diff --git a/distribute/.ipynb_checkpoints/distribute-checkpoint.ipynb b/distribute/.ipynb_checkpoints/distribute-checkpoint.ipynb
index 65f1f7dd6..f766b19ea 100644
--- a/distribute/.ipynb_checkpoints/distribute-checkpoint.ipynb
+++ b/distribute/.ipynb_checkpoints/distribute-checkpoint.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.13.7+dirty\n",
+      "0.1.13.10+dirty\n",
       "~/git/resummino/build\n"
      ]
     }
@@ -1706,22 +1706,1318 @@
     "                hepi.mapplot(rs_dl,\"MASS_1000022\",\"MASS_1000024\",\"LO\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{\\\\chi}_1^-}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "03bef720",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "................................................................................................................................................................................................................................................................................................................................................................................................................= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 941.51it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "................|...........................................|...................|...................|...................||..................||...|..||||.|.|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 954.61it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/1cde5bc1bc1fe283b2a53f516b0842a6b66d376512a4d9de392494964ac64177.out -> moved to output_13000_wino_1000023_-1000024/1cde5bc1bc1fe283b2a53f516b0842a6b66d376512a4d9de392494964ac64177.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/65de5db7c5ef809683fde63ddf64e151649bba131cee5291dbca0ca6de4be740.out -> moved to output_13000_wino_1000023_-1000024/65de5db7c5ef809683fde63ddf64e151649bba131cee5291dbca0ca6de4be740.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/59a02492d674ffb297eff95acfc1a0544e66b8dbc6a9f6e3f4ff5253575e4d74.out -> moved to output_13000_wino_1000023_-1000024/59a02492d674ffb297eff95acfc1a0544e66b8dbc6a9f6e3f4ff5253575e4d74.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/486c5954035c68530dcafec51e91889d826dfb41df1042f35f185d26ba537020.out -> moved to output_13000_wino_1000023_-1000024/486c5954035c68530dcafec51e91889d826dfb41df1042f35f185d26ba537020.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6045e86394a2e098d4646020d1949d4acf6ea77a03307542fdd4f6a308d1a573.out -> moved to output_13000_wino_1000023_-1000024/6045e86394a2e098d4646020d1949d4acf6ea77a03307542fdd4f6a308d1a573.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/887c762cb378e8ee3b05dc05f8321790d7b21596f084b06a1ad768d42e6b9199.out -> moved to output_13000_wino_1000023_-1000024/887c762cb378e8ee3b05dc05f8321790d7b21596f084b06a1ad768d42e6b9199.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/32d6bbcb4509b2f6cc65e4590197e157a1304774d33a0e1703ac5d379a1898b0.out -> moved to output_13000_wino_1000023_-1000024/32d6bbcb4509b2f6cc65e4590197e157a1304774d33a0e1703ac5d379a1898b0.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5683290246459a45675eb81b455d4f1af543b72a60065e505f1fa7af2309a197.out -> moved to output_13000_wino_1000023_-1000024/5683290246459a45675eb81b455d4f1af543b72a60065e505f1fa7af2309a197.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2c43c6ca48a2d86eb969312382b5e63e15158c5541efa2b150034efff953f58f.out -> moved to output_13000_wino_1000023_-1000024/2c43c6ca48a2d86eb969312382b5e63e15158c5541efa2b150034efff953f58f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0c37273f6cd14deef50f07eb82dd36a16420d167888573cddee281b215d1f076.out -> moved to output_13000_wino_1000023_-1000024/0c37273f6cd14deef50f07eb82dd36a16420d167888573cddee281b215d1f076.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6cea370d87e0c8a1faba8b9ef88bb5870128527b6b95a8f0e684c83325f474d6.out -> moved to output_13000_wino_1000023_-1000024/6cea370d87e0c8a1faba8b9ef88bb5870128527b6b95a8f0e684c83325f474d6.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/106a6d4bbd80e4cc4597aab2bef7bf8b717265aca5ddf91fd99197e3e2eecf3b.out -> moved to output_13000_wino_1000023_-1000024/106a6d4bbd80e4cc4597aab2bef7bf8b717265aca5ddf91fd99197e3e2eecf3b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5ba768149f6f6f544c59acaf092f4b5efab59cfbe1325f39558ba421297f05f1.out -> moved to output_13000_wino_1000023_-1000024/5ba768149f6f6f544c59acaf092f4b5efab59cfbe1325f39558ba421297f05f1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/98e64786b05af0a7919b0c3c8a208f5ed7bcdb2eae9eb699ceefffeb95136ac3.out -> moved to output_13000_wino_1000023_-1000024/98e64786b05af0a7919b0c3c8a208f5ed7bcdb2eae9eb699ceefffeb95136ac3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/fdb02b470fb6c2bfbaab44e0b9c625a642f801179f25e5633a1448400078b9ed.out -> moved to output_13000_wino_1000023_-1000024/fdb02b470fb6c2bfbaab44e0b9c625a642f801179f25e5633a1448400078b9ed.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/443830891279857ed81c72aaf63b8268bd322ae130797b9afb673e068922768a.out -> moved to output_13000_wino_1000023_-1000024/443830891279857ed81c72aaf63b8268bd322ae130797b9afb673e068922768a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/12b73e2267a01df5dba161687c38d4079f870e3e50d80469a9161fa885afd972.out -> moved to output_13000_wino_1000023_-1000024/12b73e2267a01df5dba161687c38d4079f870e3e50d80469a9161fa885afd972.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/41e400f471d4de6f4a18fd69b5b2a845343b13f9121234590832e458ef56a3fa.out -> moved to output_13000_wino_1000023_-1000024/41e400f471d4de6f4a18fd69b5b2a845343b13f9121234590832e458ef56a3fa.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c11220ae1eaa8b124820bfa8959ab5894b0a05f95106f45d905dece26d6e2d3a.out -> moved to output_13000_wino_1000023_-1000024/c11220ae1eaa8b124820bfa8959ab5894b0a05f95106f45d905dece26d6e2d3a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b67f1652f40cf71b67fee26f540d5153833008134033e0159439e49fafa7cfb5.out -> moved to output_13000_wino_1000023_-1000024/b67f1652f40cf71b67fee26f540d5153833008134033e0159439e49fafa7cfb5.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/7d22429792476e712e63fd39c1958c890382caba0fd776afbf75328d7921a569.out -> moved to output_13000_wino_1000023_-1000024/7d22429792476e712e63fd39c1958c890382caba0fd776afbf75328d7921a569.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ac4173706abf2a4078e2c5e83310bbdb55612bc4973edf49488db7fc4aa85e70.out -> moved to output_13000_wino_1000023_-1000024/ac4173706abf2a4078e2c5e83310bbdb55612bc4973edf49488db7fc4aa85e70.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8b223940891f2fcb28056a8e0073cf4a5247dc0405b47943e28acd3fae782ade.out -> moved to output_13000_wino_1000023_-1000024/8b223940891f2fcb28056a8e0073cf4a5247dc0405b47943e28acd3fae782ade.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/965d74e688ef58b6483b1e9de033fda9448f37687e7bb63e413504d5864b9100.out -> moved to output_13000_wino_1000023_-1000024/965d74e688ef58b6483b1e9de033fda9448f37687e7bb63e413504d5864b9100.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/dbb442ef83d6e176de164fbd90a68da618ed780c8ab2b13d4c3d0711acba848c.out -> moved to output_13000_wino_1000023_-1000024/dbb442ef83d6e176de164fbd90a68da618ed780c8ab2b13d4c3d0711acba848c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f9f8f20d4466b09dd113a24b0dc134f9f97fddffb708966f5c91500a0b1195dc.out -> moved to output_13000_wino_1000023_-1000024/f9f8f20d4466b09dd113a24b0dc134f9f97fddffb708966f5c91500a0b1195dc.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/22fa3467f7dbbe5e9be7d71ec28740981beb43c2751430e25508c90019a31ebc.out -> moved to output_13000_wino_1000023_-1000024/22fa3467f7dbbe5e9be7d71ec28740981beb43c2751430e25508c90019a31ebc.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/149ff84e725d26694e32425163f2df634b730c67174b37f213afcfc26a51c967.out -> moved to output_13000_wino_1000023_-1000024/149ff84e725d26694e32425163f2df634b730c67174b37f213afcfc26a51c967.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/66be5c8a641ae734564be3e56097d9acb99fc4b6de7bf9282f84b52186adefd1.out -> moved to output_13000_wino_1000023_-1000024/66be5c8a641ae734564be3e56097d9acb99fc4b6de7bf9282f84b52186adefd1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f23650e41d14af8d1f2807618076088d2a07190ad5539bb0fef661d551227e81.out -> moved to output_13000_wino_1000023_-1000024/f23650e41d14af8d1f2807618076088d2a07190ad5539bb0fef661d551227e81.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3cc12500ac31e1bdf0f205c819617eec8957ad87d43f6339c912beb31ffbe6cd.out -> moved to output_13000_wino_1000023_-1000024/3cc12500ac31e1bdf0f205c819617eec8957ad87d43f6339c912beb31ffbe6cd.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/dadde2a35dc978c0c8c24fe137f87b10e6324e3488b908645624b23e7a05a821.out -> moved to output_13000_wino_1000023_-1000024/dadde2a35dc978c0c8c24fe137f87b10e6324e3488b908645624b23e7a05a821.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/bba74d1741636688376f0af589c9920cedbcd976b854f72b2197ddafd9037758.out -> moved to output_13000_wino_1000023_-1000024/bba74d1741636688376f0af589c9920cedbcd976b854f72b2197ddafd9037758.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d3a4076e4bc82d341da73fe1d4de9a93a00eff9ced8cf09019e8fc13a1141353.out -> moved to output_13000_wino_1000023_-1000024/d3a4076e4bc82d341da73fe1d4de9a93a00eff9ced8cf09019e8fc13a1141353.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e185f41bc709f20ce45389bd460cc3250691fe614138cd2fe8f569fcf8a40359.out -> moved to output_13000_wino_1000023_-1000024/e185f41bc709f20ce45389bd460cc3250691fe614138cd2fe8f569fcf8a40359.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/680e6659a11dd83d2e3d053901d22c59d90c6ef5500e9f12e98fe713395509be.out -> moved to output_13000_wino_1000023_-1000024/680e6659a11dd83d2e3d053901d22c59d90c6ef5500e9f12e98fe713395509be.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0cf9320da7e31425f5a40277c5677120f1cb3d8c14bdd9a53809043629cd89b3.out -> moved to output_13000_wino_1000023_-1000024/0cf9320da7e31425f5a40277c5677120f1cb3d8c14bdd9a53809043629cd89b3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5bb5c74f71ee99088e294bca08f9976a2756066be641aefd9fc8e7400ff74e77.out -> moved to output_13000_wino_1000023_-1000024/5bb5c74f71ee99088e294bca08f9976a2756066be641aefd9fc8e7400ff74e77.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d795b3f406502465d06d5a750fccd38b8b9e1a1cb75dd8043278ad3544036f83.out -> moved to output_13000_wino_1000023_-1000024/d795b3f406502465d06d5a750fccd38b8b9e1a1cb75dd8043278ad3544036f83.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/7785188510ba848312f5ac1a9dacf7e5cd6b1844e862d3da140debfaa5f010ad.out -> moved to output_13000_wino_1000023_-1000024/7785188510ba848312f5ac1a9dacf7e5cd6b1844e862d3da140debfaa5f010ad.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c3edf07d2e1f3c6701d50265da0459a7706b372da42ee3377c7c997810f0b0d6.out -> moved to output_13000_wino_1000023_-1000024/c3edf07d2e1f3c6701d50265da0459a7706b372da42ee3377c7c997810f0b0d6.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d9b66190408ebff97cc0f36e35a91cf86697afaa33cb98fd2bf0b9e5d9c7355a.out -> moved to output_13000_wino_1000023_-1000024/d9b66190408ebff97cc0f36e35a91cf86697afaa33cb98fd2bf0b9e5d9c7355a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ab6c0bf36edd81cd39098a4d2915368a790afa80191d6a057afe6a690076e314.out -> moved to output_13000_wino_1000023_-1000024/ab6c0bf36edd81cd39098a4d2915368a790afa80191d6a057afe6a690076e314.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d8f0b229cd08f36620bf361f6c7844dc0e39142954ff2400d7c7278f2ce86024.out -> moved to output_13000_wino_1000023_-1000024/d8f0b229cd08f36620bf361f6c7844dc0e39142954ff2400d7c7278f2ce86024.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/343b1c5a55fa25106da604fdd368ff2cd6f77a3d6d2a2f79b2d351c996524c6a.out -> moved to output_13000_wino_1000023_-1000024/343b1c5a55fa25106da604fdd368ff2cd6f77a3d6d2a2f79b2d351c996524c6a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a3dd2acc5fa7e9c1e118dc77f99fec72ee02f40b5d71d96eda7fb448b9336a13.out -> moved to output_13000_wino_1000023_-1000024/a3dd2acc5fa7e9c1e118dc77f99fec72ee02f40b5d71d96eda7fb448b9336a13.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/90fd1fd0d96598116675d33df22e8b5dff4089d632a011e4450bb902febdd466.out -> moved to output_13000_wino_1000023_-1000024/90fd1fd0d96598116675d33df22e8b5dff4089d632a011e4450bb902febdd466.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/76338e4634bab8b28f07114914d008bcd0e61584ad6eeff90a44e8e3dc92388d.out -> moved to output_13000_wino_1000023_-1000024/76338e4634bab8b28f07114914d008bcd0e61584ad6eeff90a44e8e3dc92388d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e3163029c99ee7b64cf98820988a47cc8c5239af9774827c0aba60108809bbde.out -> moved to output_13000_wino_1000023_-1000024/e3163029c99ee7b64cf98820988a47cc8c5239af9774827c0aba60108809bbde.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5eb839258c5edaecc4ea2f3b1b5793f774ba9ba1ede1e2a7ce7eb718e52c407d.out -> moved to output_13000_wino_1000023_-1000024/5eb839258c5edaecc4ea2f3b1b5793f774ba9ba1ede1e2a7ce7eb718e52c407d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a38f7aed2bd53d2b6d7ee73364f112bb43829bb80884ba8eed52fba97e483677.out -> moved to output_13000_wino_1000023_-1000024/a38f7aed2bd53d2b6d7ee73364f112bb43829bb80884ba8eed52fba97e483677.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/46cdc35699972dbe7d3bca80ee19fae41b74c26a67497338ba1056ba3b391f99.out -> moved to output_13000_wino_1000023_-1000024/46cdc35699972dbe7d3bca80ee19fae41b74c26a67497338ba1056ba3b391f99.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a0fc91aa3337d4ec573916edf2edd825602dade95b201325eb2dc8bdef5692ca.out -> moved to output_13000_wino_1000023_-1000024/a0fc91aa3337d4ec573916edf2edd825602dade95b201325eb2dc8bdef5692ca.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/36066c6804699c468a15ccd96dedbf681ac1be96333fb3b8afd6a9cebee1dcab.out -> moved to output_13000_wino_1000023_-1000024/36066c6804699c468a15ccd96dedbf681ac1be96333fb3b8afd6a9cebee1dcab.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3f5ce62b094de2d989158a133c40ff205a599f8392ebba949b9ae6922fb74d4c.out -> moved to output_13000_wino_1000023_-1000024/3f5ce62b094de2d989158a133c40ff205a599f8392ebba949b9ae6922fb74d4c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9e33ff531124e359f240a45dc63254957defdfaa19f7b4906d8a97f3e64350a1.out -> moved to output_13000_wino_1000023_-1000024/9e33ff531124e359f240a45dc63254957defdfaa19f7b4906d8a97f3e64350a1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e74fa0c5e450cd2e6018a630a09ff8ad9fc22777cc4dd489d71bdb96e8906e25.out -> moved to output_13000_wino_1000023_-1000024/e74fa0c5e450cd2e6018a630a09ff8ad9fc22777cc4dd489d71bdb96e8906e25.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e0d82d7b22de02dc1a557beb9316183915ac10f5ffb02f94ffd70590d71ea329.out -> moved to output_13000_wino_1000023_-1000024/e0d82d7b22de02dc1a557beb9316183915ac10f5ffb02f94ffd70590d71ea329.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/625cd36cf2e4abdc4211339af23cbbcb3b8bffb6a3392f56084ffd1a509a4cc2.out -> moved to output_13000_wino_1000023_-1000024/625cd36cf2e4abdc4211339af23cbbcb3b8bffb6a3392f56084ffd1a509a4cc2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/65913a6cba20419163a9044637c26b6fd3ad52a3e593b6a4da95914399a2628e.out -> moved to output_13000_wino_1000023_-1000024/65913a6cba20419163a9044637c26b6fd3ad52a3e593b6a4da95914399a2628e.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9f84b8ff70bb5a4ca21e280e5daada126dbce8c9a79b47d2cc7c35c95e3ed472.out -> moved to output_13000_wino_1000023_-1000024/9f84b8ff70bb5a4ca21e280e5daada126dbce8c9a79b47d2cc7c35c95e3ed472.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/709b2f5f70ec559c1cdb31d9b607a0171bb1e3f6a1337dbd99a7135b79428af7.out -> moved to output_13000_wino_1000023_-1000024/709b2f5f70ec559c1cdb31d9b607a0171bb1e3f6a1337dbd99a7135b79428af7.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b93b46ec1eb0a84f5f13c7f95ddf84453ed66dc8a295cac07f3830277a90548a.out -> moved to output_13000_wino_1000023_-1000024/b93b46ec1eb0a84f5f13c7f95ddf84453ed66dc8a295cac07f3830277a90548a.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e738b28e0f2fa333509c6794dfe4c8754a231d1e5dbd959ea1a94d06a542f989.out -> moved to output_13000_wino_1000023_-1000024/e738b28e0f2fa333509c6794dfe4c8754a231d1e5dbd959ea1a94d06a542f989.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/13ebd96ab71eb472fe97352b841e21b5389b45db7c451f9c4219fff77eb17285.out -> moved to output_13000_wino_1000023_-1000024/13ebd96ab71eb472fe97352b841e21b5389b45db7c451f9c4219fff77eb17285.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/245fa83dfdcea378c40484e6af94d83f64d18877ae517bc2fd5105b842d1b2a9.out -> moved to output_13000_wino_1000023_-1000024/245fa83dfdcea378c40484e6af94d83f64d18877ae517bc2fd5105b842d1b2a9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2d68c281c437758d583970bf68deef98b665f0493e50c8c1d99ed19393556b16.out -> moved to output_13000_wino_1000023_-1000024/2d68c281c437758d583970bf68deef98b665f0493e50c8c1d99ed19393556b16.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/46acea56ef2033e2e7ea4d0a23808c2725fe72e09c398cd94b2272293ccd8705.out -> moved to output_13000_wino_1000023_-1000024/46acea56ef2033e2e7ea4d0a23808c2725fe72e09c398cd94b2272293ccd8705.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/674ba279793421b20f60af2bfe2bc784676490b5085350b815f1753327a3f046.out -> moved to output_13000_wino_1000023_-1000024/674ba279793421b20f60af2bfe2bc784676490b5085350b815f1753327a3f046.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ff6cb558bdb4ca71d3386b199a249fcbc0becf42841d0facfe416ef80bc9eefb.out -> moved to output_13000_wino_1000023_-1000024/ff6cb558bdb4ca71d3386b199a249fcbc0becf42841d0facfe416ef80bc9eefb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/396eff7a102aabcd3720419c12d5f2523746eda9097932f4f1057a45ce1177b3.out -> moved to output_13000_wino_1000023_-1000024/396eff7a102aabcd3720419c12d5f2523746eda9097932f4f1057a45ce1177b3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6d961a3635d88ac740239880e84faf1dd55af1da05ab8352dd23dd08f6d4c2c9.out -> moved to output_13000_wino_1000023_-1000024/6d961a3635d88ac740239880e84faf1dd55af1da05ab8352dd23dd08f6d4c2c9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/15e51e316ff9f8a4168a69aaaa1d90c5e6f0f8738bbdae083b447a4cfcd19526.out -> moved to output_13000_wino_1000023_-1000024/15e51e316ff9f8a4168a69aaaa1d90c5e6f0f8738bbdae083b447a4cfcd19526.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/fd1f6c5086f0e5032a7328883bf0baf0bff010f2fc32cdf02c9dbdbdc38de513.out -> moved to output_13000_wino_1000023_-1000024/fd1f6c5086f0e5032a7328883bf0baf0bff010f2fc32cdf02c9dbdbdc38de513.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9debb6bb017b2e4276cb521c4d27817e81c3d7b8a5657d9461f3d722f9e6dd07.out -> moved to output_13000_wino_1000023_-1000024/9debb6bb017b2e4276cb521c4d27817e81c3d7b8a5657d9461f3d722f9e6dd07.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/65e0bcd9de8b6b675d39141cb8f77e70fb64758bfff20a57c9a36f8637092e37.out -> moved to output_13000_wino_1000023_-1000024/65e0bcd9de8b6b675d39141cb8f77e70fb64758bfff20a57c9a36f8637092e37.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b3f57235e3e7a2156c64a1a1f78897ca539ef80190c4696e681b0197b7bd2a11.out -> moved to output_13000_wino_1000023_-1000024/b3f57235e3e7a2156c64a1a1f78897ca539ef80190c4696e681b0197b7bd2a11.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/1db3cfb0c75a8de9c79e37603d0521f240a779b986c2b9d0334233aa15133cea.out -> moved to output_13000_wino_1000023_-1000024/1db3cfb0c75a8de9c79e37603d0521f240a779b986c2b9d0334233aa15133cea.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0f8ea7d954d2ab384528d5ccaed553c0d40f1f502035e990c0a3211db81da0a8.out -> moved to output_13000_wino_1000023_-1000024/0f8ea7d954d2ab384528d5ccaed553c0d40f1f502035e990c0a3211db81da0a8.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/298b6a76b4a25e5db267c7a032e0cef580b121f1253e8691501c70da266b2fbd.out -> moved to output_13000_wino_1000023_-1000024/298b6a76b4a25e5db267c7a032e0cef580b121f1253e8691501c70da266b2fbd.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d156e85ba4a47503aa37a0eabcdb31c779be2c32388abc9e9b59f9ef027f4f9c.out -> moved to output_13000_wino_1000023_-1000024/d156e85ba4a47503aa37a0eabcdb31c779be2c32388abc9e9b59f9ef027f4f9c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/067b0fff79fd520ca0dcf587c341a80d95d89f8fc8d73faba59561a398211674.out -> moved to output_13000_wino_1000023_-1000024/067b0fff79fd520ca0dcf587c341a80d95d89f8fc8d73faba59561a398211674.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/605f4fff9435d597da9fd3f49544e2a0e8b2e50464a3f1862f8c2f921437d821.out -> moved to output_13000_wino_1000023_-1000024/605f4fff9435d597da9fd3f49544e2a0e8b2e50464a3f1862f8c2f921437d821.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3f6002aab5d2440a2023f665d11bebb4cbc512f26dbdef72ac2f31eacb0e6fd5.out -> moved to output_13000_wino_1000023_-1000024/3f6002aab5d2440a2023f665d11bebb4cbc512f26dbdef72ac2f31eacb0e6fd5.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/12b939ba374bd0aef1724a0c5aaad8c9c08568a208f08efb8e8096888db9301d.out -> moved to output_13000_wino_1000023_-1000024/12b939ba374bd0aef1724a0c5aaad8c9c08568a208f08efb8e8096888db9301d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e688041c42209e2865d7c919e35f925d4f534a3a808fb3e3f8494662dd14e5ad.out -> moved to output_13000_wino_1000023_-1000024/e688041c42209e2865d7c919e35f925d4f534a3a808fb3e3f8494662dd14e5ad.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e4658f6932c35339f50599f0c4980107827b2ceb66d60c6561fa048f3c7bc977.out -> moved to output_13000_wino_1000023_-1000024/e4658f6932c35339f50599f0c4980107827b2ceb66d60c6561fa048f3c7bc977.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/054f8c4a3900b6218de640153db88737a8868b72101adf4fef80084146e3257a.out -> moved to output_13000_wino_1000023_-1000024/054f8c4a3900b6218de640153db88737a8868b72101adf4fef80084146e3257a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2d70629cd94c51010e2f75ca052a231a46be206e40c1d5a9741668ea6380f635.out -> moved to output_13000_wino_1000023_-1000024/2d70629cd94c51010e2f75ca052a231a46be206e40c1d5a9741668ea6380f635.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ae5a551c9912ba21d9be6d07588126225bdb193465ad6edbbbca0854640d8fd5.out -> moved to output_13000_wino_1000023_-1000024/ae5a551c9912ba21d9be6d07588126225bdb193465ad6edbbbca0854640d8fd5.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0b93df97d37ef3c1a2d9f78825a0f555f9bd908676fd42ea73a84f841fe2f9b3.out -> moved to output_13000_wino_1000023_-1000024/0b93df97d37ef3c1a2d9f78825a0f555f9bd908676fd42ea73a84f841fe2f9b3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ed032dd89355130c0015ea2c1cf6d0aa67f4447d6745eebfa70be860d0f2259a.out -> moved to output_13000_wino_1000023_-1000024/ed032dd89355130c0015ea2c1cf6d0aa67f4447d6745eebfa70be860d0f2259a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/fb9a4b60f92c7e40a82951c32ace6909a3f366273d6512a42db460145bc8db21.out -> moved to output_13000_wino_1000023_-1000024/fb9a4b60f92c7e40a82951c32ace6909a3f366273d6512a42db460145bc8db21.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b0d2e35c4023069bca81d79e67011bb40caa048a9338197979e5261446a16e5a.out -> moved to output_13000_wino_1000023_-1000024/b0d2e35c4023069bca81d79e67011bb40caa048a9338197979e5261446a16e5a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5d51432b3ec1f6ff8e445a6c160e2b40305177fe6ccb355263c69b3ac90a40ad.out -> moved to output_13000_wino_1000023_-1000024/5d51432b3ec1f6ff8e445a6c160e2b40305177fe6ccb355263c69b3ac90a40ad.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/659e3eccf6c2bd555f70c21503780db7993aa6deb173ade1ce56e66246ccaa86.out -> moved to output_13000_wino_1000023_-1000024/659e3eccf6c2bd555f70c21503780db7993aa6deb173ade1ce56e66246ccaa86.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/03ac2eaa4fd7687bbd9b646e3742c6bf7fb10e5403a2a301efac0f3733e05e76.out -> moved to output_13000_wino_1000023_-1000024/03ac2eaa4fd7687bbd9b646e3742c6bf7fb10e5403a2a301efac0f3733e05e76.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8dad53b12ac08f30eb029d1791a98b31192d4ef216bfefc3f9edaa02e868546b.out -> moved to output_13000_wino_1000023_-1000024/8dad53b12ac08f30eb029d1791a98b31192d4ef216bfefc3f9edaa02e868546b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/75b5e9c99446d7c37d03b6fefb5bd0f4ae94dde2d67c78ff78198ddd3d888d43.out -> moved to output_13000_wino_1000023_-1000024/75b5e9c99446d7c37d03b6fefb5bd0f4ae94dde2d67c78ff78198ddd3d888d43.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/091b5a6bbfbf5383dc279d9130b7a46081d7d21c295126bcc7ad71daf3a30d1a.out -> moved to output_13000_wino_1000023_-1000024/091b5a6bbfbf5383dc279d9130b7a46081d7d21c295126bcc7ad71daf3a30d1a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/00ac0e51de5ca158da98368d9a8c0769e3e6b506f4efe1c3116f3fa8531dc893.out -> moved to output_13000_wino_1000023_-1000024/00ac0e51de5ca158da98368d9a8c0769e3e6b506f4efe1c3116f3fa8531dc893.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e16c5522ecbafdb0f57902b761a7c2fac6f13c3c7af9c21a12c8b66fff816915.out -> moved to output_13000_wino_1000023_-1000024/e16c5522ecbafdb0f57902b761a7c2fac6f13c3c7af9c21a12c8b66fff816915.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9576ff1c0193ca8293da4f837236131693d693014361c556e924e8249e9e898f.out -> moved to output_13000_wino_1000023_-1000024/9576ff1c0193ca8293da4f837236131693d693014361c556e924e8249e9e898f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2d471f040f5b73dfd13b8ca225c6617882d87fbf0c57418bb07be08e49fe9170.out -> moved to output_13000_wino_1000023_-1000024/2d471f040f5b73dfd13b8ca225c6617882d87fbf0c57418bb07be08e49fe9170.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/89a109c8a723ea66f87f41afdc9bb555e8d62f90e86f3a2c76064949837a21f1.out -> moved to output_13000_wino_1000023_-1000024/89a109c8a723ea66f87f41afdc9bb555e8d62f90e86f3a2c76064949837a21f1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f5492b9d88cd60a704cb775ab1969b6cee4cb89d8db784cbd75b5b44defb9e21.out -> moved to output_13000_wino_1000023_-1000024/f5492b9d88cd60a704cb775ab1969b6cee4cb89d8db784cbd75b5b44defb9e21.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e28fd8630f32106d99c5f5bd134d09879b1c03adad7817dc8571988bda17298d.out -> moved to output_13000_wino_1000023_-1000024/e28fd8630f32106d99c5f5bd134d09879b1c03adad7817dc8571988bda17298d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f59a6eef2e54b2542059ffe2f07b800701afa3388aed915f98998d0f13b6115a.out -> moved to output_13000_wino_1000023_-1000024/f59a6eef2e54b2542059ffe2f07b800701afa3388aed915f98998d0f13b6115a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/006e8ecc50d3f67327c479fb68c876b931bd5597676b294ea5347f80cf3a8a51.out -> moved to output_13000_wino_1000023_-1000024/006e8ecc50d3f67327c479fb68c876b931bd5597676b294ea5347f80cf3a8a51.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/25e0db0c117d5eea16d8764b6bb5d79f81ffb8eede4e4705b32f1a3acdc9f88f.out -> moved to output_13000_wino_1000023_-1000024/25e0db0c117d5eea16d8764b6bb5d79f81ffb8eede4e4705b32f1a3acdc9f88f.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/11cea8e623411e51547b9dc794c48c5903e184514ecf854586ab52ceeccb6d15.out -> moved to output_13000_wino_1000023_-1000024/11cea8e623411e51547b9dc794c48c5903e184514ecf854586ab52ceeccb6d15.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/af326ae8a833ee20016a0368edf66b7e2dbf0de97198666e56022478edd492f3.out -> moved to output_13000_wino_1000023_-1000024/af326ae8a833ee20016a0368edf66b7e2dbf0de97198666e56022478edd492f3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/54c7c243e62ea2608b0dac628391161e593dacc893707fbd508c12498ed78c70.out -> moved to output_13000_wino_1000023_-1000024/54c7c243e62ea2608b0dac628391161e593dacc893707fbd508c12498ed78c70.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a5377d2f915169436d3c6be6d16d111aaf60cfddbf462e02942213c480cce236.out -> moved to output_13000_wino_1000023_-1000024/a5377d2f915169436d3c6be6d16d111aaf60cfddbf462e02942213c480cce236.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/35f1d9ef26d8aff049a8749a68606043a58c18ffb8788a2777bfaf63f506881d.out -> moved to output_13000_wino_1000023_-1000024/35f1d9ef26d8aff049a8749a68606043a58c18ffb8788a2777bfaf63f506881d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2c977fd36b5dd3c8601f6de6cf68172931d201df49cd2fcff560750347b44816.out -> moved to output_13000_wino_1000023_-1000024/2c977fd36b5dd3c8601f6de6cf68172931d201df49cd2fcff560750347b44816.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/7a19bffa1552b86482b74ca7f7667d85bbb21516ecab15e0b4a960ca86e53709.out -> moved to output_13000_wino_1000023_-1000024/7a19bffa1552b86482b74ca7f7667d85bbb21516ecab15e0b4a960ca86e53709.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d454fa5595c9b71350fd0a979b7db5c24d7593117413ca2a12495cd445d0288b.out -> moved to output_13000_wino_1000023_-1000024/d454fa5595c9b71350fd0a979b7db5c24d7593117413ca2a12495cd445d0288b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8892793bbd90ea2302e085850df500c81843087d8ca8396266bf6c8980db3c31.out -> moved to output_13000_wino_1000023_-1000024/8892793bbd90ea2302e085850df500c81843087d8ca8396266bf6c8980db3c31.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e4d6406c86ecf6286197119e6d3edd3a3b20661fd9163ebf99e58f506f46730d.out -> moved to output_13000_wino_1000023_-1000024/e4d6406c86ecf6286197119e6d3edd3a3b20661fd9163ebf99e58f506f46730d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/184b8e51d47ee41a3cc0d874c46d48f57ae64b6cb0336578f761a1d9de47842f.out -> moved to output_13000_wino_1000023_-1000024/184b8e51d47ee41a3cc0d874c46d48f57ae64b6cb0336578f761a1d9de47842f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d20e4d939a2baaf12b455bd58daef09b4bc7d6f18a1e11367053d43891f5abce.out -> moved to output_13000_wino_1000023_-1000024/d20e4d939a2baaf12b455bd58daef09b4bc7d6f18a1e11367053d43891f5abce.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4f7bfe6ff2cc60e955fc65d81a3a29299fd8daf76d35803c9c6a100b0ed5fca6.out -> moved to output_13000_wino_1000023_-1000024/4f7bfe6ff2cc60e955fc65d81a3a29299fd8daf76d35803c9c6a100b0ed5fca6.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e5448173a912b031370ece8e638e9a8d4f54e13a5aa15bfccc16d02fe1b27f3e.out -> moved to output_13000_wino_1000023_-1000024/e5448173a912b031370ece8e638e9a8d4f54e13a5aa15bfccc16d02fe1b27f3e.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/7e76c8acc03635bc017f54afc2eddf0335e9121e9a31c6cd7cef176b1280b9e5.out -> moved to output_13000_wino_1000023_-1000024/7e76c8acc03635bc017f54afc2eddf0335e9121e9a31c6cd7cef176b1280b9e5.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6a8d0e27a52a2d5658f7d56ed40d2a2899585a0bf816d9a015dee7e9e26c34b2.out -> moved to output_13000_wino_1000023_-1000024/6a8d0e27a52a2d5658f7d56ed40d2a2899585a0bf816d9a015dee7e9e26c34b2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ed3efcf41a1d03ae8739d2486d61d2941131f508b19d65e45eb08b3d429dc1c1.out -> moved to output_13000_wino_1000023_-1000024/ed3efcf41a1d03ae8739d2486d61d2941131f508b19d65e45eb08b3d429dc1c1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/cc34a0925d93edb6fb1b10b7ebf8c8af279886bbdd360639e972382f447983fb.out -> moved to output_13000_wino_1000023_-1000024/cc34a0925d93edb6fb1b10b7ebf8c8af279886bbdd360639e972382f447983fb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/38e6290a490172e2c18a95197007fe3d513395226de68a9f32a0dd064718632c.out -> moved to output_13000_wino_1000023_-1000024/38e6290a490172e2c18a95197007fe3d513395226de68a9f32a0dd064718632c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/faa2d150ecfed29a96dfb377b7352cb2347415eda34cfb3a529ce434e1333c80.out -> moved to output_13000_wino_1000023_-1000024/faa2d150ecfed29a96dfb377b7352cb2347415eda34cfb3a529ce434e1333c80.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/35de6308fa1a9439c025cfc42443c9a9604db761e8f48665c68bbb45b4a80037.out -> moved to output_13000_wino_1000023_-1000024/35de6308fa1a9439c025cfc42443c9a9604db761e8f48665c68bbb45b4a80037.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8e3858963cc28454c6056cac64c7b555f0673075b1e1199739e98f6d2a304e3b.out -> moved to output_13000_wino_1000023_-1000024/8e3858963cc28454c6056cac64c7b555f0673075b1e1199739e98f6d2a304e3b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2eadb680d7bc3332c420d51c562d9eb2c83004406c30f939640fb98731ada05b.out -> moved to output_13000_wino_1000023_-1000024/2eadb680d7bc3332c420d51c562d9eb2c83004406c30f939640fb98731ada05b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4bdaabd0a74ec5ea9e1393cfd0f078e9d11682e878cc4bccc24d45a12b0fb55c.out -> moved to output_13000_wino_1000023_-1000024/4bdaabd0a74ec5ea9e1393cfd0f078e9d11682e878cc4bccc24d45a12b0fb55c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0d7d5ca6a6ee4dec77a78203bce78492d01ab94fe1ed8b51135a1b4b3febcb52.out -> moved to output_13000_wino_1000023_-1000024/0d7d5ca6a6ee4dec77a78203bce78492d01ab94fe1ed8b51135a1b4b3febcb52.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/1cfd429715d10846d44e1e4a649fa1162b26b3310c6c0e102e114c39de8d3e85.out -> moved to output_13000_wino_1000023_-1000024/1cfd429715d10846d44e1e4a649fa1162b26b3310c6c0e102e114c39de8d3e85.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/cb68c39ca9b993969f5b7f047e97ea9a326515321cd59bf4dc3929725aef02b0.out -> moved to output_13000_wino_1000023_-1000024/cb68c39ca9b993969f5b7f047e97ea9a326515321cd59bf4dc3929725aef02b0.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9bf487cc95ae279dc815a375c13c308136bae7995d1b2f35586ff7104836bd2b.out -> moved to output_13000_wino_1000023_-1000024/9bf487cc95ae279dc815a375c13c308136bae7995d1b2f35586ff7104836bd2b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e61b576ce590a9c4e7f761b4d5edbd0530561921d830912df909dff0cc56e1c8.out -> moved to output_13000_wino_1000023_-1000024/e61b576ce590a9c4e7f761b4d5edbd0530561921d830912df909dff0cc56e1c8.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2278d392ad5af960f9578e8b1dd473ef01f6c6e8cdf43707e2588a9037d4d576.out -> moved to output_13000_wino_1000023_-1000024/2278d392ad5af960f9578e8b1dd473ef01f6c6e8cdf43707e2588a9037d4d576.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c6fe8a373d5c830307b002930d8a9f25063e216b527e3b68a319b00109f332b2.out -> moved to output_13000_wino_1000023_-1000024/c6fe8a373d5c830307b002930d8a9f25063e216b527e3b68a319b00109f332b2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ab3647df9def8a1cca2f3a30eb196fdddf3fd68269c7d8a1f0deb68b74dc0055.out -> moved to output_13000_wino_1000023_-1000024/ab3647df9def8a1cca2f3a30eb196fdddf3fd68269c7d8a1f0deb68b74dc0055.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/96e4e7d644a7b9640228c155b20c5a701b2eb56b4afdc71b81d339fdef260c4d.out -> moved to output_13000_wino_1000023_-1000024/96e4e7d644a7b9640228c155b20c5a701b2eb56b4afdc71b81d339fdef260c4d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6c76cf763b02b88a02f47b4234d788912d02e4073458c6066315c2ae0be66404.out -> moved to output_13000_wino_1000023_-1000024/6c76cf763b02b88a02f47b4234d788912d02e4073458c6066315c2ae0be66404.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/83abf930f7c5729e763b908f06eb17876fd299bbcd1b2db0ae11aac62af3aa05.out -> moved to output_13000_wino_1000023_-1000024/83abf930f7c5729e763b908f06eb17876fd299bbcd1b2db0ae11aac62af3aa05.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/940a51374acafd962f9fe47a4923484c68644f1d01d295fd01c6593c00da137f.out -> moved to output_13000_wino_1000023_-1000024/940a51374acafd962f9fe47a4923484c68644f1d01d295fd01c6593c00da137f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/7d7ee4fea79517ff06af1bba4dfb4bbfe454a15646b1b57db36766670b9623b4.out -> moved to output_13000_wino_1000023_-1000024/7d7ee4fea79517ff06af1bba4dfb4bbfe454a15646b1b57db36766670b9623b4.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c45c39f8fd545ad65e8ed50f4b0d405c4a927437f19331670322e556fb51b4e9.out -> moved to output_13000_wino_1000023_-1000024/c45c39f8fd545ad65e8ed50f4b0d405c4a927437f19331670322e556fb51b4e9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/04a598c4046bbebf8d0a2bb85abd1c8b749a6e99d1b30fbb228b15d018614d5b.out -> moved to output_13000_wino_1000023_-1000024/04a598c4046bbebf8d0a2bb85abd1c8b749a6e99d1b30fbb228b15d018614d5b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e1d5bfb4ee5645a59dda7b9dd309a179840e8f3cce8663f7cb7ca04437ae9af2.out -> moved to output_13000_wino_1000023_-1000024/e1d5bfb4ee5645a59dda7b9dd309a179840e8f3cce8663f7cb7ca04437ae9af2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/00240252270c5f9390a4f3f160fcb9064e65e532171656d7603fa2353c04be9d.out -> moved to output_13000_wino_1000023_-1000024/00240252270c5f9390a4f3f160fcb9064e65e532171656d7603fa2353c04be9d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4ad50279112968b25f290a9017ca603c9ab48a00f40e1e1bfd1e7d4b1f0a02d1.out -> moved to output_13000_wino_1000023_-1000024/4ad50279112968b25f290a9017ca603c9ab48a00f40e1e1bfd1e7d4b1f0a02d1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/1dbb7644ac0d17aa1b805c2c828b32d6f5344ec68f53262a005878bd26c2044a.out -> moved to output_13000_wino_1000023_-1000024/1dbb7644ac0d17aa1b805c2c828b32d6f5344ec68f53262a005878bd26c2044a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/bf2f9844acfd2821ca3da4697ed9522c04c0891969c11fce25118578eebc883a.out -> moved to output_13000_wino_1000023_-1000024/bf2f9844acfd2821ca3da4697ed9522c04c0891969c11fce25118578eebc883a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/07d9db0404ff79630edbd9a43e8be910dfd7f0f370ad331fd679d69841c1d3c2.out -> moved to output_13000_wino_1000023_-1000024/07d9db0404ff79630edbd9a43e8be910dfd7f0f370ad331fd679d69841c1d3c2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4c59a05c6c384a9404ee808f0b13df960cafee92fde550c3c9c212c64d02412c.out -> moved to output_13000_wino_1000023_-1000024/4c59a05c6c384a9404ee808f0b13df960cafee92fde550c3c9c212c64d02412c.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4081f4faf29c04be30e88886669653d0805bb1a5a7b5e0c9a0af29d40d484469.out -> moved to output_13000_wino_1000023_-1000024/4081f4faf29c04be30e88886669653d0805bb1a5a7b5e0c9a0af29d40d484469.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8b3022fc046bd4ec8dff8380095e866cca99aa99a06e392cd6d4943d41bca391.out -> moved to output_13000_wino_1000023_-1000024/8b3022fc046bd4ec8dff8380095e866cca99aa99a06e392cd6d4943d41bca391.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/49ac30520e974d61b6bc20b115ad4aae706146ee63bf30ce275d6ef86286e396.out -> moved to output_13000_wino_1000023_-1000024/49ac30520e974d61b6bc20b115ad4aae706146ee63bf30ce275d6ef86286e396.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2848d8981b3091fc7c969284bbe26666dbf7bffb230ea3c690866448ac7e4b1f.out -> moved to output_13000_wino_1000023_-1000024/2848d8981b3091fc7c969284bbe26666dbf7bffb230ea3c690866448ac7e4b1f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8d8f643ab3aba718b01bff7dfa6238a9980e6d558889f397558fb9bd67ac467c.out -> moved to output_13000_wino_1000023_-1000024/8d8f643ab3aba718b01bff7dfa6238a9980e6d558889f397558fb9bd67ac467c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c0cc4a7e06a80ab18871983cc9e2ba9715717924fbaca971f5faac84224c2c34.out -> moved to output_13000_wino_1000023_-1000024/c0cc4a7e06a80ab18871983cc9e2ba9715717924fbaca971f5faac84224c2c34.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/efd3379dde640774d91dea31ee5023642b0c391128fc5f2d491ced20d820be49.out -> moved to output_13000_wino_1000023_-1000024/efd3379dde640774d91dea31ee5023642b0c391128fc5f2d491ced20d820be49.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/62d3ccc9ff08b5d7790d9cc05492e5a90df6e1439db91b849894dce7e816195a.out -> moved to output_13000_wino_1000023_-1000024/62d3ccc9ff08b5d7790d9cc05492e5a90df6e1439db91b849894dce7e816195a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/023e43abe480425a94a925d5898eb18154e76f1bdc30cb7c098bba1e3ec35c37.out -> moved to output_13000_wino_1000023_-1000024/023e43abe480425a94a925d5898eb18154e76f1bdc30cb7c098bba1e3ec35c37.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4677af293c8948a90235f4cfe3c01ad80aefa07d55a8438f077fc0133a408b24.out -> moved to output_13000_wino_1000023_-1000024/4677af293c8948a90235f4cfe3c01ad80aefa07d55a8438f077fc0133a408b24.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3e5713ea61fe3a76cd1a87ddcbfa8541101983fda6fd8767c4afac42139a5436.out -> moved to output_13000_wino_1000023_-1000024/3e5713ea61fe3a76cd1a87ddcbfa8541101983fda6fd8767c4afac42139a5436.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d99b37247f664e789f69c044576e54490ee3a313185acb31037ce9d9e877a088.out -> moved to output_13000_wino_1000023_-1000024/d99b37247f664e789f69c044576e54490ee3a313185acb31037ce9d9e877a088.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/96dd2df62ff5bbd4e905bee78dd75526f4226b9248ac1d0344856938adfe3abb.out -> moved to output_13000_wino_1000023_-1000024/96dd2df62ff5bbd4e905bee78dd75526f4226b9248ac1d0344856938adfe3abb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a5523a82459f51504d0a9c7b1854522602a3c2c5947ffb099b7df09f9fd00888.out -> moved to output_13000_wino_1000023_-1000024/a5523a82459f51504d0a9c7b1854522602a3c2c5947ffb099b7df09f9fd00888.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/aa671f501c59161df44c99203e192ad72416d6730d3819a813e7cf61cb564831.out -> moved to output_13000_wino_1000023_-1000024/aa671f501c59161df44c99203e192ad72416d6730d3819a813e7cf61cb564831.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/cc2efaf37d0926028b280764562f8e435ef357759fbd57ec105d5bdae0d6c3cc.out -> moved to output_13000_wino_1000023_-1000024/cc2efaf37d0926028b280764562f8e435ef357759fbd57ec105d5bdae0d6c3cc.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/48195f2727aa5f1abe9b3a5902d5a795e33d342c30806196d5dbfd765453e089.out -> moved to output_13000_wino_1000023_-1000024/48195f2727aa5f1abe9b3a5902d5a795e33d342c30806196d5dbfd765453e089.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/760c98e2b776e239940b9a04cc5041ebbf00ba6ae768752fc04faefaeed9d842.out -> moved to output_13000_wino_1000023_-1000024/760c98e2b776e239940b9a04cc5041ebbf00ba6ae768752fc04faefaeed9d842.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/48dd3ec14b2cbbf36a56b206edefbbcfa090b6de1815051edd79b91ff3125f35.out -> moved to output_13000_wino_1000023_-1000024/48dd3ec14b2cbbf36a56b206edefbbcfa090b6de1815051edd79b91ff3125f35.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d3f9f2310b55eed2e378686f40a7c2a5d59ef65f5dc421abb5930fa024decfaf.out -> moved to output_13000_wino_1000023_-1000024/d3f9f2310b55eed2e378686f40a7c2a5d59ef65f5dc421abb5930fa024decfaf.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/eff3289865a5aa67f195f0e60f51cdf1e33b55f662f1d482767f19e7a9de71d8.out -> moved to output_13000_wino_1000023_-1000024/eff3289865a5aa67f195f0e60f51cdf1e33b55f662f1d482767f19e7a9de71d8.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/93ebfe5e239ddc9ad5452299d3f15d4f6f8eae627979e0b64943dfb1cc05c893.out -> moved to output_13000_wino_1000023_-1000024/93ebfe5e239ddc9ad5452299d3f15d4f6f8eae627979e0b64943dfb1cc05c893.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/1de1f9c8606175bd14e72a22ed43e00ea1f3e21ebfa80fcd4b9c571c5cb63d9b.out -> moved to output_13000_wino_1000023_-1000024/1de1f9c8606175bd14e72a22ed43e00ea1f3e21ebfa80fcd4b9c571c5cb63d9b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/61dfe619ed9619ed494ba1f7130568a24c1797c4172decb66ca8911d93d47b71.out -> moved to output_13000_wino_1000023_-1000024/61dfe619ed9619ed494ba1f7130568a24c1797c4172decb66ca8911d93d47b71.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c032be8afefc73b04e52a75e9a5367199a323fa480530339dada065c1b3a8c60.out -> moved to output_13000_wino_1000023_-1000024/c032be8afefc73b04e52a75e9a5367199a323fa480530339dada065c1b3a8c60.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/32233105a91fa716d008252d542ce27ee64081e208163bf1b084189e1f25f80c.out -> moved to output_13000_wino_1000023_-1000024/32233105a91fa716d008252d542ce27ee64081e208163bf1b084189e1f25f80c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a975c2d3190e7d236daae08cfb816402ddc1a2e8e37bfd0cd02b33fb15cee921.out -> moved to output_13000_wino_1000023_-1000024/a975c2d3190e7d236daae08cfb816402ddc1a2e8e37bfd0cd02b33fb15cee921.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/193c7114fc3b2ef230ca0e76bf2eda26a74b9bf7787cd83376020a5e734ca5cb.out -> moved to output_13000_wino_1000023_-1000024/193c7114fc3b2ef230ca0e76bf2eda26a74b9bf7787cd83376020a5e734ca5cb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4a1a6730b03980c2e1b82dd19f7a897d66a1c262e00f44e15c4e7cfecbfc74eb.out -> moved to output_13000_wino_1000023_-1000024/4a1a6730b03980c2e1b82dd19f7a897d66a1c262e00f44e15c4e7cfecbfc74eb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/131a509706c50e147fdcccd25aa9f21387266920791aa7362e8085ced7ba6e67.out -> moved to output_13000_wino_1000023_-1000024/131a509706c50e147fdcccd25aa9f21387266920791aa7362e8085ced7ba6e67.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c4dbfef312123bfede67d1f96163fe8abf17448a5104b62470d63869ab23acf7.out -> moved to output_13000_wino_1000023_-1000024/c4dbfef312123bfede67d1f96163fe8abf17448a5104b62470d63869ab23acf7.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c345299c3dff95b3ba5bd7cc74f9de2270d64fecfc3c5a3a2e5cdc86caa6a9a9.out -> moved to output_13000_wino_1000023_-1000024/c345299c3dff95b3ba5bd7cc74f9de2270d64fecfc3c5a3a2e5cdc86caa6a9a9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a8706bc67c504e1a3d81b4e2a2bd05e2078ea477dd2e7e0ce00c159dbf2c664a.out -> moved to output_13000_wino_1000023_-1000024/a8706bc67c504e1a3d81b4e2a2bd05e2078ea477dd2e7e0ce00c159dbf2c664a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3481d4c965bd1051b840e81ecb9e552ac7321938a0c77908088aa7e7768d48d6.out -> moved to output_13000_wino_1000023_-1000024/3481d4c965bd1051b840e81ecb9e552ac7321938a0c77908088aa7e7768d48d6.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5b6bec7e8e16f0aa6e1f480cef3f8f403fa9b07731e3d9440dc4d496c3d06223.out -> moved to output_13000_wino_1000023_-1000024/5b6bec7e8e16f0aa6e1f480cef3f8f403fa9b07731e3d9440dc4d496c3d06223.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9912b4d9caf3de4b815ff2568f522adff8fa74d0ef44b7d84e684ca307ba1fa0.out -> moved to output_13000_wino_1000023_-1000024/9912b4d9caf3de4b815ff2568f522adff8fa74d0ef44b7d84e684ca307ba1fa0.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5a243db926a384b668853e08d489f2efc71872f2ee8d139d4e10d27e6ac09798.out -> moved to output_13000_wino_1000023_-1000024/5a243db926a384b668853e08d489f2efc71872f2ee8d139d4e10d27e6ac09798.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/07ef16e21efc4a5ee22f2b44216e568a77a2e01451df7761bb693642229b378c.out -> moved to output_13000_wino_1000023_-1000024/07ef16e21efc4a5ee22f2b44216e568a77a2e01451df7761bb693642229b378c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e1e088a5ada3286df7dc39461ef2910be98381921dfe959c7ae456a4adc096b1.out -> moved to output_13000_wino_1000023_-1000024/e1e088a5ada3286df7dc39461ef2910be98381921dfe959c7ae456a4adc096b1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/187d891b9e89052d63010be4644da2e89585f52a15d232ddbe51fadeb2a19843.out -> moved to output_13000_wino_1000023_-1000024/187d891b9e89052d63010be4644da2e89585f52a15d232ddbe51fadeb2a19843.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d819eca3e09dcbd4bbb07368335b52e8e9d17472bc140db5e89b5827184f812e.out -> moved to output_13000_wino_1000023_-1000024/d819eca3e09dcbd4bbb07368335b52e8e9d17472bc140db5e89b5827184f812e.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e512907ca6fe13a03088afaba086f6348d2f8cfbbd53c69e24324fcc6b060dd5.out -> moved to output_13000_wino_1000023_-1000024/e512907ca6fe13a03088afaba086f6348d2f8cfbbd53c69e24324fcc6b060dd5.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/fae381f9910bfdd1679896efbdcf80b7c75150619316045d1bf9847314b039c9.out -> moved to output_13000_wino_1000023_-1000024/fae381f9910bfdd1679896efbdcf80b7c75150619316045d1bf9847314b039c9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/656c209b3edb2b6d696680d4b5cf7e6c561c2894615c3234c75f36f76d36595e.out -> moved to output_13000_wino_1000023_-1000024/656c209b3edb2b6d696680d4b5cf7e6c561c2894615c3234c75f36f76d36595e.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e804ee137d0af330f5da0e8103c6a01e91cc469334d54f0e92f3a905d3892686.out -> moved to output_13000_wino_1000023_-1000024/e804ee137d0af330f5da0e8103c6a01e91cc469334d54f0e92f3a905d3892686.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ffb302f1deb854e61817f876cd37afd6e389fb479b73fce95d31b940f2a88c26.out -> moved to output_13000_wino_1000023_-1000024/ffb302f1deb854e61817f876cd37afd6e389fb479b73fce95d31b940f2a88c26.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c0c7cfd31f040a68a800254ee1dbfb206095c3528e2aad8a81db4f92e5a8cf00.out -> moved to output_13000_wino_1000023_-1000024/c0c7cfd31f040a68a800254ee1dbfb206095c3528e2aad8a81db4f92e5a8cf00.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/a9709234ed425dfb8c7ac5edccf502d9fb1cb4dfa60e19d809ba4205434f731d.out -> moved to output_13000_wino_1000023_-1000024/a9709234ed425dfb8c7ac5edccf502d9fb1cb4dfa60e19d809ba4205434f731d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b056d8e4ddcba4b38569e76038afed67b219e95a10884dba75325f08a2a2edd6.out -> moved to output_13000_wino_1000023_-1000024/b056d8e4ddcba4b38569e76038afed67b219e95a10884dba75325f08a2a2edd6.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/301719054f23ea8f3db6a9c7cebbfa45969276d82228ed11a1974241e128cc4c.out -> moved to output_13000_wino_1000023_-1000024/301719054f23ea8f3db6a9c7cebbfa45969276d82228ed11a1974241e128cc4c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3c54e18f12c2f269a9baefe8c5966bf6d5acf2cacd45c69b214d5eafcef7ee9b.out -> moved to output_13000_wino_1000023_-1000024/3c54e18f12c2f269a9baefe8c5966bf6d5acf2cacd45c69b214d5eafcef7ee9b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/602d1ca0a51a6522eadf57a57d750b5fc6c238baf2181ac25fce72f6b034ba73.out -> moved to output_13000_wino_1000023_-1000024/602d1ca0a51a6522eadf57a57d750b5fc6c238baf2181ac25fce72f6b034ba73.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/477809780133c3231b629c763b727baf2a3855d297bd08e6e7212642fade22cb.out -> moved to output_13000_wino_1000023_-1000024/477809780133c3231b629c763b727baf2a3855d297bd08e6e7212642fade22cb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/53c9fff45592582835e1f756379fd0ef50ebdd1311b8b5ef1f283f063e4da9cd.out -> moved to output_13000_wino_1000023_-1000024/53c9fff45592582835e1f756379fd0ef50ebdd1311b8b5ef1f283f063e4da9cd.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5aa432379574f92382dad21ec152efb82a039090ffcc0782a1971267132f922b.out -> moved to output_13000_wino_1000023_-1000024/5aa432379574f92382dad21ec152efb82a039090ffcc0782a1971267132f922b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4f104ac81267fe3b1e99079d588881533f6afaadcf1321ce3bfd77775fa7b7b2.out -> moved to output_13000_wino_1000023_-1000024/4f104ac81267fe3b1e99079d588881533f6afaadcf1321ce3bfd77775fa7b7b2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/db742148d0c1ffec91ce98ae565217a141d7ab586a1213611c83a92ddeb23a53.out -> moved to output_13000_wino_1000023_-1000024/db742148d0c1ffec91ce98ae565217a141d7ab586a1213611c83a92ddeb23a53.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/98420f2c20780d6b7c1110e8e1ca82c9390f48144eaef95c51249d9ae364f7c2.out -> moved to output_13000_wino_1000023_-1000024/98420f2c20780d6b7c1110e8e1ca82c9390f48144eaef95c51249d9ae364f7c2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c13169bdbe7b2f8b183a0836ed9a987ceb6c18975e923aac646e94de354a2b26.out -> moved to output_13000_wino_1000023_-1000024/c13169bdbe7b2f8b183a0836ed9a987ceb6c18975e923aac646e94de354a2b26.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/420e10a971462d53d572d32e3bcc5670fc1eee404fe8613abf5647ffcb93e7b3.out -> moved to output_13000_wino_1000023_-1000024/420e10a971462d53d572d32e3bcc5670fc1eee404fe8613abf5647ffcb93e7b3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5b172bbd1ac64ba60c5b49b1ef168624d70a68bd4feb27a9665a49c15849803a.out -> moved to output_13000_wino_1000023_-1000024/5b172bbd1ac64ba60c5b49b1ef168624d70a68bd4feb27a9665a49c15849803a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/887f17868031a21879b1f5cb1f3bb24fdc0c7c17bc534456066d07f4150db27a.out -> moved to output_13000_wino_1000023_-1000024/887f17868031a21879b1f5cb1f3bb24fdc0c7c17bc534456066d07f4150db27a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/079933a677c3fa1eab44b4e2f194c3e7338cfb9e4199b33fddfb8f7ff4edf641.out -> moved to output_13000_wino_1000023_-1000024/079933a677c3fa1eab44b4e2f194c3e7338cfb9e4199b33fddfb8f7ff4edf641.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/018cbbac882415fbd2f68f30945630a15d5171c35840e8f5bac444318b5a51b2.out -> moved to output_13000_wino_1000023_-1000024/018cbbac882415fbd2f68f30945630a15d5171c35840e8f5bac444318b5a51b2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/982457ee087008757245a4b321fb1a2138c7db71b40d6175249ed954bcc64007.out -> moved to output_13000_wino_1000023_-1000024/982457ee087008757245a4b321fb1a2138c7db71b40d6175249ed954bcc64007.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2317841eb7aa9abdd1eff6f551136f5f7f087ac5ac77151315f6f003f78d85d3.out -> moved to output_13000_wino_1000023_-1000024/2317841eb7aa9abdd1eff6f551136f5f7f087ac5ac77151315f6f003f78d85d3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b4ae7f37c38fe1fc23c80d070490830e654bf0678a421c26ca192c8dcf215de1.out -> moved to output_13000_wino_1000023_-1000024/b4ae7f37c38fe1fc23c80d070490830e654bf0678a421c26ca192c8dcf215de1.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/00060c78df31e0542f25593cffa521b6955881acd66bcf6d6915e89012ffa3eb.out -> moved to output_13000_wino_1000023_-1000024/00060c78df31e0542f25593cffa521b6955881acd66bcf6d6915e89012ffa3eb.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/7ec8991f85c4a9d3c37c080843a27c93a81c70f3659e12a02354e5ce75890b2d.out -> moved to output_13000_wino_1000023_-1000024/7ec8991f85c4a9d3c37c080843a27c93a81c70f3659e12a02354e5ce75890b2d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c99e5ff86fc3d8acf846c1d6fc1168021008fb6be516f06958ec0b2fae13e42f.out -> moved to output_13000_wino_1000023_-1000024/c99e5ff86fc3d8acf846c1d6fc1168021008fb6be516f06958ec0b2fae13e42f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d00c1c91846debeb50e4d8221205c58563cdfae5a2b98a0f33837ce7f16183ff.out -> moved to output_13000_wino_1000023_-1000024/d00c1c91846debeb50e4d8221205c58563cdfae5a2b98a0f33837ce7f16183ff.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9c77424f114db71abf01dbf95bd5b34deaa703fd6cd82d0223b3fd7335bfe5e2.out -> moved to output_13000_wino_1000023_-1000024/9c77424f114db71abf01dbf95bd5b34deaa703fd6cd82d0223b3fd7335bfe5e2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5f9656f6337982508617e80f45cd22c3f6000cf6ee7354c639a452a6b6ba0b40.out -> moved to output_13000_wino_1000023_-1000024/5f9656f6337982508617e80f45cd22c3f6000cf6ee7354c639a452a6b6ba0b40.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2c0ec528019f9d86c198294d4f962f2816e46d3973ce73c8f58ba128ca30cb8f.out -> moved to output_13000_wino_1000023_-1000024/2c0ec528019f9d86c198294d4f962f2816e46d3973ce73c8f58ba128ca30cb8f.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/25724ca0bf67297e6bcaadc37cad5c7f3d465061dbe2577025cf459c1f6ed1d0.out -> moved to output_13000_wino_1000023_-1000024/25724ca0bf67297e6bcaadc37cad5c7f3d465061dbe2577025cf459c1f6ed1d0.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0b9009ccb05f5fb830154423d5cb83b34b0f3048b535520d7ef750361737ad39.out -> moved to output_13000_wino_1000023_-1000024/0b9009ccb05f5fb830154423d5cb83b34b0f3048b535520d7ef750361737ad39.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b6b22c4511f4b79a8ce75a10f3b33644726a5e1d0235a1952c6658e8e18a0153.out -> moved to output_13000_wino_1000023_-1000024/b6b22c4511f4b79a8ce75a10f3b33644726a5e1d0235a1952c6658e8e18a0153.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/edce102fbc66e673f057e368713ecc760998c098be69219665df9d32a89bcb6a.out -> moved to output_13000_wino_1000023_-1000024/edce102fbc66e673f057e368713ecc760998c098be69219665df9d32a89bcb6a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5fd706e582da0a23bd5aed5fe3a28836f6d44644fce33287aa083b19717ab9e6.out -> moved to output_13000_wino_1000023_-1000024/5fd706e582da0a23bd5aed5fe3a28836f6d44644fce33287aa083b19717ab9e6.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/84392b4ba4d090f31d1673fb7a53adbd7b9afd5fc594c59f9f7af4343f50934a.out -> moved to output_13000_wino_1000023_-1000024/84392b4ba4d090f31d1673fb7a53adbd7b9afd5fc594c59f9f7af4343f50934a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6965cf2ed34588cb3f2509a456a6924d8f0faaf063b73545cb4859e28fca1a22.out -> moved to output_13000_wino_1000023_-1000024/6965cf2ed34588cb3f2509a456a6924d8f0faaf063b73545cb4859e28fca1a22.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9700eac60328f5e3d7b7e6db202250c96c17b56f79781848f681aec7f02d34e2.out -> moved to output_13000_wino_1000023_-1000024/9700eac60328f5e3d7b7e6db202250c96c17b56f79781848f681aec7f02d34e2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8bf06cebeb972c4bd381b62bc9039a265764a20d9b27b317aab0ea4680e75071.out -> moved to output_13000_wino_1000023_-1000024/8bf06cebeb972c4bd381b62bc9039a265764a20d9b27b317aab0ea4680e75071.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/636e425ce2eb75cf846e8ad780e46534801dac55001b6751e3714f15cd446fc5.out -> moved to output_13000_wino_1000023_-1000024/636e425ce2eb75cf846e8ad780e46534801dac55001b6751e3714f15cd446fc5.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/46dea7d9f9e2c18695cdf5745ed1708d3a0bd5d0d8ca965d280859438dc354c0.out -> moved to output_13000_wino_1000023_-1000024/46dea7d9f9e2c18695cdf5745ed1708d3a0bd5d0d8ca965d280859438dc354c0.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/bcedfb44813d14d092e9f831d5f62e4c57f12e18d69bb3ef679a416dd484b4c9.out -> moved to output_13000_wino_1000023_-1000024/bcedfb44813d14d092e9f831d5f62e4c57f12e18d69bb3ef679a416dd484b4c9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/545dcefe8e00ae0c11d5af7be995b907db754d6d7974635c6f4231254179b0a2.out -> moved to output_13000_wino_1000023_-1000024/545dcefe8e00ae0c11d5af7be995b907db754d6d7974635c6f4231254179b0a2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2cb3ca58ca3dc37bc0a33516d23d55b9704f6104c209a752633baca2575b1c7c.out -> moved to output_13000_wino_1000023_-1000024/2cb3ca58ca3dc37bc0a33516d23d55b9704f6104c209a752633baca2575b1c7c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b6732c27a30140bdac31fdb63ec20ed4ef4462f8270c38ec34487b60f5d1a294.out -> moved to output_13000_wino_1000023_-1000024/b6732c27a30140bdac31fdb63ec20ed4ef4462f8270c38ec34487b60f5d1a294.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d66daf9ba7e4cb437f0c8c11ead89e0301e03519a3027f6ca566b2edd4a8c224.out -> moved to output_13000_wino_1000023_-1000024/d66daf9ba7e4cb437f0c8c11ead89e0301e03519a3027f6ca566b2edd4a8c224.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d9dd4933800ca025038c5ba9f8b96472907afe7176ef457a3b7fb9da41bdd5a8.out -> moved to output_13000_wino_1000023_-1000024/d9dd4933800ca025038c5ba9f8b96472907afe7176ef457a3b7fb9da41bdd5a8.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3a47a153c163b010dfae438d6a15c2cc6db2bdab990c2c7b4185b80f24e566ed.out -> moved to output_13000_wino_1000023_-1000024/3a47a153c163b010dfae438d6a15c2cc6db2bdab990c2c7b4185b80f24e566ed.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/1d9cefbe3344550482ce066e2e6db830701a39740e1efa4cc270864044a21924.out -> moved to output_13000_wino_1000023_-1000024/1d9cefbe3344550482ce066e2e6db830701a39740e1efa4cc270864044a21924.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/388a5a802f49130576864d9ff888093ca8aa0613de503d686179b89c0d3db372.out -> moved to output_13000_wino_1000023_-1000024/388a5a802f49130576864d9ff888093ca8aa0613de503d686179b89c0d3db372.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f3dc65e8695048fcebdd7ff2daa5c1a0758b8d2420e5708610a891ce7fdef9cc.out -> moved to output_13000_wino_1000023_-1000024/f3dc65e8695048fcebdd7ff2daa5c1a0758b8d2420e5708610a891ce7fdef9cc.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/c03ae74cf30bd3f2aa359639400f589e77e7945b68001c364c0884fcdf68e362.out -> moved to output_13000_wino_1000023_-1000024/c03ae74cf30bd3f2aa359639400f589e77e7945b68001c364c0884fcdf68e362.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b80a327204ba5b5a41e35ebd51f6d42240f72ce1917299d4fa765432a85c461d.out -> moved to output_13000_wino_1000023_-1000024/b80a327204ba5b5a41e35ebd51f6d42240f72ce1917299d4fa765432a85c461d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/50b5e90492dc93c02a8e0819f042d7aed2a10c02d2cb5a7258948bba1fc9892c.out -> moved to output_13000_wino_1000023_-1000024/50b5e90492dc93c02a8e0819f042d7aed2a10c02d2cb5a7258948bba1fc9892c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/ddef4b84ed296c9806da3593579e4ff0c39d180303d7c40dae7d121e532c0361.out -> moved to output_13000_wino_1000023_-1000024/ddef4b84ed296c9806da3593579e4ff0c39d180303d7c40dae7d121e532c0361.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/aa960504c5852d703edb0c31ade6c5c4041dbf773c6b208930ff18fb7d8242e2.out -> moved to output_13000_wino_1000023_-1000024/aa960504c5852d703edb0c31ade6c5c4041dbf773c6b208930ff18fb7d8242e2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/caa07011ca3912c842ce1c007b051c846a2b4e5d4f37d9dfcc92efda4f7e9e53.out -> moved to output_13000_wino_1000023_-1000024/caa07011ca3912c842ce1c007b051c846a2b4e5d4f37d9dfcc92efda4f7e9e53.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/61a8775fd8fea11c19c8c256a46705d36749308a552649dee5e9bd37c8f50cb6.out -> moved to output_13000_wino_1000023_-1000024/61a8775fd8fea11c19c8c256a46705d36749308a552649dee5e9bd37c8f50cb6.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/cb8540cf42baba8914387eeb58298893c21cc8edf89aa092737421d8cf45ee60.out -> moved to output_13000_wino_1000023_-1000024/cb8540cf42baba8914387eeb58298893c21cc8edf89aa092737421d8cf45ee60.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/6f9812f8ed0edaf3cb4b7517144e4ffff34eacf4c70131d16de80e40a6076b68.out -> moved to output_13000_wino_1000023_-1000024/6f9812f8ed0edaf3cb4b7517144e4ffff34eacf4c70131d16de80e40a6076b68.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/09c37bd9df35ebf747957325a8aab67d7e03f678f1551867359915c72fafa9a9.out -> moved to output_13000_wino_1000023_-1000024/09c37bd9df35ebf747957325a8aab67d7e03f678f1551867359915c72fafa9a9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8ff9dbdcace4d82bcd2b5d7e1db43049802bff0fcd1591cfa30bbe93fe4d4c52.out -> moved to output_13000_wino_1000023_-1000024/8ff9dbdcace4d82bcd2b5d7e1db43049802bff0fcd1591cfa30bbe93fe4d4c52.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f40bb60635a97fd5767cb734840ce61716ef5de11c2ea69c0392be83c2cda705.out -> moved to output_13000_wino_1000023_-1000024/f40bb60635a97fd5767cb734840ce61716ef5de11c2ea69c0392be83c2cda705.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8c57e0c4fd8ec65db2f66b8dcf2b06f54a20d8a44f7f7edb2c534962965e05f7.out -> moved to output_13000_wino_1000023_-1000024/8c57e0c4fd8ec65db2f66b8dcf2b06f54a20d8a44f7f7edb2c534962965e05f7.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d26479763132ddd1a07d0418ee9ac6d327a3d6366bb683de7d20b5f26df586ea.out -> moved to output_13000_wino_1000023_-1000024/d26479763132ddd1a07d0418ee9ac6d327a3d6366bb683de7d20b5f26df586ea.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b2cc9b11eecefbd3cfe2902ddb01ed4bbcb780f8b931cbdf3877dfb5efdef8a3.out -> moved to output_13000_wino_1000023_-1000024/b2cc9b11eecefbd3cfe2902ddb01ed4bbcb780f8b931cbdf3877dfb5efdef8a3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b48f7f36751ac3b4acd561f8743dfbbc9a63500c061a8c51f8a18a63ec87b3b9.out -> moved to output_13000_wino_1000023_-1000024/b48f7f36751ac3b4acd561f8743dfbbc9a63500c061a8c51f8a18a63ec87b3b9.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/bcd1f25b79e957f47a7561fc778be7c66c6345e2953d0ba01e8d1ae9e621ab1c.out -> moved to output_13000_wino_1000023_-1000024/bcd1f25b79e957f47a7561fc778be7c66c6345e2953d0ba01e8d1ae9e621ab1c.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5c115dc257a0ff664676c550826c8237764678828324014b542856be099d49d3.out -> moved to output_13000_wino_1000023_-1000024/5c115dc257a0ff664676c550826c8237764678828324014b542856be099d49d3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/9290a87c767876c353cddee80364199569ff98247ec7f98e41f8865f3e9a6dce.out -> moved to output_13000_wino_1000023_-1000024/9290a87c767876c353cddee80364199569ff98247ec7f98e41f8865f3e9a6dce.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b6ca8e8fcbe073dabe36f7851f005372bc93e0c52a889cba10a074b7691c567a.out -> moved to output_13000_wino_1000023_-1000024/b6ca8e8fcbe073dabe36f7851f005372bc93e0c52a889cba10a074b7691c567a.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f792a7374e903606feb73f1715858c68df06ed8148f3b5e67cb5ffac60bed598.out -> moved to output_13000_wino_1000023_-1000024/f792a7374e903606feb73f1715858c68df06ed8148f3b5e67cb5ffac60bed598.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/342089548365a344f5f2a93a96bdc76609d292c2e6afd31f5b44d28854f04763.out -> moved to output_13000_wino_1000023_-1000024/342089548365a344f5f2a93a96bdc76609d292c2e6afd31f5b44d28854f04763.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/3859e60ac2d6d2fb88a11866c631a198abda03755746b1e3d9367981b79e11c4.out -> moved to output_13000_wino_1000023_-1000024/3859e60ac2d6d2fb88a11866c631a198abda03755746b1e3d9367981b79e11c4.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5a096f073adcc440188c318867ea021e61446bd43e72698076e0c52be09d32f2.out -> moved to output_13000_wino_1000023_-1000024/5a096f073adcc440188c318867ea021e61446bd43e72698076e0c52be09d32f2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/42d79c2659959b3a23ac98425588a09f4917eb95ed3460a6bbbcbca3517c1493.out -> moved to output_13000_wino_1000023_-1000024/42d79c2659959b3a23ac98425588a09f4917eb95ed3460a6bbbcbca3517c1493.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5721aa995b035f34662c9547fc661eb3f0d291cd32d5cf520d4180cd176be987.out -> moved to output_13000_wino_1000023_-1000024/5721aa995b035f34662c9547fc661eb3f0d291cd32d5cf520d4180cd176be987.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d5d10d1bd658a47555b5eb1b1c86931728af58a8247cb0c0b276c1a07bf8b424.out -> moved to output_13000_wino_1000023_-1000024/d5d10d1bd658a47555b5eb1b1c86931728af58a8247cb0c0b276c1a07bf8b424.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/54da24a694fcec913fd28653db66791a5f2f766173a6e324453d5ac2d7f88a5d.out -> moved to output_13000_wino_1000023_-1000024/54da24a694fcec913fd28653db66791a5f2f766173a6e324453d5ac2d7f88a5d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/4d488f259baf4f3c03a78a450a440f9a92907d09ab57e803817eb26d5acaaa85.out -> moved to output_13000_wino_1000023_-1000024/4d488f259baf4f3c03a78a450a440f9a92907d09ab57e803817eb26d5acaaa85.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/632bb042412343484a6ac3eb24be82652c2f4e9401cf68e2ac50b74e639140ef.out -> moved to output_13000_wino_1000023_-1000024/632bb042412343484a6ac3eb24be82652c2f4e9401cf68e2ac50b74e639140ef.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5d6ca3d62982ac75df578e3e568bd4b4f4ecfd1d267599e29229fdca44e0b592.out -> moved to output_13000_wino_1000023_-1000024/5d6ca3d62982ac75df578e3e568bd4b4f4ecfd1d267599e29229fdca44e0b592.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/b1fd8a0edc7d815a1f65955084a94f202786a675a401667a2b61ef6ddd0f2d93.out -> moved to output_13000_wino_1000023_-1000024/b1fd8a0edc7d815a1f65955084a94f202786a675a401667a2b61ef6ddd0f2d93.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/e0607234606107ef5bcb92ac56b577f9f7e8ba4edfd27a026142de50d5abf768.out -> moved to output_13000_wino_1000023_-1000024/e0607234606107ef5bcb92ac56b577f9f7e8ba4edfd27a026142de50d5abf768.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/cc1aadbfcaa193a16b2bef66b8aab298948b1be62e93c452e4c4d23b0ad8ebb2.out -> moved to output_13000_wino_1000023_-1000024/cc1aadbfcaa193a16b2bef66b8aab298948b1be62e93c452e4c4d23b0ad8ebb2.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/178c62768851e4108693e329f7939a8080729588a6a1994ece56a5b5353971b2.out -> moved to output_13000_wino_1000023_-1000024/178c62768851e4108693e329f7939a8080729588a6a1994ece56a5b5353971b2.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/117092b5b509f6a952bd245acb29b9f41c7b91b0a68d3bf66c1c0a800de8dd34.out -> moved to output_13000_wino_1000023_-1000024/117092b5b509f6a952bd245acb29b9f41c7b91b0a68d3bf66c1c0a800de8dd34.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/07fcc65ddb3406d45affb4ae5b150eb9e50e1ff120265e66029c06943b13cb56.out -> moved to output_13000_wino_1000023_-1000024/07fcc65ddb3406d45affb4ae5b150eb9e50e1ff120265e66029c06943b13cb56.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/0fecc0d634935c06f5a43544cff5af455bbab152a27193191acb8bac8f63b9e7.out -> moved to output_13000_wino_1000023_-1000024/0fecc0d634935c06f5a43544cff5af455bbab152a27193191acb8bac8f63b9e7.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/87ce2ed3e5cb3856477b61b2cc5ff85901f875c152d6d9e86342c4fe663fbd8d.out -> moved to output_13000_wino_1000023_-1000024/87ce2ed3e5cb3856477b61b2cc5ff85901f875c152d6d9e86342c4fe663fbd8d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/520fa37987d88053c85b83d2474eac33c379e0eaa6790dae74091516da3b8956.out -> moved to output_13000_wino_1000023_-1000024/520fa37987d88053c85b83d2474eac33c379e0eaa6790dae74091516da3b8956.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8f7338fcf6fbc19f37b9a797bc396cc03fddca0455c4fe167c9a4a624c0bfddf.out -> moved to output_13000_wino_1000023_-1000024/8f7338fcf6fbc19f37b9a797bc396cc03fddca0455c4fe167c9a4a624c0bfddf.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/91f0279a04ec2538409b8cdfb360977be7f25b9567bcb274cc439b60ef3fe186.out -> moved to output_13000_wino_1000023_-1000024/91f0279a04ec2538409b8cdfb360977be7f25b9567bcb274cc439b60ef3fe186.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/17845050b1e7acfa1cc5d5ea7d46d8b4d0ae20ec0dc727bfa29b6373b03c4f62.out -> moved to output_13000_wino_1000023_-1000024/17845050b1e7acfa1cc5d5ea7d46d8b4d0ae20ec0dc727bfa29b6373b03c4f62.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/35d5e27558d79c3d96efada7f843d8dc27f9c24a8271f22576e005e4baac1db3.out -> moved to output_13000_wino_1000023_-1000024/35d5e27558d79c3d96efada7f843d8dc27f9c24a8271f22576e005e4baac1db3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/d5b050f519378f4b15b8be8ba82a91a9de22fdd876e40b79e510fe4a83dcfd24.out -> moved to output_13000_wino_1000023_-1000024/d5b050f519378f4b15b8be8ba82a91a9de22fdd876e40b79e510fe4a83dcfd24.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f0dc14b3834b3c4fd1d3b442f9d466a23832a4cb8e4d89c3890294153d463676.out -> moved to output_13000_wino_1000023_-1000024/f0dc14b3834b3c4fd1d3b442f9d466a23832a4cb8e4d89c3890294153d463676.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/2486c3f9f5d6737e6925af3a8311cc133ffaf08504e45ce84741fbcab4e82d20.out -> moved to output_13000_wino_1000023_-1000024/2486c3f9f5d6737e6925af3a8311cc133ffaf08504e45ce84741fbcab4e82d20.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/f606131525486ea7d624ef8ef2f95f90485221987388545acfcc16cc4ca9f9b3.out -> moved to output_13000_wino_1000023_-1000024/f606131525486ea7d624ef8ef2f95f90485221987388545acfcc16cc4ca9f9b3.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/8df24710ee26feda3c70c9e4dd234679d8f3a5874f825a2f8a285b381fd71e6b.out -> moved to output_13000_wino_1000023_-1000024/8df24710ee26feda3c70c9e4dd234679d8f3a5874f825a2f8a285b381fd71e6b.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/73ffea16cdafee4274e7a6482c77581c8ef30f1ca229f859d2246517607ac93c.out -> moved to output_13000_wino_1000023_-1000024/73ffea16cdafee4274e7a6482c77581c8ef30f1ca229f859d2246517607ac93c.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|||||||||||||||||"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/38ea0d1fabece3dfd8ebabe1523473c2cdc04f63574d4f3e25eeac3b8ef7e06d.out -> moved to output_13000_wino_1000023_-1000024/38ea0d1fabece3dfd8ebabe1523473c2cdc04f63574d4f3e25eeac3b8ef7e06d.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/19c905631f83cc8a99bb316f3ff65b8ef8fcb83b80e63b1d39ae37a108cf9648.out -> moved to output_13000_wino_1000023_-1000024/19c905631f83cc8a99bb316f3ff65b8ef8fcb83b80e63b1d39ae37a108cf9648.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/46371b397c34c8d84646e7a1ad6e34f602ef9414cd60278748765056de47afd4.out -> moved to output_13000_wino_1000023_-1000024/46371b397c34c8d84646e7a1ad6e34f602ef9414cd60278748765056de47afd4.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/5fe648cbd062ebf87fa6dae2f5f489c64ba846f5b0b3cfe9473905bc5eeed7ff.out -> moved to output_13000_wino_1000023_-1000024/5fe648cbd062ebf87fa6dae2f5f489c64ba846f5b0b3cfe9473905bc5eeed7ff.out.old\n",
+      "  warnings.warn(\n",
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/resummino/result.py:79: RuntimeWarning: Possible hash collision in output_13000_wino_1000023_-1000024/924cfd27c83cc361c56f725c4107a022debc815f91fd53455c0db232c2d7d91c.out -> moved to output_13000_wino_1000023_-1000024/924cfd27c83cc361c56f725c4107a022debc815f91fd53455c0db232c2d7d91c.out.old\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 889.71it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 898.73it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 900.44it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 884.92it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_lo=True\n",
+    "params = [\n",
+    "    \"wino.slha\",\n",
+    "]\n",
+    "pss = [\n",
+    "    ( 1000023, +1000024),  # N1C1p\n",
+    "    ( 1000023, -1000024),  # N1C1m\n",
+    "    (+1000024, -1000024),  # C1pC1m\n",
+    "]\n",
+    "for pa, pb in pss:\n",
+    "    for energy in [13000, 13600]:\n",
+    "        name = str(energy) + \"_wino_\" + str(pa) + \"_\" + str(pb)\n",
+    "        hepi.set_output_dir(\n",
+    "               \"output_\" + name + \"/\"\n",
+    "        )\n",
+    "        rs.default_resummino_runner.set_output_dir(hepi.get_output_dir())\n",
+    "        for param in params:\n",
+    "            i = hepi.Input(\n",
+    "                hepi.Order.LO if test_lo else hepi.Order.aNNLO_PLUS_NNLL,\n",
+    "                    energy,\n",
+    "                    pa,\n",
+    "                    pb,\n",
+    "                    param,\n",
+    "                    \"PDF4LHC21_40\",\n",
+    "                    \"PDF4LHC21_40\",\n",
+    "                    1.0,\n",
+    "                    1.0,\n",
+    "                    id=\"0.0.0\",\n",
+    "                    precision=0.001,\n",
+    "                    max_iters=50,\n",
+    "                )\n",
+    "            li = [i]\n",
+    "            li = hepi.masses_scan(\n",
+    "                    li,\n",
+    "                    [\n",
+    "                        1000022,\n",
+    "                        1000023,\n",
+    "                        1000024,\n",
+    "                    ],\n",
+    "                    range(100,2010,100),\n",
+    "                )\n",
+    "            li = hepi.masses_scan(\n",
+    "                    li,\n",
+    "                    [\n",
+    "                        1000002,\n",
+    "                    ],\n",
+    "                    range(100,2010,100),diff_L_R=0,\n",
+    "                )\n",
+    "            if not test_lo:\n",
+    "\n",
+    "                li = hepi.seven_point_scan(li)\n",
+    "                li = hepi.pdf_scan(li)\n",
+    "                    # li = hepi.change_where(li, {\"precision\": 0.0001, \"max_iters\": 200}, pdfset_nlo=0)\n",
+    "                if not analyse:\n",
+    "                    rs_dl = rs.run(li, skip=False, run=False, parse=False)\n",
+    "                else:\n",
+    "                    rs_dl = rs.run(li, skip=True, run=True, parse=True,ignore_error=True)\n",
+    "                    rs_dl = hepi.pdf_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.scale_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.combine_errors(rs_dl)\n",
+    "                    if energy == 13000:\n",
+    "                        fig, axs = plt.subplots(2, 1, figsize=(6,4), sharex=True, gridspec_kw={'height_ratios': [2, 1]})         # Remove horizontal space between axes         \n",
+    "                        fig.subplots_adjust(hspace=0)\n",
+    "                        hepi.title(li[0],axe=axs[0],cms_energy=False,pdf_info=False,scenario=\"\")\n",
+    "                        frac = rs_dl[\"aNNLO_PLUS_NNLL_NOERR\"][rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull()]\n",
+    "                    #hepi.mass_plot(rs_dl,\"LO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp)\n",
+    "                    #hepi.mass_plot(rs_dl,\"NLO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp,init=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000022,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fmt=\" \",next_color=False,show=False,plot_data=False,logy=True,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,interpolate_label=\"13.0 TeV\" if energy == 13000 else \"13.6 TeV\" )\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000022,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000022,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fmt=\" \",next_color=False,show=False,plot_data=False,logy=False,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\")\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000022,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=False,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    axs[0].set_ylim(5e-7,25)\n",
+    "                    axs[1].set_ylim(-2.5,5.0)\n",
+    "                    axs[0].set_xlabel(None)\n",
+    "                    axs[1].set_xlabel('$m_{\\\\tilde{\\\\chi}}$ [GeV]')\n",
+    "                    if energy == 13600:\n",
+    "                        plt.savefig(name + \".pdf\",bbox_inches ='tight',pad_inches=0)\n",
+    "                        plt.show()\n",
+    "                    hepi.write_latex_table_transposed_header(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",\"mass_1000022\")\n",
+    "                    hepi.write_latex_table_transposed(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",yscale=1000.)\n",
+    "            else:\n",
+    "                rs_dl = rs.run(li, skip=True, run=True, parse=True)\n",
+    "                hepi.mapplot(rs_dl,\"mass_1000022_1000023_1000024\",\"mass_1000002\",\"LO\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{q}}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\")\n",
+    "                plt.show()\n",
+    "                hepi.mass_plot(rs_dl,\"LO\",1000002,data_fmt=\".\",show=True,interpolate=False)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "03bef720",
+   "id": "856af219",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||"
+     ]
+    }
+   ],
    "source": [
-    "import pandas as pd\n",
-    "pd.set_option('display.max_columns',None)\n",
-    "rs_dl"
+    "test_lo=True\n",
+    "params = [\n",
+    "    \"hino.slha\",\n",
+    "]\n",
+    "pss = [\n",
+    "    (1000022, +1000024),  # N2C1p\n",
+    "    (1000022, -1000024),  # N2C1m\n",
+    "    (+1000024, -1000024),  # C1pC2m\n",
+    "    (1000022, 1000023),  # N2N1\n",
+    "]\n",
+    "for pa, pb in pss:\n",
+    "    for energy in [13000, 13600]:\n",
+    "        # save to different folders to have some substructure\n",
+    "        name = str(energy) + \"_hino_deg_\" + str(pa) + \"_\" + str(pb)\n",
+    "        hepi.set_output_dir(\n",
+    "            \"output_\" + name + \"/\"\n",
+    "        )\n",
+    "        rs.default_resummino_runner.set_output_dir(hepi.get_output_dir())\n",
+    "        for param in params:\n",
+    "            i = hepi.Input(\n",
+    "                hepi.Order.LO if test_lo else hepi.Order.aNNLO_PLUS_NNLL,\n",
+    "                energy,\n",
+    "                pa,\n",
+    "                pb,\n",
+    "                param,\n",
+    "                \"PDF4LHC21_40\",\n",
+    "                \"PDF4LHC21_40\",\n",
+    "                1.0,\n",
+    "                1.0,\n",
+    "                id=\"0.0.0\",\n",
+    "                precision=0.001,\n",
+    "                max_iters=50,\n",
+    "            )\n",
+    "            li = [i]\n",
+    "            li = hepi.masses_scan(\n",
+    "                li,\n",
+    "                [\n",
+    "                    1000022,\n",
+    "                    1000023,\n",
+    "                    1000024,\n",
+    "                ],\n",
+    "                range(100, 1510, 100),\n",
+    "                negate=[1000022],\n",
+    "            )\n",
+    "            li = hepi.masses_scan(\n",
+    "                    li,\n",
+    "                    [\n",
+    "                        1000002,\n",
+    "                    ],\n",
+    "                    range(100,2010,100),diff_L_R=0,\n",
+    "            )\n",
+    "            if not test_lo:\n",
+    "                li = hepi.seven_point_scan(li)\n",
+    "                li = hepi.pdf_scan(li)\n",
+    "                # li = hepi.change_where(li, {\"precision\": 0.0001, \"max_iters\": 200}, pdfset_nlo=0)\n",
+    "                if not analyse:\n",
+    "                    rs_dl = rs.run(li, skip=False, run=False, parse=False)\n",
+    "                else:\n",
+    "                    rs_dl = rs.run(li, skip=True, run=True, parse=True,ignore_error=True)\n",
+    "                    rs_dl = hepi.pdf_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.scale_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.combine_errors(rs_dl)\n",
+    "                    if energy == 13000:\n",
+    "                        fig, axs = plt.subplots(2, 1, figsize=(6,4), sharex=True, gridspec_kw={'height_ratios': [2, 1]})         # Remove horizontal space between axes         \n",
+    "                        fig.subplots_adjust(hspace=0)\n",
+    "                        hepi.title(li[0],axe=axs[0],cms_energy=False,pdf_info=False,scenario=\"\")\n",
+    "                        frac = rs_dl[\"aNNLO_PLUS_NNLL_NOERR\"][rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull()]\n",
+    "                    #hepi.mass_plot(rs_dl,\"LO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp)\n",
+    "                    #hepi.mass_plot(rs_dl,\"NLO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,plot_data=False,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp,init=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000023,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fmt=\" \",next_color=False,show=False,plot_data=False,logy=True,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,interpolate_label=\"13.0 TeV\" if energy == 13000 else \"13.6 TeV\" )\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000023,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000023,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fmt=\" \",next_color=False,show=False,plot_data=False,logy=False,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\")\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000023,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=False,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    axs[0].set_ylim(5e-7,10)\n",
+    "                    axs[1].set_ylim(0,2.5)\n",
+    "                    axs[0].set_xlabel(None)\n",
+    "                    axs[1].set_xlabel('$m_{\\\\tilde{\\\\chi}}$ [GeV]')\n",
+    "                    if energy == 13600:\n",
+    "                        plt.savefig(name + \".pdf\",bbox_inches ='tight',pad_inches=0)\n",
+    "                        plt.show()\n",
+    "                    hepi.write_latex_table_transposed_header(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",\"mass_1000023\")\n",
+    "                    hepi.write_latex_table_transposed(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",yscale=1000.)\n",
+    "            else:\n",
+    "                rs_dl = rs.run(li, skip=False, run=True, parse=True)\n",
+    "                \n",
+    "                hepi.mapplot(rs_dl,\"mass_1000022_1000023_1000024\",\"mass_1000002\",\"LO\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{q}}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\")\n",
+    "                plt.show()\n",
+    "                hepi.mass_plot(rs_dl,\"LO\",1000002,data_fmt=\".\",show=True,interpolate=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "856af219",
+   "id": "d893b94d",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1729,7 +3025,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
diff --git a/distribute/distribute.ipynb b/distribute/distribute.ipynb
index 65f1f7dd6..526875aef 100644
--- a/distribute/distribute.ipynb
+++ b/distribute/distribute.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.1.13.7+dirty\n",
+      "0.1.13.10+dirty\n",
       "~/git/resummino/build\n"
      ]
     }
@@ -1708,20 +1708,421 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "03bef720",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "................................................................................................................................................................................................................................................................................................................................................................................................................= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 899.57it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4wv3bWPfA+CHSamG5avk8rl5xSl07FsUlsmOlt+A4zuukHWa+EVgKzBaRQeB6Vb1NRK4E7qMUubdBVbeEOJ4P8Y0lVOhvCCyJi+M4NklVoFT1ojrb7wXuDX08H+ILM6x29YpTxvSMQkw4g4uL4ziNsTDElxhF60F1MjyX1GR+3HkSx3GcZhRaoIrUgwoxPOfLhziOkycKLVCWelCd9H6SKCviw2qO4+SFQgtUCEKWsGm39+Or+zqOM5EptEDFHeKLM6wWuvfjw3OO40w0Ci1QnQzxhRKWpIITXJgcJxusFBW25EuIqjKNKLRAdUKSJWy89+NMJKzdjOPcSIu6BEnWy5g0wwWqAaFL2LgwOWlg4ena4s243RuppfWTrPiS9npQhRaoEGHmLixOK4TsLeT1Sb9oN2NLRYWt+FLLRoj1oOqR+nIbWeALFoanSL5a6y20u2BhlsuH1Du3IZf9sLKwXpo90zwtshmiqoyZBQuzwAUqPFZ8beUia2V10rRWoE3KTkTaq61OpPWg0sTK9dUKRV8PynE6Iu9rStWzE+dJ39LyIUVbD8pJHxcoJzOyqq5hedmPuAsWRnZ8+RCnCBRaoIpUi88SWa6oa1lcQvUW/EnfcUoUWqAs1eIrClmvqFuJNXFxHCcshRYoZzxZFq316hqO47SDC9QEIuSwWtyQXa+u4ThOM1ygCk5Sw2q+oq7jOEmTS4ESkT8A/iPwZuBWVf1eUscKlcuRlS++oq7j2MFabljWNQ6bkbpAicgG4Dxgl6qeVrF9FbAO6AbWq+pN9Wyo6neA74jITODvgEQEKlSFgMhW1tUKfFjNySNWbqQWruFQdrxYbH1uB24B7og2iEg3cCuwAhgE+kXkbkpitbbq85ep6q7y39eVPxeMJIohZl3brBIfVnNawdITuoUbadbXsJX6hGkXi82k1JGI9AKboh6UiCwBblDVleXXawBUtVqcos8LcBNwv6p+v84+q4HVAHPmzFnY19fXtp87hka4uX8/h0dhUhdcc+YUTp7Z3dJn1z66j6eGRpvuN39mF2sWTwVgeHiYnp6e4L4kQSNfrZEnXyG+vzuGRnhyzwgLZnV31EaitnZoFCY3aWv1fG3HRjWdXDtWbVTSzjWc9L0grA1lUpfEuictW7bMTi2+GgJ1AbBKVS8vv74UWKyqV9b5/J8BnwD6gR+r6pcbHS9OLb6QT4HN5n7SKBAZiolWKywt4taMS7u2YOW5DV1XEOwUiw1Z/NaLxY7HVLHYGgL1EWBllUC9W1X/NOZxokoSV2zfvj2m1/GIW9TUGu7rWEI+yGRRzbyWH3GKxYa6oUe2LNxI03xAnGjXl/VisYPACRWvjweez8iXRPC5H7tkOfld1NqCIQNyrBSL9Ws4fawIVD8wT0TmAs8BFwIXxzXqpY6KT1bikmR1jayrmUd2vOCskzVZhJlvBJYCs0VkELheVW8TkSuB+yhF7m1Q1S0BjuXFYg2TZ3FJKr/MSjVzx7FA6gKlqhfV2X4vcG/gY3kPyihFEpfQw1meBO04JawM8SWC96CSodOkxyKLi/daHCc8hRYo70GFJ07So4uL4xSLzEsdicisFuyMquqv47sTFu9BjSfL5TYqcXFx8oaV3CMrvlgpdfR8+Z802Kcb+K0gHgUkRA/KUnJsXCwttwEuLk5rWKjFZ6X+Xda+pF3qqBWB2qqqpzfaQUT+PbYnBrFULLZTG5aX23CKTd5r8VmqXWfFl6QeVuvRikC1IoPxpTIBOhnis1QsNoQNX25j4mFp+Cer5GUrATmhrj9LvkSk8bCaSamjtOm0Fl+cH7GT8jPVJUOSqm0WYshyopViSYusavF1amOi1OKL7KQl/HmpxQfJljpqWaBEZArwX4D3Agr8EPiSqu6P5VkKFKFYbMjaZiHI003fa/GV6KTH0czGRKrFlyYT7foKUYvvDuA14B/Kry8C/hn4SCzPEiREFF+oki+Waps5Y8n7cJbl4Z8i1eJz0qcdgZqvqu+seP2giDwe2qGQWMqD8tpmNsk6msliLT5PAXCs0I5A/buInKWqjwCIyGLg/yXjluO0RlaRjUWvxefi4liglUTdn1Cac5oMfFxEflF+67eAnyXom1Nwshpasz6c5UNRjlOilR7UeYl74Uw4sh5ai/DhLCePWIniy7zUkaruhPpRfME9CoiXOkqGIhWLBRcXpzUsiYKFqhZWSh1F5C6Kz0sdhaeIxWId21h40s9aFKwkL1ssdRSRuyi+uFgrdRQCC+WSwIfWJgLWegt5LnVkJZXAYqmjiAkRxWe11FFkx0pQgReLLTZ5zw2zMpycZLRn1qkEpkodichWYD5QGcW3FRgFVFV/J7h3gchrqaNavoQKKmjkRzM8Kz85QpzbNMXFSx0lYyNPbdZKqaMTG70fBVMkjYi8HbgKmA08oKpNAzXyWuoo9AUfsvzMRLuA0qDTUkdfuH8b6x7Y3nS/q5bP4+oVp9R8z0sd2SIvbRaMlDoKIUAisoFS2PouVT2tYvsqYB2ldaXWq+pNDfzYCnxaRLqAr8X1qRlZljryoIL80OlNtJ64VA9FNRKXEFhua17qaOLSSqLuj1T1jLj7lLkduIVSRGD02W7gVmAFMAj0i8jdlMRqbdXnL1PVXSLyAeAzZVu5IO5F5kEFyZHlfEsIrl5xyhHxsiQu3tacuLTSg3q7iDzR4H0BZrRyMFX9gYj0Vm1+N7BDVZ8BEJE+4IOqupY6ScKqejdwt4j8K/CNVo5dBPyCH09W4hIqmKZSXCJ/4gxFubg4RaLpHFSzuacyI6o62NIBSwK1KRriE5ELgFWqenn59aXAYlW9ss7nlwIfBo4GnlDVW+vstxpYDTBnzpyFfX19rbiXKcPDw/T09GTtRktY8HXH0Ag39+/n0ChM7oJrzpzCyTO7x+1X7evaR/fx1NBoU/vzZ3axZvHUlv04PAqTGvjRKhbObau4r8nQzNcdQyM8uWeEBbO6O25roWw8/qt9vPMtU2O1+WXLlnU2B5VC8IPUOmy9nVV1M7C5mVFV/aqIvACcP3369IV5mHCcaBOjIXOyDo7C5x59fWmyRtGRlW6HGBJbCpx+Rrg8t4nWDtIirq9WovgGdg7xdw9Evf6RjiMbO7FRe85UuGfn2GUBQ82ZtpMHlRSDwAkVr48Hng9h2CtJJEOI+ltFK/TqQ2LJ4ZUk7OSGpU07YebXq+pfxT7g+CG+ScA2YDnwHNAPXKyqWwIcK6rFd8X27c3DcKtJu5JEHp5GOw2FhuyWsM/Dea0kT/42e9K3Vkki7ysVQ9jcMAv5ZRAmD2oU+FtgFvAjoE9Vh9p0YiOlEZHZwIvA9ap6m4icC3yRUuTeBlW9sR27DY7XtkAlmWzY7CJrdmPKqlil1QTMVsnTDR9s+Bt3KCqJ3kI1SQhDPdIWBav3glo2QuSXhRCoEeBzwE+AM4APAReq6uMde5USE7mSRGI2Ajw1pTV8auGG3w5Zz5V4JYn6dizMQVnDSiWJLar62xWvTwG+rKq/F8uzBIk7xAf5ryThpY7ydbHHPbdpl8TyShLJkKc2a6KSBLBbRBaq6gCAqm4TkTfF8iphQgRJ5L2SRFLFKj0rfywhH2SyqLptNfgksuWVJCYm7QjUnwF9IjJAaZjvd4CfJ+JVICwtWGihkoSXOkqGrKOzrIqLRzY6cWmnFt/jIvIu4BzgNOBBYGNCfgUhRA/KEqGeJP2mMZYs18gKvZSJi8vEwFKQRKZLvleiqgeAfy3/c5zMsbRGVqh1fuLOlbi42MVa6H2RlnzPHSGG+DxRNznyXkcvIvR8i8+VhCdUcnlWRYVDVL3PY7JvoQUq7hBfEZd8t0KW4pJU78d/12QIJQxxnvStPAzFIY9LvrccZp5n2smD8kTd1mx0MgyVVfJkGuc1JEUJMc5yOMtiioYn6tYndh5UnvFE3fA2Oil1VMtO3J6LlzpKhrxXkqjpS4wn/TTbK9hoA61iIlE3j3iirh0btfBKErXxShLjfQkhDHGf9NPsaeepzbpAxaTTHlRI4j7pF7FAZFrk6WL3ShLjCSUMeWoHE83XEJUknBgUJVE3VCh00fBKEmOxVknCSQZTeVBOtlhJ1C1aKHSW4cNFFhcXluSwECTheVCOkzBZhw97JYn8YEEUIhtZBUx5HlRAPFG3+GRVpsgrSeSLuENRWUfRWkmyTTsPqtAC5Ym6tilKmSKvJJEsWQ1FWREFsLmyQRrz0YUWqE5IogsbSugsDTGEKBtjYYjBh8SSw2Kibtq95CR72lkHTEV2knyg8jDzBniibn0bnSTqWq0kYQ0L/nqibm07vqLueDwPKiZx8qA8Udd+AqZXkhiLJ+rWtpOn1Ig8tVnPg6pCRKYBPwCuV9VNSR4rVFh2nlfUTWJi1IfWapPVZL6VIbFq8jIU5SRDqgIlIhuA84BdqnpaxfZVwDqgG1ivqjc1MfWXwLcSczQBPFG3tq2i3DA8UXcsnqhrHyvz0Y1Iuwd1O3ALcEe0QUS6gVuBFcAg0C8id1MSq7VVn7+M0lLzPwOmpOCvKTxRNxk8UXcs3rtNjlDRvFm22WobSSbqpj4HJSK9wKaoByUiS4AbVHVl+fUaAFWtFqfo8zcC04BTgX3Ah1R1tMZ+q4HVAHPmzFnY19fXkb87hkZ4cs8IC2Z1c/LM7o5stMrw8DA9PT2JHiMURfF1x9AIN/fv59AoTO6Ca86c0tLvvPbRfTw1NK7ZjWP+zC7WLJ7akp+RL4dHlUld0rIvtex4mx1PXF/jntd22prVNptEuwdYtmyZjSCJGgJ1AbBKVS8vv74UWKyqVzax85+A3a3MQcWN4ksrD2qiTYyGoJPzmuU6P63Y8sn88OS9CK/FNhsyUddykITU2NZUNVX19qaGO6gk4XlQrdkIMe7sibq1bRVl+NTacFae5/YsttmJkqg7CJxQ8fp44PmMfAnWEEILncU8qDjjzp6oaxsLcxxx7FgSl4iQbc1Kmy1com6NIb5JwDZgOfAc0A9crKpbQh3T86CytxHSToQn6tYm73lQRU7UbZU8tdnCJOqKyEZgKTAbeJFSHtNtInIu8EVKkXsbVPXGQMeLvaJuKHzBwvC+RHaKNLcH8fzNeq6k2o8Qv68n6tqmMAKVFRZW1G2FZj+0pTmoEBd7Wk+kebrYOzm3WU6gN3uosjAHFZGndpCGr5buBZaDJBIjxHIblihaHlSR5m2yTNS1PFcS4vf1dhLejqX56EYUWqDiLrcBdhqUkxx5T9StxJq4FAkLohDHjqXlQ1ql0AIVtweVdYOqZcdFbixZ3TSsrqgLLi61yHONw1B2rNblbITPQVWRZSRS0sttRHasjDunJS55SHqstOWT+WMJmQfVzhIxluf20gowasVGknNQLlANSDsSKa0baVbrQYXwJSnht3CxRxRlMj/kvFxaUYnN/EirnaQRMBUKj+LrkBBh5mnOQSVxI/U8qHxd7GBDoOLeSJOYl6vG86Bs4AIVEw8zf/3zngdln4meqFvLF8+DsomHmTtHiDv5HSLCK7IRt1F6tFlt8jyZ7wsWpoeFnEgPM49JiDwoa8M/cfE8qGTIMg/KoriEEpbIlreTsTayqsvpYeYBiZsHFSpyLrJVJKErEnnPg7IqLkUSFsh/O0mqrSUZZl5ogeoEX24jPSx8n6yfJENf8C4utcnz0Ckkl8MU4kEmybk9D5JoQJwf0WIeVEihjNso0/w+ngeVHJ4HlX4eVGQr64e7CI/ii4kvtxH+IuskDyqr7+N5UMnheVDtESoPyhIuUB3iy22EsWE1fNjzoJLD86Bq2/E8qPG4QMXE86DC2Qg5MZrW98nTxQ6eB1XLF8+DCk8eSh15kEQTLD1dhwoRD5FLFaJRWvg+1sjzZL7nQaVHlhGF1TY8DyojPMy8NkW72EPgeVBjKWIelIURjMiGL7cxQfEw83xh4Zxknd9iVVysCAtk1zMNZcNKO/HlNpogIkuBvwG2AH2qurnZZzzMPJyNyE6el9voxEY1Sc63+GT+WPIcZt6pjbxEniY9B5WqQInIBuA8YJeqnlaxfRWwDugG1qvqTQ1s/C7wGeBF4HOquqPZcT3M3EaYeS07WSy3YTUEObJVlMl8i8NZ1aT5G1vLgwpFYaL4ROR9wDBwRyRQItINbANWAINAP3ARJbFaW2XiMmC3qo6KyBzg71X1kmbHtRDF52HmNpfbsPQ02oq/aeFh5rXtpDWcbKENtEphBKrsSC+wqUKglgA3qOrK8us1AKpaLU7Vdo4CvqGqFzQ7pgWBaoWJFGaepjDk6WkUPMy8li8eZm6TQg3xlR3pZaxAXQCsUtXLy68vBRar6pV1Pv9hYCXwRuBL9eagRGQ1sBpgzpw5C/v6+jryd8fQCE/uGWHBrG5OntndkY1WGR4epqenJ9FjhGDH0AiP/2of73zL1FjnJK1zm5fzCvHP7Y6hEW7u38+hUZjcBdecOaUlO2sf3cdTQ6NN95s/s4s1i6ceeV3v3EZ+HB6FSW34UYtQ7SRP7aCZryHOSVwbr7c1ZXKXxPqNly1bZlagPgKsrBKod6vqnwY4VqxKEmmHmefpST8vT3iQj4n8yEaRasZZaq9go82GGOLLMmAqiV4y2E7UHQROqHh9PPB8CMOdLLdhNczcWqh6nJDdkJga9sxx+HAlRQwzD0He20kel9uw0IOaRClIYjnwHKUgiYtVdUuAYwXpQWUVZp7YZHGg7PE4UXyRLQ8zr+GLgcn8VrHQK2mFuHMlabcTqwFTtewUZg5KRDYCS4HZlMLEr1fV20TkXOCLlCL3NqjqjYGOF7tYbJZh5p3YqMZiqDp4mHkjW0WZzA8llHnOg6rlh5Uw81C/T6Gi+LLAQhSfhTmokE9enTzle5h5a1jolWQZZh7CjtWHEA8zr82EFChLy220Qp4m80MtWOhh5uPJa5j5F+7fxroHml9nVy2fx9UrTqn7fpHzoFolLwKV9BCfhSCJxOgkSKIaK8MUoQgxcb3wxPjFYi1NxFuazM+qZlzaRUAbYXVpcmvtJOshvsqhU69m3gEVPaiOPp/1MEUtO1k3ypBYuuDjEjrMPI/VzK9eccqYnlGIcxLyQcZCBf4syz+FsJH2g0yhh/gi2pmDyrKsj9Xch1p2LBSLbdVGWnN7lsr6+FzJePI6dAo2g51ChplPyDmoiCyqmXdiJ0+NMqtisZ3Y8DDz5MiLQHmYeXgbkZ3ChJmnjZUw81btWG2UlorFepi5LfIQ2ONh5snZAI/ii42FMPNWyEOjDPmUn5bgeph5+4QqyePLbYy340On45mQUXwhKFpQQVwbISOi4k5+W7ER2cm6fURkOacWwobFkjzeTurbSLLsmQtUA0IFFUS2su79hCJURJQFwQ1lIxR5DjMPFeHlYebNsfQQ4mHmHdJJmLkXi23NRoinJiuibclGnsPMQwsLeJh5PRt5fwhpFZ+DakCci8zyZL6FYrF5j+ILZaPIcyVWevwREz3MvJYvWZU9q8aDJDoMkgj5xDPRi8UWKYrPw8xL5CGKL7LhYebj7WR9XiNcoLxYrKmnpiJF8XmY+dKa71mrmuBh5sngYeYxyboH1SoT5Wk0tC9ZC38oGxEWQozjDvFlPexpcUgsslOkMPOke1AeJNGAIkbxhYp68yi+8DZCkVWEl6UoPg8zb07IBGiP4usAX/K9th0rUXxFImTFkbyGmVuN4vMw89qf92KxRvAovhKWovgiW0UY4gv58FCkuRJL8ySQfRRfOzbyUD/So/gCkfUclEfxjSfvYeaWIhsr8bmS2mQVxdepjTyEmUd2PIovJh7F9/rn877ku+Weaaiq9x5mPpaQcyVp90yLHmbezNdWKYxAiUgX8DfAMcBjqvr1Zp/JugfVKnm42CMbFpZ8txJmHtkJNQflYeZjbVgazkorHcHDzEukGiQhIhuA84BdqnpaxfZVwDqgG1ivqjc1MPNB4K3AHmAwQXc9iq+BDQtLvluxEdkJcaMIcW4ttLUkwszzXiw2VFuLbGUtTJB8wFTaUXy3A7cAd0QbRKQbuBVYQUlw+kXkbkpitbbq85cB84GHVfUrInIX8EBIBz2KrzUbIRqllfDuEDbyHsUXwoalMPNKLEXxhRIWSw8hSYaZpz7EJyK9wKaoByUiS4AbVHVl+fUaAFWtFqfo838EHFTVb4nIN1X1Y3X2Ww2sBpgzZ87Cvr6+tn3dMTTCzf37OTwKk7rgmjOncPLM7pY+u/bRfTw1NNp0v/kzu1izeCoAw8PD9PT0xLLRiOj7HBqFyW1+n/E2lMld0pGNSltP7hlhwazuRG1Un9fQfoQ4r2PttHduQ7STTm3UO7dxrp1atuK2k0a+pkmr36WRr2Gv4/ZshL4nRSxbtszGHFQNgboAWKWql5dfXwosVtUr63z+DcA/AL8BnlTVW5sdM+s5KI/iG49H8YW1E2FprsTSPAlkH2aedRSfh5m35kgvYwXqI8DKKoF6t6r+aYBjxV7yPRQexedRfK3asRDFZynM3EJgT9oPMnmpH1m4MPO4Q3xtHiu2QHkUX20bHsU33o5H8Y0lyyi+WjaKkgAd2cp6DiqiMFF8degH5onIXOA54ELg4hCGOyl1VEkRo/hC4FF8te1YieILQd6j+CyWbrIWxRcqOKgwUXwishFYCswWkUHgelW9TUSuBO6jFLm3QVW3BDqer6gb2EZkx0otPksXqoUovhC+FCGKz2KYeWQn62smwqP4jDCRa/F94f5trHug+fDmVcvncfWKU5ru1+lwST07aYTNF70WXwhfQs+VhBo6jWxZGJYOQai5PWsPIdW0WyzWzBxUmliZg8pyriSEQFmPWJvoUXxW50qsDElH5DmKr1M7lXgUn1G8Fl84GyEjzdIKm59IUXxpB314sdjGhOqZWm6zhYriSxMrPahW7UyUi73SThph8xMtis9K5Gma106zzxcpis9am/VisTGJOwcVam4hzlBUZCfrHlSEFTG10jMNiYVzG7cHlfa1U43lHoeFOaiQFD3M3BShIpFCRwMWMYrP0kUWF4/is3XtFD2Kz1I0YJIUugcVd4gv9NxCu0NRlifQLUTxZRkkEdpGpR2P4kvGjoUovlax0ItuFR/ii0nWtfhatWO1vEneI9YsC78lXyKsRfEVbVi6FSaary5QGUfxtUIe5qCyjuLrxIZV4a9nx6P4ksF9TQafg+qQTipJWMZC1YRoLD7E2joWyhRZsVFpJ865tTZX4jhxKLRAxa3F59Rm4Ylh6sVZEFxLNiI7Ieocurg4RaArawccx3EcpxYuUI7jOI5JXKAcx3EckxRaoETkfBH56iuvvJK1K47jOE6bFFqgVPUeVV09Y8aMrF1xHMdx2qTQAuU4juPkFxcox3EcxyQTopKEiLwE7MzajxaYDezO2okWcV+TI0/+uq/JMNF8PVFV31S9cUIIVF4QkcdqlfuwiPuaHHny131NBve1hA/xOY7jOCZxgXIcx3FM4gJli69m7UAbuK/JkSd/3ddkcF/xOSjHcRzHKN6DchzHcUziAuU4juOYxAUqJUTkBBF5UES2isgWEbmqvP0GEXlORH5c/nduxWfWiMgOEXlKRFZm4POzIvKTsl+PlbfNEpH7RWR7+f+ZFftn4q+IzK84fz8WkVdF5M+tnFsR2SAiu0TkpxXb2j6PIrKw/HvsEJH/ISKSkq9/KyJPisgTIvIvIvLG8vZeEdlXcX6/bMDXtn/zDH39ZoWfz4rIj8vbsz6v9e5V6bdZVfV/KfwDjgPOKP89HdgGnArcAPxFjf1PBR4HjgbmAk8D3Sn7/Cwwu2rbzcBnyn9/Bvi8FX/LfnQDvwJOtHJugfcBZwA/jXMegX8DlgAC/G/g91Py9f3ApPLfn6/wtbdyvyo7Wfna9m+ela9V7/934L8ZOa/17lWpt1nvQaWEqr6gqj8q//0asBV4a4OPfBDoU9UDqvpzYAfw7uQ9bcoHga+X//468AcV2y34uxx4WlUbVQ5J1VdV/QGwp4YPLZ9HETkOOEZVH9bSlX9HxWcS9VVVv6eqh8svHwGOb2QjS18bYO68RpR7FR8FNjaykaKv9e5VqbdZF6gMEJFe4HTg0fKmK8vDJxsqus1vBX5Z8bFBGgtaEijwPREZEJHV5W1zVPUFKDVk4M3l7Rb8BbiQsRe61XPb7nl8a/nv6u1pcxmlJ+GIuSLy7yLyf0Xk7PK2rH1t5zfP2leAs4EXVXV7xTYT57XqXpV6m3WBShkR6QH+J/Dnqvoq8CXgbcC7gBcodfWh1CWuJu2cgPeo6hnA7wN/IiLva7Bv5v6KyFHAB4BvlzdZPrf1qOdb5j6LyLXAYeDO8qYXgN9S1dOB/wp8Q0SOIVtf2/3NMz+vwEWMfagycV5r3Kvq7lpjW5Bz6wKVIiIymdIPfqeq/i8AVX1RVUdUdRT4Gq8PNQ0CJ1R8/Hjg+TT9VdXny//vAv6l7NuL5a57NOSwq7x75v5SEtIfqeqLYPvc0v55HGTs0FqqPovIJ4DzgEvKwzWUh3ReLv89QGnu4ZQsfe3gN8/6vE4CPgx8M9pm4bzWuleRQZt1gUqJ8jjzbcBWVf37iu3HVez2ISCK8rkbuFBEjhaRucA8ShOOafk7TUSmR39Tmij/admvT5R3+wTwXQv+lhnzJGr13Fb40PJ5LA+pvCYiZ5Xb0scrPpMoIrIK+EvgA6r6m4rtbxKR7vLfJ5V9fSZjX9v6zbP0tcw5wJOqemQoLOvzWu9eRRZtNnQEiP+rGxnzXkrd2yeAH5f/nQv8M/CT8va7geMqPnMtpaenp0ggWqeJvydRisx5HNgCXFvefizwALC9/P8sI/6+AXgZmFGxzcS5pSSaLwCHKD1VfqqT8wgsonTDfRq4hXIlmBR83UFpjiFqt18u7/uH5bbxOPAj4HwDvrb9m2fla3n77cCnq/bN+rzWu1el3ma91JHjOI5jEh/icxzHcUziAuU4juOYxAXKcRzHMYkLlOM4jmMSFyjHcRzHJC5QjuM4jklcoBzHOJXLL1RsmyMi3xCRZ8q1Eh8WkQ81sbNZqpYWkdKyJP8oIlOltLTDQRGZndBXcZy2cIFynHzwtKq+C45k+n8H+IGqnqSqCykVyW1YZZxSsuiFVdsuBDaq6r6y/bRLPjlOXVygHCcBROTbInKLiPxQRHaKyHtF5A4R2SYit8U0/3vAQVU9spCdqu5U1X+oOP4fici/lXtFXymXzrkLOE9Eji7v0wv8B+CHMf1xnERwgXKcZHgHpfpp76W0ds5tlOrZnQZ8uEIkLhORRU0qxVfz25RK4NRERN4OfIxSNfp3ASOUiry+TKnm4KryrhcC31QvJ+MYxQXKcQIjIlOANwJfLG/aB9ympYXgDgK/AQ6W33sJOItSvbtOj3eriDwuIv3lTcuBhUB/ed5qOaXaijB2mK967SzHMYULlOOE57cpLfsxWn79TsqLU4pItOTAaSLyF6p6D6Uls3/dhv0tlJYPB0BV/4SSCL2pvEmAr6vqu8r/5qvqDeX3vgMsF5EzgKlaXjnVcSziAuU44XkHpUrUEb9DqTI0lMTqCUqrlEb7vEErlrFogf8DTBGR/1yx7Q0Vfz8AXCAibwYQkVkiciKAqg4Dm4ENeO/JMY4LlOOE5x2UliiIhvumqupQ+b1IrN4BPFFeKVXL+y4XkSXNjJfnjP4A+F0R+bmI/Bulea6/LL//M+A64Hsi8gRwP1C5TtJGSkLZF+9rOk6y+HIbjpMBIvIpYAGlJdR/raqfF5GrgH/SquW1y9F2m1T1tBT8ehZYpKq7kz6W4zRjUtYOOM5ERFVrhZq/sVqcyowAM0Tkx1EuVGhEZCrwMDAZGG2yu+OkgvegHMcIIvINSmHg31fVnzbb33GKjguU4ziOYxIPknAcx3FM4gLlOI7jmMQFynEcxzGJC5TjOI5jEhcox3EcxyQuUI7jOI5JXKAcx3Eck7hAOY7jOCb5/w0pZEnuKeO8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "................................................................................................................................................................................................................................................................................................................................................................................................................= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 909.43it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "................................................................................................................................................................................................................................................................................................................................................................................................................= 400 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:00<00:00, 898.12it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "import pandas as pd\n",
-    "pd.set_option('display.max_columns',None)\n",
-    "rs_dl"
+    "test_lo=True\n",
+    "params = [\n",
+    "    \"wino.slha\",\n",
+    "]\n",
+    "pss = [\n",
+    "    ( 1000023, +1000024),  # N1C1p\n",
+    "    ( 1000023, -1000024),  # N1C1m\n",
+    "    (+1000024, -1000024),  # C1pC1m\n",
+    "]\n",
+    "for pa, pb in pss:\n",
+    "    for energy in [13600]:\n",
+    "        name = str(energy) + \"_wino_\" + str(pa) + \"_\" + str(pb)\n",
+    "        hepi.set_output_dir(\n",
+    "               \"output_\" + name + \"/\"\n",
+    "        )\n",
+    "        rs.default_resummino_runner.set_output_dir(hepi.get_output_dir())\n",
+    "        for param in params:\n",
+    "            i = hepi.Input(\n",
+    "                hepi.Order.LO if test_lo else hepi.Order.aNNLO_PLUS_NNLL,\n",
+    "                    energy,\n",
+    "                    pa,\n",
+    "                    pb,\n",
+    "                    param,\n",
+    "                    \"PDF4LHC21_40\",\n",
+    "                    \"PDF4LHC21_40\",\n",
+    "                    1.0,\n",
+    "                    1.0,\n",
+    "                    id=\"0.0.0\",\n",
+    "                    precision=0.001,\n",
+    "                    max_iters=50,\n",
+    "                )\n",
+    "            li = [i]\n",
+    "            li = hepi.masses_scan(\n",
+    "                    li,\n",
+    "                    [\n",
+    "                        1000022,\n",
+    "                        1000023,\n",
+    "                        1000024,\n",
+    "                    ],\n",
+    "                    range(100,2010,100),\n",
+    "                )\n",
+    "            li = hepi.masses_scan(\n",
+    "                    li,\n",
+    "                    [\n",
+    "                        1000002,\n",
+    "                    ],\n",
+    "                    range(100,2010,100),diff_L_R=0,\n",
+    "                )\n",
+    "            if not test_lo:\n",
+    "\n",
+    "                li = hepi.seven_point_scan(li)\n",
+    "                li = hepi.pdf_scan(li)\n",
+    "                    # li = hepi.change_where(li, {\"precision\": 0.0001, \"max_iters\": 200}, pdfset_nlo=0)\n",
+    "                if not analyse:\n",
+    "                    rs_dl = rs.run(li, skip=False, run=False, parse=False)\n",
+    "                else:\n",
+    "                    rs_dl = rs.run(li, skip=True, run=True, parse=True,ignore_error=True)\n",
+    "                    rs_dl = hepi.pdf_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.scale_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.combine_errors(rs_dl)\n",
+    "                    if energy == 13000:\n",
+    "                        fig, axs = plt.subplots(2, 1, figsize=(6,4), sharex=True, gridspec_kw={'height_ratios': [2, 1]})         # Remove horizontal space between axes         \n",
+    "                        fig.subplots_adjust(hspace=0)\n",
+    "                        hepi.title(li[0],axe=axs[0],cms_energy=False,pdf_info=False,scenario=\"\")\n",
+    "                        frac = rs_dl[\"aNNLO_PLUS_NNLL_NOERR\"][rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull()]\n",
+    "                    #hepi.mass_plot(rs_dl,\"LO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp)\n",
+    "                    #hepi.mass_plot(rs_dl,\"NLO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp,init=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000022,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fmt=\" \",next_color=False,show=False,plot_data=False,logy=True,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,interpolate_label=\"13.0 TeV\" if energy == 13000 else \"13.6 TeV\" )\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000022,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000022,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fmt=\" \",next_color=False,show=False,plot_data=False,logy=False,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\")\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000022,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=False,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    axs[0].set_ylim(5e-7,25)\n",
+    "                    axs[1].set_ylim(-2.5,5.0)\n",
+    "                    axs[0].set_xlabel(None)\n",
+    "                    axs[1].set_xlabel('$m_{\\\\tilde{\\\\chi}}$ [GeV]')\n",
+    "                    if energy == 13600:\n",
+    "                        plt.savefig(name + \".pdf\",bbox_inches ='tight',pad_inches=0)\n",
+    "                        plt.show()\n",
+    "                    hepi.write_latex_table_transposed_header(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",\"mass_1000022\")\n",
+    "                    hepi.write_latex_table_transposed(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",yscale=1000.)\n",
+    "            else:\n",
+    "                rs_dl = rs.run(li, skip=True, run=True, parse=True)\n",
+    "                hepi.mapplot(rs_dl,\"mass_1000022_1000023_1000024\",\"mass_1000002\",\"LO\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{q}}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\")\n",
+    "                plt.show()\n",
+    "                hepi.mass_plot(rs_dl,\"LO\",1000002,data_fmt=\".\",show=True,interpolate=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "856af219",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 300 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 836.84it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 300 jobs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:00<00:00, 876.30it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apn/.local/lib/python3.9/site-packages/hepi/input.py:283: RuntimeWarning: Could not set new central scale to average of masses.\n",
+      "  warnings.warn(\"Could not set new central scale to average of masses.\",\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||= 300 jobs\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_lo=True\n",
+    "params = [\n",
+    "    \"hino_ch.slha\",\n",
+    "]\n",
+    "pss = [\n",
+    "    (1000022, +1000024),  # N2C1p\n",
+    "    (1000022, -1000024),  # N2C1m\n",
+    "    (+1000024, -1000024),  # C1pC2m\n",
+    "    (1000022, 1000023),  # N2N1\n",
+    "]\n",
+    "for pa, pb in pss:\n",
+    "    for energy in [13600]:\n",
+    "        # save to different folders to have some substructure\n",
+    "        name = str(energy) + \"_hino_deg_\" + str(pa) + \"_\" + str(pb)\n",
+    "        hepi.set_output_dir(\n",
+    "            \"output_\" + name + \"/\"\n",
+    "        )\n",
+    "        rs.default_resummino_runner.set_output_dir(hepi.get_output_dir())\n",
+    "        for param in params:\n",
+    "            i = hepi.Input(\n",
+    "                hepi.Order.LO if test_lo else hepi.Order.aNNLO_PLUS_NNLL,\n",
+    "                energy,\n",
+    "                pa,\n",
+    "                pb,\n",
+    "                param,\n",
+    "                \"PDF4LHC21_40\",\n",
+    "                \"PDF4LHC21_40\",\n",
+    "                1.0,\n",
+    "                1.0,\n",
+    "                id=\"0.0.0\",\n",
+    "                precision=0.001,\n",
+    "                max_iters=50,\n",
+    "            )\n",
+    "            li = [i]\n",
+    "            li = hepi.masses_scan(\n",
+    "                li,\n",
+    "                [\n",
+    "                    1000022,\n",
+    "                    1000023,\n",
+    "                    1000024,\n",
+    "                ],\n",
+    "                range(100, 1510, 100),\n",
+    "                negate=[1000022],\n",
+    "            )\n",
+    "            li = hepi.masses_scan(\n",
+    "                    li,\n",
+    "                    [\n",
+    "                        2000002,\n",
+    "                        1000002,\n",
+    "                        2000001,\n",
+    "                        1000001,\n",
+    "                    ],\n",
+    "                    range(100,2010,100),diff_L_R=100,\n",
+    "            )\n",
+    "            if not test_lo:\n",
+    "                li = hepi.seven_point_scan(li)\n",
+    "                li = hepi.pdf_scan(li)\n",
+    "                # li = hepi.change_where(li, {\"precision\": 0.0001, \"max_iters\": 200}, pdfset_nlo=0)\n",
+    "                if not analyse:\n",
+    "                    rs_dl = rs.run(li, skip=False, run=False, parse=False)\n",
+    "                else:\n",
+    "                    rs_dl = rs.run(li, skip=True, run=True, parse=True,ignore_error=True)\n",
+    "                    rs_dl = hepi.pdf_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.scale_errors(li,rs_dl)\n",
+    "                    rs_dl = hepi.combine_errors(rs_dl)\n",
+    "                    if energy == 13000:\n",
+    "                        fig, axs = plt.subplots(2, 1, figsize=(6,4), sharex=True, gridspec_kw={'height_ratios': [2, 1]})         # Remove horizontal space between axes         \n",
+    "                        fig.subplots_adjust(hspace=0)\n",
+    "                        hepi.title(li[0],axe=axs[0],cms_energy=False,pdf_info=False,scenario=\"\")\n",
+    "                        frac = rs_dl[\"aNNLO_PLUS_NNLL_NOERR\"][rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull()]\n",
+    "                    #hepi.mass_plot(rs_dl,\"LO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp)\n",
+    "                    #hepi.mass_plot(rs_dl,\"NLO_COMBINED\",1000022,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),show=True,logy=True,plot_data=False,interpolate=True,fill=True,interpolator='cubic',pre=np.log,post=np.exp,init=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000023,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fmt=\" \",next_color=False,show=False,plot_data=False,logy=True,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,interpolate_label=\"13.0 TeV\" if energy == 13000 else \"13.6 TeV\" )\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000023,axes=axs[0],mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=True,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_NOERR\",1000023,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fmt=\" \",next_color=False,show=False,plot_data=False,logy=False,interpolate=True,fill=False,interpolator='cubic',pre=lambda a :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\")\n",
+    "                    hepi.mass_plot(rs_dl,\"aNNLO_PLUS_NNLL_COMBINED\",1000023,axes=axs[1],yscale=1./frac,mask = rs_dl[\"aNNLO_PLUS_NNLL_COMBINED\"].notnull(),tight=False,yaxis=\"Ratio\",fit_fmt=\" \",next_color=True,show=False,plot_data=False,logy=False,interpolate=True,fill=True,interpolator='cubic',pre=lambda a  :np.log(a.clip(min=1e-22)),post=np.exp,init=False,label=\"\",interpolate_lower_uncertainty=False)\n",
+    "                    axs[0].set_ylim(5e-7,10)\n",
+    "                    axs[1].set_ylim(0,2.5)\n",
+    "                    axs[0].set_xlabel(None)\n",
+    "                    axs[1].set_xlabel('$m_{\\\\tilde{\\\\chi}}$ [GeV]')\n",
+    "                    if energy == 13600:\n",
+    "                        plt.savefig(name + \".pdf\",bbox_inches ='tight',pad_inches=0)\n",
+    "                        plt.show()\n",
+    "                    hepi.write_latex_table_transposed_header(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",\"mass_1000023\")\n",
+    "                    hepi.write_latex_table_transposed(rs_dl,\"aNNLO_PLUS_NNLL\",param+str(energy)+\".tex\",yscale=1000.)\n",
+    "            else:\n",
+    "                rs_dl = rs.run(li, skip=False, run=True, parse=True)\n",
+    "                \n",
+    "                hepi.mapplot(rs_dl,\"mass_1000022_1000023_1000024\",\"mass_2000002_1000002_2000001_1000001\",\"LO\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{q}}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\")\n",
+    "                plt.show()\n",
+    "                hepi.mass_plot(rs_dl,\"LO\",1000002,data_fmt=\".\",show=True,interpolate=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3f04afb",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1729,7 +2130,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
diff --git a/distribute/higgsino_slha_C1C1.slha13000.tex b/distribute/higgsino_slha_C1C1.slha13000.tex
deleted file mode 100644
index de637ca28..000000000
--- a/distribute/higgsino_slha_C1C1.slha13000.tex
+++ /dev/null
@@ -1,7 +0,0 @@
-$80$ 	&	 $80$ 	&	 $80$ 	&	 $80$ 	&	 $80$ 	&	 $80$ 	&	 $60$ 	&	 $80$ 	&	 $100$ 	&	 $100$ 	&	 $100$ 	&	 $100$ 	&	 $100$ 	&	 $80$ 	&	 $100$ 	&	 $100$ 	&	 $60$ 	&	 $80$ 	&	 $125$ 	&	 $125$ 	&	 $125$ 	&	 $100$ 	&	 $125$ 	&	 $125$ 	&	 $100$ 	&	 $125$ 	&	 $80$ 	&	 $125$ 	&	 $150$ 	&	 $150$ 	&	 $125$ 	&	 $150$ 	&	 $100$ 	&	 $150$ 	&	 $125$ 	&	 $150$ 	&	 $150$ 	&	 $175$ 	&	 $150$ 	&	 $175$ 	&	 $125$ 	&	 $175$ 	&	 $150$ 	&	 $175$ 	&	 $175$ 	&	 $200$ 	&	 $200$ 	&	 $200$ 	&	 $150$ 	&	 $200$ 	&	 $200$ 	&	 $200$ 	&	 $200$ 	&	 $225$ 	&	 $225$ 	&	 $200$ 	&	 $225$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $200$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $300$ 	&	 $300$ 	&	 $300$ 	&	 $250$ 	&	 $300$ 	&	 $300$ 	&	 $300$ 	&	 \\
-$-81.5$ 	&	 $-82$ 	&	 $-83$ 	&	 $-85$ 	&	 $-90$ 	&	 $-95$ 	&	 $-100$ 	&	 $-100$ 	&	 $-101.5$ 	&	 $-102$ 	&	 $-103$ 	&	 $-105$ 	&	 $-110$ 	&	 $-110$ 	&	 $-115$ 	&	 $-120$ 	&	 $-120$ 	&	 $-120$ 	&	 $-126.5$ 	&	 $-127$ 	&	 $-128$ 	&	 $-130$ 	&	 $-130$ 	&	 $-135$ 	&	 $-140$ 	&	 $-140$ 	&	 $-140$ 	&	 $-145$ 	&	 $-152$ 	&	 $-153$ 	&	 $-155$ 	&	 $-155$ 	&	 $-160$ 	&	 $-160$ 	&	 $-165$ 	&	 $-165$ 	&	 $-170$ 	&	 $-178$ 	&	 $-180$ 	&	 $-180$ 	&	 $-185$ 	&	 $-185$ 	&	 $-190$ 	&	 $-190$ 	&	 $-195$ 	&	 $-202$ 	&	 $-203$ 	&	 $-205$ 	&	 $-210$ 	&	 $-210$ 	&	 $-215$ 	&	 $-220$ 	&	 $-230$ 	&	 $-230$ 	&	 $-235$ 	&	 $-240$ 	&	 $-240$ 	&	 $-252$ 	&	 $-253$ 	&	 $-255$ 	&	 $-260$ 	&	 $-260$ 	&	 $-265$ 	&	 $-270$ 	&	 $-280$ 	&	 $-290$ 	&	 $-302$ 	&	 $-303$ 	&	 $-305$ 	&	 $-310$ 	&	 $-310$ 	&	 $-315$ 	&	 $-320$ 	&	 \\
-$80.75$ 	&	 $81$ 	&	 $81.5$ 	&	 $82.5$ 	&	 $85$ 	&	 $87.5$ 	&	 $80$ 	&	 $90$ 	&	 $100.8$ 	&	 $101$ 	&	 $101.5$ 	&	 $102.5$ 	&	 $105$ 	&	 $95$ 	&	 $107.5$ 	&	 $110$ 	&	 $90$ 	&	 $100$ 	&	 $125.8$ 	&	 $126$ 	&	 $126.5$ 	&	 $115$ 	&	 $127.5$ 	&	 $130$ 	&	 $120$ 	&	 $132.5$ 	&	 $110$ 	&	 $135$ 	&	 $151$ 	&	 $151.5$ 	&	 $140$ 	&	 $152.5$ 	&	 $130$ 	&	 $155$ 	&	 $145$ 	&	 $157.5$ 	&	 $160$ 	&	 $176.5$ 	&	 $165$ 	&	 $177.5$ 	&	 $155$ 	&	 $180$ 	&	 $170$ 	&	 $182.5$ 	&	 $185$ 	&	 $201$ 	&	 $201.5$ 	&	 $202.5$ 	&	 $180$ 	&	 $205$ 	&	 $207.5$ 	&	 $210$ 	&	 $215$ 	&	 $227.5$ 	&	 $230$ 	&	 $220$ 	&	 $232.5$ 	&	 $251$ 	&	 $251.5$ 	&	 $252.5$ 	&	 $230$ 	&	 $255$ 	&	 $257.5$ 	&	 $260$ 	&	 $265$ 	&	 $270$ 	&	 $301$ 	&	 $301.5$ 	&	 $302.5$ 	&	 $280$ 	&	 $305$ 	&	 $307.5$ 	&	 $310$ 	&	 \\
-$7765^{+2.5\%+2.8\%}_{-3.6\%-2.8\%}$ 	&	 $7630^{+2.5\%+2.8\%}_{-3.6\%-2.8\%}$ 	&	 $7368^{+2.5\%+2.8\%}_{-3.5\%-2.8\%}$ 	&	 $6882^{+2.4\%+2.8\%}_{-3.5\%-2.8\%}$ 	&	 $5837^{+2.3\%+2.8\%}_{-3.3\%-2.8\%}$ 	&	 $4993^{+2.2\%+2.9\%}_{-3.1\%-2.9\%}$ 	&	 $5206^{+2.2\%+2.9\%}_{-3.2\%-2.9\%}$ 	&	 $4303^{+2.1\%+2.9\%}_{-3.0\%-2.9\%}$ 	&	 $3438^{+2.0\%+3.0\%}_{-2.8\%-3.0\%}$ 	&	 $3392^{+2.0\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $3303^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $3134^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $2757^{+1.9\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $3260^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $2438^{+1.8\%+3.1\%}_{-2.4\%-3.1\%}$ 	&	 $2164^{+1.7\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $2950^{+1.9\%+3.0\%}_{-2.6\%-3.0\%}$ 	&	 $2523^{+1.8\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $1561^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1545^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1512^{+1.5\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1726^{+1.6\%+3.2\%}_{-2.2\%-3.2\%}$ 	&	 $1451^{+1.5\%+3.2\%}_{-2.0\%-3.2\%}$ 	&	 $1310^{+1.5\%+3.2\%}_{-1.9\%-3.2\%}$ 	&	 $1395^{+1.5\%+3.2\%}_{-2.0\%-3.2\%}$ 	&	 $1187^{+1.4\%+3.3\%}_{-1.9\%-3.3\%}$ 	&	 $1592^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1077^{+1.4\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $815.4^{+1.3\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $801.3^{+1.3\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $894.5^{+1.3\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $773.9^{+1.2\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $943.9^{+1.3\%+3.3\%}_{-1.7\%-3.3\%}$ 	&	 $710.4^{+1.2\%+3.5\%}_{-1.6\%-3.5\%}$ 	&	 $749.4^{+1.2\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $653.2^{+1.2\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $601.8^{+1.2\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $466.5^{+1.1\%+3.6\%}_{-1.3\%-3.6\%}$ 	&	 $513.2^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $452.7^{+1.1\%+3.6\%}_{-1.3\%-3.6\%}$ 	&	 $537.9^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $420.2^{+1.1\%+3.7\%}_{-1.3\%-3.7\%}$ 	&	 $440.4^{+1.1\%+3.7\%}_{-1.3\%-3.7\%}$ 	&	 $390.5^{+1.0\%+3.7\%}_{-1.2\%-3.7\%}$ 	&	 $363.5^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $294.3^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $290.4^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $282.8^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $329.6^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $264.6^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $248^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $232.5^{+0.9\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $205.1^{+0.8\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $185.5^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $174.8^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $181.6^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $164.8^{+0.8\%+4.2\%}_{-0.9\%-4.2\%}$ 	&	 $130.8^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $129.3^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $126.5^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $143.6^{+0.8\%+4.2\%}_{-0.9\%-4.2\%}$ 	&	 $119.8^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $113.5^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $107.6^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $96.84^{+0.7\%+4.5\%}_{-0.7\%-4.5\%}$ 	&	 $87.37^{+0.6\%+4.6\%}_{-0.7\%-4.6\%}$ 	&	 $66.02^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 $65.39^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $64.16^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $71.59^{+0.6\%+4.7\%}_{-0.7\%-4.7\%}$ 	&	 $61.21^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $58.41^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $55.77^{+0.6\%+4.9\%}_{-0.6\%-4.9\%}$ 	&	 \\
-$7765^{+2.5\%+2.8\%}_{-3.6\%-2.8\%}$ 	&	 $7630^{+2.5\%+2.8\%}_{-3.6\%-2.8\%}$ 	&	 $7368^{+2.5\%+2.8\%}_{-3.5\%-2.8\%}$ 	&	 $6882^{+2.4\%+2.8\%}_{-3.5\%-2.8\%}$ 	&	 $5837^{+2.3\%+2.8\%}_{-3.3\%-2.8\%}$ 	&	 $4993^{+2.2\%+2.9\%}_{-3.1\%-2.9\%}$ 	&	 $5206^{+2.2\%+2.9\%}_{-3.2\%-2.9\%}$ 	&	 $4303^{+2.1\%+2.9\%}_{-3.0\%-2.9\%}$ 	&	 $3438^{+2.0\%+3.0\%}_{-2.8\%-3.0\%}$ 	&	 $3392^{+2.0\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $3303^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $3134^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $2757^{+1.9\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $3260^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $2438^{+1.8\%+3.1\%}_{-2.4\%-3.1\%}$ 	&	 $2164^{+1.7\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $2950^{+1.9\%+3.0\%}_{-2.6\%-3.0\%}$ 	&	 $2523^{+1.8\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $1561^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1545^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1512^{+1.5\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1726^{+1.6\%+3.2\%}_{-2.2\%-3.2\%}$ 	&	 $1451^{+1.5\%+3.2\%}_{-2.0\%-3.2\%}$ 	&	 $1310^{+1.5\%+3.2\%}_{-1.9\%-3.2\%}$ 	&	 $1395^{+1.5\%+3.2\%}_{-2.0\%-3.2\%}$ 	&	 $1187^{+1.4\%+3.3\%}_{-1.9\%-3.3\%}$ 	&	 $1592^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1077^{+1.4\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $815.4^{+1.3\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $801.3^{+1.3\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $894.5^{+1.3\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $773.9^{+1.2\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $943.9^{+1.3\%+3.3\%}_{-1.7\%-3.3\%}$ 	&	 $710.4^{+1.2\%+3.5\%}_{-1.6\%-3.5\%}$ 	&	 $749.4^{+1.2\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $653.2^{+1.2\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $601.8^{+1.2\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $466.5^{+1.1\%+3.6\%}_{-1.3\%-3.6\%}$ 	&	 $513.2^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $452.7^{+1.1\%+3.6\%}_{-1.3\%-3.6\%}$ 	&	 $537.9^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $420.2^{+1.1\%+3.7\%}_{-1.3\%-3.7\%}$ 	&	 $440.4^{+1.1\%+3.7\%}_{-1.3\%-3.7\%}$ 	&	 $390.5^{+1.0\%+3.7\%}_{-1.2\%-3.7\%}$ 	&	 $363.5^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $294.3^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $290.4^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $282.8^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $329.6^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $264.6^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $248^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $232.5^{+0.9\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $205.1^{+0.8\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $185.5^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $174.8^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $181.6^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $164.8^{+0.8\%+4.2\%}_{-0.9\%-4.2\%}$ 	&	 $130.8^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $129.3^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $126.5^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $143.6^{+0.8\%+4.2\%}_{-0.9\%-4.2\%}$ 	&	 $119.8^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $113.5^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $107.6^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $96.84^{+0.7\%+4.5\%}_{-0.7\%-4.5\%}$ 	&	 $87.37^{+0.6\%+4.6\%}_{-0.7\%-4.6\%}$ 	&	 $66.02^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 $65.39^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $64.16^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $71.59^{+0.6\%+4.7\%}_{-0.7\%-4.7\%}$ 	&	 $61.21^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $58.41^{+0.6\%+4.8\%}_{-0.6\%-4.8\%}$ 	&	 $55.77^{+0.6\%+4.9\%}_{-0.6\%-4.9\%}$ 	&	 \\
-$5088^{+2.7\%+2.7\%}_{-3.7\%-2.7\%}$ 	&	 $4995^{+2.6\%+2.7\%}_{-3.7\%-2.7\%}$ 	&	 $4817^{+2.6\%+2.7\%}_{-3.7\%-2.7\%}$ 	&	 $4484^{+2.6\%+2.7\%}_{-3.6\%-2.7\%}$ 	&	 $3776^{+2.4\%+2.8\%}_{-3.4\%-2.8\%}$ 	&	 $3208^{+2.3\%+2.8\%}_{-3.3\%-2.8\%}$ 	&	 $3351^{+2.4\%+2.8\%}_{-3.3\%-2.8\%}$ 	&	 $2745^{+2.3\%+2.8\%}_{-3.1\%-2.8\%}$ 	&	 $2168^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $2138^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $2079^{+2.1\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $1967^{+2.1\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $1720^{+2.0\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $2050^{+2.1\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $1511^{+1.9\%+3.0\%}_{-2.6\%-3.0\%}$ 	&	 $1333^{+1.9\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $1848^{+2.0\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $1567^{+2.0\%+3.0\%}_{-2.6\%-3.0\%}$ 	&	 $944.3^{+1.7\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $933.9^{+1.7\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $913.3^{+1.7\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $1050^{+1.8\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $874^{+1.7\%+3.1\%}_{-2.1\%-3.1\%}$ 	&	 $784.8^{+1.7\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $838.8^{+1.7\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $706.5^{+1.6\%+3.3\%}_{-2.0\%-3.3\%}$ 	&	 $964.3^{+1.8\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $637.9^{+1.6\%+3.3\%}_{-1.9\%-3.3\%}$ 	&	 $474.6^{+1.5\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $465.9^{+1.4\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $523.8^{+1.5\%+3.4\%}_{-1.8\%-3.4\%}$ 	&	 $449^{+1.4\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $554.6^{+1.5\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $409.9^{+1.4\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $433.9^{+1.4\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $375^{+1.4\%+3.5\%}_{-1.6\%-3.5\%}$ 	&	 $343.7^{+1.3\%+3.5\%}_{-1.6\%-3.5\%}$ 	&	 $262.1^{+1.3\%+3.7\%}_{-1.5\%-3.7\%}$ 	&	 $290.2^{+1.3\%+3.6\%}_{-1.5\%-3.6\%}$ 	&	 $253.8^{+1.3\%+3.7\%}_{-1.4\%-3.7\%}$ 	&	 $305.1^{+1.3\%+3.6\%}_{-1.5\%-3.6\%}$ 	&	 $234.4^{+1.2\%+3.7\%}_{-1.4\%-3.7\%}$ 	&	 $246.5^{+1.2\%+3.7\%}_{-1.4\%-3.7\%}$ 	&	 $216.8^{+1.2\%+3.8\%}_{-1.4\%-3.8\%}$ 	&	 $200.8^{+1.2\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $160.3^{+1.1\%+3.9\%}_{-1.3\%-3.9\%}$ 	&	 $158^{+1.1\%+3.9\%}_{-1.2\%-3.9\%}$ 	&	 $153.5^{+1.1\%+4.0\%}_{-1.2\%-4.0\%}$ 	&	 $180.9^{+1.2\%+3.9\%}_{-1.3\%-3.9\%}$ 	&	 $143^{+1.1\%+4.0\%}_{-1.2\%-4.0\%}$ 	&	 $133.4^{+1.1\%+4.0\%}_{-1.2\%-4.0\%}$ 	&	 $124.5^{+1.1\%+4.1\%}_{-1.1\%-4.1\%}$ 	&	 $108.9^{+1.0\%+4.2\%}_{-1.1\%-4.2\%}$ 	&	 $97.75^{+1.0\%+4.2\%}_{-1.1\%-4.2\%}$ 	&	 $91.69^{+1.0\%+4.3\%}_{-1.0\%-4.3\%}$ 	&	 $95.52^{+1.0\%+4.2\%}_{-1.1\%-4.2\%}$ 	&	 $86.07^{+1.0\%+4.3\%}_{-1.0\%-4.3\%}$ 	&	 $67.15^{+0.9\%+4.5\%}_{-1.0\%-4.5\%}$ 	&	 $66.36^{+0.9\%+4.5\%}_{-1.0\%-4.5\%}$ 	&	 $64.8^{+0.9\%+4.5\%}_{-0.9\%-4.5\%}$ 	&	 $74.28^{+0.9\%+4.4\%}_{-1.0\%-4.4\%}$ 	&	 $61.1^{+0.9\%+4.6\%}_{-0.9\%-4.6\%}$ 	&	 $57.65^{+0.9\%+4.6\%}_{-0.9\%-4.6\%}$ 	&	 $54.43^{+0.9\%+4.7\%}_{-0.9\%-4.7\%}$ 	&	 $48.61^{+0.8\%+4.8\%}_{-0.9\%-4.8\%}$ 	&	 $43.51^{+0.8\%+4.8\%}_{-0.8\%-4.8\%}$ 	&	 $32.16^{+0.8\%+5.1\%}_{-0.7\%-5.1\%}$ 	&	 $31.83^{+0.8\%+5.1\%}_{-0.7\%-5.1\%}$ 	&	 $31.18^{+0.8\%+5.1\%}_{-0.7\%-5.1\%}$ 	&	 $35.1^{+0.8\%+5.0\%}_{-0.8\%-5.0\%}$ 	&	 $29.63^{+0.7\%+5.2\%}_{-0.7\%-5.2\%}$ 	&	 $28.18^{+0.7\%+5.2\%}_{-0.7\%-5.2\%}$ 	&	 $26.8^{+0.7\%+5.3\%}_{-0.7\%-5.3\%}$ 	&	 \\
-$6761^{+2.6\%+2.4\%}_{-3.7\%-2.4\%}$ 	&	 $6684^{+2.6\%+2.4\%}_{-3.7\%-2.4\%}$ 	&	 $6534^{+2.6\%+2.4\%}_{-3.7\%-2.4\%}$ 	&	 $6248^{+2.5\%+2.4\%}_{-3.6\%-2.4\%}$ 	&	 $5602^{+2.4\%+2.4\%}_{-3.5\%-2.4\%}$ 	&	 $5043^{+2.4\%+2.5\%}_{-3.4\%-2.5\%}$ 	&	 $6999^{+2.6\%+2.4\%}_{-3.8\%-2.4\%}$ 	&	 $4555^{+2.3\%+2.5\%}_{-3.3\%-2.5\%}$ 	&	 $3043^{+2.1\%+2.6\%}_{-2.8\%-2.6\%}$ 	&	 $3017^{+2.0\%+2.6\%}_{-2.9\%-2.6\%}$ 	&	 $2964^{+2.1\%+2.7\%}_{-2.8\%-2.7\%}$ 	&	 $2862^{+2.0\%+2.7\%}_{-2.8\%-2.7\%}$ 	&	 $2628^{+2.0\%+2.6\%}_{-2.7\%-2.6\%}$ 	&	 $3752^{+2.2\%+2.5\%}_{-3.1\%-2.5\%}$ 	&	 $2418^{+1.9\%+2.6\%}_{-2.6\%-2.6\%}$ 	&	 $2229^{+1.9\%+2.6\%}_{-2.5\%-2.6\%}$ 	&	 $4555^{+2.3\%+2.5\%}_{-3.3\%-2.5\%}$ 	&	 $3125^{+2.1\%+2.6\%}_{-2.9\%-2.6\%}$ 	&	 $1389^{+1.6\%+2.8\%}_{-2.2\%-2.8\%}$ 	&	 $1379^{+1.6\%+2.8\%}_{-2.2\%-2.8\%}$ 	&	 $1360^{+1.6\%+2.8\%}_{-2.1\%-2.8\%}$ 	&	 $1905^{+1.8\%+2.7\%}_{-2.4\%-2.7\%}$ 	&	 $1322^{+1.6\%+2.8\%}_{-2.1\%-2.8\%}$ 	&	 $1234^{+1.6\%+2.8\%}_{-2.1\%-2.8\%}$ 	&	 $1639^{+1.8\%+2.7\%}_{-2.3\%-2.7\%}$ 	&	 $1154^{+1.6\%+2.8\%}_{-2.0\%-2.8\%}$ 	&	 $2229^{+1.9\%+2.6\%}_{-2.5\%-2.6\%}$ 	&	 $1080^{+1.5\%+2.8\%}_{-2.0\%-2.8\%}$ 	&	 $725^{+1.4\%+2.9\%}_{-1.7\%-2.9\%}$ 	&	 $716.5^{+1.4\%+2.9\%}_{-1.7\%-2.9\%}$ 	&	 $949^{+1.5\%+2.9\%}_{-1.9\%-2.9\%}$ 	&	 $699.8^{+1.4\%+2.9\%}_{-1.7\%-2.9\%}$ 	&	 $1234^{+1.6\%+2.8\%}_{-2.1\%-2.8\%}$ 	&	 $660.3^{+1.3\%+3.0\%}_{-1.7\%-3.0\%}$ 	&	 $837.7^{+1.4\%+2.9\%}_{-1.8\%-2.9\%}$ 	&	 $623.5^{+1.3\%+3.0\%}_{-1.6\%-3.0\%}$ 	&	 $589.3^{+1.3\%+3.0\%}_{-1.6\%-3.0\%}$ 	&	 $413.6^{+1.2\%+3.1\%}_{-1.4\%-3.1\%}$ 	&	 $527.6^{+1.2\%+3.1\%}_{-1.5\%-3.1\%}$ 	&	 $405.2^{+1.2\%+3.1\%}_{-1.4\%-3.1\%}$ 	&	 $660.3^{+1.3\%+3.0\%}_{-1.7\%-3.0\%}$ 	&	 $385.2^{+1.1\%+3.1\%}_{-1.4\%-3.1\%}$ 	&	 $473.7^{+1.2\%+3.1\%}_{-1.5\%-3.1\%}$ 	&	 $366.3^{+1.1\%+3.2\%}_{-1.4\%-3.2\%}$ 	&	 $348.7^{+1.1\%+3.2\%}_{-1.3\%-3.2\%}$ 	&	 $257.3^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $255^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $250.4^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $385.2^{+1.1\%+3.1\%}_{-1.4\%-3.1\%}$ 	&	 $239.3^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $228.8^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $218.9^{+1.0\%+3.4\%}_{-1.1\%-3.4\%}$ 	&	 $200.6^{+1.0\%+3.4\%}_{-1.1\%-3.4\%}$ 	&	 $162.4^{+0.9\%+3.5\%}_{-1.0\%-3.5\%}$ 	&	 $155.9^{+0.9\%+3.5\%}_{-1.0\%-3.5\%}$ 	&	 $184.1^{+0.9\%+3.4\%}_{-1.1\%-3.4\%}$ 	&	 $149.7^{+0.9\%+3.5\%}_{-1.0\%-3.5\%}$ 	&	 $112^{+0.8\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $111.2^{+0.8\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $109.5^{+0.8\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $155.9^{+0.9\%+3.5\%}_{-1.0\%-3.5\%}$ 	&	 $105.5^{+0.8\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $101.6^{+0.8\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $97.93^{+0.8\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $91.02^{+0.8\%+3.8\%}_{-0.8\%-3.8\%}$ 	&	 $84.7^{+0.8\%+3.8\%}_{-0.8\%-3.8\%}$ 	&	 $55.44^{+0.7\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $55.08^{+0.7\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $54.36^{+0.7\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $73.57^{+0.7\%+3.9\%}_{-0.8\%-3.9\%}$ 	&	 $52.62^{+0.6\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $50.95^{+0.6\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $49.34^{+0.6\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 \\
diff --git a/distribute/higgsino_slha_C1C1.slha13600.tex b/distribute/higgsino_slha_C1C1.slha13600.tex
deleted file mode 100644
index 1b2db0a8b..000000000
--- a/distribute/higgsino_slha_C1C1.slha13600.tex
+++ /dev/null
@@ -1,5 +0,0 @@
-$80$ 	&	 $80$ 	&	 $80$ 	&	 $80$ 	&	 $80$ 	&	 $80$ 	&	 $60$ 	&	 $80$ 	&	 $100$ 	&	 $100$ 	&	 $100$ 	&	 $100$ 	&	 $100$ 	&	 $80$ 	&	 $100$ 	&	 $100$ 	&	 $60$ 	&	 $80$ 	&	 $125$ 	&	 $125$ 	&	 $125$ 	&	 $100$ 	&	 $125$ 	&	 $125$ 	&	 $100$ 	&	 $125$ 	&	 $80$ 	&	 $125$ 	&	 $150$ 	&	 $150$ 	&	 $125$ 	&	 $150$ 	&	 $100$ 	&	 $150$ 	&	 $125$ 	&	 $150$ 	&	 $150$ 	&	 $175$ 	&	 $150$ 	&	 $175$ 	&	 $125$ 	&	 $175$ 	&	 $150$ 	&	 $175$ 	&	 $175$ 	&	 $200$ 	&	 $200$ 	&	 $200$ 	&	 $150$ 	&	 $200$ 	&	 $200$ 	&	 $200$ 	&	 $200$ 	&	 $225$ 	&	 $225$ 	&	 $200$ 	&	 $225$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $200$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $250$ 	&	 $300$ 	&	 $300$ 	&	 $300$ 	&	 $250$ 	&	 $300$ 	&	 $300$ 	&	 $300$ 	&	 \\
-$-81.5$ 	&	 $-82$ 	&	 $-83$ 	&	 $-85$ 	&	 $-90$ 	&	 $-95$ 	&	 $-100$ 	&	 $-100$ 	&	 $-101.5$ 	&	 $-102$ 	&	 $-103$ 	&	 $-105$ 	&	 $-110$ 	&	 $-110$ 	&	 $-115$ 	&	 $-120$ 	&	 $-120$ 	&	 $-120$ 	&	 $-126.5$ 	&	 $-127$ 	&	 $-128$ 	&	 $-130$ 	&	 $-130$ 	&	 $-135$ 	&	 $-140$ 	&	 $-140$ 	&	 $-140$ 	&	 $-145$ 	&	 $-152$ 	&	 $-153$ 	&	 $-155$ 	&	 $-155$ 	&	 $-160$ 	&	 $-160$ 	&	 $-165$ 	&	 $-165$ 	&	 $-170$ 	&	 $-178$ 	&	 $-180$ 	&	 $-180$ 	&	 $-185$ 	&	 $-185$ 	&	 $-190$ 	&	 $-190$ 	&	 $-195$ 	&	 $-202$ 	&	 $-203$ 	&	 $-205$ 	&	 $-210$ 	&	 $-210$ 	&	 $-215$ 	&	 $-220$ 	&	 $-230$ 	&	 $-230$ 	&	 $-235$ 	&	 $-240$ 	&	 $-240$ 	&	 $-252$ 	&	 $-253$ 	&	 $-255$ 	&	 $-260$ 	&	 $-260$ 	&	 $-265$ 	&	 $-270$ 	&	 $-280$ 	&	 $-290$ 	&	 $-302$ 	&	 $-303$ 	&	 $-305$ 	&	 $-310$ 	&	 $-310$ 	&	 $-315$ 	&	 $-320$ 	&	 \\
-$80.75$ 	&	 $81$ 	&	 $81.5$ 	&	 $82.5$ 	&	 $85$ 	&	 $87.5$ 	&	 $80$ 	&	 $90$ 	&	 $100.8$ 	&	 $101$ 	&	 $101.5$ 	&	 $102.5$ 	&	 $105$ 	&	 $95$ 	&	 $107.5$ 	&	 $110$ 	&	 $90$ 	&	 $100$ 	&	 $125.8$ 	&	 $126$ 	&	 $126.5$ 	&	 $115$ 	&	 $127.5$ 	&	 $130$ 	&	 $120$ 	&	 $132.5$ 	&	 $110$ 	&	 $135$ 	&	 $151$ 	&	 $151.5$ 	&	 $140$ 	&	 $152.5$ 	&	 $130$ 	&	 $155$ 	&	 $145$ 	&	 $157.5$ 	&	 $160$ 	&	 $176.5$ 	&	 $165$ 	&	 $177.5$ 	&	 $155$ 	&	 $180$ 	&	 $170$ 	&	 $182.5$ 	&	 $185$ 	&	 $201$ 	&	 $201.5$ 	&	 $202.5$ 	&	 $180$ 	&	 $205$ 	&	 $207.5$ 	&	 $210$ 	&	 $215$ 	&	 $227.5$ 	&	 $230$ 	&	 $220$ 	&	 $232.5$ 	&	 $251$ 	&	 $251.5$ 	&	 $252.5$ 	&	 $230$ 	&	 $255$ 	&	 $257.5$ 	&	 $260$ 	&	 $265$ 	&	 $270$ 	&	 $301$ 	&	 $301.5$ 	&	 $302.5$ 	&	 $280$ 	&	 $305$ 	&	 $307.5$ 	&	 $310$ 	&	 \\
-$8187^{+2.6\%+2.7\%}_{-3.7\%-2.7\%}$ 	&	 $8045^{+2.5\%+2.7\%}_{-3.7\%-2.7\%}$ 	&	 $7770^{+2.5\%+2.8\%}_{-3.7\%-2.8\%}$ 	&	 $7258^{+2.5\%+2.8\%}_{-3.6\%-2.8\%}$ 	&	 $6160^{+2.4\%+2.8\%}_{-3.4\%-2.8\%}$ 	&	 $5272^{+2.3\%+2.8\%}_{-3.2\%-2.8\%}$ 	&	 $5496^{+2.3\%+2.8\%}_{-3.3\%-2.8\%}$ 	&	 $4546^{+2.2\%+2.9\%}_{-3.1\%-2.9\%}$ 	&	 $3635^{+2.0\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $3587^{+2.0\%+3.0\%}_{-2.9\%-3.0\%}$ 	&	 $3493^{+2.0\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $3314^{+2.0\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $2918^{+1.9\%+3.0\%}_{-2.6\%-3.0\%}$ 	&	 $3447^{+2.0\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $2580^{+1.8\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $2292^{+1.8\%+3.1\%}_{-2.4\%-3.1\%}$ 	&	 $3121^{+1.9\%+3.0\%}_{-2.7\%-3.0\%}$ 	&	 $2670^{+1.9\%+3.0\%}_{-2.6\%-3.0\%}$ 	&	 $1655^{+1.6\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $1638^{+1.6\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $1604^{+1.6\%+3.1\%}_{-2.1\%-3.1\%}$ 	&	 $1829^{+1.7\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $1539^{+1.6\%+3.1\%}_{-2.1\%-3.1\%}$ 	&	 $1390^{+1.5\%+3.2\%}_{-2.0\%-3.2\%}$ 	&	 $1480^{+1.6\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1260^{+1.5\%+3.2\%}_{-1.9\%-3.2\%}$ 	&	 $1688^{+1.6\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $1144^{+1.4\%+3.2\%}_{-1.9\%-3.2\%}$ 	&	 $867^{+1.3\%+3.3\%}_{-1.7\%-3.3\%}$ 	&	 $852.1^{+1.3\%+3.3\%}_{-1.7\%-3.3\%}$ 	&	 $950.7^{+1.4\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $823^{+1.3\%+3.3\%}_{-1.7\%-3.3\%}$ 	&	 $1003^{+1.4\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $755.7^{+1.3\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $797^{+1.3\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $695.4^{+1.2\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $640.7^{+1.2\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $497.4^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $546.9^{+1.1\%+3.5\%}_{-1.4\%-3.5\%}$ 	&	 $482.7^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $573.1^{+1.2\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $448.3^{+1.1\%+3.6\%}_{-1.3\%-3.6\%}$ 	&	 $469.6^{+1.1\%+3.6\%}_{-1.4\%-3.6\%}$ 	&	 $416.8^{+1.1\%+3.6\%}_{-1.3\%-3.6\%}$ 	&	 $388.1^{+1.0\%+3.7\%}_{-1.3\%-3.7\%}$ 	&	 $314.6^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $310.4^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $302.3^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $352.1^{+1.0\%+3.7\%}_{-1.2\%-3.7\%}$ 	&	 $283^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $265.3^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $248.9^{+0.9\%+3.9\%}_{-1.1\%-3.9\%}$ 	&	 $219.7^{+0.9\%+3.9\%}_{-1.0\%-3.9\%}$ 	&	 $198.9^{+0.8\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $187.4^{+0.8\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $194.6^{+0.8\%+4.0\%}_{-1.0\%-4.0\%}$ 	&	 $176.7^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $140.5^{+0.8\%+4.2\%}_{-0.9\%-4.2\%}$ 	&	 $139^{+0.8\%+4.2\%}_{-0.9\%-4.2\%}$ 	&	 $136^{+0.8\%+4.2\%}_{-0.8\%-4.2\%}$ 	&	 $154.2^{+0.8\%+4.1\%}_{-0.9\%-4.1\%}$ 	&	 $128.8^{+0.7\%+4.2\%}_{-0.8\%-4.2\%}$ 	&	 $122^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $115.7^{+0.7\%+4.3\%}_{-0.8\%-4.3\%}$ 	&	 $104.3^{+0.7\%+4.4\%}_{-0.8\%-4.4\%}$ 	&	 $94.17^{+0.7\%+4.4\%}_{-0.7\%-4.4\%}$ 	&	 $71.31^{+0.6\%+4.6\%}_{-0.7\%-4.6\%}$ 	&	 $70.64^{+0.6\%+4.7\%}_{-0.7\%-4.7\%}$ 	&	 $69.32^{+0.6\%+4.7\%}_{-0.7\%-4.7\%}$ 	&	 $77.28^{+0.6\%+4.6\%}_{-0.7\%-4.6\%}$ 	&	 $66.16^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 $63.16^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 $60.33^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 \\
-$5402^{+2.7\%+2.7\%}_{-3.8\%-2.7\%}$ 	&	 $5304^{+2.7\%+2.7\%}_{-3.8\%-2.7\%}$ 	&	 $5115^{+2.7\%+2.7\%}_{-3.8\%-2.7\%}$ 	&	 $4764^{+2.6\%+2.7\%}_{-3.7\%-2.7\%}$ 	&	 $4014^{+2.5\%+2.7\%}_{-3.5\%-2.7\%}$ 	&	 $3412^{+2.4\%+2.8\%}_{-3.4\%-2.8\%}$ 	&	 $3564^{+2.4\%+2.7\%}_{-3.4\%-2.7\%}$ 	&	 $2922^{+2.3\%+2.8\%}_{-3.2\%-2.8\%}$ 	&	 $2310^{+2.2\%+2.8\%}_{-3.0\%-2.8\%}$ 	&	 $2278^{+2.2\%+2.9\%}_{-3.0\%-2.9\%}$ 	&	 $2216^{+2.2\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $2097^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $1834^{+2.0\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $2185^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $1612^{+2.0\%+2.9\%}_{-2.7\%-2.9\%}$ 	&	 $1423^{+1.9\%+3.0\%}_{-2.5\%-3.0\%}$ 	&	 $1970^{+2.1\%+2.9\%}_{-2.8\%-2.9\%}$ 	&	 $1672^{+2.0\%+2.9\%}_{-2.7\%-2.9\%}$ 	&	 $1010^{+1.8\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $999^{+1.8\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $977.2^{+1.8\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $1122^{+1.8\%+3.0\%}_{-2.3\%-3.0\%}$ 	&	 $935.4^{+1.7\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $840.2^{+1.7\%+3.1\%}_{-2.1\%-3.1\%}$ 	&	 $897.7^{+1.7\%+3.1\%}_{-2.2\%-3.1\%}$ 	&	 $757^{+1.7\%+3.2\%}_{-2.1\%-3.2\%}$ 	&	 $1031^{+1.8\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $683.7^{+1.6\%+3.2\%}_{-2.0\%-3.2\%}$ 	&	 $509.6^{+1.5\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $500.3^{+1.5\%+3.3\%}_{-1.8\%-3.3\%}$ 	&	 $562^{+1.5\%+3.3\%}_{-1.9\%-3.3\%}$ 	&	 $482.2^{+1.5\%+3.4\%}_{-1.8\%-3.4\%}$ 	&	 $594.8^{+1.5\%+3.3\%}_{-1.9\%-3.3\%}$ 	&	 $440.5^{+1.4\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $466.1^{+1.5\%+3.4\%}_{-1.8\%-3.4\%}$ 	&	 $403.1^{+1.4\%+3.4\%}_{-1.7\%-3.4\%}$ 	&	 $369.6^{+1.4\%+3.4\%}_{-1.6\%-3.4\%}$ 	&	 $282.4^{+1.3\%+3.6\%}_{-1.5\%-3.6\%}$ 	&	 $312.4^{+1.3\%+3.5\%}_{-1.5\%-3.5\%}$ 	&	 $273.5^{+1.3\%+3.6\%}_{-1.5\%-3.6\%}$ 	&	 $328.4^{+1.3\%+3.5\%}_{-1.6\%-3.5\%}$ 	&	 $252.8^{+1.2\%+3.7\%}_{-1.5\%-3.7\%}$ 	&	 $265.7^{+1.3\%+3.6\%}_{-1.5\%-3.6\%}$ 	&	 $233.9^{+1.2\%+3.7\%}_{-1.4\%-3.7\%}$ 	&	 $216.7^{+1.2\%+3.7\%}_{-1.4\%-3.7\%}$ 	&	 $173.2^{+1.1\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $170.8^{+1.1\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $166^{+1.1\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $195.4^{+1.2\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $154.7^{+1.1\%+3.9\%}_{-1.2\%-3.9\%}$ 	&	 $144.4^{+1.1\%+3.9\%}_{-1.2\%-3.9\%}$ 	&	 $134.8^{+1.1\%+4.0\%}_{-1.2\%-4.0\%}$ 	&	 $118^{+1.1\%+4.0\%}_{-1.1\%-4.0\%}$ 	&	 $106^{+1.0\%+4.1\%}_{-1.1\%-4.1\%}$ 	&	 $99.49^{+1.0\%+4.2\%}_{-1.1\%-4.2\%}$ 	&	 $103.6^{+1.0\%+4.1\%}_{-1.1\%-4.1\%}$ 	&	 $93.43^{+1.0\%+4.2\%}_{-1.1\%-4.2\%}$ 	&	 $73.04^{+0.9\%+4.4\%}_{-1.0\%-4.4\%}$ 	&	 $72.19^{+0.9\%+4.4\%}_{-1.0\%-4.4\%}$ 	&	 $70.51^{+0.9\%+4.4\%}_{-1.0\%-4.4\%}$ 	&	 $80.73^{+1.0\%+4.3\%}_{-1.0\%-4.3\%}$ 	&	 $66.51^{+0.9\%+4.4\%}_{-1.0\%-4.4\%}$ 	&	 $62.79^{+0.9\%+4.5\%}_{-1.0\%-4.5\%}$ 	&	 $59.31^{+0.9\%+4.5\%}_{-0.9\%-4.5\%}$ 	&	 $53.02^{+0.9\%+4.6\%}_{-0.9\%-4.6\%}$ 	&	 $47.51^{+0.8\%+4.7\%}_{-0.9\%-4.7\%}$ 	&	 $35.2^{+0.8\%+4.9\%}_{-0.8\%-4.9\%}$ 	&	 $34.85^{+0.8\%+4.9\%}_{-0.8\%-4.9\%}$ 	&	 $34.15^{+0.8\%+5.0\%}_{-0.8\%-5.0\%}$ 	&	 $38.39^{+0.8\%+4.9\%}_{-0.8\%-4.9\%}$ 	&	 $32.47^{+0.8\%+5.0\%}_{-0.8\%-5.0\%}$ 	&	 $30.88^{+0.8\%+5.1\%}_{-0.7\%-5.1\%}$ 	&	 $29.39^{+0.7\%+5.1\%}_{-0.7\%-5.1\%}$ 	&	 \\
diff --git a/distribute/hino.slha13000.tex b/distribute/hino.slha13000.tex
deleted file mode 100644
index 5d0524805..000000000
--- a/distribute/hino.slha13000.tex
+++ /dev/null
@@ -1,5 +0,0 @@
-$100$ 	&	 $200$ 	&	 $300$ 	&	 $400$ 	&	 $500$ 	&	 $600$ 	&	 $700$ 	&	 $800$ 	&	 $900$ 	&	 $1000$ 	&	 $1100$ 	&	 $1200$ 	&	 $1300$ 	&	 $1400$ 	&	 $1500$ 	&	 \\
-$3600^{+2.0\%+3.0\%}_{-2.8\%-3.0\%}$ 	&	 $303.9^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $67.64^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 $21.74^{+0.5\%+5.6\%}_{-0.4\%-5.6\%}$ 	&	 $8.496^{+0.5\%+6.5\%}_{-0.3\%-6.5\%}$ 	&	 $3.746^{+0.4\%+7.5\%}_{-0.2\%-7.5\%}$ 	&	 $1.79^{+0.4\%+8.6\%}_{-0.2\%-8.6\%}$ 	&	 $0.9064^{+0.4\%+10.0\%}_{-0.2\%-10.0\%}$ 	&	 $0.4788^{+0.4\%+11.7\%}_{-0.2\%-11.7\%}$ 	&	 $0.2611^{+0.3\%+13.9\%}_{-0.2\%-13.9\%}$ 	&	 $0.146^{+0.3\%+16.9\%}_{-0.1\%-16.9\%}$ 	&	 $0.08327^{+0.3\%+20.9\%}_{-0.1\%-20.9\%}$ 	&	 $0.04826^{+0.3\%+26.2\%}_{-0.1\%-26.2\%}$ 	&	 $0.02837^{+0.3\%+33.1\%}_{-0.1\%-33.1\%}$ 	&	 $0.01687^{+0.3\%+42.0\%}_{-0.1\%-42.0\%}$ 	&	 \\
-$2275^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $165.8^{+1.1\%+3.9\%}_{-1.3\%-3.9\%}$ 	&	 $33^{+0.8\%+5.1\%}_{-0.8\%-5.1\%}$ 	&	 $9.674^{+0.6\%+6.4\%}_{-0.6\%-6.4\%}$ 	&	 $3.497^{+0.4\%+7.8\%}_{-0.4\%-7.8\%}$ 	&	 $1.442^{+0.4\%+9.4\%}_{-0.3\%-9.4\%}$ 	&	 $0.6504^{+0.3\%+11.5\%}_{-0.2\%-11.5\%}$ 	&	 $0.3135^{+0.3\%+14.0\%}_{-0.2\%-14.0\%}$ 	&	 $0.1589^{+0.3\%+17.3\%}_{-0.2\%-17.3\%}$ 	&	 $0.08376^{+0.3\%+21.4\%}_{-0.1\%-21.4\%}$ 	&	 $0.04559^{+0.3\%+26.5\%}_{-0.1\%-26.5\%}$ 	&	 $0.02548^{+0.2\%+32.8\%}_{-0.1\%-32.8\%}$ 	&	 $0.01457^{+0.2\%+40.6\%}_{-0.1\%-40.6\%}$ 	&	 $0.008485^{+0.2\%+49.9\%}_{-0.1\%-49.9\%}$ 	&	 $0.005026^{+0.2\%+61.0\%}_{-0.1\%-61.0\%}$ 	&	 \\
-$3138^{+2.1\%+2.6\%}_{-2.9\%-2.6\%}$ 	&	 $262.9^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $56.34^{+0.7\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $17.46^{+0.5\%+4.9\%}_{-0.5\%-4.9\%}$ 	&	 $6.608^{+0.4\%+5.8\%}_{-0.4\%-5.8\%}$ 	&	 $2.834^{+0.4\%+6.9\%}_{-0.3\%-6.9\%}$ 	&	 $1.323^{+0.3\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.6575^{+0.3\%+9.7\%}_{-0.2\%-9.7\%}$ 	&	 $0.3423^{+0.3\%+11.6\%}_{-0.2\%-11.6\%}$ 	&	 $0.1848^{+0.3\%+14.2\%}_{-0.2\%-14.2\%}$ 	&	 $0.1028^{+0.3\%+17.4\%}_{-0.1\%-17.4\%}$ 	&	 $0.05856^{+0.3\%+21.5\%}_{-0.1\%-21.5\%}$ 	&	 $0.03405^{+0.2\%+26.6\%}_{-0.1\%-26.6\%}$ 	&	 $0.02015^{+0.3\%+32.8\%}_{-0.1\%-32.8\%}$ 	&	 $0.01211^{+0.2\%+40.5\%}_{-0.1\%-40.5\%}$ 	&	 \\
-$3459^{+2.1\%+2.8\%}_{-2.9\%-2.8\%}$ 	&	 $256.7^{+1.1\%+3.5\%}_{-1.2\%-3.5\%}$ 	&	 $53.38^{+0.7\%+4.2\%}_{-0.7\%-4.2\%}$ 	&	 $16.3^{+0.5\%+5.0\%}_{-0.5\%-5.0\%}$ 	&	 $6.108^{+0.4\%+5.9\%}_{-0.4\%-5.9\%}$ 	&	 $2.599^{+0.4\%+6.9\%}_{-0.3\%-6.9\%}$ 	&	 $1.206^{+0.4\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.5952^{+0.4\%+9.6\%}_{-0.2\%-9.6\%}$ 	&	 $0.3081^{+0.3\%+11.4\%}_{-0.2\%-11.4\%}$ 	&	 $0.1653^{+0.3\%+13.8\%}_{-0.1\%-13.8\%}$ 	&	 $0.09133^{+0.3\%+16.8\%}_{-0.1\%-16.8\%}$ 	&	 $0.05169^{+0.3\%+20.7\%}_{-0.1\%-20.7\%}$ 	&	 $0.02986^{+0.3\%+25.6\%}_{-0.1\%-25.6\%}$ 	&	 $0.01755^{+0.3\%+31.6\%}_{-0.1\%-31.6\%}$ 	&	 $0.01048^{+0.2\%+39.0\%}_{-0.1\%-39.0\%}$ 	&	 \\
diff --git a/distribute/hino.slha13600.tex b/distribute/hino.slha13600.tex
deleted file mode 100644
index 83c600e08..000000000
--- a/distribute/hino.slha13600.tex
+++ /dev/null
@@ -1,5 +0,0 @@
-$100$ 	&	 $200$ 	&	 $300$ 	&	 $400$ 	&	 $500$ 	&	 $600$ 	&	 $700$ 	&	 $800$ 	&	 $900$ 	&	 $1000$ 	&	 $1100$ 	&	 $1200$ 	&	 $1300$ 	&	 $1400$ 	&	 $1500$ 	&	 \\
-$3806^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $324.8^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $73.05^{+0.6\%+4.6\%}_{-0.7\%-4.6\%}$ 	&	 $23.74^{+0.5\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $9.386^{+0.5\%+6.3\%}_{-0.3\%-6.3\%}$ 	&	 $4.189^{+0.4\%+7.2\%}_{-0.3\%-7.2\%}$ 	&	 $2.028^{+0.4\%+8.2\%}_{-0.2\%-8.2\%}$ 	&	 $1.041^{+0.4\%+9.5\%}_{-0.2\%-9.5\%}$ 	&	 $0.5575^{+0.4\%+11.0\%}_{-0.2\%-11.0\%}$ 	&	 $0.3085^{+0.4\%+12.9\%}_{-0.2\%-12.9\%}$ 	&	 $0.1751^{+0.3\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.1014^{+0.3\%+18.7\%}_{-0.1\%-18.7\%}$ 	&	 $0.0597^{+0.4\%+23.1\%}_{-0.1\%-23.1\%}$ 	&	 $0.03564^{+0.3\%+28.7\%}_{-0.1\%-28.7\%}$ 	&	 $0.02153^{+0.3\%+35.9\%}_{-0.1\%-35.9\%}$ 	&	 \\
-$2424^{+2.2\%+2.8\%}_{-3.0\%-2.8\%}$ 	&	 $179.2^{+1.2\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $36.12^{+0.8\%+4.9\%}_{-0.8\%-4.9\%}$ 	&	 $10.72^{+0.6\%+6.1\%}_{-0.6\%-6.1\%}$ 	&	 $3.926^{+0.5\%+7.4\%}_{-0.5\%-7.4\%}$ 	&	 $1.639^{+0.4\%+9.0\%}_{-0.4\%-9.0\%}$ 	&	 $0.7493^{+0.3\%+10.8\%}_{-0.3\%-10.8\%}$ 	&	 $0.3658^{+0.3\%+13.1\%}_{-0.2\%-13.1\%}$ 	&	 $0.1879^{+0.3\%+15.9\%}_{-0.2\%-15.9\%}$ 	&	 $0.1004^{+0.3\%+19.5\%}_{-0.2\%-19.5\%}$ 	&	 $0.05535^{+0.2\%+24.0\%}_{-0.1\%-24.0\%}$ 	&	 $0.03135^{+0.2\%+29.4\%}_{-0.1\%-29.4\%}$ 	&	 $0.01815^{+0.2\%+36.0\%}_{-0.1\%-36.0\%}$ 	&	 $0.01071^{+0.2\%+44.0\%}_{-0.2\%-44.0\%}$ 	&	 $0.006428^{+0.2\%+53.5\%}_{-0.1\%-53.5\%}$ 	&	 \\
-$3329^{+2.1\%+2.5\%}_{-3.0\%-2.5\%}$ 	&	 $282.3^{+1.1\%+3.2\%}_{-1.3\%-3.2\%}$ 	&	 $61.2^{+0.7\%+3.9\%}_{-0.7\%-3.9\%}$ 	&	 $19.2^{+0.5\%+4.7\%}_{-0.5\%-4.7\%}$ 	&	 $7.353^{+0.4\%+5.6\%}_{-0.4\%-5.6\%}$ 	&	 $3.192^{+0.4\%+6.6\%}_{-0.3\%-6.6\%}$ 	&	 $1.509^{+0.4\%+7.7\%}_{-0.2\%-7.7\%}$ 	&	 $0.7595^{+0.3\%+9.1\%}_{-0.2\%-9.1\%}$ 	&	 $0.4007^{+0.3\%+10.8\%}_{-0.2\%-10.8\%}$ 	&	 $0.2193^{+0.3\%+13.0\%}_{-0.1\%-13.0\%}$ 	&	 $0.1236^{+0.3\%+15.8\%}_{-0.1\%-15.8\%}$ 	&	 $0.07133^{+0.3\%+19.2\%}_{-0.1\%-19.2\%}$ 	&	 $0.04203^{+0.3\%+23.6\%}_{-0.1\%-23.6\%}$ 	&	 $0.02521^{+0.3\%+28.9\%}_{-0.1\%-28.9\%}$ 	&	 $0.01535^{+0.3\%+35.4\%}_{-0.1\%-35.4\%}$ 	&	 \\
-$3673^{+2.2\%+2.7\%}_{-3.0\%-2.7\%}$ 	&	 $276.1^{+1.1\%+3.4\%}_{-1.3\%-3.4\%}$ 	&	 $58.07^{+0.7\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $17.94^{+0.5\%+4.9\%}_{-0.5\%-4.9\%}$ 	&	 $6.805^{+0.4\%+5.7\%}_{-0.4\%-5.7\%}$ 	&	 $2.932^{+0.4\%+6.7\%}_{-0.3\%-6.7\%}$ 	&	 $1.378^{+0.4\%+7.7\%}_{-0.2\%-7.7\%}$ 	&	 $0.6891^{+0.4\%+9.0\%}_{-0.2\%-9.0\%}$ 	&	 $0.3613^{+0.3\%+10.6\%}_{-0.2\%-10.6\%}$ 	&	 $0.1966^{+0.3\%+12.7\%}_{-0.1\%-12.7\%}$ 	&	 $0.1101^{+0.3\%+15.3\%}_{-0.1\%-15.3\%}$ 	&	 $0.06318^{+0.3\%+18.5\%}_{-0.1\%-18.5\%}$ 	&	 $0.03699^{+0.3\%+22.7\%}_{-0.1\%-22.7\%}$ 	&	 $0.02204^{+0.3\%+27.8\%}_{-0.1\%-27.8\%}$ 	&	 $0.01333^{+0.2\%+34.1\%}_{-0.1\%-34.1\%}$ 	&	 \\
diff --git a/distribute/input/.hino_ch.slha.swp b/distribute/input/.hino_ch.slha.swp
new file mode 100644
index 000000000..1322f44d2
Binary files /dev/null and b/distribute/input/.hino_ch.slha.swp differ
diff --git a/distribute/input/.wino.slha.swp b/distribute/input/.wino.slha.swp
new file mode 100644
index 000000000..b0e442b79
Binary files /dev/null and b/distribute/input/.wino.slha.swp differ
diff --git a/distribute/input/hino_ch.slha b/distribute/input/hino_ch.slha
new file mode 100644
index 000000000..d792aef20
--- /dev/null
+++ b/distribute/input/hino_ch.slha
@@ -0,0 +1,163 @@
+BLOCK MODSEL  # Model selection
+     1     0   # Low energy spectrum
+#
+BLOCK SMINPUTS  # Standard Model inputs
+         1     1.27934000E+02   # alpha_em^-1(M_Z)^MSbar
+         2     1.16639000E-05   # G_F [GeV^-2]
+         3     1.17200000E-01   # alpha_S(M_Z)^MSbar
+         4     9.11870000E+01   # M_Z pole mass
+         5     4.25000000E+00   # mb(mb)^MSbar
+         6     1.75000000E+02   # mt pole mass
+         7     1.77700000E+00   # mtau pole mass
+#
+BLOCK MASS  # Mass Spectrum
+# PDG code           mass       particle
+        24     8.04961219E+01   # W+
+        25     1.25000000E+02   # h
+        35     1.00000000E+05   # H
+        36     1.00000000E+05   # A
+        37     1.00000000E+05   # H+
+         5     4.87884274E+00   # b-quark pole mass
+   1000001     1.00000000E+05   # ~d_L
+   2000001     1.00000000E+05   # ~d_R
+   1000002     1.00000000E+05   # ~u_L
+   2000002     1.00000000E+05   # ~u_R
+   1000003     1.00000000E+05   # ~s_L
+   2000003     1.00000000E+05   # ~s_R
+   1000004     1.00000000E+05   # ~c_L
+   2000004     1.00000000E+05   # ~c_R
+   1000005     1.00000000E+05   # ~b_1
+   2000005     1.00000000E+05   # ~b_2
+   1000006     1.00000000E+05   # ~t_1
+   2000006     1.00000000E+05   # ~t_2
+   1000011     1.00000000E+05   # ~e_L
+   2000011     1.00000000E+05   # ~e_R
+   1000012     1.00000000E+05   # ~nu_eL
+   1000013     1.00000000E+05   # ~mu_L
+   2000013     1.00000000E+05   # ~mu_R
+   1000014     1.00000000E+05   # ~nu_muL
+   1000015     1.00000000E+05   # ~tau_1
+   2000015     1.00000000E+05   # ~tau_2
+   1000016     1.00000000E+05   # ~nu_tauL
+   1000021     1.00000000E+05   # ~g
+   1000022     -@a              # ~chi_10
+   1000023     @a               # ~chi_20
+   1000025     1.00000000E+05   # ~chi_30
+   1000035     1.00000000E+05   # ~chi_40
+   1000024     @a               # ~chi_1+
+   1000037     1.00000000E+05   # ~chi_2+
+#
+BLOCK NMIX  # Neutralino Mixing Matrix
+  1  1     0.00000000E+00   # N_11
+  1  2     0.00000000E+00  # N_12
+  1  3     0.70710678E+00  # N_13
+  1  4     0.70710678E+00  # N_14
+  2  1     0.00000000E+00  # N_21
+  2  2     0.00000000E+00  # N_22
+  2  3     0.70710678E+00  # N_23
+  2  4    -0.70710678E+00  # N_24
+  3  1     0.00000000E+00  # N_31
+  3  2     0.00000000E+00  # N_32
+  3  3     1.00000000E+00  # N_33
+  3  4     0.00000000E+00  # N_34
+  4  1     0.00000000E+00  # N_41
+  4  2     0.00000000E+00  # N_42
+  4  3     0.00000000E+00  # N_43
+  4  4     1.00000000E+00  # N_44
+#
+BLOCK UMIX  # Chargino Mixing Matrix U
+  1  1     0.00000000E+00   # U_11
+  1  2     1.00000000E+00   # U_12
+  2  1     1.00000000E+00   # U_21
+  2  2     0.00000000E+00   # U_22
+#
+BLOCK VMIX  # Chargino Mixing Matrix V
+  1  1     0.00000000E+00   # V_11
+  1  2     1.00000000E+00   # V_12
+  2  1     1.00000000E+00   # V_21
+  2  2     0.00000000E+00   # V_22
+#
+BLOCK STOPMIX  # Stop Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_t)
+  1  2     0.00000000E+00   # sin(theta_t)
+  2  1     0.00000000E+00   # -sin(theta_t)
+  2  2     0.00000000E+00   # cos(theta_t)
+#
+BLOCK SBOTMIX  # Sbottom Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_b)
+  1  2     0.00000000E+00   # sin(theta_b)
+  2  1     0.00000000E+00   # -sin(theta_b)
+  2  2     0.00000000E+00   # cos(theta_b)
+#
+BLOCK STAUMIX  # Stau Mixing Matrix
+  1  1     0.00000000E+00 # cos(theta_tau)
+  1  2     0.00000000E+00 # sin(theta_tau)
+  2  1     0.00000000E+00 # -sin(theta_tau)
+  2  2     0.00000000E+00 # cos(theta_tau)
+#
+BLOCK ALPHA  # Higgs mixing
+           0.00000000E+00   # Mixing angle in the neutral Higgs boson sector
+#
+BLOCK HMIX Q=  1.00000000E+05  # DRbar Higgs Parameters
+         1     1.00000000E+05   # mu(Q)               
+         2     1.00000000E+05   # tanbeta(Q)          
+         3     1.00000000E+05   # vev(Q)              
+         4     1.00000000E+05   # MA^2(Q)             
+#
+BLOCK GAUGE Q=  1.00000000E+03  # The gauge couplings
+     1     3.60000000E-01   # gprime(Q) DRbar
+     2     6.46000000E-01   # g(Q) DRbar
+     3     1.10000000E+00   # g3(Q) DRbar
+#
+BLOCK AU Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_u(Q) DRbar
+  2  2    0.00000000E+00   # A_c(Q) DRbar
+  3  3    0.00000000E+00   # A_t(Q) DRbar
+#
+BLOCK AD Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_d(Q) DRbar
+  2  2    0.00000000E+00   # A_s(Q) DRbar
+  3  3    0.00000000E+00   # A_b(Q) DRbar
+#
+BLOCK AE Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_e(Q) DRbar
+  2  2    0.00000000E+00   # A_mu(Q) DRbar
+  3  3    0.00000000E+00   # A_tau(Q) DRbar
+#
+BLOCK Yu Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_u(Q) DRbar
+  2  2     0.00000000E+00   # y_c(Q) DRbar
+  3  3     8.74064983E-01   # y_t(Q) DRbar
+#
+BLOCK Yd Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_d(Q) DRbar
+  2  2     0.00000000E+00   # y_s(Q) DRbar
+  3  3     1.35994628E-01   # y_b(Q) DRbar
+#
+BLOCK Ye Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_e(Q) DRbar
+  2  2     0.00000000E+00   # y_mu(Q) DRbar
+  3  3     1.00415339E-01   # y_tau(Q) DRbar
+#
+BLOCK MSOFT Q=  1.00000000E+03  # The soft SUSY breaking masses at the scale Q
+         1     1.00000000E+05   # M_1
+         2     1.00000000E+05   # M_2
+         3     1.00000000E+05   # M_3
+        21     1.00000000E+05   # M^2_Hd
+        22     1.00000000E+05   # M^2_Hu
+        31     1.00000000E+05   # M_eL
+        32     1.00000000E+05   # M_muL
+        33     1.00000000E+05   # M_tauL
+        34     1.00000000E+05   # M_eR
+        35     1.00000000E+05   # M_muR
+        36     1.00000000E+05   # M_tauR
+        41     1.00000000E+05   # M_q1L
+        42     1.00000000E+05   # M_q2L
+        43     1.00000000E+05   # M_q3L
+        44     1.00000000E+05   # M_uR
+        45     1.00000000E+05   # M_cR
+        46     1.00000000E+05   # M_tR
+        47     1.00000000E+05   # M_dR
+        48     1.00000000E+05   # M_sR
+        49     1.00000000E+05   # M_bR
+
diff --git a/distribute/input/wino_ch.slha b/distribute/input/wino_ch.slha
new file mode 100644
index 000000000..0999095ef
--- /dev/null
+++ b/distribute/input/wino_ch.slha
@@ -0,0 +1,158 @@
+BLOCK MODSEL  # Model selection
+     1     0   # Low energy spectrum
+#
+BLOCK SMINPUTS  # Standard Model inputs
+         1     1.27934000E+02   # alpha_em^-1(M_Z)^MSbar
+         2     1.16639000E-05   # G_F [GeV^-2]
+         3     1.17200000E-01   # alpha_S(M_Z)^MSbar
+         4     9.11870000E+01   # M_Z pole mass
+         5     4.25000000E+00   # mb(mb)^MSbar
+         6     1.75000000E+02   # mt pole mass
+         7     1.77700000E+00   # mtau pole mass
+#
+BLOCK MASS  # Mass Spectrum
+# PDG code           mass       particle
+        24     8.04961219E+01   # W+
+        25     1.25000000E+02   # h
+        35     1.00000000E+05   # H
+        36     1.00000000E+05   # A
+        37     1.00000000E+05   # H+
+         5     4.87884274E+00   # b-quark pole mass
+   1000001     1.00000000E+05   # ~d_L
+   2000001     1.00000000E+05   # ~d_R
+   1000002     1.00000000E+05   # ~u_L
+   2000002     1.00000000E+05   # ~u_R
+   1000003     1.00000000E+05   # ~s_L
+   2000003     1.00000000E+05   # ~s_R
+   1000004     1.00000000E+05   # ~c_L
+   2000004     1.00000000E+05   # ~c_R
+   1000005     1.00000000E+05   # ~b_1
+   2000005     1.00000000E+05   # ~b_2
+   1000006     1.00000000E+05   # ~t_1
+   2000006     1.00000000E+05   # ~t_2
+   1000011     1.00000000E+05   # ~e_L
+   2000011     1.00000000E+05   # ~e_R
+   1000012     1.00000000E+05   # ~nu_eL
+   1000013     1.00000000E+05   # ~mu_L
+   2000013     1.00000000E+05   # ~mu_R
+   1000014     1.00000000E+05   # ~nu_muL
+   1000015     1.00000000E+05   # ~tau_1
+   2000015     1.00000000E+05   # ~tau_2
+   1000016     1.00000000E+05   # ~nu_tauL
+   1000021     1.00000000E+05   # ~g
+   1000022     @a              # ~chi_10
+   1000023     @a             # ~chi_20
+   1000025     1.00000000E+05   # ~chi_30
+   1000035     1.00000000E+05   # ~chi_40
+   1000024     @a             # ~chi_1+
+   1000037     1.00000000E+05   # ~chi_2+
+#
+BLOCK NMIX  # Neutralino Mixing Matrix
+  1  1     1.00000000E+00   # N_11
+  1  2     0.00000000E+00  # N_12
+  1  3     0.00000000E+00  # N_13
+  1  4     0.00000000E+00  # N_14
+  2  1     0.00000000E+00  # N_21
+  2  2     1.00000000E+00  # N_22
+  2  3     0.00000000E+00  # N_23
+  2  4     0.00000000E+00  # N_24
+  3  1     0.00000000E+00  # N_31
+  3  2     0.00000000E+00  # N_32
+  3  3     0.00000000E+00  # N_33
+  3  4     0.00000000E+00  # N_34
+  4  1     0.00000000E+00  # N_41
+  4  2     0.00000000E+00  # N_42
+  4  3     0.00000000E+00  # N_43
+  4  4     0.00000000E+00  # N_44
+#
+BLOCK UMIX  # Chargino Mixing Matrix U
+  1  1     1.00000000E+00   # U_11
+  1  2     0.00000000E+00   # U_12
+  2  1     0.00000000E+00   # U_21
+  2  2     0.00000000E+00   # U_22
+#
+BLOCK VMIX  # Chargino Mixing Matrix V
+  1  1     1.00000000E+00   # V_11
+  1  2     0.00000000E+00   # V_12
+  2  1     0.00000000E+00   # V_21
+  2  2     0.00000000E+00   # V_22
+#
+BLOCK STOPMIX  # Stop Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_t)
+  1  2     0.00000000E+00   # sin(theta_t)
+  2  1     0.00000000E+00   # -sin(theta_t)
+  2  2     0.00000000E+00   # cos(theta_t)
+#
+BLOCK SBOTMIX  # Sbottom Mixing Matrix
+  1  1     0.00000000E+00   # cos(theta_b)
+  1  2     0.00000000E+00   # sin(theta_b)
+  2  1     0.00000000E+00   # -sin(theta_b)
+  2  2     0.00000000E+00   # cos(theta_b)
+#
+BLOCK STAUMIX  # Stau Mixing Matrix
+  1  1     0.00000000E+00 # cos(theta_tau)
+  1  2     0.00000000E+00 # sin(theta_tau)
+  2  1     0.00000000E+00 # -sin(theta_tau)
+  2  2     0.00000000E+00 # cos(theta_tau)
+#
+BLOCK ALPHA  # Higgs mixing
+           0.00000000E+00   # Mixing angle in the neutral Higgs boson sector
+#
+BLOCK HMIX Q=  1.00000000E+05  # DRbar Higgs Parameters
+         1     1.00000000E+05   # mu(Q)               
+         2     1.00000000E+05   # tanbeta(Q)          
+         3     1.00000000E+05   # vev(Q)              
+         4     1.00000000E+05   # MA^2(Q)             
+
+#
+BLOCK AU Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_u(Q) DRbar
+  2  2    0.00000000E+00   # A_c(Q) DRbar
+  3  3    0.00000000E+00   # A_t(Q) DRbar
+#
+BLOCK AD Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_d(Q) DRbar
+  2  2    0.00000000E+00   # A_s(Q) DRbar
+  3  3    0.00000000E+00   # A_b(Q) DRbar
+#
+BLOCK AE Q=  1.00000000E+03  # The trilinear couplings
+  1  1    0.00000000E+00   # A_e(Q) DRbar
+  2  2    0.00000000E+00   # A_mu(Q) DRbar
+  3  3    0.00000000E+00   # A_tau(Q) DRbar
+#
+BLOCK Yu Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_u(Q) DRbar
+  2  2     0.00000000E+00   # y_c(Q) DRbar
+  3  3     8.74064983E-01   # y_t(Q) DRbar
+#
+BLOCK Yd Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_d(Q) DRbar
+  2  2     0.00000000E+00   # y_s(Q) DRbar
+  3  3     1.35994628E-01   # y_b(Q) DRbar
+#
+BLOCK Ye Q=  1.00000000E+03  # The Yukawa couplings
+  1  1     0.00000000E+00   # y_e(Q) DRbar
+  2  2     0.00000000E+00   # y_mu(Q) DRbar
+  3  3     1.00415339E-01   # y_tau(Q) DRbar
+#
+BLOCK MSOFT Q=  1.00000000E+03  # The soft SUSY breaking masses at the scale Q
+         1     1.00000000E+05   # M_1
+         2     1.00000000E+05   # M_2
+         3     1.00000000E+05   # M_3
+        21     1.00000000E+05   # M^2_Hd
+        22     1.00000000E+05   # M^2_Hu
+        31     1.00000000E+05   # M_eL
+        32     1.00000000E+05   # M_muL
+        33     1.00000000E+05   # M_tauL
+        34     1.00000000E+05   # M_eR
+        35     1.00000000E+05   # M_muR
+        36     1.00000000E+05   # M_tauR
+        41     1.00000000E+05   # M_q1L
+        42     1.00000000E+05   # M_q2L
+        43     1.00000000E+05   # M_q3L
+        44     1.00000000E+05   # M_uR
+        45     1.00000000E+05   # M_cR
+        46     1.00000000E+05   # M_tR
+        47     1.00000000E+05   # M_dR
+        48     1.00000000E+05   # M_sR
+        49     1.00000000E+05   # M_bR
diff --git a/distribute/sleptons.slha13000.tex b/distribute/sleptons.slha13000.tex
deleted file mode 100644
index e5c0802f6..000000000
--- a/distribute/sleptons.slha13000.tex
+++ /dev/null
@@ -1,9 +0,0 @@
-$100$ 	&	 $200$ 	&	 $300$ 	&	 $400$ 	&	 $500$ 	&	 $600$ 	&	 $700$ 	&	 $800$ 	&	 $900$ 	&	 $1000$ 	&	 \\
-$282.2^{+1.6\%+2.7\%}_{-2.3\%-2.7\%}$ 	&	 $22.71^{+1.0\%+3.5\%}_{-0.8\%-3.5\%}$ 	&	 $4.62^{+0.7\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $1.361^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.4901^{+0.5\%+6.7\%}_{-0.3\%-6.7\%}$ 	&	 $0.2004^{+0.4\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.0894^{+0.4\%+9.9\%}_{-0.2\%-9.9\%}$ 	&	 $0.0425^{+0.4\%+12.3\%}_{-0.2\%-12.3\%}$ 	&	 $0.02121^{+0.5\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.01101^{+0.4\%+19.3\%}_{-0.1\%-19.3\%}$ 	&	 \\
-$100.6^{+1.6\%+3.0\%}_{-2.3\%-3.0\%}$ 	&	 $8.484^{+1.1\%+3.7\%}_{-0.8\%-3.7\%}$ 	&	 $1.753^{+0.7\%+4.6\%}_{-0.5\%-4.6\%}$ 	&	 $0.5209^{+0.6\%+5.7\%}_{-0.4\%-5.7\%}$ 	&	 $0.1888^{+0.5\%+6.9\%}_{-0.3\%-6.9\%}$ 	&	 $0.07765^{+0.4\%+8.4\%}_{-0.2\%-8.4\%}$ 	&	 $0.03482^{+0.4\%+10.3\%}_{-0.2\%-10.3\%}$ 	&	 $0.01664^{+0.4\%+12.9\%}_{-0.2\%-12.9\%}$ 	&	 $0.008347^{+0.5\%+16.2\%}_{-0.2\%-16.2\%}$ 	&	 $0.004354^{+0.4\%+20.4\%}_{-0.1\%-20.4\%}$ 	&	 \\
-$282.2^{+1.6\%+2.7\%}_{-2.3\%-2.7\%}$ 	&	 $22.71^{+1.0\%+3.5\%}_{-0.8\%-3.5\%}$ 	&	 $4.62^{+0.7\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $1.361^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.4901^{+0.5\%+6.7\%}_{-0.3\%-6.7\%}$ 	&	 $0.2004^{+0.4\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.0894^{+0.4\%+9.9\%}_{-0.2\%-9.9\%}$ 	&	 $0.0425^{+0.4\%+12.3\%}_{-0.2\%-12.3\%}$ 	&	 $0.02121^{+0.5\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.01101^{+0.4\%+19.3\%}_{-0.1\%-19.3\%}$ 	&	 \\
-$282.2^{+1.6\%+2.7\%}_{-2.3\%-2.7\%}$ 	&	 $22.71^{+1.0\%+3.5\%}_{-0.8\%-3.5\%}$ 	&	 $4.62^{+0.7\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $1.361^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.4901^{+0.5\%+6.7\%}_{-0.3\%-6.7\%}$ 	&	 $0.2004^{+0.4\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.0894^{+0.4\%+9.9\%}_{-0.2\%-9.9\%}$ 	&	 $0.0425^{+0.4\%+12.3\%}_{-0.2\%-12.3\%}$ 	&	 $0.02121^{+0.5\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.01101^{+0.4\%+19.3\%}_{-0.1\%-19.3\%}$ 	&	 \\
-$282.2^{+1.6\%+2.7\%}_{-2.3\%-2.7\%}$ 	&	 $22.71^{+1.0\%+3.5\%}_{-0.8\%-3.5\%}$ 	&	 $4.62^{+0.7\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $1.361^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.4901^{+0.5\%+6.7\%}_{-0.3\%-6.7\%}$ 	&	 $0.2004^{+0.4\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.0894^{+0.4\%+9.9\%}_{-0.2\%-9.9\%}$ 	&	 $0.0425^{+0.4\%+12.3\%}_{-0.2\%-12.3\%}$ 	&	 $0.02121^{+0.5\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.01101^{+0.4\%+19.3\%}_{-0.1\%-19.3\%}$ 	&	 \\
-$282.2^{+1.6\%+2.7\%}_{-2.3\%-2.7\%}$ 	&	 $22.71^{+1.0\%+3.5\%}_{-0.8\%-3.5\%}$ 	&	 $4.62^{+0.7\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $1.361^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.4901^{+0.5\%+6.7\%}_{-0.3\%-6.7\%}$ 	&	 $0.2004^{+0.4\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $0.0894^{+0.4\%+9.9\%}_{-0.2\%-9.9\%}$ 	&	 $0.0425^{+0.4\%+12.3\%}_{-0.2\%-12.3\%}$ 	&	 $0.02121^{+0.5\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.01101^{+0.4\%+19.3\%}_{-0.1\%-19.3\%}$ 	&	 \\
-$100.6^{+1.6\%+3.0\%}_{-2.3\%-3.0\%}$ 	&	 $8.484^{+1.1\%+3.7\%}_{-0.8\%-3.7\%}$ 	&	 $1.753^{+0.7\%+4.6\%}_{-0.5\%-4.6\%}$ 	&	 $0.5209^{+0.6\%+5.7\%}_{-0.4\%-5.7\%}$ 	&	 $0.1888^{+0.5\%+6.9\%}_{-0.3\%-6.9\%}$ 	&	 $0.07765^{+0.4\%+8.4\%}_{-0.2\%-8.4\%}$ 	&	 $0.03482^{+0.4\%+10.3\%}_{-0.2\%-10.3\%}$ 	&	 $0.01664^{+0.4\%+12.9\%}_{-0.2\%-12.9\%}$ 	&	 $0.008347^{+0.5\%+16.2\%}_{-0.2\%-16.2\%}$ 	&	 $0.004354^{+0.4\%+20.4\%}_{-0.1\%-20.4\%}$ 	&	 \\
-$116.7^{+1.6\%+3.1\%}_{-2.3\%-3.1\%}$ 	&	 $10.13^{+1.1\%+3.8\%}_{-0.8\%-3.8\%}$ 	&	 $2.101^{+0.7\%+4.6\%}_{-0.5\%-4.6\%}$ 	&	 $0.6254^{+0.6\%+5.7\%}_{-0.4\%-5.7\%}$ 	&	 $0.2269^{+0.5\%+6.9\%}_{-0.3\%-6.9\%}$ 	&	 $0.09334^{+0.4\%+8.4\%}_{-0.2\%-8.4\%}$ 	&	 $0.04188^{+0.4\%+10.4\%}_{-0.2\%-10.4\%}$ 	&	 $0.02002^{+0.4\%+12.9\%}_{-0.2\%-12.9\%}$ 	&	 $0.01004^{+0.5\%+16.2\%}_{-0.2\%-16.2\%}$ 	&	 $0.00524^{+0.4\%+22.0\%}_{-0.1\%-22.0\%}$ 	&	 \\
diff --git a/distribute/sleptons.slha13600.tex b/distribute/sleptons.slha13600.tex
deleted file mode 100644
index f50625c20..000000000
--- a/distribute/sleptons.slha13600.tex
+++ /dev/null
@@ -1,7 +0,0 @@
-$100$ 	&	 $200$ 	&	 $300$ 	&	 $400$ 	&	 $500$ 	&	 $600$ 	&	 $700$ 	&	 $800$ 	&	 $900$ 	&	 $1000$ 	&	 \\
-$300.2^{+1.7\%+2.7\%}_{-2.4\%-2.7\%}$ 	&	 $24.51^{+1.1\%+3.5\%}_{-0.9\%-3.5\%}$ 	&	 $5.053^{+0.7\%+4.4\%}_{-0.5\%-4.4\%}$ 	&	 $1.509^{+0.6\%+5.4\%}_{-0.4\%-5.4\%}$ 	&	 $0.551^{+0.5\%+6.4\%}_{-0.3\%-6.4\%}$ 	&	 $0.2285^{+0.4\%+7.7\%}_{-0.3\%-7.7\%}$ 	&	 $0.1034^{+0.4\%+9.3\%}_{-0.2\%-9.3\%}$ 	&	 $0.04985^{+0.4\%+11.4\%}_{-0.2\%-11.4\%}$ 	&	 $0.02523^{+0.4\%+14.1\%}_{-0.2\%-14.1\%}$ 	&	 $0.01328^{+0.4\%+17.5\%}_{-0.1\%-17.5\%}$ 	&	 \\
-$300.2^{+1.7\%+2.7\%}_{-2.4\%-2.7\%}$ 	&	 $24.51^{+1.1\%+3.5\%}_{-0.9\%-3.5\%}$ 	&	 $5.053^{+0.7\%+4.4\%}_{-0.5\%-4.4\%}$ 	&	 $1.509^{+0.6\%+5.4\%}_{-0.4\%-5.4\%}$ 	&	 $0.551^{+0.5\%+6.4\%}_{-0.3\%-6.4\%}$ 	&	 $0.2285^{+0.4\%+7.7\%}_{-0.3\%-7.7\%}$ 	&	 $0.1034^{+0.4\%+9.3\%}_{-0.2\%-9.3\%}$ 	&	 $0.04985^{+0.4\%+11.4\%}_{-0.2\%-11.4\%}$ 	&	 $0.02523^{+0.4\%+14.1\%}_{-0.2\%-14.1\%}$ 	&	 $0.01328^{+0.4\%+17.5\%}_{-0.1\%-17.5\%}$ 	&	 \\
-$300.2^{+1.7\%+2.7\%}_{-2.4\%-2.7\%}$ 	&	 $24.51^{+1.1\%+3.5\%}_{-0.9\%-3.5\%}$ 	&	 $5.053^{+0.7\%+4.4\%}_{-0.5\%-4.4\%}$ 	&	 $1.509^{+0.6\%+5.4\%}_{-0.4\%-5.4\%}$ 	&	 $0.551^{+0.5\%+6.4\%}_{-0.3\%-6.4\%}$ 	&	 $0.2285^{+0.4\%+7.7\%}_{-0.3\%-7.7\%}$ 	&	 $0.1034^{+0.4\%+9.3\%}_{-0.2\%-9.3\%}$ 	&	 $0.04985^{+0.4\%+11.4\%}_{-0.2\%-11.4\%}$ 	&	 $0.02523^{+0.4\%+14.1\%}_{-0.2\%-14.1\%}$ 	&	 $0.01328^{+0.4\%+17.5\%}_{-0.1\%-17.5\%}$ 	&	 \\
-$300.2^{+1.7\%+2.7\%}_{-2.4\%-2.7\%}$ 	&	 $24.51^{+1.1\%+3.5\%}_{-0.9\%-3.5\%}$ 	&	 $5.053^{+0.7\%+4.4\%}_{-0.5\%-4.4\%}$ 	&	 $1.509^{+0.6\%+5.4\%}_{-0.4\%-5.4\%}$ 	&	 $0.551^{+0.5\%+6.4\%}_{-0.3\%-6.4\%}$ 	&	 $0.2285^{+0.4\%+7.7\%}_{-0.3\%-7.7\%}$ 	&	 $0.1034^{+0.4\%+9.3\%}_{-0.2\%-9.3\%}$ 	&	 $0.04985^{+0.4\%+11.4\%}_{-0.2\%-11.4\%}$ 	&	 $0.02523^{+0.4\%+14.1\%}_{-0.2\%-14.1\%}$ 	&	 $0.01328^{+0.4\%+17.5\%}_{-0.1\%-17.5\%}$ 	&	 \\
-$106.9^{+1.7\%+3.0\%}_{-2.4\%-3.0\%}$ 	&	 $9.145^{+1.1\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $1.915^{+0.8\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $0.5768^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.2121^{+0.5\%+6.6\%}_{-0.3\%-6.6\%}$ 	&	 $0.08842^{+0.4\%+7.9\%}_{-0.3\%-7.9\%}$ 	&	 $0.04021^{+0.4\%+9.7\%}_{-0.2\%-9.7\%}$ 	&	 $0.01949^{+0.4\%+11.9\%}_{-0.2\%-11.9\%}$ 	&	 $0.009911^{+0.5\%+14.8\%}_{-0.2\%-14.8\%}$ 	&	 $0.005242^{+0.4\%+18.5\%}_{-0.1\%-18.5\%}$ 	&	 \\
-$124^{+1.7\%+3.1\%}_{-2.4\%-3.1\%}$ 	&	 $10.92^{+1.1\%+3.7\%}_{-0.9\%-3.7\%}$ 	&	 $2.296^{+0.8\%+4.5\%}_{-0.5\%-4.5\%}$ 	&	 $0.6925^{+0.6\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $0.2548^{+0.5\%+6.6\%}_{-0.3\%-6.6\%}$ 	&	 $0.1063^{+0.5\%+8.0\%}_{-0.3\%-8.0\%}$ 	&	 $0.04835^{+0.4\%+9.7\%}_{-0.2\%-9.7\%}$ 	&	 $0.02344^{+0.4\%+11.9\%}_{-0.2\%-11.9\%}$ 	&	 $0.01192^{+0.5\%+14.9\%}_{-0.2\%-14.9\%}$ 	&	 $0.006309^{+0.4\%+18.5\%}_{-0.1\%-18.5\%}$ 	&	 \\
diff --git a/distribute/wino.slha13000.tex b/distribute/wino.slha13000.tex
deleted file mode 100644
index 1bad0e959..000000000
--- a/distribute/wino.slha13000.tex
+++ /dev/null
@@ -1,4 +0,0 @@
-$100$ 	&	 $200$ 	&	 $300$ 	&	 $400$ 	&	 $500$ 	&	 $600$ 	&	 $700$ 	&	 $800$ 	&	 $900$ 	&	 $1000$ 	&	 $1100$ 	&	 $1200$ 	&	 $1300$ 	&	 $1400$ 	&	 $1500$ 	&	 $1600$ 	&	 $1700$ 	&	 $1800$ 	&	 $1900$ 	&	 $2000$ 	&	 \\
-$1.44e+04^{+2.0\%+3.0\%}_{-2.8\%-3.0\%}$ 	&	 $1216^{+0.9\%+3.8\%}_{-1.1\%-3.8\%}$ 	&	 $270.5^{+0.6\%+4.7\%}_{-0.6\%-4.7\%}$ 	&	 $86.95^{+0.5\%+5.6\%}_{-0.4\%-5.6\%}$ 	&	 $33.98^{+0.5\%+6.5\%}_{-0.3\%-6.5\%}$ 	&	 $14.98^{+0.4\%+7.5\%}_{-0.2\%-7.5\%}$ 	&	 $7.159^{+0.4\%+8.6\%}_{-0.2\%-8.6\%}$ 	&	 $3.624^{+0.4\%+10.0\%}_{-0.2\%-10.0\%}$ 	&	 $1.914^{+0.4\%+11.7\%}_{-0.2\%-11.7\%}$ 	&	 $1.044^{+0.3\%+13.9\%}_{-0.2\%-13.9\%}$ 	&	 $0.5836^{+0.3\%+16.9\%}_{-0.1\%-16.9\%}$ 	&	 $0.3328^{+0.3\%+20.9\%}_{-0.1\%-20.9\%}$ 	&	 $0.1929^{+0.3\%+26.2\%}_{-0.1\%-26.2\%}$ 	&	 $0.1133^{+0.3\%+33.0\%}_{-0.1\%-33.0\%}$ 	&	 $0.06738^{+0.3\%+42.0\%}_{-0.1\%-42.0\%}$ 	&	 $0.0405^{+0.3\%+53.5\%}_{-0.1\%-53.5\%}$ 	&	 $0.02458^{+0.2\%+68.0\%}_{-0.1\%-68.0\%}$ 	&	 $0.01507^{+0.2\%+86.2\%}_{-0.0\%-86.2\%}$ 	&	 $0.00933^{+0.2\%+108.5\%}_{-0.0\%-108.5\%}$ 	&	 $0.005834^{+0.2\%+135.7\%}_{-0.0\%-135.7\%}$ 	&	 \\
-$9101^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $663.2^{+1.1\%+3.9\%}_{-1.3\%-3.9\%}$ 	&	 $132^{+0.8\%+5.1\%}_{-0.8\%-5.1\%}$ 	&	 $38.69^{+0.6\%+6.4\%}_{-0.6\%-6.4\%}$ 	&	 $13.99^{+0.4\%+7.8\%}_{-0.4\%-7.8\%}$ 	&	 $5.765^{+0.4\%+9.4\%}_{-0.3\%-9.4\%}$ 	&	 $2.601^{+0.3\%+11.5\%}_{-0.2\%-11.5\%}$ 	&	 $1.253^{+0.3\%+14.0\%}_{-0.2\%-14.0\%}$ 	&	 $0.6351^{+0.3\%+17.3\%}_{-0.2\%-17.3\%}$ 	&	 $0.3348^{+0.3\%+21.4\%}_{-0.1\%-21.4\%}$ 	&	 $0.1822^{+0.3\%+26.5\%}_{-0.1\%-26.5\%}$ 	&	 $0.1018^{+0.2\%+32.8\%}_{-0.1\%-32.8\%}$ 	&	 $0.0582^{+0.2\%+40.6\%}_{-0.1\%-40.6\%}$ 	&	 $0.0339^{+0.2\%+49.8\%}_{-0.1\%-49.8\%}$ 	&	 $0.02008^{+0.2\%+61.0\%}_{-0.1\%-61.0\%}$ 	&	 $0.01207^{+0.2\%+74.0\%}_{-0.1\%-74.0\%}$ 	&	 $0.00735^{+0.2\%+89.3\%}_{-0.1\%-89.3\%}$ 	&	 $0.004532^{+0.2\%+107.0\%}_{-0.0\%-107.0\%}$ 	&	 $0.002825^{+0.2\%+127.0\%}_{-0.0\%-127.0\%}$ 	&	 $0.001778^{+0.2\%+149.6\%}_{-0.0\%-149.6\%}$ 	&	 \\
-$1.209e+04^{+2.1\%+2.6\%}_{-2.9\%-2.6\%}$ 	&	 $941.9^{+1.0\%+3.3\%}_{-1.2\%-3.3\%}$ 	&	 $198.2^{+0.7\%+4.1\%}_{-0.7\%-4.1\%}$ 	&	 $60.87^{+0.5\%+4.9\%}_{-0.5\%-4.9\%}$ 	&	 $22.89^{+0.4\%+5.8\%}_{-0.4\%-5.8\%}$ 	&	 $9.769^{+0.4\%+6.9\%}_{-0.3\%-6.9\%}$ 	&	 $4.543^{+0.3\%+8.1\%}_{-0.2\%-8.1\%}$ 	&	 $2.248^{+0.4\%+9.5\%}_{-0.2\%-9.5\%}$ 	&	 $1.166^{+0.3\%+11.4\%}_{-0.2\%-11.4\%}$ 	&	 $0.6272^{+0.3\%+13.8\%}_{-0.1\%-13.8\%}$ 	&	 $0.3474^{+0.3\%+16.9\%}_{-0.1\%-16.9\%}$ 	&	 $0.1971^{+0.3\%+20.9\%}_{-0.1\%-20.9\%}$ 	&	 $0.1141^{+0.3\%+25.8\%}_{-0.1\%-25.8\%}$ 	&	 $0.06724^{+0.3\%+31.9\%}_{-0.1\%-31.9\%}$ 	&	 $0.04025^{+0.2\%+39.4\%}_{-0.1\%-39.4\%}$ 	&	 $0.02442^{+0.2\%+48.5\%}_{-0.1\%-48.5\%}$ 	&	 $0.01501^{+0.2\%+59.3\%}_{-0.1\%-59.3\%}$ 	&	 $0.009333^{+0.2\%+72.2\%}_{-0.0\%-72.2\%}$ 	&	 $0.005867^{+0.2\%+87.2\%}_{-0.0\%-87.2\%}$ 	&	 $0.003727^{+0.2\%+104.8\%}_{-0.0\%-104.8\%}$ 	&	 \\
diff --git a/distribute/wino.slha13600.tex b/distribute/wino.slha13600.tex
deleted file mode 100644
index 7c0fbc8f5..000000000
--- a/distribute/wino.slha13600.tex
+++ /dev/null
@@ -1,4 +0,0 @@
-$100$ 	&	 $200$ 	&	 $300$ 	&	 $400$ 	&	 $500$ 	&	 $600$ 	&	 $700$ 	&	 $800$ 	&	 $900$ 	&	 $1000$ 	&	 $1100$ 	&	 $1200$ 	&	 $1300$ 	&	 $1400$ 	&	 $1500$ 	&	 $1600$ 	&	 $1700$ 	&	 $1800$ 	&	 $1900$ 	&	 $2000$ 	&	 \\
-$1.522e+04^{+2.1\%+2.9\%}_{-2.9\%-2.9\%}$ 	&	 $1299^{+1.0\%+3.8\%}_{-1.2\%-3.8\%}$ 	&	 $292.2^{+0.6\%+4.6\%}_{-0.7\%-4.6\%}$ 	&	 $94.95^{+0.5\%+5.5\%}_{-0.4\%-5.5\%}$ 	&	 $37.54^{+0.5\%+6.3\%}_{-0.3\%-6.3\%}$ 	&	 $16.75^{+0.4\%+7.2\%}_{-0.3\%-7.2\%}$ 	&	 $8.11^{+0.4\%+8.2\%}_{-0.2\%-8.2\%}$ 	&	 $4.161^{+0.4\%+9.5\%}_{-0.2\%-9.5\%}$ 	&	 $2.229^{+0.4\%+11.0\%}_{-0.2\%-11.0\%}$ 	&	 $1.233^{+0.4\%+12.9\%}_{-0.2\%-12.9\%}$ 	&	 $0.6999^{+0.3\%+15.4\%}_{-0.2\%-15.4\%}$ 	&	 $0.4052^{+0.3\%+18.7\%}_{-0.1\%-18.7\%}$ 	&	 $0.2385^{+0.4\%+23.1\%}_{-0.1\%-23.1\%}$ 	&	 $0.1424^{+0.3\%+28.7\%}_{-0.1\%-28.7\%}$ 	&	 $0.08599^{+0.3\%+35.9\%}_{-0.1\%-35.9\%}$ 	&	 $0.05247^{+0.3\%+45.3\%}_{-0.1\%-45.3\%}$ 	&	 $0.03233^{+0.2\%+57.0\%}_{-0.1\%-57.0\%}$ 	&	 $0.0201^{+0.2\%+71.7\%}_{-0.1\%-71.7\%}$ 	&	 $0.01261^{+0.2\%+89.8\%}_{-0.0\%-89.8\%}$ 	&	 $0.007983^{+0.2\%+111.9\%}_{-0.0\%-111.9\%}$ 	&	 \\
-$9695^{+2.2\%+2.8\%}_{-3.0\%-2.8\%}$ 	&	 $716.7^{+1.2\%+3.8\%}_{-1.3\%-3.8\%}$ 	&	 $144.5^{+0.8\%+4.9\%}_{-0.8\%-4.9\%}$ 	&	 $42.89^{+0.6\%+6.1\%}_{-0.6\%-6.1\%}$ 	&	 $15.7^{+0.5\%+7.4\%}_{-0.5\%-7.4\%}$ 	&	 $6.556^{+0.4\%+9.0\%}_{-0.4\%-9.0\%}$ 	&	 $2.996^{+0.3\%+10.8\%}_{-0.3\%-10.8\%}$ 	&	 $1.463^{+0.3\%+13.1\%}_{-0.2\%-13.1\%}$ 	&	 $0.7511^{+0.3\%+15.9\%}_{-0.2\%-15.9\%}$ 	&	 $0.4012^{+0.3\%+19.5\%}_{-0.2\%-19.5\%}$ 	&	 $0.2212^{+0.2\%+24.0\%}_{-0.1\%-24.0\%}$ 	&	 $0.1253^{+0.2\%+29.4\%}_{-0.1\%-29.4\%}$ 	&	 $0.07254^{+0.2\%+36.0\%}_{-0.1\%-36.0\%}$ 	&	 $0.0428^{+0.2\%+44.0\%}_{-0.2\%-44.0\%}$ 	&	 $0.02568^{+0.2\%+53.5\%}_{-0.1\%-53.5\%}$ 	&	 $0.01563^{+0.2\%+64.7\%}_{-0.1\%-64.7\%}$ 	&	 $0.00964^{+0.2\%+77.8\%}_{-0.1\%-77.8\%}$ 	&	 $0.006017^{+0.2\%+92.9\%}_{-0.0\%-92.9\%}$ 	&	 $0.003797^{+0.2\%+110.3\%}_{-0.0\%-110.3\%}$ 	&	 $0.002419^{+0.2\%+129.8\%}_{-0.0\%-129.8\%}$ 	&	 \\
-$1.284e+04^{+2.2\%+2.6\%}_{-3.0\%-2.6\%}$ 	&	 $1012^{+1.1\%+3.3\%}_{-1.3\%-3.3\%}$ 	&	 $215.5^{+0.7\%+4.0\%}_{-0.7\%-4.0\%}$ 	&	 $66.96^{+0.5\%+4.8\%}_{-0.5\%-4.8\%}$ 	&	 $25.49^{+0.4\%+5.6\%}_{-0.4\%-5.6\%}$ 	&	 $11.01^{+0.4\%+6.6\%}_{-0.3\%-6.6\%}$ 	&	 $5.188^{+0.4\%+7.7\%}_{-0.2\%-7.7\%}$ 	&	 $2.601^{+0.4\%+9.0\%}_{-0.2\%-9.0\%}$ 	&	 $1.367^{+0.3\%+10.6\%}_{-0.2\%-10.6\%}$ 	&	 $0.7451^{+0.3\%+12.7\%}_{-0.1\%-12.7\%}$ 	&	 $0.4183^{+0.3\%+15.4\%}_{-0.1\%-15.4\%}$ 	&	 $0.2406^{+0.3\%+18.7\%}_{-0.1\%-18.7\%}$ 	&	 $0.1412^{+0.3\%+22.9\%}_{-0.1\%-22.9\%}$ 	&	 $0.08431^{+0.3\%+28.1\%}_{-0.1\%-28.1\%}$ 	&	 $0.05113^{+0.2\%+34.4\%}_{-0.1\%-34.4\%}$ 	&	 $0.03143^{+0.2\%+42.0\%}_{-0.1\%-42.0\%}$ 	&	 $0.01956^{+0.2\%+51.1\%}_{-0.1\%-51.1\%}$ 	&	 $0.01231^{+0.2\%+62.0\%}_{-0.0\%-62.0\%}$ 	&	 $0.007831^{+0.2\%+74.6\%}_{-0.0\%-74.6\%}$ 	&	 $0.005031^{+0.2\%+89.4\%}_{-0.0\%-89.4\%}$ 	&	 \\
diff --git a/setup.py b/setup.py
index 1ccd0dece..917d1ed09 100644
--- a/setup.py
+++ b/setup.py
@@ -26,7 +26,7 @@
         "scipy",
         "sympy",
         "scikit-learn",
-        "smpl==0.0.142",
+        "smpl>=0.0.142",
         "pyslha",
         "enlighten",
         "particle",