diff --git a/notebooks/interactive_triangle_shapes.ipynb b/notebooks/interactive_triangle_shapes.ipynb
index 56c1116..e0f5bd3 100644
--- a/notebooks/interactive_triangle_shapes.ipynb
+++ b/notebooks/interactive_triangle_shapes.ipynb
@@ -4,14 +4,306 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "from ipywidgets import interact, interactive, fixed\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import palettable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "colors = palettable.colorbrewer.qualitative.Paired_10.hex_colors\n",
+    "print len(colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def drawTriangleScatter(min_r,max_r,min_u,max_u,min_v,max_v,color=0,num=20):\n",
+    "    test = np.array(np.meshgrid(np.linspace(min_r,max_r,num=num),\n",
+    "               np.linspace(min_u,max_u,num=num),\n",
+    "               np.linspace(min_v,max_v,num=10*num),))\n",
+    "    d2 = test[0,:,:,:]\n",
+    "    d3 = test[0,:,:,:]*test[1,:,:,:]\n",
+    "    d1 = np.abs(test[2,:,:,:])*d3+d2 \n",
+    "    cosine = (d2**2 + d3**2 - d1**2)/(2*d2*d3+1e-9)\n",
+    "    sine = np.sqrt(1-cosine**2)\n",
+    "    points = d3*cosine,d3*sine\n",
+    "    plt.scatter(np.array(points).reshape(2,-1)[0,:],np.array(points).reshape(2,-1)[1,:],color=colors[color],alpha=.1,marker='.')\n",
+    "    #plt.plot([0,1],[0,0],color='k')\n",
+    "    #plt.plot([0,-.2,1],[0,.6,0],color='k',linestyle='--')\n",
+    "    plt.plot(1,0,color='r',marker='s')\n",
+    "    plt.xlim(-1.5,1.1)\n",
+    "    plt.ylim(-.2,1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OG/8Ip6+OCph72tSAG3/wvQ1UXzRcBaw6sJVdB8vsDsMSnVMJW+MPcEZBJ1KSErTxjwL6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\nSjmUJgCllHIoTQBKKeVQmgCUUsqh/j/uWqtxsKIFVAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f315f447790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTriangleScatter(.99,1.01,.3,1,0,.2, color=0,num=20)\n",
+    "drawTriangleScatter(.99,1.01,.3,1,.2,.4,color=1,num=20)\n",
+    "drawTriangleScatter(.99,1.01,.3,1,.4,.6,color=2,num=20)\n",
+    "drawTriangleScatter(.99,1.01,.3,1,.6,.8,color=3,num=20)\n",
+    "drawTriangleScatter(.99,1.01,.3,1,.8,1, color=4,num=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZytXWOaXKjmv6sDgw2FF0H0AhfcCjl35IO9AzBKFA6dfhmd8YZn5D+UffzK8N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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f315cfc0c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTriangleScatter(.8,1.2,.3,1,0,.2, color=0,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.2,.4,color=1,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.4,.6,color=2,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.6,.8,color=3,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.8,1, color=4,num=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f315cbb0890>]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ecQnrVr3PrRd9n98/vDktoZ/lXvh4dTnnTS9Ja9mHIqcK+zQb9c4PA/SjR+Ht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\nFCx/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f31602e3f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTriangleScatter(.8,1.2,.3,1,0,.2, color=0,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.2,.4,color=1,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.4,.6,color=2,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.6,.8,color=3,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.8,1, color=4,num=100)\n",
+    "plt.plot([.2,1],[0,0],color='k')\n",
+    "plt.plot([-.2,.2],[0,0],color='Grey',linestyle=':')\n",
+    "plt.plot([-.2,-.05,1],[0,.75,0],color='k',linestyle='--')\n",
+    "plt.plot([.2,-.05,1],[0,.75,0],color='k',linestyle='--')\n",
+    "plt.plot(0,0,marker='.',color='Grey')\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,.6,.8,color=3,num=20)\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,.4,.6,color=2,num=20)\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,.2,.4,color=1,num=20)\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,0,.2, color=0,num=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f315c1e3fd0>]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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y0pnJX0hsBvjsPqQtY+NPmMkBhdHLiwno6TRxLUlC1yoiAMlEtujkCNYdPXsOoLfPQbWY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\nHmIv8F2W2wEkU9D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PttDCEm1aScYylFA6XNT3C6Cuxhh3FbCWxmBe7fS0xivPPsXp51+M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\n3QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f315c2cedd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTriangleScatter(.8,1.2,.3,1,0,.2, color=0,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.2,.4,color=1,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.4,.6,color=2,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.6,.8,color=3,num=20)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.8,1, color=4,num=100)\n",
+    "plt.plot([0,1],[0,0],color='k')\n",
+    "plt.plot([0,-1,1],[0,.1,0],color='k')\n",
+    "plt.plot([0,.25,1],[0,.8,0],color='k')\n",
+    "plt.plot(0,0,marker='.',color='k')\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,.6,.8,color=3,num=20)\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,.4,.6,color=2,num=20)\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,.2,.4,color=1,num=20)\n",
+    "# drawTriangleScatter(.8,1.2,.3,1,0,.2, color=0,num=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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y5PrbO0oSXIDN1cC7L25IsfD8k3NW6w4XLM9enyHO8PYXN8zB0CwC03Zk2M+MQ8S1Ftd7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dcjIuKZoIDHQ5HN5FUlmUxTxUsEElAwG6aa8cEgpWRaCqX2zEug5p5UJrRSdLoinEaJDEKghSDk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\nlmNchB8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4adcdd8410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTriangleScatter(.8,1.2,.3,1,0,.2,color=0)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.2,.4,color=1)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.4,.6,color=2)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.6,.8,color=3)\n",
+    "drawTriangleScatter(.8,1.2,.3,1,.8,1,color=4)\n",
+    "drawTriangleScatter(1.6,2.4,.8,1,0,.2,color=5)\n",
+    "drawTriangleScatter(1.6,2.4,.8,1,.2,.4,color=6)\n",
+    "drawTriangleScatter(1.6,2.4,.8,1,.4,.6,color=7)\n",
+    "drawTriangleScatter(1.6,2.4,.8,1,.6,.8,color=8)\n",
+    "drawTriangleScatter(1.6,2.4,.8,1,.8,1,color=9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def drawTriangleBins(min_r,max_r,min_u,max_u,min_v,max_v):\n",
+    "    points = []\n",
+    "    def getPoint(r,u,v):\n",
+    "        d2 = float(r)\n",
+    "        d3 = u*r\n",
+    "        d1 = np.abs(v)*d3+d2 \n",
+    "        cosine = (d2**2 + d3**2 - d1**2)/(2*d2*d3+1e-9)\n",
+    "        sine = np.sqrt(1-cosine**2)\n",
+    "        points.append([d3*cosine,d3*sine])\n",
+    "    #getPoint(min_u,max_r,max_v)\n",
+    "    #getPoint(min_u,min_r,min_v)\n",
+    "    #getPoint(max_u,min_r,min_v)\n",
+    "    #getPoint(max_u,max_r,max_v)\n",
+    "    getPoint(min_u,min_r,min_v)\n",
+    "    getPoint(min_u,min_r,max_v)\n",
+    "    getPoint(min_u,max_r,min_v)\n",
+    "    getPoint(max_u,min_r,min_v)\n",
+    "    getPoint(min_u,max_r,max_v)\n",
+    "    getPoint(max_u,max_r,min_v)\n",
+    "    getPoint(max_u,min_r,max_v)\n",
+    "    getPoint(max_u,max_r,max_v)\n",
+    "    #print points\n",
+    "    p = plt.Polygon(points,closed=False)\n",
+    "    ax = plt.gca()\n",
+    "    ax.add_patch(p)\n",
+    "    plt.xlim(-1,1.1)\n",
+    "    plt.ylim(-.6,1.6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RMcqPK/TmeGsHteNLu0upm4e0mg0/DoY+fOGhDex561Sly6iYI83tnDshu8tl9Kd7SOuX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\nrRwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff63f206e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#drawTriangleBins(.8,1.2,.3,1,0,.2)\n",
+    "#drawTriangleBins(.8,1.2,.3,1,.2,.4)\n",
+    "#drawTriangleBins(.8,1.2,.3,1,.4,.6)\n",
+    "#drawTriangleBins(.8,1.2,.3,1,.6,.8)\n",
+    "drawTriangleBins(.99,1.01,.3,1,.8,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kfO5CSVkFf/lkO/e8ucHtjm1X4/j3Tf24tl9br19bhRZNDMoruibGsmDa0BoL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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff63ed340d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTriangleBins(.5,1.5,.3,1,0,.2)\n",
+    "drawTriangleBins(.5,1.5,.3,1,.2,.4)\n",
+    "drawTriangleBins(.5,1.5,.3,1,.4,.6)\n",
+    "drawTriangleBins(.5,1.5,.3,1,.6,.8)\n",
+    "drawTriangleBins(.5,1.5,.3,1,.8,1)"
    ]
   },
   {
@@ -47,9 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -118,9 +408,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -142,7 +430,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -178,9 +466,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -200,9 +486,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -237,9 +521,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -260,7 +542,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -270,9 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -295,9 +575,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -316,9 +594,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -338,9 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -360,9 +634,7 @@
   {
    "cell_type": "code",
    "execution_count": 165,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -385,9 +657,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -407,9 +677,7 @@
   {
    "cell_type": "code",
    "execution_count": 131,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -432,9 +700,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -457,9 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -506,5 +770,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/notebooks/jackknife_regions.ipynb b/notebooks/jackknife_regions.ipynb
index b599fc8..ef7fbce 100644
--- a/notebooks/jackknife_regions.ipynb
+++ b/notebooks/jackknife_regions.ipynb
@@ -40,11 +40,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data = fits.getdata('/nfs/slac/g/ki/ki19/des/mbaumer/3pt_data/jackknife_randoms5x/redmagic_data.fits')\n",
     "randoms = fits.getdata('/nfs/slac/g/ki/ki19/des/mbaumer/3pt_data/jackknife_randoms5x/redmagic_randoms_0.fits')"
@@ -60,9 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = data[data['DEC'] < -35]\n",
@@ -72,9 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -108,9 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -139,9 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -168,9 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -193,9 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = zip(spt['RA'],spt['DEC'])\n",
@@ -206,9 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
@@ -241,9 +234,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -272,9 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -301,9 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.neighbors import KNeighborsRegressor\n",
@@ -315,9 +302,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -341,9 +326,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -363,9 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -425,5 +406,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/tricorder/make_figures.py b/tricorder/make_figures.py
new file mode 100644
index 0000000..c5f2c02
--- /dev/null
+++ b/tricorder/make_figures.py
@@ -0,0 +1,575 @@
+#### Make figures for paper (in eps/pdf format)
+from __future__ import division
+import numpy as np
+
+import matplotlib
+matplotlib.use('agg')
+
+import matplotlib.pyplot as plt
+import chainconsumer
+from scipy.optimize import minimize
+import plottools
+reload(plottools)
+import palettable
+from matplotlib.colors import LogNorm
+
+from astropy.io import fits
+import paths
+import skymapper as skm
+
+matplotlib.rcParams.update({'font.size': 14})
+matplotlib.rc('xtick', labelsize=12) 
+matplotlib.rc('ytick', labelsize=12) 
+matplotlib.rc('font', family='serif')
+
+def make_easy_figures():
+    ## 
+    ##Footprint of simulated gals
+    ##
+    HD = fits.getdata(paths.rm_y1[0])
+    HL = fits.getdata(paths.rm_y1_HL[0])
+    HHL = fits.getdata(paths.rm_y1_HHL[0])
+
+    slice1 = HD[((HD['ZREDMAGIC'] > .15) & (HD['ZREDMAGIC'] < .3))]
+    slice2 = HD[((HD['ZREDMAGIC'] > .3) & (HD['ZREDMAGIC'] < .45))]
+    slice3 = HD[((HD['ZREDMAGIC'] > .45) & (HD['ZREDMAGIC'] < .6))]
+    slice4 = HL[((HL['ZREDMAGIC'] > .6) & (HL['ZREDMAGIC'] < .75))]
+    slice5 = HHL[((HHL['ZREDMAGIC'] > .75) & (HHL['ZREDMAGIC'] < .9))]
+
+    sample = np.concatenate([slice1,slice2,slice3,slice4,slice5])
+    skm.plotDensity(sample['RA'],sample['DEC'],sep=10)
+
+    plt.savefig('./figures/skymap.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/skymap.png',dpi=300,bbox_inches='tight')
+
+    plt.figure()
+    
+    ##
+    ## Redmagic n(z) histogram
+    ##
+    plt.hist(slice1['ZSPEC'],histtype='step',bins=100,range=(0,1))
+    plt.hist(slice2['ZSPEC'],histtype='step',bins=100,range=(0,1))
+    plt.hist(slice3['ZSPEC'],histtype='step',bins=100,range=(0,1))
+    plt.hist(slice4['ZSPEC'],histtype='step',bins=100,range=(0,1))
+    plt.hist(slice5['ZSPEC'],histtype='step',bins=100,range=(0,1));
+    plt.axvspan(.15,.3,alpha=.2,color='b')
+    plt.axvspan(.3,.45,alpha=.2,color='orange')
+    plt.axvspan(.45,.6,alpha=.2,color='g')
+    plt.axvspan(.6,.75,alpha=.2,color='r')
+    plt.axvspan(.75,.9,alpha=.2,color='violet')
+    plt.xlabel('True Redshift')
+    plt.ylabel('N(z)')
+
+    plt.savefig('./figures/tomobins.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/tomobins.png',dpi=300,bbox_inches='tight')
+
+    plt.figure()
+
+    ##
+    ## Redmagic errors vs redshift
+    ##
+
+    plt.hist2d(sample['ZREDMAGIC'],sample['ZREDMAGIC_E'],bins=100,norm=LogNorm());
+    plt.vlines([.3,.45,.6,.75],0.007,0.1,linestyle='--',color='r')
+    plt.ylim(0.007,0.1);
+    plt.xlabel(r'$z_{RM}$')
+    plt.ylabel(r'$\sigma(z_{RM})$')
+
+    plt.savefig('./figures/redmagic_errors.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/redmagic_errors.png',dpi=300,bbox_inches='tight')
+
+def make_triangle_diagrams():
+    colors = palettable.colorbrewer.qualitative.Paired_5.hex_colors
+    def drawTriangleScatter(min_r,max_r,min_u,max_u,min_v,max_v,color=0,num=20):
+        test = np.array(np.meshgrid(np.linspace(min_r,max_r,num=num),
+                np.linspace(min_u,max_u,num=num),
+                np.linspace(min_v,max_v,num=10*num),))
+        d2 = test[0,:,:,:]
+        d3 = test[0,:,:,:]*test[1,:,:,:]
+        d1 = np.abs(test[2,:,:,:])*d3+d2 
+        cosine = (d2**2 + d3**2 - d1**2)/(2*d2*d3+1e-9)
+        sine = np.sqrt(1-cosine**2)
+        points = d3*cosine,d3*sine
+        plt.scatter(np.array(points).reshape(2,-1)[0,:],np.array(points).reshape(2,-1)[1,:],color=colors[color],alpha=.1,marker='.')
+        plt.plot([0,20],[0,0],color='k')
+        plt.plot([0,-4,20],[0,12,0],color='k',linestyle='--')
+        plt.plot(20,0,color='r',marker='s')
+        plt.xlim(-30,25)
+        plt.ylim(-5,25)
+
+    drawTriangleScatter(19.9,20.1,.3,1,0,.2, color=0,num=50)
+    drawTriangleScatter(19.9,20.1,.3,1,.2,.4,color=1,num=50)
+    drawTriangleScatter(19.9,20.1,.3,1,.4,.6,color=2,num=50)
+    drawTriangleScatter(19.9,20.1,.3,1,.6,.8,color=3,num=50)
+    drawTriangleScatter(19.9,20.1,.3,1,.8,1, color=4,num=100)
+    plt.xlabel('RA (arcmin)')
+    plt.ylabel('DEC (arcmin)')   
+
+    plt.savefig('./figures/perfect_binning.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/perfect_binning.png',dpi=300,bbox_inches='tight')
+
+    plt.figure() 
+
+    drawTriangleScatter(16,24,.3,1,0,.2, color=0,num=50)
+    drawTriangleScatter(16,24,.3,1,.2,.4,color=1,num=50)
+    drawTriangleScatter(16,24,.3,1,.4,.6,color=2,num=50)
+    drawTriangleScatter(16,24,.3,1,.6,.8,color=3,num=50)
+    drawTriangleScatter(16,24,.3,1,.8,1, color=4,num=120)    
+
+    plt.savefig('./figures/tolerance_20.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/tolerance_20.png',dpi=300,bbox_inches='tight')
+
+
+def make_covariance_fig():
+
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+
+    zmin = .45
+    zmax = zmin+.15
+    data = plottools.load_res_indep(path, 'dm','newpaper13.1','ZSPEC',zmin,zmax,'12x20',sigma=0)
+    data2 = plottools.load_res_indep(path,'dm','newpaper13.1','ZREDMAGIC',zmin,zmax,'12x20',sigma=0)
+    galdata = plottools.load_res_indep(path, 'newbuzzardrm2','newpaper13.1','ZSPEC',zmin,zmax,'20',sigma=0)
+    galdata2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1','ZREDMAGIC',zmin,zmax,'20',sigma=0)
+    plt.figure()
+    fig,axarr = plt.subplots(2,2,figsize=(6,6))
+    
+    vmin= 0
+    vmax= 1
+    
+    axarr[0,0].imshow(np.corrcoef(plottools.compress_dv(galdata['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1))
+    axarr[0,0].set_title(r'Galaxies: True n(z)')
+    axarr[0,0].set_xticks([.1,.3,.5,.7,.9])
+    axarr[0,0].set_yticks([.1,.3,.5,.7,.9])
+    #axarr[0,0].set_xlabel('v')
+    axarr[0,0].set_ylabel('v')
+    axarr[0,1].imshow(np.corrcoef(plottools.compress_dv(galdata2['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1))
+    axarr[0,1].set_title(r'Galaxies: RedMaGiC n(z)')
+    #axarr[0,1].set_xlabel('v')
+    #axarr[0,1].set_ylabel('v')
+    axarr[0,1].set_xticks([.1,.3,.5,.7,.9])
+    axarr[0,1].set_yticks([.1,.3,.5,.7,.9])
+    axarr[1,0].imshow(np.corrcoef(plottools.compress_dv(data['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1))
+    axarr[1,0].set_title(r'DM: True n(z)')
+    axarr[1,0].set_xticks([.1,.3,.5,.7,.9])
+    axarr[1,0].set_yticks([.1,.3,.5,.7,.9])
+    axarr[1,0].set_xlabel('v')
+    axarr[1,0].set_ylabel('v')
+    im = axarr[1,1].imshow(np.corrcoef(plottools.compress_dv(data2['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1))
+    axarr[1,1].set_title(r'DM: RedMaGiC n(z)')
+    axarr[1,1].set_xlabel('v')
+    #axarr[1,1].set_ylabel('v')
+    axarr[1,1].set_xticks([.1,.3,.5,.7,.9])
+    axarr[1,1].set_yticks([.1,.3,.5,.7,.9])
+    
+    #fig.colorbar(im, ax=axarr.ravel().tolist(),shrink=.4,aspect=10)
+    #plt.suptitle(str(zmin)+r'$<z<$'+str(zmax))
+    plt.tight_layout()
+    cbar_ax = fig.add_axes([.97, 0.2, .04, 0.6])
+    fig.colorbar(im, cax=cbar_ax, orientation="vertical")
+
+    plt.savefig('./figures/corrmat_examples.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/corrmat_examples.png',dpi=300,bbox_inches='tight') 
+
+def make_data_vector_plots():
+
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+
+    for i,zmin in enumerate([.15, .3, .45, .6]):
+        zmax = zmin+.15
+        data =  plottools.load_res_indep(path,'dm','newpaper13.1','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        galdata = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1','ZSPEC',zmin,zmax,'20',sigma=0)
+        plt.figure()
+        plottools.plot_dv(data,'Q', offset=0.,indiv_runs=True,color='Blue',label=r'Dark Matter',compressed=True)
+        plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=True,color='Red',label='Galaxies',compressed=True)
+        plt.xlabel('v')
+        plt.ylabel('Q')
+        plt.title(str(zmin)+r'$<z<$'+str(zmax))
+        plt.legend() 
+
+        plt.savefig('./figures/spec_bin'+str(i+1)+'_dv.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/spec_bin'+str(i+1)+'_dv.png',dpi=300,bbox_inches='tight')
+        plt.figure()
+
+
+def get_zspec(is11k=False):
+    summaries = []
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        if zmin != .45:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/tolerance_syst/'
+            config_fname = 'newpaper14.1'
+        else:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+            config_fname = 'newpaper13.1'
+        config = plottools.load_config(config_fname)
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        if ((zmin == .15) | (zmin == .45)):
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0)
+        else:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'True redshifts')
+        
+        summary = cc.analysis.get_summary()
+        summaries.append(summary)
+       
+        if ((zmin == .15) | (zmin == .45)):
+            xi1 = plottools.load_res_xi_indep(path,'dm',config_fname,zmin,zmax,'12x20')
+            xi2 = plottools.load_res_xi_indep(path,'newbuzzardrm2',config_fname,zmin,zmax,'20')
+        else:
+            xi1 = plottools.load_res_xi_indep(path,'dm',config_fname,zmin,zmax,'10x10')
+            xi2 = plottools.load_res_xi_indep(path,'newbuzzardrm2',config_fname,zmin,zmax,'10')
+        #b1 = np.sqrt(np.mean(xi2,axis=0)/np.mean(xi1,axis=0))
+        b1 = np.sqrt(xi2/np.mean(xi1,axis=0))
+        r = np.logspace(config['2PCF']['min_sep'],np.log10(config['2PCF']['max_sep']),num=config['2PCF']['nbins'])
+        b1_mean = np.mean(b1[:,(r > config['3PCF']['min_sep']*config['3PCF']['min_u']) & (r < config['3PCF']['max_sep']*config['3PCF']['max_u'])])
+        b1_std = np.std(b1[:,(r > config['3PCF']['min_sep']*config['3PCF']['min_u']) & (r < config['3PCF']['max_sep']*config['3PCF']['max_u'])])
+        
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=18,tick_font_size=14)
+
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        figure.axes[2].axvspan(b1_mean-b1_std, b1_mean+b1_std, alpha=0.3, color='b')
+        figure.axes[2].axvspan(b1_mean-2*b1_std, b1_mean+2*b1_std, alpha=0.3, color='b')
+
+        figure.axes[2].plot(np.linspace(0,3,100),plottools.get_lazeyras_schmidt_2015(np.linspace(0,3,100)),label='Lazeyras+Schmidt 2015')
+        figure.axes[2].plot(np.linspace(0,3,100),plottools.get_hoffman_2015(np.linspace(0,3,100)),label='Hoffman 2015')
+
+        if is11k:
+            str11k = '_11k'
+        else:
+            str11k = ''
+ 
+        axarr = figure.get_axes()
+        #plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+        plt.legend()
+        plt.savefig('./figures/spec_bin'+str(i+1)+str11k+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/spec_bin'+str(i+1)+str11k+'.png',dpi=300,bbox_inches='tight')
+
+def get_zspec_both():
+    summaries = []
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        if zmin != .45:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/tolerance_syst/'
+            config_fname = 'newpaper14.1'
+        else:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+            config_fname = 'newpaper13.1'
+        config = plottools.load_config(config_fname)
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        if ((zmin == .15) | (zmin == .45)):
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0)
+        else:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=False)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'DES Y1-like',color='b')
+        samples2 = plottools.make_inference(red_qdm,red_qrm,is11k=True)
+        cc.add_chain(samples2.flatchain,parameters=['b1','b2'],name=r'12,000 sq. deg. survey', color='k')
+        
+        summary = cc.analysis.get_summary()
+        summaries.append(summary)
+       
+        if ((zmin == .15) | (zmin == .45)):
+            xi1 = plottools.load_res_xi_indep(path,'dm',config_fname,zmin,zmax,'12x20')
+            xi2 = plottools.load_res_xi_indep(path,'newbuzzardrm2',config_fname,zmin,zmax,'20')
+        else:
+            xi1 = plottools.load_res_xi_indep(path,'dm',config_fname,zmin,zmax,'10x10')
+            xi2 = plottools.load_res_xi_indep(path,'newbuzzardrm2',config_fname,zmin,zmax,'10')
+        #b1 = np.sqrt(np.mean(xi2,axis=0)/np.mean(xi1,axis=0))
+        b1 = np.sqrt(xi2/np.mean(xi1,axis=0))
+        r = np.logspace(config['2PCF']['min_sep'],np.log10(config['2PCF']['max_sep']),num=config['2PCF']['nbins'])
+        b1_mean = np.mean(b1[:,(r > config['3PCF']['min_sep']*config['3PCF']['min_u']) & (r < config['3PCF']['max_sep']*config['3PCF']['max_u'])])
+        b1_std = np.std(b1[:,(r > config['3PCF']['min_sep']*config['3PCF']['min_u']) & (r < config['3PCF']['max_sep']*config['3PCF']['max_u'])])
+        
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        figure.axes[2].axvspan(b1_mean-b1_std, b1_mean+b1_std, alpha=0.3, color='b')
+        figure.axes[2].axvspan(b1_mean-2*b1_std, b1_mean+2*b1_std, alpha=0.3, color='b')
+
+        figure.axes[2].plot(np.linspace(0,3,100),plottools.get_lazeyras_schmidt_2015(np.linspace(0,3,100)),label='Lazeyras+Schmidt 2015')
+        figure.axes[2].plot(np.linspace(0,3,100),plottools.get_hoffman_2015(np.linspace(0,3,100)),label='Hoffman 2015')
+
+        axarr = figure.get_axes()
+        plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax),y=1)
+        plt.legend()
+        plt.savefig('./figures/both_bin'+str(i+1)+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/both_bin'+str(i+1)+'.png',dpi=300,bbox_inches='tight')
+
+def get_zrm_only(is11k=False):
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+    summaries = []
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        if zmin != .45:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/tolerance_syst/'
+            config_fname = 'newpaper14.1'
+        else:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+            config_fname = 'newpaper13.1'
+        config = plottools.load_config(config_fname)
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        if zmin != .45:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0)
+        else:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'12x20',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'redMaGiC redshifts')
+        
+        summary = cc.analysis.get_summary()
+        summaries.append(summary)
+       
+        if is11k:
+            str11k = '_11k'
+        else:
+            str11k = ''
+ 
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        axarr = figure.get_axes()
+        plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax),y=1)
+        plt.legend()
+        plt.savefig('./figures/rmonly_bin'+str(i+1)+str11k+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/rmonly_bin'+str(i+1)+str11k+'.png',dpi=300,bbox_inches='tight')
+
+def get_zspec_zrm(is11k=False):
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+    summaries = []
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        if zmin != .45:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/tolerance_syst/'
+            config_fname = 'newpaper14.1'
+        else:
+            path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+            config_fname = 'newpaper13.1'
+        config = plottools.load_config(config_fname)
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        if ((zmin == .15) | (zmin == .45)):
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0)
+        else:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'True redshifts')
+        
+        if zmin != .45:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0)
+        else:
+            data1 = plottools.load_res_indep(path,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'12x20',sigma=0)
+            data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'redMaGiC redshifts')
+        
+        summary = cc.analysis.get_summary()
+        summaries.append(summary)
+       
+        if is11k:
+            str11k = '_11k'
+        else:
+            str11k = ''
+ 
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        axarr = figure.get_axes()
+        plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax),y=1)
+        plt.legend()
+        plt.savefig('./figures/rm_bin'+str(i+1)+str11k+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/rm_bin'+str(i+1)+str11k+'.png',dpi=300,bbox_inches='tight')
+
+def get_tolerance_figs(is11k=False):
+    oldpath = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/tolerance_syst/'
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        #config_fname = 'newpaper13.1'
+        #config = plottools.load_config(config_fname)
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        
+        data1 = plottools.load_res_indep(oldpath,'dm','newpaper13.1','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(oldpath,'newbuzzardrm2','newpaper13.1','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance $= 20\%$')
+        
+        data1 = plottools.load_res_indep(path,'dm','newpaper13.1_up','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1_up','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance $= 25\%$')
+        
+        data1 = plottools.load_res_indep(path,'dm','newpaper13.1_down','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1_down','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance $= 15\%$')
+        
+        data1 = plottools.load_res_indep(path,'dm','newpaper13.1_down2','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1_down2','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance $= 10\%$')
+        
+        data1 = plottools.load_res_indep(path,'dm','newpaper13.1_up2','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1_up2','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance $= 30\%$')
+
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        axarr = figure.get_axes()
+        plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax),y=1)
+        plt.legend()
+
+        if is11k:
+            str11k = '_11k'
+        else:
+            str11k = ''
+
+        plt.savefig('./figures/tol_bin'+str(i+1)+str11k+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/tol_bin'+str(i+1)+str11k+'.png',dpi=300,bbox_inches='tight')
+
+def get_u_figs(is11k=False): 
+    oldpath = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/tolerance_syst/'   
+    #testing choice of u
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        config_fname = 'newpaper13.1'
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        
+        data1 = plottools.load_res_indep(oldpath,'dm','newpaper13.1','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(oldpath,'newbuzzardrm2','newpaper13.1','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$.3 < u < 1$')
+        
+        data1 = plottools.load_res_indep(path,'dm','newpaper13.1_newu','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1_newu','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$.6 < u < 1$')
+        
+        data1 = plottools.load_res_indep(path,'dm','newpaper13.1_newu2','ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2','newpaper13.1_newu2','ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$.9 < u < 1$')
+        
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        axarr = figure.get_axes()
+        plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax),y=1)
+        plt.legend()
+        
+        if is11k:
+            str11k = '_11k'
+        else:
+            str11k = ''
+
+        plt.savefig('./figures/u_bin'+str(i+1)+str11k+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/u_bin'+str(i+1)+str11k+'.png',dpi=300,bbox_inches='tight')
+
+
+def get_gaussian_photoz(is11k=False):
+    path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/photoz_syst/'
+    summaries = []
+    for i,zmin in enumerate([.15,.3,.45,.6]):
+        config_fname = 'newpaper13.1'
+        config = plottools.load_config(config_fname)
+        zmax = zmin+.15
+        cc = chainconsumer.ChainConsumer()
+        data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma_z = 0$')
+        
+        data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0.005)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0.005)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma_z = 0.005$')
+        
+        data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0.01)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0.01)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma_z = 0.01$')
+        
+        data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'12x20',sigma=0.02)
+        data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'20',sigma=0.02)
+        red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+        red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+        samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+        cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma_z = 0.02$')
+        
+        summary = cc.analysis.get_summary()
+        summaries.append(summary)
+        
+        cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+        figure = cc.plotter.plot(figsize='column',extents=[(0,3),(-3,3)]);
+        axarr = figure.get_axes()
+        plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax),y=1)
+        plt.legend()
+
+        if is11k:
+            str11k = '_11k'
+        else:
+            str11k = ''
+
+        plt.savefig('./figures/gauss_bin'+str(i+1)+str11k+'.pdf',dpi=300,bbox_inches='tight')
+        plt.savefig('./figures/gauss_bin'+str(i+1)+str11k+'.png',dpi=300,bbox_inches='tight')
+        
+
+if __name__ == '__main__':
+    # make_easy_figures()
+    # make_data_vector_plots()
+    # make_triangle_diagrams()
+    # make_covariance_fig()
+    # get_zspec_both()
+    # get_zspec()
+    # get_zspec(is11k=True)
+    #get_zrm_only()
+    #get_zrm_only(is11k=True)
+    # get_zspec(is11k=True)
+    get_zspec_zrm()
+    get_zspec_zrm(is11k=True)
+    # get_gaussian_photoz()
+    # get_gaussian_photoz(is11k=True)
+    # get_tolerance_figs()
+    # get_tolerance_figs(is11k=True)
+    # get_u_figs()
+    # get_u_figs(is11k=True)
diff --git a/tricorder/make_figures2.py b/tricorder/make_figures2.py
new file mode 100644
index 0000000..082713a
--- /dev/null
+++ b/tricorder/make_figures2.py
@@ -0,0 +1,888 @@
+#### Make figures for paper (in eps/pdf format)
+from __future__ import division
+import matplotlib
+matplotlib.use('agg')
+
+import numpy as np
+import chainconsumer
+from scipy.optimize import minimize
+import plottools
+reload(plottools)
+import palettable
+path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/paper_validation/'
+path2 = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/paper_validation2/'
+
+import matplotlib.pyplot as plt
+import chainconsumer
+from scipy.optimize import minimize
+from matplotlib.colors import LogNorm
+
+from astropy.io import fits
+import paths
+import skymapper as skm
+
+matplotlib.rcParams.update({'font.size': 14})
+matplotlib.rc('xtick', labelsize=12) 
+matplotlib.rc('ytick', labelsize=12) 
+matplotlib.rc('font', family='serif')
+
+# Covmats 3 cols, 4 rows: cols are ZSPEC, Gaussian z, and ZRM
+# 	Or maybe 4x4 since we might need spec gals, spec dm, rm gals, rmdm
+# Contours: zspec+zrm for DES Y1 are bad
+# But 11k good: Contours: zspec, zrm, 2pt, lazeyras
+# Scale
+# Tolerance
+# U min
+
+b1_2pt = [1.71,1.78,1.9,2.22]
+b1_err_2pt = [.064,.064,.071,.074]
+
+b1_spec = [1.51,1.71,1.71,2.1]
+b1_up_spec = [.21,.17,.18,.21]
+b1_down_spec = [.22,.15,.19,.19]
+
+b2_spec = [.21,.49,.35,.7]
+b2_up_spec = [.37,.29,.31,.5]
+b2_down_spec = [.40,.34,.35,.43]
+
+b1_rm = [1.41,1.87,1.69,2.33]
+b1_up_rm = [.38,.37,.28,.98]
+b1_down_rm = [.28,.3,.2,.54]
+
+b2_rm = [-.18,.56,.37,1]
+b2_up_rm = [.52,.83,.61,3.5]
+b2_down_rm = [.42,.58,.49,1.1]
+
+sigmas_list = [0.02,0.03,0.03,0.03]
+
+
+# # data vectors ZSPEC
+
+# plt.figure()
+# config_fname = 'paper_3dval4'
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     sigma = 0
+#     zmax = zmin+.15
+#     data =  plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigma,use_alt_randoms=True)
+#     galdata = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=sigma,use_alt_randoms=True)
+#     print len(data), len(galdata)
+#     plottools.plot_dv(data,'Q', offset=0.,indiv_runs=True,color='Blue',label=r'Dark Matter',compressed=True)
+#     plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=True,color='Red',label=r'Galaxies',compressed=True)
+#     plt.xlabel('v')
+#     plt.ylabel('Q')
+#     plt.title(str(zmin)+r'$<z<$'+str(zmax))
+#     plt.ylim(-.75,3)
+#     plt.legend()
+
+#     plt.savefig('./figures/spec_bin'+str(i+1)+'_dv.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/spec_bin'+str(i+1)+'_dv.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# # data vectors ZREDMAGIC
+
+# config_fname = 'paper_3dval4'
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     sigma = 0
+#     zmax = zmin+.15
+#     data =  plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=sigma,use_alt_randoms=False)
+#     galdata = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=sigma,use_alt_randoms=False)
+#     print len(data), len(galdata)
+#     plottools.plot_dv(data,'Q', offset=0.,indiv_runs=True,color='Blue',label=r'Dark Matter',compressed=True)
+#     plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=True,color='Red',label=r'Galaxies',compressed=True)
+#     plt.xlabel('v')
+#     plt.ylabel('Q')
+#     plt.title(str(zmin)+r'$<z<$'+str(zmax))
+#     plt.ylim(-.75,3)
+#     plt.legend()
+
+#     plt.savefig('./figures/rm_bin'+str(i+1)+'_dv.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/rm_bin'+str(i+1)+'_dv.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# #covmats
+
+# def make_corr_fig(zmin):
+
+#     zmax = zmin+.15
+#     if zmin == .15:
+#         placeholder_sigma = 0.01
+#     else: 
+#         placeholder_sigma = 0.02
+#     data = plottools.load_res_indep(path, 'dm','paper_3dval4','ZSPEC',zmin,zmax,'10x10',sigma=0)
+#     data2 = plottools.load_res_indep(path,'dm','paper_3dval4','ZSPEC',zmin,zmax,'10x10',sigma=placeholder_sigma)
+#     data3 = plottools.load_res_indep(path2,'dm','paper_3dval4','ZREDMAGIC',zmin,zmax,'10x10',sigma=0)
+    
+#     galdata = plottools.load_res_indep(path, 'newbuzzardrm2','paper_3dval4','ZSPEC',zmin,zmax,'10',sigma=0)
+#     galdata2 = plottools.load_res_indep(path,'newbuzzardrm2','paper_3dval4','ZSPEC',zmin,zmax,'10',sigma=placeholder_sigma)
+#     galdata3 = plottools.load_res_indep(path,'newbuzzardrm2','paper_3dval4','ZREDMAGIC',zmin,zmax,'10',sigma=0)
+#     plt.figure()
+    
+#     print len(data),len(data2),len(data3)
+#     print len(galdata), len(galdata2), len(galdata3)
+    
+#     fig,axarr = plt.subplots(2,3,figsize=(8,7))
+    
+#     vmin= -1
+#     vmax= 1
+#     cmap_choice = 'viridis'
+    
+#     axarr[0,0].imshow(np.corrcoef(plottools.compress_dv(galdata['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1),cmap=cmap_choice)
+#     axarr[0,0].set_title(r'True redshifts')
+#     axarr[0,0].set_xticks([.1,.3,.5,.7,.9])
+#     axarr[0,0].set_yticks([.1,.3,.5,.7,.9])
+#     #axarr[0,0].set_xlabel('v')
+#     axarr[0,0].set_ylabel('v')
+#     axarr[0,1].imshow(np.corrcoef(plottools.compress_dv(galdata2['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1),cmap=cmap_choice)
+#     axarr[0,1].set_title(r'Galaxies \\ \\ $\sigma_z = $'+str(placeholder_sigma))
+#     #axarr[0,1].set_xlabel('v')
+#     #axarr[0,1].set_ylabel('v')
+#     axarr[0,1].set_xticks([.1,.3,.5,.7,.9])
+#     axarr[0,1].set_yticks([.1,.3,.5,.7,.9])
+    
+#     axarr[0,2].imshow(np.corrcoef(plottools.compress_dv(galdata3['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1),cmap=cmap_choice)
+#     axarr[0,2].set_title(r'RedMaGiC redshifts')
+#     #axarr[0,1].set_xlabel('v')
+#     #axarr[0,1].set_ylabel('v')
+#     axarr[0,2].set_xticks([.1,.3,.5,.7,.9])
+#     axarr[0,2].set_yticks([.1,.3,.5,.7,.9])
+    
+#     axarr[1,0].imshow(np.corrcoef(plottools.compress_dv(data['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1),cmap=cmap_choice)
+#     axarr[1,0].set_title(r'True redshifts')
+#     axarr[1,0].set_xticks([.1,.3,.5,.7,.9])
+#     axarr[1,0].set_yticks([.1,.3,.5,.7,.9])
+#     axarr[1,0].set_xlabel('v')
+#     axarr[1,0].set_ylabel('v')
+#     im = axarr[1,1].imshow(np.corrcoef(plottools.compress_dv(data2['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1),cmap=cmap_choice)
+#     axarr[1,1].set_title(r'Dark Matter \\ \\ $\sigma_z = $'+str(placeholder_sigma))
+#     axarr[1,1].set_xlabel('v')
+#     #axarr[1,1].set_ylabel('v')
+#     axarr[1,1].set_xticks([.1,.3,.5,.7,.9])
+#     axarr[1,1].set_yticks([.1,.3,.5,.7,.9])
+    
+#     axarr[1,2].imshow(np.corrcoef(plottools.compress_dv(data3['Q'].values.reshape(-1,10)).T),vmin=vmin,vmax=vmax,origin='lower',extent=(0,1,0,1),cmap=cmap_choice)
+#     axarr[1,2].set_title(r'RedMaGiC redshifts')
+#     axarr[1,2].set_xlabel('v')
+#     #axarr[1,1].set_ylabel('v')
+#     axarr[1,2].set_xticks([.1,.3,.5,.7,.9])
+#     axarr[1,2].set_yticks([.1,.3,.5,.7,.9])
+    
+#     #fig.colorbar(im, ax=axarr.ravel().tolist(),shrink=.4,aspect=10)
+#     #plt.suptitle(str(zmin)+r'$<z<$'+str(zmax))
+#     #plt.tight_layout()
+#     cbar_ax = fig.add_axes([.97, 0.2, .04, 0.6])
+#     fig.colorbar(im, cax=cbar_ax, orientation="vertical")
+#     plt.suptitle(str(zmin)+r'$< z <$'+str(zmax))
+
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     make_corr_fig(zmin)
+#     plt.savefig('./figures/cov_bin'+str(i+1)+'.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/cov_bin'+str(i+1)+'.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# # DES Y1 contours (spec+photoz), no 2pt or lazeyras comparison
+
+# is11k = False
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     for sigma in [0]:
+#         config_fname = 'paper_3dval4'
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigma,use_alt_randoms=False)
+#         data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=sigma,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name='Spectroscopic Redshifts')
+        
+#     data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'RedMaGiC Photometric Redshifts')
+    
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,10),(-10,10)]);
+#     axarr = figure.get_axes()
+#     figure.axes[2].axvspan(b1_2pt[i]-b1_err_2pt[i], b1_2pt[i]+b1_err_2pt[i], alpha=0.3, color='b')
+#     figure.axes[2].axvspan(b1_2pt[i]-2*b1_err_2pt[i], b1_2pt[i]+2*b1_err_2pt[i], alpha=0.3, color='b')
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+    
+#     plt.savefig('./figures/bothy1_bin'+str(i+1)+'_inf.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/bothy1_bin'+str(i+1)+'_inf.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+    
+# # 11k degree contours, 2pt and lazeyras comparison.
+
+# is11k = True
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     for sigma in [0]:
+#         config_fname = 'paper_3dval4'
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigma,use_alt_randoms=False)
+#         data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=sigma,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name='Spectroscopic Redshifts')
+
+#     #replace later once RM is fixed:   
+#     #data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     if zmin != .15:
+#         data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigmas_list[i],use_alt_randoms=False)
+#     else:
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigmas_list[i],use_alt_randoms=False)
+        
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'RedMaGiC Photometric Redshifts')
+
+#     # add 2pt
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,4),(-4,4)]);
+#     axarr = figure.get_axes()
+
+#     figure.axes[2].axvspan(b1_2pt[i]-b1_err_2pt[i], b1_2pt[i]+b1_err_2pt[i], alpha=0.3, color='b')
+#     figure.axes[2].axvspan(b1_2pt[i]-2*b1_err_2pt[i], b1_2pt[i]+2*b1_err_2pt[i], alpha=0.3, color='b')
+
+#     figure.axes[2].plot(np.linspace(0,4,100),plottools.get_lazeyras_schmidt_2015(np.linspace(0,4,100)),label='Lazeyras+Schmidt 2015')
+#     figure.axes[2].plot(np.linspace(0,4,100),plottools.get_hoffman_2015(np.linspace(0,4,100)),label='Hoffman 2015')
+
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+    
+#     plt.savefig('./figures/both11k_bin'+str(i+1)+'_inf.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/both11k_bin'+str(i+1)+'_inf.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+    
+# # tol dv redmagic
+# colors = palettable.colorbrewer.sequential.Reds_3.hex_colors
+# colors2 = palettable.colorbrewer.sequential.Blues_3.hex_colors
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     galdata = plottools.load_res_indep(path2,'newbuzzardrm2','fiducial3d_toldown','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     galdata1 = plottools.load_res_indep(path,'newbuzzardrm2','paper_3dval4','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     galdata2 = plottools.load_res_indep(path2,'newbuzzardrm2','fiducial3d_tolup','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     data = plottools.load_res_indep(path2,'dm','fiducial3d_toldown','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data1 = plottools.load_res_indep(path2,'dm','paper_3dval4','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path2,'dm','fiducial3d_tolup','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    
+#     plottools.plot_dv(data,'Q', offset=0.,indiv_runs=False,color=colors2[0]   ,compressed=True)
+#     plottools.plot_dv(data1,'Q', offset=0.01,indiv_runs=False,color=colors2[1],compressed=True)
+#     plottools.plot_dv(data2,'Q', offset=0.02,indiv_runs=False,color=colors2[2],compressed=True)
+    
+    
+#     plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'Tolerance = $\pm 2.5 Mpc$' ,compressed=True)
+#     plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'Tolerance = $\pm 5 Mpc$' ,compressed=True)
+#     plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'Tolerance = $\pm 7.5 Mpc$' ,compressed=True)
+    
+#     plt.xlabel('v')
+#     plt.ylabel('Q')
+#     plt.title(str(zmin)+r'$<z<$'+str(zmax))
+#     plt.legend()
+#     plt.savefig('./figures/tol_bin'+str(i+1)+'_dvrm.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/tol_bin'+str(i+1)+'_dvrm.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+    
+# # tol inference spec
+
+# is11k = True
+# tol_labels = ['2.5','5','7.5']
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     for j,config_fname in enumerate(['fiducial3d_toldown','paper_3dval4','fiducial3d_tolup']):
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#         data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance = $\pm$ '+tol_labels[j]+' Mpc')
+    
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,4),(-4,4)]);
+#     axarr = figure.get_axes()
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+    
+#     plt.savefig('./figures/tol_bin'+str(i+1)+'_inf.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/tol_bin'+str(i+1)+'_inf.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+    
+
+# # tol inference ZREDMAGIC
+
+# is11k = True
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin + 0.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     data1 = plottools.load_res_indep(path2,'dm','fiducial3d_toldown','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path2,'newbuzzardrm2','fiducial3d_toldown','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance = $\pm 2.5 $ Mpc')
+    
+#     data1 = plottools.load_res_indep(path2,'dm','paper_3dval4','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2','paper_3dval4','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance = $\pm$ 5 Mpc')
+    
+#     data1 = plottools.load_res_indep(path2,'dm','fiducial3d_tolup','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path2,'newbuzzardrm2','fiducial3d_tolup','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'Tolerance = $\pm$ 7.5 Mpc')
+    
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,4),(-4,4)]);
+#     axarr = figure.get_axes()
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+
+#     plt.savefig('./figures/tol_bin'+str(i+1)+'_infrm.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/tol_bin'+str(i+1)+'_infrm.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# # u data vectors
+
+# colors = palettable.colorbrewer.sequential.Reds_3.hex_colors
+# colors2 = palettable.colorbrewer.sequential.Blues_3.hex_colors
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     plt.figure()
+#     zmax = zmin+.15
+#     galdata = plottools.load_res_indep(path,'newbuzzardrm2','fiducial3d_halfu','ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     galdata1 = plottools.load_res_indep(path,'newbuzzardrm2','fiducial3d_75u','ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     galdata2 = plottools.load_res_indep(path,'newbuzzardrm2','paper_3dval4','ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     data = plottools.load_res_indep(path,'dm','fiducial3d_halfu','ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data1 = plottools.load_res_indep(path,'dm','fiducial3d_75u','ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'dm','paper_3dval4','ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    
+#     plottools.plot_dv(data,'Q', offset=0.,indiv_runs=False,color=colors2[0]    ,compressed=True)
+#     plottools.plot_dv(data1,'Q', offset=0.01,indiv_runs=False,color=colors2[1],compressed=True)
+#     plottools.plot_dv(data2,'Q', offset=0.02,indiv_runs=False,color=colors2[2],compressed=True)
+    
+    
+#     plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'$u > .5$' ,compressed=True)
+#     plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'$u > .75$' ,compressed=True)
+#     plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'$u > .9$' ,compressed=True)
+    
+#     plt.xlabel('v')
+#     plt.ylabel('Q')
+#     plt.title(str(zmin)+r'$<z<$'+str(zmax))
+#     plt.legend()
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_dv.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_dv.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# # u data vectors ZREDMAGIC
+
+# colors = palettable.colorbrewer.sequential.Reds_3.hex_colors
+# colors2 = palettable.colorbrewer.sequential.Blues_3.hex_colors
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     plt.figure()
+#     zmax = zmin+.15
+#     galdata = plottools.load_res_indep(path2,'newbuzzardrm2','fiducial3d_halfu','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     galdata1 = plottools.load_res_indep(path2,'newbuzzardrm2','fiducial3d_75u','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     galdata2 = plottools.load_res_indep(path,'newbuzzardrm2','paper_3dval4','ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    
+#     data = plottools.load_res_indep(path2,'dm','fiducial3d_halfu','ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data1 = plottools.load_res_indep(path2,'dm','fiducial3d_75u', 'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path2,'dm','paper_3dval4',   'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    
+#     plottools.plot_dv(data,'Q', offset=0.,indiv_runs=False,color=colors2[0]    ,compressed=True)
+#     plottools.plot_dv(data1,'Q', offset=0.01,indiv_runs=False,color=colors2[1],compressed=True)
+#     plottools.plot_dv(data2,'Q', offset=0.02,indiv_runs=False,color=colors2[2],compressed=True)
+    
+    
+#     plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'$u > .5$' ,compressed=True)
+#     plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'$u > .75$' ,compressed=True)
+#     plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'$u > .9$' ,compressed=True)
+    
+#     plt.xlabel('v')
+#     plt.ylabel('Q')
+#     plt.title(str(zmin)+r'$<z<$'+str(zmax))
+#     plt.legend()
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_dvrm.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_dvrm.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# # u inferences SPEC
+
+# is11k = True
+# tol_labels = ['.5','.75','.9']
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     for j,config_fname in enumerate(['fiducial3d_halfu','fiducial3d_75u','paper_3dval4']):
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#         data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$u >$ '+tol_labels[j])
+    
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,4),(-4,4)]);
+#     axarr = figure.get_axes()
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+    
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_inf.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_inf.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+    
+# # u inferences ZRM
+
+# is11k = True
+# tol_labels = ['.5','.75','.9']
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     for j,config_fname in enumerate(['fiducial3d_halfu','fiducial3d_75u','paper_3dval4']):
+#         data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#         if config_fname == 'paper_3dval4':
+#             data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#         else:
+#             data2 = plottools.load_res_indep(path2,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$u >$ '+tol_labels[j])
+    
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,4),(-4,4)]);
+#     axarr = figure.get_axes()
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_infrm.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/u_bin'+str(i+1)+'_infrm.png',dpi=300,bbox_inches='tight')
+#     plt.figure()
+
+# # gaussian z dv
+
+# colors = palettable.colorbrewer.sequential.Reds_7.hex_colors
+# config_fname = 'paper_3dval4'
+# zmin = .15
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.01,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'$\sigma(z) = 0$' ,compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'$\sigma(z) = 0.001$' ,compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'$\sigma(z) = 0.003$' ,compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3],label=r'$\sigma(z) = 0.005$' ,compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4],label=r'$\sigma(z) = 0.01$' ,compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.05,indiv_runs=False,color='k',label='ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin1_galdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin1_galdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# zmin = .3
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.01,use_alt_randoms=False)
+# galdata5 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.02,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'$\sigma(z) = 0$' ,compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'$\sigma(z) = 0.001$' ,compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'$\sigma(z) = 0.003$' ,compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3],label=r'$\sigma(z) = 0.005$' ,compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4],label=r'$\sigma(z) = 0.01$' ,compressed=True)
+# plottools.plot_dv(galdata5,'Q', offset=0.05,indiv_runs=False,color=colors[5],label=r'$\sigma(z) = 0.02$' ,compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.06,indiv_runs=False,color='k',label='ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin2_galdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin2_galdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# zmin = .45
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.01,use_alt_randoms=False)
+# galdata5 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.02,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'$\sigma(z) = 0$' ,compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'$\sigma(z) = 0.001$' ,compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'$\sigma(z) = 0.003$' ,compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3],label=r'$\sigma(z) = 0.005$' ,compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4],label=r'$\sigma(z) = 0.01$' ,compressed=True)
+# plottools.plot_dv(galdata5,'Q', offset=0.05,indiv_runs=False,color=colors[5],label=r'$\sigma(z) = 0.02$' ,compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.06,indiv_runs=False,color='k',label='ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin3_galdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin3_galdv.png',dpi=300,bbox_inches='tight')
+
+# plt.figure()
+
+# zmin = .6
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.01,use_alt_randoms=False)
+# galdata5 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.02,use_alt_randoms=False)
+# galdata6 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.03,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   ,label=r'$\sigma(z) = 0$' ,compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1],label=r'$\sigma(z) = 0.001$' ,compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2],label=r'$\sigma(z) = 0.003$' ,compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3],label=r'$\sigma(z) = 0.005$' ,compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4],label=r'$\sigma(z) = 0.01$' ,compressed=True)
+# plottools.plot_dv(galdata5,'Q', offset=0.05,indiv_runs=False,color=colors[5],label=r'$\sigma(z) = 0.02$' ,compressed=True)
+# plottools.plot_dv(galdata6,'Q', offset=0.06,indiv_runs=False,color=colors[6],label=r'$\sigma(z) = 0.03$' ,compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.07,indiv_runs=False,color='k',label='ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+
+# plt.savefig('./figures/gaussrm_bin4_galdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin4_galdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# # dv data vector figures
+
+# colors = palettable.colorbrewer.sequential.Blues_7.hex_colors
+# config_fname = 'paper_3dval4'
+# zmin = .15
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path, 'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.01,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   , label=r'$\sigma(z) = 0$',compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1], label=r'$\sigma(z) = 0.001$',compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2], label=r'$\sigma(z) = 0.003$',compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3], label=r'$\sigma(z) = 0.005$',compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4], label=r'$\sigma(z) = 0.01$',compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.05,indiv_runs=False,color='k', label=r'ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin1_dmdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin1_dmdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# zmin = .3
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path, 'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.01,use_alt_randoms=False)
+# galdata5 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.02,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   , label=r'$\sigma(z) = 0$',compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1], label=r'$\sigma(z) = 0.001$',compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2], label=r'$\sigma(z) = 0.003$',compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3], label=r'$\sigma(z) = 0.005$',compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4], label=r'$\sigma(z) = 0.01$',compressed=True)
+# plottools.plot_dv(galdata5,'Q', offset=0.05,indiv_runs=False,color=colors[5], label=r'$\sigma(z) = 0.02$',compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.06,indiv_runs=False,color='k', label=r'ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin2_dmdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin2_dmdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# zmin = .45
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path, 'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.01,use_alt_randoms=False)
+# galdata5 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.02,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   , label=r'$\sigma(z) = 0$',compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1], label=r'$\sigma(z) = 0.001$',compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2], label=r'$\sigma(z) = 0.003$',compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3], label=r'$\sigma(z) = 0.005$',compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4], label=r'$\sigma(z) = 0.01$',compressed=True)
+# plottools.plot_dv(galdata5,'Q', offset=0.05,indiv_runs=False,color=colors[5], label=r'$\sigma(z) = 0.02$',compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.06,indiv_runs=False,color='k', label=r'ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin3_dmdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin3_dmdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# zmin = .6
+# plt.figure()
+# zmax = zmin+.15
+# galdata = plottools.load_res_indep(path, 'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+# galdata1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.001,use_alt_randoms=False)
+# galdata2 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.003,use_alt_randoms=False)
+# galdata3 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.005,use_alt_randoms=False)
+# galdata4 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.01,use_alt_randoms=False)
+# galdata5 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.02,use_alt_randoms=False)
+# galdata6 = plottools.load_res_indep(path2,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.03,use_alt_randoms=False)
+# galdata8 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+
+# plottools.plot_dv(galdata,'Q', offset=0.,indiv_runs=False,color=colors[0]   , label=r'$\sigma(z) = 0$',compressed=True)
+# plottools.plot_dv(galdata1,'Q', offset=0.01,indiv_runs=False,color=colors[1], label=r'$\sigma(z) = 0.001$',compressed=True)
+# plottools.plot_dv(galdata2,'Q', offset=0.02,indiv_runs=False,color=colors[2], label=r'$\sigma(z) = 0.003$',compressed=True)
+# plottools.plot_dv(galdata3,'Q', offset=0.03,indiv_runs=False,color=colors[3], label=r'$\sigma(z) = 0.005$',compressed=True)
+# plottools.plot_dv(galdata4,'Q', offset=0.04,indiv_runs=False,color=colors[4], label=r'$\sigma(z) = 0.01$',compressed=True)
+# plottools.plot_dv(galdata5,'Q', offset=0.05,indiv_runs=False,color=colors[5], label=r'$\sigma(z) = 0.02$',compressed=True)
+# plottools.plot_dv(galdata6,'Q', offset=0.06,indiv_runs=False,color=colors[6], label=r'$\sigma(z) = 0.03$',compressed=True)
+# plottools.plot_dv(galdata8,'Q', offset=0.07,indiv_runs=False,color='k', label=r'ZREDMAGIC',compressed=True)
+
+# plt.xlabel('v')
+# plt.ylabel('Q')
+# plt.title(str(zmin)+r'$<z<$'+str(zmax))
+# plt.legend()
+# plt.savefig('./figures/gaussrm_bin4_dmdv.pdf',dpi=300,bbox_inches='tight')
+# plt.savefig('./figures/gaussrm_bin4_dmdv.png',dpi=300,bbox_inches='tight')
+# plt.figure()
+
+# #Gaussian photoz + rm inference
+
+# is11k = True
+# for i,zmin in enumerate([.15,.3,.45,.6]):
+#     zmax = zmin+.15
+#     cc = chainconsumer.ChainConsumer()
+    
+#     config_fname = 'paper_3dval4'
+#     data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name='Spectroscopic Redshifts',color='k',linewidth=3)
+    
+#     data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.005,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.005,use_alt_randoms=False)
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma(z) = 0.005$')
+    
+#     data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.01,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.01,use_alt_randoms=False)
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma(z) = 0.01$')
+    
+#     if zmin > 0.15:
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.02,use_alt_randoms=False)
+#         data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.02,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma(z) = 0.02$')
+    
+#     if zmin > .45:
+#         data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0.03,use_alt_randoms=False)
+#         data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0.03,use_alt_randoms=False)
+#         red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#         red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#         samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#         cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'$\sigma(z) = 0.03$')
+        
+#     if zmin != .15:
+#         data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigmas_list[i],use_alt_randoms=False)
+#     else:
+#         data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=sigmas_list[i],use_alt_randoms=False)
+    
+#     #fix to use RM DM later...
+#     #data1 = plottools.load_res_indep(path2,'dm',config_fname,'ZREDMAGIC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+#     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZREDMAGIC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+#     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+#     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+#     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+#     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'RedMaGiC Photometric Redshifts',linewidth=3)
+    
+#     cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+#     figure = cc.plotter.plot(figsize='column',extents=[(0,4),(-4,4)]);
+#     axarr = figure.get_axes()
+#     plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+    
+#     plt.savefig('./figures/gaussrm_bin'+str(i+1)+'_inf.pdf',dpi=300,bbox_inches='tight')
+#     plt.savefig('./figures/gaussrm_bin'+str(i+1)+'_inf.png',dpi=300,bbox_inches='tight')
+
+#     plt.figure()
+
+# # scale inferences
+
+is11k = True
+for i,zmin in enumerate([.15,.3,.45,.6]):
+    zmax = zmin+.15
+    cc = chainconsumer.ChainConsumer()
+    
+    config_fname = 'paper_3dval1'
+    data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'4 Mpc')
+    
+    config_fname = 'paper_3dval2'
+    data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'10 Mpc')
+    
+    config_fname = 'paper_3dval3'
+    data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'20 Mpc')
+    
+    #if zmin == .6:
+    config_fname = 'fiducial3d_25Mpc'
+    data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'25 Mpc')
+
+    config_fname = 'paper_3dval4'
+    data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    print len(data1),len(data2)
+    red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'30 Mpc')
+    
+    #if zmin != .45:
+    config_fname = 'fiducial3d_35Mpc'
+    data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    print len(data1),len(data2)
+    red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'35 Mpc')
+
+    # try:
+    #     config_fname = 'paper_3dval5'
+    #     data1 = plottools.load_res_indep(path,'dm',config_fname,'ZSPEC',zmin,zmax,'10x10',sigma=0,use_alt_randoms=False)
+    #     data2 = plottools.load_res_indep(path,'newbuzzardrm2',config_fname,'ZSPEC',zmin,zmax,'10',sigma=0,use_alt_randoms=False)
+    #     print len(data1),len(data2)
+    #     red_qdm = plottools.compress_dv(data1['Q'].values.reshape(-1,10))
+    #     red_qrm = plottools.compress_dv(data2['Q'].values.reshape(-1,10))
+    #     samples = plottools.make_inference(red_qdm,red_qrm,is11k=is11k)
+    #     cc.add_chain(samples.flatchain,parameters=['b1','b2'],name=r'40 Mpc')
+    # except:
+    #     print "not enough finished jobs"
+
+    cc.configure(legend_kwargs={"loc": "lower right"},label_font_size=14,tick_font_size=14)
+    figure = cc.plotter.plot(figsize='column',extents=[(0,10),(-10,10)]);
+    axarr = figure.get_axes()
+    plt.suptitle(str(zmin)+r'$ < z < $'+str(zmax))
+    
+    plt.savefig('./figures/scale_bin'+str(i+1)+'_inf.pdf',dpi=300,bbox_inches='tight')
+    plt.savefig('./figures/scale_bin'+str(i+1)+'_inf.png',dpi=300,bbox_inches='tight')
+    plt.figure()
+
+z = np.array([.15,.3,.45,.6])+0.075
+line1 = plt.errorbar(z-0.01,b1_spec,yerr=np.array([b1_down_spec,b1_up_spec]),linestyle='None',marker='o',color='b',label='Spectroscopic Redshifts')
+line2 = plt.errorbar(z+0.01,b1_rm,yerr=np.array([b1_down_rm,b1_up_rm]),linestyle='None',marker='o',color='r',label='RedMaGiC Redshifts')
+
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Rectangle, Patch
+
+errorboxes = []
+
+for i in [0,1,2,3]:
+    rect = Rectangle((z[i] - 0.02, b1_2pt[i] - b1_err_2pt[i]), 0.04, 2*b1_err_2pt[i])
+    errorboxes.append(rect)
+
+pc = PatchCollection(errorboxes, facecolor='k', alpha=0.5,
+                         edgecolor='None')
+ax = plt.gca()
+ax.add_collection(pc)
+
+red_patch = Patch(color='k', alpha=0.5, label='Linear bias from 2PCFs')
+plt.legend(handles=[line1,line2,red_patch],loc=2)
+
+plt.xlabel('z')
+plt.ylabel('Linear bias')
+plt.xlim(.15,.75)
+
+plt.savefig('./figures/b1_evolution.pdf',dpi=300,bbox_inches='tight')
+plt.savefig('./figures/b1_evolution.png',dpi=300,bbox_inches='tight')
+plt.figure()
+
+plt.errorbar(z-0.01,b2_spec,yerr=np.array([b2_down_spec,b2_up_spec]),linestyle='None',marker='o',color='b',label='Spectroscopic Redshifts')
+plt.errorbar(z+0.01,b2_rm,yerr=np.array([b2_down_rm,b2_up_rm]),linestyle='None',marker='o',color='r',label='RedMaGiC Redshifts')
+plt.hlines(0,0.2,0.7,linestyles=['--'],colors=['k'])
+
+plt.xlabel('z')
+plt.ylabel('Quadratic bias')
+plt.legend(loc=2)
+plt.xlim(.15,.75)
+plt.savefig('./figures/b2_evolution.pdf',dpi=300,bbox_inches='tight')
+plt.savefig('./figures/b2_evolution.png',dpi=300,bbox_inches='tight')
\ No newline at end of file
diff --git a/tricorder/paths.py b/tricorder/paths.py
index e9cc2a5..7def609 100644
--- a/tricorder/paths.py
+++ b/tricorder/paths.py
@@ -1,131 +1,95 @@
+
+# CAUTION: Only use NFS paths!! Otherwise you'll have disk access problems...
+
+config_dir = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/configs/'
+
+corr_out_dir = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/paper_validation'
+ang_out_dir = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/paper_validation'
+
+rm_y1_randoms = '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3a/buzzard-3_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_highdens_0.5-10_randoms.fit'
+rm_y1_HL_randoms = '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3a/buzzard-3_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_highlum_1.0-04_randoms.fit'
+rm_y1_HHL_randoms = '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3a/buzzard-3_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01_randoms.fit'
+
+#dm_y1_randoms = '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/a/buzzard_1.6-6a_run_redmapper_v6.4.18_redmagic_higherlum_1.5-01_randoms.fit'
+#dm_y1_randoms = '/nfs/slac/des/fs1/g/sims/erykoff/clusters/mocks/Buzzard/buzzard-1.6/des-y1a1/redmapper_v6.4.18/redmagic_a/buzzard_1.6-6a_run_redmapper_v6.4.18_redmagic_higherlum_1.5-01_randoms.fit'
+dm_y1_randoms = '/nfs/slac/g/ki/ki19/des/mbaumer/dark_matter_joe/buzzard_1.6-6a_run_redmapper_v6.4.18_redmagic_higherlum_1.5-01_randoms.fit'
+
 rm_y1 = [
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/a/buzzard_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/b/buzzard_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/c/buzzard_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/d/buzzard_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/e/buzzard_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/f/buzzard_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/a/buzzard-1_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/rdmagic/y1/buzzard/flock/buzzard-1/b/buzzard-1_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/c/buzzard-1_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/d/buzzard-1_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/e/buzzard-1_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/f/buzzard-1_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/a/buzzard2_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/b/buzzard2_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/c/buzzard2_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/d/buzzard2_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/e/buzzard2_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/f/buzzard2_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/a/buzzard-3_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/b/buzzard-3_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/c/buzzard-3_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/d/buzzard-3_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/e/buzzard-3_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/f/buzzard-3_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/a/buzzard5_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/b/buzzard5_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/c/buzzard5_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/d/buzzard5_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/e/buzzard5_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/f/buzzard5_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/a/buzzard21_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/b/buzzard21_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/c/buzzard21_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/d/buzzard21_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/e/buzzard21_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-    '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/f/buzzard21_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3a/buzzard-3_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3b/buzzard-3_1.9.2+1-6b_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3c/buzzard-3_1.9.2+1-6c_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3d/buzzard-3_1.9.2+1-6d_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3e/buzzard-3_1.9.2+1-6e_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3f/buzzard-3_1.9.2+1-6f_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4a/buzzard-4_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4b/buzzard-4_1.9.2+1-6b_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4c/buzzard-4_1.9.2+1-6c_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    # this one is bad b/c of joe healpix error!
+    #'/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4d/buzzard-4_1.9.2+1-6d_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4e/buzzard-4_1.9.2+1-6e_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4f/buzzard-4_1.9.2+1-6f_run_redmapper_v6.4.22_redmagic_highdens_0.5-10.fit',
+]
+
+rm_y1_HL = [
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3a/buzzard-3_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3b/buzzard-3_1.9.2+1-6b_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3c/buzzard-3_1.9.2+1-6c_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3d/buzzard-3_1.9.2+1-6d_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3e/buzzard-3_1.9.2+1-6e_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3f/buzzard-3_1.9.2+1-6f_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4a/buzzard-4_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4b/buzzard-4_1.9.2+1-6b_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4c/buzzard-4_1.9.2+1-6c_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    #'/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4d/buzzard-4_1.9.2+1-6d_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4e/buzzard-4_1.9.2+1-6e_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4f/buzzard-4_1.9.2+1-6f_run_redmapper_v6.4.22_redmagic_highlum_1.0-04.fit',
 ]
 
-lss_y1 = [
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/a/Buzzard_v1.6_Y1a_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/b/Buzzard_v1.6_Y1b_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/c/Buzzard_v1.6_Y1c_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/d/Buzzard_v1.6_Y1d_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/e/Buzzard_v1.6_Y1e_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/f/Buzzard_v1.6_Y1f_gold.fits',
-
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/a/Buzzard_v1.6_Y1a_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/b/Buzzard_v1.6_Y1b_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/c/Buzzard_v1.6_Y1c_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/d/Buzzard_v1.6_Y1d_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/e/Buzzard_v1.6_Y1e_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/f/Buzzard_v1.6_Y1f_gold.fits',
-
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/a/Buzzard_v1.6_Y1a_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/b/Buzzard_v1.6_Y1b_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/c/Buzzard_v1.6_Y1c_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/d/Buzzard_v1.6_Y1d_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/e/Buzzard_v1.6_Y1e_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/f/Buzzard_v1.6_Y1f_gold.fits',
-
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/a/Buzzard_v1.6_Y1a_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/b/Buzzard_v1.6_Y1b_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/c/Buzzard_v1.6_Y1c_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/d/Buzzard_v1.6_Y1d_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/e/Buzzard_v1.6_Y1e_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/f/Buzzard_v1.6_Y1f_gold.fits',
-
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/a/Buzzard_v1.6_Y1a_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/b/Buzzard_v1.6_Y1b_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/c/Buzzard_v1.6_Y1c_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/d/Buzzard_v1.6_Y1d_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/e/Buzzard_v1.6_Y1e_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/f/Buzzard_v1.6_Y1f_gold.fits',
-
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/a/Buzzard_v1.6_Y1a_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/b/Buzzard_v1.6_Y1b_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/c/Buzzard_v1.6_Y1c_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/d/Buzzard_v1.6_Y1d_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/e/Buzzard_v1.6_Y1e_gold.fits',
-    '/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/f/Buzzard_v1.6_Y1f_gold.fits',
+rm_y1_HHL = [
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3a/buzzard-3_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3b/buzzard-3_1.9.2+1-6b_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3c/buzzard-3_1.9.2+1-6c_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3d/buzzard-3_1.9.2+1-6d_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3e/buzzard-3_1.9.2+1-6e_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_3f/buzzard-3_1.9.2+1-6f_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4a/buzzard-4_1.9.2+1-6a_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4b/buzzard-4_1.9.2+1-6b_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4c/buzzard-4_1.9.2+1-6c_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    #'/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4d/buzzard-4_1.9.2+1-6d_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4e/buzzard-4_1.9.2+1-6e_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
+    '/nfs/slac/g/ki/ki19/des/erykoff/clusters/mocks/Buzzard/buzzard-1.9.2+1/des-y1a1/redmapper_v6.4.22/redmagic_4f/buzzard-4_1.9.2+1-6f_run_redmapper_v6.4.22_redmagic_higherlum_1.5-01.fit',
 ]
 
 dm_y1 = [
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/a/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/b/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/c/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/d/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/e/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/f/downsampled_particles.fits.downsample',
-
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-1/a/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-1/b/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-1/c/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-1/d/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-1/e/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-1/f/downsampled_particles.fits.downsample',
-
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-2/a/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-2/b/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-2/c/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-2/d/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-2/e/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-2/f/downsampled_particles.fits.downsample',
-
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-21/a/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-21/b/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-21/c/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-21/d/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-21/e/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-21/f/downsampled_particles.fits.downsample',
-
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-3/a/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-3/b/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-3/c/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-3/d/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-3/e/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-3/f/downsampled_particles.fits.downsample',
-
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-5/a/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-5/b/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-5/c/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-5/d/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-5/e/downsampled_particles.fits.downsample',
-    '/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-5/f/downsampled_particles.fits.downsample',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-0/downsample_particles/y1a/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-0/downsample_particles/y1b/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-0/downsample_particles/y1c/downsampled_particles.fits',
+    #'/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-0/downsample_particles/y1d/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-0/downsample_particles/y1e/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-0/downsample_particles/y1f/downsampled_particles.fits',
+
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-1/downsample_particles/y1a/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-1/downsample_particles/y1b/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-1/downsample_particles/y1c/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-1/downsample_particles/y1d/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-1/downsample_particles/y1e/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-1/downsample_particles/y1f/downsampled_particles.fits',
+
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-2/downsample_particles/y1a/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-2/downsample_particles/y1b/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-2/downsample_particles/y1c/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-2/downsample_particles/y1d/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-2/downsample_particles/y1e/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-2/downsample_particles/y1f/downsampled_particles.fits',
+
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-5/downsample_particles/y1a/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-5/downsample_particles/y1b/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-5/downsample_particles/y1c/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-5/downsample_particles/y1d/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-5/downsample_particles/y1e/downsampled_particles.fits',
+    '/nfs/slac/des/fs1/g/sims/jderose/BCC/Chinchilla/Herd//Chinchilla-5/downsample_particles/y1f/downsampled_particles.fits',
+
 ]
diff --git a/tricorder/plottools.py b/tricorder/plottools.py
new file mode 100644
index 0000000..9b5a3e3
--- /dev/null
+++ b/tricorder/plottools.py
@@ -0,0 +1,474 @@
+from __future__ import division
+import numpy as np
+import matplotlib.pyplot as plt
+import paths
+from astropy.io import fits
+
+import yaml
+import treecorr
+import emcee
+import chainconsumer 
+from sklearn.decomposition import PCA
+
+from glob import glob
+
+def load_config(config_fname):
+    config_path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/configs/'+config_fname+'.config'
+    with open(config_path) as f:
+        return yaml.load(f.read())
+    
+def get_q(config_name,fname):
+    config = load_config(config_name)
+    corr = treecorr.NNNCorrelation(config=config['3PCF'])
+    dd = treecorr.NNCorrelation(config=config['2PCF'])
+    zeta = np.load(fname)
+    xi = np.load(fname[:-8]+'xi.npy')
+    
+    from scipy.interpolate import UnivariateSpline
+    yfit = UnivariateSpline(dd.logr[xi != 0],np.log(xi)[xi != 0],k=4)
+    xi1 = np.exp(yfit(corr.logr*(1+corr.u*np.abs(corr.v))))
+    xi2 = np.exp(yfit(corr.logr))
+    xi3 = np.exp(yfit(corr.logr*corr.u))
+    denom = (xi1*xi2+xi2*xi3+xi3*xi1)
+    return zeta/denom
+
+def plot_qarr(qvec,**kwargs):
+    q_mean = np.mean(qvec,axis=0)
+    q_std = np.std(qvec,axis=0)
+    v = np.linspace(-1,1,qvec.shape[2])[:,np.newaxis]*np.ones_like(q_mean.T)
+    for row in np.arange(v.shape[1]):
+        plt.errorbar(v[:,row]+.03*row,q_mean.T[:,row],yerr=q_std.T[:,row],**kwargs)
+    plt.xlabel('v')
+    plt.ylabel('q')
+
+def infer_bias(q_dm_infer,q_gal_infer,icov,use_covmat=True):
+    def lnprior(theta):
+        b1, b2 = theta
+        if 0 < b1 < 10 and -10 < b2 < 10:
+            return 0.0
+        return -np.inf
+
+    def lnprob_noerror(bias):
+        ln_prior = lnprior(bias)
+        b1 = bias[0]
+        b2 = bias[1]
+        return -.5*np.sum((((q_gal_infer-q_dm_infer/b1-b2/(2*b1**2))**2)/icov)) + ln_prior
+
+    def lnprob(bias):
+        ln_prior = lnprior(bias)
+        b1 = bias[0]
+        b2 = bias[1]
+        resid = (q_gal_infer-q_dm_infer/b1-b2/(2*b1**2))
+        return -.5*np.matmul(resid,np.matmul(icov,resid)) + ln_prior
+        #return -.5*np.sum(np.dot(np.dot(resid,icov),resid)) + ln_prior
+
+    ndim, nwalkers = 2, 50
+    p0 = [np.array([10*np.random.rand(),20*np.random.rand()-10]) for i in range(nwalkers)]
+
+    if use_covmat:
+        sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)
+    else:
+        sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_noerror)
+    sampler.run_mcmc(p0, 10000)
+    
+    cc = chainconsumer.ChainConsumer()
+    cc.add_chain(sampler.flatchain,parameters=['b1','b2'],name='3pt ZSPEC')
+    summary = cc.analysis.get_summary()
+    b1=summary['b1'][1]
+    b2=summary['b2'][1]
+    resid = (q_gal_infer-q_dm_infer/b1-b2/(2*b1**2))
+    print 'chisq is: ', str(.5*np.sum(np.dot(np.dot(resid,icov),resid)))
+    print 'alt: ', .5*np.matmul(np.matmul(resid,icov),resid)
+    
+    #cc = chainconsumer.ChainConsumer()
+    #cc.add_chain(sampler.flatchain,parameters=['b1','b2'])
+    
+    #cc.plotter.plot(figsize='column',extents=[(0,3),(-1,1)]);
+    return sampler
+
+def infer_bias_2pt(xi_gal,xi_dm,icov,use_covmat=True,plot=False):
+    def lnprior(theta):
+        b1 = theta
+        if 0 < b1 < 10:
+            return 0.0
+        return -np.inf
+
+    def lnprob_noerror(bias):
+        ln_prior = lnprior(bias)
+        b1 = bias
+        return -.5*np.sum(((xi_gal-xi_dm*(b1**2))**2/icov)) + ln_prior
+
+    def lnprob(bias):
+        ln_prior = lnprior(bias)
+        b1 = bias
+        resid = (xi_gal-xi_dm*(b1**2))
+        return -5.*np.sum(np.dot(np.dot(resid.T,icov),resid)) + ln_prior
+
+    ndim, nwalkers = 1, 100
+    p0 = [np.array([3*np.random.rand()]) for i in range(nwalkers)]
+
+    if use_covmat:
+        sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)
+    else:
+        sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_noerror)
+    sampler.run_mcmc(p0, 1000)
+    
+    cc = chainconsumer.ChainConsumer()
+    cc.add_chain(sampler.flatchain,parameters=['b1'])
+    if plot:
+        cc.plotter.plot(figsize='column',extents=[(0,3)]);
+    return np.mean(sampler.flatchain), np.std(sampler.flatchain)
+
+def plot_data_vectors(qdm,qrm,v=None,b1=1,b2=0,b1MAP=1,b2MAP=0,rm_color='g',dm_color='b'):
+    qdm_mean = np.mean(qdm,axis=0)
+    qdm_std = np.std(qdm,axis=0)
+    
+    qrm_mean = np.mean(qrm,axis=0)
+    qrm_std = np.std(qrm,axis=0)
+    
+    if v is None:
+        v = np.arange(qrm.shape[1])
+        plt.xlabel('Data Vector Index')
+    else:    
+        plt.xlabel('v')
+        
+    plt.errorbar(v,qdm_mean/b1+b2/(b1**2),yerr=qdm_std/np.sqrt(qdm.shape[0]),label='DM',color=dm_color)
+    plt.errorbar(v,qrm_mean,yerr=qrm_std,label='Galaxies',color=rm_color)
+    if b1 != 1:
+        plt.title('Best-fit dark matter')
+    else:
+        plt.title('Data Vectors')
+    plt.ylabel('q')
+    plt.legend()
+    
+def make_inference(red_qdm,red_qrm,covmat_src=None,max_pca_comps=None,is11k=False):
+    
+    if max_pca_comps is None:
+        rmcov = np.cov(red_qrm.T)
+        dmcov = np.cov(red_qdm.T)
+    else: 
+        pca = PCA(n_components = max_pca_comps)
+        pca.fit(red_qrm)
+        print pca.explained_variance_ratio_
+        rmcov = pca.get_covariance()
+    
+    if covmat_src is None:
+        #icov = np.linalg.inv(dmcov/red_qdm.shape[0]+rmcov)
+        if is11k:
+            #icov = np.linalg.inv(rmcov/len(red_qrm)+dmcov/len(red_qdm))
+            # corrected for https://arxiv.org/pdf/astro-ph/0608064.pdf 
+            icov = np.linalg.inv(rmcov/len(red_qrm)+dmcov/len(red_qdm))*(len(red_qrm)-red_qrm.shape[1]-1)/(len(red_qrm)-1)
+            #Let's see how it goes taking out the DM errors:
+            #icov = np.linalg.inv(rmcov/len(red_qrm))*(len(red_qrm)-red_qrm.shape[1]-1)/(len(red_qrm)-1)
+        else:
+            #icov = np.linalg.inv(rmcov+dmcov/len(red_qdm))
+            # corrected for https://arxiv.org/pdf/astro-ph/0608064.pdf 
+            icov = np.linalg.inv(rmcov+dmcov/len(red_qdm))*(len(red_qrm)-red_qrm.shape[1]-1)/(len(red_qrm)-1)
+            #Let's see how it goes taking out the DM errors:
+            #icov = np.linalg.inv(rmcov)*(len(red_qrm)-red_qrm.shape[1]-1)/(len(red_qrm)-1)
+        qrm_mean = np.mean(red_qrm,axis=0)
+        qdm_mean = np.mean(red_qdm,axis=0)
+    else:
+        qrm_mean = red_qrm
+        qdm_mean = red_qdm
+        icov = np.linalg.inv(covmat_src)
+    samples = infer_bias(qdm_mean,qrm_mean,icov,use_covmat=True)
+    
+    #cc = chainconsumer.ChainConsumer()
+    #cc.add_chain(samples.flatchain,parameters=['b1','b2'],name='3pt ZSPEC')
+    #cc.plotter.plot(figsize='column',extents=[(0,3),(-1,1)]);
+    
+    return samples
+
+from glob import glob
+import os.path
+import matplotlib.pyplot as plt
+import treecorr
+import yaml
+import paths
+import numpy as np
+import pandas as pd
+def load_config(config_fname):
+    config_path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/configs/'+config_fname+'.config'
+    with open(config_path) as f:
+        return yaml.load(f.read())
+    
+def compress_dv(qdm1):
+    return ((qdm1[:,5:] + np.flip(qdm1[:,:5],axis=1))/2.0).reshape(qdm1.shape[0],5)[:,-5:]
+
+
+
+
+def load_res(path,dset_name,dset_id,config_fname,zvar,min_z,max_z,rsamp_str,sigma=0,norm=True):
+    first_done = False
+    for jk_id in range(15):
+        try:
+            this = load_files(path,dset_name,dset_id,config_fname,zvar,min_z,max_z,rsamp_str,sigma,return_all_norm=norm,jk_id=jk_id,config=config_fname)
+        except IOError:
+            continue
+        this['JK'] = jk_id
+        if not first_done:
+            res1 = this
+            first_done = True
+        else:
+            res1 = pd.concat([res1,this],ignore_index=True)
+    return res1
+
+def load_res_indep(path,dset_name,config_fname,zvar,min_z,max_z,rsamp_str,norm=True,sigma=0,use_alt_randoms=True):
+    first_done = False
+    for dset_id in range(24):
+        try:
+            this = load_files(path,dset_name,dset_id,config_fname,zvar,min_z,max_z,rsamp_str,sigma,return_all_norm=norm,config=config_fname,use_alt_randoms=use_alt_randoms)
+        except IOError:
+            continue
+        this['DSET'] = dset_id
+        if not first_done:
+            res1 = this
+            first_done = True
+        else:
+            res1 = pd.concat([res1,this],ignore_index=True)
+    return res1
+
+def load_res_indep2(path,dset_name,config_fname,zvar,min_z,max_z,rsamp_str,norm=False):
+    first_done = False
+    for dset_id in range(24):
+        if norm:
+            try:
+                this = load_files(path+config_fname+'_'+dset_name+'dset0_jk'+str(dset_id)+'_sigma0_'+zvar+'_'+str(min_z)+'_'+str(max_z)+'_rsamp'+rsamp_str,return_all_norm=True)
+            except IOError:
+                continue
+        else:
+            try:
+                this = load_files(path+config_fname+'_'+dset_name+'dset0_jk'+str(dset_id)+'_sigma0_'+zvar+'_'+str(min_z)+'_'+str(max_z)+'_rsamp'+rsamp_str,return_all=True)
+            except IOError:
+                continue
+        this['DSET'] = dset_id
+        if not first_done:
+            res1 = this
+            first_done = True
+        else:
+            res1 = pd.concat([res1,this],ignore_index=True)
+    return res1
+
+def load_res_xi(path,dset_name,dset_id,config_fname,min_z,max_z,rsamp_str):
+    xilist = []
+    for jk_id in range(15):
+        try:
+            this = np.load(path+config_fname+'_'+dset_name+'dset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma0_ZSPEC_'+str(min_z)+'_'+str(max_z)+'_rsamp'+rsamp_str+'.xi.npy')
+        except IOError:
+            continue
+        xilist.append(this.flatten())
+    return xilist
+
+def load_res_xi_indep(path,dset_name,config_fname,min_z,max_z,rsamp_str,zvar='ZSPEC',sigma=0):
+    xilist = []
+    for dset_id in range(24):
+        try:
+            this = np.load(path+config_fname+'_'+dset_name+'dset'+str(dset_id)+'_jk-1_sigma'+str(sigma)+'_'+zvar+'_'+str(min_z)+'_'+str(max_z)+'_rsamp'+rsamp_str+'.xi.npy')
+        except IOError:
+            continue
+        xilist.append(this.flatten())
+    return xilist
+
+def load_files(path,dset_name,dset_id,config_fname,zvar,min_z,max_z,rsamp_str,sigma=0,get_q=True,jk_id=-1,config='newpaper13.1',return_all=False,return_all_norm=False,use_alt_randoms=True):
+    runname = config_fname+'_'+dset_name+'dset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma)+'_'+zvar+'_'+str(min_z)+'_'+str(max_z)+'_rsamp'+rsamp_str
+    ddd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    ddd.read(path+runname+'.ddd')
+    ddr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    ddr.read(path+runname+'.ddr')
+    drd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    drd.read(path+runname+'.drd')
+    rdd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rdd.read(path+runname+'.rdd')
+    rrd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rrd.read(path+runname+'.rrd')
+    drr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    drr.read(path+runname+'.drr')
+    rdr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rdr.read(path+runname+'.rdr')
+    rrr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    try:
+        rrr.read(path+runname+'.rrr')
+    except IOError:
+        print 'missing randoms'
+        if use_alt_randoms:
+            print 'using new randoms'
+            newrunnamelist = glob(path+config_fname+'_'+dset_name+'dset*_jk'+str(jk_id)+'_sigma'+str(sigma)+'_'+zvar+'_'+str(min_z)+'_'+str(max_z)+'_rsamp'+rsamp_str+'.rrr')
+            if newrunnamelist != []:
+                newrunname = newrunnamelist[-1]
+                print newrunname
+                rrr.read(newrunname)
+                print rrr.ntri
+            else:
+                print 'couldnt find alternate rrr file'
+                raise IOError
+        else:
+            raise IOError
+        
+    corr = treecorr.NNNCorrelation(config = load_config(config)['3PCF'])
+    dd = treecorr.NNCorrelation(config = load_config(config)['2PCF'])
+    zeta = (ddd.ntri/ddd.tot - ddr.ntri/ddr.tot - drd.ntri/drd.tot - rdd.ntri/rdd.tot + rrd.ntri/rrd.tot + drr.ntri/drr.tot + rdr.ntri/rdr.tot - rrr.ntri/rrr.tot)/(rrr.ntri/rrr.tot)
+    dddrrr = ddd.ntri/ddd.tot/(rrr.ntri/rrr.tot)
+    oldest = (ddd.ntri/ddd.tot - ddr.ntri/ddr.tot - drd.ntri/drd.tot - rdd.ntri/rdd.tot)/(rrr.ntri/rrr.tot) + 2
+
+    xi = np.load(path+runname+'.xi.npy')
+    
+    from scipy.interpolate import UnivariateSpline
+    
+    yfit = UnivariateSpline(dd.logr[xi != 0],np.log(xi)[xi != 0],k=4)
+    xi1 = np.exp(yfit(corr.logr*(1+corr.u*np.abs(corr.v))))
+    xi2 = np.exp(yfit(corr.logr))
+    xi3 = np.exp(yfit(corr.logr*corr.u))
+    denom = (xi1*xi2+xi2*xi3+xi3*xi1)
+    
+    #insert elisabeth stuff here
+    
+    yfit = UnivariateSpline(dd.logr[xi != 0],np.log(xi)[xi != 0],k=4)
+    xi1 = np.exp(yfit(np.log(np.exp(corr.logr)*(1+corr.u*np.abs(corr.v)))))
+    xi2 = np.exp(yfit(np.log(np.exp(corr.logr))))
+    xi3 = np.exp(yfit(np.log(np.exp(corr.logr)*corr.u)))
+    correct_denom = (xi1*xi2+xi2*xi3+xi3*xi1)
+    
+    if return_all:
+        return pd.DataFrame(np.array([ddd.ntri.flatten().T,ddr.ntri.flatten().T,drd.ntri.flatten().T,
+                             rdd.ntri.flatten().T,drr.ntri.flatten().T,rdr.ntri.flatten().T, 
+                             rrd.ntri.flatten().T,rrr.ntri.flatten().T,zeta.flatten().T,(zeta/denom).flatten().T, dddrrr.flatten().T, oldest.flatten().T]).T, columns=['DDD','DDR','DRD','RDD','DRR','RDR','RRD','RRR','ZETA','Q','DDD/RRR','OLDEST'])
+    
+    if return_all_norm:
+        return pd.DataFrame(np.array([ddd.ntri.flatten().T/ddd.tot,ddr.ntri.flatten().T/ddr.tot,drd.ntri.flatten().T/drd.tot,
+                             rdd.ntri.flatten().T/rdd.tot,drr.ntri.flatten().T/drr.tot,rdr.ntri.flatten().T/rdr.tot, 
+                             rrd.ntri.flatten().T/rrd.tot,rrr.ntri.flatten().T/rrr.tot,zeta.flatten().T,denom.flatten().T, correct_denom.flatten().T, (zeta/correct_denom).flatten().T, dddrrr.flatten().T, oldest.flatten().T]).T, columns=['DDD','DDR','DRD','RDD','DRR','RDR','RRD','RRR','ZETA','DENOM','CORRECTDENOM','Q','DDD/RRR','OLDEST'])
+    
+    if get_q:
+        return zeta/denom
+    else:
+        return zeta
+
+def old_load_files(runname,rsamp,get_q=True,config='paper13.1',return_all=False):
+    path = ''
+    print len(glob(path+runname+'*'))
+    ddd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    ddd.read(path+runname+'.ddd')
+    ddr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    ddr.read(path+runname+'.ddr')
+    drd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    drd.read(path+runname+'.drd')
+    rdd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rdd.read(path+runname+'.rdd')
+    rrd = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rrd.read(path+runname+'.rrd')
+    drr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    drr.read(path+runname+'.drr')
+    rdr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rdr.read(path+runname+'.rdr')
+    rrr = treecorr.NNNCorrelation(config=load_config(config)['3PCF'])
+    rrr.read(path+runname+'.rrr')
+
+    corr = treecorr.NNNCorrelation(config = load_config(config)['3PCF'])
+    dd = treecorr.NNCorrelation(config = load_config(config)['2PCF'])
+    zeta = (ddd.ntri - ddr.ntri/rsamp - drd.ntri/rsamp - rdd.ntri/rsamp + rrd.ntri/rsamp**2 + drr.ntri/rsamp**2 + rdr.ntri/rsamp**2 - rrr.ntri/rsamp**3)/(rrr.ntri/rsamp**3)
+
+    xi = np.load(path+runname+'.xi.npy')
+    
+    from scipy.interpolate import UnivariateSpline
+    yfit = UnivariateSpline(dd.logr[xi != 0],np.log(xi)[xi != 0],k=4)
+    xi1 = np.exp(yfit(corr.logr*(1+corr.u*np.abs(corr.v))))
+    xi2 = np.exp(yfit(corr.logr))
+    xi3 = np.exp(yfit(corr.logr*corr.u))
+    denom = (xi1*xi2+xi2*xi3+xi3*xi1)
+    
+    if return_all:
+        print zeta.flatten()
+        return pd.DataFrame(np.array([ddd.ntri.flatten().T,ddr.ntri.flatten().T,drd.ntri.flatten().T,
+                             rdd.ntri.flatten().T,drr.ntri.flatten().T,rdr.ntri.flatten().T, 
+                             rrd.ntri.flatten().T,rrr.ntri.flatten().T,zeta.flatten().T,(zeta/denom).flatten().T]).T, columns=['DDD','DDR','DRD','RDD','DRR','RDR','RRD','RRR','ZETA','Q'])
+    
+    if get_q:
+        return zeta/denom
+    else:
+        return zeta
+
+def load_files2(runname,rsamp):
+    ddd = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    ddd.read(path+runname+'.ddd')
+    rrr = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    rrr.read(path+runname+'.rrr')
+    return (ddd.ntri - rrr.ntri/rsamp**3)/(rrr.ntri/rsamp**3)
+
+def load_files3(runname,rsamp):
+    ddd = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    ddd.read(path+runname+'.ddd')
+    ddr = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    ddr.read(path+runname+'.ddr')
+    drd = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    drd.read(path+runname+'.drd')
+    rdd = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    rdd.read(path+runname+'.rdd')
+    rrr = treecorr.NNNCorrelation(config=load_config('paper13.1')['3PCF'])
+    rrr.read(path+runname+'.rrr')
+    return (ddd.ntri - ddr.ntri/(rsamp) - drd.ntri/(rsamp) - rdd.ntri/(rsamp))/(rrr.ntri/rsamp**3) + 2
+
+
+def plot_data_vectors(qdm,qrm,v=None,b1=1,b2=0,b1MAP=1,b2MAP=0,rm_color='g',dm_color='b'):
+    qdm_mean = np.mean(qdm,axis=0)
+    qdm_std = np.std(qdm,axis=0)
+    
+    qrm_mean = np.mean(qrm,axis=0)
+    qrm_std = np.std(qrm,axis=0)
+    
+    if v is None:
+        v = np.arange(qrm.shape[1])
+        plt.xlabel('Data Vector Index')
+    else:    
+        plt.xlabel('v')
+        
+    plt.errorbar(v,qdm_mean/b1+b2/(b1**2),yerr=qdm_std/np.sqrt(qdm.shape[0]),label='DM',color=dm_color)
+    plt.errorbar(v,qrm_mean,yerr=qrm_std,label='Galaxies',color=rm_color)
+    if b1 != 1:
+        plt.title('Best-fit dark matter')
+    else:
+        plt.title('Data Vectors')
+    plt.ylabel('q')
+    plt.legend()
+
+def plot_dv(res,var,indiv_runs=True,offset=0,compressed=False,**kwargs):
+    if compressed:
+        if indiv_runs:
+            plt.plot(np.linspace(.1,.9,5),compress_dv(res[var].values.reshape(-1,10)).T,alpha=.3,color=kwargs['color'])
+        plt.errorbar(np.linspace(.1,.9,5)+offset,np.mean(compress_dv(res[var].values.reshape(-1,10)),axis=0),
+                     yerr=np.std(compress_dv(res[var].values.reshape(-1,10)),axis=0),**kwargs)
+    else:
+        if indiv_runs:
+            plt.plot(np.linspace(-.9,.9,10),res[var].values.reshape(-1,10).T,alpha=.3,color=kwargs['color'])
+        plt.errorbar(np.linspace(-.9,.9,10)+offset,np.mean(res[var].values.reshape(-1,10),axis=0),
+                     yerr=np.std(res[var].values.reshape(-1,10),axis=0),**kwargs)
+    
+def get_max_like(red_qdm,red_qrm):
+    b1 = np.linspace(0, 3, 300)
+    b2 = np.linspace(-3, 3, 300)
+    z = np.zeros((len(b2),len(b1)))
+    icov = np.linalg.inv(np.cov(red_qrm.T)+np.cov(red_qdm.T)/len(red_qdm))
+    for i,tb1 in enumerate(b1):
+        for j,tb2 in enumerate(b2):
+            resid = (np.mean(red_qrm,axis=0)-np.mean(red_qdm,axis=0)/tb1-tb2/(tb1**2))
+            z[j,i] = .5*np.matmul(np.matmul(resid,icov),resid)
+    print 'manual chisq: ', min(z[~np.isnan(z)])
+    return b1, b2, z
+
+def get_max_like_diag(red_qdm,red_qrm):
+    b1 = np.arange(0, 3, 0.01)
+    b2 = np.arange(-1, 1, 0.01)
+    z2 = np.zeros((len(b2),len(b1)))
+    icov = np.linalg.inv(np.diag(np.cov(red_qrm.T)+np.cov(red_qdm.T)/len(red_qdm))*np.eye(5))
+    for i,tb1 in enumerate(b1):
+        for j,tb2 in enumerate(b2):
+            resid = (np.mean(red_qrm,axis=0)-np.mean(red_qdm,axis=0)/tb1-tb2/(tb1**2))
+            z2[j,i] = .5*np.matmul(resid,np.matmul(resid,icov))
+    print 'diag chisq: ', min(z2[~np.isnan(z2)])
+    return b1, b2, z2
+
+def get_lazeyras_schmidt_2015(b1):
+    return .412-2.143*b1+.929*b1**2+.008*b1**3
+
+def get_hoffman_2015(b1):
+    return .51-2.21*b1+1*b1**2
diff --git a/tricorder/simple.py b/tricorder/simple.py
index 7b640c7..4258e4d 100644
--- a/tricorder/simple.py
+++ b/tricorder/simple.py
@@ -12,12 +12,11 @@
 import os
 
 import paths
-from simple_script import out_path, config_dir
 
 
 def load_config(config_fname):
     config_path = os.path.join(
-        simple_script.config_dir, config_fname+'.config')
+        paths.config_dir, config_fname+'.config')
     with open(config_path) as f:
         return yaml.load(f.read())
 
@@ -30,10 +29,9 @@ def calc_2pt(data, randoms, config_fname, zvar, random_zvar, ra_var='RA', dec_va
     config_2pt = load_config(config_fname)['2PCF']
     cat = treecorr.Catalog(ra=data[ra_var], dec=data[dec_var],
                            dec_units='degrees', ra_units='degrees',
-                           r=datasets.buzzard_cosmo.comoving_distance(data[zvar]).value*datasets.buzzard_cosmo.h)
+                           r=datasets.buzzard_cosmo.comoving_distance(data[zvar]).value)
     random_cat = treecorr.Catalog(ra=randoms[random_ra_var], dec=randoms[random_dec_var],
-                                  dec_units='degrees', ra_units='degrees',
-                                  r=datasets.buzzard_cosmo.comoving_distance(randoms[random_zvar]).value*datasets.buzzard_cosmo.h)
+                                  dec_units='degrees', ra_units='degrees', r=datasets.buzzard_cosmo.comoving_distance(randoms[random_zvar]).value)
     print config_2pt
     dd = treecorr.NNCorrelation(config=config_2pt)
     dr = treecorr.NNCorrelation(config=config_2pt)
@@ -50,10 +48,10 @@ def calc_3pt(data, randoms, config_fname, zvar, random_zvar, ra_var='RA', dec_va
     config_3pt = load_config(config_fname)['3PCF']
     cat = treecorr.Catalog(ra=data[ra_var], dec=data[dec_var],
                            dec_units='degrees', ra_units='degrees',
-                           r=datasets.buzzard_cosmo.comoving_distance(data[zvar]).value*datasets.buzzard_cosmo.h)
+                           r=datasets.buzzard_cosmo.comoving_distance(data[zvar]).value)
     random_cat = treecorr.Catalog(ra=randoms[random_ra_var], dec=randoms[random_dec_var],
                                   dec_units='degrees', ra_units='degrees',
-                                  r=datasets.buzzard_cosmo.comoving_distance(randoms[random_zvar]).value*datasets.buzzard_cosmo.h)
+                                  r=datasets.buzzard_cosmo.comoving_distance(randoms[random_zvar]).value)
     print config_3pt
     ddd = treecorr.NNNCorrelation(config=config_3pt)
     ddr = treecorr.NNNCorrelation(config=config_3pt)
@@ -98,11 +96,11 @@ def calc_3pt_noisy_photoz_lss(dset_id, config_fname, min_z, max_z, sigma_z, zvar
 
     xi_file_name = config_fname + '_lssdset' + \
         str(dset_id)+'_sigma'+str(sigma_z)+'_'+str(min_z)+'_'+str(max_z)+'.xi'
-    zeta_file_name = config_fname+'_lssdset' +
+    zeta_file_name = config_fname+'_lssdset' + \
         str(dset_id)+'_sigma'+str(sigma_z) + \
-            '_'+str(min_z)+'_'+str(max_z)+'.zeta'
-    np.save(os.path.join(simple_script.out_dir, xi_file_name), xi)
-    np.save(os.path.join(simple_script.out_dir, zeta_file_name), zeta)
+        '_'+str(min_z)+'_'+str(max_z)+'.zeta'
+    np.save(os.path.join(paths.corr_out_dir, xi_file_name), xi)
+    np.save(os.path.join(paths.corr_out_dir, zeta_file_name), zeta)
 
 
 def calc_3pt_noisy_photoz_dm(dset_id, config_fname, min_z, max_z, sigma_z, zvar, random_zvar):
@@ -124,14 +122,14 @@ def calc_3pt_noisy_photoz_dm(dset_id, config_fname, min_z, max_z, sigma_z, zvar,
     zeta = calc_3pt(data_slice, randoms_slice, config_fname,
                     zvar, random_zvar, ra_var=ra_var, dec_var=dec_var)
 
-    xi_file_name = config_fname +
+    xi_file_name = config_fname + \
         '_dmdset'+str(dset_id)+'_sigma'+str(sigma_z) + \
-            '_'+str(min_z)+'_'+str(max_z)+'.xi'
-    zeta_file_name = config_fname+'_dmdset' +
+        '_'+str(min_z)+'_'+str(max_z)+'.xi'
+    zeta_file_name = config_fname+'_dmdset' + \
         str(dset_id)+'_sigma'+str(sigma_z) + \
-            '_'+str(min_z)+'_'+str(max_z)+'.zeta'
-    np.save(os.path.join(simple_script.out_dir, xi_file_name), xi)
-    np.save(os.path.join(simple_script.out_dir, zeta_file_name), zeta)
+        '_'+str(min_z)+'_'+str(max_z)+'.zeta'
+    np.save(os.path.join(paths.corr_out_dir, xi_file_name), xi)
+    np.save(os.path.join(paths.corr_out_dir, zeta_file_name), zeta)
 
 
 def calc_3pt_noisy_photoz_rm(dset_id, config_fname, min_z, max_z, sigma_z, zvar, random_zvar):
@@ -157,11 +155,11 @@ def calc_3pt_noisy_photoz_rm(dset_id, config_fname, min_z, max_z, sigma_z, zvar,
     zeta = calc_3pt(data_slice, randoms_slice, config_fname,
                     zvar, random_zvar, ra_var=ra_var, dec_var=dec_var)
 
-    xi_file_name = config_fname +
+    xi_file_name = config_fname + \
         '_dset'+str(dset_id)+'_sigma'+str(sigma_z) + \
-            '_'+str(min_z)+'_'+str(max_z)+'.xi'
-    zeta_file_name = config_fname+'_dset' +
+        '_'+str(min_z)+'_'+str(max_z)+'.xi'
+    zeta_file_name = config_fname+'_dset' + \
         str(dset_id)+'_sigma'+str(sigma_z) + \
-            '_'+str(min_z)+'_'+str(max_z)+'.zeta'
-    np.save(os.path.join(simple_script.out_dir, xi_file_name), xi)
-    np.save(os.path.join(simple_script.out_dir, zeta_file_name), zeta)
+        '_'+str(min_z)+'_'+str(max_z)+'.zeta'
+    np.save(os.path.join(paths.corr_out_dir, xi_file_name), xi)
+    np.save(os.path.join(paths.corr_out_dir, zeta_file_name), zeta)
diff --git a/tricorder/simple_angular.py b/tricorder/simple_angular.py
index 14f276f..36a60ec 100644
--- a/tricorder/simple_angular.py
+++ b/tricorder/simple_angular.py
@@ -1,6 +1,8 @@
 #!/u/ki/mbaumer/anaconda/bin/python
+from __future__ import division
 import matplotlib.pyplot as plt
 from astropy.io import fits
+from astropy.table import Table
 import treecorr
 import yaml
 import numpy as np
@@ -9,23 +11,80 @@
 import tricorder
 import yaml
 import sys
+import os
+import paths
+from glob import glob
+import pandas as pd
+import pickle
+from tenacity import *
+
+from scipy.stats import binned_statistic
+
+np.random.seed(12)
+
+from make_data_randoms import (generate_randoms_radec, index_to_radec,
+                               radec_to_index)
+
+#@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=300)+wait_random(0, 10))
+def persistent_load_fits(fpath):
+    print 'attempting load of ' + fpath
+    return fits.getdata(fpath)
+
 
 def load_config(config_fname):
-    config_path = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/configs/'+config_fname+'.config'
+    config_path = os.path.join(
+        paths.config_dir, config_fname+'.config')
     with open(config_path) as f:
         return yaml.load(f.read())
 
-def get_zslice(data,min_z,max_z,zvar):
+
+def get_zslice(data, min_z, max_z, zvar):
     return data[(data[zvar] > min_z) & (data[zvar] < max_z)]
 
-def calc_2pt(data,randoms,config_fname,zvar,random_zvar,ra_var='RA',dec_var='DEC',random_ra_var='RA',random_dec_var='DEC'):
+def downselect_pz(input_data, target_cts, target_bins, input_zvar, oversamp):
+    
+    input_labels = np.digitize(input_data[input_zvar], target_bins)
+    
+    #kill overflow and underflow bins
+    input_data = input_data[(input_labels != 0) & (input_labels != len(target_bins))]
+    input_labels = input_labels[(input_labels != 0) & (input_labels != len(target_bins))]
+
+    input_cts, _ = np.histogram(
+        input_data[input_zvar], bins=target_bins, range=(0, 1))
+    input_weights = target_cts/(input_cts+1e-40)
+    return input_data[np.random.rand(len(input_data)) < oversamp*input_weights[input_labels-1]]
+
+def downselect(input_data, target, input_zvar, target_zvar, oversamp):
+    target_cts, bins = np.histogram(
+        target[target_zvar], range=(0, 1), bins=100)
+    input_labels = np.digitize(input_data[input_zvar], bins)
+    input_cts, _ = np.histogram(
+        input_data[input_zvar], bins=bins, range=(0, 1))
+    input_weights = target_cts/(input_cts+1e-40)
+    return input_data[np.random.rand(len(input_data)) < oversamp*input_weights[input_labels-1]]
+
+
+def calc_2pt(data, randoms, config_fname, do3D, ra_var='RA', dec_var='DEC',
+             random_ra_var='RA', random_dec_var='DEC', data_zvar=None, random_zvar=None,outfname=''):
+    if do3D:
+        assert data_zvar is not None
+        assert random_zvar is not None
     config_2pt = load_config(config_fname)['2PCF']
-    cat = treecorr.Catalog(ra=data[ra_var],dec=data[dec_var],
-                           dec_units='degrees',ra_units='degrees',
-                           )
-    random_cat = treecorr.Catalog(ra=randoms[random_ra_var],dec=randoms[random_dec_var],
-                            dec_units='degrees',ra_units='degrees',
-                            )
+    if not do3D:
+        cat = treecorr.Catalog(ra=data[ra_var], dec=data[dec_var],
+                               dec_units='degrees', ra_units='degrees',
+                               )
+        random_cat = treecorr.Catalog(ra=randoms[random_ra_var], dec=randoms[random_dec_var],
+                                      dec_units='degrees', ra_units='degrees',
+                                      )
+    else:
+        cat = treecorr.Catalog(ra=data[ra_var], dec=data[dec_var],
+                               dec_units='degrees', ra_units='degrees',
+                               r=datasets.buzzard_cosmo.comoving_distance(data[data_zvar]).value)
+        random_cat = treecorr.Catalog(ra=randoms[random_ra_var], dec=randoms[random_dec_var],
+                                      dec_units='degrees', ra_units='degrees',
+                                      r=datasets.buzzard_cosmo.comoving_distance(randoms[random_zvar]).value)
+
     print config_2pt
     dd = treecorr.NNCorrelation(config=config_2pt)
     dr = treecorr.NNCorrelation(config=config_2pt)
@@ -37,153 +96,581 @@ def calc_2pt(data,randoms,config_fname,zvar,random_zvar,ra_var='RA',dec_var='DEC
     xi, varxi = dd.calculateXi(dr=dr, rr=rr)
     return xi
 
-def calc_3pt(data,randoms,config_fname,zvar,random_zvar,ra_var='RA',dec_var='DEC',random_ra_var='RA',random_dec_var='DEC'):
+
+def calc_3pt(data, randoms, config_fname, do3D, ra_var='RA',
+             dec_var='DEC', random_ra_var='RA', random_dec_var='DEC',
+             data_zvar=None, random_zvar=None, outvar='zeta',outfname=''):
+
+    if do3D:
+        assert data_zvar is not None
+        assert random_zvar is not None
+
     config_3pt = load_config(config_fname)['3PCF']
-    cat = treecorr.Catalog(ra=data[ra_var],dec=data[dec_var],
-                           dec_units='degrees',ra_units='degrees',
-                           )
-    random_cat = treecorr.Catalog(ra=randoms[random_ra_var],dec=randoms[random_dec_var],
-                            dec_units='degrees',ra_units='degrees',
-                            )
+    if not do3D:
+        cat = treecorr.Catalog(ra=data[ra_var], dec=data[dec_var],
+                               dec_units='degrees', ra_units='degrees',
+                               )
+        random_cat = treecorr.Catalog(ra=randoms[random_ra_var], dec=randoms[random_dec_var],
+                                      dec_units='degrees', ra_units='degrees',
+                                      )
+    else:
+        cat = treecorr.Catalog(ra=data[ra_var], dec=data[dec_var],
+                               dec_units='degrees', ra_units='degrees',
+                               r=datasets.buzzard_cosmo.comoving_distance(data[data_zvar]).value)
+        random_cat = treecorr.Catalog(ra=randoms[random_ra_var], dec=randoms[random_dec_var],
+                                      dec_units='degrees', ra_units='degrees',
+                                      r=datasets.buzzard_cosmo.comoving_distance(randoms[random_zvar]).value)
+
     print config_3pt
-    ddd = treecorr.NNNCorrelation(config=config_3pt)
-    ddr = treecorr.NNNCorrelation(config=config_3pt)
-    drd = treecorr.NNNCorrelation(config=config_3pt)
-    rdd = treecorr.NNNCorrelation(config=config_3pt)
-    rdr = treecorr.NNNCorrelation(config=config_3pt)
-    rrd = treecorr.NNNCorrelation(config=config_3pt)
-    drr = treecorr.NNNCorrelation(config=config_3pt)
-    rrr = treecorr.NNNCorrelation(config=config_3pt)
-    ddd.process(cat, metric=config_3pt['metric'])
-    ddr.process(cat, cat,  random_cat, metric=config_3pt['metric'])
-    drd.process(cat, random_cat, cat, metric=config_3pt['metric'])
-    rdd.process(random_cat, cat, cat, metric=config_3pt['metric'])
-    rdr.process(random_cat, cat,  random_cat, metric=config_3pt['metric'])
-    rrd.process(random_cat, random_cat,  cat, metric=config_3pt['metric'])
-    drr.process(cat, random_cat,  random_cat, metric=config_3pt['metric'])
-    rrr.process(random_cat, random_cat,  random_cat, metric=config_3pt['metric'])
-    zeta, varzeta = ddd.calculateZeta(ddr=ddr,drd=drd,rdd=rdd,rrd=rrd,rdr=rdr,drr=drr,rrr=rrr)
-    return zeta
-
-dset_vec = ['/u/ki/jderose/public_html/bcc/catalog/particles/y1/buzzard/flock/buzzard-0/a/downsampled_particles.fits.downsample',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/a/buzzard_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/b/buzzard_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/c/buzzard_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/d/buzzard_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/e/buzzard_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/f/buzzard_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/a/buzzard-1_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/rdmagic/y1/buzzard/flock/buzzard-1/b/buzzard-1_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/c/buzzard-1_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/d/buzzard-1_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/e/buzzard-1_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-1/f/buzzard-1_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/a/buzzard2_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/b/buzzard2_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/c/buzzard2_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/d/buzzard2_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/e/buzzard2_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-2/f/buzzard2_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/a/buzzard-3_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/b/buzzard-3_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/c/buzzard-3_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/d/buzzard-3_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/e/buzzard-3_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-3/f/buzzard-3_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/a/buzzard5_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/b/buzzard5_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/c/buzzard5_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/d/buzzard5_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/e/buzzard5_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-5/f/buzzard5_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/a/buzzard21_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/b/buzzard21_1.6-6b_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/c/buzzard21_1.6-6c_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/d/buzzard21_1.6-6d_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/e/buzzard21_1.6-6e_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit',
-            '/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-21/f/buzzard21_1.6-6f_run_redmapper_v6.4.18_redmagic_highdens_0.5-10.fit'
-           ]
-
-dset_vec2 = [
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/a/Buzzard_v1.6_Y1a_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/b/Buzzard_v1.6_Y1b_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/c/Buzzard_v1.6_Y1c_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/d/Buzzard_v1.6_Y1d_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/e/Buzzard_v1.6_Y1e_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-0/f/Buzzard_v1.6_Y1f_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/a/Buzzard_v1.6_Y1a_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/b/Buzzard_v1.6_Y1b_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/c/Buzzard_v1.6_Y1c_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/d/Buzzard_v1.6_Y1d_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/e/Buzzard_v1.6_Y1e_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-1/f/Buzzard_v1.6_Y1f_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/a/Buzzard_v1.6_Y1a_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/b/Buzzard_v1.6_Y1b_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/c/Buzzard_v1.6_Y1c_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/d/Buzzard_v1.6_Y1d_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/e/Buzzard_v1.6_Y1e_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-2/f/Buzzard_v1.6_Y1f_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/a/Buzzard_v1.6_Y1a_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/b/Buzzard_v1.6_Y1b_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/c/Buzzard_v1.6_Y1c_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/d/Buzzard_v1.6_Y1d_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/e/Buzzard_v1.6_Y1e_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-3/f/Buzzard_v1.6_Y1f_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/a/Buzzard_v1.6_Y1a_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/b/Buzzard_v1.6_Y1b_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/c/Buzzard_v1.6_Y1c_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/d/Buzzard_v1.6_Y1d_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/e/Buzzard_v1.6_Y1e_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-5/f/Buzzard_v1.6_Y1f_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/a/Buzzard_v1.6_Y1a_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/b/Buzzard_v1.6_Y1b_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/c/Buzzard_v1.6_Y1c_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/d/Buzzard_v1.6_Y1d_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/e/Buzzard_v1.6_Y1e_gold.fits',
-'/u/ki/jderose/public_html/bcc/catalog/mergedcats/y1/buzzard/flock/buzzard-21/f/Buzzard_v1.6_Y1f_gold.fits', 
-]
-
-def calc_3pt_noisy_photoz_lss(dset_id,config_fname,min_z,max_z,sigma_z,zvar,random_zvar):
-    randoms = np.load('/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/Buzzard_v1.6_Y1_0_a/lss_sample/data/REDSHIFT0.6_1nsideNonenJack30.dset_randoms.npy')
-    randoms = randoms[np.random.rand(len(randoms)) < 0.2]
-    data = fits.getdata(dset_vec2[dset_id])
+
+    if outvar == 'zeta':
+        ddd = treecorr.NNNCorrelation(config=config_3pt)
+        ddr = treecorr.NNNCorrelation(config=config_3pt)
+        drd = treecorr.NNNCorrelation(config=config_3pt)
+        rdd = treecorr.NNNCorrelation(config=config_3pt)
+        rdr = treecorr.NNNCorrelation(config=config_3pt)
+        rrd = treecorr.NNNCorrelation(config=config_3pt)
+        drr = treecorr.NNNCorrelation(config=config_3pt)
+        rrr = treecorr.NNNCorrelation(config=config_3pt)
+        ddd.process(cat, metric=config_3pt['metric'])
+        ddr.process(cat, cat,  random_cat, metric=config_3pt['metric'])
+        drd.process(cat, random_cat, cat, metric=config_3pt['metric'])
+        rdd.process(random_cat, cat, cat, metric=config_3pt['metric'])
+        rdr.process(random_cat, cat,  random_cat, metric=config_3pt['metric'])
+        rrd.process(random_cat, random_cat,  cat, metric=config_3pt['metric'])
+        drr.process(cat, random_cat,  random_cat, metric=config_3pt['metric'])
+        rrr.process(random_cat, random_cat,  random_cat,
+                    metric=config_3pt['metric'])
+        output, varzeta = ddd.calculateZeta(
+            ddr=ddr, drd=drd, rdd=rdd, rrd=rrd, rdr=rdr, drr=drr, rrr=rrr)
+        return output
+    elif outvar == 'ddd':
+        ddd = treecorr.NNNCorrelation(config=config_3pt)
+        ddr = treecorr.NNNCorrelation(config=config_3pt)
+        drd = treecorr.NNNCorrelation(config=config_3pt)
+        rdd = treecorr.NNNCorrelation(config=config_3pt)
+        tic = time.time()
+        ddd.process(cat, metric=config_3pt['metric'])
+        toc = time.time()
+        print 'ddd took: ', toc-tic
+        ddd.write(outfname+'.ddd')
+        ddr.process(cat, cat,  random_cat, metric=config_3pt['metric'])
+        ddr.write(outfname+'.ddr')
+        drd.process(cat, random_cat, cat, metric=config_3pt['metric'])
+        drd.write(outfname+'.drd')
+        rdd.process(random_cat, cat, cat, metric=config_3pt['metric'])
+        rdd.write(outfname+'.rdd')
+        return
+    elif outvar == 'rdr':
+        rdr = treecorr.NNNCorrelation(config=config_3pt)
+        rdr.process(random_cat, cat,  random_cat, metric=config_3pt['metric'])
+        print rdr.ntri, rdr.tot
+        rdr.write(outfname+'.rdr')
+        return
+    elif outvar == 'rrd':
+        rrd = treecorr.NNNCorrelation(config=config_3pt)
+        rrd.process(random_cat, random_cat,  cat, metric=config_3pt['metric'])
+        print rrd.ntri, rrd.tot
+        rrd.write(outfname+'.rrd')
+        return
+    elif outvar == 'drr':
+        drr = treecorr.NNNCorrelation(config=config_3pt)
+        drr.process(cat, random_cat,  random_cat, metric=config_3pt['metric'])
+        print drr.ntri, drr.tot
+        drr.write(outfname+'.drr')
+        return
+    elif outvar == 'rrr':
+        rrr = treecorr.NNNCorrelation(config=config_3pt)
+        tic = time.time()
+        rrr.process(random_cat, random_cat,  random_cat, metric=config_3pt['metric'])
+        toc = time.time()
+        print 'rrr took: ', toc-tic
+        rrr.write(outfname+'.rrr')
+        return
+    else:
+        nnn = treecorr.NNNCorrelation(config=config_3pt)
+        toc = time.time()
+        setdict = {'d': cat, 'r': random_cat}
+        nnn.process(setdict[outvar[0]],
+                    setdict[outvar[1]], setdict[outvar[2]],
+                    metric=config_3pt['metric'])
+        tic = time.time()
+        print '3PCF took', tic - toc
+        output = nnn.ntri
+        return output
+
+
+def calc_3pt_noisy_photoz_lss(dset_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, random_oversamp):
+    randoms = fits.getdata(paths.lss_y1_randoms)
+    data = fits.getdata(paths.lss_y1[dset_id])
     data = data[data['lss-sample'] == 1]
+
+    ra_var = 'RA'
+    dec_var = 'DEC'
+
+    data = data[data[ra_var] < 100]
+    data = data[data[dec_var] < -20]
+
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
+    data_slice = get_zslice(data, min_z, max_z, zvar)
+    randoms_slice = get_zslice(randoms, min_z, max_z, random_zvar)
+
+    randoms_slice = randoms_slice[np.random.rand(len(randoms_slice)) < (
+        len(data_slice)/len(randoms_slice)*random_oversamp)]
+
+    xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                  ra_var=ra_var, dec_var=dec_var,
+                  data_zvar=zvar, random_zvar=random_zvar,)
+    zeta = calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                    ra_var=ra_var, dec_var=dec_var,
+                    data_zvar=zvar, random_zvar=random_zvar,)
+
+    xi_file_name = config_fname + \
+        '_lssdset'+str(dset_id)+'_sigma'+str(sigma_z) + \
+        '_'+str(zvar)+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(random_oversamp)+'.xi'
+    zeta_file_name = config_fname+'_lssdset' + \
+        str(dset_id)+'_sigma'+str(sigma_z) + \
+        '_'+str(zvar)+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(random_oversamp)+'.zeta'
+
+    if not do3D:
+        np.save(os.path.join(paths.ang_out_dir, xi_file_name), xi)
+        np.save(os.path.join(paths.ang_out_dir, zeta_file_name), zeta)
+    else:
+        np.save(os.path.join(paths.corr_out_dir, xi_file_name), xi)
+        np.save(os.path.join(paths.corr_out_dir, zeta_file_name), zeta)
+
+
+def calc_3pt_noisy_photoz_mice(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, random_oversamp, outvar='zeta'):
+
+    if min_z == .6:
+        data = fits.getdata(paths.rm_mice_y1_HL[dset_id])
+    elif min_z == .75:
+        data = fits.getdata(paths.rm_mice_y1_HHL[dset_id])
+    else:
+        data = fits.getdata(paths.rm_mice_y1[dset_id])
+
     ra_var = 'RA'
     dec_var = 'DEC'
-    print len(randoms)
 
-    data[zvar] += np.random.normal(size=len(data),scale=sigma_z)
-    data_slice = get_zslice(data,min_z,max_z,zvar)
-    randoms_slice = get_zslice(randoms,min_z,max_z,random_zvar)
+    data[dec_var] = -data[dec_var]
+    data = data[data[ra_var] > 0]
+    data = data[data[ra_var] < 90]
+    data = data[data[dec_var] > -60]
+    data = data[data[dec_var] < -40]
+
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
+    data_slice = get_zslice(data, min_z, max_z, zvar)
+    randoms_slice = generate_randoms(data_slice, random_oversamp, zvar)
+
+    #remove jk region
+    jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/rectangle_0_90_-60_-40_jk.pkl", "rb" ) )
+    data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    random_inds = jk_classifier.predict(zip(randoms_slice[ra_var],randoms_slice[dec_var]))
+    data_slice = data_slice[data_inds != jk_id]
+    randoms_slice = randoms_slice[random_inds != jk_id]
+
+    file_name = config_fname + \
+        '_MICEdset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+str(zvar)+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
     
-    xi = calc_2pt(data_slice,randoms_slice,config_fname,zvar,random_zvar,ra_var=ra_var,dec_var=dec_var)
-    zeta = calc_3pt(data_slice,randoms_slice,config_fname,zvar,random_zvar,ra_var=ra_var,dec_var=dec_var)
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+def calc_3pt_noisy_photoz_MICEdm(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, dm_oversamp, random_oversamp, rw_scheme, outvar='zeta'):
+    data = fits.getdata(paths.dm_mice_y1[dset_id])
+    data = data[data['redshift'] < 1.0]
+
+    if min_z == .6:
+        weight_data = fits.getdata(paths.rm_mice_y1_HL[0])
+    elif min_z == .75:
+        weight_data = fits.getdata(paths.rm_mice_y1_HHL[0])
+    else:
+        weight_data = fits.getdata(paths.rm_mice_y1[0])
+
+    ra_var = 'RA'
+    dec_var = 'DEC'
     
-    np.save('/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/test2_angular/'+config_fname+'_lssdset'+str(dset_id)+'_sigma'+str(sigma_z)+'_'+str(min_z)+'_'+str(max_z)+'.xi',xi)
-    np.save('/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/test2_angular/'+config_fname+'_lssdset'+str(dset_id)+'_sigma'+str(sigma_z)+'_'+str(min_z)+'_'+str(max_z)+'.zeta',zeta)
+    data[dec_var] = -data[dec_var]
+    data = data[data[ra_var] > 0]
+    data = data[data[ra_var] < 90]
+    data = data[data[dec_var] > -60]
+    data = data[data[dec_var] < -40]
+
+    weight_data_slice = get_zslice(weight_data, min_z, max_z, rw_scheme)
+
+    if rw_scheme == 'ZSPEC':
+        target_cts, target_bins = np.histogram(
+            weight_data_slice['ZSPEC'], range=(0, 1), bins=100)   
+    else:
+        target_cts, target_bins = np.histogram(
+            weight_data_slice['ZREDMAGIC']+np.random.normal(scale=weight_data_slice['ZREDMAGIC_E']), range=(0, 1), bins=100)    
+
+    data_slice = downselect_pz(data, target_cts, target_bins, 'redshift', dm_oversamp)
+    randoms_slice = generate_randoms(data_slice, random_oversamp, 'redshift')
+    #randoms_slice = downselect_pz(randoms, target_cts, target_bins, 'Z', random_oversamp)
+
+    #remove jk region
+    jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/rectangle_0_90_-60_-40_jk.pkl", "rb" ) )
+    data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    random_inds = jk_classifier.predict(zip(randoms_slice[ra_var],randoms_slice[dec_var]))
+    data_slice = data_slice[data_inds != jk_id]
+    randoms_slice = randoms_slice[random_inds != jk_id]
+
+    file_name = config_fname + \
+        '_MICEdmdset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+rw_scheme+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(dm_oversamp)+'x'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
+
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+def calc_3pt_noisy_photoz_halos(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, random_oversamp, outvar='zeta'):
+
+    data = pd.read_pickle('/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/buzzard_halos/halos-'+str(dset_id)+'.pkl')
+
+    ra_var = 'RA'
+    dec_var = 'DEC'
+
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
+    data_slice = get_zslice(data, min_z, max_z, zvar)
+    randoms_slice = generate_randoms(data_slice, random_oversamp, 'Z')
+    #randoms_slice = get_zslice(randoms, min_z, max_z, random_zvar)
+
+    #remove jk region
+    jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/rectangle_0_90_-60_-40_jk.pkl", "rb" ) )
+    data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    random_inds = jk_classifier.predict(zip(randoms_slice[ra_var],randoms_slice[dec_var]))
+    data_slice = data_slice[data_inds != jk_id]
+    randoms_slice = randoms_slice[random_inds != jk_id]
+
+    # randoms_slice = randoms_slice[np.random.rand(len(randoms_slice)) < (
+    #    len(data_slice)/len(randoms_slice)*random_oversamp)]
 
-def calc_3pt_noisy_photoz(dset_id,config_fname,min_z,max_z,sigma_z,zvar,random_zvar):
-    randoms = fits.getdata('/u/ki/jderose/public_html/bcc/catalog/redmagic/y1/buzzard/flock/buzzard-0/a/buzzard_1.6-6a_run_redmapper_v6.4.18_redmagic_highdens_0.5-10_randoms.fit')
-    randoms = randoms[np.random.rand(len(randoms)) < 0.02]
-    data = fits.getdata(dset_vec[dset_id])
+    file_name = config_fname + \
+        '_halosdset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+str(zvar)+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
     
-    if dset_id == 0:
-        ra_var = 'azim_ang'
-        dec_var = 'polar_ang'
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+
+def calc_3pt_noisy_photoz_dm(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, dm_oversamp,random_oversamp, rw_scheme, outvar='zeta'):
+    ra_var = 'azim_ang'
+    dec_var = 'polar_ang'
+
+    if min_z == .6:
+        weight_data = persistent_load_fits(paths.rm_y1_HL[0])
+    elif min_z == .75:
+        weight_data = persistent_load_fits(paths.rm_y1_HHL[0])
     else:
-        ra_var = 'RA'
-        dec_var = 'DEC'
-    print len(randoms)
+        weight_data = persistent_load_fits(paths.rm_y1[0])
+
+    data = persistent_load_fits(paths.dm_y1[dset_id])
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
 
-    data[zvar] += np.random.normal(size=len(data),scale=sigma_z)
-    data_slice = get_zslice(data,min_z,max_z,zvar)
-    randoms_slice = get_zslice(randoms,min_z,max_z,random_zvar)
+    randoms = persistent_load_fits(paths.dm_y1_randoms)
     
-    xi = calc_2pt(data_slice,randoms_slice,config_fname,zvar,random_zvar,ra_var=ra_var,dec_var=dec_var)
-    zeta = calc_3pt(data_slice,randoms_slice,config_fname,zvar,random_zvar,ra_var=ra_var,dec_var=dec_var)
+    weight_data_slice = get_zslice(weight_data, min_z, max_z, rw_scheme)
+
+    if rw_scheme == 'ZSPEC':
+        target_cts, target_bins = np.histogram(
+            weight_data_slice['ZSPEC'], range=(0, 1), bins=100)   
+    else:
+        target_cts, target_bins = np.histogram(
+           weight_data_slice['ZREDMAGIC'], range=(0, 1), bins=100)  
+        # this was for angular case -- just reweighting to n(z)
+        #target_cts, target_bins = np.histogram(
+        #    weight_data_slice['ZREDMAGIC']+np.random.normal(scale=weight_data_slice['ZREDMAGIC_E']), range=(0, 1), bins=100)    
+
+    data_slice = downselect_pz(data, target_cts, target_bins, 'redshift', dm_oversamp)
+    randoms_slice = downselect_pz(randoms, target_cts, target_bins, 'Z', random_oversamp)
+
+    if rw_scheme == 'ZREDMAGIC':
+        sigmas_vs_z, zerr_bins, _ = binned_statistic(weight_data_slice['ZREDMAGIC'],weight_data_slice['ZREDMAGIC_E'], bins=100, range=(0,1))
+        data_slice_sigmas = sigmas_vs_z[np.digitize(data_slice[zvar],zerr_bins)-1] # -1 to avoid underflow bin
+        randoms_slice_sigmas = sigmas_vs_z[np.digitize(randoms_slice[random_zvar],zerr_bins)-1]
+        data_slice[zvar] = data_slice[zvar]+np.random.normal(scale=data_slice_sigmas)
+        randoms_slice[random_zvar] = randoms_slice[random_zvar]+np.random.normal(scale=randoms_slice_sigmas)
+
+    #remove jk region
+    # jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/buzzard_jk.pkl", "rb" ) )
+    # data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    # random_inds = jk_classifier.predict(zip(randoms_slice['RA'],randoms_slice['DEC']))
+    # data_slice = data_slice[data_inds != jk_id]
+    # randoms_slice = randoms_slice[random_inds != jk_id]
+
+    file_name = config_fname + \
+        '_dmdset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+rw_scheme+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(dm_oversamp)+'x'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
+
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+def calc_3pt_noisy_photoz(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, random_oversamp, outvar='zeta'):
+
+    if min_z == .6:
+        randoms = persistent_load_fits(paths.rm_y1_HL_randoms)
+        data = persistent_load_fits(paths.rm_y1_HL[dset_id])
+    elif min_z == .75:
+        randoms = persistent_load_fits(paths.rm_y1_HHL_randoms)
+        data = persistent_load_fits(paths.rm_y1_HHL[dset_id])
+    else:
+        randoms = persistent_load_fits(paths.rm_y1_randoms)
+        data = persistent_load_fits(paths.rm_y1[dset_id])
+
+    ra_var = 'RA'
+    dec_var = 'DEC'
+
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
+    data_slice = get_zslice(data, min_z, max_z, zvar)
+    randoms_slice = get_zslice(randoms, min_z, max_z, random_zvar)
+
+    randoms_slice = randoms_slice[np.random.rand(len(randoms_slice)) < (
+        len(data_slice)/len(randoms_slice)*random_oversamp)]
+
+    #remove jk region
+    # jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/buzzard_jk.pkl", "rb" ) )
+    # data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    # random_inds = jk_classifier.predict(zip(randoms_slice[ra_var],randoms_slice[dec_var]))
+    # data_slice = data_slice[data_inds != jk_id]
+    # randoms_slice = randoms_slice[random_inds != jk_id]
+
+    file_name = config_fname + \
+        '_newbuzzardrm2dset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+str(zvar)+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
     
-    np.save('/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/test2_angular/'+config_fname+'_dset'+str(dset_id)+'_sigma'+str(sigma_z)+'_'+str(min_z)+'_'+str(max_z)+'.xi',xi)
-    np.save('/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/test2_angular/'+config_fname+'_dset'+str(dset_id)+'_sigma'+str(sigma_z)+'_'+str(min_z)+'_'+str(max_z)+'.zeta',zeta)
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+
+
+
+def calc_3pt_noisy_photoz_y3(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, random_oversamp, outvar='zeta'):
+
+    if min_z == .6:
+        randoms = fits.getdata(paths.rm_y3_HL_randoms)
+        data = fits.getdata(paths.rm_y3_HL[dset_id])
+    elif min_z == .75:
+        randoms = fits.getdata(paths.rm_y3_HHL_randoms)
+        data = fits.getdata(paths.rm_y3_HHL[dset_id])
+    else:
+        randoms = fits.getdata(paths.rm_y3_randoms)
+        data = fits.getdata(paths.rm_y3[dset_id])
+
+    ra_var = 'RA'
+    dec_var = 'DEC'
+
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
+    data_slice = get_zslice(data, min_z, max_z, zvar)
+    randoms_slice = get_zslice(randoms, min_z, max_z, random_zvar)
+
+    randoms_slice = randoms_slice[np.random.rand(len(randoms_slice)) < (
+        len(data_slice)/len(randoms_slice)*random_oversamp)]
+
+    #remove jk region
+    jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/buzzard_jk.pkl", "rb" ) )
+    data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    random_inds = jk_classifier.predict(zip(randoms_slice[ra_var],randoms_slice[dec_var]))
+    data_slice = data_slice[data_inds != jk_id]
+    randoms_slice = randoms_slice[random_inds != jk_id]
+
+    file_name = config_fname + \
+        '_rmy3dset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+str(zvar)+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
     
-if __name__ == '__main__':
-    calc_3pt_noisy_photoz(dset_id,config_fname,min_z,max_z,sigma_z,zvar,random_zvar)
\ No newline at end of file
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+
+
+def calc_3pt_randxrand(dset_id, jk_id, config_fname, do3D, min_z, max_z, sigma_z, zvar, random_zvar, dm_oversamp,random_oversamp, rw_scheme, outvar='zeta'):
+    np.random.seed(dset_id)
+    randoms = fits.getdata(paths.dm_y1_randoms2)
+    data = fits.getdata(paths.dm_y1_randoms)
+    data[zvar] += np.random.normal(size=len(data), scale=sigma_z)
+
+    ra_var = 'RA'
+    dec_var = 'DEC'
+
+    if min_z == .6:
+        weight_data = fits.getdata(paths.rm_y1_HL[0])
+    elif min_z == .75:
+        weight_data = fits.getdata(paths.rm_y1_HHL[0])
+    else:
+        weight_data = fits.getdata(paths.rm_y1[0])
+    
+    weight_data_slice = get_zslice(weight_data, min_z, max_z, rw_scheme)
+
+    if rw_scheme == 'ZSPEC':
+        target_cts, target_bins = np.histogram(
+            weight_data_slice['ZSPEC'], range=(0, 1), bins=100)   
+    else:
+        target_cts, target_bins = np.histogram(
+            weight_data_slice['ZREDMAGIC']+np.random.normal(scale=weight_data_slice['ZREDMAGIC_E']), range=(0, 1), bins=100)    
+
+    data_slice = downselect_pz(data, target_cts, target_bins, 'Z', dm_oversamp)
+    randoms_slice = downselect_pz(randoms, target_cts, target_bins, 'Z', random_oversamp)
+
+    #remove jk region
+    jk_classifier = pickle.load( open( "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/jk_classifiers/buzzard_jk.pkl", "rb" ) )
+    data_inds = jk_classifier.predict(zip(data_slice[ra_var],data_slice[dec_var]))
+    random_inds = jk_classifier.predict(zip(randoms_slice['RA'],randoms_slice['DEC']))
+    data_slice = data_slice[data_inds != jk_id]
+    randoms_slice = randoms_slice[random_inds != jk_id]
+
+    file_name = config_fname + \
+        '_randxranddset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+rw_scheme+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+str(dm_oversamp)+'x'+str(random_oversamp)
+        
+    if not do3D:
+        xi_file_path = os.path.join(paths.ang_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.ang_out_dir, file_name)
+    else: 
+        xi_file_path = os.path.join(paths.corr_out_dir, file_name+'.xi')
+        output_file_path = os.path.join(paths.corr_out_dir, file_name)
+
+    if (outvar == 'zeta') | (outvar == 'ddd'):
+        xi = calc_2pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar,)
+        np.save(xi_file_path, xi)
+    calc_3pt(data_slice, randoms_slice, config_fname, do3D,
+                      ra_var=ra_var, dec_var=dec_var,
+                      data_zvar=zvar, random_zvar=random_zvar, outvar=outvar, outfname=output_file_path)
+
+
+def generate_randoms(data, oversamp, zvar,
+                     Ngen=1000000, Ntries_max=10000):
+
+    Ncurrent = 0
+    Ntry = 0
+    Ntot = int(len(data) * oversamp)
+
+    minra = 0
+    maxra = 90
+    mindec = -60
+    maxdec = -40
+
+    zdist = data[zvar]
+
+    random_ra = []
+    random_dec = []
+    random_z = []
+    while ((Ncurrent < Ntot) & (Ntry < Ntries_max)):
+        if Ntry % 100 == 1:
+            print(Ntry, Ntries_max, Ncurrent, Ntot, Ngen)
+        # generate random ra and dec
+        ra_i, dec_i = generate_randoms_radec(minra, maxra,
+                                             mindec, maxdec, Ngen)
+        indices_i = radec_to_index(dec_i, ra_i, 4096)
+
+        z_i = np.random.choice(zdist, len(ra_i))
+
+        random_ra += list(ra_i)
+        random_dec += list(dec_i)
+        random_z += list(z_i)
+
+        Ncurrent = len(random_ra)
+        Ntry += 1
+    if Ntry >= Ntries_max:
+        print('Warning! We gave up after {0} tries, finding {1} objects instead of the desired {2} objects!'.format(
+            Ntry, Ncurrent, Ntot))
+
+    randoms = np.zeros(len(random_ra), dtype=[
+        ('RA', '>f4'), ('DEC', '>f4'), ('Z', '>f4')])
+    randoms['RA'] = random_ra
+    randoms['DEC'] = random_dec
+    randoms['Z'] = random_z
+
+    if len(randoms) > Ntot:
+        inds_to_keep = np.random.choice(
+            np.arange(len(randoms)), size=Ntot, replace=False)
+        randoms = randoms[inds_to_keep]
+
+    return randoms
diff --git a/tricorder/simple_angular_script.py b/tricorder/simple_angular_script.py
index 24e9769..88ae196 100644
--- a/tricorder/simple_angular_script.py
+++ b/tricorder/simple_angular_script.py
@@ -1,15 +1,98 @@
 import subprocess
+import paths
+from time import sleep
+import os.path
 
-for config_fname in ['test7_angular','test8_angular']:
-    for sigma_z in [0,0.01,0.02,0.03,0.04]:
-        for min_z in [.15,.3,.45]:
-            max_z = min_z + 0.15
-            command_str = "import simple_angular; simple_angular.calc_3pt_noisy_photoz(" + str(0) + ", '" + config_fname + "', " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'REDSHIFT','Z')"
-            print command_str
-            subprocess.call(["bsub", "-W", "47:00", "-R", "rusage[mem=4000]", "-o", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.out","-e", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.err", "python", "-c", command_str])
-            for dset_id in range(1,37):
-            #for dset_id in range(36):
-                command_str = "import simple; simple_angular.calc_3pt_noisy_photoz(" + str(dset_id) + ", '" + config_fname + "', " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'ZSPEC','Z')"
-                #command_str = "import simple_angular; simple_angular.calc_3pt_noisy_photoz_lss(" + str(dset_id) + ", '" + config_fname + "', " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'redshift','REDSHIFT')"
-                print command_str
-                subprocess.call(["bsub", "-W", "47:00", "-R", "rusage[mem=4000]", "-o", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.out","-e", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.err", "python", "-c", command_str])
\ No newline at end of file
+do3Ds = [True, True, True, True, True, True]
+outlogpath = "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/logs4/%J.out"
+errlogpath = "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new3/logs4/%J.err"
+ncpus = "4"
+primary_dset_id = 0
+walltime = '47:00'
+memlimit = '2000'
+
+
+def nice_job_submit(do3D, outvar, config_fname, dset_flavor, dset_id, jk_id, sigma_z, rw_scheme, min_z, max_z, random_oversamp, dm_oversamp=None, sleep_time=.1):
+
+    if do3D:
+        checkpath = paths.corr_out_dir
+    else:
+        checkpath = paths.ang_out_dir
+
+    if outvar == 'ddd':
+        checkoutvar = 'rdd'  # last one to be written in a ddd job.
+    else:
+        checkoutvar = outvar
+
+    if dset_flavor == 'dm':
+        oversamp_str = str(dm_oversamp) + 'x' + str(random_oversamp)
+    elif dset_flavor == 'newbuzzardrm2':
+        oversamp_str = str(random_oversamp)
+    else:
+        raise ValueError('Unknown dset_flavor: '+dset_flavor)
+
+    outfile = checkpath + '/' + config_fname + \
+        '_'+dset_flavor+'dset'+str(dset_id)+'_jk'+str(jk_id)+'_sigma'+str(sigma_z) + \
+        '_'+rw_scheme+'_'+str(min_z)+'_'+str(max_z) + \
+        '_rsamp'+oversamp_str + '.' + checkoutvar
+
+    if os.path.exists(outfile):
+        print 'already done; continuing'
+        return
+    else:
+        if dset_flavor == 'dm':
+            command_str = "import simple_angular; simple_angular.calc_3pt_noisy_photoz_dm(" + str(
+                dset_id) + ", " + str(jk_id) + ", '" + config_fname + "', "+str(do3D)+", " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + ",'redshift','Z',"+str(dm_oversamp)+","+str(random_oversamp)+", '"+rw_scheme+"', outvar='"+outvar+"')"
+        else:
+            command_str = "import simple_angular; simple_angular.calc_3pt_noisy_photoz(" + str(
+                dset_id) + ", " + str(jk_id) + ", '" + config_fname + "', "+str(do3D)+", " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + ",'"+rw_scheme+"','Z',"+str(random_oversamp)+", outvar='"+outvar+"')"
+
+        print command_str
+        subprocess.call(["bsub", "-W", walltime, "-n", ncpus, "-C", "1", "-R", "span[hosts=1] rusage[mem="+memlimit+"] select[hname!=deft0001 && hname!=deft0002 && hname!=deft0003 && hname!=deft0004 && hname!=deft0005 && hname!=deft0006 && hname!=deft0007 && hname!=deft0008 && hname!=deft0009 && hname!=deft0010 && hname!=deft0011 && hname!=deft0012 && hname!=deft0013 && hname!=deft0014 && hname!=deft0015 && hname!=deft0016 && hname!=deft0017 && hname!=deft0018 && hname!=deft0019 && hname!=deft0020 && hname!=deft0021 && hname!=deft0022 && hname!=deft0023 && hname!=deft0024 && hname!=deft0025 && hname!=deft0026 && hname!=deft0027 && hname!=deft0028 && hname!=kiso0030 && hname!=kiso0032 && hname!=kiso0033 && hname!=kiso0034 && hname!=kiso0035 && hname!=kiso0036 && hname!=kiso0037 && hname!=kiso0038 && hname!=kiso0039 && hname!=kiso0010 && hname!=kiso0011 && hname!=kiso0012 && hname!=kiso0013 && hname!=kiso0014 && hname!=kiso0015 && hname!=kiso0016 && hname!=kiso0060 && hname!=kiso0017 && hname!=kiso0061 && hname!=kiso0018 && hname!=kiso0062 && hname!=kiso0019 && hname!=kiso0063 && hname!=kiso0064 && hname!=kiso0065 && hname!=kiso0067 && hname!=kiso0068 && hname!=kiso0040 && hname!=kiso0041 && hname!=kiso0042 && hname!=kiso0043 && hname!=kiso0044 && hname!=kiso0045 && hname!=kiso0046 && hname!=kiso0047 && hname!=kiso0048 && hname!=kiso0049 && hname!=kiso0020 && hname!=kiso0021 && hname!=kiso0022 && hname!=kiso0024 && hname!=kiso0025 && hname!=kiso0026 && hname!=kiso0027 && hname!=kiso0028 && hname!=kiso0029 && hname!=kiso0002 && hname!=kiso0003 && hname!=kiso0004 && hname!=kiso0005 && hname!=kiso0006 && hname!=kiso0050 && hname!=kiso0051 && hname!=kiso0008 && hname!=kiso0052 && hname!=kiso0054 && hname!=kiso0055 && hname!=kiso0056 && hname!=kiso0057 && hname!=kiso0058 && hname!=kiso0059 && hname!=bubble0003 && hname!=bubble0004 && hname!=bubble0005 && hname!=bubble0006]",
+                         "-o", outlogpath,
+                         "-e", errlogpath, "python", "-c", command_str])
+        sleep(sleep_time)
+        return
+
+
+#sigma_z_list = [0.01, 0.02, 0.02, 0.03]
+sigma_z_list = [0, 0, 0, 0]
+
+if __name__ == '__main__':
+    for i, config_fname in enumerate(['fiducial3d_25Mpc', 'fiducial3d_35Mpc']):
+        #do3D = False
+        for z_width in [0.15]:
+            # for sigma_z in [0.02]:
+            for which_sigma, min_z in enumerate([.15, .45, .3, .6]):
+                sigma_z = sigma_z_list[which_sigma]
+                max_z = min_z + z_width
+                for random_oversamp in [10]:
+                    for jk_id in [-1]:
+                        for outvar in ['ddd', 'rrr', 'drr', 'rdr', 'rrd']:
+                            if sigma_z == 0:
+                                zlist = ['ZSPEC']
+                            else:
+                                zlist = ['ZSPEC']
+
+                            for rw_scheme in zlist:
+
+                                for dm_oversamp in [10]:
+
+                                    if jk_id == -1:
+                                        dset_ids = range(len(paths.dm_y1))
+                                    else:
+                                        dset_ids = [primary_dset_id]
+                                    for dset_id in dset_ids:
+
+                                        nice_job_submit(do3Ds[i], outvar, config_fname, 'dm', dset_id,
+                                                        jk_id, sigma_z, rw_scheme, min_z, max_z, random_oversamp,
+                                                        dm_oversamp=dm_oversamp)
+
+                                # RMY1
+                                if jk_id == -1:
+                                    dset_ids = range(len(paths.rm_y1))
+                                else:
+                                    dset_ids = [primary_dset_id]
+                                for dset_id in dset_ids:
+                                    nice_job_submit(do3Ds[i], outvar, config_fname, 'newbuzzardrm2', dset_id,
+                                                    jk_id, sigma_z, rw_scheme, min_z, max_z, random_oversamp)
diff --git a/tricorder/simple_script.py b/tricorder/simple_script.py
index 80c600f..b52d266 100644
--- a/tricorder/simple_script.py
+++ b/tricorder/simple_script.py
@@ -1,17 +1,14 @@
 import subprocess
 import paths
 
-config_dir = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/configs/'
-out_dir = '/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/test2/'
-
-for config_fname in ['test7_3d']:
-    for sigma_z in [0.001, 0.002, 0.003, 0.004, .005, 0.01, 0.02, 0.03, 0.04]:
-        for min_z in [.15, .3, .45]:
-            max_z = min_z + 0.15
-            for dset_id in range(len(paths.dm_y1)):
-                command_str = "import simple; simple.calc_3pt_noisy_photoz_dm(" + str(dset_id) + ", '" + config_fname + "', " + str(
-                    min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'ZSPEC','Z')"
-                #command_str = "import simple_angular; simple_angular.calc_3pt_noisy_photoz_lss(" + str(dset_id) + ", '" + config_fname + "', " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'redshift','REDSHIFT')"
-                print command_str
-                subprocess.call(["bsub", "-W", "47:00", "-R", "rusage[mem=4000]", "-o", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.out",
-                                 "-e", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.err", "python", "-c", command_str])
+if __name__ == '__main__':
+    for config_fname in ['test7_3d']:
+        for sigma_z in [0]: #[0.001, 0.002, 0.003, 0.004, .005, 0.01, 0.02, 0.03, 0.04]:
+            for min_z in [.15, .3, .45]:
+                max_z = min_z + 0.15
+                for dset_id in range(len(paths.dm_y1)):
+                    command_str = "import simple; simple.calc_3pt_noisy_photoz_dm(" + str(dset_id) + ", '" + config_fname + "', " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'redshift','Z')"
+                    #command_str = "import simple_angular; simple_angular.calc_3pt_noisy_photoz_lss(" + str(dset_id) + ", '" + config_fname + "', " + str(min_z) + "," + str(max_z) + "," + str(sigma_z) + "," + "'redshift','REDSHIFT')"
+                    print command_str
+                    subprocess.call(["bsub", "-W", "47:00", "-R", "rusage[mem=4000]", "-o", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.out",
+                                     "-e", "/nfs/slac/des/fs1/g/sims/mbaumer/3pt_sims/new2/logs/%J.err", "python", "-c", command_str])