess-'+plate+'-'+fiber+'.pdf')\n",
    "    \n",
    "    plt.clf()\n",
    "    velocity_input=np.zeros((shape(data_stel_vel)[0],shape(data_stel_vel)[0]))\n",
    "    for j in range(shape(data_stel_vel)[0]):\n",
    "        for k in range(shape(data_stel_vel)[0]):\n",
    "            vel=data_stel_vel[j][k]\n",
    "            vel_e=np.sqrt(1./data_stel_vel_ivar[j][k])\n",
    "            sig=data_stel_sig[j][k]\n",
    "            sig_e=np.sqrt(1./data_stel_sig_ivar[j][k])\n",
    "            if vel < -500:\n",
    "            #if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "                none=1\n",
    "                velocity_input[j][k]=0\n",
    "            else:\n",
    "                #file = open(\"../MaNGA/kinemetry_input_\"+plate+'-'+fiber+\".txt\",\"w\")\n",
    "                #file.write('#'+'\\t'+'XBIN'+'\\t'+'YBIN'+'\\t'+'VEL'+'\\t'+'ER_VEL'+'\\t'+'SIG'+'\\t'+'ER_SIG'+'\\n')\n",
    "    \n",
    "                file.write(str(counter)+'\\t'+str(0.5*(j-shape(data_stel_vel)[0]/2))+'\\t'+str(0.5*(k-shape(data_stel_vel)[0]/2))+'\\t')\n",
    "                file.write(str(round(vel,2))+'\\t'+str(round(vel_e,2))+'\\t'+str(round(sig,2))+'\\t'+str(round(sig_e,2))+'\\n')\n",
    "                counter += 1\n",
    "                velocity_input[j][k]=vel\n",
    "    plt.clf()\n",
    "    print('counter', counter)\n",
    "    plt.imshow(velocity_input, vmin=-100, vmax=100)\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    file.close()\n",
    "    \n",
    "    \n",
    "    \n",
    "def kinemetry_gas(plate, fiber):\n",
    "    \n",
    "    plt.clf()\n",
    "    \n",
    "    \n",
    "    path_spec='MaNGA_J0749/manga-'+plate+'-'+fiber+'-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "    b=pyfits.open(path_spec)\n",
    "    #print(b[0].header)\n",
    "\n",
    "    \n",
    "    '''Now time to download the stellar velocities and create images of them'''\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    file = open(\"../MaNGA/kinemetry_input_oiii_\"+plate+'-'+fiber+\".txt\",\"w\")\n",
    "    file.write('#'+'\\t'+'XBIN'+'\\t'+'YBIN'+'\\t'+'VEL'+'\\t'+'ER_VEL'+'\\t'+'SIG'+'\\t'+'ER_SIG'+'\\n')\n",
    "    \n",
    "    #print(b_stellar[0].header)\n",
    "    elines_vel=b['EMLINE_GVEL'].data\n",
    "    elines_vel_ivar=b['EMLINE_GVEL_IVAR'].data\n",
    "    elines_sig=np.sqrt(b['EMLINE_GSIGMA'].data**2-b['EMLINE_INSTSIGMA'].data**2)\n",
    "    elines_sig_ivar=b['EMLINE_GSIGMA_IVAR'].data\n",
    "    \n",
    "    oiii_elines=elines_vel[3]\n",
    "    elines_vel_ivar=elines_vel_ivar[3]\n",
    "    elines_sig=elines_sig[3]\n",
    "    elines_sig_ivar=elines_sig_ivar[3]\n",
    "    \n",
    "    counter=1\n",
    "    plt.clf()\n",
    "    \n",
    "    fig=plt.figure()\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.set_title(r'[OIII] Velocity [km s$^{-1}$]', y=1.08)\n",
    "    '''if cen_x[i] ==0 and cen_y[i]==0:\n",
    "        print('doin nothing')\n",
    "    else:\n",
    "        ax.scatter(len(data_stel_vel)/2-cen_x[i],len(data_stel_vel)/2-cen_y[i], color='black', marker='*')\n",
    "    '''\n",
    "    im=ax.imshow(oiii_elines, vmin=-200, vmax=200)\n",
    "    cbar=plt.colorbar(im)\n",
    "    \n",
    "    \n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    savefig('../MaNGA/sigma_vel/kinemetry-maps-pre-kinemetry-oiii-'+plate+'-'+fiber+'.pdf')\n",
    "    \n",
    "    \n",
    "    \n",
    "    plt.clf()\n",
    "    velocity_input=np.zeros((shape(oiii_elines)[0],shape(oiii_elines)[0]))\n",
    "    for j in range(shape(oiii_elines)[0]):\n",
    "        for k in range(shape(oiii_elines)[0]):\n",
    "            vel=oiii_elines[j][k]\n",
    "            vel_e=np.sqrt(1./elines_vel_ivar[j][k])\n",
    "            sig=elines_sig[j][k]\n",
    "            sig_e=np.sqrt(1./elines_sig_ivar[j][k])\n",
    "            if vel < -500:\n",
    "            #if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "                none=1\n",
    "                velocity_input[j][k]=0\n",
    "            else:\n",
    "                #file = open(\"../MaNGA/kinemetry_input_\"+plate+'-'+fiber+\".txt\",\"w\")\n",
    "                #file.write('#'+'\\t'+'XBIN'+'\\t'+'YBIN'+'\\t'+'VEL'+'\\t'+'ER_VEL'+'\\t'+'SIG'+'\\t'+'ER_SIG'+'\\n')\n",
    "    \n",
    "                file.write(str(counter)+'\\t'+str(0.5*(j-shape(oiii_elines)[0]/2))+'\\t'+str(0.5*(k-shape(oiii_elines)[0]/2))+'\\t')\n",
    "                file.write(str(round(vel,2))+'\\t'+str(round(vel_e,2))+'\\t'+str(round(sig,2))+'\\t'+str(round(sig_e,2))+'\\n')\n",
    "                counter += 1\n",
    "                velocity_input[j][k]=vel\n",
    "    plt.clf()\n",
    "    print('counter', counter)\n",
    "    plt.imshow(velocity_input, vmin=-100, vmax=100)\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "velcirc=[   0.0 \n",
    ",\n",
    "   0.0\n",
    ",\n",
    "   0.0\n",
    ",\n",
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    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counter=1\n",
    "file = open(\"../MaNGA/kinemetry_output_8141_1901.txt\",\"w\")\n",
    "file.write('#'+'\\t'+'XBIN'+'\\t'+'YBIN'+'\\t'+'VEL'+'\\n')\n",
    "for j in range(shape(data_stel_vel)[0]):\n",
    "        for k in range(shape(data_stel_vel)[0]):\n",
    "            vel=data_stel_vel[j][k]\n",
    "            \n",
    "            if vel < -500 or vel==0.0:\n",
    "            #if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "                none=1\n",
    "                \n",
    "            else:\n",
    "                #file = open(\"../MaNGA/kinemetry_input_\"+plate+'-'+fiber+\".txt\",\"w\")\n",
    "                #file.write('#'+'\\t'+'XBIN'+'\\t'+'YBIN'+'\\t'+'VEL'+'\\t'+'ER_VEL'+'\\t'+'SIG'+'\\t'+'ER_SIG'+'\\n')\n",
    "                #print('counter', counter)\n",
    "                file.write(str(counter)+'\\t'+str(0.5*(j-shape(data_stel_vel)[0]/2))+'\\t'+str(0.5*(k-shape(data_stel_vel)[0]/2))+'\\t')\n",
    "                try:\n",
    "                    if velcirc[counter]>500:\n",
    "                        vell=0.0\n",
    "                    else:\n",
    "                        vell=velcirc[counter]\n",
    "                    file.write(str(round(vell,2))+'\\n')\n",
    "                except IndexError:\n",
    "                    continue\n",
    "                counter += 1  \n",
    "file.close()\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12db4ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "for i,j,k in zip(xs,ys,velcirc):\n",
    "    circle = plt.Circle((i,j), 0.1, color = 'k')\n",
    "    fig.gca().add_artist(circle)   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
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      "34\n",
      "0.0\n",
      "400\n"
     ]
    },
    {
     "data": {
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aScbWrcDu3UC7dmLKIgUHDwIbNpA9c6qJTFMiSkqApUsZtM84IzMlkFCINkpx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v8+ADQ7m6WXtGvlAdUFo41mZ+tZJipKsw8dGvbQJ1VnAd9Ph+rN9WOzOWmmIUH6LGofKy+YLfrq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\nzbiwm5nVjAu7mVnN/AsBvPCFu8rf/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d717400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAADIAAAD8CAYAAAA13fd1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU0bI/wCrZdMTQ+tD1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d7b3b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(type(velcirc))\n",
    "print(velcirc)\n",
    "print(shape(data_stel_vel)[0])\n",
    "\n",
    "print(float(velcirc[0]))\n",
    "velcirc_mask=[]\n",
    "for i in range(len(velcirc)):\n",
    "    if (velcirc[i])==123456790:\n",
    "        velcirc_mask.append('-100.0')\n",
    "    else:\n",
    "        velcirc_mask.append(str(velcirc[i]))\n",
    "#print(velcirc_mask)\n",
    "velcirc_mask=[float(y) for y in velcirc_mask]\n",
    "cmap_here=plt.cm.rainbow(velcirc_mask)\n",
    "\n",
    "\n",
    "print(len(velcirc_mask))\n",
    "velcirc_mask=[float(y) for y in velcirc_mask]\n",
    "plt.scatter(xs,ys,c=cmap_here)\n",
    "#plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(cmap_here)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iOOZTO3cN9DOKoM+hkteo6nUtivC/CXxVVS8Bvpr+PjAMZTrgoWl3wYM2GGF6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\nRgxF4C5QoECBEcP/B2LlDru4RoZeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a6d4390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file='../MaNGA/kinemetry_output_8141_1901.txt'\n",
    "\n",
    "with open(file, 'r') as f:\n",
    "    data = f.readlines()\n",
    "    title=[]\n",
    "    xs=[]\n",
    "    ys=[]\n",
    "    output_vel=[]\n",
    "   \n",
    "\n",
    "    for x in data:\n",
    "        words = x.split()\n",
    "        if words[0] != '#':\n",
    "            xs.append(words[1])\n",
    "            ys.append(words[2])\n",
    "            output_vel.append(words[3])\n",
    "         \n",
    "output_vel=[float(y) for y in output_vel]\n",
    "\n",
    "plt.scatter(xs,ys,c=output_vel,s=200)\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len 400\n"
     ]
    },
    {
     "data": {
      "image/png": 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VhMXr+MK5lePV3CGr1Nj1kFYWJkWLgh1yZzrOzVXbx7il3cF3K7idymtUYv/V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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6d49b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file='../MaNGA/kinemetry_input_8141-1901.txt'\n",
    "\n",
    "with open(file, 'r') as f:\n",
    "    data = f.readlines()\n",
    "    title=[]\n",
    "    xs=[]\n",
    "    ys=[]\n",
    "    input_vel=[]\n",
    "   \n",
    "\n",
    "    for x in data:\n",
    "        words = x.split()\n",
    "        if words[0] != '#':\n",
    "            xs.append(words[1])\n",
    "            ys.append(words[2])\n",
    "            input_vel.append(words[3])\n",
    "         \n",
    "input_vel=[float(y) for y in input_vel]\n",
    "\n",
    "#rgb=plt.cm.jet(float(input_vel))\n",
    "print('len', len(input_vel))\n",
    "plt.scatter(xs,ys,c=input_vel, s=200)\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119690898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.         0.         0.       ..., -80.946943 -84.801627   0.      ]\n",
      " [-87.459469 -89.943158 -90.994447 ..., -49.65619  -51.497496   0.      ]\n",
      " [-54.058065 -56.784852 -61.196862 ..., -31.57987  -29.790166   0.      ]\n",
      " ..., \n",
      " [  0.         0.         0.       ...,   0.         0.         0.      ]\n",
      " [  0.         0.         0.       ...,   0.         0.         0.      ]\n",
      " [  0.         0.         0.       ...,   0.         0.         0.      ]]\n"
     ]
    }
   ],
   "source": [
    "##step 1: make a new .txt file\n",
    "\n",
    "\n",
    "\n",
    "path_spec='MaNGA_J0749/manga-8141-1901-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "b=pyfits.open(path_spec)\n",
    "#print(b[0].header)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#b_stellar=pyfits.open('../MaNGA/'+plate+'/mangadap-'+plate+'-'+fiber+'-default.fits')\n",
    "data_stel_vel=b['STELLAR_VEL'].data#'STELLAR_VEL'\n",
    "data_stel_vel_ivar=b['STELLAR_VEL_IVAR'].data\n",
    "data_stel_vel_mask=b['STELLAR_VEL_MASK'].data\n",
    "data_stel_sig=np.sqrt(b['STELLAR_SIGMA'].data**2-b['STELLAR_SIGMACORR'].data**2)\n",
    "data_stel_sig_ivar=b['STELLAR_SIGMA_IVAR'].data\n",
    "\n",
    "print(velcirc[0])\n",
    "#create an empty 2x2 array to populate with the model velocity\n",
    "model_vel = np.zeros(shape=(shape(data_stel_vel)[0],shape(data_stel_vel)[0]))\n",
    "print(shape(model_vel))\n",
    "print(len(velcirc))\n",
    "\n",
    "##bins are binned by 0.5 units\n",
    "\n",
    "'''You need to pull the position from the txt file because they are in order of XBIN YBIN'''\n",
    "'''Practice on the txt file input because you know what its supposed to look like'''\n",
    "n=0\n",
    "'''for j in xrange(shape(data_stel_vel)[0]):\n",
    "        for k in xrange(shape(data_stel_vel)[0]):\n",
    "            vel=data_stel_vel[j][k]\n",
    "            vel_e=np.sqrt(1./data_stel_vel_ivar[j][k])\n",
    "            sig=data_stel_sig[j][k]\n",
    "            sig_e=np.sqrt(1./data_stel_sig_ivar[j][k])\n",
    "            if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "                none=1\n",
    "            else:\n",
    "                file.write(str(counter)+'\\t'+str(0.5*(j-shape(data_stel_vel)[0]/2))+'\\t'+str(0.5*(k-shape(data_stel_vel)[0]/2))+'\\t')\n",
    "                file.write(str(round(vel,2))+'\\t'+str(round(vel_e,2))+'\\t'+str(round(sig,2))+'\\t'+str(round(sig_e,2))+'\\n')\n",
    "                counter += 1'''\n",
    "'''n=0\n",
    "for j in range(20):\n",
    "    for k in range(20):\n",
    "        vel=data_stel_vel[j][k]\n",
    "        vel_e=np.sqrt(1./data_stel_vel_ivar[j][k])\n",
    "        sig=data_stel_sig[j][k]\n",
    "        sig_e=np.sqrt(1./data_stel_sig_ivar[j][k])\n",
    "        if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "            none=1\n",
    "        else:\n",
    "        xbin=(j-shape(data_stel_vel)[0]/2)\n",
    "        ybin=(k-shape(data_stel_vel)[0]/2)\n",
    "        #print('xbin,ybin', xbin,ybin)\n",
    "        xbin_corr=xbin+34/2\n",
    "        ybin_corr=ybin+34/2\n",
    "        #print('corr', xbin_corr,ybin_corr)\n",
    "\n",
    "\n",
    "        #print(n)\n",
    "        print(xbin_corr, ybin_corr, n)\n",
    "        model_vel[xbin_corr][ybin_corr]=velcirc[n]\n",
    "        if velcirc[n] > 400000:\n",
    "            model_vel[xbin_corr][ybin_corr]=0\n",
    "        #if xbin**2+ybin**2 > 370:\n",
    "        #    model_vel[xbin_corr][ybin_corr]=0\n",
    "        #    print('xcor,ycor', xbin_corr, ybin_corr, ybin**2+xbin**2)\n",
    "        if xbin_corr < 20 and ybin_corr > 35:\n",
    "            model_vel[xbin_corr][ybin_corr]=0\n",
    "        if ybin_corr <10:\n",
    "            model_vel[xbin_corr][ybin_corr]=0\n",
    "        if ybin_corr < 17 and xbin_corr < 11:\n",
    "            model_vel[xbin_corr][ybin_corr]=0\n",
    "        #ybin_corr >25 cuts off anything to the right of 0\n",
    "        n+=1'''\n",
    "                \n",
    "model_vel = np.zeros(shape=(54,54))\n",
    "         \n",
    "                \n",
    "for j in range(53):\n",
    "    for k in range(53):\n",
    "        xbin=(j)\n",
    "        ybin=(k)\n",
    "        #print('xbin,ybin', xbin,ybin)\n",
    "        xbin_corr=xbin\n",
    "        ybin_corr=ybin\n",
    "            \n",
    "            \n",
    "        print(n)\n",
    "        try:\n",
    "            model_vel[xbin_corr][ybin_corr]=velcirc[n]\n",
    "        except IndexError:\n",
    "            continue\n",
    "        n+=1\n",
    "plt.clf()\n",
    "\n",
    "plt.imshow(np.flipud(model_vel), vmin=-100, vmax=100)\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "print(model_vel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "(34, 34)\n",
      "400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:26: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:28: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d783400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 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.]]\n"
     ]
    }
   ],
   "source": [
    "##step 1: make a new .txt file\n",
    "\n",
    "print(velcirc[0])\n",
    "#create an empty 2x2 array to populate with the model velocity\n",
    "model_vel = np.zeros(shape=(shape(data_stel_vel)[0],shape(data_stel_vel)[0]))\n",
    "print(shape(model_vel))\n",
    "print(len(velcirc))\n",
    "\n",
    "##bins are binned by 0.5 units\n",
    "n=0\n",
    "'''for j in xrange(shape(data_stel_vel)[0]):\n",
    "        for k in xrange(shape(data_stel_vel)[0]):\n",
    "            vel=data_stel_vel[j][k]\n",
    "            vel_e=np.sqrt(1./data_stel_vel_ivar[j][k])\n",
    "            sig=data_stel_sig[j][k]\n",
    "            sig_e=np.sqrt(1./data_stel_sig_ivar[j][k])\n",
    "            if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "                none=1\n",
    "            else:\n",
    "                file.write(str(counter)+'\\t'+str(0.5*(j-shape(data_stel_vel)[0]/2))+'\\t'+str(0.5*(k-shape(data_stel_vel)[0]/2))+'\\t')\n",
    "                file.write(str(round(vel,2))+'\\t'+str(round(vel_e,2))+'\\t'+str(round(sig,2))+'\\t'+str(round(sig_e,2))+'\\n')\n",
    "                counter += 1'''\n",
    "for j in range(shape(data_stel_vel)[0]):\n",
    "    for k in range(shape(data_stel_vel)[0]):\n",
    "        vel=data_stel_vel[j][k]\n",
    "        vel_e=np.sqrt(1./data_stel_vel_ivar[j][k])\n",
    "        sig=data_stel_sig[j][k]\n",
    "        sig_e=np.sqrt(1./data_stel_sig_ivar[j][k])\n",
    "        if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "            none=1\n",
    "        else:\n",
    "            xbin=(j-shape(data_stel_vel)[0]/2)\n",
    "            ybin=(k-shape(data_stel_vel)[0]/2)\n",
    "            #print('xbin,ybin', xbin,ybin)\n",
    "            xbin_corr=xbin+54/2\n",
    "            ybin_corr=ybin+54/2\n",
    "            \n",
    "            \n",
    "            xbin_corr=j\n",
    "            ybin_corr=k\n",
    "            \n",
    "            \n",
    "            #print(n)\n",
    "            try:\n",
    "                model_vel[xbin_corr][ybin_corr]=velcirc[n]\n",
    "                if velcirc[n] > 40000:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0\n",
    "                #if xbin**2+ybin**2 > 370:\n",
    "                #    model_vel[xbin_corr][ybin_corr]=0\n",
    "                #    print('xcor,ycor', xbin_corr, ybin_corr, ybin**2+xbin**2)\n",
    "                '''if xbin_corr < 20 and ybin_corr > 35:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0\n",
    "                if ybin_corr <10:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0\n",
    "                if ybin_corr < 17 and xbin_corr < 11:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0'''\n",
    "                #ybin_corr >25 cuts off anything to the right of 0\n",
    "                n+=1\n",
    "            except IndexError:\n",
    "                n+=1 \n",
    "                continue\n",
    "plt.clf()\n",
    "plt.imshow(np.flipud(model_vel), vmin=-100, vmax=100)\n",
    "plt.show()\n",
    "print(model_vel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]\n",
      " ..., \n",
      " [-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]]\n"
     ]
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#print(data_stel_vel)\n",
    "print(masked_stel_vel)\n",
    "#masked_stel_vel_model=ma.masked_where(abs(model_vel) > 10000 , model_vel)#(1/np.sqrt(data_stel_vel_ivar))/data_stel_vel > 1.0\n",
    "#masked_stel_vel_model=ma.masked_where(model_vel ==0, masked_stel_vel_model)\n",
    "masked_stel_vel_data=ma.masked_where(model_vel==0, model_vel)\n",
    "masked_stel=ma.masked_where(data_stel_vel< -150, data_stel_vel)\n",
    "##masked_stel_vel_model=ma.masked_where(model_vel ==0, masked_stel_vel_model)\n",
    "##masked_stel_vel=ma.masked_where(abs((1/np.sqrt(data_stel_vel_ivar))/data_stel_vel) > 1.0, abs(data_stel_vel) > 100, masked_stel_vel)\n",
    "im=ax.imshow(masked_stel-masked_stel_vel_data, vmin=-50,vmax=50,  cmap='rainbow')#,vmin=-25,vmax=25)#, vmin=-100, vmax=100)#,vmin=-max(masked_stel_vel), vmax=max(masked_stel_vel), interpolation='None')#, vmin=-300, vmax=300)\n",
    "'''-(masked_stel_vel_model-masked_stel_vel)'''\n",
    "#print(max(masked_stel_vel))\n",
    "ax.set_adjustable('box-forced')\n",
    "ax.annotate('Residuals', xy=(0.05,0.92),xycoords='axes fraction', size=15)\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(model_vel)/2,10)\n",
    "middle=len(model_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "    \n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=15)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=15)\n",
    "\n",
    "#plt.title(r'Stellar Velocity [km s$^{-1}$]')\n",
    "#im=plt.imshow(np.flipud(data_stel_vel), vmin=-300, vmax=300)\n",
    "#ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "\n",
    "#if circle=='yes':\n",
    "#    ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='black')\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "#cbar1.ax.set_ticks([-150,-100,50,0,50,100,150])\n",
    "#colorbar(im)\n",
    "cbar1.set_label(label=r'Stellar Velocity [km s$^{-1}$]',size=15,weight='normal', labelpad=10)\n",
    "plt.xlim([0,len(model_vel)])\n",
    "plt.ylim([0,len(model_vel)])\n",
    "plt.xlabel('Arcsec', size=15)\n",
    "plt.ylabel('Arcsec', size=15)\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "savefig('../stellar-vel-resids-8141-1901.pdf')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''velcirc for oiii'''\n",
    "velcirc_oiii=[   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       94.566624\n",
    ",\n",
    "       98.440350\n",
    ",\n",
    "       100.77599\n",
    ",\n",
    "       101.66244\n",
    ",\n",
    "       100.88470\n",
    ",\n",
    "       98.499185\n",
    ",\n",
    "       94.708075\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       87.659005\n",
    ",\n",
    "       94.309980\n",
    ",\n",
    "       100.10556\n",
    ",\n",
    "       104.55457\n",
    ",\n",
    "       107.34515\n",
    ",\n",
    "       108.37909\n",
    ",\n",
    "       107.49426\n",
    ",\n",
    "       104.75749\n",
    ",\n",
    "       100.28463\n",
    ",\n",
    "       94.609005\n",
    ",\n",
    "       87.998403\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       82.746515\n",
    ",\n",
    "       91.028455\n",
    ",\n",
    "       98.573145\n",
    ",\n",
    "       105.50298\n",
    ",\n",
    "       110.84881\n",
    ",\n",
    "       114.42932\n",
    ",\n",
    "       115.69890\n",
    ",\n",
    "       114.28889\n",
    ",\n",
    "       110.49755\n",
    ",\n",
    "       105.34282\n",
    ",\n",
    "       98.795274\n",
    ",\n",
    "       91.116432\n",
    ",\n",
    "       82.906861\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       74.949359\n",
    ",\n",
    "       84.023975\n",
    ",\n",
    "       92.922936\n",
    ",\n",
    "       101.18333\n",
    ",\n",
    "       109.63944\n",
    ",\n",
    "       116.82071\n",
    ",\n",
    "       121.49458\n",
    ",\n",
    "       123.02519\n",
    ",\n",
    "       121.28945\n",
    ",\n",
    "       116.38074\n",
    ",\n",
    "       109.87093\n",
    ",\n",
    "       101.89362\n",
    ",\n",
    "       93.001120\n",
    ",\n",
    "       84.172195\n",
    ",\n",
    "       75.211584\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       64.699989\n",
    ",\n",
    "       74.117629\n",
    ",\n",
    "       83.398805\n",
    ",\n",
    "       92.115864\n",
    ",\n",
    "       102.64572\n",
    ",\n",
    "       112.46536\n",
    ",\n",
    "       121.42672\n",
    ",\n",
    "       126.96564\n",
    ",\n",
    "       129.47073\n",
    ",\n",
    "       127.45659\n",
    ",\n",
    "       121.60232\n",
    ",\n",
    "       112.80278\n",
    ",\n",
    "       102.38259\n",
    ",\n",
    "       92.188041\n",
    ",\n",
    "       83.352044\n",
    ",\n",
    "       74.288304\n",
    ",\n",
    "       65.291623\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       61.351096\n",
    ",\n",
    "       70.814834\n",
    ",\n",
    "       79.029369\n",
    ",\n",
    "       88.989797\n",
    ",\n",
    "       100.69308\n",
    ",\n",
    "       113.67770\n",
    ",\n",
    "       124.48000\n",
    ",\n",
    "       132.34548\n",
    ",\n",
    "       133.76920\n",
    ",\n",
    "       132.35073\n",
    ",\n",
    "       124.68125\n",
    ",\n",
    "       113.67942\n",
    ",\n",
    "       100.53541\n",
    ",\n",
    "       89.012923\n",
    ",\n",
    "       78.237787\n",
    ",\n",
    "       70.613560\n",
    ",\n",
    "       61.897454\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       49.363221\n",
    ",\n",
    "       56.781811\n",
    ",\n",
    "       63.691547\n",
    ",\n",
    "       70.987227\n",
    ",\n",
    "       82.311128\n",
    ",\n",
    "       96.483887\n",
    ",\n",
    "       108.57315\n",
    ",\n",
    "       113.08385\n",
    ",\n",
    "       119.48883\n",
    ",\n",
    "       123.35583\n",
    ",\n",
    "       119.51732\n",
    ",\n",
    "       113.13547\n",
    ",\n",
    "       108.53446\n",
    ",\n",
    "       95.375060\n",
    ",\n",
    "       82.127544\n",
    ",\n",
    "       71.436459\n",
    ",\n",
    "       63.090396\n",
    ",\n",
    "       56.534606\n",
    ",\n",
    "       49.481973\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       43.144901\n",
    ",\n",
    "       47.867704\n",
    ",\n",
    "       52.904661\n",
    ",\n",
    "       60.046259\n",
    ",\n",
    "       71.087603\n",
    ",\n",
    "       83.443395\n",
    ",\n",
    "       87.510891\n",
    ",\n",
    "       91.168296\n",
    ",\n",
    "       99.678877\n",
    ",\n",
    "       105.84685\n",
    ",\n",
    "       99.667443\n",
    ",\n",
    "       91.564908\n",
    ",\n",
    "       87.492766\n",
    ",\n",
    "       83.467073\n",
    ",\n",
    "       71.861531\n",
    ",\n",
    "       59.985519\n",
    ",\n",
    "       52.557613\n",
    ",\n",
    "       48.266648\n",
    ",\n",
    "       42.762195\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       34.904519\n",
    ",\n",
    "       37.436427\n",
    ",\n",
    "       38.976034\n",
    ",\n",
    "       45.078007\n",
    ",\n",
    "       54.986640\n",
    ",\n",
    "       60.399034\n",
    ",\n",
    "       62.964892\n",
    ",\n",
    "       66.057017\n",
    ",\n",
    "       72.682871\n",
    ",\n",
    "       75.723426\n",
    ",\n",
    "       72.786048\n",
    ",\n",
    "       66.385031\n",
    ",\n",
    "       62.958708\n",
    ",\n",
    "       60.419231\n",
    ",\n",
    "       55.369669\n",
    ",\n",
    "       44.862529\n",
    ",\n",
    "       39.350017\n",
    ",\n",
    "       36.938892\n",
    ",\n",
    "       34.238285\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       23.803322\n",
    ",\n",
    "       25.354867\n",
    ",\n",
    "       24.601528\n",
    ",\n",
    "       24.147573\n",
    ",\n",
    "       29.704550\n",
    ",\n",
    "       33.965593\n",
    ",\n",
    "       35.317153\n",
    ",\n",
    "       38.153961\n",
    ",\n",
    "       41.309495\n",
    ",\n",
    "       44.465028\n",
    ",\n",
    "       48.337431\n",
    ",\n",
    "       44.517232\n",
    ",\n",
    "       41.506842\n",
    ",\n",
    "       38.496452\n",
    ",\n",
    "       35.486062\n",
    ",\n",
    "       33.971366\n",
    ",\n",
    "       26.774035\n",
    ",\n",
    "       23.959637\n",
    ",\n",
    "       23.932167\n",
    ",\n",
    "       24.858782\n",
    ",\n",
    "       24.364786\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       16.020650\n",
    ",\n",
    "       15.162752\n",
    ",\n",
    "       11.124659\n",
    ",\n",
    "       7.6205688\n",
    ",\n",
    "       9.4258221\n",
    ",\n",
    "       9.5366475\n",
    ",\n",
    "       10.909089\n",
    ",\n",
    "       12.281531\n",
    ",\n",
    "       13.653973\n",
    ",\n",
    "       15.026415\n",
    ",\n",
    "       16.049999\n",
    ",\n",
    "       15.026892\n",
    ",\n",
    "       13.655901\n",
    ",\n",
    "       12.284911\n",
    ",\n",
    "       10.913920\n",
    ",\n",
    "       9.5429292\n",
    ",\n",
    "       9.3068634\n",
    ",\n",
    "       7.8223971\n",
    ",\n",
    "       12.846175\n",
    ",\n",
    "       15.414233\n",
    ",\n",
    "       16.655987\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "       8.2725476\n",
    ",\n",
    "       5.0955924\n",
    ",\n",
    "      -1.6802395\n",
    ",\n",
    "      -8.4203704\n",
    ",\n",
    "      -11.055315\n",
    ",\n",
    "      -15.183271\n",
    ",\n",
    "      -15.108911\n",
    ",\n",
    "      -15.034552\n",
    ",\n",
    "      -14.960192\n",
    ",\n",
    "      -14.826313\n",
    ",\n",
    "      -16.764744\n",
    ",\n",
    "      -14.780442\n",
    ",\n",
    "      -14.960023\n",
    ",\n",
    "      -15.034305\n",
    ",\n",
    "      -15.108588\n",
    ",\n",
    "      -15.182870\n",
    ",\n",
    "      -11.413168\n",
    ",\n",
    "      -8.0773420\n",
    ",\n",
    "     -0.14806616\n",
    ",\n",
    "       5.4063478\n",
    ",\n",
    "       8.8983082\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "      -4.0937752\n",
    ",\n",
    "      -12.571004\n",
    ",\n",
    "      -22.600891\n",
    ",\n",
    "      -30.004393\n",
    ",\n",
    "      -36.368376\n",
    ",\n",
    "      -39.699625\n",
    ",\n",
    "      -39.627768\n",
    ",\n",
    "      -41.579946\n",
    ",\n",
    "      -43.766758\n",
    ",\n",
    "      -44.770270\n",
    ",\n",
    "      -43.696983\n",
    ",\n",
    "      -41.358122\n",
    ",\n",
    "      -39.628167\n",
    ",\n",
    "      -39.699560\n",
    ",\n",
    "      -36.503018\n",
    ",\n",
    "      -30.204903\n",
    ",\n",
    "      -21.894039\n",
    ",\n",
    "      -11.546278\n",
    ",\n",
    "      -3.6413375\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "      -11.730705\n",
    ",\n",
    "      -20.057130\n",
    ",\n",
    "      -31.688064\n",
    ",\n",
    "      -44.397901\n",
    ",\n",
    "      -54.313889\n",
    ",\n",
    "      -64.469800\n",
    ",\n",
    "      -65.589516\n",
    ",\n",
    "      -66.611493\n",
    ",\n",
    "      -71.015232\n",
    ",\n",
    "      -74.893697\n",
    ",\n",
    "      -70.837826\n",
    ",\n",
    "      -66.610071\n",
    ",\n",
    "      -65.582617\n",
    ",\n",
    "      -64.472053\n",
    ",\n",
    "      -54.494753\n",
    ",\n",
    "      -44.417804\n",
    ",\n",
    "      -32.544740\n",
    ",\n",
    "      -20.225941\n",
    ",\n",
    "      -11.137646\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "      -17.507188\n",
    ",\n",
    "      -26.305303\n",
    ",\n",
    "      -38.644304\n",
    ",\n",
    "      -54.221131\n",
    ",\n",
    "      -67.036815\n",
    ",\n",
    "      -78.211008\n",
    ",\n",
    "      -89.072193\n",
    ",\n",
    "      -91.157026\n",
    ",\n",
    "      -94.670502\n",
    ",\n",
    "      -96.927699\n",
    ",\n",
    "      -94.683427\n",
    ",\n",
    "      -91.158523\n",
    ",\n",
    "      -89.062084\n",
    ",\n",
    "      -77.928989\n",
    ",\n",
    "      -66.299321\n",
    ",\n",
    "      -53.651456\n",
    ",\n",
    "      -39.361487\n",
    ",\n",
    "      -26.452204\n",
    ",\n",
    "   1.2345679e+08\n",
    ",\n",
    "      -30.903984\n",
    ",\n",
    "      -41.919415\n",
    ",\n",
    "      -57.135481\n",
    ",\n",
    "      -73.146849\n",
    ",\n",
    "      -85.319993\n",
    ",\n",
    "      -95.443599\n",
    ",\n",
    "      -106.32314\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 34)\n",
      "400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:12: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:14: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    },
    {
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4CbAlIk4Uu16mXZ0tO2YvsBdgdMPG5ZbTzGq02qqrAEi6AvgO8NmImHMDKSKScT0ipiJi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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11588c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 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.]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#create an empty 2x2 array to populate with the model velocity\n",
    "model_vel_oiii = np.zeros(shape=(shape(data_stel_vel)[0],shape(data_stel_vel)[0]))\n",
    "print(shape(model_vel_oiii))\n",
    "print(len(velcirc))\n",
    "\n",
    "##bins are binned by 0.5 units\n",
    "n=0\n",
    "for j in range(shape(data_stel_vel)[0]):\n",
    "    for k in range(shape(data_stel_vel)[0]):\n",
    "        vel=data_stel_vel[j][k]\n",
    "        vel_e=np.sqrt(1./data_stel_vel_ivar[j][k])\n",
    "        sig=data_stel_sig[j][k]\n",
    "        sig_e=np.sqrt(1./data_stel_sig_ivar[j][k])\n",
    "        if vel==0.0 or vel_e > 10000 or sig_e > 10000:\n",
    "            none=1\n",
    "        else:\n",
    "            xbin=(j-shape(data_stel_vel)[0]/2)\n",
    "            ybin=(k-shape(data_stel_vel)[0]/2)\n",
    "            #print('xbin,ybin', xbin,ybin)\n",
    "            xbin_corr=xbin+54/2\n",
    "            ybin_corr=ybin+54/2\n",
    "            \n",
    "            \n",
    "            xbin_corr=j\n",
    "            ybin_corr=k\n",
    "            \n",
    "            \n",
    "            #print(n)\n",
    "            try:\n",
    "                model_vel_oiii[xbin_corr][ybin_corr]=velcirc_oiii[n]\n",
    "                if velcirc_oiii[n] > 40000:\n",
    "                    model_vel_oiii[xbin_corr][ybin_corr]=0\n",
    "                #if xbin**2+ybin**2 > 370:\n",
    "                #    model_vel[xbin_corr][ybin_corr]=0\n",
    "                #    print('xcor,ycor', xbin_corr, ybin_corr, ybin**2+xbin**2)\n",
    "                '''if xbin_corr < 20 and ybin_corr > 35:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0\n",
    "                if ybin_corr <10:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0\n",
    "                if ybin_corr < 17 and xbin_corr < 11:\n",
    "                    model_vel[xbin_corr][ybin_corr]=0'''\n",
    "                #ybin_corr >25 cuts off anything to the right of 0\n",
    "                n+=1\n",
    "            except IndexError:\n",
    "                n+=1 \n",
    "                continue\n",
    "plt.clf()\n",
    "plt.imshow(np.flipud(model_vel_oiii))\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "print(model_vel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]\n",
      " ..., \n",
      " [-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]\n",
      " [-- -- -- ..., -- -- --]]\n"
     ]
    }
   ],
   "source": [
    "'''Make some beautiful plots for the OIII modeling :)'''\n",
    "\n",
    "\n",
    "path_spec='MaNGA_J0749/manga-8141-1901-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "        #path_spec='../MaNGA/'+plate+'/manga-'+plate+'-'+fiber+'-LOGCUBE.fits'\n",
    "b=pyfits.open(path_spec)\n",
    "#print(b[0].header)\n",
    "\n",
    "\n",
    "\n",
    "#print(b_stellar[0].header)\n",
    "elines_vel=b['EMLINE_GVEL'].data\n",
    "\n",
    "oiii_elines=elines_vel[3]\n",
    "\n",
    "\n",
    "plt.clf()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#print(data_stel_vel)\n",
    "print(masked_stel_vel)\n",
    "#masked_stel_vel_model=ma.masked_where(abs(model_vel) > 10000 , model_vel)#(1/np.sqrt(data_stel_vel_ivar))/data_stel_vel > 1.0\n",
    "#masked_stel_vel_model=ma.masked_where(model_vel ==0, masked_stel_vel_model)\n",
    "masked_stel_vel_data=ma.masked_where(model_vel_oiii==0, model_vel_oiii)\n",
    "masked_stel=ma.masked_where(data_stel_vel< -150, oiii_elines)\n",
    "##masked_stel_vel_model=ma.masked_where(model_vel ==0, masked_stel_vel_model)\n",
    "##masked_stel_vel=ma.masked_where(abs((1/np.sqrt(data_stel_vel_ivar))/data_stel_vel) > 1.0, abs(data_stel_vel) > 100, masked_stel_vel)\n",
    "im=ax.imshow(masked_stel-masked_stel_vel_data,vmin=-50,vmax=50,  cmap='rainbow')#,vmin=-25,vmax=25)#, vmin=-100, vmax=100)#,vmin=-max(masked_stel_vel), vmax=max(masked_stel_vel), interpolation='None')#, vmin=-300, vmax=300)\n",
    "'''-(masked_stel_vel_model-masked_stel_vel)'''\n",
    "#print(max(masked_stel_vel))\n",
    "ax.set_adjustable('box-forced')\n",
    "ax.annotate('Residuals', xy=(0.05,0.92),xycoords='axes fraction', size=15)\n",
    "\n",
    "ax.get_yaxis().set_ticks_position(\"left\")\n",
    "ax.get_xaxis().set_ticks_position(\"bottom\")\n",
    "ax.get_yaxis().set_tick_params(direction='out')\n",
    "ax.get_xaxis().set_tick_params(direction='out')\n",
    "#put ticks every 5\n",
    "\n",
    "x_ticks=np.arange(0,len(model_vel)/2,10)\n",
    "middle=len(model_vel)/2\n",
    "#print('total locs', len(data_stel_vel),'x_tick_locs', x_tick_locs)\n",
    "    \n",
    "x_tick_locs_neg=[middle-x for x in x_ticks]\n",
    "x_tick_locs_pos=[middle+x for x in x_ticks]\n",
    "x_tick_locs=np.concatenate([x_tick_locs_neg, x_tick_locs_pos])\n",
    "\n",
    "x_tick_lbls=[int((x-middle)*0.5) for x in x_tick_locs]\n",
    "y_tick_lbls=[-x for x in x_tick_lbls]\n",
    "\n",
    "ax.xaxis.set_ticks(x_tick_locs)\n",
    "ax.xaxis.set_ticklabels(x_tick_lbls, size=15)\n",
    "\n",
    "ax.yaxis.set_ticks(x_tick_locs)\n",
    "ax.yaxis.set_ticklabels(y_tick_lbls, size=15)\n",
    "\n",
    "#plt.title(r'Stellar Velocity [km s$^{-1}$]')\n",
    "#im=plt.imshow(np.flipud(data_stel_vel), vmin=-300, vmax=300)\n",
    "#ax.scatter(popt[1], popt[2], color='black', marker='x', s=100)\n",
    "\n",
    "#if circle=='yes':\n",
    "#    ax.scatter(x_coord,y_coord, s=200,facecolors='none', edgecolors='black')\n",
    "cbar1=colorbar(im)\n",
    "cbar1.ax.tick_params(labelsize=20, direction='out')\n",
    "#cbar1.ax.set_ticks([-150,-100,50,0,50,100,150])\n",
    "#colorbar(im)\n",
    "cbar1.set_label(label=r'Stellar Velocity [km s$^{-1}$]',size=15,weight='normal', labelpad=10)\n",
    "plt.xlim([0,len(model_vel)])\n",
    "plt.ylim([0,len(model_vel)])\n",
    "plt.xlabel('Arcsec', size=15)\n",
    "plt.ylabel('Arcsec', size=15)\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "savefig('../gas-vel-resids-8141-1901.pdf')\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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