ldaFig4.ipynb

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   "cells": [
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Figure 04 -- Final "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Determination of the error in U0/t0 due to the \n",
      "# systematic uncertainty in the lattice depth\n",
      "\n",
      "from uncertainties import ufloat,unumpy\n",
      "from scipy import stats\n",
      "from scipy.interpolate import interp1d\n",
      "\n",
      "\n",
      "wF = np.loadtxt('dataplots/EDAT/tANDU.dat')\n",
      "wF = wF[ np.logical_and(  wF[ :, 0] > 6.5 , wF[:,0]<7.5  ) ] \n",
      "z = np.polyfit( wF[:,0], np.power( wF[:,2], 1./3.) , 1)  \n",
      "p = np.poly1d(z) \n",
      "#\n",
      "# get the slope of wF^(1/3)  in units of wF^(1/3) \n",
      "slope_wF13 = p[0] / p(7.) \n",
      "#print slope_wF13  \n",
      "#x = np.linspace( 6., 8., 20) \n",
      "#plt.plot( wF[:,0], np.power(wF[:,2], 1./3.), '.', color='black')\n",
      "#plt.plot( x, p(x), '-' )  \n",
      "#plt.show()\n",
      "\n",
      "# get the uncertainty in the lattice depth for x,y,z\n",
      "sx = ufloat( 21.8, 0.3)\n",
      "sy = ufloat( 21.6, 0.2)\n",
      "sz = ufloat( 18.6, 0.5)\n",
      "\n",
      "def get_err_sq( s ):\n",
      "    mW = 7.0 * s.nominal_value \n",
      "    return  ( (mW / sx).std_dev )**2.\n",
      " \n",
      "s0_err =  np.sqrt(  get_err_sq( sx ) \\\n",
      "             + get_err_sq( sy ) + get_err_sq( sz ) )\n",
      "\n",
      "U_err_factor = slope_wF13 * s0_err \n",
      "\n",
      "#print slope_wF13\n",
      "#print get_err_sq( sx ) \n",
      "#print get_err_sq( sy ) \n",
      "#print get_err_sq( sz ) \n",
      "#print  s0_err \n",
      "print U_err_factor\n",
      "\n",
      "import glob \n",
      "from colorChooser import rgb_to_hex, cmapCycle\n",
      "\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'] = 0.65\n",
      "plt.rcParams['patch.linewidth'] = 0.5\n",
      "\n",
      "fig = plt.figure(figsize=(3.50, 2.8))\n",
      "gs = matplotlib.gridspec.GridSpec( 1,1, wspace=0.25, hspace=0.11,\\\n",
      "                    top=0.865, left=0.13, right=0.80, bottom=0.14)\n",
      "ax = fig.add_subplot( gs[0] )  \n",
      "\n",
      "def fx(x):\n",
      "    return  x * 11.1 / 290. \n",
      "\n",
      "    \n",
      "\n",
      "    \n",
      "# YLABELS \n",
      "for tick in ax.get_yaxis().get_major_ticks():\n",
      "    tick.set_pad(2.5)\n",
      "#ax.set_ylabel( r'$\\mathrm{Spin\\ structure\\ factor}\\ S_{\\mathbf{Q}}$', rotation=90, fontsize=7, labelpad=5. )\n",
      "ax.set_ylabel( r'$S_{\\mathbf{Q}}$', rotation=0, fontsize=10., labelpad=10. )\n",
      "ax.set_xlabel( r'$U_{0}/t_{0}$', fontsize=10., labelpad=5.) \n",
      "    \n",
      "# AXES GENERAL PROPERTIES \n",
      "#ax.grid(alpha=0.3)\n",
      "ax.axhline( 1.0, ls='-', color='0.7', lw=1.2, zorder=0.1)\n",
      "ax.set_xlim(2.0, 23.)\n",
      "ax.set_ylim(0.55, 2.25 ) \n",
      "\n",
      "\n",
      "ax.yaxis.set_major_locator( \\\n",
      "    matplotlib.ticker.MultipleLocator( 0.4 ))\n",
      "ax.yaxis.set_minor_locator( \\\n",
      "    matplotlib.ticker.MultipleLocator( 0.2 ))\n",
      "ax.xaxis.set_major_locator( \\\n",
      "    matplotlib.ticker.MaxNLocator( nbins=6))\n",
      "\n",
      "\n",
      "# Setup scattering length ticks on the top\n",
      "def ascatt(x):\n",
      "    return x * 290. / 11.1\n",
      "def Ut(x):\n",
      "    return x * 11.1 / 290. \n",
      "aSs =  [ 80., 200., 290., 380., 470, 560. ] \n",
      "ax2 = ax.twiny()\n",
      "ax2.set_xticks( [ Ut(xi) for xi in  aSs ] )  \n",
      "ax2.set_xticklabels( [ r'$%d$' % xi for xi in aSs ] )\n",
      "ax2.tick_params(axis='x', labelsize=7)\n",
      "ax2.set_xlim( 2. , 23. )\n",
      "#ax2.set_xlabel('$\\mathrm{scattering\\ length}\\ (a_{0})$', fontsize=7, labelpad=3.5) \n",
      "ax2.set_xlabel('$a_{s}\\ (a_{0})$', fontsize=10., labelpad=6.5) \n",
      "\n",
      "for tick in ax2.get_xaxis().get_major_ticks():\n",
      "    tick.set_pad(1.1)\n",
      "\n",
      "\n",
      "\n",
      "#### PLOT THE QMC COMPARISON INCLUDING LDA \n",
      "\n",
      "import glob\n",
      "from colorChooser import rgb_to_hex, cmapCycle\n",
      "\n",
      "aSs = [200, 290, 380, 470]\n",
      "Tsets = []\n",
      "for aS in aSs:\n",
      "    fdats  = sorted(glob.glob('dataplots/THERM/{:03d}/*.dat'.format(int(aS))))\n",
      "    Tspis  = [ float(f.split('Tspi')[1].split('.dat')[0]) for f in fdats ] \n",
      "    print aS\n",
      "    print Tspis\n",
      "    Tsets.append( set(Tspis))\n",
      "    \n",
      "    \n",
      "Tcommon = sorted(set.intersection(*Tsets))\n",
      "print \"TCommon = \", Tcommon\n",
      "\n",
      "Tcommon = [ 0.53, 0.55, 0.57, 0.61, 0.68, 0.80, 1.09, 1.99]\n",
      "\n",
      "m  =  ['D','s','^','o','v','p', '<', '>' ]\n",
      "ms =  [3.6, 3.8, 4.1, 3.8, 4.1, 4.3, 4.1, 4.1]\n",
      "ms = [ 0.85*msi for msi in ms ]\n",
      "\n",
      "\n",
      "cs = [ cmapCycle(matplotlib.cm.jet, 0.15 ),\\\n",
      "       cmapCycle(matplotlib.cm.jet, 0.77 ),\\\n",
      "       cmapCycle(matplotlib.cm.spectral, 0.45 ),\\\n",
      "       cmapCycle(matplotlib.cm.jet, 0.86 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.8 ),\\\n",
      "       cmapCycle(matplotlib.cm.jet, 0.6 ),\\\n",
      "       cmapCycle(matplotlib.cm.jet, 0.2 ),\\\n",
      "       cmapCycle(matplotlib.cm.jet, 0.2 ),\\\n",
      "       ]\n",
      "\n",
      "cs = [ 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.hsv, 0.28, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.42, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.56, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.70, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.84, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.98, lbound=0, ubound=1.20 ),\\\n",
      "       ]\n",
      "\n",
      "cs = [ cmapCycle(matplotlib.cm.hsv, 1.00, lbound=0, ubound=1.00 ),\\\n",
      "      cmapCycle(matplotlib.cm.autumn, 0.6, lbound=0, ubound=1.0 ),\\\n",
      "       cmapCycle(matplotlib.cm.spring, 1.0, lbound=0, ubound=1.0 ),\\\n",
      "       'blue',\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.56, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.70, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.84, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.98, lbound=0, ubound=1.20 ),\\\n",
      "       ]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "cs = [ 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.hsv, 0.28, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.42, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.56, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.70, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.84, lbound=0, ubound=1.20 ),\\\n",
      "       cmapCycle(matplotlib.cm.hsv, 0.98, lbound=0, ubound=1.20 ),\\\n",
      "       ]\n",
      "\n",
      "cs = [  cmapCycle( matplotlib.cm.jet, float(i), lbound=0.,  ubound=7., inv=False) for i in range(8) ]\n",
      "\n",
      "cs = ['#e41a1c',\\\n",
      "      '#377eb8',\\\n",
      "'#4daf4a',\\\n",
      "'#984ea3',\\\n",
      "'#ff7f00',\\\n",
      "'dodgerblue',\\\n",
      "'#a65628',\\\n",
      "'#f781bf' ]\n",
      "\n",
      "cs = ['blue'] * 8 \n",
      "\n",
      "cs = [ 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",
      "resultsThermometer = [] \n",
      "Tstars = [] \n",
      "\n",
      "import os\n",
      "rawdir = 'dataplots/THERM/04_rawdat/'\n",
      "if not os.path.exists(rawdir):\n",
      "        os.makedirs(rawdir)\n",
      "rawctr = 0\n",
      "\n",
      "for i,Tspi in enumerate(Tcommon):\n",
      "    \n",
      "    color = cs[i] \n",
      "      \n",
      "    if Tspi == 0.50:\n",
      "        continue\n",
      "    result = []\n",
      "    # Columns are :  N/1e5,  SpiBulk, Frac, S/N_Bulk, t*, T*, U*\n",
      "    for j,(aS,U,bestN) in enumerate([\\\n",
      "            ( 80,3,0), \\\n",
      "            (120,4,0), \\\n",
      "            (160,6,0), \\\n",
      "            (200,7,0), \\\n",
      "            (245,9,0), \\\n",
      "            (290,11,0), \\\n",
      "            (335,12,0), \\\n",
      "            (380,14,0), \\\n",
      "            (425,16,0), \\\n",
      "            (470,17,0), \\\n",
      "            (515,19,0), \\\n",
      "            (560,21,0), \\\n",
      "            ]):\n",
      "        try:\n",
      "            fname = 'dataplots/THERM/{a:03d}/aS{a:03d}_U{U:02d}_Tspi{T:0.2f}.dat'.format(a=aS,U=U,T=Tspi)\n",
      "            dat = np.loadtxt( fname )\n",
      "            dat = np.atleast_2d( dat ) \n",
      "            # 0 is number\n",
      "            # 1 is Spi Bulk\n",
      "            # 3 is Entropy Bulk\n",
      "            # 4 is t^{*}\n",
      "            # 5 is T/t^{*} \n",
      "            # 6 is U^{*} / t^{*}\n",
      "            try:\n",
      "                sth = dat[bestN][7]\n",
      "            except:\n",
      "                sth = np.nan\n",
      "                \n",
      "            result.append( [ fx(aS), dat[bestN][1], dat[bestN][0], dat[bestN][3] , dat[bestN][5], dat[bestN][6], sth ] ) \n",
      "            \n",
      "            print \"U0/t0 = {:0.2f}  T/t0 = {:0.2f},  T/t* = {:0.2f}\".format( U, Tspi, dat[bestN][5])\n",
      "            \n",
      "            if aS == 290:\n",
      "                resultsThermometer.append( [  dat[bestN][5], dat[bestN][1] ]  )\n",
      "        except Exception as e:\n",
      "            print \"Did not find aS={:03d}, T={:0.2f}\".format(aS,Tspi)\n",
      "            print e\n",
      "            pass\n",
      "    result = np.array(result)\n",
      "    print \"U/t star  =\",  result[:,5]\n",
      "    #print \"mean Tstar = \", np.mean( result[:,4] ) \n",
      "    #print \" std Tstar = \", np.std( result[:,4] )\n",
      "    #print result \n",
      "    \n",
      "    # Choose what goes on the legend title\n",
      "    Tlabel = np.mean(result[:,4] )\n",
      "    Tstars.append(Tlabel) \n",
      "    print \"TSTARS:\"\n",
      "    print result[:,4]\n",
      "    #~Tlabel = Tspi\n",
      "\n",
      "    lwidth = 0.85\n",
      "    # Dummy plot for label \n",
      "    ax.plot( result[:,0], 5.0*np.ones_like(result[:,0]), #'-', \\\n",
      "            m[i%len(m)]+'-', \\\n",
      "            ms=ms[i], mew=0.25, lw=lwidth, color=color, alpha=1.0,\\\n",
      "               mfc='white', mec=color, label='${:0.2f}$'.format( np.round(Tlabel,2)) , zorder=0.9)\n",
      "    #SPI\n",
      "    ax.plot( result[:,0], result[:,1], #'-', \\\n",
      "            m[i%len(m)], \\\n",
      "            ms=ms[i], mew=0.25, lw=0.6, color=color, alpha=1.0,\\\n",
      "               mfc='white', mec=color, zorder=0.9)\n",
      "    \n",
      "    flda = interp1d( result[:,0], result[:,1], kind='cubic')\n",
      "    xnew = np.linspace( result[:,0].min(), result[:,0].max(), 120 )\n",
      "    ax.plot( xnew, flda(xnew), '-',  lw=lwidth, alpha=0.8, color=color, zorder=0.5)\n",
      "    \n",
      "    np.savetxt( rawdir + '{:02d}_T{:0.2f}_SPI.dat'.format(rawctr,np.round(Tlabel,2)), \\\n",
      "               np.column_stack((result[:,0], result[:,1])), fmt='%.4g',\\\n",
      "               header='U0/t0  Spi')\n",
      "    rawctr = rawctr  + 1 \n",
      "    \n",
      "        \n",
      "    #STH\n",
      "    if np.around(Tlabel,2) in [1.68, 0.70, 0.47]:\n",
      "        \n",
      "        \n",
      "        x = result[:,0]\n",
      "        y = result[:,6] \n",
      "        subset = ~np.isnan(y)\n",
      "        \n",
      "        if Tlabel < 0.68:\n",
      "            subset = ~np.isnan(y) & (x<16.)\n",
      "        \n",
      "        ax.plot( x[subset], y[subset], #'-', \\\n",
      "                m[i%len(m)], \\\n",
      "                ms=ms[i], mew=0.25, lw=0.6, color=color, alpha=1.0,\\\n",
      "                   mfc='white', mec=color, zorder=0.9)\n",
      "        np.savetxt( rawdir + '{:02d}_T{:0.2f}_STH.dat'.format(rawctr,np.round(Tlabel,2)), \\\n",
      "                   np.column_stack((x[subset], y[subset])), fmt='%.4g',\\\n",
      "                   header='U0/t0  Sth')\n",
      "        rawctr = rawctr  + 1 \n",
      "        try:\n",
      "            flda = interp1d( x[subset], y[subset], kind='cubic')\n",
      "            xnew = np.linspace( x[subset].min(), x[subset].max(), 120 )\n",
      "            ax.plot( xnew, flda(xnew), '-', dashes=[1.,1.], lw=lwidth*0.8, alpha=1.0, color=color, zorder=0.5)\n",
      "        except:\n",
      "            pass\n",
      "    \n",
      "    \n",
      "    \n",
      "\n",
      "\n",
      "figLDA = plt.figure(figsize=(3.4, 3.0))\n",
      "gsLDA = matplotlib.gridspec.GridSpec(1,1,\\\n",
      "            wspace=0.1, hspace=0.34,\\\n",
      "            left=0.17, right=0.97, bottom=0.16, top=0.97)\n",
      "axTH = figLDA.add_subplot( gsLDA[0] )\n",
      "\n",
      "axTH.set_xlabel(r'$T/t_{*}$', fontsize=12., labelpad=6.)\n",
      "#axTH.set_ylabel(r'\\mathrm{Spin\\ structure\\ factor}\\ $S_{\\bm{\\pi}}$', rotation=90., labelpad=4., fontsize=7)\n",
      "axTH.set_ylabel(r'$S_{\\bm{\\pi}}$', rotation=0., labelpad=15., fontsize=12.)\n",
      "axTH.axhspan( 1.962-0.115, 1.962+0.115, facecolor='gray', alpha=0.6)\n",
      "resultsThermometer = np.array( resultsThermometer )\n",
      "\n",
      "x = resultsThermometer[:,0]\n",
      "x = np.array(Tstars)\n",
      "#print Tstars\n",
      "y = resultsThermometer[:,1]\n",
      "axTH.plot( x, y, 'D', mfc='white', mec='black', ms=4.4, mew=1.2, color='black')\n",
      "axTH.set_xlim(0.43, 0.74)\n",
      "axTH.set_ylim(1.3, 2.33)\n",
      "#axTH.grid(alpha=0.3)\n",
      "\n",
      "axTH.xaxis.set_major_locator( matplotlib.ticker.MultipleLocator(0.1) )\n",
      "axTH.xaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(0.05) )\n",
      "\n",
      "axTH.yaxis.set_major_locator( matplotlib.ticker.MultipleLocator(0.2) )\n",
      "axTH.yaxis.set_minor_locator( matplotlib.ticker.MultipleLocator(0.1) )\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "#for tick in axTH.get_xaxis().get_major_ticks():\n",
      "#    tick.set_pad(2.5)\n",
      "for tick in axTH.get_yaxis().get_major_ticks():\n",
      "    tick.set_pad(1.2)\n",
      "    \n",
      "    \n",
      "axTH.text(0.13, 0.08, '$U_{0}/t_{0}=11.1$',\\\n",
      "          ha='left', va='bottom', transform=axTH.transAxes, fontsize=12., linespacing=1.5) \n",
      "          #bbox={'boxstyle':'square', 'facecolor':'white'})\n",
      "    \n",
      "\n",
      "\n",
      "\n",
      "handles, labels = ax.get_legend_handles_labels()\n",
      "l0 = legend = ax.legend(handles, labels, title='$T/t_{*}$',bbox_to_anchor=(1.02,0.92), loc='upper left', numpoints=1, \\\n",
      "    prop={'size':9.}, handlelength=1.1, handletextpad=0.4, ncol=1, columnspacing=0.7, labelspacing=0.55)\n",
      "plt.setp( legend.get_title(), fontsize=9.)\n",
      "ax.add_artist(l0)\n",
      "\n",
      "######################################################################\n",
      "\n",
      "def plotexp( ax, dat, mfc, lstr, **kwargs):\n",
      "    \n",
      "    global rawdir\n",
      "    global rawctr\n",
      "    \n",
      "    cols = kwargs.pop('cols',(0,3,4))\n",
      "    \n",
      "    X=fx(dat[:,cols[0]]); Y=dat[:,cols[1]];YERR=dat[:,cols[2]]\n",
      "    # In the compressibility paper we found  +/-5% in the lattice depth \n",
      "    # is +/-14% in U/t \n",
      "    XERR = X * U_err_factor / 2. \n",
      "    XERR = X * 0.14\n",
      "    XERR = X * U_err_factor\n",
      "    XERR = X * 0.\n",
      "    marker = kwargs.pop('marker','o')\n",
      "    mec=kwargs.pop('mec','black')\n",
      "    \n",
      "    f2B = interp1d(X, Y-YERR, kind='cubic')\n",
      "    f2T = interp1d(X, Y+YERR, kind='cubic')\n",
      "    xnew = np.linspace(X.min(), X.max(),120)\n",
      "\n",
      "    glw = kwargs.pop('guide_lw',3.)\n",
      "    gcl = kwargs.pop('guide_color','gray')\n",
      "    gal = kwargs.pop('guide_alpha', 0.4)\n",
      "    if kwargs.pop('guide', False):\n",
      "        ax.fill_between( xnew, f2B(xnew), f2T(xnew), lw=0., color=gcl, alpha=gal, zorder=0)\n",
      "    \n",
      "    ax.errorbar( X, Y, yerr=YERR, xerr=XERR, \\\n",
      "            fmt='.', \\\n",
      "            #capsize=0.6, elinewidth=0.4, \\\n",
      "            capsize=0., elinewidth=0.8, \\\n",
      "            mew=0.9, marker=marker,\\\n",
      "            mfc=mfc, mec=mec, ms=4.2, ecolor=mec, **kwargs )\n",
      "    \n",
      "    ax.plot( X, Y,  '.', marker=marker, ms=4.2, mew=0.9, mfc=mfc, mec=mec, label=lstr, **kwargs)\n",
      "    \n",
      "    np.savetxt( rawdir + '{:02d}_expdata.dat'.format(rawctr), \\\n",
      "           np.column_stack((X,Y,YERR)), fmt='%.4g',\\\n",
      "           header='U0/t0 Spi Spi_err')\n",
      "    rawctr = rawctr  + 1 \n",
      "\n",
      "\n",
      "\n",
      "datSpi = np.loadtxt('dataplots/EDAT/experiment_data_Spi.txt')\n",
      "datSth = np.loadtxt('dataplots/EDAT/experiment_data_Sth.txt')\n",
      "\n",
      "plotexp( ax, datSpi, '0.65', r'$\\mathbf{Q}=\\bm{\\pi}$', mec='black',marker='o',\\\n",
      "        guide_lw=12., guide_color='0.65', guide_alpha=0.6, guide=False)\n",
      "plotexp( ax, datSth, 'white', r'$\\mathbf{Q}=\\bm{\\theta}$', mec='black', marker='o',\\\n",
      "        guide_lw=4., guide_color='0.68', guide_alpha=0.4, guide=False)\n",
      "\n",
      "#datSpi = np.loadtxt('dataplots/EDAT/sdata_A2.dat')\n",
      "#datSth = np.loadtxt('dataplots/EDAT/sdata_A1.dat')\n",
      "#plotexp( ax, datSpi, '0.65', r'$\\mathbf{Q}=\\bm{\\pi}$', mec='black',marker='s',\\\n",
      "#        guide_lw=12., guide_color='0.65', guide_alpha=0.6, guide=False, cols=(0,1,2))\n",
      "#plotexp( ax, datSth, 'white', r'$\\mathbf{Q}=\\bm{\\theta}$', mec='black', marker='s',\\\n",
      "#        guide_lw=4., guide_color='0.68', guide_alpha=0.4, guide=False, cols=(0,1,2))\n",
      "\n",
      "#for dat,mfc,lstr in [(datSpi,'black',r'$\\mathbf{Q}=\\bm{\\pi}$' ), (datSth,'white', r'$\\mathbf{Q}=\\bm{\\theta}$' )]:\n",
      "#    plotexp( ax, dat, mfc, lstr)\n",
      "    \n",
      "handles, labels = ax.get_legend_handles_labels()\n",
      "legend = ax.legend( handles[-2:], labels[-2:], bbox_to_anchor=(1.,1.), loc='upper right',numpoints=1,prop={'size':9.},\\\n",
      "    handlelength=0.6,handletextpad=0.5, labelspacing=0.26)\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "ax.tick_params(axis='both', which='major', labelsize=10., length=2.)\n",
      "ax2.tick_params(axis='both', which='major', labelsize=10., length=2.)\n",
      "\n",
      "axTH.tick_params(axis='both', which='major', labelsize=10.,  length=1.5)\n",
      "axTH.tick_params(axis='both', which='minor', labelsize=10.,  length=1.0)\n",
      "\n",
      "\n",
      "    \n",
      "import datetime\n",
      "datestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S_')\n",
      "datestamp = ''\n",
      "\n",
      "#fig.savefig('dataplots/SEXP/' + datestamp+'udat_final_onerow_XU.png', dpi=350)\n",
      "fig.savefig('dataplots/THERM/04_SpiLDA_partA.png', dpi=400)\n",
      "fig.savefig('dataplots/THERM/fig-04.pdf')\n",
      "figLDA.savefig('dataplots/THERM/04_SpiLDA_partB.png', dpi=400)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.106844960754\n",
        "200"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[0.53, 0.55, 0.57, 0.61, 0.68, 0.8, 1.09, 1.99]\n",
        "290\n",
        "[0.53, 0.55, 0.57, 0.61, 0.68, 0.8, 1.09, 1.99]\n",
        "380\n",
        "[0.53, 0.55, 0.57, 0.61, 0.68, 0.8, 1.09, 1.99]\n",
        "470\n",
        "[0.53, 0.55, 0.57, 0.61, 0.68, 0.8, 1.09, 1.99]\n",
        "TCommon =  [0.53, 0.55, 0.57, 0.61, 0.68, 0.8, 1.09, 1.99]\n",
        "U0/t0 = 3.00  T/t0 = 0.53,  T/t* = 0.45\n",
        "U0/t0 = 4.00  T/t0 = 0.53,  T/t* = 0.47\n",
        "U0/t0 = 6.00  T/t0 = 0.53,  T/t* = 0.47\n",
        "U0/t0 = 7.00  T/t0 = 0.53,  T/t* = 0.47\n",
        "U0/t0 = 9.00  T/t0 = 0.53,  T/t* = 0.48\n",
        "U0/t0 = 11.00  T/t0 = 0.53,  T/t* = 0.48\n",
        "U0/t0 = 12.00  T/t0 = 0.53,  T/t* = 0.48\n",
        "U0/t0 = 14.00  T/t0 = 0.53,  T/t* = 0.47\n",
        "U0/t0 = 16.00  T/t0 = 0.53,  T/t* = 0.47\n",
        "U0/t0 = 17.00  T/t0 = 0.53,  T/t* = 0.46\n",
        "U0/t0 = 19.00  T/t0 = 0.53,  T/t* = 0.46\n",
        "U0/t0 = 21.00  T/t0 = 0.53,  T/t* = 0.46\n",
        "U/t star  = [  2.369   3.76    5.013   6.267   7.878   9.555  10.77   11.91   13.32\n",
        "  14.33   15.7    17.07 ]\n",
        "TSTARS:\n",
        "[ 0.452   0.468   0.468   0.468   0.4755  0.4828  0.4755  0.468   0.468\n",
        "  0.4602  0.4602  0.4602]\n",
        "U0/t0 = 3.00  T/t0 = 0.55,  T/t* = 0.47\n",
        "U0/t0 = 4.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 6.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 7.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 9.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 11.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 12.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 14.00  T/t0 = 0.55,  T/t* = 0.48\n",
        "U0/t0 = 16.00  T/t0 = 0.55,  T/t* = 0.49\n",
        "U0/t0 = 17.00  T/t0 = 0.55,  T/t* = 0.48\n",
        "U0/t0 = 19.00  T/t0 = 0.55,  T/t* = 0.48\n",
        "U0/t0 = 21.00  T/t0 = 0.55,  T/t* = 0.48\n",
        "U/t star  = [  2.369   3.76    5.013   6.267   7.878   9.087  10.77   11.59   13.32\n",
        "  14.33   15.7    17.07 ]\n",
        "TSTARS:\n",
        "[ 0.4691  0.4856  0.4856  0.4856  0.4935  0.4856  0.4935  0.4776  0.4856\n",
        "  0.4776  0.4776  0.4776]\n",
        "U0/t0 = 3.00  T/t0 = 0.57,  T/t* = 0.49\n",
        "U0/t0 = 4.00  T/t0 = 0.57,  T/t* = 0.49\n",
        "U0/t0 = 6.00  T/t0 = 0.57,  T/t* = 0.50\n",
        "U0/t0 = 7.00  T/t0 = 0.57,  T/t* = 0.50\n",
        "U0/t0 = 9.00  T/t0 = 0.57,  T/t* = 0.51\n",
        "U0/t0 = 11.00  T/t0 = 0.57,  T/t* = 0.52\n",
        "U0/t0 = 12.00  T/t0 = 0.57,  T/t* = 0.51\n",
        "U0/t0 = 14.00  T/t0 = 0.57,  T/t* = 0.50\n",
        "U0/t0 = 16.00  T/t0 = 0.57,  T/t* = 0.49\n",
        "U0/t0 = 17.00  T/t0 = 0.57,  T/t* = 0.50\n",
        "U0/t0 = 19.00  T/t0 = 0.57,  T/t* = 0.49\n",
        "U0/t0 = 21.00  T/t0 = 0.57,  T/t* = 0.49\n",
        "U/t star  = [  2.369   3.659   5.013   6.267   7.878   9.555  10.77   11.91   12.96\n",
        "  14.73   15.7    17.07 ]\n",
        "TSTARS:\n",
        "[ 0.4861  0.495   0.5033  0.5033  0.5114  0.5192  0.5114  0.5033  0.495\n",
        "  0.5033  0.495   0.495 ]\n",
        "U0/t0 = 3.00  T/t0 = 0.61,  T/t* = 0.52\n",
        "U0/t0 = 4.00  T/t0 = 0.61,  T/t* = 0.53\n",
        "U0/t0 = 6.00  T/t0 = 0.61,  T/t* = 0.55\n",
        "U0/t0 = 7.00  T/t0 = 0.61,  T/t* = 0.54\n",
        "U0/t0 = 9.00  T/t0 = 0.61,  T/t* = 0.55\n",
        "U0/t0 = 11.00  T/t0 = 0.61,  T/t* = 0.55\n",
        "U0/t0 = 12.00  T/t0 = 0.61,  T/t* = 0.55\n",
        "U0/t0 = 14.00  T/t0 = 0.61,  T/t* = 0.54\n",
        "U0/t0 = 16.00  T/t0 = 0.61,  T/t* = 0.54\n",
        "U0/t0 = 17.00  T/t0 = 0.61,  T/t* = 0.54\n",
        "U0/t0 = 19.00  T/t0 = 0.61,  T/t* = 0.54\n",
        "U0/t0 = 21.00  T/t0 = 0.61,  T/t* = 0.53\n",
        "U/t star  = [  2.369   3.659   5.145   6.267   7.878   9.325  10.77   11.91   13.32\n",
        "  14.73   16.14   17.07 ]\n",
        "TSTARS:\n",
        "[ 0.5202  0.5297  0.5473  0.5386  0.5473  0.5473  0.5473  0.5386  0.5386\n",
        "  0.5386  0.5386  0.5297]\n",
        "U0/t0 = 3.00  T/t0 = 0.68,  T/t* = 0.58\n",
        "U0/t0 = 4.00  T/t0 = 0.68,  T/t* = 0.59\n",
        "U0/t0 = 6.00  T/t0 = 0.68,  T/t* = 0.60"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "U0/t0 = 7.00  T/t0 = 0.68,  T/t* = 0.60\n",
        "U0/t0 = 9.00  T/t0 = 0.68,  T/t* = 0.61\n",
        "U0/t0 = 11.00  T/t0 = 0.68,  T/t* = 0.61\n",
        "U0/t0 = 12.00  T/t0 = 0.68,  T/t* = 0.61\n",
        "U0/t0 = 14.00  T/t0 = 0.68,  T/t* = 0.59\n",
        "U0/t0 = 16.00  T/t0 = 0.68,  T/t* = 0.60\n",
        "U0/t0 = 17.00  T/t0 = 0.68,  T/t* = 0.59\n",
        "U0/t0 = 19.00  T/t0 = 0.68,  T/t* = 0.59\n",
        "U0/t0 = 21.00  T/t0 = 0.68,  T/t* = 0.59\n",
        "U/t star  = [  2.369   3.659   5.013   6.267   7.878   9.325  10.77   11.59   13.32\n",
        "  14.33   15.7    17.07 ]\n",
        "TSTARS:\n",
        "[ 0.5799  0.5905  0.6004  0.6004  0.6101  0.6101  0.6101  0.5905  0.6004\n",
        "  0.5905  0.5905  0.5905]\n",
        "U0/t0 = 3.00  T/t0 = 0.80,  T/t* = 0.67\n",
        "U0/t0 = 4.00  T/t0 = 0.80,  T/t* = 0.69\n",
        "U0/t0 = 6.00  T/t0 = 0.80,  T/t* = 0.69\n",
        "U0/t0 = 7.00  T/t0 = 0.80,  T/t* = 0.71\n",
        "U0/t0 = 9.00  T/t0 = 0.80,  T/t* = 0.73\n",
        "U0/t0 = 11.00  T/t0 = 0.80,  T/t* = 0.72\n",
        "U0/t0 = 12.00  T/t0 = 0.80,  T/t* = 0.71\n",
        "U0/t0 = 14.00  T/t0 = 0.80,  T/t* = 0.71\n",
        "U0/t0 = 16.00  T/t0 = 0.80,  T/t* = 0.71\n",
        "U0/t0 = 17.00  T/t0 = 0.80,  T/t* = 0.71\n",
        "U0/t0 = 19.00  T/t0 = 0.80,  T/t* = 0.69\n",
        "U0/t0 = 21.00  T/t0 = 0.80,  T/t* = 0.69\n",
        "U/t star  = [  2.294   3.659   4.878   6.267   8.073   9.325  10.5    11.91   13.32\n",
        "  14.73   15.7    17.07 ]\n",
        "TSTARS:\n",
        "[ 0.6689  0.6947  0.6947  0.7064  0.7288  0.7178  0.7064  0.7064  0.7064\n",
        "  0.7064  0.6947  0.6947]\n",
        "U0/t0 = 3.00  T/t0 = 1.09,  T/t* = 0.89\n",
        "U0/t0 = 4.00  T/t0 = 1.09,  T/t* = 0.95\n",
        "U0/t0 = 6.00  T/t0 = 1.09,  T/t* = 0.93\n",
        "U0/t0 = 7.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 9.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 11.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 12.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 14.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 16.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 17.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 19.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U0/t0 = 21.00  T/t0 = 1.09,  T/t* = 0.96\n",
        "U/t star  = [  2.22    3.659   4.737   6.267   7.677   9.087  10.5    11.91   13.32\n",
        "  14.73   16.14   17.55 ]\n",
        "TSTARS:\n",
        "[ 0.8931  0.9465  0.9296  0.9625  0.9625  0.9625  0.9625  0.9625  0.9625\n",
        "  0.9625  0.9625  0.9625]\n",
        "U0/t0 = 3.00  T/t0 = 1.99,  T/t* = 1.53\n",
        "U0/t0 = 4.00  T/t0 = 1.99,  T/t* = 1.53\n",
        "U0/t0 = 6.00  T/t0 = 1.99,  T/t* = 1.56\n",
        "U0/t0 = 7.00  T/t0 = 1.99,  T/t* = 1.66\n",
        "U0/t0 = 9.00  T/t0 = 1.99,  T/t* = 1.73\n",
        "U0/t0 = 11.00  T/t0 = 1.99,  T/t* = 1.73\n",
        "U0/t0 = 12.00  T/t0 = 1.99,  T/t* = 1.70\n",
        "U0/t0 = 14.00  T/t0 = 1.99,  T/t* = 1.76\n",
        "U0/t0 = 16.00  T/t0 = 1.99,  T/t* = 1.73\n",
        "U0/t0 = 17.00  T/t0 = 1.99,  T/t* = 1.73\n",
        "U0/t0 = 19.00  T/t0 = 1.99,  T/t* = 1.76\n",
        "U0/t0 = 21.00  T/t0 = 1.99,  T/t* = 1.70\n",
        "U/t star  = [  1.993   2.99    4.141   5.736   7.47    8.842   9.919  11.91   12.96\n",
        "  14.33   16.14   16.58 ]\n",
        "TSTARS:\n",
        "[ 1.528  1.528  1.564  1.664  1.728  1.728  1.697  1.757  1.728  1.728\n",
        "  1.757  1.697]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/SeTFZLYcib+ldS+ddtCHUbgQTCdeYSCrKCaL/Uwlmu8o1jo86qAOUvPeuvfw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\nMdDjeasgPT1vWEbGaIB/b958cuHWrYsvjXkdpv1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       "text": [
        "<matplotlib.figure.Figure at 0x7fd03f15f550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPMAAADcCAYAAAClDJfcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDhYREREREREREREREREREXHO/wG/5VoD5Yk74gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd016b38350>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": []
    }
   ],
   "metadata": {}
  }
 ]
}