diff --git a/debug/histo1d.ipynb b/debug/histo1d.ipynb
index 0672685..f1a4b4f 100644
--- a/debug/histo1d.ipynb
+++ b/debug/histo1d.ipynb
@@ -7,18 +7,105 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import babyyoda as grogu"
+    "import babyyoda as grogu\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "7baf67bd-162a-4740-a694-1eed5c44e028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0 1.541103500742244\n",
+      "3.0 0.9354143466934853\n",
+      "4.0 1.0606601717798212\n",
+      "5.0 1.1726039399558574\n",
+      "6.0 1.2747548783981961\n",
+      "7.0 1.3693063937629153\n",
+      "8.0 1.4577379737113252\n",
+      "8.0 1.9364916731037085\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 9.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5, 15. ])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hists = grogu.read_yoda(\"../tests/test_histo1d_v2.yoda\")\n",
+    "for b in hists[\"/\"]:\n",
+    "    print(b.sumW(), b.errW() / 2)\n",
+    "hists[\"/\"].variances()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b75dee9c-0432-4ddb-99f6-53f294b5e122",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0 1.541103500742244\n",
+      "3.0 0.9354143466934853\n",
+      "4.0 1.0606601717798212\n",
+      "5.0 1.1726039399558574\n",
+      "6.0 1.2747548783981961\n",
+      "7.0 1.3693063937629153\n",
+      "8.0 1.4577379737113252\n",
+      "8.0 1.9364916731037085\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 9.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5, 15. ])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hists = grogu.read_grogu(\"../tests/test_histo1d_v2.yoda\")\n",
+    "for b in hists[\"/\"]:\n",
+    "    print(b.sumW(), b.errW() / 2)\n",
+    "hists[\"/\"].variances()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "9e7f5fc2-0126-4a97-b4ab-2b1539245987",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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LolKqz2293v7B4TMe3esXR6WU/4+JxgvzmxeoPeIDalylVF9IILy1dkNhawPnL0+7AABJiQ8AICnxAQAkJT4AgKTEBzWjt38wZnbsjpkdu3P95VcApCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApHxWNkAVVEoN8eoDy6s9BlSFMx8AQFLiAwBISnwAAEmJDwAgqcLjo7OzM+rq6mLNmjVFbwUA1IBC42Pfvn2xffv2+PCHP1zkNgBADSnsrbZvvvlm3HzzzfGNb3wjNm7cWNQ28IFlWRanB4YKWbu3f3DU7z/4usXMC5BCYfGxatWqWL58eSxZsuRd46Ovry/6+vqGL/f09BQ1Eozq9MBQzL3nycL3adv4dOF7ANSCQuLjO9/5Thw4cCD27dv3nrft7OyMe++9t4gxAIAxKPf4OHr0aKxevTqeeuqpmDBhwnveft26ddHe3j58uaenJ1pbW/MeC96X7vVLolKqz2293v7B4TMe3esXR6WUf+83XpjfvAAp5P6TcP/+/XH8+PFYsGDB8LGhoaHYu3dvbN26Nfr6+qK+/v9+WJbL5SiXy3mPAb+TSqm+kEB4a+2GwtYGqCW5/yRcvHhxvPjiiyOOffGLX4w5c+bE2rVrR4QHAHD+yT0+mpqaYv78+SOOXXTRRTF58uSzjgMA5x+/4RQASCrJE9A/+tGPUmwDANQAZz4AgKTEBwCQlPgAAJISHwBAUuIDAEjKr1sEal6l1BCvPrC82mMA75MzHwBAUuIDAEhKfAAASYmP81Rv/2DM7NgdMzt2R2//YLXHAeA8Ij4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJOWD5chVlmVxemCokLV/85eh5fmL0Xr7i5kXgNGJD3J1emAo5t7zZOH7tG18uvA9ACiGp10AgKSc+aAw3euXRKVUn9t6vf2Dw2c8utcvjkop/7++jRfmNy8AoxMfFKZSqi8kEN5au6GwtQEolqddAICkxAcAkJT4AACS8qQ5MEKl1BCvPrC82mMA45gzHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkso9Pjo7O+Oaa66JpqammDJlSnz605+Ol19+Oe9tAIAalXt87NmzJ1atWhXPP/98dHV1xeDgYCxdujROnTqV91YAQA1qyHvBf/3Xfx1x+bHHHospU6bE/v374+Mf/3je241rWZbF6YGhQtbu7R8c9fsPvm4x8wIwfuQeH7/txIkTERExadKkUa/v6+uLvr6+4cs9PT1Fj1QzTg8Mxdx7nix8n7aNTxe+BwC8rdD4yLIs2tvbY+HChTF//vxRb9PZ2Rn33ntvkWMUrrd/cDgSfn7fJ6NSKrzpqBGVUkO8+sDyao8BMKYU+ih55513xs9+9rN49tln3/E269ati/b29uHLPT090draWuRYNal7/ZKolOpzW6+3f3D4jEf3+sWFBFPjhfnNC8D4UVh83HXXXbFr167Yu3dvXHLJJe94u3K5HOVyuagxxo1Kqb6wMyqVUoOzNQAkk/sjTpZlcdddd8Xjjz8eP/rRj2LWrFl5bwEA1LDc42PVqlWxY8eO+P73vx9NTU1x7NixiIhobm6OxsbGvLcDAGpM7r/nY9u2bXHixIlYtGhRTJ8+ffhr586deW8FANSgQp52AQB4Jz7bBQBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAklfsHy0FRKqWGePWB5dUeA4APyJkPACAp8QEAJCU+AICkxAcAkJT4AACSOm/e7ZJlWZweGCpk7d7+wVG//+DrFjMvAFTTeRMfpweGYu49Txa+T9vGpwvfAwBqmaddAICkzpszH7+pe/2SqJTqc1uvt39w+IxH9/rFUSnlf7c2XpjfvABQTedlfFRK9YUEwltrNxS2NgCMB552AQCSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU34Z1nqqUGuLVB5ZXewwAzkPOfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgqcLi45FHHolZs2bFhAkTYsGCBfHjH/+4qK0AgBpSSHzs3Lkz1qxZE3fffXe88MIL8bGPfSyWLVsWR44cKWI7AKCGFBIfmzdvjj//8z+Pv/iLv4jLL788tmzZEq2trbFt27YitgMAakjun2rb398f+/fvj46OjhHHly5dGs8999xZt+/r64u+vr7hyydOnIiIiJ6enlzn6u0fjDN9vcNrD5by+59e5NoAkJciH6/eftzOsuw9b5v7o+SvfvWrGBoaiqlTp444PnXq1Dh27NhZt+/s7Ix77733rOOtra15jzZs+pbCli50bQDIS1GPVydPnozm5uZ3vU1h/0Svq6sbcTnLsrOORUSsW7cu2tvbhy+fOXMm/vd//zcmT5486u0/iJ6enmhtbY2jR4/GxIkTc12b/+N+TsP9nI77Og33cxpF3c9ZlsXJkyejpaXlPW+be3x86EMfivr6+rPOchw/fvyssyEREeVyOcrl8ohjF198cd5jjTBx4kR/sRNwP6fhfk7HfZ2G+zmNIu7n9zrj8bbcX3BaKpViwYIF0dXVNeJ4V1dXXHvttXlvBwDUmEKedmlvb48vfOEL0dbWFh/96Edj+/btceTIkbj99tuL2A4AqCGFxMef/dmfxf/8z//EfffdF//93/8d8+fPj3/5l3+JSy+9tIjt3rdyuRxf//rXz3qah3y5n9NwP6fjvk7D/ZzGWLif67L3854YAICc+GwXACAp8QEAJCU+AICkxAcAkNR5Ex+PPPJIzJo1KyZMmBALFiyIH//4x9Ueadzp7OyMa665JpqammLKlCnx6U9/Ol5++eVqjzXudXZ2Rl1dXaxZs6bao4w7v/zlL+OWW26JyZMnR6VSiauuuir2799f7bHGlcHBwVi/fn3MmjUrGhsb47LLLov77rsvzpw5U+3Rat7evXtjxYoV0dLSEnV1dfHEE0+MuD7LstiwYUO0tLREY2NjLFq0KF566aUks50X8bFz585Ys2ZN3H333fHCCy/Exz72sVi2bFkcOXKk2qONK3v27IlVq1bF888/H11dXTE4OBhLly6NU6dOVXu0cWvfvn2xffv2+PCHP1ztUcadN954I6677rq48MIL4wc/+EH8/Oc/j7/7u78r/Dcwn28efPDBePTRR2Pr1q3xH//xH/HQQw/F3/7t38bDDz9c7dFq3qlTp+LKK6+MrVu3jnr9Qw89FJs3b46tW7fGvn37Ytq0afGJT3wiTp48Wfxw2XngIx/5SHb77bePODZnzpyso6OjShOdH44fP55FRLZnz55qjzIunTx5Mps9e3bW1dWVXX/99dnq1aurPdK4snbt2mzhwoXVHmPcW758eXbbbbeNOPaZz3wmu+WWW6o00fgUEdnjjz8+fPnMmTPZtGnTsgceeGD42K9//eusubk5e/TRRwufZ9yf+ejv74/9+/fH0qVLRxxfunRpPPfcc1Wa6vxw4sSJiIiYNGlSlScZn1atWhXLly+PJUuWVHuUcWnXrl3R1tYWn/3sZ2PKlClx9dVXxze+8Y1qjzXuLFy4MJ5++uk4dOhQRET89Kc/jWeffTb+6I/+qMqTjW+HDx+OY8eOjXhsLJfLcf311yd5bCzsU23Hil/96lcxNDR01ofaTZ069awPvyM/WZZFe3t7LFy4MObPn1/tccad73znO3HgwIHYt29ftUcZt37xi1/Etm3bor29Pf76r/86fvKTn8SXv/zlKJfLceutt1Z7vHFj7dq1ceLEiZgzZ07U19fH0NBQ3H///fH5z3++2qONa28//o322Pjaa68Vvv+4j4+31dXVjbicZdlZx8jPnXfeGT/72c/i2WefrfYo487Ro0dj9erV8dRTT8WECROqPc64debMmWhra4tNmzZFRMTVV18dL730Umzbtk185Gjnzp3xrW99K3bs2BHz5s2LgwcPxpo1a6KlpSVWrlxZ7fHGvWo9No77+PjQhz4U9fX1Z53lOH78+FnFRz7uuuuu2LVrV+zduzcuueSSao8z7uzfvz+OHz8eCxYsGD42NDQUe/fuja1bt0ZfX1/U19dXccLxYfr06TF37twRxy6//PL47ne/W6WJxqevfvWr0dHRETfeeGNERFxxxRXx2muvRWdnp/go0LRp0yLirTMg06dPHz6e6rFx3L/mo1QqxYIFC6Krq2vE8a6urrj22murNNX4lGVZ3HnnnfG9730vfvjDH8asWbOqPdK4tHjx4njxxRfj4MGDw19tbW1x8803x8GDB4VHTq677rqz3ip+6NChqn9A5njT29sbF1ww8qGovr7eW20LNmvWrJg2bdqIx8b+/v7Ys2dPksfGcX/mIyKivb09vvCFL0RbW1t89KMfje3bt8eRI0fi9ttvr/Zo48qqVatix44d8f3vfz+ampqGzzY1NzdHY2NjlacbP5qams56Hc1FF10UkydP9vqaHH3lK1+Ja6+9NjZt2hSf+9zn4ic/+Uls3749tm/fXu3RxpUVK1bE/fffHzNmzIh58+bFCy+8EJs3b47bbrut2qPVvDfffDNeeeWV4cuHDx+OgwcPxqRJk2LGjBmxZs2a2LRpU8yePTtmz54dmzZtikqlEjfddFPxwxX+fpox4u///u+zSy+9NCuVStkf/MEfePtnASJi1K/HHnus2qONe95qW4x/+qd/yubPn5+Vy+Vszpw52fbt26s90rjT09OTrV69OpsxY0Y2YcKE7LLLLsvuvvvurK+vr9qj1bxnnnlm1J/JK1euzLLsrbfbfv3rX8+mTZuWlcvl7OMf/3j24osvJpmtLsuyrPjEAQB4y7h/zQcAMLaIDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKT+HxY3ELk6B5bCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f51a6e5fc20>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -29,18 +116,21 @@
    ],
    "source": [
     "hists = grogu.read_grogu(\"../tests/test_histo1d_v2.yoda\")\n",
-    "hists[\"/\"]"
+    "hists[\"/\"].plot()\n",
+    "plt.ylim([2, 4.5])\n",
+    "plt.grid()\n",
+    "plt.axhline(y=4 - 1.9364916731037085, color=\"black\", linestyle=\"-\", linewidth=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "159c7b2f-e18e-43f6-a630-c6f9ac062a25",
+   "execution_count": 5,
+   "id": "88fe7219-6233-4574-a5f2-8a7ae69975c9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -56,17 +146,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
+   "id": "4d72c698-ab00-4ff8-be33-833260b1fc01",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "39.0 28.0\n",
+      "0.2727272727272727 648.0\n",
+      "0.17391304347826086 1239.6521739130435\n",
+      "0.1282051282051282 2030.7692307692307\n",
+      "0.1016949152542373 274.5762711864407\n",
+      "0.08433734939759036 382.5542168674699\n",
+      "0.07207207207207207 508.5405405405405\n",
+      "1.1428571428571428 1157.142857142857\n"
+     ]
+    }
+   ],
+   "source": [
+    "for b in hists[\"/\"]:\n",
+    "    print(b.variance(), b.xVariance())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "159c7b2f-e18e-43f6-a630-c6f9ac062a25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hists = grogu.read_yoda(\"../tests/test_histo1d_v2.yoda\")\n",
+    "# hists[\"/\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "2773a80a-4f50-456c-b2a5-b4db8d3d719b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Dbn1D(mean=1.0, stddev=0.0)>"
+       "<Dbn1D(mean=1.0, stddev=0.35805743701971643)>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,24 +204,183 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "d0cecc9f-4f12-4b70-b4cc-72442582dabc",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3., 3., 4., 5., 6., 7., 8., 8.])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hists[\"/\"].values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bef63e37-e04d-4541-8eb5-bf11b315fcf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 9.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5, 15. ])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hists[\"/\"].variances()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9391df8c-07f5-4777-87dc-619e3ff9c8e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hists[\"/\"].counts()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "26407638-40d0-4f73-81d1-6a795a8a6d5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 9.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5, 15. ])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hists[\"/\"].sumW2s()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "878a5396-f255-4d82-a21e-0c00c356d532",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0 1.541103500742244\n",
+      "3.0 0.9354143466934853\n",
+      "4.0 1.0606601717798212\n",
+      "5.0 1.1726039399558574\n",
+      "6.0 1.2747548783981961\n",
+      "7.0 1.3693063937629153\n",
+      "8.0 1.4577379737113252\n",
+      "8.0 1.9364916731037085\n"
+     ]
+    }
+   ],
+   "source": [
+    "for b in hists[\"/\"]:\n",
+    "    print(b.sumW(), b.errW() / 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0cbcb048-ab07-49ad-8759-3f8a371a0a42",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'HISTO1D_V2' object and target have no attribute 'yVals'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhists\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myVals\u001b[49m()\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/babyyoda/Histo1D_v2.py:24\u001b[0m, in \u001b[0;36mHISTO1D_V2.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name):\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget, name)\n\u001b[0;32m---> 24\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object and target have no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     26\u001b[0m )\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'HISTO1D_V2' object and target have no attribute 'yVals'"
+     ]
+    }
+   ],
+   "source": [
+    "hists[\"/\"].yVals()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f639161-6496-4c52-a9ae-fff6cf1292a9",
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import yoda\n",
+    "\n",
+    "hists = yoda.read(\"../tests/test_histo1d_v2.yoda\")"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bef63e37-e04d-4541-8eb5-bf11b315fcf8",
+   "id": "0b9b6237-bf2c-42d8-87d9-f9e45d108a7b",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "h = hists[\"/\"]"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9391df8c-07f5-4777-87dc-619e3ff9c8e5",
+   "id": "a78c6ed3-af01-4ae5-8ee2-098c0d1ddb5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h.mkScatter().yErrs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4268e817-fb1c-4b80-933c-384f7e7cb984",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h.binAt(5).val()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c50b2e7e-ab28-47b3-93b1-4d887326c2d1",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/debug/histo2d.ipynb b/debug/histo2d.ipynb
index c217b0c..5f7bf0d 100644
--- a/debug/histo2d.ipynb
+++ b/debug/histo2d.ipynb
@@ -26,7 +26,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 684.8x480 with 2 Axes>"
       ]
@@ -56,7 +56,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 684.8x480 with 2 Axes>"
       ]
@@ -2609,7 +2609,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 684.8x480 with 2 Axes>"
       ]
@@ -2639,7 +2639,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 684.8x480 with 2 Axes>"
       ]
diff --git a/src/babyyoda/Histo1D_v2.py b/src/babyyoda/Histo1D_v2.py
index f263f9e..464ebcc 100644
--- a/src/babyyoda/Histo1D_v2.py
+++ b/src/babyyoda/Histo1D_v2.py
@@ -48,12 +48,16 @@ def __call__(self, *args, **kwargs):
     ########################################################
 
     def overflow(self):
-        # This is a YODA-1 feature that is not present in YODA-2
-        return self.bins()[-1]
+        # if target has overflow method, call it
+        if hasattr(self.target, "overflow"):
+            return self.target.overflow()
+        return self.bins(includeOverflows=True)[-1]
 
     def underflow(self):
-        # This is a YODA-1 feature that is not present in YODA-2
-        return self.bins()[0]
+        # if target has underflow method, call it
+        if hasattr(self.target, "underflow"):
+            return self.target.underflow()
+        return self.bins(includeOverflows=True)[0]
 
     def xMins(self):
         return np.array([b.xMin() for b in self.bins()])
@@ -64,6 +68,9 @@ def xMaxs(self):
     def sumWs(self):
         return np.array([b.sumW() for b in self.bins()])
 
+    def sumW2s(self):
+        return np.array([b.sumW2() for b in self.bins()])
+
     ########################################################
     # Generic UHI code
     ########################################################
@@ -74,7 +81,7 @@ def axes(self):
 
     @property
     def kind(self):
-        return "COUNT"
+        return "MEAN"
 
     def counts(self):
         return np.array([b.numEntries() for b in self.bins()])
@@ -83,7 +90,7 @@ def values(self):
         return np.array([b.sumW() for b in self.bins()])
 
     def variances(self):
-        return np.array([b.sumW2() for b in self.bins()])
+        return np.array([(b.sumW2()) for b in self.bins()])
 
     def __setitem__(self, slices, value):
         # integer index
@@ -150,10 +157,12 @@ def __get_index(self, slices):
             index = overflow
         return index
 
-    def plot(self, *args, w2method="sqrt", **kwargs):
+    def plot(self, *args, binwnorm=1.0, **kwargs):
         import mplhep as hep
 
-        hep.histplot(self, w2=self.variances(), *args, w2method=w2method, **kwargs)
+        hep.histplot(
+            self, *args, yerr=self.variances() ** 0.5, binwnorm=binwnorm, **kwargs
+        )
 
     def _ipython_display_(self):
         try:
diff --git a/src/babyyoda/Histo2D_v2.py b/src/babyyoda/Histo2D_v2.py
index 83b0ffb..e5cba3c 100644
--- a/src/babyyoda/Histo2D_v2.py
+++ b/src/babyyoda/Histo2D_v2.py
@@ -46,13 +46,20 @@ def __call__(self, *args, **kwargs):
     # YODA compatibility code (dropped legacy code?)
     ########################################################
 
-    def overflow(self):
-        # This is a YODA-1 feature that is not present in YODA-2
-        return self.bins(includeOverflows=True)[-1]
+    def bins(self):
+        # fix order
+        return np.array(sorted(self.target.bins(), key=lambda b: (b.xMin(), b.yMin())))
 
-    def underflow(self):
-        # This is a YODA-1 feature that is not present in YODA-2
-        return self.bins(includeOverflows=True)[0]
+    def bin(self, *indices):
+        return self.bins()[indices]
+
+    # def overflow(self):
+    #    # This is a YODA-1 feature that is not present in YODA-2
+    #    return self.bins(includeOverflows=True)[-1]
+
+    # def underflow(self):
+    #    # This is a YODA-1 feature that is not present in YODA-2
+    #    return self.bins(includeOverflows=True)[0]
 
     def xMins(self):
         return np.array(sorted(list(set([b.xMin() for b in self.bins()]))))
@@ -141,7 +148,17 @@ def __getitem__(self, slices):
     def plot(self, *args, **kwargs):
         import mplhep as hep
 
+        # Hack in the temporary division by dVol
+        saved_values = self.values
+
+        def temp_values():
+            return np.array([b.sumW() / b.dVol() for b in self.bins()]).reshape(
+                (len(self.axes[0]), len(self.axes[1]))
+            )
+
+        self.values = temp_values
         hep.hist2dplot(self, *args, **kwargs)
+        self.values = saved_values
 
     def _ipython_display_(self):
         try:
diff --git a/src/babyyoda/grogu/histo1d_v2.py b/src/babyyoda/grogu/histo1d_v2.py
index 31e24fd..b7d1f42 100644
--- a/src/babyyoda/grogu/histo1d_v2.py
+++ b/src/babyyoda/grogu/histo1d_v2.py
@@ -48,6 +48,39 @@ def sumWX(self):
         def sumWX2(self):
             return self.d_sumwx2
 
+        def variance(self):
+            if self.d_sumw**2 - self.d_sumw2 == 0:
+                return 0
+            return abs(
+                (self.d_sumw2 * self.d_sumw - self.d_sumw**2)
+                / (self.d_sumw**2 - self.d_sumw2)
+            )
+            # return self.d_sumw2/self.d_numentries - (self.d_sumw/self.d_numentries)**2
+
+        def errW(self):
+            return self.d_sumw2**0.5
+
+        def stdDev(self):
+            return self.variance() ** 0.5
+
+        def effNumEntries(self):
+            return self.sumW() ** 2 / self.sumW2()
+
+        def stdErr(self):
+            return self.stdDev() / self.effNumEntries() ** 0.5
+
+        def dVol(self):
+            return self.d_xmax - self.d_xmin
+
+        def xVariance(self):
+            # return self.d_sumwx2/self.d_sumw - (self.d_sumwx/self.d_sumw)**2
+            if self.d_sumw**2 - self.d_sumw2 == 0:
+                return 0
+            return abs(
+                (self.d_sumwx2 * self.d_sumw - self.d_sumwx**2)
+                / (self.d_sumw**2 - self.d_sumw2)
+            )
+
         def numEntries(self):
             return self.d_numentries
 
diff --git a/src/babyyoda/grogu/histo2d_v2.py b/src/babyyoda/grogu/histo2d_v2.py
index 957bea1..aec7015 100644
--- a/src/babyyoda/grogu/histo2d_v2.py
+++ b/src/babyyoda/grogu/histo2d_v2.py
@@ -70,6 +70,9 @@ def sumWY2(self):
         def sumWXY(self):
             return self.d_sumwxy
 
+        def dVol(self):
+            return (self.d_xmax - self.d_xmin) * (self.d_ymax - self.d_ymin)
+
         def crossTerm(self, x, y):
             assert (x == 0 and y == 1) or (x == 1 and y == 0)
             return self.sumWXY()
diff --git a/tests/test_histo1d_v2.yoda b/tests/test_histo1d_v2.yoda
index 7d5bb1f..41e1076 100644
--- a/tests/test_histo1d_v2.yoda
+++ b/tests/test_histo1d_v2.yoda
@@ -10,14 +10,12 @@ Total   	Total   	6.600000e+01	6.600000e+01	4.400000e+02	3.410000e+03	6.600000e+
 Underflow	Underflow	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
 Overflow	Overflow	1.100000e+01	1.100000e+01	1.100000e+02	1.100000e+03	1.100000e+01
 # xlow	 xhigh	 sumw	 sumw2	 sumwx	 sumwx2	 numEntries
-0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00
-1.000000e+00	2.000000e+00	2.000000e+00	2.000000e+00	2.000000e+00	2.000000e+00	2.000000e+00
-2.000000e+00	3.000000e+00	3.000000e+00	3.000000e+00	6.000000e+00	1.200000e+01	3.000000e+00
-3.000000e+00	4.000000e+00	4.000000e+00	4.000000e+00	1.200000e+01	3.600000e+01	4.000000e+00
-4.000000e+00	5.000000e+00	5.000000e+00	5.000000e+00	2.000000e+01	8.000000e+01	5.000000e+00
-5.000000e+00	6.000000e+00	6.000000e+00	6.000000e+00	3.000000e+01	1.500000e+02	6.000000e+00
-6.000000e+00	7.000000e+00	7.000000e+00	7.000000e+00	4.200000e+01	2.520000e+02	7.000000e+00
-7.000000e+00	8.000000e+00	8.000000e+00	8.000000e+00	5.600000e+01	3.920000e+02	8.000000e+00
-8.000000e+00	9.000000e+00	9.000000e+00	9.000000e+00	7.200000e+01	5.760000e+02	9.000000e+00
-9.000000e+00	1.000000e+01	1.000000e+01	1.000000e+01	9.000000e+01	8.100000e+02	1.000000e+01
+0.000000e+00	2.000000e+00	3.000000e+00	9.500000e+00	2.000000e+00	6.000000e+00	5.000000e+00
+2.000000e+00	3.000000e+00	3.000000e+00	3.500000e+00	6.000000e+00	1.200000e+03	3.000000e+00
+3.000000e+00	4.000000e+00	4.000000e+00	4.500000e+00	1.200000e+01	3.600000e+03	4.000000e+00
+4.000000e+00	5.000000e+00	5.000000e+00	5.500000e+00	2.000000e+01	8.000000e+03	5.000000e+00
+5.000000e+00	6.000000e+00	6.000000e+00	6.500000e+00	3.000000e+01	1.500000e+03	6.000000e+00
+6.000000e+00	7.000000e+00	7.000000e+00	7.500000e+00	4.200000e+01	2.520000e+03	7.000000e+00
+7.000000e+00	8.000000e+00	8.000000e+00	8.500000e+00	5.600000e+01	3.920000e+03	8.000000e+00
+8.000000e+00	1.000000e+01	8.000000e+00	1.500000e+01	9.000000e+01	8.100000e+03	1.000000e+01
 END YODA_HISTO1D_V2
diff --git a/tests/yoda/uhi/test_yd_histo2d_access.py b/tests/yoda/uhi/test_yd_histo2d_access.py
index 27b674b..7ead0cc 100644
--- a/tests/yoda/uhi/test_yd_histo2d_access.py
+++ b/tests/yoda/uhi/test_yd_histo2d_access.py
@@ -39,7 +39,7 @@ def test_access_index():
     assert_bin2d(h[0, 2], h.bin(2))
 
     assert_bin2d(g[1, 0], g.bin(10))
-    assert_bin2d(g[0, 2], h.bin(2))
+    assert_bin2d(g[0, 2], g.bin(2))
 
 
 def test_access_loc():