Snowmass.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "from numpy.random import *\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib import colors\n",
    "import matplotlib.ticker as mticker\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import matplotlib.cm as cm\n",
    "from scipy.stats import norm\n",
    "from PlotFuncs import col_alpha,CurvedText\n",
    "from PlotFuncs import MySaveFig\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def FigSetup(xlab=r'Dark photon mass, $m_{\\gamma^\\prime}$ [eV]',ylab='Kinetic mixing, $\\epsilon$',\\\n",
    "             chi_min = 1.0e-18,chi_max = 1.0e0,\\\n",
    "             m_min = 3e-18,m_max = 1e5,\\\n",
    "             lw=2.5,lfs=40,tfs=25,tickdir='out',\\\n",
    "             Grid=False,Shape='Rectangular',mathpazo=True,\\\n",
    "             TopAndRightTicks=False,FrequencyAxis=True,FrequencyLabels=True,UnitAxis=True,f_rescale=1,\\\n",
    "            tick_rotation = 20,width=20,height=10,upper_tickdir='out'):\n",
    "\n",
    "    plt.rcParams['axes.linewidth'] = lw\n",
    "    plt.rc('text', usetex=True)\n",
    "    plt.rc('font', family='serif',size=tfs)\n",
    "\n",
    "    if mathpazo:\n",
    "        mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{mathpazo}']\n",
    "\n",
    "    if Shape=='Wide':\n",
    "        fig = plt.figure(figsize=(16.5,5))\n",
    "    elif Shape=='Rectangular':\n",
    "        fig = plt.figure(figsize=(16.5,11))\n",
    "    elif Shape=='Custom':\n",
    "        fig = plt.figure(figsize=(width,height))\n",
    "\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    ax.set_xlabel(xlab,fontsize=lfs)\n",
    "    ax.set_ylabel(ylab,fontsize=lfs)\n",
    "\n",
    "    ax.tick_params(which='major',direction=tickdir,width=2.5,length=13,right=TopAndRightTicks,top=TopAndRightTicks,pad=7)\n",
    "    ax.tick_params(which='minor',direction=tickdir,width=1,length=10,right=TopAndRightTicks,top=TopAndRightTicks)\n",
    "\n",
    "\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_xlim([m_min,m_max])\n",
    "    ax.set_ylim([chi_min,chi_max])\n",
    "\n",
    "    locmaj = mpl.ticker.LogLocator(base=10.0, subs=(1.0, ), numticks=50)\n",
    "    locmin = mpl.ticker.LogLocator(base=10.0, subs=arange(2, 10)*.1,numticks=100)\n",
    "    ax.xaxis.set_major_locator(locmaj)\n",
    "    ax.xaxis.set_minor_locator(locmin)\n",
    "    ax.xaxis.set_minor_formatter(mpl.ticker.NullFormatter())\n",
    "\n",
    "    locmaj = mpl.ticker.LogLocator(base=10.0, subs=(1.0, ), numticks=100)\n",
    "    locmin = mpl.ticker.LogLocator(base=10.0, subs=arange(2, 10)*.1,numticks=100)\n",
    "    ax.yaxis.set_major_locator(locmaj)\n",
    "    ax.yaxis.set_minor_locator(locmin)\n",
    "    ax.yaxis.set_minor_formatter(mpl.ticker.NullFormatter())\n",
    "\n",
    "    if Shape=='Rectangular':\n",
    "        plt.xticks(rotation=tick_rotation)\n",
    "\n",
    "    if Grid:\n",
    "        ax.grid(zorder=0)\n",
    "\n",
    "    if FrequencyAxis:\n",
    "        ax2 = ax.twiny()\n",
    "\n",
    "\n",
    "\n",
    "        ax2.set_xscale('log')\n",
    "        ax2.tick_params(which='major',direction=upper_tickdir,width=2.5,length=13,pad=7)\n",
    "        ax2.tick_params(which='minor',direction=upper_tickdir,width=1,length=10)\n",
    "        locmaj = mpl.ticker.LogLocator(base=10.0, subs=(1.0, ), numticks=50)\n",
    "        locmin = mpl.ticker.LogLocator(base=10.0, subs=arange(2, 10)*.1,numticks=100)\n",
    "        ax2.xaxis.set_major_locator(locmaj)\n",
    "        ax2.xaxis.set_minor_locator(locmin)\n",
    "        ax2.xaxis.set_minor_formatter(mpl.ticker.NullFormatter())\n",
    "\n",
    "        if FrequencyLabels:\n",
    "            ax2.set_xticks([1e-3,1e0,1e3,1e6,1e9,1e12,1*241.8*1e12,1000*241.8*1e12])\n",
    "            ax2.set_xticklabels(['mHz','Hz','kHz','MHz','GHz','THz','eV','keV'])\n",
    "        ax2.set_xlim([m_min*241.8*1e12/f_rescale,m_max*241.8*1e12/f_rescale])\n",
    "\n",
    "        plt.sca(ax)\n",
    "    return fig,ax\n",
    "    \n",
    "    \n",
    "import matplotlib.patheffects as pe\n",
    "pek=[pe.Stroke(linewidth=7, foreground='k'), pe.Normal()]\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "def Haloscopes(ax,col=[0.75, 0.2, 0.2],fs=17,projection=True,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    zo = 0.3\n",
    "    \n",
    "    HAYSTAC_col = 'indianred'\n",
    "    CAPP_col = 'crimson'\n",
    "    QUAX_col = 'r'\n",
    "    ADMX_col = 'firebrick'\n",
    "    \n",
    "    # ADMX\n",
    "    costh = sqrt(0.019)\n",
    "    B = 7.6\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/ADMX.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=ADMX_col,zorder=0.1,lw=3)\n",
    "\n",
    "    B = 6.8\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/ADMX2018.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=ADMX_col,zorder=0.1)\n",
    "\n",
    "    B = 7.6\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/ADMX2019_1.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=ADMX_col,zorder=0.1)\n",
    "\n",
    "    B = 7.6\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/ADMX2019_2.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=ADMX_col,zorder=0.1)\n",
    "    \n",
    "    B = 7.6\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/ADMX2021.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=ADMX_col,zorder=0.1)\n",
    "\n",
    "#     B = 3.11\n",
    "#     dat = loadtxt(\"limit_data/AxionPhoton/ADMX_Sidecar_AC.txt\")\n",
    "#     dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "#     plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=ADMX_col,facecolor=ADMX_col,zorder=0.1)\n",
    "\n",
    "#     B = 5.0\n",
    "#     dat = loadtxt(\"limit_data/AxionPhoton/ADMX_SLIC.txt\")\n",
    "#     dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "#     plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=ADMX_col,facecolor=ADMX_col,zorder=100)\n",
    "\n",
    "    \n",
    "    \n",
    "    B = 9\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/HAYSTAC_highres.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=HAYSTAC_col,zorder=0.1)\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/HAYSTAC_2020_highres.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=HAYSTAC_col,zorder=0.1)\n",
    "\n",
    "    \n",
    "    # CAPP\n",
    "    B = 7.3\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/CAPP-1.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=CAPP_col,zorder=0.1)\n",
    "\n",
    "    B = 7.8\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/CAPP-2.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=CAPP_col,zorder=0.1)\n",
    "\n",
    "    B = 7.9\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/CAPP-3.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*costh*dat[:,0]))\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor='none',facecolor=CAPP_col,zorder=0.1)\n",
    "\n",
    "    # CAPP-3 [KSVZ]\n",
    "    dat_min = dat[argmin(dat[:,1]),:]\n",
    "    dat_min[1] = dat_min[1]*costh/sqrt(0.2)\n",
    "    plt.plot([dat_min[0],dat_min[0]],[1e-10,dat_min[1]],'-',color=CAPP_col,lw=1.5,zorder=0.1)\n",
    "\n",
    "\n",
    "    B = 8.1\n",
    "    costh = sqrt(0.03)\n",
    "    dat = loadtxt(\"limit_data/AxionPhoton/QUAX.txt\")\n",
    "    dat[:,1] = 1e-9*dat[:,1]*(B/(1.444e-3*0.023*dat[:,0]))\n",
    "    plt.fill_between([dat[0,0],dat[0,0]],[y2,dat[0,1]],y2=y2,color=QUAX_col,zorder=0.1)\n",
    "\n",
    "\n",
    "    if text_on: \n",
    "        plt.text(1.4e-6,0.5e-14,r'{\\bf ADMX}',fontsize=fs,color=ADMX_col,rotation=90,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.8e-5,0.1e-13,r'{\\bf CAPP}',fontsize=fs-2,color=CAPP_col,rotation=90,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.19e-4,3e-15,r'{\\bf HAYSTAC}',fontsize=fs-5,color=HAYSTAC_col,rotation=90,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.55e-4,5e-12,r'{\\bf QUAX}',fontsize=fs-5,color=QUAX_col,rotation=-90,rotation_mode='anchor',ha='center',va='center')\n",
    "\n",
    "    return\n",
    "    \n",
    "    \n",
    "def StellarBounds(ax,fs=19,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    # Stellar physics constraints\n",
    "\n",
    "    # Globular clusters \n",
    "    HB_col = [0.01, 0.75, 0.24]\n",
    "    HB = loadtxt(\"limit_data/DarkPhoton/RG.txt\")\n",
    "    plt.plot(HB[:,0],HB[:,1],color='k',alpha=0.5,zorder=0.9,lw=2)\n",
    "    plt.fill_between(HB[:,0],HB[:,1],y2=y2,edgecolor=None,facecolor=HB_col,zorder=0.9)\n",
    "    \n",
    "    # Globular clusters \n",
    "    HB_col = 'DarkGreen'\n",
    "    HB = loadtxt(\"limit_data/DarkPhoton/HB.txt\")\n",
    "    plt.plot(HB[:,0],HB[:,1],color='k',alpha=0.5,zorder=0.95,lw=2)\n",
    "    plt.fill_between(HB[:,0],HB[:,1],y2=y2,edgecolor=None,facecolor=HB_col,zorder=0.95)\n",
    "\n",
    "    # Solar bound\n",
    "    Solar_col = 'ForestGreen'\n",
    "    Solar = loadtxt(\"limit_data/DarkPhoton/Solar.txt\")\n",
    "    plt.plot(Solar[:,0],Solar[:,1],color='k',alpha=0.5,zorder=1.02,lw=2)\n",
    "    plt.fill_between(Solar[:,0],Solar[:,1],y2=y2,edgecolor=None,facecolor=Solar_col,zorder=1.02)\n",
    "\n",
    "    Solar = loadtxt(\"limit_data/DarkPhoton/Solar-Global.txt\")\n",
    "    plt.plot(Solar[:,0],Solar[:,1]/Solar[:,0],color='k',alpha=0.5,zorder=1.021,lw=2)\n",
    "    plt.fill_between(Solar[:,0],Solar[:,1]/Solar[:,0],y2=y2,edgecolor=None,facecolor=Solar_col,zorder=1.021)\n",
    "\n",
    "    if text_on:\n",
    "        plt.text(0.8e2*(1-0.01),1.5e-14*(1+0.05),r'{\\bf Solar}',fontsize=fs,color='k',rotation=-41,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1e3*(1-0.01),0.7e-14*(1+0.05),r'{\\bf HB}',fontsize=fs,color='k',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.8e4*(1-0.01),0.7e-14*(1+0.05),r'{\\bf RG}',fontsize=fs,color='k',rotation=-37,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.8e2,1.5e-14,r'{\\bf Solar}',fontsize=fs,color='w',rotation=-41,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1e3,0.7e-14,r'{\\bf HB}',fontsize=fs,color='w',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.8e4,0.7e-14,r'{\\bf RG}',fontsize=fs,color='w',rotation=-37,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "    \n",
    "def Xenon(ax,col='crimson',fs=23,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Xenon1T.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "\n",
    "    plt.plot(1e3*dat[:,0],dat[:,1],color='k',alpha=0.5,zorder=0.5,lw=2)\n",
    "    plt.fill_between(1e3*dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.5)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Xenon1T_S1S2.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=0.5,zorder=0.5,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.5)\n",
    "    \n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Xenon100.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=0.5,zorder=0.5,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.5)\n",
    "    if text_on: \n",
    "        plt.text(8e2,3e-17,r'{\\bf XENON}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    \n",
    "    return\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def DAMIC(ax,col='salmon',fs=21,text_on=True):\n",
    "    m1,y1 = loadtxt(\"limit_data/DarkPhoton/DM_combined.txt\",unpack=True)\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/DAMIC.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "\n",
    "    y2 = interp(dat[:,0],m1,y1)\n",
    "    dat[0,1] = y2[0]\n",
    "    dat[-1,1] = y2[-1]\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.001,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.001)\n",
    "    if text_on: \n",
    "        plt.text(6e-1,1.3e-14,r'{\\bf DAMIC}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([5e0,1e1],[3e-14,6e-14],'k-',lw=2.5,color=col)\n",
    "    return\n",
    "\n",
    "\n",
    "def FUNK(ax,col='red',fs=21,text_on=True):\n",
    "    m1,y1 = loadtxt(\"limit_data/DarkPhoton/DM_combined.txt\",unpack=True)\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/FUNK.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)*sqrt(2/3/0.27)\n",
    "\n",
    "    y2 = interp(dat[:,0],m1,y1)\n",
    "    dat[0,1] = y2[0]/1.1\n",
    "    dat[-1,1] = y2[-1]/1.1\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.3,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.3)\n",
    "    if text_on: \n",
    "        plt.text(2.6e-1,1e-13,r'{\\bf FUNK}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([9e-1,3e0],[3e-13,1e-12],'k-',lw=2.5,color=col)\n",
    "    return\n",
    "\n",
    "def SENSEI(ax,col='firebrick',fs=21,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SENSEI.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1)\n",
    "    if text_on: \n",
    "        plt.text(1.7e0,1e-15,r'{\\bf SENSEI}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([7e0,1e1],[3e-15,9e-15],'k-',lw=2.5,color=col)\n",
    "    return\n",
    "\n",
    "def SuperCDMS(ax,col=[0.4,0,0],fs=18,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SuperCDMS.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=0.5,zorder=0.6,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.6)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(0.5e1,1.5e-16,r'{\\bf SuperCDMS}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([5e1,0.8e2],[3e-16,9e-16],'k-',lw=2.5,color=col)\n",
    "    return\n",
    "\n",
    "def Nanowire(ax,col='pink',fs=22,text_on=True):\n",
    "    m1,y1 = loadtxt(\"limit_data/DarkPhoton/DM_combined.txt\",unpack=True)\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/WSi_Nanowire.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "    y2 = interp(dat[:,0],m1,y1)\n",
    "    dat[0,1] = y2[0]/1.1\n",
    "    dat[-1,1] = y2[-1]/1.1\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.3,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.3)\n",
    "    if text_on: \n",
    "        plt.text(5e-4,1e-10,r'{\\bf WSi Nanowire}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([9e-3,3e-3],[3e-10,9e-10],'k-',lw=2.5,color=col)\n",
    "    return\n",
    "\n",
    "def LAMPOST(ax,col='red',fs=15,text_on=True):\n",
    "    m1,y1 = loadtxt(\"limit_data/DarkPhoton/DM_combined.txt\",unpack=True)\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/LAMPOST.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.4/0.45)*sqrt(2/3/0.27)\n",
    "\n",
    "    y2 = interp(dat[:,0],m1,y1)\n",
    "    dat[0,1] = y2[0]/1.1\n",
    "    dat[-1,1] = y2[-1]/1.1\n",
    "    plt.plot(dat[:,0],dat[:,1],color=col,alpha=1,zorder=0,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0)\n",
    "    if text_on: \n",
    "        plt.text(0.3e-1,5e-13,r'{\\bf LAMPOST}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([3e-1,0.6e0],[6e-13,1e-12],'-',lw=1.5,color=col)\n",
    "    return\n",
    "\n",
    "def Tokyo(ax,col='darkred',fs=15,text_on=True):\n",
    "    m1,y1 = loadtxt(\"limit_data/DarkPhoton/DM_combined.txt\",unpack=True)\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Tokyo-Dish.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(2/3/0.6)\n",
    "    y2 = interp(dat[:,0],m1,y1)\n",
    "    dat[0,1] = y2[0]/1.1\n",
    "    dat[-1,1] = y2[-1]/1.1\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.4,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.4)\n",
    "\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Tokyo-Knirck.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(1/3/0.175)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=1e0,edgecolor='k',facecolor=col,zorder=1.09)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Tokyo-Tomita.txt\")\n",
    "    plt.plot([dat[1,0],dat[1,0]],[dat[1,1],1e0],'-',color=col,lw=3,zorder=0.2)\n",
    "    if text_on: \n",
    "        plt.text(2.3e-4,2.5e-10,r'{\\bf Tokyo-3}',fontsize=fs,color=col,rotation=-90,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.45e-3,3e-8,r'{\\bf Tokyo-2}',fontsize=fs-2,color='k',rotation=90,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.3e-1,4e-12,r'{\\bf Tokyo-1}',fontsize=fs+4,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.plot([3e-1,4e0],[5e-12,8e-12],'-',lw=2.5,color=col)\n",
    "    return\n",
    "    \n",
    "\n",
    "def Jupiter(ax,col='Green',fs=15,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Jupiter.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=2,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=2)\n",
    "    if text_on: \n",
    "        plt.text(0.1e-14*(1-0.02),4.5e-1*(1+0.07),r'{\\bf Jupiter}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.1e-14,4.5e-1,r'{\\bf Jupiter}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def Earth(ax,col='DarkGreen',fs=17,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Earth.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.9,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.9)\n",
    "    if text_on: \n",
    "        plt.text(0.4e-13*(1-0.01),2e-1*(1+0.05),r'{\\bf Earth}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.4e-13,2e-1,r'{\\bf Earth}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "\n",
    "def Crab(ax,col=[0.1,0.4,0.1],fs=17,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Crab.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.09999,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.09999)\n",
    "    \n",
    "#     dat = loadtxt(\"limit_data/DarkPhoton/Crab_2.txt\")\n",
    "#     plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.9,lw=2)\n",
    "#     plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.9)\n",
    "    if text_on: \n",
    "        plt.text(0.5e-6*(1-0.02),3e-1*(1+0.07),r'{\\bf Crab}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.5e-6,3e-1,r'{\\bf Crab}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    \n",
    "        plt.text(0.8e-6*(1-0.02),0.9e-1*(1+0.07),r'{\\bf nebula}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.8e-6,0.9e-1,r'{\\bf nebula}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    \n",
    "    return\n",
    "\n",
    "\n",
    "def SHUKET(ax,col='maroon',fs=13,text_on=True,edge_on=False,lw=0.8):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SHUKET.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)*sqrt(1/3/0.038)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.2)\n",
    "    if edge_on:\n",
    "        plt.plot(dat[:,0],dat[:,1],'k-',lw=lw,zorder=0.2)\n",
    "    if text_on: \n",
    "        plt.text(3.5e-5,0.13e-12,r'{\\bf SHUKET}',fontsize=fs,color=col,rotation=-90,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def DarkEfield(ax,col='darkred',fs=17,text_on=True,edge_on=False,lw=0.8):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/DarkEfield.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(1.64/5) # convert from 5 sigma CL to 95%\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)*sqrt(1/3/0.129)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.2)\n",
    "    if edge_on:\n",
    "        plt.plot(dat[:,0],dat[:,1],'k-',lw=lw,zorder=0.2)\n",
    "    if text_on: \n",
    "        plt.text(0.8e-7/1.2,0.2e-12,r'{\\bf Dark}',fontsize=fs,color=col,rotation=90,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(2e-7/1.2,0.2e-12,r'{\\bf E-field}',fontsize=fs,color=col,rotation=90,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def WISPDMX(ax,col='crimson',fs=12,text_on=True,edge_on=False,lw=0.8):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/WISPDMX.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.3/0.45)*sqrt(1/3/0.23)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.201)\n",
    "    if edge_on:\n",
    "        plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=0.201,lw=lw)\n",
    "\n",
    "    if text_on: \n",
    "        plt.text(9e-7,4.1e-12/1.2,r'{\\bf WISP}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(9e-7,1.8e-12/1.2,r'{\\bf DMX}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "\n",
    "    return\n",
    "\n",
    "def SQuAD(ax,col=[0.7,0,0],fs=12,text_on=True,lw=0.5,point_on=False,ms=10):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SQuAD.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(0.4/0.45)*sqrt(1/3/0.019)\n",
    "    plt.plot([dat[0,0],dat[0,0]],[y2,dat[0,1]],lw=lw,color=col,alpha=1,zorder=0.2)\n",
    "    if point_on:\n",
    "        plt.plot(dat[0,0],dat[0,1],'o',mfc=col,mec='k',mew=lw+1,zorder=0.2,markersize=ms)\n",
    "    if text_on: \n",
    "        plt.text(36e-6,0.25e-14,r'{\\bf SQuAD}',fontsize=fs,color=col,rotation=-90,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def DMPathfinder(ax,col='pink',fs=13,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/DM-Pathfinder.txt\")\n",
    "    dat[:,1] = dat[:,1]*sqrt(1/0.075)\n",
    "    plt.plot([dat[0,0],dat[0,0]],[y2,dat[0,1]],lw=2,color=col,alpha=1,zorder=0.6)\n",
    "    if text_on: \n",
    "        plt.text(2.1e-9,0.5e-8/1.9,r'{\\bf DM}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(2.1e-9,0.2e-8/1.9,r'{\\bf Pathfinder}',fontsize=fs,color=col,rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "\n",
    "    return\n",
    "\n",
    "def DarkMatter(ax,Witte_col='royalblue',Caputo_col='dodgerblue',Arias_col='navy',fs=20,projection=True,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    zo = 0.3\n",
    "    \n",
    "    # Combined limits\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/DM_combined.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],'-',color='w',alpha=1,zorder=zo+0.1,lw=2.5,path_effects=pek)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor='lightgray',zorder=zo,alpha=1.0)\n",
    "    plt.plot([1e-16,dat[0,0]],[dat[0,1],dat[0,1]],'--',color='w',alpha=1,zorder=zo+0.1,lw=2.5,path_effects=pek)\n",
    "    plt.fill_between([1e-16,dat[0,0]],[dat[0,1],dat[0,1]],y2=y2,edgecolor=None,facecolor='lightgray',zorder=zo+0.1,alpha=1.0)\n",
    "    plt.plot(dat[40:,0],dat[40:,1],'--',color='w',alpha=1,lw=2.5,zorder=1000,solid_capstyle='round')\n",
    "    \n",
    "    # Individual limits\n",
    "    dat2 = loadtxt(\"limit_data/DarkPhoton/Cosmology_Witte_inhomogeneous.txt\")\n",
    "    dat4 = loadtxt(\"limit_data/DarkPhoton/Cosmology_Caputo_HeII.txt\",delimiter=',')\n",
    "    dat5 = loadtxt(\"limit_data/DarkPhoton/Cosmology_Arias.txt\")\n",
    "    \n",
    "    plt.fill_between(dat2[:,0],dat2[:,1],y2=y2,edgecolor='k',facecolor=Witte_col,zorder=0.305,alpha=0.8)\n",
    "    plt.fill_between(dat4[:,0],dat4[:,1],y2=y2,edgecolor='k',facecolor=Caputo_col,zorder=0.305,alpha=0.8)\n",
    "    plt.fill_between(dat5[:,0],dat5[:,1],y2=y2,edgecolor='k',facecolor=Arias_col,zorder=0.306,alpha=1)\n",
    "\n",
    "    if text_on: \n",
    "        plt.gcf().text(0.295,0.42-0.04,r'{\\bf DPDM} HeII',fontsize=15,color='w',ha='center')\n",
    "        plt.gcf().text(0.295,0.4-0.04,r'Reionisation',fontsize=15,color='w',ha='center')\n",
    "        plt.gcf().text(0.295,0.38-0.04,r'(Caputo et al.)',fontsize=13,color='w',ha='center')\n",
    "\n",
    "        plt.gcf().text(0.365,0.37,r'{\\bf DPDM}',fontsize=17,color='w',ha='center')\n",
    "        plt.gcf().text(0.365,0.35,r'(Witte et al.)',fontsize=13,color='w',ha='center')\n",
    "\n",
    "        plt.gcf().text(0.49,0.48,r'{\\bf DPDM}',fontsize=18,color='w',ha='center')\n",
    "        plt.gcf().text(0.49,0.46,r'(Arias et al.)',fontsize=16,color='w',ha='center')\n",
    "\n",
    "    return\n",
    "    \n",
    "def COBEFIRAS(ax,col=[0.1,0.2,0.5],text_on=True):\n",
    "    y2 = ax.get_ylim()[1]   \n",
    "    dat3 = loadtxt(\"limit_data/DarkPhoton/COBEFIRAS.txt\",delimiter=',')\n",
    "    plt.fill_between(dat3[:,0],dat3[:,1],y2=y2,edgecolor='k',facecolor=col,zorder=0.5,alpha=1)\n",
    "    if text_on: \n",
    "        plt.gcf().text(0.29,0.70,r'{\\bf COBE/FIRAS}',fontsize=22,color='w',ha='center')\n",
    "        plt.gcf().text(0.29,0.67,r'$\\gamma \\rightarrow X$',fontsize=22,color='w',ha='center')\n",
    "    return\n",
    "\n",
    "\n",
    "def LSW(ax,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SPring-8.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.1001,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.45, 0.05, 0.1],zorder=1.1001)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/ALPS.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.091,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.55, 0.0, 0.16],zorder=1.091)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/LSW_UWA.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.09,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.6, 0.0, 0.2],zorder=1.09)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/LSW_ADMX.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.089,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.65, 0.1, 0.24],zorder=1.089)\n",
    "    \n",
    "#     dat = loadtxt(\"limit_data/DarkPhoton/LSW_CERN.txt\")\n",
    "#     plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.089,lw=2)\n",
    "#     plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.65, 0.15, 0.2],zorder=1.089)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/CROWS.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.08,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.7, 0.2, 0.2],zorder=1.08)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(0.4e-6,0.15e-3,r'{\\bf LSW-ADMX}',fontsize=17,color='w',rotation=-58,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1e-5,5e-5,r'{\\bf LSW-UWA}',fontsize=14,color='w',rotation=-56,rotation_mode='anchor',ha='center',va='center')\n",
    "        \n",
    "        plt.text(0.55e0*(1-0.02),0.9e-4*(1+0.08),r'{\\bf LSW-SPring-8}',fontsize=13,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.55e0,0.9e-4,r'{\\bf LSW-SPring-8}',fontsize=13,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "\n",
    "        \n",
    "        plt.text(1.2e-4*(1-0.02),0.9e-5*(1+0.08),r'{\\bf ALPS}',fontsize=25,color='k',rotation=-56,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1.2e-4,0.9e-5,r'{\\bf ALPS}',fontsize=25,color='w',rotation=-56,rotation_mode='anchor',ha='center',va='center')\n",
    "\n",
    "        plt.text(0.75e-7*(1-0.01),9.9e-5*(1+0.05),r'{\\bf CROWS}',fontsize=24,color='k',rotation=-56,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.75e-7,9.9e-5,r'{\\bf CROWS}',fontsize=24,color='w',rotation=-56,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def Coulomb(ax,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Cavendish.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.07,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.7,0,0],zorder=1.07)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/PlimptonLawton.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.071,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor='crimson',zorder=1.071)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/Spectroscopy.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.11,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.4, 0.0, 0.13],zorder=1.11)\n",
    "    \n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/AFM.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.5,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=[0.4, 0.2, 0.2],zorder=1.5)\n",
    "    if text_on: \n",
    "        plt.text(2.5e-10*(1-0.02),0.35e-1*(1+0.08),r'{\\bf Plimpton-Lawton}',fontsize=15,color='k',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(2.5e-10,0.35e-1,r'{\\bf Plimpton-Lawton}',fontsize=15,color='w',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        \n",
    "        plt.text(3e1*(1-0.02),3e-1*(1+0.08),r'{\\bf AFM}',fontsize=20,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(3e1,3e-1,r'{\\bf AFM}',fontsize=20,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "\n",
    "        plt.text(0.5e-8*(1-0.02),4e-6*(1+0.08),r'{\\bf Cavendish-Coulomb}',fontsize=23,color='k',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.5e-8,4e-6,r'{\\bf Cavendish-Coulomb}',fontsize=23,color='w',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        \n",
    "        plt.text(0.2e2*(1-0.01),1e-3*(1+0.08),r'{\\bf Spectroscopy}',fontsize=23,color='k',rotation=-34,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.2e2,1e-3,r'{\\bf Spectroscopy}',fontsize=23,color='w',rotation=-34,rotation_mode='anchor',ha='center',va='center')\n",
    "    \n",
    "    return\n",
    "\n",
    "def NeutronStarCooling(ax,col='#004d00',fs=18,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/NeutronStarCooling.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.1001,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.1001)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(0.9e4*(1-0.03),0.4e-6*(1+0.05),r'{\\bf Neutron stars}',fontsize=fs,color='k',rotation=-43,rotation_mode='anchor',ha='center',va='center')    \n",
    "        plt.text(0.9e4,0.4e-6,r'{\\bf Neutron stars}',fontsize=fs,color='w',rotation=-43,rotation_mode='anchor',ha='center',va='center')    \n",
    "    return\n",
    "\n",
    "def CAST(ax,col='maroon',fs=27,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/CAST.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.1,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.1)\n",
    "    if text_on: \n",
    "        plt.text(4e-3*(1-0.01),0.8e-6*(1+0.08),r'{\\bf CAST}',fontsize=fs,color='k',rotation=-59,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(4e-3,0.8e-6,r'{\\bf CAST}',fontsize=fs,color='w',rotation=-59,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def SHIPS(ax,col='indianred',fs=20,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SHIPS.txt\")\n",
    "    dat[:,1] = dat[:,1]/dat[:,0]\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.09,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.09)\n",
    "    if text_on: \n",
    "        plt.text(0.6e-1*(1-0.05),0.08e-8*(1+0.1),r'{\\bf SHIPS}',fontsize=fs,color='k',rotation=-32,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.6e-1,0.08e-8,r'{\\bf SHIPS}',fontsize=fs,color='w',rotation=-32,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def TEXONO(ax,col=[0.5, 0.0, 0.13],fs=15,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/TEXONO.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1.101,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1.101)\n",
    "    if text_on: \n",
    "        plt.text(0.25e2*(1-0.01),0.1e-4*(1+0.08),r'{\\bf TEXONO}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.25e2,0.1e-4,r'{\\bf TEXONO}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def IGM(ax,col='seagreen',fs=18,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/IGM.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=0.5,zorder=0.49,lw=2)\n",
    "\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.49)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(4e-12*(1-0.05),0.03e-7*(1+0.07),r'{\\bf IGM}',fontsize=fs,color='k',rotation=-39,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(4e-12,0.03e-7,r'{\\bf IGM}',fontsize=fs,color='w',rotation=-39,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.gcf().text(0.233*(1-0.005),0.565*(1+0.003),r'{\\bf DPDM heating}',color='k',fontsize=23)\n",
    "        plt.gcf().text(0.233,0.565,r'{\\bf DPDM heating}',color='w',fontsize=23)\n",
    "\n",
    "    return\n",
    "\n",
    "def LeoT(ax,col='mediumseagreen',fs=18,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/LeoT.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=0.48,zorder=0.3061,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.3061)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(7e-13*(1-0.05),0.2e-9*(1+0.07),r'{\\bf Leo T}',fontsize=fs,color='k',rotation=-39,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(7e-13,0.2e-9,r'{\\bf Leo T}',fontsize=fs,color='w',rotation=-39,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def GasClouds(ax,col='#00cc66',fs=18,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/GasClouds.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=0.48,zorder=0.306,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=0.306)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(0.86e-13*(1-0.07),1e-10*(1+0.07),r'{\\bf Gas clouds}',fontsize=fs,color='k',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(0.86e-13,1e-10,r'{\\bf Gas clouds}',fontsize=fs,color='w',rotation=-38,rotation_mode='anchor',ha='center',va='center')\n",
    "    return\n",
    "\n",
    "def SuperMAG(ax,col='#b5403e',fs=18,text_on=True):\n",
    "    y2 = ax.get_ylim()[1]\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/SuperMAG.txt\")\n",
    "    plt.plot(dat[:,0],dat[:,1],color='k',alpha=1,zorder=1,lw=2)\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=1)\n",
    "    \n",
    "    if text_on: \n",
    "        plt.text(1.5e-17*(1-0.05),1e-1*(1+0.05)/1.4,r'{\\bf Super}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1.5e-17,1e-1/1.4,r'{\\bf Super}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1.5e-17*(1-0.05),0.2e-1*(1+0.05)/1.4,r'{\\bf MAG}',fontsize=fs,color='k',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "        plt.text(1.5e-17,0.2e-1/1.4,r'{\\bf MAG}',fontsize=fs,color='w',rotation=0,rotation_mode='anchor',ha='center',va='center')\n",
    "    \n",
    "    return\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/c5/ms4qz_795ll784bwyy3sqj3m0000gn/T/ipykernel_12819/2148017572.py:31: MatplotlibDeprecationWarning: Support for setting an rcParam that expects a str value to a non-str value is deprecated since 3.5 and support will be removed two minor releases later.\n",
      "  mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{mathpazo}']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1188x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = FigSetup(m_min=1e-10,m_max=10.0,chi_max=3e-9,tick_rotation=0)\n",
    "y2 = 1\n",
    "\n",
    "def PlotLimSimple(filename,col,zo,alph,delimiter='\\t',rescale_m=False):\n",
    "    dat = loadtxt(\"limit_data/DarkPhoton/\"+filename+'.txt',delimiter=delimiter)\n",
    "    if rescale_m:\n",
    "        dat[:,1] = dat[:,1]/dat[:,0]\n",
    "    plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col,zorder=zo,alpha=alph)\n",
    "    return\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/DP_Combined_Stellar.txt')\n",
    "plt.plot(dat[:,0],dat[:,1],'-',color='w',alpha=1,zorder=-10,lw=2.5,path_effects=pek)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col_alpha('darkgreen',0.5),zorder=-10)\n",
    "\n",
    "plt.plot([1e-13,1e10],[1e-13,1e-13],'-',color='w',alpha=1,zorder=-10,lw=2.5,path_effects=pek)\n",
    "plt.fill_between([1e-13,1e10],[1e-13,1e-13],y2=y2,edgecolor=None,facecolor='green',alpha=0.1,zorder=-10)\n",
    "\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/DM_combined.txt')\n",
    "zo =0.3\n",
    "plt.plot(dat[:,0],dat[:,1],'-',color='w',alpha=1,zorder=1,lw=2.5,path_effects=pek)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor=col_alpha('steelblue',0.5),zorder=1)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',color='w',alpha=0.2,zorder=1,lw=2.5)\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/DP_Combined_DarkMatterSearches.txt')\n",
    "plt.plot(dat[:,0],dat[:,1],'-',color='k',alpha=1,zorder=-1,lw=1.5)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor='crimson',zorder=-1)\n",
    "\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/DP_Combined_AxionHaloscopes.txt')\n",
    "plt.plot(dat[:,0],dat[:,1],'-',color='k',alpha=1,zorder=-1,lw=1.5)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,edgecolor=None,facecolor='purple',zorder=-1)\n",
    "\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/Projections/ALPHA.txt')\n",
    "chi_a = dat[:,1]*sqrt(1/3/0.0036)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',lw=2,color='Brown',zorder=-1)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,color='Brown',alpha=0.1,zorder=-100,lw=2.5)\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/Projections/MADMAX.txt')\n",
    "chi_a = dat[:,1]*sqrt(2/3/0.13)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',lw=2,color='Brown',zorder=-1)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,color='Brown',alpha=0.1,zorder=-100,lw=2.5)\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/Projections/SuperCDMS.txt')\n",
    "dat[:,1] = dat[:,1]*sqrt(0.3/0.45)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',lw=2,color='Brown',zorder=-1)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,color='Brown',alpha=0.05,zorder=-100,lw=2.5)\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/Projections/DMRadio.txt')\n",
    "dat[:,1] = dat[:,1]/sqrt(1/3) # <--- need to correct for the fact they don't account for the polarisation to begin with\n",
    "chi_a = dat[:,1]*sqrt(1/3/0.0036)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',lw=2,color='Brown',zorder=-1)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,color='Brown',alpha=0.05,zorder=-100,lw=2.5)\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/Projections/DarkEfield.txt')\n",
    "dat[:,1] = dat[:,1]*sqrt(1.64/5) # convert from 5 sigma CL to 95%\n",
    "chi_a = dat[:,1]*sqrt(1/3/0.0036)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',lw=2,color='Brown',zorder=-1)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,color='Brown',alpha=0.05,zorder=-100,lw=2.5)\n",
    "\n",
    "\n",
    "dat = loadtxt('limit_data/DarkPhoton/Projections/BREAD.txt')\n",
    "chi_a = dat[:,1]*sqrt(2/3/0.13)\n",
    "plt.plot(dat[:,0],dat[:,1],'--',lw=2,color='Brown',zorder=-1)\n",
    "plt.fill_between(dat[:,0],dat[:,1],y2=y2,color='Brown',alpha=0.05,zorder=-100,lw=2.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "MySaveFig(fig,'DarkPhotonBounds_Snowmass')"
   ]
  }
 ],
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