Galkin_prueba.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import galkin\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import galkin.plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import auxiliary modules\n",
    "import galkin.processdata   # routines to process kinematic data\n",
    "import galkin.readparsFile  # routines to read and check input parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Galactic parameters\n",
    "R0=8.0# Galactocentric distance (kpc)\n",
    "V0=230.# local circular velocity (km/s)\n",
    "UsunINUSE=11.10# solar motion in the U-direction (km/s), e.g. from Schoenrich+ '10, MNRAS 403, 1829 (2010)\n",
    "VsunINUSE=12.24# solar motion in the V-direction (km/s), e.g. from Schoenrich+ '10, MNRAS 403, 1829 (2010)\n",
    "WsunINUSE=07.25# solar motion in the W-direction (km/s), e.g. from Schoenrich+ '10, MNRAS 403, 1829 (2010)\n",
    "SYSTDISP=0.# systematic dispersion due to spiral arm streaming (km/s), e.g. 11.8 km/s from Brand & Blitz '93, A&A 275, 67 (1993)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flags\n",
    "flagPROPERMOTIONS=0\t \t\t\t# proper motions not supported in current version - please keep at 0\n",
    "flagHITERMINAL=1\t\t\t\t# whether to use HI terminal velocities\n",
    "flagFich89tab2=1\t\t\t\t# whether to use Fich+ '89, ApJ 342, 272 (1989) (Table 2)\n",
    "flagMalhotra95=1\t\t\t\t# whether to use Malhotra '95, ApJ 448, 138 (1995)\n",
    "flagMcClureGriffithsDickey07=1\t\t\t# whether to use McClure-Griffiths & Dickey '07, ApJ 671, 427 (2007)\n",
    "flagHITHICKNESS=1\t\t\t\t# whether to use the HI thickness method\n",
    "flagHonmaSofue97=1\t\t\t\t# whether to use Honma & Sofue '97, PASJ 49, 453 (1997)\n",
    "flagCOTERMINAL=1\t\t\t\t# whether to use CO terminal velocities\n",
    "flagBurtonGordon78=1\t\t\t\t# whether to use Burton & Gordon '78, A&A 63, 7 (1978)\n",
    "flagClemens85=1\t\t\t\t\t# whether to use Clemens '85, ApJ 295, 422 (1985)\n",
    "flagKnapp85=1\t\t\t\t\t# whether to use Knapp+ '85, AJ 90, 2 (1985)\n",
    "flagLuna06=1\t\t\t\t\t# whether to use Luna+ '06, ApJ 641, 938 (2006)\n",
    "flagHIIREGIONS=1\t\t\t\t# whether to use HII regions\n",
    "flagBlitz79=1\t\t\t\t\t# whether to use Blitz '79, ApJL 231, L115 (1979)\n",
    "flagFich89tab1=1\t\t\t\t# whether to use Fich+ '89, ApJ 342, 272 (1989) (Table 1)\t\n",
    "flagTurbideMoffat93=1\t\t\t\t# whether to use Turbide & Moffat '93, AJ 105, 5 (1993)\n",
    "flagBrandBlitz93=1\t\t\t\t# whether to use Brand & Blitz '93, A&A 275, 67 (1993)\n",
    "flagHou09tabA1=1\t\t\t\t# whether to use Hou+ '09, A&A 499, 473 (2009) (Table A1)\n",
    "flagGMC=1\t\t\t\t\t# whether to use giant molecular clouds\n",
    "flagHou09tabA2=1\t\t\t\t# whether to use Hou+ '09, A&A 499, 473 (2009) (Table A2)\n",
    "flagOPENCLUSTERS=1\t\t\t\t# whether to use open clusters\n",
    "flagFrinchaboyMajewski08=1\t\t\t# whether to use Frinchaboy & Majewski '08, AJ 136, 118 (2008)\n",
    "flagPLANETARYNEBULAE=1\t\t\t\t# whether to use planetary nebulae\n",
    "flagDurand98=1\t\t\t\t\t# whether to use Durand+ '98, A&AS 132, 13 (1998)\n",
    "flagCEPHEIDS=1\t\t\t\t\t# whether to use classical cepheids\n",
    "flagPont94=1\t\t\t\t\t# whether to use Pont+ '94, A&A 285, 415 (1994)\n",
    "flagPont97=1\t\t\t\t\t# whether to use Pont+ '97, A&A 318, 416 (1997)\n",
    "flagCSTARS=1\t\t\t\t\t# whether to use carbon stars\n",
    "flagDemersBattinelli07=1\t\t\t# whether to use Demers & Battinelli '07, A&A 473, 143 (2007)\n",
    "flagBattinelli12=1\t\t\t\t# whether to use Battinelli+ '12, Ap 56, 68 (2013)\n",
    "flagMASERS=1\t\t\t\t\t# whether to use masers\n",
    "flagReid14=1\t\t\t\t\t# whether to use Reid+ '14, ApJ 783, 130 (2014)\n",
    "flagHonma12=1\t\t\t\t\t# whether to use Honma+ '12, PASJ 64, 136 (2012)\n",
    "flagStepanishchevBobylev11=1\t\t\t# whether to use Stepanishchev & Bobylev '11, AstL 37, 4 (2011)\n",
    "flagXu13=1\t\t\t\t\t# whether to use Xu+ '13, ApJ 769, 15 (2013)\n",
    "flagBobylevBajkova13=1\t\t\t\t# whether to use Bobylev & Bajkova '13, AstL 39, 809 (2013)\n",
    "flagastropy=0\t\t\t\t \t# whether to use astropy for equatorial-to-galactic conversions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputpars=(R0,V0,UsunINUSE,VsunINUSE,WsunINUSE,SYSTDISP, flagPROPERMOTIONS,flagHITERMINAL,flagFich89tab2,flagMalhotra95,flagMcClureGriffithsDickey07, flagHITHICKNESS,flagHonmaSofue97,flagCOTERMINAL,flagBurtonGordon78,flagClemens85,flagKnapp85,flagLuna06, flagHIIREGIONS,flagBlitz79,flagFich89tab1,flagTurbideMoffat93,flagBrandBlitz93,flagHou09tabA1, flagGMC,flagHou09tabA2,flagOPENCLUSTERS,flagFrinchaboyMajewski08,flagPLANETARYNEBULAE,flagDurand98,flagCEPHEIDS,flagPont94,flagPont97, flagCSTARS,flagDemersBattinelli07,flagBattinelli12, flagMASERS,flagReid14,flagHonma12,flagStepanishchevBobylev11,flagXu13,flagBobylevBajkova13,flagastropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking validity of input parameters...\n",
      "printing input parameters...\n",
      " R0=  8.0  kpc\n",
      " V0=  230.0  km/s\n",
      " (Usun,Vsun,Wsun) = ( 11.1 , 12.24 , 7.25 ) km/s\n",
      " systematic dispersion =  0.0  km/s\n",
      " use HI terminal velocities?            1\n",
      "  use Fich+ 89(Table 2)?                  1\n",
      "  use Malhotra 95?                        1\n",
      "  use McClure-Griffiths & Dickey 07?      1\n",
      " use HI thickness method?               1\n",
      "  use Honma & Sofue 97?                   1\n",
      " use CO terminal velocities?            1\n",
      "  use Burton & Gordon 78?                 1\n",
      "  use Clemens 85?                         1\n",
      "  use Knapp+ 85?                          1\n",
      "  use Luna+ 06?                           1\n",
      " use HII regions?                       1\n",
      "  use Blitz 79?                           1\n",
      "  use Fich+ 89 (Table 1)?                 1\n",
      "  use Turbide & Moffat 93?                1\n",
      "  use Brand & Blitz 93?                   1\n",
      "  use Hou+ 09 (Table A1)?                 1\n",
      " use giant molecular clouds?            1\n",
      "  use Hou+ 09 (Table A2)?                 1\n",
      " use open clusters?                     1\n",
      "  use Frinchaboy & Majewski 08?           1\n",
      " use planetary nebulae?                 1\n",
      "  use Durand+ 98?                         1\n",
      " use cepheids?                          1\n",
      "  use Pont+ 94?                           1\n",
      "  use Pont+ 97?                           1\n",
      " use C stars?                           1\n",
      "  use Demers & Battinelli 07?             1\n",
      "  use Battinelli+ 12?                     1\n",
      " use masers?                            1\n",
      "  use Stepanishchev & Bobylev 11?         1\n",
      "  use Honma+ 12?                          1\n",
      "  use Xu+ 13?                             1\n",
      "  use Bobylev & Bajkova 13?               1\n",
      "  use Reid+ 14?                           1\n",
      " use astropy?                           0\n"
     ]
    }
   ],
   "source": [
    "galkin.readparsFile.CheckAndPrintParameters(inputpars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing HI terminal velocities...\n",
      " processing Fich+ 89 (Table 2)...\n",
      "  selected  149  HI terminal velocities\n",
      " processing Malhotra 95...\n",
      "  selected  110  HI terminal velocities\n",
      " processing McClure-Griffiths & Dickey 07...\n",
      "  selected  701  out of the total sample of  761  HI terminal velocities\n",
      "processing HI thickness method...\n",
      " processing Honma & Sofue 97...\n",
      "  selected  13  HI thickness rings\n",
      "processing CO terminal velocities...\n",
      " processing Burton & Gordon 78...\n",
      "  selected  284  CO terminal velocities\n",
      " processing Clemens 85...\n",
      "  selected  143  CO terminal velocities\n",
      " processing Knapp+ 85...\n",
      "  selected  37  CO terminal velocities\n",
      " processing Luna+ 06...\n",
      "  selected  272  out of the total sample of  457  CO terminal velocities\n",
      "processing HII regions...\n",
      " processing Blitz 79...\n",
      "  selected  3  HII regions\n",
      " processing Fich+ 89 (Table 1)...\n",
      "  selected  5  out of the total sample of  104  HII regions\n",
      " processing Turbide & Moffat 93...\n",
      "  selected  5  out of the total sample of  8  HII regions\n",
      " processing Brand & Blitz 93...\n",
      "  selected  148  out of the total sample of  206  HII regions\n",
      " processing Hou+ 09 (Table A1)...\n",
      "  selected  274  out of the total sample of  815  HII regions\n",
      "processing giant molecular clouds...\n",
      " processing Hou+ 09 (Table A2)...\n",
      "  selected  30  out of the total sample of  963  giant molecular clouds\n",
      "processing open clusters...\n",
      " processing Frinchaboy & Majewski 08...\n",
      "  selected  60  out of the total sample of  71  open clusters\n",
      "processing planetary nebulae...\n",
      " processing Durand+ 98...\n",
      "  selected  79  out of the total sample of  867  planetary nebulae\n",
      "processing classical cepheids...\n",
      " processing Pont+ 94...\n",
      "  selected  245  out of the total sample of  278  cepheids\n",
      " processing Pont+ 97...\n",
      "  selected  32  out of the total sample of  48  cepheids\n",
      "processing carbon stars...\n",
      " processing Demers & Battinelli 07...\n",
      "  selected  55  out of the total sample of  103  C stars\n",
      " processing Battinelli+ 12...\n",
      "  selected  35  out of the total sample of  36  C stars\n",
      "processing masers...\n",
      " processing Reid+ 14...\n",
      "  selected  80  out of the total sample of  103  masers\n",
      " processing Honma+ 12...\n",
      "*** warning: found more than 1 coincidence!\n",
      "  selected  11  out of the total sample of  52  masers\n",
      " processing Stepanishchev & Bobylev 11...\n",
      "  selected  1  out of the total sample of  1  masers\n",
      " processing Xu+ 13...\n",
      "  selected  6  out of the total sample of  30  masers\n",
      "  selected  1  out of the total sample of  6  masers\n",
      " processing Bobylev & Bajkova 13...\n",
      "  selected  14  out of the total sample of  31  masers\n",
      "  selected  7  out of the total sample of  14  masers\n"
     ]
    }
   ],
   "source": [
    "vecout=galkin.processdata.ProcessData(inputpars)\n",
    "totallistvc=vecout[0]; # list of {R[kpc], delta_R[kpc], vc[km/s], delta_vc[km/s], wc[km/s/kpc], delta_wc[km/s/kpc], Reference}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.070552360820166, 0.0, 204.7522462861832, 4.5, 98.88774133926218, 2.173333109150404, 'Fich89_Table2']\n"
     ]
    }
   ],
   "source": [
    "print totallistvc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.612212812065508, 0.010544186304777303, 236.3513518499254, 8.658320507429012, 31.048967978838405, 1.1366115200893554, 'BobylevBajkova13']\n"
     ]
    }
   ],
   "source": [
    "print totallistvc[len(totallistvc)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "vcfile = open(\"/Users/atalia/galkin/bin/output/vcdata.dat\",'r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<open file '/Users/atalia/galkin/bin/output/vcdata.dat', mode 'r' at 0x1080bcf60>\n"
     ]
    }
   ],
   "source": [
    "print r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "totallistvc=[]\n",
    "i=0\n",
    "for line in vcfile:\n",
    "    i+=1\n",
    "    values=line.split()\n",
    "    if(i>2):\n",
    "        totallistvc.append([float(values[0]),float(values[1]),float(values[2]),float(values[3]),float(values[4]),float(values[5]),values[6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print totallistvc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "posfile = open(\"/Users/atalia/galkin/bin/output/posdata.dat\",'r')\n",
    "totallistpos=[]\n",
    "i=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for line in posfile:\n",
    "    i+=1\n",
    "    values=line.split()\n",
    "    if(i>2):\n",
    "        totallistpos.append([float(values[0]),float(values[1]),float(values[2]),float(values[3]),float(values[4]),float(values[5]),float(values[6]),values[7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot rotation curve measurements\n",
    "galkin.plots.PlotRotationCurve(totallistvc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot positions in galactic plane\n",
    "galkin.plots.PlotPositions(totallistpos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mgalkin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotRotationCurve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotallistvc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda2/lib/python2.7/site-packages/galkin-1.0-py2.7.egg/galkin/plots.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?galkin.plots.PlotRotationCurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}