ldaFig3.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:226e35150655760e2b311d16c0649e0fc2a5bb05e1993c6663758ff1a44f8398"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import rc\n",
      "rc('font', **{'family':'serif'})\n",
      "rc('text', usetex=True)\n",
      "\n",
      "import pickle\n",
      "from colorChooser import rgb_to_hex, cmapCycle\n",
      "\n",
      "\n",
      "fig = plt.figure( figsize=(2.5,2.2))\n",
      "gs = matplotlib.gridspec.GridSpec( 1,1,\\\n",
      "        left=0.19, right=0.95, bottom=0.18, top=0.95)\n",
      "axsingle = fig.add_subplot( gs[0] )\n",
      "\n",
      "figPaper = plt.figure( figsize=(4.8,2.2))\n",
      "gsPaper = matplotlib.gridspec.GridSpec( 1,2,\\\n",
      "        left=0.1, right=0.95, bottom=0.18, top=0.95)\n",
      "ax0 = figPaper.add_subplot( gsPaper[0] )\n",
      "ax1 = figPaper.add_subplot( gsPaper[1] )\n",
      "\n",
      "for ax in [axsingle, ax0, ax1]:\n",
      "    ax.set_xlabel('$r\\ (\\mu\\mathrm{m})$')\n",
      "    ax.set_xlim(0.,30.)\n",
      "    ax.grid(alpha=0.3)\n",
      "\n",
      "for ax in [axsingle, ax0]:\n",
      "    ax.set_ylabel('$n(r)$')\n",
      "    ax.set_ylim(-0.1, 1.5)    \n",
      "    #ax.set_yscale('log')\n",
      "\n",
      "cval = [0., 0.7, 1.8, 3.5]\n",
      "gr = 3.700\n",
      "savedir = 'dataplots/PROF/'\n",
      "for ii, (aS, mu) in enumerate([(200,0.107), (290,0.079), (380,0.029), (470,-0.027)]):\n",
      "    color = cmapCycle( matplotlib.cm.spectral, cval[ii], lbound=0., ubound=4.)\n",
      "    \n",
      "    fname = savedir + 'aS{:03d}_gr{:0.3f}_mu{:0.3f}.pck'.format(aS,gr,mu)\n",
      "    spis = pickle.load( open( fname,\"rb\" ) )\n",
      "    for ax in [axsingle, ax0]:\n",
      "        ax.plot( spis['r111'], spis['n111'], color=color, lw=1.5, alpha=1.0,\\\n",
      "            label='${:0.1f}$'.format(aS*11.1/290.))\n",
      "        \n",
      "    ax1.plot( spis['r111'], spis['spi111']*spis['n111'] * 4*np.pi * spis['r111']**2, \\\n",
      "             color=color, lw=1.5, alpha=1.0)\n",
      "    \n",
      "    print spis['Number']\n",
      "\n",
      "for ax in [axsingle,ax0]:\n",
      "    legend = ax.legend( bbox_to_anchor=(1.0,1.0), loc='upper right', numpoints=1,\\\n",
      "                   prop={'size':8}, title='$U/t$', handlelength=1.1,\\\n",
      "                   handletextpad=0.5)\n",
      "    plt.setp( legend.get_title(), fontsize=8.)    \n",
      "\n",
      "fig.savefig('dataplots/PROF/profilesLDA.png', dpi=300)\n",
      "figPaper.savefig('dataplots/PROF/spi-profiles-paper.png', dpi=300)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "150929.163045\n",
        "168883.066599\n",
        "177438.53843\n",
        "183122.555636\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L7oGpsuF2pNCgl+r9+z3yGfaofYNqNBrlueeeExGlTPWRI0dERBl/t7FmzZq6WpszZsxw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\nNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NNTl/wMhAxC+nYleBQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f01a8df8550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5u8C1ugs8fF3hXssDztXdUc3HFW5ejvDxkcPXVw5//z44diwav/yyDgsWLEDbtm3xwQcf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\niIiIiIiIiIiIiK3w/17pgJMZF8QsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f01a8e0dc50>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Figure 03 in the paper"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import rc\n",
      "rc('font', **{'family':'serif'})\n",
      "rc('text', usetex=True)\n",
      "\n",
      "from uncertainties import unumpy\n",
      "\n",
      "fig = plt.figure( figsize=(3.8,2.8))\n",
      "gs = matplotlib.gridspec.GridSpec( 1,1,\\\n",
      "        left=0.135, right=0.95, bottom=0.14, top=0.95)\n",
      "ax= fig.add_subplot( gs[0] )\n",
      "ax.set_xlim( 0., 1.45 )\n",
      "ax.set_ylim( 0., 8.2 ) \n",
      "#ax.grid(alpha=0.3)\n",
      "ax.set_xlabel('$n$', fontsize=15, labelpad=-1)\n",
      "ax.set_ylabel(r'$\\frac{s_{\\pi}}{n}$',rotation=0, fontsize=18, labelpad=15.)\n",
      "\n",
      "T066 = np.loadtxt('/home/pmd/sandbox/hubbard-lda/QMC_Extra2/Thereza_U08_L6_dt0.05_l30_T0.667.dat')\n",
      "T050 = np.loadtxt('/home/pmd/sandbox/hubbard-lda/QMC_Extra2/Thereza_U08_L6_dt0.05_l40_T0.5.dat')\n",
      "T040 = np.loadtxt('/home/pmd/sandbox/hubbard-lda/QMC_Extra2/Thereza_U08_L6_dt0.05_l50_T0.4.dat')\n",
      "\n",
      "# columns are \n",
      "# mu, dens, +-, spi, +- \n",
      "\n",
      "def plotspi( ax, dat, err=False, **kwargs):\n",
      "    xdat = np.concatenate(( 2.-1.*dat[::-1,1], dat[:,1]))\n",
      "    ydat = np.concatenate((  dat[::-1,3], dat[:,3] ))\n",
      "    ax.plot(xdat,ydat/xdat, **kwargs) \n",
      "    \n",
      "    if err:\n",
      "        yerr = np.concatenate(( dat[::-1,4], dat[:,4] )) \n",
      "        yunc = unumpy.uarray( ydat, yerr )\n",
      "        \n",
      "        # per particle \n",
      "        ypp = yunc / xdat \n",
      "        \n",
      "        ypp_val = unumpy.nominal_values( ypp )\n",
      "        ypp_err = unumpy.std_devs( ypp )\n",
      "        \n",
      "        ax.fill_between( xdat, ypp_val - ypp_err, ypp_val + ypp_err, color='none', facecolor='gray', alpha=0.5)\n",
      "        \n",
      "plotspi( ax, T066, err=False, label='$0.66$', marker='o', ms=2.5, c='k', mec='black', mfc='white')\n",
      "plotspi( ax, T050, err=False, label='$0.50$', marker='s', ms=2.5, c='k', mec='black', mfc='white')\n",
      "plotspi( ax, T040, err=False, label='$0.40$', marker='D', ms=2.5, c='k', mec='black', mfc='white')\n",
      "\n",
      "legend = ax.legend( bbox_to_anchor=(0.97,0.85), loc='upper right', numpoints=1,\\\n",
      "       prop={'size':8}, title='$T/t$', handlelength=1.1,\\\n",
      "       handletextpad=0.5)\n",
      "plt.setp( legend.get_title(), fontsize=8.)  \n",
      "\n",
      "ax.text(0.95,1.02, '$U/t=8$', ha='right', va='top', transform=ax.transAxes,\\\n",
      "        bbox={'boxstyle':'round','facecolor':'white'}, fontsize=12)\n",
      "\n",
      "ax.text(0.05,0.02, r'\\textbf{a}', ha='left', va='bottom', transform=ax.transAxes,\\\n",
      "         fontsize=12.3)\n",
      "\n",
      "\n",
      "# Density profiles \n",
      "\n",
      "import pickle\n",
      "from colorChooser import rgb_to_hex, cmapCycle\n",
      "\n",
      "gsInset = matplotlib.gridspec.GridSpec( 2,1,\\\n",
      "        height_ratios = [1.4,2.8],\\\n",
      "        hspace=0.005,\\\n",
      "        left=0.25, right=0.58, bottom=0.39, top=0.91)\n",
      "axn = fig.add_subplot( gsInset[1] )\n",
      "axspi = fig.add_subplot( gsInset[0] )\n",
      "\n",
      "axn.set_xlabel('$r\\ (\\mu\\mathrm{m})$', labelpad=0.8, fontsize=9)\n",
      "axn.set_ylabel('$n(r)$',rotation=90, labelpad=2., fontsize=9)\n",
      "axn.set_xlim(0.,30.)\n",
      "#axn.grid(alpha=0.3)\n",
      "axn.set_ylim(-0.1, 1.38)    \n",
      "\n",
      "axspi.set_xlim(0.,30.)\n",
      "axspi.set_ylim(-0.4, 4.4)\n",
      "axspi.xaxis.set_ticklabels([])\n",
      "#axspi.yaxis.set_ticklabels([])\n",
      "#axspi.set_ylabel(r'$\\frac{4\\pi r^{2}}{N} S_{\\pi}(r) $')\n",
      "#axspi.set_ylabel(r'$\\frac{S_{\\pi}(r) }{S_{\\pi}(r_{*}) }$', rotation=90, fontsize=8, labelpad=2.)\n",
      "axspi.set_ylabel(r'$s_{\\mathbf{\\pi}}(r)$', rotation=90, fontsize=8, labelpad=4.)\n",
      "#axspi.set_ylim(-0.07, 1.07) \n",
      "\n",
      "\n",
      "\n",
      "cval = [0., 0.7, 1.8, 3.5]\n",
      "dash = [(2.4,1.4), (3.2,1,1,1),(1,0.001),(1,1.05)]\n",
      "\n",
      "colors = ['0.65','black','0.65', 'black']\n",
      "savedir = 'dataplots/PROF/'\n",
      "gr = 3.700\n",
      "\n",
      "for ii, (aS, mu) in enumerate([(200,0.107), (290,0.079), (380,0.029), (470,-0.027)]):\n",
      "    #color = cmapCycle( matplotlib.cm.spectral, cval[ii], lbound=0., ubound=4.)\n",
      "    \n",
      "    color = colors[ii] \n",
      "    \n",
      "#for ii, (aS, mu) in enumerate([(290,0.095)]):\n",
      "#    color = 'black'\n",
      "    \n",
      "    fname = savedir + 'aS{:03d}_gr{:0.3f}_mu{:0.3f}.pck'.format(aS,gr,mu)\n",
      "    spis = pickle.load( open( fname,\"rb\" ) )\n",
      "    if ii == 3 or True:\n",
      "        axn.plot( spis['r111'], spis['n111'], color=color, lw=0.9, alpha=1.0,\\\n",
      "            label='${:0.1f}$'.format(aS*11.1/290.), dashes=dash[ii] )\n",
      "    else:\n",
      "        axn.plot( spis['r111'], spis['n111'], color=color, lw=1.2, alpha=1.0,\\\n",
      "            label='${:0.1f}$'.format(aS*11.1/290.))# dashes=dash[ii] )\n",
      "    \n",
      "    peak = np.argmax( spis['spi111'] )\n",
      "    #axspi.plot( spis['r111'],  spis['n111'] *spis['spi111'] / ( spis['n111'][peak] * spis['spi111'][peak] ),\n",
      "    #axspi.plot( spis['r111'],  spis['n111'] *spis['spi111'],\n",
      "\n",
      "    x = spis['r111_fine']\n",
      "    y = spis['n111_fine']*spis['spi111_fine']\n",
      "    \n",
      "    def smooth(y,win):\n",
      "        return np.convolve(y, np.ones(win)/win)[win/2-1:-win/2]\n",
      "    smoothed = smooth(y,15)\n",
      "    \n",
      "    if ii == 3 or True:\n",
      "        axspi.plot( x,smoothed, color=color,  lw=0.9, alpha=1.0, dashes=dash[ii])\n",
      "    else:\n",
      "        axspi.plot( x,smoothed, color=color,  lw=1., alpha=1.0)\n",
      "        \n",
      "        \n",
      "    \n",
      "    # find the largest radius at which n=1 \n",
      "    n1 =  np.where( np.abs(spis['n111'] -1.0)  < 0.02 )[0][-1]\n",
      "    print spis['r111'][n1]\n",
      "\n",
      "    if ii == 1:\n",
      "        axn.axvspan( 10.3 , 11.3, lw=0., facecolor=color, alpha=0.3 )\n",
      "        axspi.axvspan( 10.3, 11.3, lw=0., facecolor=color, alpha=0.3 )\n",
      "\n",
      "#axspi.text( 11.6, 5.35, r'$r_{*}$', ha='center', va='bottom', color='blue')\n",
      "#\n",
      "#axspi.annotate(r'', xy=(10.8, 4.2),  xycoords='data',\n",
      "#            xytext=(0, 10), textcoords='offset points',\n",
      "#            size=6, ha='center',\n",
      "#            #bbox=dict(boxstyle=\"round\", fc=\"0.8\"),\n",
      "#            arrowprops=dict(arrowstyle=\"simple\",\n",
      "#                            fc=\"blue\", ec=\"none\",\n",
      "#                            ),#connectionstyle=\"arc3,rad=0.3\"),\n",
      "#            )\n",
      "\n",
      "axn.yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(0.2))\n",
      "axn.xaxis.set_major_locator( matplotlib.ticker.MultipleLocator(10))\n",
      "axn.xaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(5))\n",
      "\n",
      "axspi.yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(2.))\n",
      "axspi.yaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(1.))\n",
      "#axspi.yaxis.set_ticklabels(['','2','4'])\n",
      "\n",
      "#axn.text(0.93, 0.98, '$U/t_{0}=11.1$', ha='right', va='top', fontsize=7.5,\\\n",
      "#         transform=axn.transAxes, bbox={'boxstyle':'round','facecolor':'white'})\n",
      "\n",
      "\n",
      "legend = axn.legend( bbox_to_anchor=(1.15,1.01), loc='upper right', numpoints=1,\\\n",
      "               prop={'size':7.}, title='$U_{0}/t_{0}$', handlelength=1.45,\\\n",
      "               handletextpad=0.5)\n",
      "plt.setp( legend.get_title(), fontsize=7.)    \n",
      "\n",
      "axn.tick_params(axis='both', which='major', labelsize=7)\n",
      "for tick in axn.get_xaxis().get_major_ticks():\n",
      "    tick.set_pad(2.)\n",
      "for tick in axn.get_yaxis().get_major_ticks():\n",
      "    tick.set_pad(2.)\n",
      "    \n",
      "axspi.tick_params(axis='both', which='major', labelsize=7)\n",
      "    \n",
      "    \n",
      "axn.text(0.09,0.07, r'\\textbf{b}', ha='left', va='bottom', transform=axn.transAxes,\\\n",
      "         fontsize=12.3)\n",
      "axspi.text(0.91,0.87, r'\\textbf{c}', ha='right', va='top', transform=axspi.transAxes,\\\n",
      "         fontsize=12.3)\n",
      "\n",
      "\n",
      "fig.savefig('dataplots/PROF/03_Inhomog_.png', dpi=300)\n",
      "\n",
      "    \n",
      "\n",
      "\n",
      "#plt.plot( T066[:,1], T066[:,3])\n",
      "#plt.plot( T050[:,1], T050[:,3])\n",
      "#plt.plot( T040[:,1], T040[:,3])\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "11.0126582278\n",
        "11.0126582278\n",
        "11.0126582278\n",
        "11.0126582278\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4xz/+gaenJwsXLmTNmjX885//JD4+ntdff53/+7//4/PPP+fRRx/l2rVrLd5f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\nb5BIJLz//vt4enry2WefNes6tARafQ7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/K4BwC/TBILYHyNZ2a267pQkLd6CgyncvC/TB\nhg0bjcRSCSGs1bBqw4YNA1jipY0DDqK1WcwAfICEGttkA35m7pcNGzbgLDDU0p3QEQTMv/v/Uqyo\nYzZs2DCMJdSQzLt/w9BOn56xQB8szXS05z+/nu2WmqEvbYn4OtY19J4Yu935dz+mCpOuq20dLeY5\nq+8mmfIm1ndsU9zI4Lvt6o5vaOo4HG25BGNT3znr+meJ623Ke70ArXqrj4beE2O3G4ZWDQfYe/e7\nMamrbR0Nfs4snfG2Pp8LU/pk1HfsMCAdrdOYL8a7kQ2dOjaFC2tDrmcskIZ21sqc1zsI7fWQ3f1r\nbP+bxLvH1Ycpp/Prate3SluKu9+NSV1t62jwc2ZpYVHfTTLlTazv2Ka6kQ2ZOg7i3ktlTOo75xmA\nLqAmgXsqoznahntDZl8jt10flprOT+KeB3Mw9669uWjUc2ZpYVHfTTLlTazv2Ka8kfXNQnkasa2q\n1HfOIXeXBWF8Pba+tjOB3LvbFGB+LDmdHwxkYH77XaOeM0sLC6j/JpnyJjbk2Ma+kUru3SQP4GaN\n9aYaVeio75z/4N6v+vS6NjRy2+5o9ev5aIW0Tx3bGpv67ompCQOWmbnNRj9nlhYW9d0kU97Ehh7b\n2DdyD/dUGh+0PiegfVm4u246WuOUJ8bV3es755tof9112w43Y9vzgU1o7SWRaFUiU6O75obuianb\nBe191vkZGdvAWVfbjX7OLC0s6ntxTHkT62sbTHMjDU0dp9/9m3b3IwBuGNfQWd85p1ZZ7w6cNGPb\nALfv/pWhvTbGZAZaNevZKst019yU0/l1tRuOdqYtG63qZWyjdl1tm/I5MxnzqT1ddrqe9eZoOxzt\nDdTdyNaSdqsh13s6sM4CbS/FdNO2NmzYsGHDhg0bNmzYsGHDhg0bNmzYsGHDhg0bTWc+sBHtFNz0\nu5+NFu2RDRs2rA6d/8lBYEmV5QcAb7P3xoYNG1aLzi27AOhYZbkl4jts2LBh5fhS3bnKl/rzKNho\nYVja3dtG6yCc6q74M7hns3Azf3ds2LBhreylujv8AbSBScaOWrVhQWwjCxvGwJ3qakjK/2/fDgkA\nAGAYht2/6oFbGFvioqD3KdKewQAAAAAAAAAAAABgSACOFscyHmLSTAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f01a90b5f10>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import uncertainties as unc\n",
      "print dir(unc)\n",
      "\n",
      "print unc.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['AffineScalarFunc', 'CONSTANT_TYPES', 'CallableStdDev', 'EXP_LETTERS', 'EXP_PRINT', 'FLOAT_LIKE_TYPES', 'FROM_SUPERSCRIPT', 'GROUP_SYMBOLS', 'IndexableIter', 'NUMBER_WITH_UNCERT_GLOBAL_EXP_RE_MATCH', 'NUMBER_WITH_UNCERT_RE_MATCH', 'NUMBER_WITH_UNCERT_RE_STR', 'NotParenUncert', 'NotUpcast', 'NumericalDerivatives', 'PDG_precision', 'POSITIVE_DECIMAL_UNSIGNED_OR_NAN', 'PRETTY_PRINT_MATCH', 'STEP_SIZE', 'TO_SUPERSCRIPT', 'UFloat', 'Variable', '__all__', '__author__', '__builtins__', '__doc__', '__file__', '__name__', '__package__', '__path__', '__version__', '__version_info__', 'add_operators_to_AffineScalarFunc', 'cannot_parse_ufloat_msg_pat', 'collections', 'copy', 'correlated_values', 'correlated_values_norm', 'correlation_matrix', 'covariance_matrix', 'custom_ops', 'deprecation', 'division', 'eq_on_aff_funcs', 'first_digit', 'force_aff_func_args', 'format_num', 'from_superscript', 'ge_on_aff_funcs', 'get_ops_with_reflection', 'gt_on_aff_funcs', 'inspect', 'isnan', 'itertools', 'le_on_aff_funcs', 'log', 'lt_on_aff_funcs', 'math', 'modified_operators', 'modified_ops_with_reflection', 'nan_if_exception', 'ne_on_aff_funcs', 'nominal_value', 'numpy', 'ops_with_reflection', 'parse_error_in_parentheses', 'partial_derivative', 're', 'robust_align', 'robust_format', 'set_doc', 'signif_d_to_limit', 'sqrt', 'std_dev', 'str_to_number_with_uncert', 'sys', 'to_affine_scalar', 'to_float', 'to_superscript', 'ufloat', 'ufloat_fromstr', 'ufloat_obsolete', 'umath', 'unumpy', 'warnings', 'wrap']\n",
        "2.4\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from uncertainties import unumpy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.interpolate import UnivariateSpline\n",
      "\n",
      "def movingaverage(interval, window_size):\n",
      "    window = numpy.ones(int(window_size))/float(window_size)\n",
      "    return numpy.convolve(interval, window, 'same')\n",
      "\n",
      "cval = [0., 0.7, 1.8, 3.5]\n",
      "dash = [(5,1), (2,1,1,1), (1,1), (3,3)]\n",
      "savedir = 'dataplots/PROF/'\n",
      "for ii, (aS, mu) in enumerate([(200,0.107), (290,0.079), (380,0.029), (470,-0.027)]):\n",
      "    color = cmapCycle( matplotlib.cm.spectral, cval[ii], lbound=0., ubound=4.)\n",
      "#for ii, (aS, mu) in enumerate([(290,0.095)]):\n",
      "#    color = 'black'\n",
      "    \n",
      "    fname = savedir + 'aS{:03d}_gr{:0.3f}_mu{:0.3f}.pck'.format(aS,gr,mu)\n",
      "    spis = pickle.load( open( fname,\"rb\" ) )\n",
      "\n",
      "    x = spis['r111_fine']\n",
      "    y = spis['n111_fine']*spis['spi111_fine']\n",
      "    plt.plot( x,y, color=color,  lw=1., alpha=1.0)\n",
      "    \n",
      "    \n",
      "    #z = np.polyfit(x, y, 96)\n",
      "    #p = np.poly1d(z)\n",
      "    #yz = p(x)\n",
      "    #plt.plot( x, yz, lw=3.)\n",
      "    \n",
      "    s = UnivariateSpline(x, y, k=2, s=1)\n",
      "    ys = s(x)\n",
      "    #plt.plot( x, ys+4, color=color, lw=5., alpha=0.5 )\n",
      "    \n",
      "    def smooth(y,win):\n",
      "        return np.convolve(y, np.ones(win)/win)[win/2-1:-win/2]\n",
      "    \n",
      "    smoothed = smooth(y,12)\n",
      "    plt.plot( x, smoothed, color=color, lw=5., alpha=0.5)\n",
      "    \n",
      "    \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dtj0b6pBtuNw4fUNTuVhOj1oZ0bZRZMoX8uMfHcdmm3hiTavVcuTIEfbt25db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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f01a90e2290>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Updated version of Figure 03 in the paper"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "from colorChooser import rgb_to_hex, cmapCycle\n",
      "\n",
      "# Start matplotlib\n",
      "from matplotlib import rc\n",
      "rc('text', usetex=True)\n",
      "plt.rcParams['text.latex.preamble'] = [\n",
      "       r'\\usepackage{bm}',        # for bold math\n",
      "       r'\\usepackage{siunitx}',   # i need upright \\micro symbols, but you need...\n",
      "       r'\\sisetup{detect-all}',   # ...this to force siunitx to actually use your fonts\n",
      "       r'\\usepackage[textlf]{MyriadPro}',\n",
      "       #r'\\usepackage{helvet}',    # set the normal font here\n",
      "       r'\\usepackage{sansmath}',  # load up the sansmath so that math -> helvet\n",
      "       r'\\sansmath',               # <- tricky! -- gotta actually tell tex to use!\n",
      "       #r'\\renewcommand{\\familydefault}{\\sfdefault}'\n",
      "]    \n",
      "plt.rcParams['axes.linewidth'] = 0.55\n",
      "plt.rcParams['patch.linewidth'] = 0.4\n",
      "\n",
      "\n",
      "rawdir = 'dataplots/PROF/03_rawdat/'\n",
      "rawctr = 0\n",
      "\n",
      "from uncertainties import unumpy\n",
      "\n",
      "fig = plt.figure( figsize=(3.5,2.10))\n",
      "gs = matplotlib.gridspec.GridSpec( 1,1,\\\n",
      "        left=0.085, right=0.46, bottom=0.12, top=0.94)\n",
      "ax= fig.add_subplot( gs[0] )\n",
      "ax.set_xlim( 0., 1.45 )\n",
      "ax.set_ylim( 0., 9.5 ) \n",
      "#ax.grid(alpha=0.3)\n",
      "ax.set_xlabel('$n$', fontsize=7, labelpad=0.8)\n",
      "#ax.set_ylabel(r'$\\frac{s_{\\pi}}{n}$',rotation=0, fontsize=18, labelpad=15.)\n",
      "ax.set_ylabel(r'$s_{\\bm{\\pi}}$',rotation=0, fontsize=7, labelpad=7.)\n",
      "\n",
      "ax.yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(2.))\n",
      "ax.yaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(1.))\n",
      "\n",
      "ax.xaxis.set_major_locator( matplotlib.ticker.MultipleLocator(0.4))\n",
      "ax.xaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(0.2))\n",
      "\n",
      "\n",
      "#T100 = np.loadtxt('QMC_Final/U08/T1.000.dat')\n",
      "#T066 = np.loadtxt('QMC_Final/U08/T0.667.dat')\n",
      "#T050 = np.loadtxt('QMC_Final/U08/T0.500.dat')\n",
      "#T040 = np.loadtxt('QMC_Final/U08/T0.400.dat')\n",
      "\n",
      "T040 = np.loadtxt('/home/pmd/sandbox/hubbard-lda2/QMCdatacopy/U08/saf6u8T0.40dt0.05.dat')\n",
      "T050 = np.loadtxt('/home/pmd/sandbox/hubbard-lda2/QMCdatacopy/U08/saf6u8T0.50dt0.05.dat')\n",
      "T066 = np.loadtxt('/home/pmd/sandbox/hubbard-lda2/QMCdatacopy/U08/saf6u8T0.67dt0.05.dat')\n",
      "T100 = np.loadtxt('/home/pmd/sandbox/hubbard-lda2/QMCdatacopy/U08/saf6u8T1.00dt0.05.dat')\n",
      "\n",
      "# columns are \n",
      "# mu, dens, +-, spi, +- \n",
      "\n",
      "def plotspi( ax, dat, err=False, **kwargs):\n",
      "    global rawdir\n",
      "    global rawctr\n",
      "    \n",
      "    #xdat = np.concatenate(( 2.-1.*dat[::-1,1], dat[:,1]))\n",
      "    #ydat = np.concatenate((  dat[::-1,3], dat[:,3] ))\n",
      "    #ax.plot(xdat,ydat/xdat, **kwargs) \n",
      "    \n",
      "    #xdat = dat[:,1]\n",
      "    #ydat = dat[:,3]\n",
      "    #ax.plot(xdat,ydat, **kwargs) \n",
      "    \n",
      "    xdat = dat[:,1]\n",
      "    ydat = dat[:,3]*dat[:,1]\n",
      "    ax.plot(xdat,ydat, **kwargs) \n",
      "    \n",
      "    T = float(kwargs.get('label','0.0').split('$')[1].split('$')[0])\n",
      "    saveset= xdat < 1.35\n",
      "    np.savetxt( rawdir + '{:02d}_T{:0.2f}.dat'.format(rawctr,T), np.column_stack((xdat[saveset], ydat[saveset])), fmt='%.4g')\n",
      "    rawctr = rawctr  + 1 \n",
      "    \n",
      "    if err:\n",
      "        #yerr = np.concatenate(( dat[::-1,4], dat[:,4] )) \n",
      "        #yunc = unumpy.uarray( ydat, yerr )\n",
      "        #yerr = dat[:,4] \n",
      "        \n",
      "        spin = unumpy.uarray( dat[:,3], dat[:,4] ) \n",
      "        n    = unumpy.uarray( dat[:,1], dat[:,2] )\n",
      "        spi = spin * n \n",
      "        \n",
      "        yerr = unumpy.std_devs( spi )\n",
      "        \n",
      "        \n",
      "        # per particle \n",
      "        #ypp = yunc / xdat \n",
      "        \n",
      "        #ypp_val = unumpy.nominal_values( ypp )\n",
      "        #ypp_err = unumpy.std_devs( ypp )\n",
      "        \n",
      "        #ax.fill_between( xdat, ypp_val - ypp_err, ypp_val + ypp_err, color='none', facecolor='gray', alpha=0.5)\n",
      "        ax.fill_between( xdat, ydat - yerr, ydat + yerr,  color='none', facecolor='gray', alpha=0.5)\n",
      "        \n",
      "cs = [ cmapCycle(matplotlib.cm.cool, 0.66, lbound=0, ubound=1.0 ),\\\n",
      "       cmapCycle(matplotlib.cm.cool, 0.33, lbound=0, ubound=1.0 ),\\\n",
      "       cmapCycle(matplotlib.cm.YlGnBu, 0.75, lbound=0, ubound=1.0 ),\\\n",
      "       cmapCycle(matplotlib.cm.YlGnBu, 0.95, lbound=0, ubound=1.0 ),\\\n",
      "       'blue'\n",
      "     ]\n",
      "\n",
      "plotspi( ax, T040, err=True, label='$0.40$', marker='D', ms=2.0, mew=0.4,  c=cs[0], mec=cs[0], mfc='white', lw=0.6)\n",
      "plotspi( ax, T050, err=False, label='$0.50$', marker='s', ms=2.0, mew=0.4, c=cs[1], mec=cs[1], mfc='white', lw=0.6)\n",
      "plotspi( ax, T066, err=False, label='$0.66$', marker='o', ms=2.0, mew=0.4, c=cs[2], mec=cs[2], mfc='white', lw=0.6)\n",
      "plotspi( ax, T100, err=True, label='$1.00$', marker='^', ms=2.0, mew=0.4,  c=cs[3], mec=cs[3], mfc='white', lw=0.6)\n",
      "\n",
      "\n",
      "legend = ax.legend( bbox_to_anchor=(0.1,0.75), loc='upper left', numpoints=1,\\\n",
      "       prop={'size':6}, title='$T/t$', handlelength=1.1,\\\n",
      "       handletextpad=0.5)\n",
      "plt.setp( legend.get_title(), fontsize=6.)  \n",
      "\n",
      "ax.text(0.1,0.94, '$U/t=8$', ha='left', va='top', transform=ax.transAxes,\\\n",
      "        #bbox={'boxstyle':'round','facecolor':'white'},\n",
      "         fontsize=7)\n",
      "\n",
      "ax.text(0.90,0.93, r'\\textbf{a}', ha='right', va='top', transform=ax.transAxes,\\\n",
      "         fontsize=8)\n",
      "\n",
      "\n",
      "# Density profiles \n",
      "\n",
      "import pickle\n",
      "from colorChooser import rgb_to_hex, cmapCycle\n",
      "\n",
      "gsInset = matplotlib.gridspec.GridSpec( 2,1,\\\n",
      "        height_ratios = [2.0,2.8],\\\n",
      "        hspace=0.005,\\\n",
      "        left=0.595, right=0.975, bottom=0.12, top=0.94)\n",
      "axn = fig.add_subplot( gsInset[1] )\n",
      "axspi = fig.add_subplot( gsInset[0] )\n",
      "\n",
      "axn.set_xlabel('$r$\\ (\\si{\\micro\\metre})', labelpad=0.8, fontsize=7)\n",
      "axn.set_ylabel('$n(r)$',rotation=0, labelpad=8., fontsize=7)\n",
      "axn.set_xlim(0.,30.)\n",
      "#axn.grid(alpha=0.3)\n",
      "axn.set_ylim(-0.1, 1.38)    \n",
      "\n",
      "axspi.set_xlim(0.,30.)\n",
      "axspi.set_ylim(-0.4, 4.4)\n",
      "axspi.xaxis.set_ticklabels([])\n",
      "#axspi.yaxis.set_ticklabels([])\n",
      "#axspi.set_ylabel(r'$\\frac{4\\pi r^{2}}{N} S_{\\pi}(r) $')\n",
      "#axspi.set_ylabel(r'$\\frac{S_{\\pi}(r) }{S_{\\pi}(r_{*}) }$', rotation=90, fontsize=8, labelpad=2.)\n",
      "axspi.set_ylabel(r'$s_{\\bm{\\pi}}(r)$', rotation=0, fontsize=7, labelpad=11.)\n",
      "#axspi.set_ylim(-0.07, 1.07) \n",
      "\n",
      "\n",
      "\n",
      "cval = [0., 0.7, 1.8, 3.5]\n",
      "dash = [(2.4,1.4), (3.2,1,1,1),(1,0.001),(1,1.05)]\n",
      "\n",
      "colors = ['0.65','black','0.65', 'black']\n",
      "\n",
      "colors = [ cmapCycle(matplotlib.cm.hsv, 0.0, lbound=0, ubound=1.0 ),\\\n",
      "          cmapCycle(matplotlib.cm.hot, 0.55, lbound=0, ubound=1.0 ),\\\n",
      "          cmapCycle(matplotlib.cm.spectral, 0.45, lbound=0, ubound=1.0 ),\\\n",
      "             cmapCycle(matplotlib.cm.hsv, 0.65, lbound=0, ubound=1.0 ),\\\n",
      "       ]\n",
      "\n",
      "colors = colors[::-1]\n",
      "\n",
      "colors = [ cmapCycle(matplotlib.cm.hsv, 0.00, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.14, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.spectral, 0.55, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.28, lbound=0, ubound=1.20 ),\\\n",
      "       #cmapCycle(matplotlib.cm.hsv, 0.56, lbound=0, ubound=1.20 ),\\\n",
      "       #'#006666',\\\n",
      "       'steelblue',\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.70, lbound=0, ubound=1.20 ),\\\n",
      "       \n",
      "       cmapCycle(matplotlib.cm.hsv, 0.84, lbound=0, ubound=1.20 ),\\\n",
      "       \n",
      "       cmapCycle(matplotlib.cm.hsv, 0.98, lbound=0, ubound=1.20 ),\\\n",
      "       ]\n",
      "\n",
      "colors = [ \n",
      "cmapCycle(matplotlib.cm.hsv, 0.0, lbound=0, ubound=1.0 ),\\\n",
      "cmapCycle(matplotlib.cm.spectral, 0.45, lbound=0, ubound=1.0 ),\\\n",
      "cmapCycle(matplotlib.cm.hsv, 0.65, lbound=0, ubound=1.0 ),\\\n",
      "cmapCycle(matplotlib.cm.hot, 0.55, lbound=0, ubound=1.0 ),\\\n",
      "       ]\n",
      "\n",
      "gr = 3.700\n",
      "savedir = 'dataplots/PROF/'\n",
      "for ii, (aS, mu) in enumerate([(200,0.107), (290,0.079), (380,0.029), (470,-0.027)]):\n",
      "    #color = cmapCycle( matplotlib.cm.spectral, cval[ii], lbound=0., ubound=4.)\n",
      "    \n",
      "    color = colors[ii] \n",
      "    \n",
      "    fname = savedir + 'aS{:03d}_gr{:0.3f}_mu{:0.3f}.pck'.format(aS,gr,mu)\n",
      "    spis = pickle.load( open( fname,\"rb\" ) )\n",
      "    #axn.plot( spis['r111'], spis['n111'], color=color, lw=0.9, alpha=1.0,\\\n",
      "    #    label='${:0.1f}$'.format(aS*11.1/290.), dashes=dash[ii] )\n",
      "    \n",
      "    axn.plot( spis['r111_fine'], spis['n111_fine'], color=color, lw=0.9, alpha=1.0,\\\n",
      "        label='${:0.1f}$'.format(aS*11.1/290.), dashes=dash[ii] )\n",
      "    \n",
      "    saveset = (spis['r111_fine'] >= 0.) & ( spis['r111_fine']<= 30. )\n",
      "    np.savetxt( rawdir + '{:02d}_U{:0.2f}_density.dat'.format(rawctr,aS*11.1/290.), \\\n",
      "               np.column_stack((spis['r111_fine'][saveset], spis['n111_fine'][saveset])), fmt='%.4g')\n",
      "    rawctr = rawctr  + 1 \n",
      "    \n",
      "    peak = np.argmax( spis['spi111'] )\n",
      "    #axspi.plot( spis['r111'],  spis['n111'] *spis['spi111'] / ( spis['n111'][peak] * spis['spi111'][peak] ),\n",
      "    #axspi.plot( spis['r111'],  spis['n111'] *spis['spi111'],\n",
      "\n",
      "    x = spis['r111_fine']\n",
      "    y = spis['n111_fine']*spis['spi111_fine']\n",
      "    \n",
      "    def smooth(y,win):\n",
      "        return np.convolve(y, np.ones(win)/win)[win/2-1:-win/2]\n",
      "    smoothed = smooth(y,15)\n",
      "    \n",
      "    if ii == 3 or True:\n",
      "        axspi.plot( x,smoothed, color=color,  lw=0.9, alpha=1.0, dashes=dash[ii])\n",
      "    else:\n",
      "        axspi.plot( x,smoothed, color=color,  lw=1., alpha=1.0)\n",
      "        \n",
      "    saveset = (x>=0) & (x<=30) \n",
      "    np.savetxt( rawdir + '{:02d}_U{:0.2f}_spin.dat'.format(rawctr,aS*11.1/290.), np.column_stack((x[saveset], smoothed[saveset])), fmt='%.4g')\n",
      "    rawctr = rawctr  + 1 \n",
      "        \n",
      "        \n",
      "    \n",
      "    # find the largest radius at which n=1 \n",
      "    n1 =  np.where( np.abs(spis['n111'] -1.0)  < 0.02 )[0][-1]\n",
      "    print spis['r111'][n1]\n",
      "    \n",
      "    print \"max spi rad = \", spis['r111'][ np.argmax(spis['spi111']) ]\n",
      "\n",
      "    if ii == 1:\n",
      "        \n",
      "        maxRad = np.abs(spis['r111'][ np.argmax(spis['spi111']) ] )\n",
      "        \n",
      "        axn.axvspan( maxRad-0.3 , maxRad, lw=0., facecolor=color, alpha=0.6 )\n",
      "        axspi.axvspan( maxRad-0.3 , maxRad, lw=0., facecolor=color, alpha=0.6 )\n",
      "\n",
      "\n",
      "\n",
      "axn.yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(0.5))\n",
      "axn.yaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(0.25))\n",
      "axn.xaxis.set_major_locator( matplotlib.ticker.MultipleLocator(10))\n",
      "axn.xaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(5))\n",
      "\n",
      "axspi.yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(1.))\n",
      "axspi.yaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(0.5))\n",
      "axspi.xaxis.set_major_locator( matplotlib.ticker.MultipleLocator(10))\n",
      "axspi.xaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(5))\n",
      "\n",
      "\n",
      "\n",
      "legend = axn.legend( bbox_to_anchor=(1.00,1.00), loc='upper right', numpoints=1,\\\n",
      "               prop={'size':6.}, title='$U_{0}/t_{0}$', handlelength=1.61,\\\n",
      "               handletextpad=0.5)\n",
      "plt.setp( legend.get_title(), fontsize=6.)    \n",
      "\n",
      "axn.tick_params(axis='both', which='major', labelsize=7, length=2.)\n",
      "axn.tick_params(axis='both', which='minor', length=1.3)\n",
      "\n",
      "#for tick in axn.get_xaxis().get_major_ticks():\n",
      "#    tick.set_pad(2.)\n",
      "for tick in axn.get_yaxis().get_major_ticks():\n",
      "    tick.set_pad(2.)\n",
      "    \n",
      "axspi.tick_params(axis='both', which='major', labelsize=7, length=2.)\n",
      "axspi.tick_params(axis='both', which='minor', length=1.3)\n",
      "ax.tick_params(axis='both', which='major', labelsize=7, length=2.)\n",
      "ax.tick_params(axis='both', which='minor', length=1.3)\n",
      "    \n",
      "    \n",
      "axn.text(0.09,0.07, r'\\textbf{b}', ha='left', va='bottom', transform=axn.transAxes,\\\n",
      "         fontsize=8)\n",
      "axspi.text(0.91,0.87, r'\\textbf{c}', ha='right', va='top', transform=axspi.transAxes,\\\n",
      "         fontsize=8)\n",
      "\n",
      "\n",
      "fig.savefig('dataplots/PROF/03_Inhomog.png', dpi=350)\n",
      "fig.savefig('dataplots/PROF/fig-03.pdf')\n",
      "\n",
      "    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "11.0126582278\n",
        "max spi rad =  -10.253164557\n",
        "11.0126582278\n",
        "max spi rad =  -11.0126582278\n",
        "11.0126582278\n",
        "max spi rad =  -11.0126582278\n",
        "11.0126582278\n",
        "max spi rad =  -11.7721518987\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ul+D8JC2XtwCUHhaMlZe3IjqDkzM3/bHu23oBvnBCnR1CJpMRfO9clky5gUUB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\nll3EriOV5B918gmQ6DgJCaD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       "text": [
        "<matplotlib.figure.Figure at 0x7f7ddcbbc5d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}