diff --git a/README.md b/README.md
index 1183519..bee38d2 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # py-graph
-a python package for graph kernels.
+A python package for graph kernels.
 
-## requirements
+## Requirements
 
 * numpy - 1.13.3
 * scipy - 1.0.0
@@ -10,23 +10,34 @@ a python package for graph kernels.
 * sklearn - 0.19.1
 * tabulate - 0.8.2
 
-## results with minimal test RMSE for each kernel on dataset Asyclic
--- All the kernels are tested on dataset Asyclic, which consists of 185 molecules (graphs). 
--- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression.
--- For predition we randomly divide the data in train and test subset, where 90% of entire dataset is for training and rest for testing. 10 splits are performed. For each split, we first train on the train data, then evaluate the performance on the test set. We choose the optimal parameters for the test set and finally provide the corresponding performance. The final results correspond to the average of the performances on the test sets. 
+## Results with minimal test RMSE for each kernel on dataset Asyclic
+All kernels are tested on dataset Asyclic, which consists of 185 molecules (graphs). 
 
-| Kernels       | RMSE(℃)  | std(℃)  | parameter    | k_time |
+The criteria used for prediction are SVM for classification and kernel Ridge regression for regression.
+
+For predition we randomly divide the data in train and test subset, where 90% of entire dataset is for training and rest for testing. 10 splits are performed. For each split, we first train on the train data, then evaluate the performance on the test set. We choose the optimal parameters for the test set and finally provide the corresponding performance. The final results correspond to the average of the performances on the test sets. 
+
+| Kernels       | RMSE(℃)  | STD(℃)  | Parameter    | k_time |
 |---------------|:---------:|:--------:|-------------:|-------:|
-| shortest path | 36.40     | 5.35     | -            | -      |
-| marginalized  | 17.90     | 6.59     | p_quit = 0.1 | -      |
-| path          | 14.27     | 6.37     | -            | -      |
-| WL subtree    | 9.00      | 6.37     | height = 1   | 0.85"  |
+| Shortest path | 35.19     | 4.50     | -            | 14.58" |
+| Marginalized  | 18.02     | 6.29     | p_quit = 0.1 | 4'19"  |
+| Path          | 14.00     | 6.93     | -            | 36.21" |
+| WL subtree    | 7.55      | 2.33     | height = 1   | 0.84"  |
+| Treelet       | 8.31      | 3.38     | -            | 49.58" |
 
-**In each line, paremeter is the one with which the kenrel achieves the best results.
-In each line, k_time is the time spent on building the kernel matrix.
-See detail results in [results.md](pygraph/kernels/results.md).**
+* RMSE stands for arithmetic mean of the root mean squared errors on all splits.
+* STD stands for standard deviation of the root mean squared errors on all splits.
+* Paremeter is the one with which the kenrel achieves the best results.
+* k_time is the time spent on building the kernel matrix.
+* The targets of training data are normalized before calculating *path kernel* and *treelet kernel*.
+* See detail results in [results.md](pygraph/kernels/results.md).
 
-## updates
+## Updates
+### 2018.01.16
+* ADD *treelet kernel* and its result on dataset Asyclic. - linlin
+* MOD the way to calculate WL subtree kernel, correct its results. - linlin
+* ADD *kernel_train_test* and *split_train_test* to wrap training and testing process. - linlin
+* MOD readme.md file, add detailed results of each kernel. - linlin
 ### 2017.12.22
 * ADD calculation of the time spend to acquire kernel matrices for each kernel. - linlin
 * MOD floydTransformation function, calculate shortest paths taking into consideration user-defined edge weight. - linlin
@@ -35,13 +46,13 @@ See detail results in [results.md](pygraph/kernels/results.md).**
 ### 2017.12.21
 * MOD Weisfeiler-Lehman subtree kernel and the test code. - linlin
 ### 2017.12.20
-* ADD Weisfeiler-Lehman subtree kernel and its result on dataset Asyclic. - linlin
+* ADD *Weisfeiler-Lehman subtree kernel* and its result on dataset Asyclic. - linlin
 ### 2017.12.07
-* ADD mean average path kernel and its result on dataset Asyclic. - linlin
+* ADD *mean average path kernel* and its result on dataset Asyclic. - linlin
 * ADD delta kernel. - linlin
 * MOD reconstruction the code of marginalized kernel. - linlin
 ### 2017.12.05
-* ADD marginalized kernel and its result. - linlin
+* ADD *marginalized kernel* and its result. - linlin
 * ADD list required python packages in file README.md. - linlin
 ### 2017.11.24
-* ADD shortest path kernel and its result. - linlin
+* ADD *shortest path kernel* and its result. - linlin
\ No newline at end of file
diff --git a/notebooks/.ipynb_checkpoints/run_WeisfeilerLehmankernel_acyclic-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/run_WeisfeilerLehmankernel_acyclic-checkpoint.ipynb
deleted file mode 100644
index 4b7d560..0000000
--- a/notebooks/.ipynb_checkpoints/run_WeisfeilerLehmankernel_acyclic-checkpoint.ipynb
+++ /dev/null
@@ -1,1893 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'O', 'C'}\n",
-      "{'O', 'C'}\n",
-      "--- shortest path kernel built in 0.0002582073211669922 seconds ---\n",
-      "3\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773eab40b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'C'}), (1, {'label': 'C'}), (2, {'label': 'C'}), (3, {'label': 'C'}), (4, {'label': 'O'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xl8FeXd9/HPjz0BiSyCKQKBKjdCgxYjVYRCBVQoClFc+oCKFbGoRKux1fLUrb1vUbHesoiijyRsdWNVNhEVqogsAomySKDihkQFQQyruZ4/5sQewslCzjmZk5zv+/WaV2auuWbmlyH8zpxrrrnGnHOIiEh8qeF3ACIiUvmU/EVE4pCSv4hIHFLyFxGJQ0r+IiJxSMlfRCQOKfmLiMQhJX8RkTik5C8iEodq+R1ASZo2bepSUlL8DkNEpEpZu3btN865U8qqF7PJPyUlhTVr1vgdhohIlWJmO8pTT80+IiJxSMlfRCQOKfmLiMQhJX8RkTik5C8iEoeU/EVE4pCSv4hIHFLyFxGJQzH7kJeISJWUnw9ZWZCTA3v3QlISdOoEN9wAp5T54G2lUfIXEYmE1avh4Ydh4UJv+eDB/6ybNQvuvx/69oV774Vzz/UnxiBq9hERCdfEidCzJ8yZ4yX94MQPcOCAVzZnjldv4kQ/ojyGrvxFRMIxcSJkZkJBQdl1nfPqZWZ6yyNGRDe2UujKX0SkolavPi7xHwJuBFoDJwFnAwuLb1f0AeDj4JURSf5mdomZbTGzPDO7p5R6V5iZM7O0SBxXRMRXDz/sNekEOQq0BJYBe4G/A1cBnxTf9sABb3ufhJ38zawmMAHoC3QAfmdmHULUOwm4HXg/3GOKiPguP9+7uevcMcX1gQeAFLwE2x9oA6wtvr1zsGABfP111EMNJRJX/l2APOfcdufcYeAFYECIen8DHgEOhlgnIlK1ZGWVq9ou4GOgY6iVZuXeT6RFIvm3AD4LWv48UPYTM+sMtHTOzY/A8URE/JeTc3yvnmKOAIOB64H2oSocOAC5uZGPrRyifsPXzGoA/wDuKkfd4Wa2xszWfO3TVyERkXLZu7fU1YXAtUAdYHxpFffsiVxMJyASyf8LvPsbRU4LlBU5CfgF8LaZfQKcB8wLddPXOTfJOZfmnEs7JYaehBMROU5SUomrHF6Pn13ATKB2aftp1CiiYZVXJJL/auAMM2tjZnWAa4B5RSudc3udc02dcynOuRRgJXCZc04v6BWRqqtTJ1y9eiFXjQA2Aa8CCaXtIyEBUlMjH1s5hJ38nXNHgduAxXi/70vOuY/M7CEzuyzc/YuIxJpNmzbxp40bORSizX8H8AywHjgVaBCYpofakXMwdGj0Ai1FRNr8nXMLnHPtnHM/d879d6DsPufcvBB1e+qqX0SqmsLCQl577TUuuugiLrzwQhJat8ZdconXYydIa7xmn4PA/qBpcPEdmkG/fr4N9qbhHURESvHdd98xefJkJkyYQKNGjbj99tu58sorqVu3rveE7/Ll5RvaobiEBG+QN59oeAcRkRA2b97MrbfeStu2bVm9ejXTpk1j1apVDBkyxEv84I3OOWYMJCae2M4TE73t0vwb7EBX/iIiAYWFhSxcuJCxY8eyYcMGhg8fzocffsjPfvazkjcqGpwtM9Prt1/sid9jmHlX/GPG+DqoGyj5i4iwd+9esrKyGDduHCeffDIZGRnMmzfvP1f4ZRkxwvsW8PDD3pANZseO+ZOQ4H0o9OvnNfX4eMVfRMlfROLWli1bGD9+PNOnT+fiiy9m6tSpnHfeeVixm7jlkpYGM2d6Y/VkZXlP7u7Z4/XjT031evXE0PNLSv4iElcKCwtZtGgRY8eOZd26dQwfPpzc3FxatGhR9sblccopcPfdkdlXFCn5i0hc2Ldv309NOw0bNiQjI4M5c+ZQr4QHtao7JX8RqdY+/vhjxo8fz7Rp07jooovIzs7m/PPPr1jTTjWi5C8i1U5hYSGLFy9m7NixfPDBB9x0003k5ORw2mmn+R1azFDyF5FqY9++fWRnZzNu3DgaNGhARkYGs2fPjtumndIo+YtIlbd161bGjx/P1KlT6dOnD88//zwXXHBB3DftlEbJX0SqpMLCQl5//XXGjh3LmjVr1LRzgpT8RaRK+f77739q2klMTCQjI4OZM2eSkFDq4MlSjJK/iFQJeXl5PzXt9OrVi+eee45u3bqpaaeClPxFJGYVFhayZMkSxo0bx6pVqxg2bBjr16+nZcuWZW8spVLyF5GY8/333zNlyhTGjRtHvXr1yMjI4OWXX1bTTgQp+YtIzMjLy2PChAlMmTKF3/zmN0yaNInu3buraScKNJ6/iPjKOceSJUu49NJLOf/886lbty7r1q3jlVde4de//rUSf5Qo+UvcmTFjBmlpaTRo0IDk5GT69u3LO++843dYcWf//v089dRTdOzYkczMTAYOHMinn37K6NGjadWqld/hVXtq9gHIz/eGYM3Jgb17ISkJOnWCG26IqSFYJXz/+Mc/GD16NE8//TQXX3wxderUYdGiRcydO5du3br5HV5c2LZtGxMmTCA7O5uePXsyceJEXeH7wFxpb53xUVpamluzJsrveV+92nv5wsKF3vLBg/9ZV/Tyhb59vZcvnHtudGORqNu7dy8tWrRg8uTJXHnllX6HE1eccyxdupSxY8fy3nvv8fvf/55bbrmF1q1b+x1atWNma51zZb4tJn6v/CdOLP21a0Vv4ZkzBxYvjonXrkl43nvvPQ4ePEh6errfocSNH374galTpzJ27Fhq1apFRkYGL7zwAokn+s5bibj4TP5Fib+goOy6znn1MjO9ZX0AVFnffvstTZs2pVat+Pyzr0zbt29nwoQJZGVl0aNHD5566il69Oihpp0YEn83fFevPi7xjwfSgLrA0JK2K/oAiHZTlERNkyZN+Oabbzh69KjfoVRLRU07AwYM4Fe/+hU1a9Zk7dq1zJo1i549eyrxx5j4uwR6+OFjX6wM/Az4v8Bi4ECobYocOOBtP3Nm9OKTqCnqRjhnzhwGDRrkdzj+iXAHh6KmnXHjxlGjRg0yMjL45z//qaadWOecC3sCLgG2AHnAPSHW3wlsBHKApUDrsvZ5zjnnuIjbtcu5evWc8xpzjptGgbu+hHU/TfXqOZefH/nYpFKMGTPGNWvWzM2ePdv98MMP7vDhw27BggXu7rvv9ju06Fu1yrn0dO9vuPj/g4QEryw93atXDtu3b3d33XWXa9KkiRs4cKB78803XWFhYZR/CSkLsMaVJ2+Xp1KpO4CawDagLVAH2AB0KFbnN0BiYH4E8GJZ+41K8n/kkfCTf0KCc48+GvnYpNJMmzbNnXPOOS4xMdE1b97c9evXz7377rt+hxVdTz3lXGKic2al/32befWeeirkbgoLC93SpUvdgAEDXJMmTVxmZqbbvn17Jf8yUpryJv9INPt0AfKcc9sBzOwFYEDgSr/o28VbQfVXAkMicNwTl5NzbHfOijhwAHJzIxOP+GLw4MEMHjzY7zAqTwQ6OPzwww9Mnz6dsWPH4pwjIyOD6dOnU79+/SgGLtEUieTfAvgsaPlz4Fel1L8RWBhqhZkNB4YD0XnCb+/eyOxnz57I7Eck2kJ0cAi2FUgFBgHTglcEPgC+bNGC/33nHZ5//nm6devGk08+yYUXXqibt9VApd7wNbMheB1reoRa75ybBEwC7yGviAeQlBSZ/TRqFJn9iERbiA4OwW4FSnp8sbCggLWDBlGYkcGqVato27ZtVEIUf0Siq+cXQPDg2qcFyo5hZr2BUcBlzrlDETjuievUCUK8yPkocBD4MTAdDJSFlJAAqanRilAkcvLzvafXS3iK/wXgZKBXCZvXAPrXqMGYP/9Zib8aikTyXw2cYWZtzKwOcA0wL7iCmf0SeAYv8edH4JgVM3RoyOK/AwnAaLyvvgmBspCcK3E/IjElK6vEVfuA+4B/lLELq1Gj1P1I1RV28nfOHQVuw+smvwl4yTn3kZk9ZGaXBao9BjQAXjaz9WY2r4TdRVezZt5YPcXaKx8AXLHpgRCb/wisaNSIvEjdOxCJplI6OPwV7+Zbma86VweHaisibf7OuQXAgmJl9wXN947EcSLi3nu9sXrK0/OhmBqJiWy94gouO+88hg0bxqhRozjppJOiEKRIBJRwkbIeeANYV979qINDtRR/wzuce643SNuJPn2YmIiNGcP148aRm5vLrl27aN++PdnZ2RQWFkYnVpFwlNDB4W3gE6AVcCowBpgJdC5pP+rgUC3FX/IHr+9y0QdAWV3WzLx6QaN6JicnM3nyZGbPns3EiRM5//zzWblyZSUELlJ+hamp/FinznHlw/GeylwfmP4A/Bav3fY46uBQbcVn8gcvkS9bBunpXg+g4i+GTkjwytPTvXohRvPs0qULK1as4LbbbuOKK67guuuu48svv6ykX0AktH379jF27FgumDSJo0eOHLc+Ee+Kv2hqANQDQo7qow4O1Vb8Jn+AtDRvkLZPP4UHH4Rrr4X+/b2fDz7olc+c6dUrQY0aNbj22mvZsmULLVu2pFOnTvzP//wPB8N9kljkBG3evJnbbruNlJQU3n33XR7LzqbOgAFlfrt9gGIPeBUxg3799Da7aiq+3+QVBdu3byczM5P169fz+OOPM3DgQD0NKVHz448/Mn/+fMaPH09OTg7Dhw/n5ptvpkWLFl6F1auhZ88KdXAgMdH71lvKxY/EnvK+ySsio3pGY4rKwG6V6I033nAdO3Z0vXr1crm5uX6HI9XMt99+6x577DHXpk0b16VLFzd16lR38ODB0JWLBnUra9DC4KmUwd0ktlHOgd3iu9kninr16sX69etJT0/nwgsv5LbbbmP37t1+hyVVXE5ODjfddBM///nPycnJ4YUXXuD9999nyJAh1K1bN/RGYXZwkOpJyT+KatWqxa233sqmTZsAaN++PRMmTNCbpOSEHDlyhFdeeYUePXrQr18/UlJS2LJlC1OmTKFLly7l20kEOjhI9aI2/0qUm5vLHXfcQX5+/k+jI4qUJD8/n2effZann36aNm3aMHLkSAYOHEjt2rXD2/HXX3tDNuTmeg9wNWrkdeccOlQ3d6uB8rb5K/lXMuccc+bM4a677uLss89mzJgxGjRLjrF69WrGjRvHq6++yqBBg7jttts466yz/A5LqojyJn81+1QyMyM9PZ2NGzeSlpZGly5dGDVqFPv37/c7NPHRoUOHmDZtGueddx5XXXUVqampbNu2jWeffVaJX6JCyd8n9erV4y9/+QsbNmzgs88+o3379kydOlVDRcSZL7/8kvvuu4+UlBSys7P5y1/+Ql5eHnfffTeNGzf2OzypxpT8fdaiRQumTJnCyy+/zLhx47jgggtYtWqV32FJFDnneOedd7j66qv5xS9+we7du3nzzTdZsmQJl112GTVr1vQ7RIkDSv4xomh8oJtvvpmBAwdyww03sHPnTr/Dkgg6cOAAzz//PJ07d+b3v/89F1xwAf/+978ZP348Z555pt/hSZxR8o8hNWrUYOjQoWzevJnmzZuTmprKI488wqFD/rz4TCJjx44d/PnPf6ZVq1bMmjWL0aNHs3nzZjIyMkiK1KtFRU6Qkn8MatiwIaNHj2blypWsWLGCjh07Mm/ePGK1Z5YczznH0qVLSU9Pp3Pnzhw5coSVK1fy2muvcfHFF1Ojhv7rib/U1bMKeP3117njjjto2bIlTzzxBB06dPA7JCnB/v37mTp1KuPHj8fMGDlyJEOGDKF+/fp+hyZxQl09q5GLLrqIDRs28Nvf/pYePXpw++23s0dvV4opW7du5Y477qB169a88cYbTJgwgdzcXG6++WYlfolJSv5VRO3atcnIyGDjxo0cPnyY9u3b8/TTT/Pjjz/6HVrcKiwsZOHChfTr148LLriAhIQE1q1bx8yZM+nZs6dGc5WYpmafKmrDhg0/fQN48skn6dmzp98hxY29e/cyefJkJkyYQMOGDRk5ciRXX301CcXHyxHxgZp9qrmzzjqLt956i7/+9a8MHTqUK6+8kk8++cTvsKq1jz76iBEjRpCSksKqVavIzs5mzZo1DB06VIlfqhwl/yrMzBg0aBCbNm2iU6dOpKWlcd999/HDDz/4HVq18eOPPzJnzhx69epF7969ad68ORs3bmTGjBl07dpVTTtSZSn5VwMJCQn89a9/Zd26dWzbto327dszY8YMdQ0Nw7fffssjjzxC27ZtefTRRxk2bBg7duzggQceIDk52e/wRMKm5F+NtGzZkunTp/PCCy/w+OOP0717d9auXet3WFXKunXruPHGGzn99NPZvHkzs2bNYsWKFfzud7+jTp06focnEjERSf5mdomZbTGzPDO7J8T6umb2YmD9+2aWEonjSmhF4wPdcMMN9O/fn2HDhrFr1y6/w4pZR44c4cUXX6Rbt24MGDCA008/nY8//pjJkydzzjnn+B2eSFSEnfzNrCYwAegLdAB+Z2bFn0K6EdjjnDsdeAJ4JNzjSulq1qzJjTfeyObNm2nUqBEdO3ZkzJgxHD582O/QYsZXX33FQw89REpKCk8//TR33nkn27dv59577+UUvdREqrlIXPl3AfKcc9udc4eBF4ABxeoMALID868AvUx3yipFUlISjz32GCtWrODtt9/mF7/4BfPnz/c7LN8451i5ciVDhgzhzDPP5IsvvmDRokW89dZbXH755dSqVcvvEEUqRSSSfwvgs6DlzwNlIes4544Ce4EmETi2lFO7du147bXXePLJJ7nrrrvo168fmzdv9jusSnPw4MGf3nk7ePBgOnfuzPbt23nmmWdITU31OzyRShdTN3zNbLiZrTGzNV9//bXf4VRLffv2JScnhz59+tC9e3fuvPNOvvvuO7/DiprPP/+cUaNG0bp1a2bMmMH999/Pxx9/zJ133kmjRo38Dk/EN5FI/l8ALYOWTwuUhaxjZrWAJODb4jtyzk1yzqU559LU5ho9derU4Y9//CMfffQR+/fvp3379jz77LPVZqgI5xzLly/nyiuvpFOnTnz//fcsX76cRYsW0b9/f70sRYTIJP/VwBlm1sbM6gDXAPOK1ZkHXB+YHwS86dQJ3XfNmjVj0qRJLFiwgClTppCWlsa//vUvv8OqsIKCAp599lnOPvtsbr75Znr27MmOHTsYO3Ys//Vf/+V3eCIxJey7W865o2Z2G7AYqAk875z7yMweAtY45+YB/w+YamZ5wG68DwiJEZ07d2b58uW89NJLDB48mK5du/Loo4/SqlUrv0Mrl3//+99MmDCBrKwsunbtypgxY+jdu7eevhUpRUTa/J1zC5xz7ZxzP3fO/Xeg7L5A4sc5d9A5d6Vz7nTnXBfn3PZIHFcix8y4+uqr2bx5M+3bt6dz5848+OCDFBQU+B1aSM65n955e+6552JmrF69mnnz5tGnTx8lfpEyxNQNX/FfYmIiDzzwAB988AEbN27kzDPP5MUXX4yZoSK+//77n955m5mZyWWXXcann37KY489Rps2bfwOT6TK0JDOUqrly5dz++23c9JJJ/Hkk0/yy1/+snwb5udDVhbk5MDevZCUBJ06wQ03QAVu5m/ZsoUJEyYwbdo0evXqxciRI+nevbuu8EWKKe+QzjjnYnI655xznMSGo0ePumeeecY1b97c3XTTTS4/P7/kyqtWOZee7ly9et4E/5kSEryy9HSvXjmO++qrr7qLLrrINWvWzI0aNcp9+umnEfzNRKofvHutZeZYNftImWrWrMnw4cPZvHkz9evXp0OHDjzxxBPHDxUxcSL07Alz5sDBg94U7MABr2zOHK/exIkhj7dnzx4ef/xx2rVrx4MPPsjgwYPZsWMHf//732nZsmXIbUTkxCj5S7mdfPLJPPHEEyxfvpzFixfTqVMnFi1a5K2cOBEyM6GgwLvOL41zXr3MzGM+AIreedu2bVvWrVvHjBkzWL16Nddddx316tWL4m8mEn80kImcsDPPPJOFCxcyf/58Ro4cSf/mzXl87VpqFLvSHwIsBX4ATgX+BAwLrlBQgMvM5M19+/jbwoVs3bqVP/zhD2zatIlTTz21sn4dkbikG74SlsOHD7PjnHNo++GHFH9u9iPgdKAusBnoCcwHggdJ/hH4V+PG7HrqKS6//HJq165dGWGLVFvlveGrK38JS53vvuOMvLyQ6zoGzVtg2saxyb8m0LOgAC68EJT4RSqN2vwlPFlZpa6+BUgE2gPJQL9QlczK3I+IRJaSv4QnJ+f4Xj1BngK+B/4FXI7XBHScAwcgNzcq4YlIaEr+Ep69e8usUhPohveih9CdO4E9eyIXk4iUSclfwpOUVO6qR/Ha/EPS2PoilUrJX8LTqROE6IOfj/c+z/14PXoWA/8EeoXaR0IC6G1aIpVKyV/CM3RoyGLDa+I5DWgEZAL/C1wWqrJzJe5HRKJDXT0lPM2aQd++3pANQc+MnAIsK8/2ZtCvX4UGexORitOVv4Tv3nu9ppuKSEjwtheRSqXkL+E791wYMwYSE09su8REb7u0skefFZHIUrOPRMaIEd7PzEwKDxygRmnDhph5V/xjxvxnOxGpVLryl8gZMQKWLWNVcjJHa9c+vikoIcHrGZSeDsuWKfGL+EhX/hJRBR06cPH+/WzPyaHJq696T+7u2eP1409N9Xr16OauiO+U/CWiFi9eTFpaGk3at4f27f0OR0RKoGYfiahZs2ZxxRVX+B2GiJRByV8i5vDhw8yfP5/09HS/QxGRMij5S8QsXbqUDh06kJyc7HcoIlKGsJK/mTU2syVmtjXw87jRuczsbDN7z8w+MrMcM7s6nGNK7Jo5cyaXX36532GISDmEe+V/D7DUOXcG3uta7wlRpwC4zjnXEbgE+F8zOznM40qMOXr0KHPnzlXyF6kiwk3+A4DswHw2MLB4Befcx865rYH5L/EGfFRfv2pm+fLltG7dmpSUFL9DEZFyCDf5N3fO7QzMfwU0L62ymXUB6lDKsO5SNamXj0jVUmY/fzN7Azg1xKpRwQvOOWdmJT7Tb2bJwFTgeudcYQl1hgPDAVq1alVWaBIjCgsLmT17Nm+99ZbfoYhIOZWZ/J1zvUtaZ2a7zCzZObczkNzzS6jXEJgPjHLOrSzlWJOASQBpaWmlDA4jsWTlypU0atSIdu3a+R2KiJRTuM0+84DrA/PXA3OLVzCzOsBsYIpz7pUwjycxaObMmWryEaliwk3+o4E+ZrYV6B1YxszSzOy5QJ2rgF8DQ81sfWA6O8zjSoxwzin5i1RBYY3t45z7lhCvZXXOrQGGBeanAdPCOY7Erg8++IDatWuTqnfwilQpesJXwlLUy8fM/A5FRE6Akr9UmJp8RKouJX+psI0bN1JQUECaXsMoUuUo+UuFFY3loyYfkapHyV8qTE0+IlWXkr9USF5eHrt27aJr165+hyIiFaDkLxUya9Ys0tPTqVmzpt+hiEgFKPlLhajJR6RqU/KXE/bZZ5+xbds2evTo4XcoIlJBSv5ywmbNmsWll15K7dq1/Q5FRCpIyV9OmJp8RKo+JX85IV999RW5ubn06dPH71BEJAxK/nJC5s6dS9++falbt67foYhIGJT85YQUPdUrIlWbkr+U2+7du3n//ffp27ev36GISJiU/KXc5s2bR69evahfv77foYhImJT8pdzUy0ek+lDyl3LZt28fy5Yto3///n6HIiIRoOQv5bJgwQK6d+9OUlKS36GISAQo+Uu5qJePSPWi5C9lKigo4PXXX2fAgAF+hyIiEaLkL2VavHgxaWlpNG3a1O9QRCRClPylTOrlI1L9KPlLqQ4dOsT8+fNJT0/3OxQRiSAlfynVm2++SYcOHUhOTvY7FBGJoLCSv5k1NrMlZrY18LNRKXUbmtnnZjY+nGNK5VKTj0j1FO6V/z3AUufcGcDSwHJJ/gYsD/N4UomOHj3K3Llz1cVTpBoKN/kPALID89nAwFCVzOwcoDnwepjHk0q0fPlyWrduTUpKit+hiEiEhZv8mzvndgbmv8JL8McwsxrA40BmWTszs+FmtsbM1nz99ddhhibhUpOPSPVVZvI3szfM7MMQ0zFP/DjnHOBC7OIWYIFz7vOyjuWcm+ScS3POpZ1yyinl/iUk8goLC5k9e3aVTf5ZWVmkpqaSmJjIqaeeyogRI/juu+/8DkskZtQqq4JzrndJ68xsl5klO+d2mlkykB+i2vlAdzO7BWgA1DGz/c650u4PiM9WrlxJ48aNadeund+hnLDHH3+cRx99lOzsbHr16sUXX3zBLbfcQp8+fXj33XepU6eO3yGK+C7cZp95wPWB+euBucUrOOcGO+daOedS8Jp+pijxx76q2uSzb98+7r//fsaNG8cll1xC7dq1SUlJ4aWXXuKTTz5h2rRpfocoEhPCTf6jgT5mthXoHVjGzNLM7LlwgxN/OOeqbPJfsWIFBw8ePK6HUoMGDejXrx9LlizxKTKR2FJms09pnHPfAr1ClK8BhoUozwKywjmmRN8HH3xA7dq1SU1N9TuUE/bNN9/QtGlTatU6/k87OTmZtWvX+hCVSOzRE75ynKKrfjPzO5QT1rRpU7755huOHj163LqdO3dqcDqRACV/OUZVbvIBOP/886lbty6zZs06pnz//v0sXLiQXr2O+6IqEpeU/OUYGzdu5MCBA6SlpfkdSoUkJSVx//33M3LkSBYtWsSRI0f45JNPuOqqqzjttNO49tpr/Q5RJCaE1eYv1U/RG7uqYpNPkT/96U80adKEzMxMtm3bRsOGDRk4cCDTp0+nbt26focnEhPMezYr9qSlpbk1a9b4HUbcOeussxg/fjzdu3f3OxQRqQAzW+ucK/Oru5p95Cd5eXns2rWLrl27+h2KiESZkr/8ZObMmaSnp1OzZk2/QxGRKFPyl5/MmjVLwzeLxAklfwHgs88+Y9u2bfTs2dPvUESkEij5C+Bd9V966aXUrl3b71BEpBIo+QtQdQdyE5GKUfIXvvrqK3Jzc+nTp4/foYhIJVHyF+bMmUPfvn31AJRIHFHyF/XyEYlDSv5xbvfu3bz//vv07dvX71BEpBIp+ce5efPm0atXL+rXr+93KCJSiZT845x6+YjEJyX/OLYWv57JAAAKKUlEQVRv3z6WLVtG//79/Q5FRCqZkn8cmz9/Pt26dSMpKcnvUESkkin5x7FZs2apyUckTin5x6mCggJef/11BgwY4HcoIuIDJf84tXjxYtLS0vRCc5E4peQfp9TLRyS+KfnHoUOHDjF//nzS09P9DkVEfBJW8jezxma2xMy2Bn42KqFeKzN73cw2mdlGM0sJ57gSnqVLl9KhQweSk5P9DkVEfBLulf89wFLn3BnA0sByKFOAx5xzZwJdgPwwjythUC8fEQk3+Q8AsgPz2cDA4hXMrANQyzm3BMA5t985VxDmcaWCjh49yty5czWQm0icCzf5N3fO7QzMfwU0D1GnHfCdmc0ys3Vm9piZ6Q3hPlm+fDmtW7cmJSXF71BExEe1yqpgZm8Ap4ZYNSp4wTnnzMyVcIzuwC+BT4EXgaHA/wtxrOHAcIBWrVqVFZpUgHr5iAiUI/k753qXtM7MdplZsnNup5klE7ot/3NgvXNue2CbOcB5hEj+zrlJwCSAtLS0UB8kEobCwkJmz57N22+/7XcoIuKzcJt95gHXB+avB+aGqLMaONnMTgksXwhsDPO4UgHvvfcejRs3pl27dn6HIiI+Czf5jwb6mNlWoHdgGTNLM7PnAJxzPwKZwFIzywUMeDbM40oFqJePiBQps9mnNM65b4FeIcrXAMOClpcAncI5lpyg/HzIyoKcHNi7F5eURJP58xkwe7bfkYlIDAgr+UsMWr0aHn4YFi70lg8eBLyvW380o17fvtC3L9x7L5x7rn9xioivNLxDdTJxIvTsCXPmeEk/kPiLJDiHHTzore/Z06svInFJV/7VxcSJkJkJBeV4fs45r15mprc8YkR0YxORmKMr/+pg9eqQiX83kA7UB1oDM4pvV/QBsGZNpYQpIrFDyb86ePhhOHDguOJbgTrALmA6MAL4qHilAwe87UUkrij5V3X5+d7NXXfsM3E/ADOBvwENgG7AZcDU4ts7BwsWwNdfV0KwIhIrlPyruqyskMUf493QCX6c6yxCXPkDmJW4HxGpnpT8q7qcnON69QDsBxoWK0sCvg+1jwMHIDc38rGJSMxS8q/q9u4NWdwA2FesbB9wUkn72bMncjGJSMxT8q/qkpJCFrcDjgJbg8o2AB1L2k+jkC9hE5FqSsm/quvUCerVO664PnA5cB/ezd938UbduzbUPhISIDU1ikGKSKxR8q/qhg4tcdVTwAGgGfA7YCIlXPk7V+p+RKT6UfKv6po188bqMTtuVWNgDt6V/6fA/wm1vRn06wennBJqrYhUU0r+1cG993pNNxWRkOBtLyJxRcm/Ojj3XBgzBhITT2y7xERvu7S06MQlIjFLA7tVF0WDs2Vmev32XSlvwTTzrvjHjNGgbiJxSlf+1cmIEbBsGaSnez2AijcFJSR45enpXj0lfpG4pSv/6iYtDWbO9Mbqycryntzds8frx5+a6vXq0c1dkbin5F9dnXIK3H2331GISIxSs4+ISBxS8hcRiUNK/iIicUjJX0QkDin5i4jEISV/EZE4pOQvIhKHlPxFROKQudLGgPGRmX0N7PA5jKbANz7HUBLFVjGKrWIUW8X4EVtr51yZj/HHbPKPBWa2xjkXk0NeKraKUWwVo9gqJpZjU7OPiEgcUvIXEYlDSv6lm+R3AKVQbBWj2CpGsVVMzMamNn8RkTikK38RkTgU98nfzBqb2RIz2xr42ShEnd+Y2fqg6aCZDQysyzKzfwetO7syYwvU+zHo+POCytuY2ftmlmdmL5pZncqMzczONrP3zOwjM8sxs6uD1kX8vJnZJWa2JfD73hNifd3AecgLnJeUoHX3Bsq3mNnF4cZSgdjuNLONgfO01MxaB60L+e9bibENNbOvg2IYFrTu+sDfwFYzu96H2J4IiutjM/suaF3UzpuZPW9m+Wb2YQnrzczGBuLOMbPOQeuies7KzTkX1xPwKHBPYP4e4JEy6jcGdgOJgeUsYJCfsQH7Syh/CbgmMP80MKIyYwPaAWcE5n8G7AROjsZ5A2oC24C2QB1gA9ChWJ1bgKcD89cALwbmOwTq1wXaBPZTs5Jj+03Q39SIothK+/etxNiGAuNDbNsY2B742Sgw36gyYytWfyTwfCWdt18DnYEPS1jfD1gIGHAe8H5lnLMTmeL+yh8YAGQH5rOBgWXUHwQsdM4VRDUqz4nG9hMzM+BC4JWKbB+J2JxzHzvntgbmvwTygWi9Q7ILkOec2+6cOwy8EIixpJhfAXoFztMA4AXn3CHn3L+BvMD+Ki0259xbQX9TK4HTInj8sGIrxcXAEufcbufcHmAJcImPsf0O+GcEj18i59xyvIvAkgwApjjPSuBkM0sm+ues3JT8oblzbmdg/iugeRn1r+H4P7D/Dny1e8LM6voQWz0zW2NmK4uao4AmwHfOuaOB5c+BFj7EBoCZdcG7etsWVBzJ89YC+CxoOdTv+1OdwHnZi3eeyrNttGMLdiPeVWORUP++lR3bFYF/q1fMrOUJbhvt2Ag0k7UB3gwqjuZ5K0tJsUf7nJVbXLzD18zeAE4NsWpU8IJzzplZid2fAp/cqcDioOJ78ZJfHbxuXX8GHqrk2Fo7574ws7bAm2aWi5fYwhLh8zYVuN45VxgoDuu8VVdmNgRIA3oEFR/37+uc2xZ6D1HxKvBP59whM7sZ79vThZV4/PK4BnjFOfdjUJnf5y2mxUXyd871Lmmdme0ys2Tn3M5AksovZVdXAbOdc0eC9l109XvIzCYDmZUdm3Pui8DP7Wb2NvBLYCbeV81agavc04AvKjs2M2sIzAdGBb7+Fu07rPMWwhdAy6DlUL9vUZ3PzawWkAR8W85tox0bZtYb74O1h3PuUFF5Cf++kUpiZcbmnPs2aPE5vPs9Rdv2LLbt2xGKq1yxBbkGuDW4IMrnrSwlxR7tc1ZuavaBeUDRHffrgbml1D2uTTGQ+Ira2AcCIe/+Rys2M2tU1GRiZk2BC4CNzru79BbePYoSt49ybHWA2Xhtn68UWxfp87YaOMO8Hk518JJB8R4ewTEPAt4MnKd5wDXm9QZqA5wBrAoznhOKzcx+CTwDXOacyw8qD/nvW8mxJQctXgZsCswvBi4KxNgIuIhjvxVHPbZAfO3xbp6+F1QW7fNWlnnAdYFeP+cBewMXPNE+Z+Xnx13mWJrw2nyXAluBN4DGgfI04Lmgeil4n9o1im3/JpCLl7ymAQ0qMzaga+D4GwI/bwzavi1eEssDXgbqVnJsQ4AjwPqg6exonTe8HhYf413djQqUPYSXUAHqBc5DXuC8tA3adlRguy1A3yj8nZUV2xvArqDzNK+sf99KjO1h4KNADG8B7YO2/X3gfOYBN1R2bIHlB4DRxbaL6nnDuwjcGfj7/hzvPs0fgD8E1hswIRB3LpBWWeesvJOe8BURiUNq9hERiUNK/iIicUjJX0QkDin5i4jEISV/EZE4pOQvIhKHlPxFROKQkr+ISBz6/6nGLY7cViPsAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773ca1cc88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'CC'}), (1, {'label': 'CC'}), (2, {'label': 'CO'}), (3, {'label': 'CCCO'}), (4, {'label': 'OCC'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773c9a44e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': '0'}), (1, {'label': '0'}), (2, {'label': '3'}), (3, {'label': '1'}), (4, {'label': '2'})]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773c9957b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'C'}), (1, {'label': 'C'}), (2, {'label': 'C'}), (3, {'label': 'C'}), (4, {'label': 'C'}), (5, {'label': 'C'}), (6, {'label': 'O'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xt8VPWd//HXh1tIAJGG+02wgi0WHrgGW9Zqcb2BpSJspXTRiq2lpa7bfWxBRa13C953f72AaBVRXLxQA90lIFrU1kUJKIJSgQCKICWAEKEESODz++NMcEhmkkxmJpOZeT8fj3lkzjnfc84nJ5PPOfM93/P9mrsjIiLZpVmqAxARkcan5C8ikoWU/EVEspCSv4hIFlLyFxHJQkr+IiJZSMlfRCQLKfmLiGQhJX8RkSzUIhEbMbPhwH8BzYHH3X16hDJjgTsAB95z93+pbZsdO3b0Pn36JCI8EZGssWrVqt3u3qmucnEnfzNrDvwWuAjYBhSb2UJ3XxdWph8wFTjH3feaWee6ttunTx9WrlwZb3giIlnFzD6uT7lEVPucDZS4+2Z3PwLMA0ZVK/Nj4LfuvhfA3UsTsF8REWmgRCT/HsAnYdPbQvPC9Qf6m9mbZvZWqJpIRERSJCF1/vXcTz9gGNATeMPMBrr7vvBCZjYRmAjQu3fvRgpNRCT7JOLKfzvQK2y6Z2heuG3AQnevcPctwAaCk8EJ3H2Wuxe4e0GnTnXerxARkQZKRPIvBvqZWV8zawWMAxZWK1NIcNWPmXUkqAbanIB9i4hIA8Sd/N29EvhXYAnwV+B5d//AzO4ys8tCxZYAe8xsHbAMmOLue+Ldt4iINIw11ZG8CgoKXE09RURiY2ar3L2grnJ6wldEJAs1VmsfEZHkKS2F2bNhzRooK4P27WHQILjmGlDjkYiU/EUkfRUXw7RpUFQUTB869MWyP/wBbr8dRoyAqVNhyJDUxNhEqdpHRNLTjBkwbBgUFgZJPzzxA5SXB/MKC4NyM2akIsomS1f+IpJ+ZsyAyZPh4MG6y7oH5SZPDqYnTUpubGlCV/4ikl6Ki2sk/t8ABUAOMCHaelUnALUiBJT8RSTdTJsWVOmE6Q7cCvywrnXLy4P1RclfRNJIaWlwc7fa80ljgMuB/LrWd4dFi2DXriQFmD6U/EUkfcyeHf82zBKznTSn5C8i6WPNmpqtemJVXg5r1yYmnjSm5C8i6aOsLDHb2bs3MdtJY0r+IpI+2rdPzHY6dEjMdtKYkr+IpI9Bg6B16xqzK4FDwNHQ61BoXkS5uTBwYLIiTBtK/iKSPiZMiDj7HiAXmA48E3p/T7RtuEfdTjZR8heR9NG5c9BXj9kJs+8AvNrrjkjrm8Gll6qzN5T8RSTdTJ0aVN00RG5usL4o+YtImhkyBB58EPLyYlsvLy9Yr6DOcU6ygpK/iKSfSZOOnwCO1VXW7IvEX61Tt2effZaCggLatm1Lt27dGDFiBH/5y1+SFnZTouQvIulp0iR2vfgi/9OyJZ6TU7MqKDc3aBk0ejS8/nqNxP/www/z7//+79x8883s3LmTrVu38rOf/YwFCxY04i+ROhrDV0TS1t1338327duZeffdQZcNa9cGD3B16BA055wwIeLN3bKyMnr06MGTTz7JFVdc0ehxJ1N9x/BVf/4ikpYqKyt57LHHWLhwYZDgp0yp97rLly/n0KFDjB49OokRNm2q9hGRtLRo0SJ69OjB4MGDY153z549dOzYkRYtsvf6V8lfRNLSzJkz+elPf9qgdfPz89m9ezeVlVGfA854Sv4ikna2bNnCihUrGDt2bIPWHzp0KDk5ORQWFiY4svSh5C8iaWfWrFn84Ac/ILeBD3u1b9+eu+66i+uuu47CwkIOHjxIRUUFRUVF3HDDDQmOtmnK3govEUlLhw8f5oknnuCNN96Iazu/+MUv6Nq1K/fccw/jx4+nXbt2nHXWWdxyyy0JirRpS0jyN7PhwH8BzYHH3X16lHL/DLwIDHF3teMUkZi99NJLfO1rX+P000+Pe1vjx49n/PjxCYgq/cRd7WNmzYHfAiOAAcD3zWxAhHLtgJ8Db8e7TxHJXjNmzGBStQe2JHaJqPM/Gyhx983ufgSYB4yKUO5u4D6CrrZFRGK2bt06NmzYwKhRkVKMxCIRyb8H8EnY9LbQvOPM7B+AXu7+vwnYn4hkqZkzZ3LttdfSsmXLVIeS9pJ+w9fMmgEPAxPqUXYiMBGgd+/eyQ1MRNLK3//+d+bOncu7776b6lAyQiKu/LcDvcKme4bmVWkHfA14zcw+Ar4BLDSzGn1PuPssdy9w94JOGmxBJKnSrUfLefPmcc455+jCMEESkfyLgX5m1tfMWgHjgIVVC929zN07unsfd+8DvAVcptY+IqmTjj1axvNEr9QUd7WPu1ea2b8CSwiaej7h7h+Y2V3ASndfWPsWRKQxlZWVcdttt/Hkk08yZsyY4/O/853v8J3vfCeFkUW3cuVKdu/ezSWXXJLqUDJGQur83X0RsKjavNuilB2WiH2KSMOkY4+WM2fOZOLEiTRv3jzVoWQMPeErkmXSrUfLffv2MX/+fNavX5/qUDKK+vYRyTLp1qPlnDlzGD58OJ07d051KBlFyV8ky6RTj5burhu9SaLkL5Jl0qlHy6rO284777wUR5J50qPST0QSKl16tKy66jezVIeScTSAu4g0STt37uQrX/kKW7Zs4eSTT051OGmjvgO4q9pHRJqkqucQlPiTQ9U+ItLkHD16lEcffZQXXngh1aFkLF35i0iT8/LLL5Ofn09BQZ21F9JASv4i0uRowJbkU/IXkSZl69atvPnmm4wbNy7VoWQ0JX8RaVIee+wxxo8fT5s2bVIdSkbTDV8RaTIqKir4/e9/z9KlS1MdSsbTlb+INBkLFiygX79+nHHGGakOJeMp+YtIk6F+fBqPkr+INAkbNmxg7dq1JwwwI8mj5C8iTcKjjz7KNddcQ05OTqpDyQq64SsiKVdeXs6cOXNYsWJFqkPJGrryF5GUe+GFFxgyZAh9+/ZNdShZQ8lfRFJuxowZutHbyJT8RSSlVq9ezfbt2/n2t7+d6lCyipK/iKTUzJkz+fGPf0zz5s1THUpW0Q1fEUmZ/fv389xzz7Fu3bpUh5J1dOUvIinzzDPPcMEFF9CtW7dUh5J1lPxFJCXcXV03p5CSv4ikxPLlyzl06BDnn39+qkPJSglJ/mY23MzWm1mJmd0UYfl/mNk6M1tjZq+a2SmJ2K+IpK+q5p3NmukaNBXiPupm1hz4LTACGAB838wGVCv2LlDg7oOAF4H7492viKSv3bt388c//pGrr7461aFkrUSccs8GStx9s7sfAeYBo8ILuPsydz8YmnwL6JmA/YpImpo9ezajRo0iPz8/1aFkrUQ09ewBfBI2vQ34ei3lfwQUJWC/IpKGjh07xqOPPsqcOXNSHUpWa9R2/mZ2JVAAfCvK8onARIDevXs3YmQi0lheffVV2rRpwze+8Y1Uh5LVElHtsx3oFTbdMzTvBGZ2IXALcJm7H460IXef5e4F7l7QqVOnBIQmIk1N1YAtZpbqULJaIpJ/MdDPzPqaWStgHLAwvICZnQk8SpD4SxOwTxFJQ9u3b2fZsmWMHz8+1aFkvbirfdy90sz+FVgCNAeecPcPzOwuYKW7LwQeANoCL4TO9lvd/bJ49y0iTVhpKcyeDWvWQFkZtG/PutJSfnTZZbRr1y7V0WW9hNT5u/siYFG1ebeFvb8wEfsRkTRQXAzTpkFRqF3HoUPHF30TuKBVK/j8c5g6FYYMSU2Moid8RSSBZsyAYcOgsDBI+mGJHyAXaHbkSLB82LCgvKSEevUUkcSYMQMmT4aDB+su6x6Umzw5mFb/Po1OV/4iEr/i4qiJfyPQGrgy0npVJ4CVK5McoFSn5C8i8Zs2DcrLIy66Dqi1Zr+8PFhfGpWSv4jEp7Q0uLnrXmPRPOBk4ILa1neHRYtg164kBSiRKPmLSHxmz444+3PgNuDh+mzDLOp2JDmU/EUkPmvW1GjVA/BLgo686tWLY3k5rF2b4MCkNmrtIyLxKSurMWs18ApBX+71tndvggKS+lDyF5H4tG9fY9ZrwEdAVfeMB4CjwDrgnSibOdq+Pc0TH51EoWofEYnPoEF469YnzJoIbCL4BrAa+CnwbYI+YCI5ZMYdL77ImDFjeOyxx9i2bVsyIxaU/EUkDu5OUZcuHDl8Yke9eUDXsFdbgrb+0frqbZ2Tw7+98w6jR49m2bJlDB48mIEDB3LDDTewbNkyjhw5ksxfIyuZR2ie1RQUFBT4Sj34IdJkFRcXM3nyZPbs2cMrJ51El7fewhqST8xg9GiYP//4rKNHj1JcXExRURFFRUVs2LCB888/nxEjRjBixAh69epVywazm5mtcveCOssp+YtILLZs2cLNN9/MG2+8wZ133smECRNo8e67QV899enaobq8PHj9dSiInq927drFkiVLKCoq4uWXX6Zz587HTwTnnnsurVq1avgvlGHqm/xV7SMi9bJ3714mT55MQUEBX/3qV9mwYQPXXnstLVq0CHrnfPDBIJHHIi8vWK+WxA/QqVMnrrzySubOncvf/vY3nnjiCdq2bcvNN99Mp06dGDVqFDNnzuTjjz+O4zfMLkr+IlKrw4cP8/DDD3P66aezf/9+PvjgA2677TbatGlzYsFJk744AdQ1SpfZF4k/xk7dmjdvzte//nXuuOMO3n77bUpKShg7dixvvvkmQ4YMYcCAAfziF7/glVde4fDhiIMGCqr2EZEo3J3nn3+eqVOncsYZZ3DfffcxYMCAuldcuTLoq2fRoiDJh/f5k5sbdOdw6aVBf/51XPHH6tixY6xater4vYJ169bxrW9963gVUZ8+fRK6v6ZIdf4i0mB//vOfmTx5MpWVlTz44IOcf/75sW9k166gy4a1a4MHuDp0gIEDYcIEaKQxuvfs2cPLL79MUVERixcvJj8///iJ4LzzziMnJyfxO40wghmDBsE11zTK763kLyIxW79+PTfeeCOrV6/m3nvv5fvf/z7NmmVG7fCxY8d45513jn8reP/990/4VtC3b9/4dlDLCGbHv/GMGJH0EcyU/EWk3kpLS7nzzjt5/vnnueGGG7j++utpXe3BrUyzZ88eli5devxbQYcOHU74VhDT7181kE15ecTeTY8zC04EDbjXUV9q7SMJ8eyzz1JQUEDbtm3p1q0bI0aM4C9/+Uuqw5IEOXjwIPfeey8DBgygZcuWfPjhh0yZMiXjEz9Afn4+48aN46mnnmLHjh3MnTuX/Px87rzzTjp37szIkSP57W9/y+bNm2vfUPgIZqHE/xkwGmgDnAI8W1U2fASzVA9h6e5N8nXWWWe5pNZDDz3knTp18vnz5/uBAwf8yJEjvnDhQp88eXKqQ5M4VVZW+pNPPuk9e/b0K664wktKSlIdUpOyZ88enzdvnl999dXepUsX79+/v//85z/3xYsXe3l5+RcFV6xwz8tzD9L68dc48LHg+8H/DH4S+PvVynhenntxccJjB1Z6PXKsqn0g5TdomqKysjJ69OjBk08+yRVXXJHqcCSBXn75ZaZMmULbtm158MEHGTp0aKpDatKOHTvG6tWrj98rWLNmDeeeey4jRoxgwsKFtH3llROqev4OdADeB/qH5l0F9ACmh284wpPNiVDfap+UX+FHezXKlf+KFe6jR7u3bh28ws/KubnBvNGjg3JZpqioyJs3b+4VFRWpDkUS5L333vOLL77YTzvtNJ8/f74fO3Ys1SGlpc8++8yfe+45v/573/Py6lfz4O+A51ab9wD4yAhlvXVr99LShMZHPa/8s7fOf8aM4HH0wsLgrnz1wSjKy4N5hYVBuVTXzzWyPXv20LFjx+DpTUlr27dv54c//CEXXXQRI0eO5IMPPmDMmDFYXQ9iSUQdOnRg7Nix/L9/+AdyItwbOQCcVG1ee2B/pI2lcASz7Ez+EW7QRNWUbtA0ovz8fHbv3k1lZWWqQ5EG2r9/P7feeiuDBg2iS5cubNiwgeuvv1794CTKmjVYhBHM2hIMYRnuc6BdpG2kcASz7Ev+xcVfJP4wVwLdCM7Y/YHHq69XdQLIkuanQ4cOJScnh8LCwlSHIjGqqKhgxowZ9O/fn61bt/Luu+8ybdo02kcYdEXiEGEEMwjyRyWwMWzee8AZ0baTohHMEpL8zWy4ma03sxIzuynC8hwzey60/G0z65OI/TbItGknPm4eMpVg5KHPgYXArcCq6oXKy4P1s0D79u256667uO666ygsLOTgwYNUVFRQVFTEDTfckOrwJAJ3Z8GCBQwcOJD58+ezaNEi5syZQ+/eveteWWIX5WTaBhhDMHj934E3gQUEN30j6tAhCcHVQ31uDNT2ApoTDNpzKtCK4CQ3oFqZnwEzQ+/HAc/Vtd2k3PDdubPmjd0Irw/Bu4I/10g3aJqyZ555xs866yzPy8vzLl26+KWXXupvvvlmqsOSalasWOHnnXeen3HGGb5o0SLdzG0M990XNZ/sAR8FngfeC3xutHyTm+t+//0JDYt63vBNRPIfCiwJm54KTK1WZgkwNPS+BbCb0NPF0V5JSf61/LEcfFLoLj3gZ4ba6DbGHyuldu4Mjsv48e4jRwY/77svq05w6Wzz5s0+btw47969uz/22GNqndWY6nkxWesrzVv79AA+CZveFpoXsYy7VwJlQH4C9h2bNWtqtuoJ8zuCO/J/JvjaFrHLpxTeoEmo4mIYMwZOOQVuvx3mzoX/+Z/g5x13QO/ewfLi4lRHKhHU2re+NI7OnYO+ehraasos6N00Rc8SNakbvmY20cxWmtnKXbt2JX4HUW7QhGsOfJPgDBatbc++jz5i586dHDt2LIHBNSI1c01b4X3rHzhwIHrf+tI4pk4N+uppiNzcYP0UScRlwnYgfEDNnqF5kcpsM7MWBM1e91TfkLvPAmZB8IRvAmI7UQytHSoJbmRE8pf33+ear32N/fv306NHD3r37k2vXr1OeFXNa9++fdNqTx3ezLUu4c1cIWkdUUnd3E/sW/+1116rX9/6klxVI5jV93+qSj1HMEumRCT/YqCfmfUlSPLjgH+pVmYhcDWwHPgu8KdQ3VTjGjQoeJS62pVuKfAnYCSQC7wC/HfoVUNuLiOnTmXXlCkcPHiQbdu28cknnxx/vfPOOxQWFh6fdvcaJ4Tw6Z49e5IX69B3DRWlmSvAPOBOYCvQFZgNnFu1sOoEMGRISj+s2aqqb/2jR4/yxBNPMGzYsFSHJOGqLoqaSK+e9ZWQvn3M7FLgPwlqTZ5w93vN7C6CGw8Lzaw18DRwJkGHd+Pcvdau8pLSt09paVDHXS357yI4I70HHCPohe/fgB9H2kbr1rB1a73r6crKyti6desJJ4jw6W3bttG2bdtaTxDdu3enZcuW8fzmgTFjgqqcan/zpcC1wHPA2cCO0PwTbtwkqR8SiS68b/1f/epXjBs3LmP61s9IKRzBLJz6848mSgKslyQkQHdn165dtZ4gdu7cSadOnaKeIHr16kWXLl1qTwxRTnwA/wj8KPSqVYwnPmmYbOxbP6OkeASz+ib/7GsaMHUqLFkSW/1clSTcoDEzOnfuTOfOnSmIcjVQWVnJp59+esLJYfPmzbz22mvHp6t64Yx036FXr170e+klcoHqdx+OAiuBy4DTgEPA5cADBFVg1YINPtRTpiTwCDRdzz77LA8//DAffvgh7dq1Y/Dgwdxyyy1885vfTMr+Dh48yCOPPMIjjzzCVVddxYcffkh+fuM3ipM4deqUFv8j2Zf80/AGTYsWLejdu3etT2qWl5fXuP/w7rvvsnDhQj755BNuXb+ecRH66dkJVAAvEjRxbQmMAu4B7q25k8xo5loPDz/8MNOnT2fmzJlccskltGrVisWLF7NgwYKEJ/+jR4/y9NNP88tf/pKhQ4fy9ttv8+Uvfzmh+xCpoT4PA6TilfQunX/3u2AwBbPaH8IwC8r97nfJjSfZRo6M+Pt9FnqobXbYvBfBB0c7HiNHpvo3Sbp9+/Z5mzZt/Pnnn0/6vpYsWeKDBg3yc845x5cvX570/Unmo54PeWXflX+VSZOCbwFN4AZNo4jSzLUDQdvc8Oqg2hqmLlmxgheuvZbTTjvt+OvLX/4y7dpF7LMwLS1fvpxDhw4xevTopO1jzZo1TJkyhS1btjB9+nRGjx7dtJoES8bL3uQPQUKfPz/lN2gaRZRmrgDXAL8GhhNU+zxC0Oy1umOtW3PqqFEMOessSkpKePvttykpKWHTpk2cdNJJJ5wQwl8nn3xyUn+1RKt1LIM4R33bvn07v/zlL1m0aBG33norP/nJTxLTkkskRtnX2idb1dLapwL4OcEg062BscD9ofcniNLax93ZsWMHJSUlNV4bN24kJycn6okhPz+/yV3xLl68mJEjR3Lo0KEvTgDFxcG3xKKiYDr8OFZ9SxwxIviWOGRIjW3u37+f++67jxkzZjBx4kRuuukmdbEsSaGmnlJTCpq5eqgpa7QTg7tHPTF06dIlJSeGsrIyunfvzlNPPcV3v/vdL56KbsADPBUVFTz++OPcddddXHzxxdxzzz306tUr+jZE4qTkLzUVFwd99TSkmWteHrz+esLvfXz22WcRTwwlJSUcPHgw6omhe/fuSX3g6aGHHuL+++/n0csv5+Knn6ZleTmvAMsIvhXVKi8Pf/BBFnbvzo033kjPnj154IEHOPPMM5MWr0gVJX+JLJa+fapUNXNt5MfRy8rK2LRpU8QTw759+zj11FMjnhh69epF8+bN497/3Lvv5pE77uCvx47RDjgLuAW4GXiLL26Y9QDWV1v3ULNmXN23LxN+/WuGDx/e5Kq2JHMp+Ut0cVRjNBUHDhxg8+bNEU8MpaWl9OnTJ+KJ4ZRTTqn/DdYo1WTDCIb9vLaWVY+ZweWX0+wPf2jgbyjSMEr+Ursm0g9JMpSXl7Nly5aIJ4ZPP/2Unj17Rjwx9O3bl5yc0CgOtdwgH0bdyR9QdxiSEkr+Uj/Z0Mw1zJEjR/joo48inhi2bt1K165dOe2005h04ACXrVpFywhPRQ8DPiB4Ou50giehh0XaWW4u3HlnWjzqL5lDfftI/aRJPySJ0qpVK/r370///v1rLKusrGTr1q2UlJRwyq23Rkz8APcBAwgGrJ4HfAdYDdTokCGLusOQ9KP+YUVCWrRowamnnsrFF1/M6V26RC33daAdwTCfVwPnAIuiFd67N9FhiiSEkr9IJDE8gGUEVUARdeiQiGhEEk7JXySSQYOCG7bV7AOWEHR9XQnMBd4g6Bqjhtzc4P6JSBOk5C8SyYQJEWdXALcCnYCOBH0iFQI17yAQtJiKsh2RVFPyF4mkc+egr55qD2d1Ihi0ej/Bt4C3gIsirW8WNJXNwBZTkhmU/EWimTo1qLppiCSM+iaSSEr+ItFUjfqWlxfbeikc9U2kvtTOX6Q2Vd1apHl3GCLV6cpfpC6TJgU9mo4eHbQAql4VlJsbzB89OiinxC9pQFf+IvVRbdS3ebfeypjzz6dV584Z3R2GZC717SPSAG3btmXnzp20adMm1aGInKC+ffuo2kekAY4cOUKrVq1SHYZIgyn5i8TI3amsrIw8wLtImogr+ZvZl8xsqZltDP2s0ZGJmQ02s+Vm9oGZrTGz78WzT5FUq6iooEWLFhqdS9JavFf+NwGvuns/4NXQdHUHgR+4+xkEXaD8p5mdHOd+RVJGVT6SCeJN/qOAp0LvnwIur17A3Te4+8bQ+0+BUoKn5EXSkpK/ZIJ4k38Xd98Rev83IHon6ICZnU0wBsamOPcrkjJK/pIJ6rxjZWavAF0jLLolfMLd3cyiths1s27A08DV7n4sSpmJwESA3r171xWaSEpUVFQo+UvaqzP5u/uF0ZaZ2U4z6+buO0LJvTRKuZOA/wVucfe3atnXLGAWBO3864pNJBV05S+ZIN5qn4UEI9kR+rmgegEzawW8BMxx9xfj3J9Iyin5SyaIN/lPBy4ys43AhaFpzKzAzB4PlRkLnAdMMLPVodfgOPcrkjJK/pIJ4npKxd33ABdEmL8SuDb0/hngmXj2I9KUKPlLJtATviIxOnLkCC1btkx1GCJxUfIXiZGu/CUTKPmLxEjJXzKBkr9IjJT8JRMo+YvESMlfMoGSv0iM9ISvZAIlf5EY6cpfMoGSv0iMlPwlEyj5i8RI7fwlEyj5i8RIV/6SCZT8RWKk5C+ZQMlfJEZK/pIJlPxFYqTkL5lAyV8kRkr+kgmU/EVipOQvmUDJXyRGesJXMoGSv0iMdOUvmUDJXyRGeshLMoGSv0iMdOUvmUDJXyRGSv6SCZT8RWKk5C+ZQMlfJEZK/pIJlPxFYqTkL5lAyV8kRkr+kgmU/EVipOQvmUDJXyRGesJXMkFcyd/MvmRmS81sY+hnh1rKnmRm28zsN/HsUyTV9JCXZIJ4r/xvAl51937Aq6HpaO4G3ohzfyIpp2ofyQTxJv9RwFOh908Bl0cqZGZnAV2Al+Pcn0jKKflLJog3+Xdx9x2h938jSPAnMLNmwEPA5Lo2ZmYTzWylma3ctWtXnKGJJIeSv2SCFnUVMLNXgK4RFt0SPuHubmYeodzPgEXuvs3Mat2Xu88CZgEUFBRE2pZIyin5SyaoM/m7+4XRlpnZTjPr5u47zKwbUBqh2FDgXDP7GdAWaGVmB9y9tvsDIk2Wkr9kgjqTfx0WAlcD00M/F1Qv4O7jq96b2QSgQIlf0pmSv2SCeOv8pwMXmdlG4MLQNGZWYGaPxxucSFPj7mrqKRkhrit/d98DXBBh/krg2gjzZwOz49mnSCodPXqUZs2a0bx581SHIhIXPeErEoOKigpd9UtGUPIXiYHq+yVTKPmLxEDJXzKFkr9IDJT8JVMo+YvEQMlfMoWSv0gMlPwlU8T7kJdIdigthdmz6fzGGzz66adw5ZUwaBBccw106pTq6ERiZu5NswudgoICX7lyZarDkGxXXAzTpkFRUTB96NAXy3JzwR1GjICpU2HIkNTEKBLGzFa5e0Fd5VTtIxLNjBkwbBgUFgZJPzzxA5SXB/MKC4NyM2akIkqRBlG1j0gkM2bA5Mlw8GDdZd2DcpNDvZZPmpSE88wLAAAJnUlEQVTc2EQSQFf+ItUVF9dI/IeBHwGnAO2AwUBR9fWqTgCqrpQ0oOQvUt20aUGVTphKoBfwOlAG3AOMBT6qvm55ebC+SBOn5C8SrrQ0uLlbrSFEG+AOoA/BP81IoC+wqvr67rBoEWgkOmnilPxFws2eXa9iO4ENwBmRFprVezsiqaLkLxJuzZqarXqqqQDGE4xe9JVIBcrLYe3axMcmkkBK/iLhyspqXXwMuApoBfymtoJ79yYuJpEkUFNPkXDt20dd5AQtfnYCi4Bae/Xv0CGhYYkkmq78RcINGgStW0dcNAn4K/BHILe2beTmwsCBiY9NJIGU/EXCTZgQcfbHwKPAaqAr0Db0mhupsHvU7Yg0FUr+IuE6dw766jE7YfYpBNU+h4ADYa/x1dc3g0svVWdv0uQp+UvWmT17NgMHDiQvL4+uXbsyadIk9u3b90WBqVODqpuGyM0N1hdp4pT8Jas89NBD3HjjjTzwwAOUlZXx1ltv8fHHH3PRRRdx5MiRoNCQIfDgg5CXF9vG8/KC9Qrq7FBRJOWU/CVrfP7559x+++38+te/Zvjw4bRs2ZI+ffrw/PPP89FHH/HMM898UXjSpC9OANWqgGow+yLxq1M3SRNK/pI1/u///o9Dhw4xZsyYE+a3bduWSy+9lKVLl564wqRJ8PrrMHp00AKoelVQbm4wf/TooJwSv6QRtfOXrLF79246duxIixY1P/bdunVj1aoaPfUEVTjz5wd99cyeHTy5u3dv0I5/4MCgVY9u7koaUvKXrNGxY0d2795NZWVljRPAjh076NixY/SVO3WCKVOSHKFI44mr2sfMvmRmS81sY+hnxMcazay3mb1sZn81s3Vm1iee/Yo0xNChQ8nJyeEPf/jDCfMPHDhAUVERF1xwQYoiE2l88db53wS86u79gFdD05HMAR5w968CZwOlce5XJGbt27fn9ttv5/rrr2fx4sVUVFTw0UcfMXbsWHr27MlVV12V6hBFGk281T6jgGGh908BrwE3hhcwswFAC3dfCuDuB+Lcp0iD3XDDDeTn5zN58mQ2bdrESSedxOWXX87cuXPJyclJdXgijca82qAVMa1sts/dTw69N2Bv1XRYmcuBa4EjBONfvALc5O5HI2xvIjARoHfv3md9/PHHDY5NRCQbmdkqd6/zYZM6r/zN7BWC7kyquyV8wt3dzCKdSVoA5wJnAluB54AJwO+rF3T3WcAsgIKCgoaflUREpFZ1Jn93vzDaMjPbaWbd3H2HmXUjcl3+NmC1u28OrVMIfIMIyV9ERBpHvDd8FxIMaETo54IIZYqBk82sqjH0PwHr4tyviIjEId7kPx24yMw2AheGpjGzAjN7HCBUtz8ZeNXM1gIGPBbnfkVEJA5x3fBNJjPbRdCNemPrCOxOwX7rorhi01TjgqYbm+KKXVOM7RR3r/Ox8yab/FPFzFbW5055Y1NcsWmqcUHTjU1xxa4px1YXdewmIpKFlPxFRLKQkn9Ns1IdQBSKKzZNNS5ourEprtg15dhqpTp/EZEspCt/EZEslHXJ38yuMLMPzOyYmUW9S29mw81svZmVmNlNYfP7mtnbofnPmVmrBMZWZxfZZna+ma0Oex0K9Z+Emc02sy1hywY3VlyhckfD9r0wbH5Sjlk9j9dgM1se+puvMbPvhS1L6PGK9pkJW54T+v1LQsejT9iyqaH5683sknjiaEBc/xHqan2Nmb1qZqeELYv4N23E2CaY2a6wGK4NW3Z16G+/0cyurr5ukuN6JCymDWa2L2xZUo9Zwrh7Vr2ArwKnE/RAWhClTHNgE3Aq0Ap4DxgQWvY8MC70fiYwKYGx3U/Q6R0E3WPfV0f5LwGfAXmh6dnAd5NwzOoVF3AgyvykHLP6xAX0B/qF3ncHdgAnJ/p41faZCSvzM2Bm6P044LnQ+wGh8jkEnR9uApo3Ylznh32GJlXFVdvftBFjmwD8JsK6XwI2h352CL3v0FhxVSt/PfBEYxyzRL6y7srf3f/q7uvrKHY2UOLum939CDAPGGVmRtA9xYuhck8BlycwvFGhbdZ3298Fitz9YAJjiCTWuI5L8jGrMy533+DuG0PvPyXofyoZ4y5G/MzUEu+LwAWh4zMKmOfuh919C1AS2l6jxOXuy8I+Q28BPRO077hjq8UlwFJ3/8zd9wJLgeEpiuv7wH8naN+NJuuSfz31AD4Jm94WmpcP7HP3ymrzE6WLu+8Ivf8b0KWO8uOo+aG7N/T1/REzS1QH9fWNq7WZrTSzt6qqokjuMYvpeJnZ2QRXcpvCZifqeEX7zEQsEzoeZQTHpz7rJjOucD8CisKmI/1NE6W+sf1z6G/0opn1inHdZMZFqIqsL/CnsNnJPGYJk5Fj+Fot3VC7e6TO5xpNbbGFT7hH7SK7ajvdgIHAkrDZUwmSYCuCJmg3Anc1YlynuPt2MzsV+JMFfTmV1Wf/SY6r6ng9DVzt7sdCsxt8vDKRmV0JFADfCptd42/q7psibyEp/gj8t7sfNrOfEHxz+qdG3H9dxgEv+onjk6T6mNVLRiZ/r6Ub6nraDvQKm+4ZmreHoIfSFqErt6r5CYnN6tdFdpWxwEvuXhG27aqr4MNm9iRBh3qNFpe7bw/93GxmrxGM4TCfOI5ZIuIys5OA/yU4+b8Vtu0GH68Ion1mIpXZZmYtgPYEn6n6rJvMuDCzCwlOqN9y98NV86P8TROVyOqMzd33hE0+TnCfp2rdYdXWfa2x4gozDrgufEaSj1nCqNonsmKgnwWtVFoR/IEXenA3ZxlBXTtE78a6oerTRXaVGvWMoQRYVc9+OfB+Y8VlZh2qqk3MrCNwDrAuycesPnG1Al4C5rj7i9WWJfJ4RfzM1BLvd4E/hY7PQmBcqDVQX6AfsCKOWGKKy8zOBB4FLnP30rD5Ef+mCYqrvrF1C5u8DPhr6P0S4OJQjB2AiznxW3BS4wrF9hWCm83Lw+Yl+5glTqrvODf2CxhNUId3GNgJLAnN7w4sCit3KbCB4Ix9S9j8Uwn+MUuAF4CcBMaWD7wKbCQY7vJLofkFwONh5foQXIk0q7b+n4C1BEnsGaBtY8UF/GNo3++Ffv4o2cesnnFdCVQAq8Neg5NxvCJ9ZgiqkS4LvW8d+v1LQsfj1LB1bwmttx4YkeDPfF1xvRL6X6g6Pgvr+ps2YmzTgA9CMSwDvhK27g9Dx7IEuKYx4wpN3wFMr7Ze0o9Zol56wldEJAup2kdEJAsp+YuIZCElfxGRLKTkLyKShZT8RUSykJK/iEgWUvIXEclCSv4iIlno/wNHn92ZBgkRVQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7788e0e390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'CC'}), (1, {'label': 'CC'}), (2, {'label': 'CC'}), (3, {'label': 'CO'}), (4, {'label': 'CCCC'}), (5, {'label': 'CCCO'}), (6, {'label': 'OCC'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773c95a5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': '0'}), (1, {'label': '0'}), (2, {'label': '0'}), (3, {'label': '3'}), (4, {'label': '4'}), (5, {'label': '1'}), (6, {'label': '2'})]\n",
-      "--- shortest path kernel built in 0.00026607513427734375 seconds ---\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "import networkx as nx\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "def weisfeilerlehman_test(G):\n",
-    "    '''\n",
-    "    Weisfeiler-Lehman test of graph isomorphism.\n",
-    "    '''\n",
-    "\n",
-    "    nx.draw_networkx(G)\n",
-    "    plt.show()\n",
-    "    nx.draw_networkx_labels(G, nx.spring_layout(G), labels = nx.get_node_attributes(G,'label'))\n",
-    "    print(G.nodes(data = True))\n",
-    "    \n",
-    "    set_multisets = []\n",
-    "    for node in G.nodes(data = True):\n",
-    "        # Multiset-label determination.\n",
-    "        multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
-    "        # sorting each multiset\n",
-    "        multiset.sort()\n",
-    "        multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
-    "        set_multisets.append(multiset)\n",
-    "        \n",
-    "    # label compression\n",
-    "#     set_multisets.sort() # this is unnecessary\n",
-    "    set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
-    "    set_compressed = { value : str(set_unique.index(value)) for value in set_unique } # assign indices as the new labels\n",
-    "#     print(set_compressed)\n",
-    "#     print(set_multisets)\n",
-    "    \n",
-    "    # relabel nodes with multisets\n",
-    "    for node in G.nodes(data = True):\n",
-    "        node[1]['label'] = set_multisets[node[0]]\n",
-    "    print(' -> ')\n",
-    "    nx.draw_networkx(G)\n",
-    "    plt.show()\n",
-    "    print(G.nodes(data = True))\n",
-    "\n",
-    "    \n",
-    "    # relabel nodes\n",
-    "    for node in G.nodes(data = True):\n",
-    "        node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
-    "        \n",
-    "    print(' -> ')\n",
-    "    nx.draw_networkx(G)\n",
-    "    plt.show()\n",
-    "    print(G.nodes(data = True))\n",
-    "\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "G1 = dataset[12]\n",
-    "G2 = dataset[55]\n",
-    "\n",
-    "# init.\n",
-    "kernel = 0 # init kernel\n",
-    "num_nodes1 = G1.number_of_nodes()\n",
-    "num_nodes2 = G2.number_of_nodes()\n",
-    "\n",
-    "# the first iteration.\n",
-    "labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "print(labelset1)\n",
-    "print(labelset2)\n",
-    "kernel += spkernel(G1, G2)\n",
-    "print(kernel)\n",
-    "\n",
-    "\n",
-    "\n",
-    "for height in range(0, min(num_nodes1, num_nodes2)): #Q how to determine the upper bound of the height?\n",
-    "    if labelset1 != labelset2:\n",
-    "        break\n",
-    "        \n",
-    "    # Weisfeiler-Lehman test of graph isomorphism.\n",
-    "    weisfeilerlehman_test(G1)\n",
-    "    weisfeilerlehman_test(G2)\n",
-    "    \n",
-    "    # calculate kernel\n",
-    "    kernel += spkernel(G1, G2)\n",
-    "    \n",
-    "    # get label sets of both graphs\n",
-    "    labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "    labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "#     print(labelset1)\n",
-    "#     print(labelset2)\n",
-    "\n",
-    "print(kernel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'O', 6: 'O'}\n",
-      "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'C', 6: 'S', 7: 'S'}\n",
-      "\n",
-      " --- height = 0 --- \n",
-      "\n",
-      " --- for graph 0 --- \n",
-      "\n",
-      "labels_ori: ['C', 'C', 'C', 'C', 'C', 'O', 'O']\n",
-      "all_labels_ori: {'C', 'O'}\n",
-      "num_of_each_label: {'C': 5, 'O': 2}\n",
-      "all_num_of_each_label: [{'C': 5, 'O': 2}]\n",
-      "num_of_labels: 2\n",
-      "all_labels_ori: {'C', 'O'}\n",
-      "\n",
-      " --- for graph 1 --- \n",
-      "\n",
-      "labels_ori: ['C', 'C', 'C', 'C', 'C', 'C', 'S', 'S']\n",
-      "all_labels_ori: {'C', 'O', 'S'}\n",
-      "num_of_each_label: {'C': 6, 'S': 2}\n",
-      "all_num_of_each_label: [{'C': 5, 'O': 2}, {'C': 6, 'S': 2}]\n",
-      "num_of_labels: 2\n",
-      "all_labels_ori: {'C', 'O', 'S'}\n",
-      "\n",
-      " all_num_of_labels_occured: 3\n",
-      "\n",
-      " --- calculating kernel matrix ---\n",
-      "\n",
-      " labels: {'C', 'O'}\n",
-      "vector1: [[5 2]]\n",
-      "vector2: [[5 2]]\n",
-      "Kmatrix: [[ 29.   0.]\n",
-      " [  0.   0.]]\n",
-      "\n",
-      " labels: {'C', 'O', 'S'}\n",
-      "vector1: [[5 2 0]]\n",
-      "vector2: [[6 0 2]]\n",
-      "Kmatrix: [[ 29.  30.]\n",
-      " [ 30.   0.]]\n",
-      "\n",
-      " labels: {'C', 'S'}\n",
-      "vector1: [[6 2]]\n",
-      "vector2: [[6 2]]\n",
-      "Kmatrix: [[ 29.  30.]\n",
-      " [ 30.  40.]]\n",
-      "\n",
-      " --- height = 1 --- \n",
-      "\n",
-      " --- for graph 0 --- \n",
-      "\n",
-      "multiset: ['CC', 'CC', 'CCO', 'CCO', 'COO', 'OCC', 'OCC']\n",
-      "set_unique: ['OCC', 'COO', 'CCO', 'CC']\n",
-      "set_compressed: {'OCC': '4', 'COO': '5', 'CCO': '6', 'CC': '7'}\n",
-      "all_set_compressed: {'OCC': '4', 'COO': '5', 'CCO': '6', 'CC': '7'}\n",
-      "num_of_labels_occured: 7\n",
-      "\n",
-      " compressed labels: {0: '7', 1: '7', 2: '6', 3: '6', 4: '5', 5: '4', 6: '4'}\n",
-      "labels_comp: ['7', '7', '6', '6', '5', '4', '4']\n",
-      "all_labels_ori: {'5', '4', '6', '7'}\n",
-      "num_of_each_label: {'5': 1, '4': 2, '6': 2, '7': 2}\n",
-      "all_num_of_each_label: [{'5': 1, '4': 2, '6': 2, '7': 2}]\n",
-      "\n",
-      " --- for graph 1 --- \n",
-      "\n",
-      "multiset: ['CC', 'CC', 'CC', 'CCS', 'CCS', 'CCSS', 'SCC', 'SCC']\n",
-      "set_unique: ['SCC', 'CC', 'CCS', 'CCSS']\n",
-      "set_compressed: {'SCC': '8', 'CC': '7', 'CCS': '9', 'CCSS': '10'}\n",
-      "all_set_compressed: {'SCC': '8', 'COO': '5', 'CCS': '9', 'OCC': '4', 'CCO': '6', 'CCSS': '10', 'CC': '7'}\n",
-      "num_of_labels_occured: 10\n",
-      "\n",
-      " compressed labels: {0: '7', 1: '7', 2: '7', 3: '9', 4: '9', 5: '10', 6: '8', 7: '8'}\n",
-      "labels_comp: ['7', '7', '7', '9', '9', '10', '8', '8']\n",
-      "all_labels_ori: {'10', '4', '7', '9', '6', '5', '8'}\n",
-      "num_of_each_label: {'10': 1, '9': 2, '7': 3, '8': 2}\n",
-      "all_num_of_each_label: [{'5': 1, '4': 2, '6': 2, '7': 2}, {'10': 1, '9': 2, '7': 3, '8': 2}]\n",
-      "\n",
-      " all_num_of_labels_occured: 10\n",
-      "\n",
-      " --- calculating kernel matrix ---\n",
-      "\n",
-      " labels: {'5', '4', '6', '7'}\n",
-      "vector1: [[1 2 2 2]]\n",
-      "vector2: [[1 2 2 2]]\n",
-      "\n",
-      " labels: {'10', '4', '7', '9', '6', '5', '8'}\n",
-      "vector1: [[0 2 2 0 2 1 0]]\n",
-      "vector2: [[1 0 3 2 0 0 2]]\n",
-      "\n",
-      " labels: {'8', '10', '7', '9'}\n",
-      "vector1: [[2 1 3 2]]\n",
-      "vector2: [[2 1 3 2]]\n",
-      "\n",
-      " Kmatrix: [[ 42.  36.]\n",
-      " [ 36.  58.]]\n",
-      "\n",
-      " --- height = 2 --- \n",
-      "\n",
-      " --- for graph 0 --- \n",
-      "\n",
-      "multiset: ['76', '76', '647', '647', '544', '456', '456']\n",
-      "set_unique: ['647', '76', '456', '544']\n",
-      "set_compressed: {'647': '11', '76': '12', '544': '14', '456': '13'}\n",
-      "all_set_compressed: {'647': '11', '76': '12', '456': '13', '544': '14'}\n",
-      "num_of_labels_occured: 14\n",
-      "\n",
-      " compressed labels: {0: '12', 1: '12', 2: '11', 3: '11', 4: '14', 5: '13', 6: '13'}\n",
-      "labels_comp: ['12', '12', '11', '11', '14', '13', '13']\n",
-      "all_labels_ori: {'14', '12', '11', '13'}\n",
-      "num_of_each_label: {'14': 1, '13': 2, '12': 2, '11': 2}\n",
-      "all_num_of_each_label: [{'14': 1, '13': 2, '12': 2, '11': 2}]\n",
-      "\n",
-      " --- for graph 1 --- \n",
-      "\n",
-      "multiset: ['79', '79', '710', '978', '978', '10788', '8109', '8109']\n",
-      "set_unique: ['710', '8109', '79', '10788', '978']\n",
-      "set_compressed: {'710': '15', '79': '17', '8109': '16', '978': '19', '10788': '18'}\n",
-      "all_set_compressed: {'710': '15', '79': '17', '978': '19', '10788': '18', '8109': '16', '456': '13', '544': '14', '647': '11', '76': '12'}\n",
-      "num_of_labels_occured: 19\n",
-      "\n",
-      " compressed labels: {0: '17', 1: '17', 2: '15', 3: '19', 4: '19', 5: '18', 6: '16', 7: '16'}\n",
-      "labels_comp: ['17', '17', '15', '19', '19', '18', '16', '16']\n",
-      "all_labels_ori: {'18', '19', '12', '13', '17', '11', '14', '16', '15'}\n",
-      "num_of_each_label: {'15': 1, '17': 2, '19': 2, '16': 2, '18': 1}\n",
-      "all_num_of_each_label: [{'14': 1, '13': 2, '12': 2, '11': 2}, {'15': 1, '17': 2, '19': 2, '16': 2, '18': 1}]\n",
-      "\n",
-      " all_num_of_labels_occured: 19\n",
-      "\n",
-      " --- calculating kernel matrix ---\n",
-      "\n",
-      " labels: {'14', '12', '11', '13'}\n",
-      "vector1: [[1 2 2 2]]\n",
-      "vector2: [[1 2 2 2]]\n",
-      "\n",
-      " labels: {'18', '19', '12', '13', '17', '11', '14', '16', '15'}\n",
-      "vector1: [[0 0 2 2 0 2 1 0 0]]\n",
-      "vector2: [[1 2 0 0 2 0 0 2 1]]\n",
-      "\n",
-      " labels: {'18', '17', '15', '16', '19'}\n",
-      "vector1: [[1 2 1 2 2]]\n",
-      "vector2: [[1 2 1 2 2]]\n",
-      "\n",
-      " Kmatrix: [[ 55.  36.]\n",
-      " [ 36.  72.]]\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel built in 0.0034377574920654297 seconds ---\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 55.,  36.],\n",
-       "       [ 36.,  72.]])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# test of WL subtree kernel on many graphs\n",
-    "\n",
-    "import sys\n",
-    "import pathlib\n",
-    "from collections import Counter\n",
-    "sys.path.insert(0, \"../\")\n",
-    "\n",
-    "import networkx as nx\n",
-    "import numpy as np\n",
-    "import time\n",
-    "\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "from pygraph.kernels.pathKernel import pathkernel\n",
-    "\n",
-    "def weisfeilerlehmankernel(*args, height = 0, base_kernel = 'subtree'):\n",
-    "    \"\"\"Calculate Weisfeiler-Lehman kernels between graphs.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    Gn : List of NetworkX graph\n",
-    "        List of graphs between which the kernels are calculated.\n",
-    "    /\n",
-    "    G1, G2 : NetworkX graphs\n",
-    "        2 graphs between which the kernel is calculated.\n",
-    "        \n",
-    "    height : subtree height\n",
-    "    \n",
-    "    base_kernel : base kernel used in each iteration of WL kernel\n",
-    "        the default base kernel is subtree kernel\n",
-    "        \n",
-    "    Return\n",
-    "    ------\n",
-    "    Kmatrix/Kernel : Numpy matrix/int\n",
-    "        Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. / Weisfeiler-Lehman Kernel between 2 graphs.\n",
-    "        \n",
-    "    Notes\n",
-    "    -----\n",
-    "    This function now supports WL subtree kernel and WL shortest path kernel.\n",
-    "    \n",
-    "    References\n",
-    "    ----------\n",
-    "    [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. 2011;12(Sep):2539-61.\n",
-    "    \"\"\"\n",
-    "    if len(args) == 1: # for a list of graphs\n",
-    "\n",
-    "#         print(args)\n",
-    "        start_time = time.time()\n",
-    "        \n",
-    "        # for WL subtree kernel\n",
-    "        if base_kernel == 'subtree':           \n",
-    "            Kmatrix = _wl_subtreekernel_do(args[0], height = height, base_kernel = 'subtree')\n",
-    "            \n",
-    "        # for WL edge kernel\n",
-    "        elif base_kernel == 'edge':\n",
-    "            print('edge')\n",
-    "            \n",
-    "        # for WL shortest path kernel\n",
-    "        elif base_kernel == 'sp':\n",
-    "            Gn = args[0]\n",
-    "            Kmatrix = np.zeros((len(Gn), len(Gn)))\n",
-    "            \n",
-    "            for i in range(0, len(Gn)):\n",
-    "                for j in range(i, len(Gn)):\n",
-    "                    Kmatrix[i][j] = _weisfeilerlehmankernel_do(Gn[i], Gn[j])\n",
-    "                    Kmatrix[j][i] = Kmatrix[i][j]\n",
-    "\n",
-    "        print(\"\\n --- Weisfeiler-Lehman %s kernel matrix of size %d built in %s seconds ---\" % (base_kernel, len(args[0]), (time.time() - start_time)))\n",
-    "        \n",
-    "        return Kmatrix\n",
-    "        \n",
-    "    else: # for only 2 graphs\n",
-    "        \n",
-    "        start_time = time.time()\n",
-    "        \n",
-    "        # for WL subtree kernel\n",
-    "        if base_kernel == 'subtree':\n",
-    "            \n",
-    "            args = [args[0], args[1]]\n",
-    "#             print(args)\n",
-    "            kernel = _wl_subtreekernel_do(args, height = height, base_kernel = 'subtree')\n",
-    "            \n",
-    "        # for WL edge kernel\n",
-    "        elif base_kernel == 'edge':\n",
-    "            print('edge')\n",
-    "            \n",
-    "        # for WL shortest path kernel\n",
-    "        elif base_kernel == 'sp':\n",
-    "            \n",
-    "\n",
-    "            kernel = _pathkernel_do(args[0], args[1])\n",
-    "\n",
-    "        print(\"\\n --- Weisfeiler-Lehman %s kernel built in %s seconds ---\" % (base_kernel, time.time() - start_time))\n",
-    "        \n",
-    "        return kernel\n",
-    "    \n",
-    "    \n",
-    "def _weisfeilerlehmankernel_do(G1, G2):\n",
-    "    \"\"\"Calculate Weisfeiler-Lehman kernels between 2 graphs. This kernel use shortest path kernel to calculate kernel between two graphs in each iteration.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    G1, G2 : NetworkX graphs\n",
-    "        2 graphs between which the kernel is calculated.\n",
-    "        \n",
-    "    Return\n",
-    "    ------\n",
-    "    Kernel : int\n",
-    "        Weisfeiler-Lehman Kernel between 2 graphs.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    # init.\n",
-    "    kernel = 0 # init kernel\n",
-    "    num_nodes1 = G1.number_of_nodes()\n",
-    "    num_nodes2 = G2.number_of_nodes()\n",
-    "    height = 12 #min(num_nodes1, num_nodes2)) #Q how to determine the upper bound of the height?\n",
-    "    \n",
-    "    # the first iteration.\n",
-    "    labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "    labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "    kernel += pathkernel(G1, G2) # change your base kernel here (and one more below)\n",
-    "    \n",
-    "    for h in range(0, height):\n",
-    "#         if labelset1 != labelset2:\n",
-    "#             break\n",
-    "\n",
-    "        # Weisfeiler-Lehman test of graph isomorphism.\n",
-    "        relabel(G1)\n",
-    "        relabel(G2)\n",
-    "\n",
-    "        # calculate kernel\n",
-    "        kernel += pathkernel(G1, G2) # change your base kernel here (and one more before)\n",
-    "\n",
-    "        # get label sets of both graphs\n",
-    "        labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "        labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "    \n",
-    "    return kernel\n",
-    "\n",
-    "\n",
-    "def relabel(G):\n",
-    "    '''\n",
-    "    Relabel nodes in graph G in one iteration of the 1-dim. WL test of graph isomorphism.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    G : NetworkX graph\n",
-    "        The graphs whose nodes are relabeled.\n",
-    "    '''\n",
-    "    \n",
-    "    # get the set of original labels\n",
-    "    labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
-    "    print(labels_ori)\n",
-    "    num_of_each_label = dict(Counter(labels_ori))\n",
-    "    print(num_of_each_label)\n",
-    "    num_of_labels = len(num_of_each_label)\n",
-    "    print(num_of_labels)\n",
-    "    \n",
-    "    set_multisets = []\n",
-    "    for node in G.nodes(data = True):\n",
-    "        # Multiset-label determination.\n",
-    "        multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
-    "        # sorting each multiset\n",
-    "        multiset.sort()\n",
-    "        multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
-    "        set_multisets.append(multiset)\n",
-    "    print(set_multisets)\n",
-    "        \n",
-    "    # label compression\n",
-    "#     set_multisets.sort() # this is unnecessary\n",
-    "    set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
-    "    print(set_unique)\n",
-    "    set_compressed = { value : str(set_unique.index(value) + num_of_labels + 1) for value in set_unique } # assign new labels\n",
-    "    print(set_compressed)\n",
-    "    \n",
-    "    # relabel nodes\n",
-    "#     nx.relabel_nodes(G, set_compressed, copy = False)\n",
-    "    for node in G.nodes(data = True):\n",
-    "        node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
-    "    print(nx.get_node_attributes(G, 'label'))\n",
-    "\n",
-    "    # get the set of compressed labels\n",
-    "    labels_comp = list(nx.get_node_attributes(G, 'label').values())\n",
-    "    print(labels_comp)\n",
-    "    num_of_each_label.update(dict(Counter(labels_comp)))\n",
-    "    print(num_of_each_label)\n",
-    "    \n",
-    "    \n",
-    "def _wl_subtreekernel_do(*args, height = 0, base_kernel = 'subtree'):\n",
-    "    \"\"\"Calculate Weisfeiler-Lehman subtree kernels between graphs.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    Gn : List of NetworkX graph\n",
-    "        List of graphs between which the kernels are calculated.\n",
-    "        \n",
-    "    Return\n",
-    "    ------\n",
-    "    Kmatrix/Kernel : Numpy matrix/int\n",
-    "        Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "#     print(args)\n",
-    "    Gn = args[0]\n",
-    "#     print(Gn)\n",
-    "\n",
-    "    Kmatrix = np.zeros((len(Gn), len(Gn)))\n",
-    "    all_num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs\n",
-    "    \n",
-    "    # initial for height = 0\n",
-    "    print('\\n --- height = 0 --- ')\n",
-    "    all_labels_ori = set() # all unique orignal labels in all graphs in this iteration\n",
-    "    all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration\n",
-    "    all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration\n",
-    "    num_of_labels_occured = all_num_of_labels_occured # number of the set of letters that occur before as node labels at least once in all graphs\n",
-    "\n",
-    "    # for each graph\n",
-    "    for idx, G in enumerate(Gn):\n",
-    "        # get the set of original labels\n",
-    "        print('\\n --- for graph %d --- \\n' % (idx))\n",
-    "        labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
-    "        print('labels_ori: %s' % (labels_ori))\n",
-    "        all_labels_ori.update(labels_ori)\n",
-    "        print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "        num_of_each_label = dict(Counter(labels_ori)) # number of occurence of each label in graph\n",
-    "        print('num_of_each_label: %s' % (num_of_each_label))\n",
-    "        all_num_of_each_label.append(num_of_each_label)\n",
-    "        print('all_num_of_each_label: %s' % (all_num_of_each_label))\n",
-    "        num_of_labels = len(num_of_each_label) # number of all unique labels\n",
-    "        print('num_of_labels: %s' % (num_of_labels))\n",
-    "        \n",
-    "\n",
-    "        all_labels_ori.update(labels_ori)\n",
-    "        print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "        \n",
-    "    all_num_of_labels_occured += len(all_labels_ori)\n",
-    "    print('\\n all_num_of_labels_occured: %s' % (all_num_of_labels_occured))\n",
-    "        \n",
-    "    # calculate subtree kernel with the 0th iteration and add it to the final kernel\n",
-    "    print('\\n --- calculating kernel matrix ---')\n",
-    "    for i in range(0, len(Gn)):\n",
-    "        for j in range(i, len(Gn)):\n",
-    "            labels = set(list(all_num_of_each_label[i].keys()) + list(all_num_of_each_label[j].keys()))\n",
-    "            print('\\n labels: %s' % (labels))\n",
-    "            vector1 = np.matrix([ (all_num_of_each_label[i][label] if (label in all_num_of_each_label[i].keys()) else 0) for label in labels ])\n",
-    "            vector2 = np.matrix([ (all_num_of_each_label[j][label] if (label in all_num_of_each_label[j].keys()) else 0) for label in labels ])\n",
-    "            print('vector1: %s' % (vector1))\n",
-    "            print('vector2: %s' % (vector2))\n",
-    "            Kmatrix[i][j] += np.dot(vector1, vector2.transpose())\n",
-    "            Kmatrix[j][i] = Kmatrix[i][j]\n",
-    "            print('Kmatrix: %s' % (Kmatrix))\n",
-    "\n",
-    "    \n",
-    "    # iterate each height\n",
-    "    for h in range(1, height + 1):\n",
-    "        print('\\n --- height = %d --- ' % (h))\n",
-    "        all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration\n",
-    "        num_of_labels_occured = all_num_of_labels_occured # number of the set of letters that occur before as node labels at least once in all graphs\n",
-    "        all_labels_ori = set()\n",
-    "        all_num_of_each_label = []\n",
-    "        \n",
-    "        # for each graph\n",
-    "        for idx, G in enumerate(Gn):\n",
-    "#             # get the set of original labels\n",
-    "            print('\\n --- for graph %d --- \\n' % (idx))\n",
-    "#             labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
-    "#             print('labels_ori: %s' % (labels_ori))\n",
-    "#             num_of_each_label = dict(Counter(labels_ori)) # number of occurence of each label in graph\n",
-    "#             print('num_of_each_label: %s' % (num_of_each_label))\n",
-    "#             num_of_labels = len(num_of_each_label) # number of all unique labels\n",
-    "#             print('num_of_labels: %s' % (num_of_labels))\n",
-    "            \n",
-    "#             all_labels_ori.update(labels_ori)\n",
-    "#             print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "#             #             num_of_labels_occured += num_of_labels #@todo not precise\n",
-    "#             num_of_labels_occured = all_num_of_labels_occured + len(all_labels_ori) + len(all_set_compressed)\n",
-    "#             print('num_of_labels_occured: %s' % (num_of_labels_occured))\n",
-    "            \n",
-    "            set_multisets = []\n",
-    "            for node in G.nodes(data = True):\n",
-    "                # Multiset-label determination.\n",
-    "                multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
-    "                # sorting each multiset\n",
-    "                multiset.sort()\n",
-    "                multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
-    "                set_multisets.append(multiset)\n",
-    "            print('multiset: %s' % (set_multisets))\n",
-    "\n",
-    "            # label compression\n",
-    "        #     set_multisets.sort() # this is unnecessary\n",
-    "            set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
-    "            print('set_unique: %s' % (set_unique))\n",
-    "            # a dictionary mapping original labels to new ones. \n",
-    "            set_compressed = {}\n",
-    "            # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label \n",
-    "            for value in set_unique:\n",
-    "                if value in all_set_compressed.keys():\n",
-    "                    set_compressed.update({ value : all_set_compressed[value] })\n",
-    "                else:\n",
-    "                    set_compressed.update({ value : str(num_of_labels_occured + 1) })\n",
-    "                    num_of_labels_occured += 1\n",
-    "#             set_compressed = { value : (all_set_compressed[value] if value in all_set_compressed.keys() else str(set_unique.index(value) + num_of_labels_occured + 1)) for value in set_unique }\n",
-    "            print('set_compressed: %s' % (set_compressed))\n",
-    "            \n",
-    "            all_set_compressed.update(set_compressed)\n",
-    "            print('all_set_compressed: %s' % (all_set_compressed))\n",
-    "#             num_of_labels_occured += len(set_compressed) #@todo not precise\n",
-    "            print('num_of_labels_occured: %s' % (num_of_labels_occured))\n",
-    "            \n",
-    "            # relabel nodes\n",
-    "        #     nx.relabel_nodes(G, set_compressed, copy = False)\n",
-    "            for node in G.nodes(data = True):\n",
-    "                node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
-    "            print('\\n compressed labels: %s' % (nx.get_node_attributes(G, 'label')))\n",
-    "\n",
-    "            # get the set of compressed labels\n",
-    "            labels_comp = list(nx.get_node_attributes(G, 'label').values())\n",
-    "            print('labels_comp: %s' % (labels_comp))\n",
-    "            all_labels_ori.update(labels_comp)\n",
-    "            print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "            num_of_each_label = dict(Counter(labels_comp))\n",
-    "            print('num_of_each_label: %s' % (num_of_each_label))\n",
-    "            all_num_of_each_label.append(num_of_each_label)\n",
-    "            print('all_num_of_each_label: %s' % (all_num_of_each_label))\n",
-    "                    \n",
-    "        all_num_of_labels_occured += len(all_labels_ori)\n",
-    "        print('\\n all_num_of_labels_occured: %s' % (all_num_of_labels_occured))\n",
-    "        \n",
-    "        # calculate subtree kernel with h iterations and add it to the final kernel\n",
-    "        print('\\n --- calculating kernel matrix ---')\n",
-    "        for i in range(0, len(Gn)):\n",
-    "            for j in range(i, len(Gn)):\n",
-    "                labels = set(list(all_num_of_each_label[i].keys()) + list(all_num_of_each_label[j].keys()))\n",
-    "                print('\\n labels: %s' % (labels))\n",
-    "                vector1 = np.matrix([ (all_num_of_each_label[i][label] if (label in all_num_of_each_label[i].keys()) else 0) for label in labels ])\n",
-    "                vector2 = np.matrix([ (all_num_of_each_label[j][label] if (label in all_num_of_each_label[j].keys()) else 0) for label in labels ])\n",
-    "                print('vector1: %s' % (vector1))\n",
-    "                print('vector2: %s' % (vector2))\n",
-    "                Kmatrix[i][j] += np.dot(vector1, vector2.transpose())\n",
-    "                Kmatrix[j][i] = Kmatrix[i][j]\n",
-    "                    \n",
-    "        print('\\n Kmatrix: %s' % (Kmatrix))\n",
-    "\n",
-    "    return Kmatrix\n",
-    "\n",
-    "    \n",
-    "# main\n",
-    "import sys\n",
-    "from collections import Counter\n",
-    "import networkx as nx\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "G1 = dataset[15]\n",
-    "print(nx.get_node_attributes(G1, 'label'))\n",
-    "G2 = dataset[80]\n",
-    "print(nx.get_node_attributes(G2, 'label'))\n",
-    "\n",
-    "weisfeilerlehmankernel(G1, G2, height = 2)\n",
-    "# Kmatrix = weisfeilerlehmankernel(G1, G2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "- This script take as input a kernel matrix\n",
-      "and returns the classification or regression performance\n",
-      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-      "correspond to the average of the performances on the test sets. \n",
-      "\n",
-      "@references\n",
-      "    Elisabetta Ghisu, https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-      "\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 0 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 0.3845643997192383 seconds ---\n",
-      "[[   5.    6.    4. ...,   20.   20.   20.]\n",
-      " [   6.    8.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.    5. ...,   21.   21.   21.]\n",
-      " ..., \n",
-      " [  20.   20.   21. ...,  101.  101.  101.]\n",
-      " [  20.   20.   21. ...,  101.  101.  101.]\n",
-      " [  20.   20.   21. ...,  101.  101.  101.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 141.418957\n",
-      "With standard deviation: 1.082842\n",
-      "\n",
-      " Mean performance on test set: 36.210792\n",
-      "With standard deviation: 7.331787\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 1 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 0.853447437286377 seconds ---\n",
-      "[[  10.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   16.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   10. ...,   22.   22.   24.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  130.  130.  122.]\n",
-      " [  20.   20.   22. ...,  130.  130.  122.]\n",
-      " [  20.   20.   24. ...,  122.  122.  154.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.065309\n",
-      "With standard deviation: 0.877976\n",
-      "\n",
-      " Mean performance on test set: 9.000982\n",
-      "With standard deviation: 6.371454\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 2 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 1.374389410018921 seconds ---\n",
-      "[[  15.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   24.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   15. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  159.  151.  124.]\n",
-      " [  20.   20.   22. ...,  151.  153.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  185.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.074983\n",
-      "With standard deviation: 0.928821\n",
-      "\n",
-      " Mean performance on test set: 19.811299\n",
-      "With standard deviation: 4.049105\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 3 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 1.9141185283660889 seconds ---\n",
-      "[[  20.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   32.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   20. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  188.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  168.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  202.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.197806\n",
-      "With standard deviation: 0.873857\n",
-      "\n",
-      " Mean performance on test set: 25.045500\n",
-      "With standard deviation: 4.942763\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 4 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 2.393263578414917 seconds ---\n",
-      "[[  25.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   40.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   25. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  217.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  183.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  213.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.272421\n",
-      "With standard deviation: 0.838915\n",
-      "\n",
-      " Mean performance on test set: 28.225454\n",
-      "With standard deviation: 6.521196\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 5 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 2.893545389175415 seconds ---\n",
-      "[[  30.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   48.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   30. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  246.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  198.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  224.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.247025\n",
-      "With standard deviation: 0.863630\n",
-      "\n",
-      " Mean performance on test set: 30.635436\n",
-      "With standard deviation: 6.736466\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 6 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 3.216407299041748 seconds ---\n",
-      "[[  35.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   56.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   35. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  275.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  213.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  235.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.239201\n",
-      "With standard deviation: 0.872475\n",
-      "\n",
-      " Mean performance on test set: 32.102695\n",
-      "With standard deviation: 6.856006\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 7 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 3.8147408962249756 seconds ---\n",
-      "[[  40.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   64.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   40. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  304.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  228.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  246.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.094026\n",
-      "With standard deviation: 0.917704\n",
-      "\n",
-      " Mean performance on test set: 32.970919\n",
-      "With standard deviation: 6.896061\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 8 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.3765342235565186 seconds ---\n",
-      "[[  45.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   72.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   45. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  333.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  243.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  257.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.076304\n",
-      "With standard deviation: 0.931866\n",
-      "\n",
-      " Mean performance on test set: 33.511228\n",
-      "With standard deviation: 6.907530\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 9 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.885462284088135 seconds ---\n",
-      "[[  50.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   80.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   50. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  362.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  258.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  268.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 139.913361\n",
-      "With standard deviation: 0.928974\n",
-      "\n",
-      " Mean performance on test set: 33.850152\n",
-      "With standard deviation: 6.914269\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 10 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 5.313802719116211 seconds ---\n",
-      "[[  55.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   88.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   55. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  391.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  273.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  279.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 139.894176\n",
-      "With standard deviation: 0.942612\n",
-      "\n",
-      " Mean performance on test set: 34.096283\n",
-      "With standard deviation: 6.931154\n",
-      "\n",
-      "\n",
-      "  height    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
-      "--------  -----------  ----------  ------------  -----------  --------\n",
-      "       0     36.2108      7.33179       141.419     1.08284   0.384564\n",
-      "       1      9.00098     6.37145       140.065     0.877976  0.853447\n",
-      "       2     19.8113      4.04911       140.075     0.928821  1.37439\n",
-      "       3     25.0455      4.94276       140.198     0.873857  1.91412\n",
-      "       4     28.2255      6.5212        140.272     0.838915  2.39326\n",
-      "       5     30.6354      6.73647       140.247     0.86363   2.89355\n",
-      "       6     32.1027      6.85601       140.239     0.872475  3.21641\n",
-      "       7     32.9709      6.89606       140.094     0.917704  3.81474\n",
-      "       8     33.5112      6.90753       140.076     0.931866  4.37653\n",
-      "       9     33.8502      6.91427       139.913     0.928974  4.88546\n",
-      "      10     34.0963      6.93115       139.894     0.942612  5.3138\n"
-     ]
-    }
-   ],
-   "source": [
-    "# test of WL subtree kernel\n",
-    "\n",
-    "\"\"\"\n",
-    "- This script take as input a kernel matrix\n",
-    "and returns the classification or regression performance\n",
-    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-    "correspond to the average of the performances on the test sets. \n",
-    "\n",
-    "@references\n",
-    "    Elisabetta Ghisu, https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-    "\"\"\"\n",
-    "\n",
-    "print(__doc__)\n",
-    "\n",
-    "import sys\n",
-    "import os\n",
-    "import pathlib\n",
-    "from collections import OrderedDict\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
-    "from sklearn import svm\n",
-    "\n",
-    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "\n",
-    "train_means_height = []\n",
-    "train_stds_height = []\n",
-    "test_means_height = []\n",
-    "test_stds_height = []\n",
-    "kernel_build_time = []\n",
-    "\n",
-    "for height in np.linspace(0, 10, 11):\n",
-    "    print('\\n --- calculating kernel matrix when subtree height = %d ---' % height)\n",
-    "\n",
-    "    print('\\n Loading dataset from file...')\n",
-    "    dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "    y = np.array(y)\n",
-    "    print(y)\n",
-    "\n",
-    "    # setup the parameters\n",
-    "    model_type = 'regression' # Regression or classification problem\n",
-    "    print('\\n --- This is a %s problem ---' % model_type)\n",
-    "\n",
-    "    datasize = len(dataset)\n",
-    "    trials = 100 # Trials for hyperparameters random search\n",
-    "    splits = 10 # Number of splits of the data\n",
-    "    alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
-    "    C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
-    "    random.seed(20) # Set the seed for uniform parameter distribution\n",
-    "\n",
-    "    # set the output path\n",
-    "    kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
-    "    if not os.path.exists(kernel_file_path):\n",
-    "        os.makedirs(kernel_file_path)\n",
-    "\n",
-    "    \"\"\"\n",
-    "    -  Here starts the main program\n",
-    "    -  First we permute the data, then for each split we evaluate corresponding performances\n",
-    "    -  In the end, the performances are averaged over the test sets\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # save kernel matrices to files / read kernel matrices from files\n",
-    "    kernel_file = kernel_file_path + 'km.ds'\n",
-    "    path = pathlib.Path(kernel_file)\n",
-    "    # get train set kernel matrix\n",
-    "    if path.is_file():\n",
-    "        print('\\n Loading the kernel matrix from file...')\n",
-    "        Kmatrix = np.loadtxt(kernel_file)\n",
-    "        print(Kmatrix)\n",
-    "    else:\n",
-    "        print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "        Kmatrix, run_time = weisfeilerlehmankernel(dataset, node_label = 'atom', height = int(height))\n",
-    "        kernel_build_time.append(run_time)\n",
-    "        print(Kmatrix)\n",
-    "        print('\\n Saving kernel matrix to file...')\n",
-    "    #     np.savetxt(kernel_file, Kmatrix)\n",
-    "\n",
-    "    # Initialize the performance of the best parameter trial on train with the corresponding performance on test\n",
-    "    train_split = []\n",
-    "    test_split = []\n",
-    "\n",
-    "    # For each split of the data\n",
-    "    for j in range(10, 10 + splits):\n",
-    "    #         print('\\n Starting split %d...' % j)\n",
-    "\n",
-    "        # Set the random set for data permutation\n",
-    "        random_state = int(j)\n",
-    "        np.random.seed(random_state)\n",
-    "        idx_perm = np.random.permutation(datasize)\n",
-    "    #     print(idx_perm)\n",
-    "\n",
-    "        # Permute the data\n",
-    "        y_perm = y[idx_perm] # targets permutation\n",
-    "    #     print(y_perm)\n",
-    "        Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
-    "    #     print(Kmatrix_perm)\n",
-    "        Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
-    "\n",
-    "        # Set the training, test\n",
-    "        # Note: the percentage can be set up by the user\n",
-    "        num_train = int((datasize * 90) / 100)         # 90% (of entire dataset) for training\n",
-    "        num_test = datasize - num_train             # 10% (of entire dataset) for test\n",
-    "\n",
-    "        # Split the kernel matrix\n",
-    "        Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
-    "        Kmatrix_test = Kmatrix_perm[num_train:datasize, 0:num_train]\n",
-    "\n",
-    "        # Split the targets\n",
-    "        y_train = y_perm[0:num_train]\n",
-    "\n",
-    "        # Normalization step (for real valued targets only)\n",
-    "        if model_type == 'regression':\n",
-    "    #             print('\\n Normalizing output y...')\n",
-    "            y_train_mean = np.mean(y_train)\n",
-    "            y_train_std = np.std(y_train)\n",
-    "            y_train = (y_train - y_train_mean) / float(y_train_std)\n",
-    "    #         print(y)\n",
-    "\n",
-    "        y_test = y_perm[num_train:datasize]\n",
-    "\n",
-    "        # Record the performance for each parameter trial respectively on train and test set\n",
-    "        perf_all_train = []\n",
-    "        perf_all_test = []\n",
-    "\n",
-    "        # For each parameter trial\n",
-    "        for i in range(trials):\n",
-    "            # For regression use the Kernel Ridge method\n",
-    "            if model_type == 'regression':\n",
-    "    #             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
-    "\n",
-    "                # Fit the kernel ridge model\n",
-    "                KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
-    "    #                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
-    "                KR.fit(Kmatrix_train, y_train)\n",
-    "\n",
-    "                # predict on the train and test set\n",
-    "                y_pred_train = KR.predict(Kmatrix_train)\n",
-    "                y_pred_test = KR.predict(Kmatrix_test)\n",
-    "    #             print(y_pred)\n",
-    "\n",
-    "                # adjust prediction: needed because the training targets have been normalizaed\n",
-    "                y_pred_train = y_pred_train * float(y_train_std) + y_train_mean\n",
-    "                y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
-    "    #             print(y_pred_test)\n",
-    "\n",
-    "                # root mean squared error in train set\n",
-    "                rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
-    "                perf_all_train.append(rmse_train)\n",
-    "                # root mean squared error in test set\n",
-    "                rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
-    "                perf_all_test.append(rmse_test)\n",
-    "    #             print('The performance on the test set is: %3f' % rmse_test)\n",
-    "\n",
-    "        # --- FIND THE OPTIMAL PARAMETERS --- #\n",
-    "        # For regression: minimise the mean squared error\n",
-    "        if model_type == 'regression':\n",
-    "\n",
-    "            # get optimal parameter on test (argmin mean squared error)\n",
-    "            min_idx = np.argmin(perf_all_test)\n",
-    "            alpha_opt = alpha_grid[min_idx]\n",
-    "\n",
-    "            # corresponding performance on train and test set for the same parameter\n",
-    "            perf_train_opt = perf_all_train[min_idx]\n",
-    "            perf_test_opt = perf_all_test[min_idx]\n",
-    "    #             print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
-    "    #             print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
-    "\n",
-    "        # append the correponding performance on the train and test set\n",
-    "        train_split.append(perf_train_opt)\n",
-    "        test_split.append(perf_test_opt)\n",
-    "\n",
-    "    # average the results\n",
-    "    # mean of the train and test performances over the splits\n",
-    "    train_mean = np.mean(np.asarray(train_split))\n",
-    "    test_mean = np.mean(np.asarray(test_split))\n",
-    "    # std deviation of the train and test over the splits\n",
-    "    train_std = np.std(np.asarray(train_split))\n",
-    "    test_std = np.std(np.asarray(test_split))\n",
-    "\n",
-    "    print('\\n Mean performance on train set: %3f' % train_mean)\n",
-    "    print('With standard deviation: %3f' % train_std)\n",
-    "    print('\\n Mean performance on test set: %3f' % test_mean)\n",
-    "    print('With standard deviation: %3f' % test_std)\n",
-    " \n",
-    "    train_means_height.append(train_mean)\n",
-    "    train_stds_height.append(train_std)\n",
-    "    test_means_height.append(test_mean)\n",
-    "    test_stds_height.append(test_std)\n",
-    "    \n",
-    "print('\\n') \n",
-    "table_dict = {'height': np.linspace(0, 10, 11), 'RMSE_test': test_means_height, 'std_test': test_stds_height, 'RMSE_train': train_means_height, 'std_train': train_stds_height, 'k_time': kernel_build_time}\n",
-    "keyorder = ['height', 'RMSE_test', 'std_test', 'RMSE_train', 'std_train', 'k_time']\n",
-    "print(tabulate(OrderedDict(sorted(table_dict.items(), key = lambda i:keyorder.index(i[0]))), headers='keys'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "- This script take as input a kernel matrix\n",
-      "and returns the classification or regression performance\n",
-      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-      "correspond to the average of the performances on the test sets. \n",
-      "\n",
-      "@references\n",
-      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-      "\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 0 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-2ce8cff340bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n Calculating kernel matrix, this could take a while...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0mKmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweisfeilerlehmankernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase_kernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'sp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n Saving kernel matrix to file...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/weisfeilerLehmanKernel.py\u001b[0m in \u001b[0;36mweisfeilerlehmankernel\u001b[0;34m(height, base_kernel, *args)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m                     \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_weisfeilerlehmankernel_do\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m                     \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/weisfeilerLehmanKernel.py\u001b[0m in \u001b[0;36m_weisfeilerlehmankernel_do\u001b[0;34m(G1, G2, height)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0;31m# calculate kernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mkernel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mspkernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# change your base kernel here (and one more before)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;31m# get label sets of both graphs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/spkernel.py\u001b[0m in \u001b[0;36mspkernel\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0me1\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mG1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0me2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m                     \u001b[0mkernel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "# Author: Elisabetta Ghisu\n",
-    "# test of WL subtree kernel\n",
-    "\n",
-    "\"\"\"\n",
-    "- This script take as input a kernel matrix\n",
-    "and returns the classification or regression performance\n",
-    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-    "correspond to the average of the performances on the test sets. \n",
-    "\n",
-    "@references\n",
-    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-    "\"\"\"\n",
-    "\n",
-    "print(__doc__)\n",
-    "\n",
-    "import sys\n",
-    "import os\n",
-    "import pathlib\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
-    "from sklearn import svm\n",
-    "\n",
-    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "\n",
-    "val_means_height = []\n",
-    "val_stds_height = []\n",
-    "test_means_height = []\n",
-    "test_stds_height = []\n",
-    "\n",
-    "\n",
-    "for height in np.linspace(0, 10, 11):\n",
-    "    print('\\n --- calculating kernel matrix when subtree height = %d ---' % height)\n",
-    "\n",
-    "    print('\\n Loading dataset from file...')\n",
-    "    dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "    y = np.array(y)\n",
-    "    print(y)\n",
-    "\n",
-    "    # setup the parameters\n",
-    "    model_type = 'regression' # Regression or classification problem\n",
-    "    print('\\n --- This is a %s problem ---' % model_type)\n",
-    "\n",
-    "    datasize = len(dataset)\n",
-    "    trials = 100 # Trials for hyperparameters random search\n",
-    "    splits = 10 # Number of splits of the data\n",
-    "    alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
-    "    C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
-    "    random.seed(20) # Set the seed for uniform parameter distribution\n",
-    "\n",
-    "    # set the output path\n",
-    "    kernel_file_path = 'kernelmatrices_weisfeilerlehman_acyclic/'\n",
-    "    if not os.path.exists(kernel_file_path):\n",
-    "        os.makedirs(kernel_file_path)\n",
-    "\n",
-    "\n",
-    "    \"\"\"\n",
-    "    -  Here starts the main program\n",
-    "    -  First we permute the data, then for each split we evaluate corresponding performances\n",
-    "    -  In the end, the performances are averaged over the test sets\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # save kernel matrices to files / read kernel matrices from files\n",
-    "    kernel_file = kernel_file_path + 'km.ds'\n",
-    "    path = pathlib.Path(kernel_file)\n",
-    "    # get train set kernel matrix\n",
-    "    if path.is_file():\n",
-    "        print('\\n Loading the kernel matrix from file...')\n",
-    "        Kmatrix = np.loadtxt(kernel_file)\n",
-    "        print(Kmatrix)\n",
-    "    else:\n",
-    "        print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "        Kmatrix = weisfeilerlehmankernel(dataset, node_label = 'atom', height = int(height), base_kernel = 'sp')\n",
-    "        print(Kmatrix)\n",
-    "        print('\\n Saving kernel matrix to file...')\n",
-    "#         np.savetxt(kernel_file, Kmatrix)\n",
-    "\n",
-    "    # Initialize the performance of the best parameter trial on validation with the corresponding performance on test\n",
-    "    val_split = []\n",
-    "    test_split = []\n",
-    "\n",
-    "    # For each split of the data\n",
-    "    for j in range(10, 10 + splits):\n",
-    "    #         print('\\n Starting split %d...' % j)\n",
-    "\n",
-    "        # Set the random set for data permutation\n",
-    "        random_state = int(j)\n",
-    "        np.random.seed(random_state)\n",
-    "        idx_perm = np.random.permutation(datasize)\n",
-    "    #     print(idx_perm)\n",
-    "\n",
-    "        # Permute the data\n",
-    "        y_perm = y[idx_perm] # targets permutation\n",
-    "    #     print(y_perm)\n",
-    "        Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
-    "    #     print(Kmatrix_perm)\n",
-    "        Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
-    "\n",
-    "        # Set the training, validation and test\n",
-    "        # Note: the percentage can be set up by the user\n",
-    "        num_train_val = int((datasize * 90) / 100)         # 90% (of entire dataset) for training and validation\n",
-    "        num_test = datasize - num_train_val              # 10% (of entire dataset) for test\n",
-    "        num_train = int((num_train_val * 90) / 100) # 90% (of train + val) for training\n",
-    "        num_val = num_train_val - num_train # 10% (of train + val) for validation\n",
-    "\n",
-    "        # Split the kernel matrix\n",
-    "        Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
-    "        Kmatrix_val = Kmatrix_perm[num_train:(num_train + num_val), 0:num_train]\n",
-    "        Kmatrix_test = Kmatrix_perm[(num_train + num_val):datasize, 0:num_train]\n",
-    "\n",
-    "        # Split the targets\n",
-    "        y_train = y_perm[0:num_train]\n",
-    "\n",
-    "        # Normalization step (for real valued targets only)\n",
-    "        if model_type == 'regression':\n",
-    "    #             print('\\n Normalizing output y...')\n",
-    "            y_train_mean = np.mean(y_train)\n",
-    "            y_train_std = np.std(y_train)\n",
-    "            y_train = (y_train - y_train_mean) / float(y_train_std)\n",
-    "    #         print(y)\n",
-    "\n",
-    "        y_val = y_perm[num_train:(num_train + num_val)]\n",
-    "        y_test = y_perm[(num_train + num_val):datasize]\n",
-    "\n",
-    "        # Record the performance for each parameter trial respectively on validation and test set\n",
-    "        perf_all_train = []\n",
-    "        perf_all_test = []\n",
-    "\n",
-    "        # For each parameter trial\n",
-    "        for i in range(trials):\n",
-    "            # For regression use the Kernel Ridge method\n",
-    "            if model_type == 'regression':\n",
-    "    #             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
-    "\n",
-    "                # Fit the kernel ridge model\n",
-    "                KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
-    "    #                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
-    "                KR.fit(Kmatrix_train, y_train)\n",
-    "\n",
-    "                # predict on the validation and test set\n",
-    "                y_pred = KR.predict(Kmatrix_val)\n",
-    "                y_pred_test = KR.predict(Kmatrix_test)\n",
-    "    #             print(y_pred)\n",
-    "\n",
-    "                # adjust prediction: needed because the training targets have been normalizaed\n",
-    "                y_pred = y_pred * float(y_train_std) + y_train_mean\n",
-    "    #             print(y_pred)\n",
-    "                y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
-    "    #             print(y_pred_test)\n",
-    "\n",
-    "                # root mean squared error on validation\n",
-    "                rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n",
-    "                perf_all_val.append(rmse)\n",
-    "\n",
-    "                # root mean squared error in test \n",
-    "                rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
-    "                perf_all_test.append(rmse_test)\n",
-    "\n",
-    "    #             print('The performance on the validation set is: %3f' % rmse)\n",
-    "    #             print('The performance on the test set is: %3f' % rmse_test)\n",
-    "\n",
-    "        # --- FIND THE OPTIMAL PARAMETERS --- #\n",
-    "        # For regression: minimise the mean squared error\n",
-    "        if model_type == 'regression':\n",
-    "\n",
-    "            # get optimal parameter on validation (argmin mean squared error)\n",
-    "            min_idx = np.argmin(perf_all_test)\n",
-    "            alpha_opt = alpha_grid[min_idx]\n",
-    "\n",
-    "            # performance corresponding to optimal parameter on val\n",
-    "            perf_val_opt = perf_all_val[min_idx]\n",
-    "\n",
-    "            # corresponding performance on test for the same parameter\n",
-    "            perf_test_opt = perf_all_test[min_idx]\n",
-    "\n",
-    "    #             print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
-    "    #             print('The best performance on the validation set is: %3f' % perf_val_opt)\n",
-    "    #             print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
-    "\n",
-    "        # append the best performance on validation\n",
-    "        # at the current split\n",
-    "        val_split.append(perf_val_opt)\n",
-    "\n",
-    "        # append the correponding performance on the test set\n",
-    "        test_split.append(perf_test_opt)\n",
-    "\n",
-    "    # average the results\n",
-    "    # mean of the validation performances over the splits\n",
-    "    val_mean = np.mean(np.asarray(val_split))\n",
-    "    # std deviation of validation over the splits\n",
-    "    val_std = np.std(np.asarray(val_split))\n",
-    "\n",
-    "    # mean of the test performances over the splits\n",
-    "    test_mean = np.mean(np.asarray(test_split))\n",
-    "    # std deviation of the test oer the splits\n",
-    "    test_std = np.std(np.asarray(test_split))\n",
-    "\n",
-    "    print('\\n Mean performance on val set: %3f' % val_mean)\n",
-    "    print('With standard deviation: %3f' % val_std)\n",
-    "    print('\\n Mean performance on test set: %3f' % test_mean)\n",
-    "    print('With standard deviation: %3f' % test_std)\n",
-    "    \n",
-    "    val_means_height.append(val_mean)\n",
-    "    val_stds_height.append(val_std)\n",
-    "    test_means_height.append(test_mean)\n",
-    "    test_stds_height.append(test_std)\n",
-    "    \n",
-    "print('\\n') \n",
-    "print(tabulate({'height': np.linspace(1, 12, 11), 'RMSE': test_means_height, 'std': test_stds_height}, headers='keys'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'O', 6: 'O'}"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# a = [0, 1, 3, 2]\n",
-    "# b = [3, 2, 1, 0]\n",
-    "# print(1 if a == b else 0)\n",
-    "\n",
-    "# max(1 ,2)\n",
-    "\n",
-    "# x = [ 'r', 'a', 's' ]\n",
-    "# x.sort()\n",
-    "# print(x)\n",
-    "\n",
-    "# def test1(*args, base = 'subtree'):\n",
-    "#     if base == 'subtree':\n",
-    "#         print('subtree')\n",
-    "#     elif base == 'edge':\n",
-    "#         print('edge')\n",
-    "#     else:\n",
-    "#         print('sp')\n",
-    "\n",
-    "# # function parameter usage test\n",
-    "# test1('hello', 'hi', base = 'edge')\n",
-    "\n",
-    "# # python matrix calculation speed test\n",
-    "# import numpy as np\n",
-    "# import time\n",
-    "\n",
-    "# size = 100\n",
-    "# m1 = np.random.random((size, size))\n",
-    "# m2 = np.random.random((size, size))\n",
-    "# itr = 1\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for i in range(itr):\n",
-    "#     np.dot(m1, m2)\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for j in range(itr):\n",
-    "#     result = np.zeros((size, size))\n",
-    "#     for i1 in range(size):\n",
-    "#         for i2 in range(size):\n",
-    "#             for i3 in range(size):\n",
-    "#                 result[i1][i2] += m1[i1][i3] * m2[i3][i2]\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for i in range(itr):\n",
-    "#     print(np.dot(m1, m2))\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for j in range(itr):\n",
-    "#     result = np.zeros((size, size))\n",
-    "#     for i1 in range(size):\n",
-    "#         for i2 in range(size):\n",
-    "#             for i3 in range(size):\n",
-    "#                 result[i1][i2] += m1[i1][i3] * m2[i3][i2]\n",
-    "#     print(result)\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# help(np.sum)\n",
-    "\n",
-    "# test dict\n",
-    "import sys\n",
-    "from collections import Counter\n",
-    "import networkx as nx\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "G1 = dataset[15]\n",
-    "nx.get_node_attributes(G1, 'label')\n",
-    "listhqhq = list(nx.get_node_attributes(G1, 'label').values())\n",
-    "dicthaha = dict(Counter(listhqhq))\n",
-    "len(dicthaha)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/.ipynb_checkpoints/run_marginalizedkernel_acyclic-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/run_marginalizedkernel_acyclic-checkpoint.ipynb
index 08c2d33..93f1626 100644
--- a/notebooks/.ipynb_checkpoints/run_marginalizedkernel_acyclic-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/run_marginalizedkernel_acyclic-checkpoint.ipynb
@@ -2,7 +2,369 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The line_profiler extension is already loaded. To reload it, use:\n",
+      "  %reload_ext line_profiler\n",
+      "\n",
+      " --- This is a regression problem ---\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.1 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 258.76952958106995 seconds ---\n",
+      "[[ 0.0287062   0.0124634   0.00444444 ...,  0.00606061  0.00606061\n",
+      "   0.00606061]\n",
+      " [ 0.0124634   0.01108958  0.00333333 ...,  0.00454545  0.00454545\n",
+      "   0.00454545]\n",
+      " [ 0.00444444  0.00333333  0.0287062  ...,  0.00819912  0.00819912\n",
+      "   0.00975875]\n",
+      " ..., \n",
+      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02846735  0.02836907\n",
+      "   0.02896354]\n",
+      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02836907  0.02831424\n",
+      "   0.0288712 ]\n",
+      " [ 0.00606061  0.00454545  0.00975875 ...,  0.02896354  0.0288712\n",
+      "   0.02987915]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 12.186285\n",
+      "With standard deviation: 7.038988\n",
+      "\n",
+      " Mean performance on test set: 18.024312\n",
+      "With standard deviation: 6.292466\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.2 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.3271746635437 seconds ---\n",
+      "[[ 0.06171557  0.03856471  0.01777778 ...,  0.02424242  0.02424242\n",
+      "   0.02424242]\n",
+      " [ 0.03856471  0.03579176  0.01333333 ...,  0.01818182  0.01818182\n",
+      "   0.01818182]\n",
+      " [ 0.01777778  0.01333333  0.06171557 ...,  0.02994207  0.02994207\n",
+      "   0.03262072]\n",
+      " ..., \n",
+      " [ 0.02424242  0.01818182  0.02994207 ...,  0.07442109  0.07434207\n",
+      "   0.07383563]\n",
+      " [ 0.02424242  0.01818182  0.02994207 ...,  0.07434207  0.07430377\n",
+      "   0.07376068]\n",
+      " [ 0.02424242  0.01818182  0.03262072 ...,  0.07383563  0.07376068\n",
+      "   0.07366354]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 13.955359\n",
+      "With standard deviation: 7.544068\n",
+      "\n",
+      " Mean performance on test set: 18.337589\n",
+      "With standard deviation: 5.854545\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.3 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 255.61398577690125 seconds ---\n",
+      "[[ 0.09803909  0.07202114  0.04       ...,  0.05454545  0.05454545\n",
+      "   0.05454545]\n",
+      " [ 0.07202114  0.06853421  0.03       ...,  0.04090909  0.04090909\n",
+      "   0.04090909]\n",
+      " [ 0.04        0.03        0.09803909 ...,  0.06368916  0.06368916\n",
+      "   0.06678704]\n",
+      " ..., \n",
+      " [ 0.05454545  0.04090909  0.06368916 ...,  0.12892852  0.12891455\n",
+      "   0.12734365]\n",
+      " [ 0.05454545  0.04090909  0.06368916 ...,  0.12891455  0.12892664\n",
+      "   0.12733207]\n",
+      " [ 0.05454545  0.04090909  0.06678704 ...,  0.12734365  0.12733207\n",
+      "   0.1261675 ]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 13.939071\n",
+      "With standard deviation: 7.958123\n",
+      "\n",
+      " Mean performance on test set: 18.495992\n",
+      "With standard deviation: 5.734918\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.4 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 254.89703965187073 seconds ---\n",
+      "[[ 0.13888889  0.11120616  0.07111111 ...,  0.0969697   0.0969697\n",
+      "   0.0969697 ]\n",
+      " [ 0.11120616  0.10756609  0.05333333 ...,  0.07272727  0.07272727\n",
+      "   0.07272727]\n",
+      " [ 0.07111111  0.05333333  0.13888889 ...,  0.10909713  0.10909713\n",
+      "   0.11216176]\n",
+      " ..., \n",
+      " [ 0.0969697   0.07272727  0.10909713 ...,  0.19178929  0.19182091\n",
+      "   0.18963212]\n",
+      " [ 0.0969697   0.07272727  0.10909713 ...,  0.19182091  0.19186661\n",
+      "   0.18966477]\n",
+      " [ 0.0969697   0.07272727  0.11216176 ...,  0.18963212  0.18966477\n",
+      "   0.18786824]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 16.259313\n",
+      "With standard deviation: 6.693580\n",
+      "\n",
+      " Mean performance on test set: 19.449149\n",
+      "With standard deviation: 5.371295\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.5 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.75693798065186 seconds ---\n",
+      "[[ 0.18518519  0.15591398  0.11111111 ...,  0.15151515  0.15151515\n",
+      "   0.15151515]\n",
+      " [ 0.15591398  0.15254237  0.08333333 ...,  0.11363636  0.11363636\n",
+      "   0.11363636]\n",
+      " [ 0.11111111  0.08333333  0.18518519 ...,  0.16617791  0.16617791\n",
+      "   0.16890214]\n",
+      " ..., \n",
+      " [ 0.15151515  0.11363636  0.16617791 ...,  0.26386999  0.26391515\n",
+      "   0.26158184]\n",
+      " [ 0.15151515  0.11363636  0.16617791 ...,  0.26391515  0.26396688\n",
+      "   0.26162729]\n",
+      " [ 0.15151515  0.11363636  0.16890214 ...,  0.26158184  0.26162729\n",
+      "   0.25964592]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 17.018055\n",
+      "With standard deviation: 6.844372\n",
+      "\n",
+      " Mean performance on test set: 19.785683\n",
+      "With standard deviation: 5.550543\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.6 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.5566437244415 seconds ---\n",
+      "[[ 0.23809524  0.20664506  0.16       ...,  0.21818182  0.21818182\n",
+      "   0.21818182]\n",
+      " [ 0.20664506  0.20385906  0.12       ...,  0.16363636  0.16363636\n",
+      "   0.16363636]\n",
+      " [ 0.16        0.12        0.23809524 ...,  0.2351024   0.2351024\n",
+      "   0.23727718]\n",
+      " ..., \n",
+      " [ 0.21818182  0.16363636  0.2351024  ...,  0.34658956  0.34662512\n",
+      "   0.34454945]\n",
+      " [ 0.21818182  0.16363636  0.2351024  ...,  0.34662512  0.34666325\n",
+      "   0.34458505]\n",
+      " [ 0.21818182  0.16363636  0.23727718 ...,  0.34454945  0.34458505\n",
+      "   0.34279503]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 17.661762\n",
+      "With standard deviation: 6.567179\n",
+      "\n",
+      " Mean performance on test set: 20.192158\n",
+      "With standard deviation: 5.591223\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.7 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 254.9531705379486 seconds ---\n",
+      "[[ 0.2991453   0.26444601  0.21777778 ...,  0.2969697   0.2969697\n",
+      "   0.2969697 ]\n",
+      " [ 0.26444601  0.26246188  0.16333333 ...,  0.22272727  0.22272727\n",
+      "   0.22272727]\n",
+      " [ 0.21777778  0.16333333  0.2991453  ...,  0.31614548  0.31614548\n",
+      "   0.31765009]\n",
+      " ..., \n",
+      " [ 0.2969697   0.22272727  0.31614548 ...,  0.44189997  0.44191814\n",
+      "   0.44038348]\n",
+      " [ 0.2969697   0.22272727  0.31614548 ...,  0.44191814  0.44193708\n",
+      "   0.44040164]\n",
+      " [ 0.2969697   0.22272727  0.31765009 ...,  0.44038348  0.44040164\n",
+      "   0.43906772]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 20.588213\n",
+      "With standard deviation: 5.746009\n",
+      "\n",
+      " Mean performance on test set: 21.661372\n",
+      "With standard deviation: 6.026849\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.8 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 252.80415797233582 seconds ---\n",
+      "[[ 0.37037037  0.33093141  0.28444444 ...,  0.38787879  0.38787879\n",
+      "   0.38787879]\n",
+      " [ 0.33093141  0.32983023  0.21333333 ...,  0.29090909  0.29090909\n",
+      "   0.29090909]\n",
+      " [ 0.28444444  0.21333333  0.37037037 ...,  0.4096795   0.4096795\n",
+      "   0.41049599]\n",
+      " ..., \n",
+      " [ 0.38787879  0.29090909  0.4096795  ...,  0.55242487  0.55243009\n",
+      "   0.5515636 ]\n",
+      " [ 0.38787879  0.29090909  0.4096795  ...,  0.55243009  0.55243545\n",
+      "   0.55156881]\n",
+      " [ 0.38787879  0.29090909  0.41049599 ...,  0.5515636   0.55156881\n",
+      "   0.55081257]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 23.594332\n",
+      "With standard deviation: 3.806374\n",
+      "\n",
+      " Mean performance on test set: 22.996018\n",
+      "With standard deviation: 6.083466\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.9 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.7384788990021 seconds ---\n",
+      "[[ 0.45454545  0.40839542  0.36       ...,  0.49090909  0.49090909\n",
+      "   0.49090909]\n",
+      " [ 0.40839542  0.40805534  0.27       ...,  0.36818182  0.36818182\n",
+      "   0.36818182]\n",
+      " [ 0.36        0.27        0.45454545 ...,  0.51619708  0.51619708\n",
+      "   0.51644564]\n",
+      " ..., \n",
+      " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172189  0.68172233\n",
+      "   0.68145294]\n",
+      " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172233  0.68172277\n",
+      "   0.68145338]\n",
+      " [ 0.49090909  0.36818182  0.51644564 ...,  0.68145294  0.68145338\n",
+      "   0.68121781]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Mean performance on train set: 25.808155\n",
+      "With standard deviation: 3.312074\n",
+      "\n",
+      " Mean performance on test set: 24.424089\n",
+      "With standard deviation: 4.951191\n",
+      "\n",
+      "\n",
+      "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+      "--------  -----------  ----------  ------------  -----------  --------\n",
+      "     0.1      18.0243     6.29247       12.1863      7.03899   258.77\n",
+      "     0.2      18.3376     5.85454       13.9554      7.54407   256.327\n",
+      "     0.3      18.496      5.73492       13.9391      7.95812   255.614\n",
+      "     0.4      19.4491     5.3713        16.2593      6.69358   254.897\n",
+      "     0.5      19.7857     5.55054       17.0181      6.84437   256.757\n",
+      "     0.6      20.1922     5.59122       17.6618      6.56718   256.557\n",
+      "     0.7      21.6614     6.02685       20.5882      5.74601   254.953\n",
+      "     0.8      22.996      6.08347       23.5943      3.80637   252.804\n",
+      "     0.9      24.4241     4.95119       25.8082      3.31207   256.738\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext line_profiler\n",
+    "\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from pygraph.utils.utils import kernel_train_test\n",
+    "from pygraph.kernels.marginalizedKernel import marginalizedkernel, _marginalizedkernel_do\n",
+    "\n",
+    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
+    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
+    "\n",
+    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', itr = 20)\n",
+    "\n",
+    "kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
+    "    hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)\n",
+    "\n",
+    "# %lprun -f _marginalizedkernel_do \\\n",
+    "#     kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
+    "#     hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results\n",
+    "\n",
+    "# with y normalization\n",
+    "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "--------  -----------  ----------  ------------  -----------  --------\n",
+    "     0.1      18.0192     6.27867       12.1642      6.99821   266.905\n",
+    "     0.2      18.3374     5.84775       13.9376      7.51398   256.288\n",
+    "     0.3      18.4955     5.73774       13.9291      7.9416    254.441\n",
+    "     0.4      19.4498     5.37509       16.2538      6.68378   257.581\n",
+    "     0.5      19.7851     5.55018       17.0142      6.83653   248.562\n",
+    "     0.6      20.1911     5.58951       17.6595      6.56211   249.667\n",
+    "     0.7      21.6606     6.02589       20.5872      5.74395   243.046\n",
+    "     0.8      22.9959     6.08344       23.5941      3.80595   252.36\n",
+    "     0.9      24.424      4.9512        25.8082      3.31202   248.077\n",
+    "\n",
+    "# without y normalization\n",
+    "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "--------  -----------  ----------  ------------  -----------  --------\n",
+    "     0.1      18.0243     6.29247       12.1863      7.03899   258.77\n",
+    "     0.2      18.3376     5.85454       13.9554      7.54407   256.327\n",
+    "     0.3      18.496      5.73492       13.9391      7.95812   255.614\n",
+    "     0.4      19.4491     5.3713        16.2593      6.69358   254.897\n",
+    "     0.5      19.7857     5.55054       17.0181      6.84437   256.757\n",
+    "     0.6      20.1922     5.59122       17.6618      6.56718   256.557\n",
+    "     0.7      21.6614     6.02685       20.5882      5.74601   254.953\n",
+    "     0.8      22.996      6.08347       23.5943      3.80637   252.804\n",
+    "     0.9      24.4241     4.95119       25.8082      3.31207   256.738"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -49,7 +411,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.1 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 246.21349620819092 seconds ---\n",
       "[[ 0.0287062   0.0124634   0.00444444 ...,  0.00606061  0.00606061\n",
       "   0.00606061]\n",
       " [ 0.0124634   0.01108958  0.00333333 ...,  0.00454545  0.00454545\n",
@@ -64,6 +428,8 @@
       " [ 0.00606061  0.00454545  0.00975875 ...,  0.02896354  0.0288712\n",
       "   0.02987915]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 51.192412\n",
       "With standard deviation: 58.804642\n",
       "\n",
@@ -72,7 +438,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.2 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 240.73209404945374 seconds ---\n",
       "[[ 0.06171557  0.03856471  0.01777778 ...,  0.02424242  0.02424242\n",
       "   0.02424242]\n",
       " [ 0.03856471  0.03579176  0.01333333 ...,  0.01818182  0.01818182\n",
@@ -87,6 +455,8 @@
       " [ 0.02424242  0.01818182  0.03262072 ...,  0.07383563  0.07376068\n",
       "   0.07366354]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 56.692288\n",
       "With standard deviation: 58.162153\n",
       "\n",
@@ -95,7 +465,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.3 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 244.91414594650269 seconds ---\n",
       "[[ 0.09803909  0.07202114  0.04       ...,  0.05454545  0.05454545\n",
       "   0.05454545]\n",
       " [ 0.07202114  0.06853421  0.03       ...,  0.04090909  0.04090909\n",
@@ -110,6 +482,8 @@
       " [ 0.05454545  0.04090909  0.06678704 ...,  0.12734365  0.12733207\n",
       "   0.1261675 ]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 54.360795\n",
       "With standard deviation: 61.733054\n",
       "\n",
@@ -118,7 +492,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.4 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 246.01012706756592 seconds ---\n",
       "[[ 0.13888889  0.11120616  0.07111111 ...,  0.0969697   0.0969697\n",
       "   0.0969697 ]\n",
       " [ 0.11120616  0.10756609  0.05333333 ...,  0.07272727  0.07272727\n",
@@ -133,6 +509,8 @@
       " [ 0.0969697   0.07272727  0.11216176 ...,  0.18963212  0.18966477\n",
       "   0.18786824]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 44.518253\n",
       "With standard deviation: 44.478206\n",
       "\n",
@@ -141,7 +519,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.5 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 241.62482810020447 seconds ---\n",
       "[[ 0.18518519  0.15591398  0.11111111 ...,  0.15151515  0.15151515\n",
       "   0.15151515]\n",
       " [ 0.15591398  0.15254237  0.08333333 ...,  0.11363636  0.11363636\n",
@@ -156,6 +536,8 @@
       " [ 0.15151515  0.11363636  0.16890214 ...,  0.26158184  0.26162729\n",
       "   0.25964592]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 42.848719\n",
       "With standard deviation: 39.189276\n",
       "\n",
@@ -164,7 +546,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.6 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 240.8926112651825 seconds ---\n",
       "[[ 0.23809524  0.20664506  0.16       ...,  0.21818182  0.21818182\n",
       "   0.21818182]\n",
       " [ 0.20664506  0.20385906  0.12       ...,  0.16363636  0.16363636\n",
@@ -179,6 +563,8 @@
       " [ 0.21818182  0.16363636  0.23727718 ...,  0.34454945  0.34458505\n",
       "   0.34279503]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 39.983104\n",
       "With standard deviation: 32.270969\n",
       "\n",
@@ -187,7 +573,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.7 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 240.47843861579895 seconds ---\n",
       "[[ 0.2991453   0.26444601  0.21777778 ...,  0.2969697   0.2969697\n",
       "   0.2969697 ]\n",
       " [ 0.26444601  0.26246188  0.16333333 ...,  0.22272727  0.22272727\n",
@@ -202,6 +590,14 @@
       " [ 0.2969697   0.22272727  0.31765009 ...,  0.44038348  0.44040164\n",
       "   0.43906772]]\n",
       "\n",
+      " Saving kernel matrix to file...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       " Mean performance on val set: 37.530308\n",
       "With standard deviation: 29.730795\n",
       "\n",
@@ -210,7 +606,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.8 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 242.16377139091492 seconds ---\n",
       "[[ 0.37037037  0.33093141  0.28444444 ...,  0.38787879  0.38787879\n",
       "   0.38787879]\n",
       " [ 0.33093141  0.32983023  0.21333333 ...,  0.29090909  0.29090909\n",
@@ -225,6 +623,8 @@
       " [ 0.38787879  0.29090909  0.41049599 ...,  0.5515636   0.55156881\n",
       "   0.55081257]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 37.110483\n",
       "With standard deviation: 21.287120\n",
       "\n",
@@ -233,7 +633,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.9 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 238.44418454170227 seconds ---\n",
       "[[ 0.45454545  0.40839542  0.36       ...,  0.49090909  0.49090909\n",
       "   0.49090909]\n",
       " [ 0.40839542  0.40805534  0.27       ...,  0.36818182  0.36818182\n",
@@ -246,13 +648,9 @@
       " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172233  0.68172277\n",
       "   0.68145338]\n",
       " [ 0.49090909  0.36818182  0.51644564 ...,  0.68145294  0.68145338\n",
-      "   0.68121781]]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "   0.68121781]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
       "\n",
       " Mean performance on val set: 30.572040\n",
       "With standard deviation: 11.057046\n",
@@ -261,17 +659,17 @@
       "With standard deviation: 4.891587\n",
       "\n",
       "\n",
-      "    std     RMSE    p_quit\n",
-      "-------  -------  --------\n",
-      "7.749    18.5188       0.1\n",
-      "6.59104  17.8991       0.2\n",
-      "7.10161  18.3924       0.3\n",
-      "6.24807  19.6233       0.4\n",
-      "6.29951  19.9936       0.5\n",
-      "6.26173  20.5466       0.6\n",
-      "6.33531  21.7018       0.7\n",
-      "6.10246  23.1489       0.8\n",
-      "4.89159  24.7157       0.9\n"
+      "  p_quit      std     RMSE\n",
+      "--------  -------  -------\n",
+      "     0.1  7.749    18.5188\n",
+      "     0.2  6.59104  17.8991\n",
+      "     0.3  7.10161  18.3924\n",
+      "     0.4  6.24807  19.6233\n",
+      "     0.5  6.29951  19.9936\n",
+      "     0.6  6.26173  20.5466\n",
+      "     0.7  6.33531  21.7018\n",
+      "     0.8  6.10246  23.1489\n",
+      "     0.9  4.89159  24.7157\n"
      ]
     }
    ],
@@ -357,7 +755,7 @@
     "        print(Kmatrix)\n",
     "    else:\n",
     "        print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "        Kmatrix, run_time = marginalizedkernel(dataset, p_quit, 20, node_label = 'atom', edge_label = 'bond_type')\n",
+    "        Kmatrix, run_time = marginalizedkernel(dataset, p_quit = p_quit, itr = 20, node_label = 'atom', edge_label = 'bond_type')\n",
     "        print(Kmatrix)\n",
     "        print('\\n Saving kernel matrix to file...')\n",
     "        np.savetxt(kernel_file, Kmatrix)\n",
diff --git a/notebooks/.ipynb_checkpoints/run_pathkernel_acyclic-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/run_pathkernel_acyclic-checkpoint.ipynb
index 86bd8fc..bdb4b16 100644
--- a/notebooks/.ipynb_checkpoints/run_pathkernel_acyclic-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/run_pathkernel_acyclic-checkpoint.ipynb
@@ -2,146 +2,238 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0, 3, 1], [0, 3, 4, 2], [0, 3], [0, 3, 4], [1, 3, 4, 2], [1, 3], [1, 3, 4], [2, 4, 3], [2, 4], [3, 4]]\n",
-      "10\n",
-      "[[0, 4, 1], [0, 4, 5, 2], [0, 4, 5, 6, 3], [0, 4], [0, 4, 5], [0, 4, 5, 6], [1, 4, 5, 2], [1, 4, 5, 6, 3], [1, 4], [1, 4, 5], [1, 4, 5, 6], [2, 5, 6, 3], [2, 5, 4], [2, 5], [2, 5, 6], [3, 6, 5, 4], [3, 6, 5], [3, 6], [4, 5], [4, 5, 6], [5, 6]]\n",
-      "21\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "0.10952380952380952\n"
+      "The line_profiler extension is already loaded. To reload it, use:\n",
+      "  %reload_ext line_profiler\n",
+      "\n",
+      " --- This is a regression problem ---\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- mean average path kernel matrix of size 185 built in 132.2242877483368 seconds ---\n",
+      "[[ 0.55555556  0.22222222  0.         ...,  0.          0.          0.        ]\n",
+      " [ 0.22222222  0.27777778  0.         ...,  0.          0.          0.        ]\n",
+      " [ 0.          0.          0.55555556 ...,  0.03030303  0.03030303\n",
+      "   0.03030303]\n",
+      " ..., \n",
+      " [ 0.          0.          0.03030303 ...,  0.08297521  0.05553719\n",
+      "   0.05256198]\n",
+      " [ 0.          0.          0.03030303 ...,  0.05553719  0.07239669\n",
+      "   0.0538843 ]\n",
+      " [ 0.          0.          0.03030303 ...,  0.05256198  0.0538843\n",
+      "   0.07438017]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 3.761907\n",
+      "With standard deviation: 0.702594\n",
+      "\n",
+      " Mean performance on test set: 14.001515\n",
+      "With standard deviation: 6.936023\n",
+      "\n",
+      "\n",
+      "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+      "-----------  ----------  ------------  -----------  --------\n",
+      "    14.0015     6.93602       3.76191     0.702594   132.224\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext line_profiler\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from pygraph.utils.utils import kernel_train_test\n",
+    "from pygraph.kernels.pathKernel import pathkernel, _pathkernel_do\n",
+    "\n",
+    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
+    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
+    "\n",
+    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type')\n",
+    "\n",
+    "kernel_train_test(datafile, kernel_file_path, pathkernel, kernel_para, normalize = True)\n",
+    "\n",
+    "# %lprun -f _pathkernel_do \\\n",
+    "#     kernel_train_test(datafile, kernel_file_path, pathkernel, kernel_para, normalize = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results\n",
+    "\n",
+    "# with y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "    14.0015     6.93602       3.76191     0.702594   37.5759\n",
+    "\n",
+    "# without y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "    18.4189     10.7811       3.61995     0.512351   37.0017"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- This script take as input a kernel matrix\n",
+      "and returns the classification or regression performance\n",
+      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
+      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
+      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
+      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
+      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
+      "correspond to the average of the performances on the test sets. \n",
+      "\n",
+      "@references\n",
+      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "IndentationError",
+     "evalue": "unindent does not match any outer indentation level (utils.py, line 106)",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
+      "  File \u001b[1;32m\"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\"\u001b[0m, line \u001b[1;32m2910\u001b[0m, in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\n",
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-0b5b9ebb5cc4>\"\u001b[0;36m, line \u001b[0;32m31\u001b[0;36m, in \u001b[0;35m<module>\u001b[0;36m\u001b[0m\n\u001b[0;31m    from pygraph.utils.utils import split_train_test\u001b[0m\n",
+      "\u001b[0;36m  File \u001b[0;32m\"../pygraph/utils/utils.py\"\u001b[0;36m, line \u001b[0;32m106\u001b[0m\n\u001b[0;31m    train_means_list = []\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unindent does not match any outer indentation level\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Author: Elisabetta Ghisu\n",
+    "\n",
+    "\"\"\"\n",
+    "- This script take as input a kernel matrix\n",
+    "and returns the classification or regression performance\n",
+    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
+    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
+    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
+    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
+    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
+    "correspond to the average of the performances on the test sets. \n",
+    "\n",
+    "@references\n",
+    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
+    "\"\"\"\n",
+    "\n",
+    "print(__doc__)\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "import pathlib\n",
+    "from collections import OrderedDict\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from tabulate import tabulate\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pygraph.kernels.pathKernel import pathkernel\n",
+    "from pygraph.utils.graphfiles import loadDataset\n",
+    "from pygraph.utils.utils import split_train_test\n",
+    "\n",
+    "train_means_list = []\n",
+    "train_stds_list = []\n",
+    "test_means_list = []\n",
+    "test_stds_list = []\n",
+    "kernel_time_list = []\n",
+    "\n",
+    "print('\\n Loading dataset from file...')\n",
+    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
+    "y = np.array(y)\n",
+    "print(y)\n",
+    "\n",
+    "# setup the parameters\n",
+    "model_type = 'regression' # Regression or classification problem\n",
+    "print('\\n --- This is a %s problem ---' % model_type)\n",
+    "\n",
+    "trials = 100 # Trials for hyperparameters random search\n",
+    "splits = 10 # Number of splits of the data\n",
+    "alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
+    "C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
+    "\n",
+    "# set the output path\n",
+    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
+    "if not os.path.exists(kernel_file_path):\n",
+    "    os.makedirs(kernel_file_path)\n",
+    "\n",
+    "\"\"\"\n",
+    "-  Here starts the main program\n",
+    "-  First we permute the data, then for each split we evaluate corresponding performances\n",
+    "-  In the end, the performances are averaged over the test sets\n",
+    "\"\"\"\n",
+    "\n",
+    "# save kernel matrices to files / read kernel matrices from files\n",
+    "kernel_file = kernel_file_path + 'km.ds'\n",
+    "path = pathlib.Path(kernel_file)\n",
+    "# get train set kernel matrix\n",
+    "if path.is_file():\n",
+    "    print('\\n Loading the kernel matrix from file...')\n",
+    "    Kmatrix = np.loadtxt(kernel_file)\n",
+    "    print(Kmatrix)\n",
+    "else:\n",
+    "    print('\\n Calculating kernel matrix, this could take a while...')\n",
+    "    Kmatrix, run_time = pathkernel(dataset, node_label = 'atom', edge_label = 'bond_type')\n",
+    "    kernel_time_list.append(run_time)\n",
+    "    print(Kmatrix)\n",
+    "    print('\\n Saving kernel matrix to file...')\n",
+    "#     np.savetxt(kernel_file, Kmatrix)\n",
+    "    \n",
+    "train_mean, train_std, test_mean, test_std = \\\n",
+    "    split_train_test(Kmatrix, y, alpha_grid, C_grid, splits, trials, model_type, normalize = True)\n",
+    "    \n",
+    "train_means_list.append(train_mean)\n",
+    "train_stds_list.append(train_std)\n",
+    "test_means_list.append(test_mean)\n",
+    "test_stds_list.append(test_std)\n",
+    "        \n",
+    "print('\\n') \n",
+    "table_dict = {'RMSE_test': test_means_list, 'std_test': test_stds_list, \\\n",
+    "    'RMSE_train': train_means_list, 'std_train': train_stds_list, 'k_time': kernel_time_list}\n",
+    "keyorder = ['RMSE_test', 'std_test', 'RMSE_train', 'std_train', 'k_time']\n",
+    "print(tabulate(OrderedDict(sorted(table_dict.items(), key = lambda i:keyorder.index(i[0]))), headers='keys'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'deltaKernel'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-51fa7de99690>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"../\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpygraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphfiles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpygraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeltaKernel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeltaKernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'deltaKernel'"
      ]
     }
    ],
@@ -545,280 +637,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "- This script take as input a kernel matrix\n",
-      "and returns the classification or regression performance\n",
-      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-      "correspond to the average of the performances on the test sets. \n",
-      "\n",
-      "@references\n",
-      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-      "\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "--- mean average path kernel matrix of size 185 built in 38.70095658302307 seconds ---\n",
-      "[[ 0.55555556  0.22222222  0.         ...,  0.          0.          0.        ]\n",
-      " [ 0.22222222  0.27777778  0.         ...,  0.          0.          0.        ]\n",
-      " [ 0.          0.          0.55555556 ...,  0.03030303  0.03030303\n",
-      "   0.03030303]\n",
-      " ..., \n",
-      " [ 0.          0.          0.03030303 ...,  0.08297521  0.05553719\n",
-      "   0.05256198]\n",
-      " [ 0.          0.          0.03030303 ...,  0.05553719  0.07239669\n",
-      "   0.0538843 ]\n",
-      " [ 0.          0.          0.03030303 ...,  0.05256198  0.0538843\n",
-      "   0.07438017]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on val set: 11.907089\n",
-      "With standard deviation: 4.781924\n",
-      "\n",
-      " Mean performance on test set: 14.270816\n",
-      "With standard deviation: 6.366698\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Author: Elisabetta Ghisu\n",
-    "\n",
-    "\"\"\"\n",
-    "- This script take as input a kernel matrix\n",
-    "and returns the classification or regression performance\n",
-    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-    "correspond to the average of the performances on the test sets. \n",
-    "\n",
-    "@references\n",
-    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-    "\"\"\"\n",
-    "\n",
-    "print(__doc__)\n",
-    "\n",
-    "import sys\n",
-    "import os\n",
-    "import pathlib\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
-    "from sklearn import svm\n",
-    "\n",
-    "from pygraph.kernels.pathKernel import pathkernel\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "\n",
-    "print('\\n Loading dataset from file...')\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "y = np.array(y)\n",
-    "print(y)\n",
-    "\n",
-    "# setup the parameters\n",
-    "model_type = 'regression' # Regression or classification problem\n",
-    "print('\\n --- This is a %s problem ---' % model_type)\n",
-    "\n",
-    "datasize = len(dataset)\n",
-    "trials = 100 # Trials for hyperparameters random search\n",
-    "splits = 10 # Number of splits of the data\n",
-    "alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
-    "C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
-    "random.seed(20) # Set the seed for uniform parameter distribution\n",
-    "\n",
-    "# set the output path\n",
-    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
-    "if not os.path.exists(kernel_file_path):\n",
-    "    os.makedirs(kernel_file_path)\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "-  Here starts the main program\n",
-    "-  First we permute the data, then for each split we evaluate corresponding performances\n",
-    "-  In the end, the performances are averaged over the test sets\n",
-    "\"\"\"\n",
-    "\n",
-    "# save kernel matrices to files / read kernel matrices from files\n",
-    "kernel_file = kernel_file_path + 'km.ds'\n",
-    "path = pathlib.Path(kernel_file)\n",
-    "# get train set kernel matrix\n",
-    "if path.is_file():\n",
-    "    print('\\n Loading the kernel matrix from file...')\n",
-    "    Kmatrix = np.loadtxt(kernel_file)\n",
-    "    print(Kmatrix)\n",
-    "else:\n",
-    "    print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "    Kmatrix, run_time = pathkernel(dataset, node_label = 'atom', edge_label = 'bond_type')\n",
-    "    print(Kmatrix)\n",
-    "    print('\\n Saving kernel matrix to file...')\n",
-    "    np.savetxt(kernel_file, Kmatrix)\n",
-    "\n",
-    "# Initialize the performance of the best parameter trial on validation with the corresponding performance on test\n",
-    "val_split = []\n",
-    "test_split = []\n",
-    "\n",
-    "# For each split of the data\n",
-    "for j in range(10, 10 + splits):\n",
-    "#         print('\\n Starting split %d...' % j)\n",
-    "\n",
-    "    # Set the random set for data permutation\n",
-    "    random_state = int(j)\n",
-    "    np.random.seed(random_state)\n",
-    "    idx_perm = np.random.permutation(datasize)\n",
-    "#     print(idx_perm)\n",
-    "\n",
-    "    # Permute the data\n",
-    "    y_perm = y[idx_perm] # targets permutation\n",
-    "#     print(y_perm)\n",
-    "    Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
-    "#     print(Kmatrix_perm)\n",
-    "    Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
-    "\n",
-    "    # Set the training, validation and test\n",
-    "    # Note: the percentage can be set up by the user\n",
-    "    num_train_val = int((datasize * 90) / 100)         # 90% (of entire dataset) for training and validation\n",
-    "    num_test = datasize - num_train_val              # 10% (of entire dataset) for test\n",
-    "    num_train = int((num_train_val * 90) / 100) # 90% (of train + val) for training\n",
-    "    num_val = num_train_val - num_train # 10% (of train + val) for validation\n",
-    "\n",
-    "    # Split the kernel matrix\n",
-    "    Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
-    "    Kmatrix_val = Kmatrix_perm[num_train:(num_train + num_val), 0:num_train]\n",
-    "    Kmatrix_test = Kmatrix_perm[(num_train + num_val):datasize, 0:num_train]\n",
-    "\n",
-    "    # Split the targets\n",
-    "    y_train = y_perm[0:num_train]\n",
-    "\n",
-    "    # Normalization step (for real valued targets only)\n",
-    "    if model_type == 'regression':\n",
-    "#             print('\\n Normalizing output y...')\n",
-    "        y_train_mean = np.mean(y_train)\n",
-    "        y_train_std = np.std(y_train)\n",
-    "        y_train = (y_train - y_train_mean) / float(y_train_std)\n",
-    "#         print(y)\n",
-    "\n",
-    "    y_val = y_perm[num_train:(num_train + num_val)]\n",
-    "    y_test = y_perm[(num_train + num_val):datasize]\n",
-    "\n",
-    "    # Record the performance for each parameter trial respectively on validation and test set\n",
-    "    perf_all_val = []\n",
-    "    perf_all_test = []\n",
-    "\n",
-    "    # For each parameter trial\n",
-    "    for i in range(trials):\n",
-    "        # For regression use the Kernel Ridge method\n",
-    "        if model_type == 'regression':\n",
-    "#             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
-    "\n",
-    "            # Fit the kernel ridge model\n",
-    "            KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
-    "#                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
-    "            KR.fit(Kmatrix_train, y_train)\n",
-    "\n",
-    "            # predict on the validation and test set\n",
-    "            y_pred = KR.predict(Kmatrix_val)\n",
-    "            y_pred_test = KR.predict(Kmatrix_test)\n",
-    "#             print(y_pred)\n",
-    "\n",
-    "            # adjust prediction: needed because the training targets have been normalizaed\n",
-    "            y_pred = y_pred * float(y_train_std) + y_train_mean\n",
-    "#             print(y_pred)\n",
-    "            y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
-    "#             print(y_pred_test)\n",
-    "\n",
-    "            # root mean squared error on validation\n",
-    "            rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n",
-    "            perf_all_val.append(rmse)\n",
-    "\n",
-    "            # root mean squared error in test \n",
-    "            rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
-    "            perf_all_test.append(rmse_test)\n",
-    "\n",
-    "#             print('The performance on the validation set is: %3f' % rmse)\n",
-    "#             print('The performance on the test set is: %3f' % rmse_test)\n",
-    "\n",
-    "    # --- FIND THE OPTIMAL PARAMETERS --- #\n",
-    "    # For regression: minimise the mean squared error\n",
-    "    if model_type == 'regression':\n",
-    "\n",
-    "        # get optimal parameter on validation (argmin mean squared error)\n",
-    "        min_idx = np.argmin(perf_all_test)\n",
-    "        alpha_opt = alpha_grid[min_idx]\n",
-    "\n",
-    "        # performance corresponding to optimal parameter on val\n",
-    "        perf_val_opt = perf_all_val[min_idx]\n",
-    "\n",
-    "        # corresponding performance on test for the same parameter\n",
-    "        perf_test_opt = perf_all_test[min_idx]\n",
-    "\n",
-    "#             print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
-    "#             print('The best performance on the validation set is: %3f' % perf_val_opt)\n",
-    "#             print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
-    "\n",
-    "    # append the best performance on validation\n",
-    "    # at the current split\n",
-    "    val_split.append(perf_val_opt)\n",
-    "\n",
-    "    # append the correponding performance on the test set\n",
-    "    test_split.append(perf_test_opt)\n",
-    "\n",
-    "# average the results\n",
-    "# mean of the validation performances over the splits\n",
-    "val_mean = np.mean(np.asarray(val_split))\n",
-    "# std deviation of validation over the splits\n",
-    "val_std = np.std(np.asarray(val_split))\n",
-    "\n",
-    "# mean of the test performances over the splits\n",
-    "test_mean = np.mean(np.asarray(test_split))\n",
-    "# std deviation of the test oer the splits\n",
-    "test_std = np.std(np.asarray(test_split))\n",
-    "\n",
-    "print('\\n Mean performance on val set: %3f' % val_mean)\n",
-    "print('With standard deviation: %3f' % val_std)\n",
-    "print('\\n Mean performance on test set: %3f' % test_mean)\n",
-    "print('With standard deviation: %3f' % test_std)"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
diff --git a/notebooks/.ipynb_checkpoints/run_spkernel_acyclic-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/run_spkernel_acyclic-checkpoint.ipynb
index b3e0f40..8466693 100644
--- a/notebooks/.ipynb_checkpoints/run_spkernel_acyclic-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/run_spkernel_acyclic-checkpoint.ipynb
@@ -2,6 +2,87 @@
  "cells": [
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The line_profiler extension is already loaded. To reload it, use:\n",
+      "  %reload_ext line_profiler\n",
+      "\n",
+      " --- This is a regression problem ---\n",
+      "\n",
+      "\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "--- shortest path kernel matrix of size 185 built in 14.576777696609497 seconds ---\n",
+      "[[  3.   1.   3. ...,   1.   1.   1.]\n",
+      " [  1.   6.   1. ...,   0.   0.   3.]\n",
+      " [  3.   1.   3. ...,   1.   1.   1.]\n",
+      " ..., \n",
+      " [  1.   0.   1. ...,  55.  21.   7.]\n",
+      " [  1.   0.   1. ...,  21.  55.   7.]\n",
+      " [  1.   3.   1. ...,   7.   7.  55.]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 28.360361\n",
+      "With standard deviation: 1.357183\n",
+      "\n",
+      " Mean performance on test set: 35.191954\n",
+      "With standard deviation: 4.495767\n",
+      "\n",
+      "\n",
+      "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+      "-----------  ----------  ------------  -----------  --------\n",
+      "     35.192     4.49577       28.3604      1.35718   14.5768\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext line_profiler\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from pygraph.utils.utils import kernel_train_test\n",
+    "from pygraph.kernels.spKernel import spkernel\n",
+    "\n",
+    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
+    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
+    "\n",
+    "kernel_para = dict(edge_weight = 'atom')\n",
+    "\n",
+    "kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)\n",
+    "\n",
+    "# %lprun -f spkernel \\\n",
+    "#     kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results\n",
+    "\n",
+    "# with y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "    35.6337     5.23183       32.3805      3.92531   14.9301\n",
+    "\n",
+    "# without y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "     35.192     4.49577       28.3604      1.35718   14.5768"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "scrolled": false
diff --git a/notebooks/kernelmatrices_marginalized_acyclic/README.md b/notebooks/kernelmatrices_marginalized_acyclic/README.md
deleted file mode 100644
index 9f7296d..0000000
--- a/notebooks/kernelmatrices_marginalized_acyclic/README.md
+++ /dev/null
@@ -1 +0,0 @@
-This folder contains marginalized kernel matrices results for acyclic dataset. Each file records a kernel matrix corresponding to a termination probability (p_quit).
diff --git a/notebooks/kernelmatrices_path_acyclic/km.ds b/notebooks/kernelmatrices_path_acyclic/km.ds
deleted file mode 100644
index 4ecfd76..0000000
--- a/notebooks/kernelmatrices_path_acyclic/km.ds
+++ /dev/null
@@ -1,185 +0,0 @@
-5.555555555555555802e-01 2.222222222222222099e-01 0.000000000000000000e+00 0.000000000000000000e+00 2.777777777777777901e-01 1.333333333333333315e-01 3.333333333333333148e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 1.666666666666666574e-01 1.666666666666666574e-01 8.888888888888889228e-02 8.888888888888889228e-02 1.587301587301587213e-01 2.222222222222222099e-01 2.222222222222222099e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 1.587301587301587213e-01 1.587301587301587213e-01 1.587301587301587213e-01 1.587301587301587213e-01 1.587301587301587213e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 1.190476190476190410e-01 1.190476190476190410e-01 1.190476190476190410e-01 1.190476190476190410e-01 1.190476190476190410e-01 1.190476190476190410e-01 1.190476190476190410e-01 1.190476190476190410e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.259259259259258745e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 2.962962962962963076e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.407407407407406996e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 2.424242424242424240e-02 6.060606060606060774e-02 6.060606060606060774e-02 6.060606060606060774e-02 6.060606060606060774e-02 6.060606060606060774e-02 6.060606060606060774e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 2.380952380952380820e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
-2.222222222222222099e-01 2.777777777777777901e-01 0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 1.666666666666666574e-01 1.333333333333333315e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 1.111111111111111049e-01 1.111111111111111049e-01 6.349206349206348854e-02 8.888888888888889228e-02 8.888888888888889228e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 3.703703703703703498e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 3.030303030303030387e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.190476190476190410e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
-0.000000000000000000e+00 0.000000000000000000e+00 5.555555555555555802e-01 2.222222222222222099e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.777777777777777901e-01 1.333333333333333315e-01 3.333333333333333148e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 1.666666666666666574e-01 1.666666666666666574e-01 8.888888888888889228e-02 2.222222222222222099e-01 2.222222222222222099e-01 2.222222222222222099e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 1.111111111111111049e-01 6.349206349206348854e-02 6.349206349206348854e-02 1.587301587301587213e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 7.936507936507936067e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 1.190476190476190410e-01 1.190476190476190410e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 9.259259259259258745e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.962962962962963076e-02 7.407407407407406996e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.703703703703703498e-02 3.703703703703703498e-02 6.060606060606060774e-02 6.060606060606060774e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222099e-01 2.777777777777777901e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 1.666666666666666574e-01 1.333333333333333315e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 1.111111111111111049e-01 8.888888888888889228e-02 8.888888888888889228e-02 8.888888888888889228e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 7.936507936507936067e-02 7.936507936507936067e-02 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 4.761904761904761640e-02 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.481481481481481538e-02 1.481481481481481538e-02 2.424242424242424240e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02 1.212121212121212120e-02
-2.777777777777777901e-01 1.111111111111111049e-01 0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222099e-01 1.000000000000000056e-01 1.666666666666666574e-01 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 1.333333333333333315e-01 1.833333333333333204e-01 1.833333333333333204e-01 8.888888888888889228e-02 8.888888888888889228e-02 1.269841269841269771e-01 1.666666666666666574e-01 1.444444444444444309e-01 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.000000000000000056e-01 1.333333333333333315e-01 1.555555555555555580e-01 1.000000000000000056e-01 1.222222222222222182e-01 1.555555555555555580e-01 1.269841269841269771e-01 1.587301587301587213e-01 1.111111111111111049e-01 1.269841269841269771e-01 1.269841269841269771e-01 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.587301587301587213e-02 7.936507936507936067e-02 9.523809523809523281e-02 1.031746031746031689e-01 1.190476190476190410e-01 9.523809523809523281e-02 7.936507936507936067e-02 7.936507936507936067e-02 1.190476190476190410e-01 9.523809523809523281e-02 1.349206349206349131e-01 1.111111111111111049e-01 9.523809523809523281e-02 1.250000000000000000e-01 8.928571428571428770e-02 1.369047619047619180e-01 8.928571428571428770e-02 1.130952380952380959e-01 8.928571428571428770e-02 1.130952380952380959e-01 1.011904761904761918e-01 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.547619047619047949e-02 7.738095238095238360e-02 8.333333333333332871e-02 9.523809523809523281e-02 7.738095238095238360e-02 7.738095238095238360e-02 1.071428571428571369e-01 7.738095238095238360e-02 1.190476190476190410e-01 8.928571428571428770e-02 1.071428571428571369e-01 8.928571428571428770e-02 9.523809523809523281e-02 8.333333333333332871e-02 1.203703703703703637e-01 1.018518518518518462e-01 1.018518518518518462e-01 9.259259259259258745e-02 7.407407407407406996e-02 8.333333333333332871e-02 8.333333333333332871e-02 7.870370370370370627e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 1.851851851851851749e-02 6.944444444444444753e-02 6.944444444444444753e-02 6.481481481481481122e-02 7.870370370370370627e-02 8.796296296296296502e-02 7.870370370370370627e-02 6.481481481481481122e-02 6.018518518518518184e-02 8.796296296296296502e-02 1.064814814814814825e-01 9.722222222222222376e-02 5.555555555555555247e-02 7.870370370370370627e-02 5.925925925925926152e-02 1.000000000000000056e-01 8.518518518518518323e-02 6.296296296296295669e-02 8.518518518518518323e-02 7.777777777777777901e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.851851851851851749e-02 5.925925925925926152e-02 5.555555555555555247e-02 5.925925925925926152e-02 7.407407407407406996e-02 5.925925925925926152e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 4.814814814814814825e-02 4.814814814814814825e-02 6.296296296296295669e-02 4.814814814814814825e-02 7.037037037037037479e-02 5.151515151515151381e-02 5.454545454545454281e-02 6.666666666666666574e-02 7.272727272727272374e-02 8.484848484848485362e-02 7.272727272727272374e-02 8.484848484848485362e-02 2.592592592592592518e-02 2.592592592592592518e-02 1.818181818181818094e-02 1.818181818181818094e-02 4.848484848484848481e-02 6.060606060606060774e-02 6.363636363636362980e-02 6.363636363636362980e-02 5.952380952380952051e-02 5.151515151515151381e-02 5.151515151515151381e-02 5.757575757575757874e-02 5.151515151515151381e-02 5.151515151515151381e-02 4.848484848484848481e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02
-1.333333333333333315e-01 1.666666666666666574e-01 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000056e-01 1.400000000000000133e-01 8.000000000000000167e-02 1.666666666666666644e-02 1.000000000000000021e-02 0.000000000000000000e+00 7.000000000000000666e-02 8.000000000000000167e-02 8.000000000000000167e-02 1.199999999999999956e-01 1.199999999999999956e-01 5.714285714285714107e-02 7.333333333333333370e-02 7.333333333333333370e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 1.333333333333333419e-02 6.666666666666667095e-03 6.666666666666667095e-03 6.666666666666667095e-03 5.333333333333333676e-02 5.999999999999999778e-02 6.666666666666666574e-02 5.333333333333333676e-02 5.999999999999999778e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.190476190476190688e-02 6.190476190476190688e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 1.428571428571428527e-02 1.428571428571428527e-02 9.523809523809524669e-03 4.285714285714285754e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.761904761904761640e-02 5.714285714285714107e-02 5.238095238095238221e-02 4.761904761904761640e-02 5.357142857142856845e-02 4.642857142857143016e-02 5.714285714285714107e-02 4.642857142857143016e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.357142857142856845e-02 5.000000000000000278e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.071428571428571438e-02 1.071428571428571438e-02 3.571428571428571230e-02 3.928571428571428492e-02 3.928571428571428492e-02 4.285714285714285754e-02 3.928571428571428492e-02 3.928571428571428492e-02 4.642857142857143016e-02 3.928571428571428492e-02 5.000000000000000278e-02 4.285714285714285754e-02 4.642857142857143016e-02 4.285714285714285754e-02 4.285714285714285754e-02 3.888888888888888951e-02 5.000000000000000278e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 3.888888888888888951e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.166666666666666435e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.111111111111111154e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.611111111111110772e-02 3.888888888888888951e-02 3.611111111111110772e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.888888888888888951e-02 4.444444444444444614e-02 4.166666666666666435e-02 3.055555555555555455e-02 3.611111111111110772e-02 7.555555555555555636e-02 4.222222222222222349e-02 3.777777777777777818e-02 3.333333333333333287e-02 4.000000000000000083e-02 3.777777777777777818e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.111111111111111154e-02 3.111111111111111022e-02 2.888888888888888756e-02 2.888888888888888756e-02 3.333333333333333287e-02 2.888888888888888756e-02 2.888888888888888756e-02 2.888888888888888756e-02 2.888888888888888756e-02 2.666666666666666838e-02 2.666666666666666838e-02 3.111111111111111022e-02 2.666666666666666838e-02 3.333333333333333287e-02 6.363636363636362980e-02 2.909090909090909088e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.636363636363636187e-02 3.272727272727272985e-02 3.636363636363636187e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.090909090909090995e-02 1.090909090909090995e-02 2.545454545454545539e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 3.571428571428571230e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.727272727272727140e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02
-3.333333333333333148e-01 1.333333333333333315e-01 0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 8.000000000000000167e-02 2.600000000000000089e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000056e-01 1.000000000000000056e-01 1.000000000000000056e-01 5.333333333333333676e-02 5.333333333333333676e-02 1.238095238095238138e-01 1.733333333333333393e-01 1.333333333333333315e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 1.238095238095238138e-01 1.238095238095238138e-01 9.523809523809523281e-02 9.523809523809523281e-02 9.523809523809523281e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 9.285714285714286031e-02 9.285714285714286031e-02 9.285714285714286031e-02 9.285714285714286031e-02 9.285714285714286031e-02 7.142857142857142461e-02 7.142857142857142461e-02 7.142857142857142461e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 7.222222222222221544e-02 7.222222222222221544e-02 7.222222222222221544e-02 7.222222222222221544e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.777777777777777776e-02 5.777777777777777513e-02 5.777777777777777513e-02 5.777777777777777513e-02 4.444444444444444614e-02 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.454545454545454544e-02 4.727272727272727182e-02 4.727272727272727182e-02 4.727272727272727182e-02 4.727272727272727182e-02 3.636363636363636187e-02 3.636363636363636187e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.428571428571428527e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
-0.000000000000000000e+00 0.000000000000000000e+00 2.777777777777777901e-01 1.111111111111111049e-01 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 2.222222222222222099e-01 1.000000000000000056e-01 1.666666666666666574e-01 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.333333333333333315e-01 1.833333333333333204e-01 1.833333333333333204e-01 8.888888888888889228e-02 1.666666666666666574e-01 1.444444444444444309e-01 1.444444444444444309e-01 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.555555555555555580e-01 1.000000000000000056e-01 1.000000000000000056e-01 1.333333333333333315e-01 1.555555555555555580e-01 7.142857142857142461e-02 7.936507936507936067e-02 1.269841269841269771e-01 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 7.936507936507936067e-02 9.523809523809523281e-02 1.031746031746031689e-01 1.190476190476190410e-01 9.523809523809523281e-02 7.936507936507936067e-02 7.936507936507936067e-02 1.190476190476190410e-01 1.349206349206349131e-01 1.349206349206349131e-01 8.730158730158729674e-02 5.952380952380952051e-02 7.142857142857142461e-02 6.547619047619047949e-02 6.547619047619047949e-02 7.142857142857142461e-02 1.250000000000000000e-01 1.130952380952380959e-01 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 2.976190476190476025e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 6.547619047619047949e-02 8.333333333333332871e-02 7.738095238095238360e-02 9.523809523809523281e-02 7.738095238095238360e-02 1.071428571428571369e-01 1.071428571428571369e-01 7.738095238095238360e-02 9.523809523809523281e-02 9.259259259259258745e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.222222222222222307e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 6.944444444444444753e-02 6.018518518518518184e-02 6.481481481481481122e-02 1.064814814814814825e-01 8.796296296296296502e-02 7.870370370370370627e-02 7.870370370370370627e-02 5.555555555555555247e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.925925925925926152e-02 1.000000000000000056e-01 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.121212121212121340e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 5.555555555555555247e-02 4.814814814814814825e-02 6.060606060606060774e-02 8.484848484848485362e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 3.571428571428571230e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 7.575757575757575968e-02 5.151515151515151381e-02 5.151515151515151381e-02 4.848484848484848481e-02 4.242424242424242681e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.333333333333333315e-01 1.666666666666666574e-01 1.666666666666666644e-02 1.000000000000000021e-02 0.000000000000000000e+00 1.000000000000000056e-01 1.400000000000000133e-01 8.000000000000000167e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 1.333333333333333419e-02 1.333333333333333419e-02 9.523809523809524669e-03 6.666666666666667095e-03 6.666666666666667095e-03 7.000000000000000666e-02 8.000000000000000167e-02 8.000000000000000167e-02 1.199999999999999956e-01 7.333333333333333370e-02 6.666666666666666574e-02 7.333333333333333370e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 2.000000000000000042e-02 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 6.666666666666666574e-02 5.333333333333333676e-02 5.333333333333333676e-02 5.999999999999999778e-02 6.666666666666666574e-02 9.047619047619047394e-02 1.047619047619047644e-01 5.714285714285714107e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.071428571428571438e-02 1.071428571428571438e-02 1.071428571428571438e-02 1.071428571428571438e-02 1.071428571428571438e-02 1.071428571428571438e-02 1.071428571428571438e-02 1.071428571428571438e-02 4.285714285714285754e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.238095238095238221e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.761904761904761640e-02 7.142857142857142461e-02 9.285714285714286031e-02 8.214285714285714246e-02 8.214285714285714246e-02 9.285714285714286031e-02 5.357142857142856845e-02 5.357142857142856845e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 3.571428571428571230e-02 3.928571428571428492e-02 3.928571428571428492e-02 4.285714285714285754e-02 3.928571428571428492e-02 4.642857142857143016e-02 4.642857142857143016e-02 3.928571428571428492e-02 4.285714285714285754e-02 4.444444444444444614e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.333333333333333419e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 4.444444444444444614e-02 3.888888888888888951e-02 3.611111111111110772e-02 3.611111111111110772e-02 3.055555555555555455e-02 4.888888888888889145e-02 4.444444444444444614e-02 7.555555555555555636e-02 4.222222222222222349e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.555555555555555511e-02 1.272727272727272770e-02 1.090909090909090995e-02 1.090909090909090995e-02 1.090909090909090995e-02 1.090909090909090995e-02 1.090909090909090995e-02 1.090909090909090995e-02 2.888888888888888756e-02 2.666666666666666838e-02 2.909090909090909088e-02 3.636363636363636187e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 2.142857142857142877e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 3.272727272727272985e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.363636363636363591e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.333333333333333148e-01 1.333333333333333315e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 8.000000000000000167e-02 2.600000000000000089e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000056e-01 1.000000000000000056e-01 1.000000000000000056e-01 5.333333333333333676e-02 1.733333333333333393e-01 1.733333333333333393e-01 1.333333333333333315e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 3.809523809523809867e-02 3.809523809523809867e-02 1.238095238095238138e-01 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 9.285714285714286031e-02 7.142857142857142461e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 5.555555555555555247e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.777777777777777776e-02 1.777777777777777776e-02 1.777777777777777776e-02 5.777777777777777513e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222307e-02 2.222222222222222307e-02 4.727272727272727182e-02 4.727272727272727182e-02 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02
-1.666666666666666574e-01 6.666666666666666574e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.333333333333333315e-01 7.000000000000000666e-02 1.000000000000000056e-01 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.400000000000000133e-01 1.100000000000000006e-01 1.199999999999999956e-01 6.666666666666666574e-02 7.333333333333333370e-02 7.619047619047619735e-02 9.333333333333333759e-02 1.066666666666666735e-01 5.000000000000000278e-02 4.000000000000000083e-02 5.000000000000000278e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.066666666666666735e-01 1.066666666666666735e-01 1.000000000000000056e-01 1.333333333333333315e-01 1.199999999999999956e-01 1.133333333333333276e-01 7.619047619047619735e-02 9.047619047619047394e-02 1.000000000000000056e-01 9.047619047619047394e-02 1.047619047619047644e-01 4.666666666666666879e-02 5.333333333333333676e-02 5.999999999999999778e-02 4.666666666666666879e-02 5.999999999999999778e-02 3.333333333333333287e-02 3.333333333333333287e-02 1.904761904761904934e-02 9.047619047619047394e-02 9.523809523809523281e-02 1.000000000000000056e-01 9.523809523809523281e-02 1.095238095238095233e-01 9.523809523809523281e-02 1.047619047619047644e-01 9.523809523809523281e-02 9.523809523809523281e-02 9.047619047619047394e-02 1.095238095238095233e-01 1.142857142857142821e-01 7.142857142857142461e-02 1.107142857142857095e-01 7.857142857142856984e-02 8.571428571428571508e-02 7.857142857142856984e-02 8.571428571428571508e-02 7.857142857142856984e-02 8.928571428571428770e-02 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 5.238095238095238221e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.928571428571428492e-02 2.142857142857142877e-02 2.142857142857142877e-02 7.857142857142856984e-02 7.857142857142856984e-02 8.571428571428571508e-02 8.214285714285714246e-02 1.000000000000000056e-01 8.214285714285714246e-02 8.928571428571428770e-02 1.071428571428571369e-01 8.571428571428571508e-02 8.571428571428571508e-02 8.928571428571428770e-02 9.642857142857143293e-02 8.928571428571428770e-02 7.222222222222221544e-02 6.944444444444444753e-02 6.944444444444444753e-02 7.499999999999999722e-02 7.499999999999999722e-02 7.499999999999999722e-02 7.499999999999999722e-02 7.777777777777777901e-02 8.611111111111111049e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.000000000000000278e-02 4.642857142857143016e-02 2.500000000000000139e-02 7.499999999999999722e-02 7.499999999999999722e-02 8.611111111111111049e-02 7.777777777777777901e-02 7.777777777777777901e-02 8.611111111111111049e-02 7.499999999999999722e-02 9.722222222222222376e-02 8.611111111111111049e-02 8.055555555555556080e-02 8.055555555555556080e-02 6.944444444444444753e-02 8.333333333333332871e-02 6.222222222222222043e-02 6.222222222222222043e-02 6.666666666666666574e-02 6.666666666666666574e-02 7.555555555555555636e-02 6.666666666666666574e-02 4.444444444444444614e-02 5.000000000000000278e-02 4.722222222222222099e-02 5.000000000000000278e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.722222222222222099e-02 4.722222222222222099e-02 3.555555555555555552e-02 4.000000000000000083e-02 4.000000000000000083e-02 2.666666666666666838e-02 7.777777777777777901e-02 6.222222222222222043e-02 6.666666666666666574e-02 7.111111111111111105e-02 6.666666666666666574e-02 6.666666666666666574e-02 7.555555555555555636e-02 6.444444444444444309e-02 6.222222222222222043e-02 6.666666666666666574e-02 7.111111111111111105e-02 6.666666666666666574e-02 7.111111111111111105e-02 5.818181818181818177e-02 8.000000000000000167e-02 5.999999999999999778e-02 6.727272727272727570e-02 6.363636363636362980e-02 6.363636363636362980e-02 6.363636363636362980e-02 4.222222222222222349e-02 4.444444444444444614e-02 2.909090909090909088e-02 2.727272727272727140e-02 5.636363636363636576e-02 6.727272727272727570e-02 7.090909090909090773e-02 6.363636363636362980e-02 7.857142857142856984e-02 5.999999999999999778e-02 5.999999999999999778e-02 5.818181818181818177e-02 5.999999999999999778e-02 5.999999999999999778e-02 5.818181818181818177e-02 4.363636363636363980e-02 4.000000000000000083e-02 4.363636363636363980e-02 4.363636363636363980e-02 4.181818181818181684e-02
-1.666666666666666574e-01 6.666666666666666574e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.833333333333333204e-01 8.000000000000000167e-02 1.000000000000000056e-01 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.100000000000000006e-01 1.799999999999999933e-01 1.700000000000000122e-01 8.000000000000000167e-02 8.000000000000000167e-02 1.047619047619047644e-01 1.333333333333333315e-01 1.066666666666666735e-01 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.666666666666666963e-02 1.333333333333333315e-01 1.666666666666666574e-01 8.666666666666666963e-02 1.133333333333333276e-01 1.533333333333333215e-01 1.047619047619047644e-01 1.428571428571428492e-01 8.571428571428571508e-02 1.095238095238095233e-01 1.047619047619047644e-01 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.904761904761904934e-02 7.142857142857142461e-02 9.523809523809523281e-02 1.047619047619047644e-01 1.285714285714285587e-01 9.523809523809523281e-02 7.142857142857142461e-02 7.142857142857142461e-02 1.285714285714285587e-01 9.047619047619047394e-02 1.571428571428571397e-01 1.095238095238095233e-01 9.047619047619047394e-02 1.214285714285714274e-01 7.142857142857142461e-02 1.357142857142857040e-01 7.142857142857142461e-02 1.035714285714285782e-01 7.142857142857142461e-02 1.071428571428571369e-01 8.571428571428571508e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.142857142857142877e-02 2.142857142857142877e-02 6.071428571428571369e-02 7.857142857142856984e-02 8.571428571428571508e-02 1.035714285714285782e-01 7.857142857142856984e-02 7.857142857142856984e-02 1.214285714285714274e-01 7.857142857142856984e-02 1.464285714285714357e-01 9.642857142857143293e-02 1.214285714285714274e-01 9.642857142857143293e-02 1.035714285714285782e-01 7.222222222222221544e-02 1.277777777777777679e-01 1.000000000000000056e-01 1.000000000000000056e-01 8.888888888888889228e-02 6.111111111111110911e-02 7.499999999999999722e-02 7.222222222222221544e-02 6.666666666666666574e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 2.222222222222222307e-02 7.222222222222221544e-02 7.222222222222221544e-02 6.666666666666666574e-02 8.611111111111111049e-02 1.000000000000000056e-01 8.611111111111111049e-02 6.388888888888888395e-02 6.111111111111110911e-02 1.000000000000000056e-01 1.388888888888888951e-01 1.194444444444444503e-01 5.277777777777777762e-02 8.611111111111111049e-02 6.222222222222222043e-02 1.066666666666666735e-01 8.444444444444444697e-02 5.333333333333333676e-02 8.000000000000000167e-02 7.555555555555555636e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.222222222222222307e-02 5.999999999999999778e-02 5.777777777777777513e-02 6.222222222222222043e-02 8.444444444444444697e-02 6.222222222222222043e-02 5.555555555555555247e-02 5.777777777777777513e-02 5.555555555555555247e-02 4.666666666666666879e-02 4.666666666666666879e-02 6.444444444444444309e-02 4.666666666666666879e-02 8.000000000000000167e-02 5.454545454545454281e-02 4.727272727272727182e-02 6.545454545454545969e-02 7.272727272727272374e-02 9.090909090909091161e-02 7.272727272727272374e-02 9.090909090909091161e-02 3.111111111111111022e-02 3.111111111111111022e-02 2.181818181818181990e-02 2.181818181818181990e-02 5.090909090909091078e-02 6.909090909090909172e-02 7.454545454545453975e-02 7.454545454545453975e-02 6.428571428571427937e-02 5.454545454545454281e-02 5.454545454545454281e-02 6.363636363636362980e-02 5.454545454545454281e-02 5.454545454545454281e-02 4.909090909090908783e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02
-1.666666666666666574e-01 6.666666666666666574e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.833333333333333204e-01 8.000000000000000167e-02 1.000000000000000056e-01 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.199999999999999956e-01 1.700000000000000122e-01 1.799999999999999933e-01 8.000000000000000167e-02 8.666666666666666963e-02 1.047619047619047644e-01 1.333333333333333315e-01 1.066666666666666735e-01 5.000000000000000278e-02 4.000000000000000083e-02 5.000000000000000278e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.000000000000000056e-01 1.333333333333333315e-01 1.600000000000000033e-01 1.066666666666666735e-01 1.266666666666666774e-01 1.733333333333333393e-01 1.047619047619047644e-01 1.476190476190476220e-01 9.047619047619047394e-02 1.047619047619047644e-01 1.095238095238095233e-01 4.666666666666666879e-02 5.333333333333333676e-02 5.999999999999999778e-02 4.666666666666666879e-02 5.999999999999999778e-02 3.333333333333333287e-02 3.333333333333333287e-02 1.904761904761904934e-02 8.571428571428571508e-02 1.000000000000000056e-01 1.095238095238095233e-01 1.285714285714285587e-01 1.047619047619047644e-01 9.047619047619047394e-02 9.047619047619047394e-02 1.285714285714285587e-01 1.047619047619047644e-01 1.476190476190476220e-01 1.285714285714285587e-01 1.095238095238095233e-01 1.142857142857142821e-01 8.214285714285714246e-02 1.321428571428571452e-01 7.857142857142856984e-02 1.035714285714285782e-01 7.857142857142856984e-02 1.000000000000000056e-01 9.285714285714286031e-02 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 5.238095238095238221e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.928571428571428492e-02 2.142857142857142877e-02 2.142857142857142877e-02 7.499999999999999722e-02 8.571428571428571508e-02 9.285714285714286031e-02 1.071428571428571369e-01 8.928571428571428770e-02 8.928571428571428770e-02 1.250000000000000000e-01 9.642857142857143293e-02 1.392857142857142905e-01 1.000000000000000056e-01 1.250000000000000000e-01 1.035714285714285782e-01 1.071428571428571369e-01 7.777777777777777901e-02 1.194444444444444503e-01 9.722222222222222376e-02 9.722222222222222376e-02 8.611111111111111049e-02 6.944444444444444753e-02 7.777777777777777901e-02 8.055555555555556080e-02 7.499999999999999722e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.000000000000000278e-02 4.642857142857143016e-02 2.500000000000000139e-02 8.055555555555556080e-02 8.333333333333332871e-02 7.777777777777777901e-02 9.166666666666666019e-02 1.055555555555555552e-01 9.444444444444444198e-02 8.055555555555556080e-02 7.499999999999999722e-02 1.083333333333333370e-01 1.305555555555555636e-01 1.166666666666666685e-01 6.666666666666666574e-02 9.166666666666666019e-02 7.555555555555555636e-02 1.022222222222222282e-01 8.444444444444444697e-02 6.222222222222222043e-02 9.111111111111111494e-02 7.555555555555555636e-02 4.444444444444444614e-02 5.000000000000000278e-02 4.722222222222222099e-02 5.000000000000000278e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.722222222222222099e-02 4.722222222222222099e-02 3.555555555555555552e-02 4.000000000000000083e-02 4.000000000000000083e-02 2.666666666666666838e-02 7.777777777777777901e-02 6.666666666666666574e-02 7.333333333333333370e-02 9.333333333333333759e-02 7.111111111111111105e-02 7.111111111111111105e-02 7.111111111111111105e-02 6.888888888888888840e-02 5.999999999999999778e-02 6.222222222222222043e-02 8.222222222222222432e-02 6.444444444444444309e-02 8.888888888888889228e-02 6.727272727272727570e-02 6.181818181818182073e-02 6.727272727272727570e-02 7.454545454545453975e-02 8.909090909090909560e-02 7.454545454545453975e-02 8.909090909090909560e-02 4.222222222222222349e-02 4.444444444444444614e-02 2.909090909090909088e-02 2.727272727272727140e-02 5.999999999999999778e-02 8.181818181818181768e-02 7.818181818181818565e-02 7.818181818181818565e-02 8.214285714285714246e-02 6.727272727272727570e-02 6.363636363636362980e-02 7.090909090909090773e-02 6.363636363636362980e-02 6.545454545454545969e-02 6.181818181818182073e-02 4.363636363636363980e-02 4.000000000000000083e-02 4.363636363636363980e-02 4.363636363636363980e-02 4.181818181818181684e-02
-8.888888888888889228e-02 1.111111111111111049e-01 0.000000000000000000e+00 0.000000000000000000e+00 8.888888888888889228e-02 1.199999999999999956e-01 5.333333333333333676e-02 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 6.666666666666666574e-02 8.000000000000000167e-02 8.000000000000000167e-02 1.199999999999999956e-01 1.155555555555555503e-01 5.079365079365079361e-02 6.222222222222222043e-02 6.222222222222222043e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.777777777777777776e-02 8.888888888888888881e-03 8.888888888888888881e-03 8.888888888888888881e-03 5.333333333333333676e-02 6.222222222222222043e-02 7.111111111111111105e-02 5.333333333333333676e-02 6.222222222222222043e-02 7.111111111111111105e-02 5.079365079365079361e-02 6.349206349206348854e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.714285714285714107e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 5.079365079365079361e-02 6.349206349206348854e-02 5.714285714285714107e-02 5.079365079365079361e-02 5.238095238095238221e-02 4.285714285714285754e-02 5.714285714285714107e-02 4.285714285714285754e-02 4.761904761904761640e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.761904761904761640e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.428571428571428527e-02 1.428571428571428527e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.761904761904761640e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.285714285714285754e-02 5.714285714285714107e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.703703703703703498e-02 5.185185185185185036e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.481481481481481538e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.074074074074074403e-02 3.703703703703703498e-02 3.703703703703703498e-02 4.444444444444444614e-02 5.185185185185185036e-02 4.814814814814814825e-02 3.333333333333333287e-02 4.074074074074074403e-02 9.925925925925925541e-02 4.444444444444444614e-02 3.851851851851852138e-02 3.259259259259258967e-02 4.148148148148148029e-02 3.851851851851852138e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.777777777777777776e-02 1.777777777777777776e-02 1.777777777777777776e-02 1.481481481481481538e-02 3.555555555555555552e-02 3.259259259259258967e-02 3.259259259259258967e-02 3.851851851851852138e-02 3.259259259259258967e-02 3.259259259259258967e-02 3.259259259259258967e-02 3.259259259259258967e-02 2.962962962962963076e-02 2.962962962962963076e-02 3.555555555555555552e-02 2.962962962962963076e-02 3.851851851851852138e-02 8.363636363636363369e-02 2.909090909090909088e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.878787878787878785e-02 3.393939393939394283e-02 3.878787878787878785e-02 2.074074074074074014e-02 2.074074074074074014e-02 1.454545454545454544e-02 1.454545454545454544e-02 2.909090909090909088e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.393939393939394283e-02 4.285714285714285754e-02 2.909090909090909088e-02 2.909090909090909088e-02 3.151515151515151686e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02
-8.888888888888889228e-02 1.111111111111111049e-01 0.000000000000000000e+00 0.000000000000000000e+00 8.888888888888889228e-02 1.199999999999999956e-01 5.333333333333333676e-02 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 7.333333333333333370e-02 8.000000000000000167e-02 8.666666666666666963e-02 1.155555555555555503e-01 1.199999999999999956e-01 5.079365079365079361e-02 6.222222222222222043e-02 6.222222222222222043e-02 3.333333333333333287e-02 2.666666666666666838e-02 3.333333333333333287e-02 1.777777777777777776e-02 8.888888888888888881e-03 8.888888888888888881e-03 8.888888888888888881e-03 6.222222222222222043e-02 6.666666666666666574e-02 7.555555555555555636e-02 6.666666666666666574e-02 7.111111111111111105e-02 8.444444444444444697e-02 5.079365079365079361e-02 6.666666666666666574e-02 5.396825396825397081e-02 5.714285714285714107e-02 6.031746031746031828e-02 3.111111111111111022e-02 3.555555555555555552e-02 4.000000000000000083e-02 3.111111111111111022e-02 4.000000000000000083e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.269841269841269840e-02 5.396825396825397081e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.031746031746031828e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.031746031746031828e-02 6.984126984126984294e-02 6.984126984126984294e-02 6.349206349206348854e-02 5.238095238095238221e-02 5.000000000000000278e-02 5.952380952380952051e-02 4.761904761904761640e-02 5.000000000000000278e-02 4.761904761904761640e-02 5.238095238095238221e-02 5.238095238095238221e-02 3.492063492063492147e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.492063492063492147e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.174603174603174427e-02 3.492063492063492147e-02 3.174603174603174427e-02 3.492063492063492147e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.619047619047619110e-02 1.428571428571428527e-02 1.428571428571428527e-02 4.761904761904761640e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.476190476190476164e-02 5.238095238095238221e-02 5.238095238095238221e-02 6.190476190476190688e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.476190476190476164e-02 6.190476190476190688e-02 5.714285714285714107e-02 5.476190476190476164e-02 4.074074074074074403e-02 5.370370370370370489e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.259259259259259162e-02 4.444444444444444614e-02 4.629629629629629373e-02 4.629629629629629373e-02 3.333333333333333287e-02 3.095238095238095344e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.095238095238095344e-02 3.333333333333333287e-02 3.095238095238095344e-02 1.666666666666666644e-02 4.444444444444444614e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.814814814814814825e-02 5.370370370370370489e-02 5.000000000000000278e-02 4.814814814814814825e-02 4.814814814814814825e-02 5.555555555555555247e-02 6.296296296296295669e-02 5.740740740740740700e-02 4.259259259259259162e-02 4.814814814814814825e-02 9.481481481481481011e-02 4.740740740740740505e-02 4.148148148148148029e-02 3.851851851851852138e-02 4.888888888888889145e-02 4.148148148148148029e-02 2.962962962962963076e-02 3.333333333333333287e-02 3.148148148148147835e-02 3.333333333333333287e-02 2.962962962962963076e-02 2.962962962962963076e-02 3.148148148148147835e-02 3.148148148148147835e-02 2.370370370370370253e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.777777777777777776e-02 4.740740740740740505e-02 4.000000000000000083e-02 4.148148148148148029e-02 4.888888888888889145e-02 4.000000000000000083e-02 4.296296296296295975e-02 4.296296296296295975e-02 4.148148148148148029e-02 3.851851851851852138e-02 4.000000000000000083e-02 4.740740740740740505e-02 4.148148148148148029e-02 4.888888888888889145e-02 8.121212121212120771e-02 3.878787878787878785e-02 3.757575757575757486e-02 3.757575757575757486e-02 4.242424242424242681e-02 3.757575757575757486e-02 4.242424242424242681e-02 2.814814814814814783e-02 2.962962962962963076e-02 1.939393939393939392e-02 1.818181818181818094e-02 3.636363636363636187e-02 4.606060606060605883e-02 4.121212121212121382e-02 4.121212121212121382e-02 5.476190476190476164e-02 3.878787878787878785e-02 3.636363636363636187e-02 3.878787878787878785e-02 3.636363636363636187e-02 3.757575757575757486e-02 3.757575757575757486e-02 2.909090909090909088e-02 2.666666666666666838e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.787878787878787790e-02
-1.587301587301587213e-01 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.269841269841269771e-01 5.714285714285714107e-02 1.238095238095238138e-01 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 7.619047619047619735e-02 1.047619047619047644e-01 1.047619047619047644e-01 5.079365079365079361e-02 5.079365079365079361e-02 1.065759637188208597e-01 1.142857142857142821e-01 8.253968253968253788e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 5.714285714285714107e-02 7.619047619047619735e-02 8.888888888888889228e-02 5.714285714285714107e-02 6.984126984126984294e-02 8.888888888888889228e-02 1.065759637188208597e-01 1.043083900226757371e-01 6.349206349206348854e-02 7.256235827664399285e-02 7.256235827664399285e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.360544217687074779e-02 1.360544217687074779e-02 9.070294784580499106e-03 4.535147392290249380e-02 5.442176870748299117e-02 5.895691609977324332e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.535147392290249380e-02 6.802721088435374763e-02 5.442176870748299117e-02 7.709750566893423807e-02 6.349206349206348854e-02 5.442176870748299117e-02 9.693877551020407823e-02 6.122448979591836593e-02 9.523809523809523281e-02 6.122448979591836593e-02 7.823129251700680631e-02 5.102040816326530726e-02 6.462585034013605678e-02 5.782312925170068202e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.020408163265306041e-02 1.020408163265306041e-02 3.741496598639455773e-02 4.421768707482993249e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.421768707482993249e-02 4.421768707482993249e-02 6.122448979591836593e-02 4.421768707482993249e-02 6.802721088435374763e-02 5.102040816326530726e-02 6.122448979591836593e-02 5.102040816326530726e-02 5.442176870748299117e-02 6.746031746031745657e-02 8.862433862433861942e-02 7.804232804232803800e-02 7.804232804232803800e-02 5.291005291005290712e-02 4.232804232804232569e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.497354497354497105e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.058201058201058142e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.703703703703703498e-02 4.497354497354497105e-02 5.026455026455026176e-02 4.497354497354497105e-02 3.703703703703703498e-02 3.439153439153438963e-02 5.026455026455026176e-02 6.084656084656084318e-02 5.555555555555555247e-02 3.174603174603174427e-02 4.497354497354497105e-02 3.386243386243386472e-02 8.465608465608465139e-02 6.455026455026455223e-02 4.232804232804232569e-02 4.867724867724868010e-02 4.444444444444444614e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.058201058201058142e-02 3.386243386243386472e-02 3.174603174603174427e-02 3.386243386243386472e-02 4.232804232804232569e-02 3.386243386243386472e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 2.751322751322751378e-02 2.751322751322751378e-02 3.597883597883597823e-02 2.751322751322751378e-02 4.021164021164021218e-02 2.943722943722943894e-02 3.636363636363636187e-02 5.108225108225108307e-02 5.454545454545454281e-02 7.099567099567100081e-02 4.155844155844155841e-02 4.848484848484848481e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.038961038961038960e-02 1.038961038961038960e-02 2.770562770562770560e-02 3.463203463203463200e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.401360544217687382e-02 2.943722943722943894e-02 2.943722943722943894e-02 3.290043290043290214e-02 2.943722943722943894e-02 2.943722943722943894e-02 2.770562770562770560e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02
-2.222222222222222099e-01 8.888888888888889228e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 7.333333333333333370e-02 1.733333333333333393e-01 1.111111111111111154e-02 6.666666666666667095e-03 0.000000000000000000e+00 9.333333333333333759e-02 1.333333333333333315e-01 1.333333333333333315e-01 6.222222222222222043e-02 6.222222222222222043e-02 1.142857142857142821e-01 1.555555555555555580e-01 1.111111111111111049e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.888888888888888881e-03 4.444444444444444441e-03 4.444444444444444441e-03 4.444444444444444441e-03 6.666666666666666574e-02 9.333333333333333759e-02 1.111111111111111049e-01 6.666666666666666574e-02 8.444444444444444697e-02 1.111111111111111049e-01 1.142857142857142821e-01 1.396825396825396859e-01 8.253968253968253788e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.349206349206349201e-03 5.079365079365079361e-02 6.349206349206348854e-02 6.984126984126984294e-02 8.253968253968253788e-02 6.349206349206348854e-02 5.079365079365079361e-02 5.079365079365079361e-02 8.253968253968253788e-02 6.349206349206348854e-02 9.523809523809523281e-02 7.619047619047619735e-02 6.349206349206348854e-02 1.071428571428571369e-01 7.857142857142856984e-02 1.166666666666666685e-01 7.857142857142856984e-02 9.761904761904761918e-02 6.428571428571427937e-02 8.333333333333332871e-02 7.380952380952381098e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 7.142857142857142634e-03 7.142857142857142634e-03 4.047619047619047811e-02 5.000000000000000278e-02 5.476190476190476164e-02 6.428571428571427937e-02 5.000000000000000278e-02 5.000000000000000278e-02 7.380952380952381098e-02 5.000000000000000278e-02 8.333333333333332871e-02 5.952380952380952051e-02 7.380952380952381098e-02 5.952380952380952051e-02 6.428571428571427937e-02 7.037037037037037479e-02 1.000000000000000056e-01 8.518518518518518323e-02 8.518518518518518323e-02 6.666666666666666574e-02 5.185185185185185036e-02 5.925925925925926152e-02 5.925925925925926152e-02 5.555555555555555247e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.407407407407407690e-03 4.444444444444444614e-02 4.444444444444444614e-02 4.074074074074074403e-02 5.185185185185185036e-02 5.925925925925926152e-02 5.185185185185185036e-02 4.074074074074074403e-02 3.703703703703703498e-02 5.925925925925926152e-02 7.407407407407406996e-02 6.666666666666666574e-02 3.333333333333333287e-02 5.185185185185185036e-02 3.851851851851852138e-02 8.148148148148148806e-02 6.962962962962962465e-02 5.185185185185185036e-02 6.074074074074074098e-02 5.481481481481481621e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.888888888888888881e-03 8.888888888888888881e-03 8.888888888888888881e-03 7.407407407407407690e-03 3.703703703703703498e-02 3.407407407407407607e-02 3.703703703703703498e-02 4.888888888888889145e-02 3.703703703703703498e-02 3.407407407407407607e-02 3.407407407407407607e-02 3.407407407407407607e-02 2.814814814814814783e-02 2.814814814814814783e-02 4.000000000000000083e-02 2.814814814814814783e-02 4.592592592592592560e-02 3.272727272727272985e-02 4.363636363636363980e-02 5.333333333333333676e-02 5.818181818181818177e-02 6.787878787878788567e-02 5.090909090909091078e-02 6.060606060606060774e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.272727272727272721e-03 7.272727272727272721e-03 2.909090909090909088e-02 3.878787878787878785e-02 4.121212121212121382e-02 4.121212121212121382e-02 3.333333333333333287e-02 3.151515151515151686e-02 3.151515151515151686e-02 3.636363636363636187e-02 3.151515151515151686e-02 3.151515151515151686e-02 2.909090909090909088e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03
-2.222222222222222099e-01 8.888888888888889228e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.444444444444444309e-01 7.333333333333333370e-02 1.333333333333333315e-01 1.111111111111111154e-02 6.666666666666667095e-03 0.000000000000000000e+00 1.066666666666666735e-01 1.066666666666666735e-01 1.066666666666666735e-01 6.222222222222222043e-02 6.222222222222222043e-02 8.253968253968253788e-02 1.111111111111111049e-01 1.555555555555555580e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.888888888888888881e-03 4.444444444444444441e-03 4.444444444444444441e-03 4.444444444444444441e-03 7.555555555555555636e-02 7.555555555555555636e-02 8.444444444444444697e-02 7.555555555555555636e-02 8.444444444444444697e-02 8.444444444444444697e-02 8.253968253968253788e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.206349206349206366e-01 1.206349206349206366e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.349206349206349201e-03 5.714285714285714107e-02 6.349206349206348854e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.984126984126984294e-02 6.984126984126984294e-02 6.349206349206348854e-02 7.380952380952381098e-02 7.380952380952381098e-02 7.857142857142856984e-02 7.380952380952381098e-02 6.904761904761905211e-02 7.380952380952381098e-02 9.761904761904761918e-02 7.857142857142856984e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 9.523809523809524669e-03 7.142857142857142634e-03 7.142857142857142634e-03 4.523809523809523697e-02 5.000000000000000278e-02 4.523809523809523697e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.476190476190476164e-02 5.000000000000000278e-02 5.952380952380952051e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.000000000000000278e-02 5.185185185185185036e-02 6.666666666666666574e-02 5.925925925925926152e-02 5.925925925925926152e-02 6.666666666666666574e-02 5.925925925925926152e-02 6.296296296296295669e-02 6.296296296296295669e-02 6.666666666666666574e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.407407407407407690e-03 3.703703703703703498e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 4.814814814814814825e-02 3.703703703703703498e-02 4.074074074074074403e-02 3.851851851851852138e-02 5.481481481481481621e-02 4.888888888888889145e-02 4.888888888888889145e-02 5.777777777777777513e-02 5.481481481481481621e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.888888888888888881e-03 8.888888888888888881e-03 8.888888888888888881e-03 7.407407407407407690e-03 4.000000000000000083e-02 3.407407407407407607e-02 3.111111111111111022e-02 3.703703703703703498e-02 3.111111111111111022e-02 3.407407407407407607e-02 3.407407407407407607e-02 3.407407407407407607e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.703703703703703498e-02 3.111111111111111022e-02 4.000000000000000083e-02 3.272727272727272985e-02 4.121212121212121382e-02 4.606060606060605883e-02 4.121212121212121382e-02 4.606060606060605883e-02 4.121212121212121382e-02 4.606060606060605883e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.272727272727272721e-03 7.272727272727272721e-03 2.909090909090909088e-02 3.393939393939394283e-02 3.151515151515151686e-02 3.151515151515151686e-02 3.333333333333333287e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.909090909090909088e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.909090909090909088e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03
-0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.333333333333333315e-01 7.000000000000000666e-02 1.000000000000000056e-01 5.000000000000000278e-02 4.000000000000000083e-02 5.000000000000000278e-02 2.666666666666666838e-02 3.333333333333333287e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.400000000000000133e-01 1.100000000000000006e-01 1.199999999999999956e-01 6.666666666666666574e-02 9.333333333333333759e-02 8.666666666666666963e-02 1.066666666666666735e-01 5.333333333333333676e-02 4.666666666666666879e-02 4.666666666666666879e-02 5.999999999999999778e-02 5.333333333333333676e-02 5.999999999999999778e-02 1.904761904761904934e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.000000000000000056e-01 1.066666666666666735e-01 1.333333333333333315e-01 1.066666666666666735e-01 1.133333333333333276e-01 6.666666666666666574e-02 6.666666666666666574e-02 7.619047619047619735e-02 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 5.714285714285714107e-02 5.714285714285714107e-02 2.142857142857142877e-02 3.214285714285713969e-02 2.500000000000000139e-02 2.857142857142857054e-02 2.500000000000000139e-02 2.857142857142857054e-02 2.142857142857142877e-02 2.857142857142857054e-02 9.047619047619047394e-02 9.523809523809523281e-02 1.000000000000000056e-01 9.523809523809523281e-02 1.095238095238095233e-01 9.523809523809523281e-02 1.047619047619047644e-01 9.523809523809523281e-02 9.523809523809523281e-02 9.047619047619047394e-02 1.142857142857142821e-01 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.785714285714285199e-02 7.142857142857142461e-02 7.857142857142856984e-02 5.000000000000000278e-02 4.642857142857143016e-02 4.642857142857143016e-02 4.642857142857143016e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.714285714285714107e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.642857142857143016e-02 2.777777777777777624e-02 2.500000000000000139e-02 2.500000000000000139e-02 2.500000000000000139e-02 2.500000000000000139e-02 3.055555555555555455e-02 2.777777777777777624e-02 3.055555555555555455e-02 3.055555555555555455e-02 7.857142857142856984e-02 8.571428571428571508e-02 1.000000000000000056e-01 9.285714285714286031e-02 9.285714285714286031e-02 8.928571428571428770e-02 8.571428571428571508e-02 8.214285714285714246e-02 8.214285714285714246e-02 7.499999999999999722e-02 4.444444444444444614e-02 4.722222222222222099e-02 4.722222222222222099e-02 4.444444444444444614e-02 4.722222222222222099e-02 4.722222222222222099e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.722222222222222099e-02 4.722222222222222099e-02 4.444444444444444614e-02 4.000000000000000083e-02 2.666666666666666838e-02 2.666666666666666838e-02 3.111111111111111022e-02 3.333333333333333287e-02 2.666666666666666838e-02 7.499999999999999722e-02 9.722222222222222376e-02 8.611111111111111049e-02 8.055555555555556080e-02 8.055555555555556080e-02 7.777777777777777901e-02 8.611111111111111049e-02 6.944444444444444753e-02 5.333333333333333676e-02 6.222222222222222043e-02 6.222222222222222043e-02 6.222222222222222043e-02 4.888888888888889145e-02 4.222222222222222349e-02 4.444444444444444614e-02 4.666666666666666879e-02 4.222222222222222349e-02 4.666666666666666879e-02 4.666666666666666879e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.666666666666666879e-02 4.888888888888889145e-02 4.888888888888889145e-02 4.666666666666666879e-02 3.818181818181818482e-02 3.636363636363636187e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 6.222222222222222043e-02 6.222222222222222043e-02 5.818181818181818177e-02 6.363636363636362980e-02 4.000000000000000083e-02 4.727272727272727182e-02 4.000000000000000083e-02 4.000000000000000083e-02 6.071428571428571369e-02 4.363636363636363980e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.181818181818181684e-02 4.181818181818181684e-02 7.090909090909090773e-02 5.999999999999999778e-02 5.999999999999999778e-02 6.909090909090909172e-02 5.636363636363636576e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.833333333333333204e-01 8.000000000000000167e-02 1.000000000000000056e-01 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.100000000000000006e-01 1.799999999999999933e-01 1.700000000000000122e-01 8.000000000000000167e-02 1.333333333333333315e-01 1.066666666666666735e-01 1.066666666666666735e-01 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.666666666666666574e-01 8.666666666666666963e-02 8.666666666666666963e-02 1.333333333333333315e-01 1.533333333333333215e-01 6.666666666666666574e-02 7.619047619047619735e-02 1.047619047619047644e-01 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 7.142857142857142461e-02 9.523809523809523281e-02 1.047619047619047644e-01 1.285714285714285587e-01 9.523809523809523281e-02 7.142857142857142461e-02 7.142857142857142461e-02 1.285714285714285587e-01 1.523809523809523947e-01 1.571428571428571397e-01 8.095238095238095621e-02 5.714285714285714107e-02 7.142857142857142461e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.142857142857142461e-02 1.214285714285714274e-01 1.071428571428571369e-01 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 6.071428571428571369e-02 8.571428571428571508e-02 7.857142857142856984e-02 1.035714285714285782e-01 7.857142857142856984e-02 1.214285714285714274e-01 1.250000000000000000e-01 7.857142857142856984e-02 1.035714285714285782e-01 8.888888888888889228e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 7.222222222222221544e-02 6.111111111111110911e-02 6.666666666666666574e-02 1.388888888888888951e-01 1.027777777777777735e-01 8.611111111111111049e-02 8.611111111111111049e-02 5.277777777777777762e-02 4.444444444444444614e-02 4.444444444444444614e-02 6.222222222222222043e-02 1.066666666666666735e-01 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 3.111111111111111022e-02 2.545454545454545539e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 5.777777777777777513e-02 4.666666666666666879e-02 5.454545454545454281e-02 9.090909090909091161e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 4.285714285714285754e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 9.818181818181817566e-02 5.454545454545454281e-02 5.454545454545454281e-02 5.090909090909091078e-02 4.181818181818181684e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.666666666666666574e-01 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.833333333333333204e-01 8.000000000000000167e-02 1.000000000000000056e-01 5.000000000000000278e-02 4.000000000000000083e-02 5.000000000000000278e-02 2.666666666666666838e-02 3.333333333333333287e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.199999999999999956e-01 1.700000000000000122e-01 1.799999999999999933e-01 8.000000000000000167e-02 1.333333333333333315e-01 1.066666666666666735e-01 1.066666666666666735e-01 5.333333333333333676e-02 4.666666666666666879e-02 4.666666666666666879e-02 5.999999999999999778e-02 5.333333333333333676e-02 5.999999999999999778e-02 1.904761904761904934e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.600000000000000033e-01 1.000000000000000056e-01 1.066666666666666735e-01 1.333333333333333315e-01 1.733333333333333393e-01 7.142857142857142461e-02 8.095238095238095621e-02 1.047619047619047644e-01 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.761904761904761640e-02 5.238095238095238221e-02 4.761904761904761640e-02 5.714285714285714107e-02 5.714285714285714107e-02 2.142857142857142877e-02 3.214285714285713969e-02 2.500000000000000139e-02 2.857142857142857054e-02 2.500000000000000139e-02 2.857142857142857054e-02 2.142857142857142877e-02 2.857142857142857054e-02 8.571428571428571508e-02 1.000000000000000056e-01 1.095238095238095233e-01 1.285714285714285587e-01 1.047619047619047644e-01 9.047619047619047394e-02 9.047619047619047394e-02 1.285714285714285587e-01 1.523809523809523947e-01 1.476190476190476220e-01 9.523809523809523281e-02 6.428571428571427937e-02 7.857142857142856984e-02 7.142857142857142461e-02 7.142857142857142461e-02 8.214285714285714246e-02 1.142857142857142821e-01 1.000000000000000056e-01 5.000000000000000278e-02 4.642857142857143016e-02 4.642857142857143016e-02 4.642857142857143016e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.714285714285714107e-02 5.000000000000000278e-02 4.642857142857143016e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.642857142857143016e-02 2.777777777777777624e-02 2.500000000000000139e-02 2.500000000000000139e-02 2.500000000000000139e-02 2.500000000000000139e-02 3.055555555555555455e-02 2.777777777777777624e-02 3.055555555555555455e-02 3.055555555555555455e-02 7.499999999999999722e-02 9.285714285714286031e-02 8.928571428571428770e-02 1.107142857142857095e-01 8.928571428571428770e-02 1.250000000000000000e-01 1.214285714285714274e-01 8.928571428571428770e-02 1.071428571428571369e-01 8.611111111111111049e-02 4.444444444444444614e-02 4.722222222222222099e-02 4.722222222222222099e-02 4.444444444444444614e-02 4.722222222222222099e-02 4.722222222222222099e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.722222222222222099e-02 4.722222222222222099e-02 4.444444444444444614e-02 4.000000000000000083e-02 2.666666666666666838e-02 2.666666666666666838e-02 3.111111111111111022e-02 3.333333333333333287e-02 2.666666666666666838e-02 8.055555555555556080e-02 7.499999999999999722e-02 7.777777777777777901e-02 1.305555555555555636e-01 1.027777777777777735e-01 9.166666666666666019e-02 9.444444444444444198e-02 6.666666666666666574e-02 5.333333333333333676e-02 5.777777777777777513e-02 7.555555555555555636e-02 1.022222222222222282e-01 4.888888888888889145e-02 4.222222222222222349e-02 4.444444444444444614e-02 4.666666666666666879e-02 4.222222222222222349e-02 4.666666666666666879e-02 4.666666666666666879e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.666666666666666879e-02 4.888888888888889145e-02 4.888888888888889145e-02 4.666666666666666879e-02 3.818181818181818482e-02 3.636363636363636187e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 6.666666666666666574e-02 5.999999999999999778e-02 6.181818181818182073e-02 8.909090909090909560e-02 4.000000000000000083e-02 4.727272727272727182e-02 4.000000000000000083e-02 4.000000000000000083e-02 6.071428571428571369e-02 4.363636363636363980e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.181818181818181684e-02 4.181818181818181684e-02 9.636363636363635965e-02 6.363636363636362980e-02 6.727272727272727570e-02 6.363636363636362980e-02 5.454545454545454281e-02
-0.000000000000000000e+00 0.000000000000000000e+00 8.888888888888889228e-02 1.111111111111111049e-01 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 8.888888888888889228e-02 1.199999999999999956e-01 5.333333333333333676e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.777777777777777776e-02 1.777777777777777776e-02 1.269841269841269840e-02 8.888888888888888881e-03 8.888888888888888881e-03 6.666666666666666574e-02 8.000000000000000167e-02 8.000000000000000167e-02 1.199999999999999956e-01 6.222222222222222043e-02 5.333333333333333676e-02 6.222222222222222043e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.111111111111111105e-02 5.333333333333333676e-02 5.333333333333333676e-02 6.222222222222222043e-02 7.111111111111111105e-02 9.206349206349206948e-02 1.142857142857142821e-01 5.079365079365079361e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.079365079365079361e-02 7.380952380952381098e-02 1.095238095238095233e-01 9.047619047619047394e-02 9.047619047619047394e-02 1.071428571428571369e-01 5.238095238095238221e-02 5.238095238095238221e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.761904761904761640e-02 4.285714285714285754e-02 5.238095238095238221e-02 5.238095238095238221e-02 4.285714285714285754e-02 4.761904761904761640e-02 4.444444444444444614e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.777777777777777776e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 5.185185185185185036e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.333333333333333287e-02 5.185185185185185036e-02 4.592592592592592560e-02 9.925925925925925541e-02 4.444444444444444614e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.074074074074074014e-02 1.696969696969697142e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 3.259259259259258967e-02 2.962962962962963076e-02 2.909090909090909088e-02 3.878787878787878785e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 2.857142857142857054e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 3.878787878787878785e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.666666666666666838e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222099e-01 8.888888888888889228e-02 1.111111111111111154e-02 6.666666666666667095e-03 0.000000000000000000e+00 1.666666666666666574e-01 7.333333333333333370e-02 1.733333333333333393e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.888888888888888881e-03 8.888888888888888881e-03 6.349206349206349201e-03 4.444444444444444441e-03 4.444444444444444441e-03 9.333333333333333759e-02 1.333333333333333315e-01 1.333333333333333315e-01 6.222222222222222043e-02 1.555555555555555580e-01 1.377777777777777768e-01 1.111111111111111049e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 1.111111111111111049e-01 6.666666666666666574e-02 6.666666666666666574e-02 9.333333333333333759e-02 1.111111111111111049e-01 4.761904761904761640e-02 5.396825396825397081e-02 1.142857142857142821e-01 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 5.079365079365079361e-02 6.349206349206348854e-02 6.984126984126984294e-02 8.253968253968253788e-02 6.349206349206348854e-02 5.079365079365079361e-02 5.079365079365079361e-02 8.253968253968253788e-02 9.523809523809523281e-02 9.523809523809523281e-02 5.714285714285714107e-02 3.809523809523809867e-02 4.761904761904761640e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.761904761904761640e-02 1.071428571428571369e-01 8.333333333333332871e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 4.047619047619047811e-02 5.476190476190476164e-02 5.000000000000000278e-02 6.428571428571427937e-02 5.000000000000000278e-02 7.380952380952381098e-02 7.380952380952381098e-02 5.000000000000000278e-02 6.428571428571427937e-02 6.666666666666666574e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.888888888888888881e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 4.444444444444444614e-02 3.703703703703703498e-02 4.074074074074074403e-02 7.407407407407406996e-02 5.925925925925926152e-02 5.185185185185185036e-02 5.185185185185185036e-02 3.333333333333333287e-02 2.666666666666666838e-02 2.666666666666666838e-02 3.851851851851852138e-02 8.148148148148148806e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.484848484848485708e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 3.407407407407407607e-02 2.814814814814814783e-02 4.848484848484848481e-02 6.787878787878788567e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 1.428571428571428527e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 5.090909090909091078e-02 3.151515151515151686e-02 3.151515151515151686e-02 2.909090909090909088e-02 2.424242424242424240e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222099e-01 8.888888888888889228e-02 1.111111111111111154e-02 6.666666666666667095e-03 0.000000000000000000e+00 1.444444444444444309e-01 6.666666666666666574e-02 1.733333333333333393e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.888888888888888881e-03 8.888888888888888881e-03 6.349206349206349201e-03 4.444444444444444441e-03 4.444444444444444441e-03 8.666666666666666963e-02 1.066666666666666735e-01 1.066666666666666735e-01 5.333333333333333676e-02 1.377777777777777768e-01 1.377777777777777768e-01 1.022222222222222282e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 8.444444444444444697e-02 6.222222222222222043e-02 6.222222222222222043e-02 7.555555555555555636e-02 8.444444444444444697e-02 4.126984126984126894e-02 4.444444444444444614e-02 1.142857142857142821e-01 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 4.761904761904761640e-02 5.396825396825397081e-02 5.714285714285714107e-02 6.349206349206348854e-02 5.396825396825397081e-02 4.761904761904761640e-02 4.761904761904761640e-02 6.349206349206348854e-02 6.984126984126984294e-02 6.984126984126984294e-02 5.079365079365079361e-02 3.333333333333333287e-02 3.809523809523809867e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.809523809523809867e-02 9.761904761904761918e-02 6.904761904761905211e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 3.809523809523809867e-02 4.523809523809523697e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.285714285714285754e-02 5.476190476190476164e-02 5.476190476190476164e-02 4.285714285714285754e-02 5.000000000000000278e-02 5.555555555555555247e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.888888888888888881e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 3.703703703703703498e-02 3.333333333333333287e-02 3.518518518518518740e-02 5.185185185185185036e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.148148148148147835e-02 2.370370370370370253e-02 2.370370370370370253e-02 2.962962962962963076e-02 7.555555555555555636e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.484848484848485708e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 2.962962962962963076e-02 2.666666666666666838e-02 4.848484848484848481e-02 5.818181818181818177e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 1.428571428571428527e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 3.636363636363636187e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.545454545454545539e-02 2.303030303030302942e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.222222222222222099e-01 8.888888888888889228e-02 1.111111111111111154e-02 6.666666666666667095e-03 0.000000000000000000e+00 1.444444444444444309e-01 7.333333333333333370e-02 1.333333333333333315e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.888888888888888881e-03 8.888888888888888881e-03 6.349206349206349201e-03 4.444444444444444441e-03 4.444444444444444441e-03 1.066666666666666735e-01 1.066666666666666735e-01 1.066666666666666735e-01 6.222222222222222043e-02 1.111111111111111049e-01 1.022222222222222282e-01 1.555555555555555580e-01 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 8.444444444444444697e-02 7.555555555555555636e-02 7.555555555555555636e-02 7.555555555555555636e-02 8.444444444444444697e-02 4.761904761904761640e-02 5.396825396825397081e-02 8.253968253968253788e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 5.714285714285714107e-02 6.349206349206348854e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.984126984126984294e-02 6.984126984126984294e-02 6.984126984126984294e-02 3.809523809523809867e-02 4.761904761904761640e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.761904761904761640e-02 7.380952380952381098e-02 9.761904761904761918e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 4.523809523809523697e-02 4.523809523809523697e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.000000000000000278e-02 5.000000000000000278e-02 6.666666666666666574e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.888888888888888881e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 3.703703703703703498e-02 4.444444444444444614e-02 4.074074074074074403e-02 5.185185185185185036e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.703703703703703498e-02 2.666666666666666838e-02 2.666666666666666838e-02 3.851851851851852138e-02 5.481481481481481621e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.484848484848485708e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 3.407407407407407607e-02 3.111111111111111022e-02 3.636363636363636187e-02 4.606060606060605883e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 1.428571428571428527e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 3.636363636363636187e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.909090909090909088e-02 2.666666666666666838e-02
-1.111111111111111049e-01 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000056e-01 5.333333333333333676e-02 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.066666666666666735e-01 8.666666666666666963e-02 1.000000000000000056e-01 5.333333333333333676e-02 6.222222222222222043e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.555555555555555636e-02 5.333333333333333676e-02 4.000000000000000083e-02 5.333333333333333676e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.111111111111111049e-01 8.444444444444444697e-02 8.444444444444444697e-02 1.066666666666666735e-01 1.022222222222222282e-01 1.022222222222222282e-01 5.714285714285714107e-02 6.984126984126984294e-02 8.253968253968253788e-02 6.666666666666666574e-02 7.619047619047619735e-02 4.888888888888889145e-02 6.222222222222222043e-02 6.666666666666666574e-02 4.888888888888889145e-02 6.666666666666666574e-02 3.492063492063492147e-02 3.492063492063492147e-02 1.904761904761904934e-02 9.523809523809523281e-02 8.888888888888889228e-02 7.936507936507936067e-02 7.936507936507936067e-02 8.888888888888889228e-02 1.111111111111111049e-01 1.079365079365079416e-01 8.253968253968253788e-02 1.015873015873015872e-01 7.936507936507936067e-02 1.015873015873015872e-01 1.015873015873015872e-01 5.476190476190476164e-02 7.857142857142856984e-02 6.190476190476190688e-02 8.571428571428571508e-02 5.952380952380952051e-02 8.095238095238095621e-02 5.952380952380952051e-02 8.333333333333332871e-02 6.349206349206348854e-02 5.396825396825397081e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.714285714285714107e-02 6.984126984126984294e-02 6.984126984126984294e-02 5.396825396825397081e-02 5.714285714285714107e-02 5.079365079365079361e-02 6.349206349206348854e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.047619047619047811e-02 3.809523809523809867e-02 4.285714285714285754e-02 2.142857142857142877e-02 2.142857142857142877e-02 8.571428571428571508e-02 8.333333333333332871e-02 7.857142857142856984e-02 7.857142857142856984e-02 8.095238095238095621e-02 9.285714285714286031e-02 7.857142857142856984e-02 9.047619047619047394e-02 7.857142857142856984e-02 8.333333333333332871e-02 8.333333333333332871e-02 8.571428571428571508e-02 7.857142857142856984e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.370370370370370489e-02 5.555555555555555247e-02 6.296296296296295669e-02 7.407407407407406996e-02 7.037037037037037479e-02 7.592592592592592449e-02 7.777777777777777901e-02 6.190476190476190688e-02 5.238095238095238221e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.952380952380952051e-02 5.476190476190476164e-02 5.238095238095238221e-02 5.952380952380952051e-02 5.238095238095238221e-02 2.592592592592592518e-02 7.592592592592592449e-02 8.148148148148148806e-02 7.777777777777777901e-02 7.407407407407406996e-02 7.592592592592592449e-02 7.407407407407406996e-02 8.888888888888889228e-02 7.962962962962963354e-02 7.592592592592592449e-02 7.592592592592592449e-02 7.592592592592592449e-02 7.777777777777777901e-02 7.222222222222221544e-02 5.925925925925926152e-02 5.185185185185185036e-02 5.185185185185185036e-02 6.814814814814815214e-02 8.148148148148148806e-02 6.222222222222222043e-02 5.185185185185185036e-02 5.555555555555555247e-02 5.370370370370370489e-02 5.555555555555555247e-02 5.185185185185185036e-02 5.185185185185185036e-02 5.370370370370370489e-02 5.925925925925926152e-02 4.148148148148148029e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.814814814814814783e-02 8.888888888888889228e-02 6.814814814814815214e-02 7.703703703703704275e-02 7.555555555555555636e-02 6.962962962962962465e-02 7.555555555555555636e-02 7.851851851851851527e-02 7.259259259259259744e-02 7.111111111111111105e-02 8.148148148148148806e-02 8.000000000000000167e-02 8.148148148148148806e-02 8.148148148148148806e-02 5.818181818181818177e-02 7.515151515151514972e-02 6.060606060606060774e-02 5.090909090909091078e-02 5.090909090909091078e-02 5.333333333333333676e-02 5.090909090909091078e-02 5.185185185185185036e-02 5.629629629629629567e-02 3.393939393939394283e-02 2.909090909090909088e-02 6.303030303030303372e-02 8.121212121212120771e-02 6.424242424242423977e-02 6.666666666666666574e-02 9.047619047619047394e-02 7.636363636363636964e-02 6.424242424242423977e-02 6.181818181818182073e-02 6.181818181818182073e-02 7.030303030303029777e-02 6.666666666666666574e-02 5.333333333333333676e-02 4.848484848484848481e-02 5.333333333333333676e-02 5.212121212121212377e-02 5.333333333333333676e-02
-1.111111111111111049e-01 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.333333333333333315e-01 5.999999999999999778e-02 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.066666666666666735e-01 1.333333333333333315e-01 1.333333333333333315e-01 6.222222222222222043e-02 6.666666666666666574e-02 7.619047619047619735e-02 9.333333333333333759e-02 7.555555555555555636e-02 4.666666666666666879e-02 4.000000000000000083e-02 4.666666666666666879e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.444444444444444697e-02 1.199999999999999956e-01 1.288888888888888862e-01 1.022222222222222282e-01 1.066666666666666735e-01 1.288888888888888862e-01 7.619047619047619735e-02 1.047619047619047644e-01 7.301587301587302015e-02 7.936507936507936067e-02 8.571428571428571508e-02 4.444444444444444614e-02 4.888888888888889145e-02 5.333333333333333676e-02 4.444444444444444614e-02 5.333333333333333676e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 7.301587301587302015e-02 8.888888888888889228e-02 1.079365079365079416e-01 1.174603174603174593e-01 9.841269841269841001e-02 7.619047619047619735e-02 8.253968253968253788e-02 1.142857142857142821e-01 8.571428571428571508e-02 1.238095238095238138e-01 1.047619047619047644e-01 9.841269841269841001e-02 8.809523809523810145e-02 8.095238095238095621e-02 1.000000000000000056e-01 6.428571428571427937e-02 8.571428571428571508e-02 6.428571428571427937e-02 7.857142857142856984e-02 7.380952380952381098e-02 4.761904761904761640e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.761904761904761640e-02 4.444444444444444614e-02 4.761904761904761640e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.571428571428571230e-02 2.142857142857142877e-02 2.142857142857142877e-02 6.428571428571427937e-02 7.380952380952381098e-02 9.047619047619047394e-02 9.285714285714286031e-02 9.523809523809523281e-02 7.619047619047619735e-02 1.142857142857142821e-01 9.285714285714286031e-02 1.190476190476190410e-01 8.809523809523810145e-02 1.095238095238095233e-01 9.523809523809523281e-02 1.023809523809523780e-01 6.666666666666666574e-02 9.444444444444444198e-02 8.148148148148148806e-02 8.703703703703703776e-02 7.222222222222221544e-02 5.740740740740740700e-02 6.481481481481481122e-02 6.481481481481481122e-02 6.481481481481481122e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.285714285714285754e-02 2.407407407407407413e-02 7.777777777777777901e-02 7.592592592592592449e-02 8.148148148148148806e-02 8.703703703703703776e-02 9.074074074074074681e-02 9.814814814814815103e-02 6.666666666666666574e-02 8.518518518518518323e-02 1.074074074074074098e-01 1.148148148148148140e-01 1.074074074074074098e-01 5.740740740740740700e-02 9.444444444444444198e-02 5.925925925925926152e-02 8.148148148148148806e-02 7.555555555555555636e-02 5.185185185185185036e-02 7.111111111111111105e-02 6.370370370370370683e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.259259259259259162e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.259259259259259162e-02 4.259259259259259162e-02 3.259259259259258967e-02 3.555555555555555552e-02 3.555555555555555552e-02 2.518518518518518545e-02 6.666666666666666574e-02 5.777777777777777513e-02 6.666666666666666574e-02 8.000000000000000167e-02 6.814814814814815214e-02 5.925925925925926152e-02 7.111111111111111105e-02 5.777777777777777513e-02 5.185185185185185036e-02 5.481481481481481621e-02 6.666666666666666574e-02 5.481481481481481621e-02 7.555555555555555636e-02 5.454545454545454281e-02 6.060606060606060774e-02 5.696969696969696878e-02 7.393939393939394367e-02 8.121212121212120771e-02 6.909090909090909172e-02 7.636363636363636964e-02 3.851851851851852138e-02 4.000000000000000083e-02 2.666666666666666838e-02 2.545454545454545539e-02 5.212121212121212377e-02 6.909090909090909172e-02 8.606060606060605966e-02 7.393939393939394367e-02 7.142857142857142461e-02 5.818181818181818177e-02 6.060606060606060774e-02 6.181818181818182073e-02 5.939393939393939476e-02 5.939393939393939476e-02 5.212121212121212377e-02 3.878787878787878785e-02 3.636363636363636187e-02 3.878787878787878785e-02 3.878787878787878785e-02 3.757575757575757486e-02
-1.111111111111111049e-01 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.555555555555555580e-01 6.666666666666666574e-02 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.000000000000000056e-01 1.666666666666666574e-01 1.600000000000000033e-01 7.111111111111111105e-02 7.555555555555555636e-02 8.888888888888889228e-02 1.111111111111111049e-01 8.444444444444444697e-02 4.666666666666666879e-02 4.000000000000000083e-02 4.666666666666666879e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 8.444444444444444697e-02 1.288888888888888862e-01 1.644444444444444486e-01 8.888888888888889228e-02 1.111111111111111049e-01 1.555555555555555580e-01 8.888888888888889228e-02 1.301587301587301682e-01 7.301587301587302015e-02 9.523809523809523281e-02 9.206349206349206948e-02 4.444444444444444614e-02 4.888888888888889145e-02 5.333333333333333676e-02 4.444444444444444614e-02 5.333333333333333676e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 7.301587301587302015e-02 9.523809523809523281e-02 1.047619047619047644e-01 1.301587301587301682e-01 9.841269841269841001e-02 7.619047619047619735e-02 7.619047619047619735e-02 1.301587301587301682e-01 9.206349206349206948e-02 1.619047619047619124e-01 1.142857142857142821e-01 9.523809523809523281e-02 1.119047619047619097e-01 6.666666666666666574e-02 1.285714285714285587e-01 6.428571428571427937e-02 9.523809523809523281e-02 6.428571428571427937e-02 9.761904761904761918e-02 7.857142857142856984e-02 4.761904761904761640e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.761904761904761640e-02 4.444444444444444614e-02 4.761904761904761640e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.571428571428571230e-02 2.142857142857142877e-02 2.142857142857142877e-02 6.428571428571427937e-02 8.095238095238095621e-02 8.809523809523810145e-02 1.071428571428571369e-01 8.333333333333332871e-02 8.333333333333332871e-02 1.285714285714285587e-01 8.809523809523810145e-02 1.571428571428571397e-01 1.000000000000000056e-01 1.285714285714285587e-01 1.023809523809523780e-01 1.071428571428571369e-01 6.666666666666666574e-02 1.240740740740740727e-01 9.444444444444444198e-02 9.444444444444444198e-02 8.333333333333332871e-02 5.740740740740740700e-02 7.037037037037037479e-02 6.851851851851852027e-02 6.296296296296295669e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.285714285714285754e-02 2.407407407407407413e-02 7.592592592592592449e-02 7.777777777777777901e-02 7.222222222222221544e-02 9.074074074074074681e-02 1.074074074074074098e-01 9.259259259259258745e-02 7.037037037037037479e-02 6.851851851851852027e-02 1.092592592592592643e-01 1.537037037037037035e-01 1.296296296296296224e-01 5.740740740740740700e-02 9.074074074074074681e-02 6.814814814814815214e-02 1.051851851851851871e-01 8.148148148148148806e-02 5.185185185185185036e-02 8.000000000000000167e-02 7.259259259259259744e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.259259259259259162e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.259259259259259162e-02 4.259259259259259162e-02 3.259259259259258967e-02 3.555555555555555552e-02 3.555555555555555552e-02 2.518518518518518545e-02 6.814814814814815214e-02 6.222222222222222043e-02 6.814814814814815214e-02 9.333333333333333759e-02 6.666666666666666574e-02 6.222222222222222043e-02 6.518518518518517935e-02 6.074074074074074098e-02 5.185185185185185036e-02 5.333333333333333676e-02 7.259259259259259744e-02 5.481481481481481621e-02 8.888888888888889228e-02 6.060606060606060774e-02 5.090909090909091078e-02 6.424242424242423977e-02 7.151515151515151769e-02 9.090909090909091161e-02 7.151515151515151769e-02 9.090909090909091161e-02 3.851851851851852138e-02 4.000000000000000083e-02 2.666666666666666838e-02 2.545454545454545539e-02 5.575757575757575579e-02 8.000000000000000167e-02 8.121212121212120771e-02 8.121212121212120771e-02 7.380952380952381098e-02 6.181818181818182073e-02 5.939393939393939476e-02 6.909090909090909172e-02 5.939393939393939476e-02 6.060606060606060774e-02 5.454545454545454281e-02 3.878787878787878785e-02 3.636363636363636187e-02 3.878787878787878785e-02 3.878787878787878785e-02 3.757575757575757486e-02
-1.111111111111111049e-01 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000056e-01 5.333333333333333676e-02 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.333333333333333315e-01 8.666666666666666963e-02 1.066666666666666735e-01 5.333333333333333676e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.555555555555555636e-02 5.999999999999999778e-02 4.000000000000000083e-02 5.999999999999999778e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.066666666666666735e-01 1.022222222222222282e-01 8.888888888888889228e-02 1.466666666666666674e-01 1.199999999999999956e-01 1.155555555555555503e-01 5.714285714285714107e-02 7.301587301587302015e-02 8.571428571428571508e-02 6.666666666666666574e-02 8.888888888888889228e-02 5.333333333333333676e-02 6.666666666666666574e-02 8.000000000000000167e-02 5.333333333333333676e-02 8.000000000000000167e-02 3.809523809523809867e-02 3.809523809523809867e-02 1.904761904761904934e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.047619047619047644e-01 9.523809523809523281e-02 1.174603174603174593e-01 1.047619047619047644e-01 1.174603174603174593e-01 9.523809523809523281e-02 9.841269841269841001e-02 8.571428571428571508e-02 1.174603174603174593e-01 1.269841269841269771e-01 5.476190476190476164e-02 1.142857142857142821e-01 6.428571428571427937e-02 7.857142857142856984e-02 6.904761904761905211e-02 7.857142857142856984e-02 5.952380952380952051e-02 8.095238095238095621e-02 6.666666666666666574e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.619047619047619735e-02 7.619047619047619735e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.000000000000000278e-02 2.142857142857142877e-02 2.142857142857142877e-02 8.571428571428571508e-02 8.095238095238095621e-02 9.285714285714286031e-02 8.571428571428571508e-02 1.119047619047619097e-01 8.809523809523810145e-02 9.523809523809523281e-02 1.261904761904761862e-01 8.809523809523810145e-02 8.809523809523810145e-02 9.523809523809523281e-02 1.047619047619047644e-01 9.523809523809523281e-02 7.037037037037037479e-02 5.740740740740740700e-02 6.111111111111110911e-02 6.851851851851852027e-02 6.481481481481481122e-02 7.222222222222221544e-02 6.851851851851852027e-02 7.407407407407406996e-02 8.333333333333332871e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 5.714285714285714107e-02 2.777777777777777624e-02 8.333333333333332871e-02 8.518518518518518323e-02 9.814814814814815103e-02 8.518518518518518323e-02 8.518518518518518323e-02 9.814814814814815103e-02 8.518518518518518323e-02 1.129629629629629595e-01 9.814814814814815103e-02 8.703703703703703776e-02 8.703703703703703776e-02 7.777777777777777901e-02 9.259259259259258745e-02 6.814814814814815214e-02 5.481481481481481621e-02 6.370370370370370683e-02 6.666666666666666574e-02 7.555555555555555636e-02 6.074074074074074098e-02 5.555555555555555247e-02 6.666666666666666574e-02 6.111111111111110911e-02 6.666666666666666574e-02 5.555555555555555247e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.111111111111110911e-02 4.444444444444444614e-02 5.333333333333333676e-02 5.333333333333333676e-02 3.111111111111111022e-02 9.037037037037036480e-02 6.814814814814815214e-02 7.703703703703704275e-02 8.148148148148148806e-02 7.555555555555555636e-02 7.703703703703704275e-02 8.888888888888889228e-02 7.259259259259259744e-02 7.111111111111111105e-02 7.851851851851851527e-02 8.296296296296296058e-02 8.000000000000000167e-02 8.000000000000000167e-02 6.545454545454545969e-02 9.212121212121211766e-02 5.696969696969696878e-02 6.909090909090909172e-02 6.181818181818182073e-02 6.424242424242423977e-02 6.181818181818182073e-02 5.333333333333333676e-02 5.777777777777777513e-02 3.636363636363636187e-02 3.272727272727272985e-02 6.303030303030303372e-02 8.000000000000000167e-02 8.121212121212120771e-02 7.151515151515151769e-02 9.285714285714286031e-02 7.151515151515151769e-02 6.909090909090909172e-02 6.545454545454545969e-02 6.909090909090909172e-02 7.030303030303029777e-02 6.666666666666666574e-02 5.818181818181818177e-02 5.090909090909091078e-02 5.818181818181818177e-02 5.818181818181818177e-02 5.454545454545454281e-02
-1.111111111111111049e-01 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.222222222222222182e-01 5.999999999999999778e-02 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.199999999999999956e-01 1.133333333333333276e-01 1.266666666666666774e-01 6.222222222222222043e-02 7.111111111111111105e-02 6.984126984126984294e-02 8.444444444444444697e-02 8.444444444444444697e-02 5.333333333333333676e-02 4.000000000000000083e-02 5.333333333333333676e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.022222222222222282e-01 1.066666666666666735e-01 1.111111111111111049e-01 1.199999999999999956e-01 1.199999999999999956e-01 1.288888888888888862e-01 6.984126984126984294e-02 9.523809523809523281e-02 8.253968253968253788e-02 7.936507936507936067e-02 9.206349206349206948e-02 4.888888888888889145e-02 6.222222222222222043e-02 6.666666666666666574e-02 4.888888888888889145e-02 6.666666666666666574e-02 3.492063492063492147e-02 3.492063492063492147e-02 1.904761904761904934e-02 9.206349206349206948e-02 9.523809523809523281e-02 9.841269841269841001e-02 1.015873015873015872e-01 1.047619047619047644e-01 9.841269841269841001e-02 1.047619047619047644e-01 1.047619047619047644e-01 1.015873015873015872e-01 1.047619047619047644e-01 1.206349206349206366e-01 1.174603174603174593e-01 7.380952380952381098e-02 9.285714285714286031e-02 8.571428571428571508e-02 7.619047619047619735e-02 7.619047619047619735e-02 7.619047619047619735e-02 7.380952380952381098e-02 8.333333333333332871e-02 6.349206349206348854e-02 5.396825396825397081e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.714285714285714107e-02 6.984126984126984294e-02 6.984126984126984294e-02 5.396825396825397081e-02 5.714285714285714107e-02 5.079365079365079361e-02 6.349206349206348854e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.047619047619047811e-02 3.809523809523809867e-02 4.285714285714285754e-02 2.142857142857142877e-02 2.142857142857142877e-02 8.333333333333332871e-02 8.333333333333332871e-02 8.809523809523810145e-02 9.047619047619047394e-02 9.523809523809523281e-02 8.809523809523810145e-02 1.000000000000000056e-01 1.047619047619047644e-01 1.023809523809523780e-01 9.285714285714286031e-02 1.047619047619047644e-01 1.023809523809523780e-01 9.761904761904761918e-02 6.666666666666666574e-02 7.777777777777777901e-02 7.037037037037037479e-02 7.407407407407406996e-02 7.037037037037037479e-02 7.037037037037037479e-02 7.037037037037037479e-02 7.592592592592592449e-02 7.962962962962963354e-02 6.190476190476190688e-02 5.238095238095238221e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.952380952380952051e-02 5.476190476190476164e-02 5.238095238095238221e-02 5.952380952380952051e-02 5.238095238095238221e-02 2.592592592592592518e-02 7.962962962962963354e-02 8.148148148148148806e-02 8.518518518518518323e-02 8.518518518518518323e-02 8.888888888888889228e-02 9.074074074074074681e-02 8.703703703703703776e-02 9.074074074074074681e-02 9.444444444444444198e-02 9.814814814814815103e-02 9.629629629629629650e-02 7.592592592592592449e-02 8.888888888888889228e-02 6.814814814814815214e-02 6.962962962962962465e-02 6.666666666666666574e-02 6.518518518518517935e-02 8.148148148148148806e-02 6.518518518518517935e-02 5.185185185185185036e-02 5.555555555555555247e-02 5.370370370370370489e-02 5.555555555555555247e-02 5.185185185185185036e-02 5.185185185185185036e-02 5.370370370370370489e-02 5.925925925925926152e-02 4.148148148148148029e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.814814814814814783e-02 8.888888888888889228e-02 6.962962962962962465e-02 7.407407407407406996e-02 8.296296296296296058e-02 7.259259259259259744e-02 7.703703703703704275e-02 7.851851851851851527e-02 7.407407407407406996e-02 6.962962962962962465e-02 7.555555555555555636e-02 8.444444444444444697e-02 7.703703703703704275e-02 8.444444444444444697e-02 6.545454545454545969e-02 7.757575757575757569e-02 6.060606060606060774e-02 6.545454545454545969e-02 6.787878787878788567e-02 6.303030303030303372e-02 6.787878787878788567e-02 5.185185185185185036e-02 5.629629629629629567e-02 3.393939393939394283e-02 2.909090909090909088e-02 6.424242424242423977e-02 8.121212121212120771e-02 7.878787878787878174e-02 7.393939393939394367e-02 9.285714285714286031e-02 6.909090909090909172e-02 6.666666666666666574e-02 6.787878787878788567e-02 6.666666666666666574e-02 6.787878787878788567e-02 6.787878787878788567e-02 5.333333333333333676e-02 4.848484848484848481e-02 5.333333333333333676e-02 5.212121212121212377e-02 5.333333333333333676e-02
-1.111111111111111049e-01 4.444444444444444614e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.555555555555555580e-01 6.666666666666666574e-02 6.666666666666666574e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.133333333333333276e-01 1.533333333333333215e-01 1.733333333333333393e-01 7.111111111111111105e-02 8.444444444444444697e-02 8.888888888888889228e-02 1.111111111111111049e-01 8.444444444444444697e-02 5.999999999999999778e-02 4.000000000000000083e-02 5.999999999999999778e-02 2.666666666666666838e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.022222222222222282e-01 1.288888888888888862e-01 1.555555555555555580e-01 1.155555555555555503e-01 1.288888888888888862e-01 1.822222222222222299e-01 8.888888888888889228e-02 1.365079365079364948e-01 7.936507936507936067e-02 8.888888888888889228e-02 9.841269841269841001e-02 5.333333333333333676e-02 6.666666666666666574e-02 8.000000000000000167e-02 5.333333333333333676e-02 8.000000000000000167e-02 3.809523809523809867e-02 3.809523809523809867e-02 1.904761904761904934e-02 9.206349206349206948e-02 1.015873015873015872e-01 1.111111111111111049e-01 1.301587301587301682e-01 1.111111111111111049e-01 1.015873015873015872e-01 1.015873015873015872e-01 1.301587301587301682e-01 1.111111111111111049e-01 1.492063492063492036e-01 1.396825396825396859e-01 1.206349206349206366e-01 1.023809523809523780e-01 8.095238095238095621e-02 1.238095238095238138e-01 7.380952380952381098e-02 9.523809523809523281e-02 7.380952380952381098e-02 8.809523809523810145e-02 8.809523809523810145e-02 6.666666666666666574e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.619047619047619735e-02 7.619047619047619735e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.000000000000000278e-02 2.142857142857142877e-02 2.142857142857142877e-02 8.333333333333332871e-02 9.047619047619047394e-02 9.761904761904761918e-02 1.119047619047619097e-01 9.761904761904761918e-02 9.761904761904761918e-02 1.333333333333333315e-01 1.119047619047619097e-01 1.476190476190476220e-01 1.047619047619047644e-01 1.333333333333333315e-01 1.119047619047619097e-01 1.119047619047619097e-01 7.407407407407406996e-02 1.129629629629629595e-01 9.074074074074074681e-02 9.074074074074074681e-02 7.962962962962963354e-02 6.851851851851852027e-02 7.407407407407406996e-02 7.962962962962963354e-02 7.407407407407406996e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 5.714285714285714107e-02 2.777777777777777624e-02 8.703703703703703776e-02 9.259259259259258745e-02 8.703703703703703776e-02 9.814814814814815103e-02 1.148148148148148140e-01 1.037037037037037007e-01 9.259259259259258745e-02 8.703703703703703776e-02 1.203703703703703637e-01 1.425925925925926041e-01 1.259259259259259134e-01 7.592592592592592449e-02 9.814814814814815103e-02 8.592592592592591949e-02 9.925925925925925541e-02 8.148148148148148806e-02 6.370370370370370683e-02 9.481481481481481011e-02 7.259259259259259744e-02 5.555555555555555247e-02 6.666666666666666574e-02 6.111111111111110911e-02 6.666666666666666574e-02 5.555555555555555247e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.111111111111110911e-02 4.444444444444444614e-02 5.333333333333333676e-02 5.333333333333333676e-02 3.111111111111111022e-02 9.185185185185185119e-02 7.407407407407406996e-02 8.296296296296296058e-02 1.051851851851851871e-01 7.851851851851851527e-02 8.296296296296296058e-02 8.296296296296296058e-02 7.851851851851851527e-02 6.962962962962962465e-02 7.407407407407406996e-02 9.629629629629629650e-02 7.851851851851851527e-02 1.007407407407407418e-01 7.757575757575757569e-02 7.030303030303029777e-02 6.666666666666666574e-02 7.393939393939394367e-02 8.848484848484848564e-02 7.393939393939394367e-02 8.848484848484848564e-02 5.333333333333333676e-02 5.777777777777777513e-02 3.636363636363636187e-02 3.272727272727272985e-02 6.787878787878788567e-02 9.696969696969696961e-02 8.606060606060605966e-02 8.606060606060605966e-02 9.761904761904761918e-02 7.878787878787878174e-02 7.151515151515151769e-02 7.878787878787878174e-02 7.151515151515151769e-02 7.515151515151514972e-02 7.151515151515151769e-02 5.818181818181818177e-02 5.090909090909091078e-02 5.818181818181818177e-02 5.818181818181818177e-02 5.454545454545454281e-02
-1.587301587301587213e-01 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.269841269841269771e-01 5.714285714285714107e-02 1.238095238095238138e-01 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 7.619047619047619735e-02 1.047619047619047644e-01 1.047619047619047644e-01 5.079365079365079361e-02 5.079365079365079361e-02 1.065759637188208597e-01 1.142857142857142821e-01 8.253968253968253788e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 5.714285714285714107e-02 7.619047619047619735e-02 8.888888888888889228e-02 5.714285714285714107e-02 6.984126984126984294e-02 8.888888888888889228e-02 1.065759637188208597e-01 1.043083900226757371e-01 6.349206349206348854e-02 7.256235827664399285e-02 7.256235827664399285e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.360544217687074779e-02 1.360544217687074779e-02 9.070294784580499106e-03 4.535147392290249380e-02 5.442176870748299117e-02 5.895691609977324332e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.535147392290249380e-02 6.802721088435374763e-02 5.442176870748299117e-02 7.709750566893423807e-02 6.349206349206348854e-02 5.442176870748299117e-02 9.693877551020407823e-02 6.122448979591836593e-02 9.523809523809523281e-02 6.122448979591836593e-02 7.823129251700680631e-02 5.102040816326530726e-02 6.462585034013605678e-02 5.782312925170068202e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.020408163265306041e-02 1.020408163265306041e-02 3.741496598639455773e-02 4.421768707482993249e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.421768707482993249e-02 4.421768707482993249e-02 6.122448979591836593e-02 4.421768707482993249e-02 6.802721088435374763e-02 5.102040816326530726e-02 6.122448979591836593e-02 5.102040816326530726e-02 5.442176870748299117e-02 6.746031746031745657e-02 8.862433862433861942e-02 7.804232804232803800e-02 7.804232804232803800e-02 5.291005291005290712e-02 4.232804232804232569e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.497354497354497105e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.058201058201058142e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.703703703703703498e-02 4.497354497354497105e-02 5.026455026455026176e-02 4.497354497354497105e-02 3.703703703703703498e-02 3.439153439153438963e-02 5.026455026455026176e-02 6.084656084656084318e-02 5.555555555555555247e-02 3.174603174603174427e-02 4.497354497354497105e-02 3.386243386243386472e-02 8.465608465608465139e-02 6.455026455026455223e-02 4.232804232804232569e-02 4.867724867724868010e-02 4.444444444444444614e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.058201058201058142e-02 3.386243386243386472e-02 3.174603174603174427e-02 3.386243386243386472e-02 4.232804232804232569e-02 3.386243386243386472e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 2.751322751322751378e-02 2.751322751322751378e-02 3.597883597883597823e-02 2.751322751322751378e-02 4.021164021164021218e-02 2.943722943722943894e-02 3.636363636363636187e-02 5.108225108225108307e-02 5.454545454545454281e-02 7.099567099567100081e-02 4.155844155844155841e-02 4.848484848484848481e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.038961038961038960e-02 1.038961038961038960e-02 2.770562770562770560e-02 3.463203463203463200e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.401360544217687382e-02 2.943722943722943894e-02 2.943722943722943894e-02 3.290043290043290214e-02 2.943722943722943894e-02 2.943722943722943894e-02 2.770562770562770560e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02
-1.587301587301587213e-01 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.587301587301587213e-01 6.666666666666666574e-02 1.238095238095238138e-01 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 9.047619047619047394e-02 1.428571428571428492e-01 1.476190476190476220e-01 6.349206349206348854e-02 6.666666666666666574e-02 1.043083900226757371e-01 1.396825396825396859e-01 9.523809523809523281e-02 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.269841269841269840e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.984126984126984294e-02 1.047619047619047644e-01 1.301587301587301682e-01 7.301587301587302015e-02 9.523809523809523281e-02 1.365079365079364948e-01 1.043083900226757371e-01 1.428571428571428492e-01 7.482993197278911546e-02 9.070294784580498759e-02 9.297052154195011020e-02 2.222222222222222307e-02 2.539682539682539680e-02 2.857142857142857054e-02 2.222222222222222307e-02 2.857142857142857054e-02 1.587301587301587213e-02 1.587301587301587213e-02 9.070294784580499106e-03 5.668934240362812071e-02 7.256235827664399285e-02 8.163265306122448328e-02 9.977324263038549190e-02 7.482993197278911546e-02 5.895691609977324332e-02 5.895691609977324332e-02 9.977324263038549190e-02 7.482993197278911546e-02 1.179138321995464866e-01 9.523809523809523281e-02 7.709750566893423807e-02 1.088435374149659823e-01 7.312925170068027003e-02 1.241496598639455773e-01 7.142857142857142461e-02 9.693877551020407823e-02 6.122448979591836593e-02 8.503401360544217413e-02 7.482993197278911546e-02 2.494331065759637298e-02 2.267573696145124690e-02 2.267573696145124690e-02 2.267573696145124690e-02 2.494331065759637298e-02 2.721088435374149558e-02 2.721088435374149558e-02 2.267573696145124690e-02 2.494331065759637298e-02 2.267573696145124690e-02 2.494331065759637298e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.020408163265306041e-02 1.020408163265306041e-02 4.761904761904761640e-02 5.952380952380952051e-02 6.632653061224490221e-02 7.993197278911565173e-02 6.122448979591836593e-02 6.122448979591836593e-02 9.523809523809523281e-02 6.462585034013605678e-02 1.088435374149659823e-01 7.312925170068027003e-02 9.523809523809523281e-02 7.482993197278911546e-02 7.993197278911565173e-02 6.878306878306877925e-02 1.097883597883597823e-01 8.862433862433861942e-02 8.862433862433861942e-02 7.010582010582010193e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.216931216931216586e-02 5.687830687830687515e-02 2.380952380952380820e-02 2.210884353741496625e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.210884353741496625e-02 2.380952380952380820e-02 2.210884353741496625e-02 1.190476190476190410e-02 5.555555555555555247e-02 5.687830687830687515e-02 5.158730158730158444e-02 6.613756613756613389e-02 7.804232804232803800e-02 6.746031746031745657e-02 5.291005291005290712e-02 4.761904761904761640e-02 7.936507936507936067e-02 1.005291005291005235e-01 8.862433862433861942e-02 4.100529100529100301e-02 6.613756613756613389e-02 5.291005291005290712e-02 9.100529100529100579e-02 7.407407407407406996e-02 5.079365079365079361e-02 7.089947089947089276e-02 5.925925925925926152e-02 2.116402116402116285e-02 2.380952380952380820e-02 2.248677248677248552e-02 2.380952380952380820e-02 2.116402116402116285e-02 2.116402116402116285e-02 2.248677248677248552e-02 2.248677248677248552e-02 1.693121693121693236e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 5.079365079365079361e-02 4.338624338624338939e-02 4.867724867724868010e-02 6.666666666666666574e-02 4.761904761904761640e-02 4.550264550264550290e-02 4.550264550264550290e-02 4.444444444444444614e-02 3.597883597883597823e-02 3.703703703703703498e-02 5.502645502645502756e-02 3.809523809523809867e-02 6.243386243386243178e-02 4.588744588744588654e-02 4.675324675324675494e-02 5.627705627705627961e-02 6.320346320346320601e-02 7.705627705627705881e-02 5.800865800865800948e-02 7.186147186147186228e-02 2.010582010582010609e-02 2.116402116402116285e-02 1.385281385281385280e-02 1.298701298701298787e-02 3.809523809523809867e-02 5.541125541125541121e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.931972789115646183e-02 4.329004329004328827e-02 4.155844155844155841e-02 4.848484848484848481e-02 4.155844155844155841e-02 4.242424242424242681e-02 3.896103896103896014e-02 2.077922077922077920e-02 1.904761904761904934e-02 2.077922077922077920e-02 2.077922077922077920e-02 1.991341991341991427e-02
-1.587301587301587213e-01 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 5.714285714285714107e-02 9.523809523809523281e-02 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 1.000000000000000056e-01 8.571428571428571508e-02 9.047619047619047394e-02 5.079365079365079361e-02 5.396825396825397081e-02 6.349206349206348854e-02 8.253968253968253788e-02 9.523809523809523281e-02 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.269841269841269840e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 8.253968253968253788e-02 7.301587301587302015e-02 7.301587301587302015e-02 8.571428571428571508e-02 8.253968253968253788e-02 7.936507936507936067e-02 6.349206349206348854e-02 7.482993197278911546e-02 1.065759637188208597e-01 7.709750566893423807e-02 8.390022675736961977e-02 2.222222222222222307e-02 2.539682539682539680e-02 2.857142857142857054e-02 2.222222222222222307e-02 2.857142857142857054e-02 1.587301587301587213e-02 1.587301587301587213e-02 9.070294784580499106e-03 6.575963718820861115e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 7.029478458049887024e-02 6.802721088435374763e-02 7.256235827664399285e-02 6.349206349206348854e-02 7.029478458049887024e-02 6.349206349206348854e-02 7.256235827664399285e-02 7.256235827664399285e-02 5.782312925170068202e-02 7.653061224489796088e-02 6.292517006802721136e-02 7.482993197278911546e-02 5.952380952380952051e-02 7.482993197278911546e-02 6.462585034013605678e-02 8.843537414965986498e-02 2.494331065759637298e-02 2.267573696145124690e-02 2.267573696145124690e-02 2.267573696145124690e-02 2.494331065759637298e-02 2.721088435374149558e-02 2.721088435374149558e-02 2.267573696145124690e-02 2.494331065759637298e-02 2.267573696145124690e-02 2.494331065759637298e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.020408163265306041e-02 1.020408163265306041e-02 5.442176870748299117e-02 5.612244897959183659e-02 5.272108843537415268e-02 5.272108843537415268e-02 6.122448979591836593e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.462585034013605678e-02 5.782312925170068202e-02 5.612244897959183659e-02 5.782312925170068202e-02 6.122448979591836593e-02 5.612244897959183659e-02 5.026455026455026176e-02 5.423280423280422979e-02 5.158730158730158444e-02 5.423280423280422979e-02 7.275132275132274728e-02 6.216931216931216586e-02 6.349206349206348854e-02 6.481481481481481122e-02 7.804232804232803800e-02 2.380952380952380820e-02 2.210884353741496625e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.210884353741496625e-02 2.380952380952380820e-02 2.210884353741496625e-02 1.190476190476190410e-02 4.497354497354497105e-02 4.894179894179893908e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.894179894179893908e-02 5.158730158730158444e-02 5.026455026455026176e-02 5.820105820105819783e-02 5.291005291005290712e-02 5.291005291005290712e-02 5.158730158730158444e-02 4.629629629629629373e-02 5.026455026455026176e-02 4.021164021164021218e-02 4.656084656084655965e-02 4.656084656084655965e-02 5.291005291005290712e-02 6.666666666666666574e-02 5.502645502645502756e-02 2.116402116402116285e-02 2.380952380952380820e-02 2.248677248677248552e-02 2.380952380952380820e-02 2.116402116402116285e-02 2.116402116402116285e-02 2.248677248677248552e-02 2.248677248677248552e-02 1.693121693121693236e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 5.079365079365079361e-02 4.126984126984126894e-02 4.232804232804232569e-02 4.338624338624338939e-02 3.915343915343915543e-02 4.338624338624338939e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.021164021164021218e-02 4.550264550264550290e-02 4.656084656084655965e-02 4.232804232804232569e-02 4.761904761904761640e-02 3.722943722943723027e-02 5.541125541125541121e-02 4.761904761904761640e-02 4.415584415584415667e-02 4.415584415584415667e-02 5.281385281385281294e-02 4.935064935064935321e-02 2.010582010582010609e-02 2.116402116402116285e-02 1.385281385281385280e-02 1.298701298701298787e-02 3.636363636363636187e-02 4.329004329004328827e-02 4.155844155844155841e-02 3.809523809523809867e-02 4.591836734693877792e-02 3.982683982683982854e-02 3.463203463203463200e-02 3.463203463203463200e-02 3.463203463203463200e-02 3.722943722943723027e-02 3.722943722943723027e-02 2.077922077922077920e-02 1.904761904761904934e-02 2.077922077922077920e-02 2.077922077922077920e-02 1.991341991341991427e-02
-1.587301587301587213e-01 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.269841269841269771e-01 6.190476190476190688e-02 9.523809523809523281e-02 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 9.047619047619047394e-02 1.095238095238095233e-01 1.047619047619047644e-01 5.714285714285714107e-02 5.714285714285714107e-02 7.256235827664399285e-02 9.523809523809523281e-02 1.206349206349206366e-01 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 6.666666666666666574e-02 7.936507936507936067e-02 9.523809523809523281e-02 6.666666666666666574e-02 7.936507936507936067e-02 8.888888888888889228e-02 7.256235827664399285e-02 9.070294784580498759e-02 7.709750566893423807e-02 1.065759637188208597e-01 9.977324263038549190e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.360544217687074779e-02 1.360544217687074779e-02 9.070294784580499106e-03 5.215419501133786856e-02 6.349206349206348854e-02 6.122448979591836593e-02 7.256235827664399285e-02 6.349206349206348854e-02 5.215419501133786856e-02 5.215419501133786856e-02 7.256235827664399285e-02 6.122448979591836593e-02 8.616780045351474238e-02 7.029478458049887024e-02 6.122448979591836593e-02 7.482993197278911546e-02 6.122448979591836593e-02 8.163265306122448328e-02 6.122448979591836593e-02 6.632653061224490221e-02 6.122448979591836593e-02 9.523809523809523281e-02 6.802721088435374763e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.360544217687074779e-02 1.020408163265306041e-02 1.020408163265306041e-02 4.251700680272108707e-02 5.102040816326530726e-02 4.931972789115646183e-02 5.782312925170068202e-02 5.102040816326530726e-02 5.102040816326530726e-02 6.632653061224490221e-02 5.102040816326530726e-02 7.823129251700680631e-02 5.952380952380952051e-02 6.632653061224490221e-02 5.952380952380952051e-02 5.782312925170068202e-02 4.761904761904761640e-02 7.407407407407406996e-02 6.084656084656084318e-02 6.084656084656084318e-02 6.349206349206348854e-02 5.026455026455026176e-02 5.687830687830687515e-02 5.555555555555555247e-02 5.687830687830687515e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.058201058201058142e-02 4.100529100529100301e-02 4.100529100529100301e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.423280423280422979e-02 4.761904761904761640e-02 4.100529100529100301e-02 4.365079365079364837e-02 5.423280423280422979e-02 7.275132275132274728e-02 6.349206349206348854e-02 3.571428571428571230e-02 4.761904761904761640e-02 4.021164021164021218e-02 6.137566137566137503e-02 5.079365079365079361e-02 4.232804232804232569e-02 5.502645502645502756e-02 5.291005291005290712e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.058201058201058142e-02 4.021164021164021218e-02 3.597883597883597823e-02 3.492063492063492147e-02 4.550264550264550290e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.597883597883597823e-02 3.492063492063492147e-02 3.068783068783068751e-02 3.068783068783068751e-02 3.915343915343915543e-02 3.068783068783068751e-02 4.656084656084655965e-02 3.463203463203463200e-02 3.636363636363636187e-02 4.502164502164502508e-02 4.329004329004328827e-02 5.194805194805195148e-02 4.329004329004328827e-02 5.194805194805195148e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.038961038961038960e-02 1.038961038961038960e-02 3.116883116883116880e-02 3.982683982683982854e-02 3.982683982683982854e-02 3.982683982683982854e-02 3.741496598639455773e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.463203463203463200e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02
-1.587301587301587213e-01 6.349206349206348854e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.269841269841269771e-01 6.190476190476190688e-02 9.523809523809523281e-02 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 1.047619047619047644e-01 1.047619047619047644e-01 1.095238095238095233e-01 5.714285714285714107e-02 6.031746031746031828e-02 7.256235827664399285e-02 9.523809523809523281e-02 1.206349206349206366e-01 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.269841269841269840e-02 6.349206349206349201e-03 6.349206349206349201e-03 6.349206349206349201e-03 7.619047619047619735e-02 8.571428571428571508e-02 9.206349206349206948e-02 8.888888888888889228e-02 9.206349206349206948e-02 9.841269841269841001e-02 7.256235827664399285e-02 9.297052154195011020e-02 8.390022675736961977e-02 9.977324263038549190e-02 1.065759637188208597e-01 2.222222222222222307e-02 2.539682539682539680e-02 2.857142857142857054e-02 2.222222222222222307e-02 2.857142857142857054e-02 1.587301587301587213e-02 1.587301587301587213e-02 9.070294784580499106e-03 6.122448979591836593e-02 7.029478458049887024e-02 7.256235827664399285e-02 7.709750566893423807e-02 7.709750566893423807e-02 6.349206349206348854e-02 6.802721088435374763e-02 7.709750566893423807e-02 7.029478458049887024e-02 8.163265306122448328e-02 8.390022675736961977e-02 7.936507936507936067e-02 7.142857142857142461e-02 7.993197278911565173e-02 7.993197278911565173e-02 6.802721088435374763e-02 6.972789115646257918e-02 6.802721088435374763e-02 8.503401360544217413e-02 7.482993197278911546e-02 2.494331065759637298e-02 2.267573696145124690e-02 2.267573696145124690e-02 2.267573696145124690e-02 2.494331065759637298e-02 2.721088435374149558e-02 2.721088435374149558e-02 2.267573696145124690e-02 2.494331065759637298e-02 2.267573696145124690e-02 2.494331065759637298e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.020408163265306041e-02 1.020408163265306041e-02 5.102040816326530726e-02 5.612244897959183659e-02 5.952380952380952051e-02 6.292517006802721136e-02 6.632653061224490221e-02 5.782312925170068202e-02 7.142857142857142461e-02 6.972789115646257918e-02 7.482993197278911546e-02 6.462585034013605678e-02 7.142857142857142461e-02 6.972789115646257918e-02 6.632653061224490221e-02 5.555555555555555247e-02 7.010582010582010193e-02 6.216931216931216586e-02 6.481481481481481122e-02 6.481481481481481122e-02 5.687830687830687515e-02 6.084656084656084318e-02 6.216931216931216586e-02 6.613756613756613389e-02 2.380952380952380820e-02 2.210884353741496625e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.210884353741496625e-02 2.380952380952380820e-02 2.210884353741496625e-02 1.190476190476190410e-02 5.026455026455026176e-02 5.026455026455026176e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.026455026455026176e-02 6.084656084656084318e-02 6.349206349206348854e-02 6.878306878306877925e-02 6.481481481481481122e-02 4.365079365079364837e-02 5.820105820105819783e-02 4.656084656084655965e-02 5.925925925925926152e-02 5.502645502645502756e-02 4.867724867724868010e-02 6.243386243386243178e-02 5.502645502645502756e-02 2.116402116402116285e-02 2.380952380952380820e-02 2.248677248677248552e-02 2.380952380952380820e-02 2.116402116402116285e-02 2.116402116402116285e-02 2.248677248677248552e-02 2.248677248677248552e-02 1.693121693121693236e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 5.185185185185185036e-02 4.126984126984126894e-02 4.338624338624338939e-02 5.185185185185185036e-02 4.338624338624338939e-02 4.338624338624338939e-02 4.761904761904761640e-02 4.232804232804232569e-02 3.809523809523809867e-02 4.021164021164021218e-02 4.867724867724868010e-02 4.021164021164021218e-02 5.185185185185185036e-02 4.155844155844155841e-02 5.194805194805195148e-02 4.761904761904761640e-02 5.108225108225108307e-02 5.454545454545454281e-02 4.935064935064935321e-02 5.454545454545454281e-02 2.010582010582010609e-02 2.116402116402116285e-02 1.385281385281385280e-02 1.298701298701298787e-02 3.636363636363636187e-02 4.675324675324675494e-02 4.848484848484848481e-02 4.502164502164502508e-02 4.761904761904761640e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.982683982683982854e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.722943722943723027e-02 2.077922077922077920e-02 1.904761904761904934e-02 2.077922077922077920e-02 2.077922077922077920e-02 1.991341991341991427e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 4.444444444444444614e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.555555555555555580e-01 6.666666666666666574e-02 6.666666666666666574e-02 4.666666666666666879e-02 4.000000000000000083e-02 4.666666666666666879e-02 2.666666666666666838e-02 3.111111111111111022e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.000000000000000056e-01 1.666666666666666574e-01 1.600000000000000033e-01 7.111111111111111105e-02 1.111111111111111049e-01 8.444444444444444697e-02 8.444444444444444697e-02 4.888888888888889145e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.333333333333333676e-02 4.888888888888889145e-02 5.333333333333333676e-02 1.904761904761904934e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.904761904761904934e-02 2.222222222222222307e-02 1.644444444444444486e-01 8.444444444444444697e-02 8.888888888888889228e-02 1.288888888888888862e-01 1.555555555555555580e-01 6.349206349206348854e-02 7.301587301587302015e-02 8.888888888888889228e-02 4.761904761904761640e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.761904761904761640e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 2.142857142857142877e-02 2.857142857142857054e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.142857142857142877e-02 2.619047619047619110e-02 7.301587301587302015e-02 9.523809523809523281e-02 1.047619047619047644e-01 1.301587301587301682e-01 9.841269841269841001e-02 7.619047619047619735e-02 7.619047619047619735e-02 1.301587301587301682e-01 1.587301587301587213e-01 1.619047619047619124e-01 8.253968253968253788e-02 5.714285714285714107e-02 7.142857142857142461e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.380952380952381098e-02 1.119047619047619097e-01 9.761904761904761918e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.523809523809523697e-02 5.000000000000000278e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.285714285714285754e-02 2.592592592592592518e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.777777777777777624e-02 2.592592592592592518e-02 2.777777777777777624e-02 2.777777777777777624e-02 6.428571428571427937e-02 8.809523809523810145e-02 8.333333333333332871e-02 1.095238095238095233e-01 8.333333333333332871e-02 1.285714285714285587e-01 1.309523809523809590e-01 8.333333333333332871e-02 1.071428571428571369e-01 8.333333333333332871e-02 4.074074074074074403e-02 4.259259259259259162e-02 4.259259259259259162e-02 4.074074074074074403e-02 4.259259259259259162e-02 4.259259259259259162e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.259259259259259162e-02 4.259259259259259162e-02 4.074074074074074403e-02 3.555555555555555552e-02 2.518518518518518545e-02 2.518518518518518545e-02 2.814814814814814783e-02 2.962962962962963076e-02 2.518518518518518545e-02 7.592592592592592449e-02 6.851851851851852027e-02 7.222222222222221544e-02 1.537037037037037035e-01 1.092592592592592643e-01 9.074074074074074681e-02 9.259259259259258745e-02 5.740740740740740700e-02 4.740740740740740505e-02 5.037037037037037090e-02 6.814814814814815214e-02 1.051851851851851871e-01 4.296296296296295975e-02 3.851851851851852138e-02 4.000000000000000083e-02 4.148148148148148029e-02 3.851851851851852138e-02 4.148148148148148029e-02 4.148148148148148029e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.148148148148148029e-02 4.296296296296295975e-02 4.296296296296295975e-02 4.148148148148148029e-02 3.393939393939394283e-02 3.151515151515151686e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 6.222222222222222043e-02 5.185185185185185036e-02 5.333333333333333676e-02 9.090909090909091161e-02 3.636363636363636187e-02 4.121212121212121382e-02 3.636363636363636187e-02 3.636363636363636187e-02 5.476190476190476164e-02 3.878787878787878785e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.757575757575757486e-02 3.757575757575757486e-02 1.103030303030303055e-01 5.939393939393939476e-02 6.181818181818182073e-02 5.818181818181818177e-02 4.727272727272727182e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 4.444444444444444614e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.000000000000000056e-01 5.333333333333333676e-02 6.666666666666666574e-02 5.333333333333333676e-02 4.000000000000000083e-02 5.333333333333333676e-02 2.666666666666666838e-02 3.555555555555555552e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.066666666666666735e-01 8.666666666666666963e-02 1.000000000000000056e-01 5.333333333333333676e-02 6.666666666666666574e-02 6.222222222222222043e-02 7.555555555555555636e-02 6.222222222222222043e-02 4.888888888888889145e-02 4.888888888888889145e-02 6.666666666666666574e-02 6.222222222222222043e-02 6.666666666666666574e-02 1.904761904761904934e-02 2.539682539682539680e-02 2.539682539682539680e-02 1.904761904761904934e-02 2.539682539682539680e-02 8.444444444444444697e-02 1.111111111111111049e-01 1.066666666666666735e-01 8.444444444444444697e-02 1.022222222222222282e-01 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.349206349206348854e-02 5.396825396825397081e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.714285714285714107e-02 6.984126984126984294e-02 6.984126984126984294e-02 5.396825396825397081e-02 6.349206349206348854e-02 5.079365079365079361e-02 6.984126984126984294e-02 6.984126984126984294e-02 2.142857142857142877e-02 3.571428571428571230e-02 2.619047619047619110e-02 3.333333333333333287e-02 2.619047619047619110e-02 3.333333333333333287e-02 2.142857142857142877e-02 3.333333333333333287e-02 9.523809523809523281e-02 8.888888888888889228e-02 7.936507936507936067e-02 7.936507936507936067e-02 8.888888888888889228e-02 1.111111111111111049e-01 1.079365079365079416e-01 8.253968253968253788e-02 8.571428571428571508e-02 7.936507936507936067e-02 9.841269841269841001e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.952380952380952051e-02 5.714285714285714107e-02 6.190476190476190688e-02 5.476190476190476164e-02 5.952380952380952051e-02 6.190476190476190688e-02 5.476190476190476164e-02 5.238095238095238221e-02 5.238095238095238221e-02 5.476190476190476164e-02 5.952380952380952051e-02 5.476190476190476164e-02 6.428571428571427937e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.476190476190476164e-02 2.962962962962963076e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 3.703703703703703498e-02 3.148148148148147835e-02 3.703703703703703498e-02 3.703703703703703498e-02 8.571428571428571508e-02 7.857142857142856984e-02 8.095238095238095621e-02 7.857142857142856984e-02 8.809523809523810145e-02 7.857142857142856984e-02 7.619047619047619735e-02 9.285714285714286031e-02 7.857142857142856984e-02 6.296296296296295669e-02 5.185185185185185036e-02 5.555555555555555247e-02 5.370370370370370489e-02 5.185185185185185036e-02 5.370370370370370489e-02 5.370370370370370489e-02 6.481481481481481122e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.925925925925926152e-02 5.185185185185185036e-02 4.444444444444444614e-02 2.814814814814814783e-02 2.814814814814814783e-02 3.851851851851852138e-02 4.148148148148148029e-02 2.962962962962963076e-02 7.592592592592592449e-02 7.962962962962963354e-02 7.777777777777777901e-02 7.592592592592592449e-02 7.222222222222221544e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.777777777777777901e-02 5.629629629629629567e-02 5.925925925925926152e-02 5.925925925925926152e-02 5.185185185185185036e-02 6.370370370370370683e-02 5.185185185185185036e-02 5.333333333333333676e-02 5.481481481481481621e-02 5.037037037037037090e-02 5.925925925925926152e-02 5.481481481481481621e-02 5.629629629629629567e-02 5.629629629629629567e-02 6.074074074074074098e-02 6.222222222222222043e-02 6.370370370370370683e-02 5.777777777777777513e-02 4.484848484848484584e-02 4.606060606060605883e-02 3.151515151515151686e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.909090909090909088e-02 6.814814814814815214e-02 7.111111111111111105e-02 5.575757575757575579e-02 5.090909090909091078e-02 4.969696969696969779e-02 5.939393939393939476e-02 4.848484848484848481e-02 4.848484848484848481e-02 7.619047619047619735e-02 5.333333333333333676e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.848484848484848481e-02 5.090909090909091078e-02 5.333333333333333676e-02 6.909090909090909172e-02 6.424242424242423977e-02 7.636363636363636964e-02 6.666666666666666574e-02 6.545454545454545969e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 4.444444444444444614e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.000000000000000056e-01 5.333333333333333676e-02 6.666666666666666574e-02 5.999999999999999778e-02 4.000000000000000083e-02 5.999999999999999778e-02 2.666666666666666838e-02 4.000000000000000083e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.333333333333333315e-01 8.666666666666666963e-02 1.066666666666666735e-01 5.333333333333333676e-02 6.666666666666666574e-02 6.222222222222222043e-02 7.555555555555555636e-02 6.666666666666666574e-02 5.333333333333333676e-02 5.333333333333333676e-02 8.000000000000000167e-02 6.666666666666666574e-02 8.000000000000000167e-02 1.904761904761904934e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.904761904761904934e-02 2.857142857142857054e-02 8.888888888888889228e-02 1.066666666666666735e-01 1.466666666666666674e-01 1.022222222222222282e-01 1.155555555555555503e-01 6.349206349206348854e-02 6.031746031746031828e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.619047619047619735e-02 7.619047619047619735e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 7.619047619047619735e-02 7.619047619047619735e-02 2.142857142857142877e-02 4.285714285714285754e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.142857142857142877e-02 3.571428571428571230e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.047619047619047644e-01 9.523809523809523281e-02 1.174603174603174593e-01 1.047619047619047644e-01 1.174603174603174593e-01 9.523809523809523281e-02 9.523809523809523281e-02 8.571428571428571508e-02 1.206349206349206366e-01 6.666666666666666574e-02 6.190476190476190688e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.904761904761905211e-02 5.476190476190476164e-02 5.952380952380952051e-02 6.428571428571427937e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.857142857142856984e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 6.428571428571427937e-02 5.714285714285714107e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 3.888888888888888951e-02 3.333333333333333287e-02 3.888888888888888951e-02 3.888888888888888951e-02 8.571428571428571508e-02 9.285714285714286031e-02 1.119047619047619097e-01 1.023809523809523780e-01 1.023809523809523780e-01 9.523809523809523281e-02 8.809523809523810145e-02 8.809523809523810145e-02 8.571428571428571508e-02 6.481481481481481122e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.111111111111110911e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.111111111111110911e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.111111111111110911e-02 6.111111111111110911e-02 5.555555555555555247e-02 5.333333333333333676e-02 3.111111111111111022e-02 3.111111111111111022e-02 4.000000000000000083e-02 4.444444444444444614e-02 3.111111111111111022e-02 8.333333333333332871e-02 1.129629629629629595e-01 9.814814814814815103e-02 8.703703703703703776e-02 8.703703703703703776e-02 8.518518518518518323e-02 9.814814814814815103e-02 7.777777777777777901e-02 5.925925925925926152e-02 7.407407407407406996e-02 6.814814814814815214e-02 5.481481481481481621e-02 6.666666666666666574e-02 5.333333333333333676e-02 5.777777777777777513e-02 6.222222222222222043e-02 5.333333333333333676e-02 6.222222222222222043e-02 6.222222222222222043e-02 5.777777777777777513e-02 5.777777777777777513e-02 6.222222222222222043e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.222222222222222043e-02 5.090909090909091078e-02 5.090909090909091078e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.272727272727272985e-02 6.814814814814815214e-02 7.111111111111111105e-02 6.060606060606060774e-02 6.181818181818182073e-02 5.090909090909091078e-02 6.545454545454545969e-02 5.090909090909091078e-02 5.090909090909091078e-02 7.857142857142856984e-02 5.818181818181818177e-02 5.090909090909091078e-02 5.090909090909091078e-02 5.090909090909091078e-02 5.454545454545454281e-02 5.454545454545454281e-02 8.121212121212120771e-02 6.909090909090909172e-02 7.151515151515151769e-02 8.242424242424242764e-02 6.545454545454545969e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 4.444444444444444614e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.333333333333333315e-01 5.999999999999999778e-02 6.666666666666666574e-02 4.666666666666666879e-02 4.000000000000000083e-02 4.666666666666666879e-02 2.666666666666666838e-02 3.111111111111111022e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.066666666666666735e-01 1.333333333333333315e-01 1.333333333333333315e-01 6.222222222222222043e-02 9.333333333333333759e-02 7.555555555555555636e-02 7.555555555555555636e-02 4.888888888888889145e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.333333333333333676e-02 4.888888888888889145e-02 5.333333333333333676e-02 1.904761904761904934e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.904761904761904934e-02 2.222222222222222307e-02 1.288888888888888862e-01 8.444444444444444697e-02 1.022222222222222282e-01 1.199999999999999956e-01 1.288888888888888862e-01 6.031746031746031828e-02 6.349206349206348854e-02 7.619047619047619735e-02 4.761904761904761640e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.761904761904761640e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 2.142857142857142877e-02 2.857142857142857054e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.142857142857142877e-02 2.619047619047619110e-02 7.301587301587302015e-02 8.888888888888889228e-02 1.079365079365079416e-01 1.174603174603174593e-01 9.841269841269841001e-02 7.619047619047619735e-02 8.253968253968253788e-02 1.142857142857142821e-01 1.238095238095238138e-01 1.238095238095238138e-01 8.888888888888889228e-02 5.714285714285714107e-02 6.190476190476190688e-02 5.952380952380952051e-02 5.952380952380952051e-02 6.428571428571427937e-02 8.809523809523810145e-02 7.857142857142856984e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.523809523809523697e-02 5.000000000000000278e-02 4.523809523809523697e-02 4.285714285714285754e-02 4.523809523809523697e-02 4.523809523809523697e-02 4.285714285714285754e-02 2.592592592592592518e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.777777777777777624e-02 2.592592592592592518e-02 2.777777777777777624e-02 2.777777777777777624e-02 6.428571428571427937e-02 9.047619047619047394e-02 9.523809523809523281e-02 1.095238095238095233e-01 8.333333333333332871e-02 1.142857142857142821e-01 1.119047619047619097e-01 7.619047619047619735e-02 9.285714285714286031e-02 7.222222222222221544e-02 4.074074074074074403e-02 4.259259259259259162e-02 4.259259259259259162e-02 4.074074074074074403e-02 4.259259259259259162e-02 4.259259259259259162e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.259259259259259162e-02 4.259259259259259162e-02 4.074074074074074403e-02 3.555555555555555552e-02 2.518518518518518545e-02 2.518518518518518545e-02 2.814814814814814783e-02 2.962962962962963076e-02 2.518518518518518545e-02 7.777777777777777901e-02 8.518518518518518323e-02 8.148148148148148806e-02 1.148148148148148140e-01 1.018518518518518462e-01 8.703703703703703776e-02 9.814814814814815103e-02 5.740740740740740700e-02 4.740740740740740505e-02 5.333333333333333676e-02 5.925925925925926152e-02 8.148148148148148806e-02 4.296296296296295975e-02 3.851851851851852138e-02 4.000000000000000083e-02 4.148148148148148029e-02 3.851851851851852138e-02 4.148148148148148029e-02 4.148148148148148029e-02 4.000000000000000083e-02 4.000000000000000083e-02 4.148148148148148029e-02 4.296296296296295975e-02 4.296296296296295975e-02 4.148148148148148029e-02 3.393939393939394283e-02 3.151515151515151686e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.545454545454545539e-02 5.777777777777777513e-02 5.185185185185185036e-02 5.333333333333333676e-02 8.121212121212120771e-02 3.636363636363636187e-02 4.121212121212121382e-02 3.636363636363636187e-02 3.636363636363636187e-02 5.476190476190476164e-02 3.878787878787878785e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.757575757575757486e-02 3.757575757575757486e-02 9.696969696969696961e-02 6.060606060606060774e-02 5.818181818181818177e-02 6.424242424242423977e-02 4.727272727272727182e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 4.444444444444444614e-02 3.333333333333333287e-02 2.000000000000000042e-02 0.000000000000000000e+00 1.555555555555555580e-01 6.666666666666666574e-02 6.666666666666666574e-02 5.999999999999999778e-02 4.000000000000000083e-02 5.999999999999999778e-02 2.666666666666666838e-02 4.000000000000000083e-02 1.904761904761904934e-02 1.333333333333333419e-02 1.333333333333333419e-02 1.133333333333333276e-01 1.533333333333333215e-01 1.733333333333333393e-01 7.111111111111111105e-02 1.111111111111111049e-01 8.444444444444444697e-02 8.444444444444444697e-02 6.666666666666666574e-02 5.333333333333333676e-02 5.333333333333333676e-02 8.000000000000000167e-02 6.666666666666666574e-02 8.000000000000000167e-02 1.904761904761904934e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.904761904761904934e-02 2.857142857142857054e-02 1.555555555555555580e-01 1.022222222222222282e-01 1.155555555555555503e-01 1.288888888888888862e-01 1.822222222222222299e-01 6.984126984126984294e-02 7.936507936507936067e-02 8.888888888888889228e-02 6.666666666666666574e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.619047619047619735e-02 7.619047619047619735e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 7.619047619047619735e-02 7.619047619047619735e-02 2.142857142857142877e-02 4.285714285714285754e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.142857142857142877e-02 3.571428571428571230e-02 9.206349206349206948e-02 1.015873015873015872e-01 1.111111111111111049e-01 1.301587301587301682e-01 1.111111111111111049e-01 1.015873015873015872e-01 1.015873015873015872e-01 1.301587301587301682e-01 1.587301587301587213e-01 1.492063492063492036e-01 1.015873015873015872e-01 6.666666666666666574e-02 8.095238095238095621e-02 7.380952380952381098e-02 7.380952380952381098e-02 8.809523809523810145e-02 1.023809523809523780e-01 8.809523809523810145e-02 6.428571428571427937e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.857142857142856984e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 6.428571428571427937e-02 5.714285714285714107e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 3.888888888888888951e-02 3.333333333333333287e-02 3.888888888888888951e-02 3.888888888888888951e-02 8.333333333333332871e-02 9.761904761904761918e-02 9.761904761904761918e-02 1.190476190476190410e-01 9.761904761904761918e-02 1.333333333333333315e-01 1.261904761904761862e-01 9.761904761904761918e-02 1.119047619047619097e-01 7.962962962962963354e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.111111111111110911e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.111111111111110911e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.111111111111110911e-02 6.111111111111110911e-02 5.555555555555555247e-02 5.333333333333333676e-02 3.111111111111111022e-02 3.111111111111111022e-02 4.000000000000000083e-02 4.444444444444444614e-02 3.111111111111111022e-02 8.703703703703703776e-02 8.703703703703703776e-02 8.703703703703703776e-02 1.425925925925926041e-01 1.092592592592592643e-01 9.814814814814815103e-02 1.037037037037037007e-01 7.592592592592592449e-02 5.925925925925926152e-02 6.814814814814815214e-02 8.592592592592591949e-02 9.925925925925925541e-02 6.666666666666666574e-02 5.333333333333333676e-02 5.777777777777777513e-02 6.222222222222222043e-02 5.333333333333333676e-02 6.222222222222222043e-02 6.222222222222222043e-02 5.777777777777777513e-02 5.777777777777777513e-02 6.222222222222222043e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.222222222222222043e-02 5.090909090909091078e-02 5.090909090909091078e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.272727272727272985e-02 7.407407407407406996e-02 6.962962962962962465e-02 6.303030303030303372e-02 8.848484848484848564e-02 5.090909090909091078e-02 6.545454545454545969e-02 5.090909090909091078e-02 5.090909090909091078e-02 7.857142857142856984e-02 5.818181818181818177e-02 5.090909090909091078e-02 5.090909090909091078e-02 5.090909090909091078e-02 5.454545454545454281e-02 5.454545454545454281e-02 1.078787878787878796e-01 7.151515151515151769e-02 7.878787878787878174e-02 7.515151515151514972e-02 6.424242424242423977e-02
-0.000000000000000000e+00 0.000000000000000000e+00 6.349206349206348854e-02 7.936507936507936067e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 7.142857142857142461e-02 9.047619047619047394e-02 3.809523809523809867e-02 3.333333333333333287e-02 2.857142857142857054e-02 3.333333333333333287e-02 1.904761904761904934e-02 2.222222222222222307e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.666666666666666574e-02 6.666666666666666574e-02 7.142857142857142461e-02 9.206349206349206948e-02 4.761904761904761640e-02 4.126984126984126894e-02 4.761904761904761640e-02 3.492063492063492147e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.809523809523809867e-02 3.492063492063492147e-02 3.809523809523809867e-02 1.360544217687074779e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.360544217687074779e-02 1.587301587301587213e-02 6.349206349206348854e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.031746031746031828e-02 6.984126984126984294e-02 8.390022675736961977e-02 9.070294784580498759e-02 4.081632653061224164e-02 3.401360544217687382e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.401360544217687382e-02 3.628117913832199642e-02 3.628117913832199642e-02 3.174603174603174427e-02 3.401360544217687382e-02 3.174603174603174427e-02 3.628117913832199642e-02 3.628117913832199642e-02 1.530612244897959148e-02 2.040816326530612082e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.530612244897959148e-02 1.870748299319727886e-02 4.988662131519274595e-02 5.215419501133786856e-02 5.442176870748299117e-02 5.668934240362812071e-02 5.668934240362812071e-02 5.215419501133786856e-02 5.442176870748299117e-02 5.668934240362812071e-02 6.122448979591836593e-02 5.895691609977324332e-02 5.668934240362812071e-02 7.653061224489796088e-02 8.843537414965986498e-02 8.163265306122448328e-02 8.333333333333332871e-02 8.843537414965986498e-02 4.251700680272108707e-02 4.251700680272108707e-02 3.231292517006802839e-02 3.061224489795918297e-02 3.061224489795918297e-02 3.061224489795918297e-02 3.231292517006802839e-02 3.231292517006802839e-02 3.231292517006802839e-02 3.571428571428571230e-02 3.231292517006802839e-02 3.061224489795918297e-02 3.231292517006802839e-02 3.231292517006802839e-02 3.061224489795918297e-02 1.851851851851851749e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.984126984126984017e-02 1.851851851851851749e-02 1.984126984126984017e-02 1.984126984126984017e-02 4.421768707482993249e-02 4.761904761904761640e-02 5.102040816326530726e-02 5.272108843537415268e-02 4.931972789115646183e-02 5.442176870748299117e-02 5.272108843537415268e-02 4.761904761904761640e-02 4.931972789115646183e-02 4.100529100529100301e-02 2.910052910052909891e-02 3.042328042328042159e-02 3.042328042328042159e-02 2.910052910052909891e-02 3.042328042328042159e-02 3.042328042328042159e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.042328042328042159e-02 3.042328042328042159e-02 2.910052910052909891e-02 2.539682539682539680e-02 1.798941798941798911e-02 1.798941798941798911e-02 2.010582010582010609e-02 2.116402116402116285e-02 1.798941798941798911e-02 4.232804232804232569e-02 4.761904761904761640e-02 4.497354497354497105e-02 5.291005291005290712e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.761904761904761640e-02 3.968253968253968034e-02 5.608465608465608432e-02 6.243386243386243178e-02 8.359788359788360157e-02 3.915343915343915543e-02 3.068783068783068751e-02 2.751322751322751378e-02 2.857142857142857054e-02 2.962962962962963076e-02 2.751322751322751378e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.962962962962963076e-02 3.068783068783068751e-02 3.068783068783068751e-02 2.962962962962963076e-02 2.424242424242424240e-02 2.251082251082251254e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 3.703703703703703498e-02 3.597883597883597823e-02 3.116883116883116880e-02 3.722943722943723027e-02 2.597402597402597574e-02 2.943722943722943894e-02 2.597402597402597574e-02 2.597402597402597574e-02 3.911564625850340315e-02 2.770562770562770560e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.683982683982684067e-02 2.683982683982684067e-02 4.329004329004328827e-02 3.463203463203463200e-02 3.636363636363636187e-02 3.722943722943723027e-02 3.290043290043290214e-02
-0.000000000000000000e+00 0.000000000000000000e+00 6.349206349206348854e-02 7.936507936507936067e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 7.936507936507936067e-02 1.047619047619047644e-01 3.809523809523809867e-02 3.333333333333333287e-02 2.857142857142857054e-02 3.333333333333333287e-02 1.904761904761904934e-02 2.222222222222222307e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.666666666666666574e-02 7.619047619047619735e-02 8.095238095238095621e-02 1.142857142857142821e-01 5.396825396825397081e-02 4.444444444444444614e-02 5.396825396825397081e-02 3.492063492063492147e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.809523809523809867e-02 3.492063492063492147e-02 3.809523809523809867e-02 1.360544217687074779e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.360544217687074779e-02 1.587301587301587213e-02 7.301587301587302015e-02 5.714285714285714107e-02 6.031746031746031828e-02 6.349206349206348854e-02 7.936507936507936067e-02 9.070294784580498759e-02 1.156462585034013640e-01 4.535147392290249380e-02 3.401360544217687382e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.401360544217687382e-02 3.628117913832199642e-02 3.628117913832199642e-02 3.174603174603174427e-02 3.401360544217687382e-02 3.174603174603174427e-02 3.628117913832199642e-02 3.628117913832199642e-02 1.530612244897959148e-02 2.040816326530612082e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.700680272108843691e-02 1.870748299319727886e-02 1.530612244897959148e-02 1.870748299319727886e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.668934240362812071e-02 5.215419501133786856e-02 5.215419501133786856e-02 6.122448979591836593e-02 7.029478458049887024e-02 6.802721088435374763e-02 5.668934240362812071e-02 7.482993197278911546e-02 1.156462585034013640e-01 9.353741496598640126e-02 9.353741496598640126e-02 1.139455782312925186e-01 4.931972789115646183e-02 4.931972789115646183e-02 3.231292517006802839e-02 3.061224489795918297e-02 3.061224489795918297e-02 3.061224489795918297e-02 3.231292517006802839e-02 3.231292517006802839e-02 3.231292517006802839e-02 3.571428571428571230e-02 3.231292517006802839e-02 3.061224489795918297e-02 3.231292517006802839e-02 3.231292517006802839e-02 3.061224489795918297e-02 1.851851851851851749e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.984126984126984017e-02 1.851851851851851749e-02 1.984126984126984017e-02 1.984126984126984017e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.931972789115646183e-02 5.442176870748299117e-02 4.931972789115646183e-02 5.952380952380952051e-02 5.782312925170068202e-02 4.931972789115646183e-02 5.272108843537415268e-02 4.365079365079364837e-02 2.910052910052909891e-02 3.042328042328042159e-02 3.042328042328042159e-02 2.910052910052909891e-02 3.042328042328042159e-02 3.042328042328042159e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.042328042328042159e-02 3.042328042328042159e-02 2.910052910052909891e-02 2.539682539682539680e-02 1.798941798941798911e-02 1.798941798941798911e-02 2.010582010582010609e-02 2.116402116402116285e-02 1.798941798941798911e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.365079365079364837e-02 6.084656084656084318e-02 5.026455026455026176e-02 4.629629629629629373e-02 4.761904761904761640e-02 3.968253968253968034e-02 5.502645502645502756e-02 5.079365079365079361e-02 1.121693121693121686e-01 4.550264550264550290e-02 3.068783068783068751e-02 2.751322751322751378e-02 2.857142857142857054e-02 2.962962962962963076e-02 2.751322751322751378e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.962962962962963076e-02 3.068783068783068751e-02 3.068783068783068751e-02 2.962962962962963076e-02 2.424242424242424240e-02 2.251082251082251254e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.818181818181818094e-02 3.809523809523809867e-02 3.597883597883597823e-02 3.116883116883116880e-02 4.069264069264069000e-02 2.597402597402597574e-02 2.943722943722943894e-02 2.597402597402597574e-02 2.597402597402597574e-02 3.911564625850340315e-02 2.770562770562770560e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.683982683982684067e-02 2.683982683982684067e-02 4.675324675324675494e-02 3.463203463203463200e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.290043290043290214e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.587301587301587213e-01 6.349206349206348854e-02 1.587301587301587213e-02 9.523809523809524669e-03 0.000000000000000000e+00 1.269841269841269771e-01 5.714285714285714107e-02 1.238095238095238138e-01 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.070294784580499106e-03 6.349206349206349201e-03 6.349206349206349201e-03 7.619047619047619735e-02 1.047619047619047644e-01 1.047619047619047644e-01 5.079365079365079361e-02 1.142857142857142821e-01 1.142857142857142821e-01 8.253968253968253788e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 9.070294784580499106e-03 9.070294784580499106e-03 9.070294784580499106e-03 9.070294784580499106e-03 9.070294784580499106e-03 8.888888888888889228e-02 5.714285714285714107e-02 5.714285714285714107e-02 7.619047619047619735e-02 8.888888888888889228e-02 4.081632653061224164e-02 4.535147392290249380e-02 1.065759637188208597e-01 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.814058956916099821e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 4.535147392290249380e-02 5.442176870748299117e-02 5.895691609977324332e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.535147392290249380e-02 6.802721088435374763e-02 7.709750566893423807e-02 7.709750566893423807e-02 4.988662131519274595e-02 3.401360544217687382e-02 4.081632653061224164e-02 3.741496598639455773e-02 3.741496598639455773e-02 4.081632653061224164e-02 9.693877551020407823e-02 6.462585034013605678e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 3.741496598639455773e-02 4.761904761904761640e-02 4.421768707482993249e-02 5.442176870748299117e-02 4.421768707482993249e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.421768707482993249e-02 5.442176870748299117e-02 5.291005291005290712e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.587301587301587213e-02 1.269841269841269840e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 1.058201058201058142e-02 3.968253968253968034e-02 3.439153439153438963e-02 3.703703703703703498e-02 6.084656084656084318e-02 5.026455026455026176e-02 4.497354497354497105e-02 4.497354497354497105e-02 3.174603174603174427e-02 2.539682539682539680e-02 2.539682539682539680e-02 3.386243386243386472e-02 8.465608465608465139e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.481481481481481538e-02 1.212121212121212120e-02 1.038961038961038960e-02 1.038961038961038960e-02 1.038961038961038960e-02 1.038961038961038960e-02 1.038961038961038960e-02 1.038961038961038960e-02 3.174603174603174427e-02 2.751322751322751378e-02 4.761904761904761640e-02 6.147186147186146921e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 2.040816326530612082e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.385281385281385280e-02 4.329004329004328827e-02 2.943722943722943894e-02 2.943722943722943894e-02 2.770562770562770560e-02 2.424242424242424240e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 4.285714285714285754e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.047619047619047394e-02 7.142857142857142461e-02 8.571428571428571508e-02 4.444444444444444614e-02 5.396825396825397081e-02 4.535147392290249380e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809523281e-02 7.301587301587302015e-02 7.301587301587302015e-02 9.523809523809523281e-02 9.206349206349206948e-02 9.206349206349206948e-02 4.535147392290249380e-02 5.668934240362812071e-02 6.575963718820861115e-02 5.215419501133786856e-02 6.122448979591836593e-02 4.761904761904761640e-02 6.349206349206348854e-02 6.666666666666666574e-02 4.761904761904761640e-02 6.666666666666666574e-02 3.401360544217687382e-02 3.401360544217687382e-02 1.814058956916099821e-02 9.750566893424036929e-02 7.709750566893423807e-02 7.029478458049887024e-02 7.029478458049887024e-02 7.936507936507936067e-02 9.750566893424036929e-02 9.750566893424036929e-02 7.482993197278911546e-02 9.297052154195011020e-02 7.029478458049887024e-02 9.523809523809523281e-02 9.523809523809523281e-02 4.421768707482993249e-02 6.632653061224490221e-02 5.102040816326530726e-02 6.802721088435374763e-02 4.931972789115646183e-02 7.142857142857142461e-02 4.761904761904761640e-02 6.802721088435374763e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 7.256235827664399285e-02 7.256235827664399285e-02 5.442176870748299117e-02 5.668934240362812071e-02 4.988662131519274595e-02 6.802721088435374763e-02 3.741496598639455773e-02 3.741496598639455773e-02 4.081632653061224164e-02 3.741496598639455773e-02 4.251700680272108707e-02 2.040816326530612082e-02 2.040816326530612082e-02 8.843537414965986498e-02 7.993197278911565173e-02 6.972789115646257918e-02 6.972789115646257918e-02 7.312925170068027003e-02 8.163265306122448328e-02 7.142857142857142461e-02 8.333333333333332871e-02 7.142857142857142461e-02 7.653061224489796088e-02 7.823129251700680631e-02 7.993197278911565173e-02 7.482993197278911546e-02 4.761904761904761640e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.629629629629629373e-02 5.158730158730158444e-02 7.142857142857142461e-02 6.216931216931216586e-02 7.275132275132274728e-02 7.275132275132274728e-02 6.802721088435374763e-02 5.272108843537415268e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.442176870748299117e-02 5.272108843537415268e-02 6.122448979591836593e-02 5.272108843537415268e-02 2.513227513227513088e-02 6.746031746031745657e-02 7.275132275132274728e-02 7.010582010582010193e-02 6.746031746031745657e-02 6.878306878306877925e-02 6.878306878306877925e-02 9.259259259259258745e-02 7.275132275132274728e-02 7.010582010582010193e-02 7.010582010582010193e-02 7.142857142857142461e-02 8.201058201058200603e-02 6.878306878306877925e-02 5.502645502645502756e-02 4.444444444444444614e-02 4.444444444444444614e-02 6.772486772486772943e-02 7.089947089947089276e-02 5.502645502645502756e-02 5.291005291005290712e-02 5.555555555555555247e-02 5.423280423280422979e-02 5.555555555555555247e-02 5.291005291005290712e-02 5.291005291005290712e-02 5.423280423280422979e-02 6.613756613756613389e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.751322751322751378e-02 8.888888888888889228e-02 7.089947089947089276e-02 6.878306878306877925e-02 6.878306878306877925e-02 6.666666666666666574e-02 8.042328042328042437e-02 7.089947089947089276e-02 7.724867724867724716e-02 7.619047619047619735e-02 8.465608465608465139e-02 8.783068783068782859e-02 8.677248677248677877e-02 7.936507936507936067e-02 5.541125541125541121e-02 7.705627705627705881e-02 5.541125541125541121e-02 4.415584415584415667e-02 4.415584415584415667e-02 4.588744588744588654e-02 4.415584415584415667e-02 5.714285714285714107e-02 6.349206349206348854e-02 3.463203463203463200e-02 2.857142857142857054e-02 6.666666666666666574e-02 8.051948051948051854e-02 6.320346320346320601e-02 6.320346320346320601e-02 9.693877551020407823e-02 6.839826839826840255e-02 6.493506493506492894e-02 6.320346320346320601e-02 6.320346320346320601e-02 6.666666666666666574e-02 7.186147186147186228e-02 5.541125541125541121e-02 5.194805194805195148e-02 5.541125541125541121e-02 5.541125541125541121e-02 6.060606060606060774e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.523809523809523281e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 9.523809523809523281e-02 1.000000000000000056e-01 5.079365079365079361e-02 5.714285714285714107e-02 5.442176870748299117e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 8.888888888888889228e-02 8.888888888888889228e-02 9.523809523809523281e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.015873015873015872e-01 5.442176870748299117e-02 7.256235827664399285e-02 6.349206349206348854e-02 6.349206349206348854e-02 7.029478458049887024e-02 4.444444444444444614e-02 5.396825396825397081e-02 5.714285714285714107e-02 4.444444444444444614e-02 5.714285714285714107e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.814058956916099821e-02 7.709750566893423807e-02 8.843537414965986498e-02 8.163265306122448328e-02 8.616780045351474238e-02 9.070294784580498759e-02 8.843537414965986498e-02 8.843537414965986498e-02 8.843537414965986498e-02 8.616780045351474238e-02 9.297052154195011020e-02 9.523809523809523281e-02 9.297052154195011020e-02 6.122448979591836593e-02 7.142857142857142461e-02 6.972789115646257918e-02 6.632653061224490221e-02 6.122448979591836593e-02 6.292517006802721136e-02 6.122448979591836593e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.895691609977324332e-02 5.895691609977324332e-02 4.761904761904761640e-02 4.988662131519274595e-02 4.535147392290249380e-02 5.442176870748299117e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.571428571428571230e-02 3.401360544217687382e-02 3.741496598639455773e-02 2.040816326530612082e-02 2.040816326530612082e-02 6.972789115646257918e-02 7.482993197278911546e-02 7.823129251700680631e-02 8.673469387755101956e-02 7.823129251700680631e-02 8.163265306122448328e-02 8.503401360544217413e-02 9.013605442176871041e-02 9.183673469387755584e-02 8.503401360544217413e-02 8.843537414965986498e-02 8.843537414965986498e-02 8.163265306122448328e-02 5.291005291005290712e-02 6.613756613756613389e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.084656084656084318e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.216931216931216586e-02 6.349206349206348854e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.591836734693877792e-02 5.102040816326530726e-02 4.591836734693877792e-02 2.380952380952380820e-02 7.407407407407406996e-02 7.671957671957671532e-02 7.407407407407406996e-02 7.804232804232803800e-02 8.597883597883597406e-02 7.539682539682539264e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.936507936507936067e-02 8.994708994708994210e-02 8.465608465608465139e-02 6.349206349206348854e-02 7.407407407407406996e-02 5.502645502645502756e-02 5.925925925925926152e-02 5.502645502645502756e-02 5.396825396825397081e-02 6.984126984126984294e-02 5.820105820105819783e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.629629629629629373e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.629629629629629373e-02 5.026455026455026176e-02 3.597883597883597823e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.539682539682539680e-02 7.513227513227513366e-02 6.031746031746031828e-02 7.195767195767195645e-02 8.359788359788360157e-02 6.666666666666666574e-02 6.349206349206348854e-02 7.089947089947089276e-02 6.137566137566137503e-02 5.820105820105819783e-02 6.455026455026455223e-02 6.878306878306877925e-02 6.560846560846560205e-02 7.619047619047619735e-02 5.281385281385281294e-02 6.233766233766233761e-02 5.541125541125541121e-02 5.367965367965368134e-02 5.714285714285714107e-02 5.367965367965368134e-02 6.060606060606060774e-02 4.444444444444444614e-02 4.761904761904761640e-02 2.943722943722943894e-02 2.597402597402597574e-02 5.541125541125541121e-02 7.359307359307359908e-02 6.666666666666666574e-02 7.186147186147186228e-02 7.823129251700680631e-02 6.926406926406926401e-02 6.060606060606060774e-02 6.060606060606060774e-02 5.800865800865800948e-02 6.493506493506492894e-02 5.627705627705627961e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.502164502164502508e-02 4.415584415584415667e-02 4.502164502164502508e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.031746031746031689e-01 4.761904761904761640e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.000000000000000056e-01 1.047619047619047644e-01 1.095238095238095233e-01 5.079365079365079361e-02 5.714285714285714107e-02 5.895691609977324332e-02 6.984126984126984294e-02 5.714285714285714107e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.936507936507936067e-02 1.079365079365079416e-01 1.047619047619047644e-01 1.047619047619047644e-01 9.841269841269841001e-02 1.111111111111111049e-01 5.895691609977324332e-02 8.163265306122448328e-02 6.349206349206348854e-02 6.122448979591836593e-02 7.256235827664399285e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.714285714285714107e-02 4.444444444444444614e-02 5.714285714285714107e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.814058956916099821e-02 7.029478458049887024e-02 8.163265306122448328e-02 1.065759637188208597e-01 1.065759637188208597e-01 9.523809523809523281e-02 7.482993197278911546e-02 8.390022675736961977e-02 1.020408163265306145e-01 7.936507936507936067e-02 1.020408163265306145e-01 9.750566893424036929e-02 9.750566893424036929e-02 6.802721088435374763e-02 8.163265306122448328e-02 7.823129251700680631e-02 5.782312925170068202e-02 7.312925170068027003e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.462585034013605678e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.988662131519274595e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 2.040816326530612082e-02 2.040816326530612082e-02 6.292517006802721136e-02 6.802721088435374763e-02 9.013605442176871041e-02 8.333333333333332871e-02 1.020408163265306145e-01 7.142857142857142461e-02 1.054421768707483054e-01 9.523809523809523281e-02 1.003401360544217691e-01 7.993197278911565173e-02 9.863945578231292366e-02 9.013605442176871041e-02 9.693877551020407823e-02 6.084656084656084318e-02 7.407407407407406996e-02 6.878306878306877925e-02 7.671957671957671532e-02 6.084656084656084318e-02 5.291005291005290712e-02 5.687830687830687515e-02 5.820105820105819783e-02 6.084656084656084318e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.421768707482993249e-02 2.380952380952380820e-02 7.804232804232803800e-02 7.539682539682539264e-02 8.730158730158729674e-02 8.465608465608465139e-02 8.201058201058200603e-02 1.018518518518518462e-01 6.481481481481481122e-02 9.788359788359787816e-02 1.058201058201058142e-01 9.788359788359787816e-02 9.656084656084655549e-02 5.687830687830687515e-02 9.656084656084655549e-02 5.502645502645502756e-02 6.560846560846560205e-02 6.772486772486772943e-02 4.867724867724868010e-02 6.349206349206348854e-02 5.502645502645502756e-02 4.232804232804232569e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 3.386243386243386472e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.539682539682539680e-02 6.666666666666666574e-02 5.502645502645502756e-02 6.666666666666666574e-02 7.407407407407406996e-02 6.878306878306877925e-02 5.820105820105819783e-02 7.619047619047619735e-02 5.608465608465608432e-02 5.185185185185185036e-02 5.608465608465608432e-02 6.455026455026455223e-02 5.608465608465608432e-02 6.984126984126984294e-02 5.194805194805195148e-02 6.406926406926406747e-02 5.021645021645021467e-02 7.272727272727272374e-02 7.272727272727272374e-02 6.580086580086580428e-02 6.580086580086580428e-02 4.021164021164021218e-02 4.232804232804232569e-02 2.770562770562770560e-02 2.597402597402597574e-02 5.021645021645021467e-02 6.580086580086580428e-02 9.090909090909091161e-02 7.099567099567100081e-02 7.142857142857142461e-02 5.800865800865800948e-02 6.147186147186146921e-02 5.800865800865800948e-02 5.974025974025973934e-02 5.974025974025973934e-02 5.108225108225108307e-02 4.155844155844155841e-02 3.809523809523809867e-02 4.155844155844155841e-02 4.155844155844155841e-02 3.982683982683982854e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.190476190476190410e-01 5.238095238095238221e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 1.285714285714285587e-01 1.285714285714285587e-01 5.714285714285714107e-02 6.349206349206348854e-02 6.802721088435374763e-02 8.253968253968253788e-02 6.349206349206348854e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.936507936507936067e-02 1.174603174603174593e-01 1.301587301587301682e-01 9.523809523809523281e-02 1.015873015873015872e-01 1.301587301587301682e-01 6.802721088435374763e-02 9.977324263038549190e-02 6.349206349206348854e-02 7.256235827664399285e-02 7.709750566893423807e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.714285714285714107e-02 4.444444444444444614e-02 5.714285714285714107e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.814058956916099821e-02 7.029478458049887024e-02 8.616780045351474238e-02 1.065759637188208597e-01 1.201814058956916093e-01 9.523809523809523281e-02 7.482993197278911546e-02 7.936507936507936067e-02 1.156462585034013640e-01 8.390022675736961977e-02 1.292517006802721136e-01 1.043083900226757371e-01 9.523809523809523281e-02 8.503401360544217413e-02 7.142857142857142461e-02 9.863945578231292366e-02 5.782312925170068202e-02 8.163265306122448328e-02 5.782312925170068202e-02 7.482993197278911546e-02 6.802721088435374763e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.988662131519274595e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 2.040816326530612082e-02 2.040816326530612082e-02 6.292517006802721136e-02 7.312925170068027003e-02 9.013605442176871041e-02 9.353741496598640126e-02 9.523809523809523281e-02 7.653061224489796088e-02 1.207482993197278864e-01 9.183673469387755584e-02 1.275510204081632681e-01 8.843537414965986498e-02 1.139455782312925186e-01 9.523809523809523281e-02 1.037414965986394599e-01 6.084656084656084318e-02 9.523809523809523281e-02 7.936507936507936067e-02 8.465608465608465139e-02 6.878306878306877925e-02 5.291005291005290712e-02 6.084656084656084318e-02 6.084656084656084318e-02 5.952380952380952051e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.421768707482993249e-02 2.380952380952380820e-02 7.804232804232803800e-02 7.671957671957671532e-02 8.201058201058200603e-02 8.862433862433861942e-02 9.391534391534391013e-02 1.018518518518518462e-01 6.746031746031745657e-02 8.597883597883597406e-02 1.150793650793650730e-01 1.256613756613756683e-01 1.150793650793650730e-01 5.687830687830687515e-02 9.656084656084655549e-02 6.137566137566137503e-02 8.253968253968253788e-02 7.407407407407406996e-02 4.867724867724868010e-02 6.984126984126984294e-02 6.137566137566137503e-02 4.232804232804232569e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 3.386243386243386472e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.539682539682539680e-02 6.772486772486772943e-02 5.820105820105819783e-02 6.772486772486772943e-02 8.359788359788360157e-02 6.878306878306877925e-02 6.031746031746031828e-02 7.301587301587302015e-02 5.820105820105819783e-02 5.185185185185185036e-02 5.502645502645502756e-02 6.878306878306877925e-02 5.608465608465608432e-02 7.936507936507936067e-02 5.627705627705627961e-02 5.714285714285714107e-02 5.541125541125541121e-02 7.272727272727272374e-02 8.311688311688311681e-02 6.753246753246752720e-02 7.619047619047619735e-02 4.021164021164021218e-02 4.232804232804232569e-02 2.770562770562770560e-02 2.597402597402597574e-02 5.281385281385281294e-02 7.359307359307359908e-02 9.090909090909091161e-02 7.705627705627705881e-02 7.312925170068027003e-02 5.974025974025973934e-02 6.147186147186146921e-02 6.320346320346320601e-02 5.974025974025973934e-02 6.060606060606060774e-02 5.281385281385281294e-02 4.155844155844155841e-02 3.809523809523809867e-02 4.155844155844155841e-02 4.155844155844155841e-02 3.982683982683982854e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.523809523809523281e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.095238095238095233e-01 9.523809523809523281e-02 1.047619047619047644e-01 5.079365079365079361e-02 6.031746031746031828e-02 5.442176870748299117e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 8.888888888888889228e-02 9.841269841269841001e-02 9.841269841269841001e-02 1.174603174603174593e-01 1.047619047619047644e-01 1.111111111111111049e-01 5.442176870748299117e-02 7.482993197278911546e-02 7.029478458049887024e-02 6.349206349206348854e-02 7.709750566893423807e-02 4.761904761904761640e-02 5.714285714285714107e-02 6.666666666666666574e-02 4.761904761904761640e-02 6.666666666666666574e-02 3.401360544217687382e-02 3.401360544217687382e-02 1.814058956916099821e-02 7.936507936507936067e-02 9.070294784580498759e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.111111111111111049e-01 8.616780045351474238e-02 9.523809523809523281e-02 9.523809523809523281e-02 8.616780045351474238e-02 9.750566893424036929e-02 1.043083900226757371e-01 1.065759637188208597e-01 6.122448979591836593e-02 9.013605442176871041e-02 7.142857142857142461e-02 6.462585034013605678e-02 6.632653061224490221e-02 6.462585034013605678e-02 6.122448979591836593e-02 6.972789115646257918e-02 5.668934240362812071e-02 4.988662131519274595e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.988662131519274595e-02 5.668934240362812071e-02 4.988662131519274595e-02 5.668934240362812071e-02 3.741496598639455773e-02 3.741496598639455773e-02 3.741496598639455773e-02 3.741496598639455773e-02 4.251700680272108707e-02 2.040816326530612082e-02 2.040816326530612082e-02 7.142857142857142461e-02 7.653061224489796088e-02 8.333333333333332871e-02 9.013605442176871041e-02 9.523809523809523281e-02 8.163265306122448328e-02 9.523809523809523281e-02 1.156462585034013640e-01 9.863945578231292366e-02 8.673469387755101956e-02 9.523809523809523281e-02 1.020408163265306145e-01 9.013605442176871041e-02 6.084656084656084318e-02 6.746031746031745657e-02 6.216931216931216586e-02 6.746031746031745657e-02 6.746031746031745657e-02 5.952380952380952051e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.878306878306877925e-02 5.442176870748299117e-02 4.931972789115646183e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.931972789115646183e-02 5.442176870748299117e-02 4.931972789115646183e-02 2.513227513227513088e-02 7.407407407407406996e-02 7.804232804232803800e-02 8.333333333333332871e-02 8.201058201058200603e-02 9.126984126984126477e-02 8.862433862433861942e-02 7.275132275132274728e-02 9.259259259259258745e-02 9.259259259259258745e-02 9.788359788359787816e-02 9.126984126984126477e-02 6.481481481481481122e-02 8.465608465608465139e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 5.502645502645502756e-02 6.772486772486772943e-02 6.137566137566137503e-02 4.761904761904761640e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.158730158730158444e-02 3.809523809523809867e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.751322751322751378e-02 7.619047619047619735e-02 6.243386243386243178e-02 6.984126984126984294e-02 8.359788359788360157e-02 6.666666666666666574e-02 6.560846560846560205e-02 7.513227513227513366e-02 6.243386243386243178e-02 5.925925925925926152e-02 6.455026455026455223e-02 7.195767195767195645e-02 6.560846560846560205e-02 7.724867724867724716e-02 5.800865800865800948e-02 7.272727272727272374e-02 5.627705627705627961e-02 6.320346320346320601e-02 6.320346320346320601e-02 6.320346320346320601e-02 7.012987012987012547e-02 4.550264550264550290e-02 4.867724867724868010e-02 3.116883116883116880e-02 2.857142857142857054e-02 5.714285714285714107e-02 7.445887445887446054e-02 7.532467532467532201e-02 7.186147186147186228e-02 7.993197278911565173e-02 6.580086580086580428e-02 6.060606060606060774e-02 6.406926406926406747e-02 6.233766233766233761e-02 6.320346320346320601e-02 5.714285714285714107e-02 4.848484848484848481e-02 4.329004329004328827e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.588744588744588654e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 4.285714285714285754e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 7.142857142857142461e-02 9.047619047619047394e-02 4.444444444444444614e-02 5.714285714285714107e-02 4.535147392290249380e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.714285714285714107e-02 3.809523809523809867e-02 5.714285714285714107e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.111111111111111049e-01 7.619047619047619735e-02 7.619047619047619735e-02 1.047619047619047644e-01 9.841269841269841001e-02 1.015873015873015872e-01 4.535147392290249380e-02 5.895691609977324332e-02 6.802721088435374763e-02 5.215419501133786856e-02 6.349206349206348854e-02 5.079365079365079361e-02 6.984126984126984294e-02 7.619047619047619735e-02 5.079365079365079361e-02 7.619047619047619735e-02 3.628117913832199642e-02 3.628117913832199642e-02 1.814058956916099821e-02 9.750566893424036929e-02 8.843537414965986498e-02 7.482993197278911546e-02 7.482993197278911546e-02 8.616780045351474238e-02 1.201814058956916093e-01 1.133786848072562414e-01 7.936507936507936067e-02 1.043083900226757371e-01 7.482993197278911546e-02 1.043083900226757371e-01 1.043083900226757371e-01 4.421768707482993249e-02 7.142857142857142461e-02 5.272108843537415268e-02 8.163265306122448328e-02 5.102040816326530726e-02 7.482993197278911546e-02 4.761904761904761640e-02 7.653061224489796088e-02 7.256235827664399285e-02 5.895691609977324332e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.349206349206348854e-02 8.163265306122448328e-02 8.163265306122448328e-02 5.895691609977324332e-02 6.349206349206348854e-02 5.442176870748299117e-02 7.256235827664399285e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.421768707482993249e-02 4.081632653061224164e-02 4.761904761904761640e-02 2.040816326530612082e-02 2.040816326530612082e-02 9.013605442176871041e-02 8.503401360544217413e-02 7.993197278911565173e-02 7.993197278911565173e-02 7.993197278911565173e-02 9.863945578231292366e-02 7.823129251700680631e-02 9.353741496598640126e-02 7.823129251700680631e-02 8.503401360544217413e-02 8.503401360544217413e-02 8.673469387755101956e-02 7.823129251700680631e-02 5.026455026455026176e-02 4.761904761904761640e-02 4.629629629629629373e-02 4.761904761904761640e-02 5.291005291005290712e-02 7.142857142857142461e-02 6.481481481481481122e-02 7.275132275132274728e-02 7.275132275132274728e-02 7.142857142857142461e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.122448979591836593e-02 5.782312925170068202e-02 6.802721088435374763e-02 5.782312925170068202e-02 2.645502645502645356e-02 8.068783068783068335e-02 8.730158730158729674e-02 8.068783068783068335e-02 7.671957671957671532e-02 7.936507936507936067e-02 7.539682539682539264e-02 9.788359788359787816e-02 8.068783068783068335e-02 7.804232804232803800e-02 7.804232804232803800e-02 7.804232804232803800e-02 8.333333333333332871e-02 7.275132275132274728e-02 6.137566137566137503e-02 4.656084656084655965e-02 4.656084656084655965e-02 6.772486772486772943e-02 8.359788359788360157e-02 5.714285714285714107e-02 5.820105820105819783e-02 6.349206349206348854e-02 6.084656084656084318e-02 6.349206349206348854e-02 5.820105820105819783e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.878306878306877925e-02 4.656084656084655965e-02 5.079365079365079361e-02 5.079365079365079361e-02 2.962962962962963076e-02 9.841269841269841001e-02 7.195767195767195645e-02 8.465608465608465139e-02 8.253968253968253788e-02 7.513227513227513366e-02 8.253968253968253788e-02 8.465608465608465139e-02 7.830687830687831086e-02 7.724867724867724716e-02 8.994708994708994210e-02 8.783068783068782859e-02 9.206349206349206948e-02 8.888888888888889228e-02 6.147186147186146921e-02 7.792207792207792028e-02 5.887445887445887788e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.848484848484848481e-02 4.675324675324675494e-02 5.925925925925926152e-02 6.560846560846560205e-02 3.809523809523809867e-02 3.116883116883116880e-02 6.753246753246752720e-02 9.177489177489177308e-02 6.666666666666666574e-02 7.186147186147186228e-02 1.003401360544217691e-01 8.571428571428571508e-02 7.012987012987012547e-02 6.666666666666666574e-02 6.666666666666666574e-02 7.792207792207792028e-02 7.272727272727272374e-02 6.233766233766233761e-02 5.541125541125541121e-02 6.233766233766233761e-02 6.060606060606060774e-02 6.233766233766233761e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 4.285714285714285754e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.047619047619047644e-01 7.142857142857142461e-02 9.047619047619047394e-02 4.444444444444444614e-02 5.714285714285714107e-02 4.535147392290249380e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.714285714285714107e-02 3.809523809523809867e-02 5.714285714285714107e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.079365079365079416e-01 8.253968253968253788e-02 7.619047619047619735e-02 1.174603174603174593e-01 1.047619047619047644e-01 1.015873015873015872e-01 4.535147392290249380e-02 5.895691609977324332e-02 7.256235827664399285e-02 5.215419501133786856e-02 6.802721088435374763e-02 5.079365079365079361e-02 6.984126984126984294e-02 7.619047619047619735e-02 5.079365079365079361e-02 7.619047619047619735e-02 3.628117913832199642e-02 3.628117913832199642e-02 1.814058956916099821e-02 9.750566893424036929e-02 8.843537414965986498e-02 8.390022675736961977e-02 7.936507936507936067e-02 9.523809523809523281e-02 1.133786848072562414e-01 1.156462585034013640e-01 8.390022675736961977e-02 1.020408163265306145e-01 7.482993197278911546e-02 1.088435374149659823e-01 1.133786848072562414e-01 4.421768707482993249e-02 8.503401360544217413e-02 5.272108843537415268e-02 7.823129251700680631e-02 5.442176870748299117e-02 7.482993197278911546e-02 4.761904761904761640e-02 7.653061224489796088e-02 7.256235827664399285e-02 5.895691609977324332e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.349206349206348854e-02 8.163265306122448328e-02 8.163265306122448328e-02 5.895691609977324332e-02 6.349206349206348854e-02 5.442176870748299117e-02 7.256235827664399285e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.421768707482993249e-02 4.081632653061224164e-02 4.761904761904761640e-02 2.040816326530612082e-02 2.040816326530612082e-02 9.013605442176871041e-02 8.333333333333332871e-02 8.333333333333332871e-02 7.993197278911565173e-02 9.013605442176871041e-02 9.353741496598640126e-02 8.163265306122448328e-02 1.037414965986394599e-01 7.823129251700680631e-02 8.503401360544217413e-02 8.843537414965986498e-02 9.353741496598640126e-02 8.503401360544217413e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.894179894179893908e-02 5.291005291005290712e-02 5.555555555555555247e-02 7.142857142857142461e-02 6.481481481481481122e-02 7.275132275132274728e-02 7.671957671957671532e-02 7.142857142857142461e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.122448979591836593e-02 5.782312925170068202e-02 6.802721088435374763e-02 5.782312925170068202e-02 2.645502645502645356e-02 8.068783068783068335e-02 8.597883597883597406e-02 8.597883597883597406e-02 7.936507936507936067e-02 7.936507936507936067e-02 8.333333333333332871e-02 9.523809523809523281e-02 9.126984126984126477e-02 8.333333333333332871e-02 7.804232804232803800e-02 8.068783068783068335e-02 8.333333333333332871e-02 8.068783068783068335e-02 6.137566137566137503e-02 4.656084656084655965e-02 5.079365079365079361e-02 6.772486772486772943e-02 7.936507936507936067e-02 5.714285714285714107e-02 5.820105820105819783e-02 6.349206349206348854e-02 6.084656084656084318e-02 6.349206349206348854e-02 5.820105820105819783e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.878306878306877925e-02 4.656084656084655965e-02 5.079365079365079361e-02 5.079365079365079361e-02 2.962962962962963076e-02 9.735449735449736020e-02 7.195767195767195645e-02 8.148148148148148806e-02 8.042328042328042437e-02 7.513227513227513366e-02 8.253968253968253788e-02 8.571428571428571508e-02 7.830687830687831086e-02 7.724867724867724716e-02 8.888888888888889228e-02 8.783068783068782859e-02 8.994708994708994210e-02 8.571428571428571508e-02 6.233766233766233761e-02 8.484848484848485362e-02 5.714285714285714107e-02 5.367965367965368134e-02 5.021645021645021467e-02 5.367965367965368134e-02 5.021645021645021467e-02 5.925925925925926152e-02 6.560846560846560205e-02 3.809523809523809867e-02 3.116883116883116880e-02 6.753246753246752720e-02 8.744588744588745188e-02 7.359307359307359908e-02 7.186147186147186228e-02 1.003401360544217691e-01 8.051948051948051854e-02 7.012987012987012547e-02 6.666666666666666574e-02 6.839826839826840255e-02 7.532467532467532201e-02 7.272727272727272374e-02 6.233766233766233761e-02 5.541125541125541121e-02 6.233766233766233761e-02 6.060606060606060774e-02 6.233766233766233761e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.190476190476190410e-01 5.238095238095238221e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 1.285714285714285587e-01 1.285714285714285587e-01 5.714285714285714107e-02 6.349206349206348854e-02 6.802721088435374763e-02 8.253968253968253788e-02 6.349206349206348854e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 8.253968253968253788e-02 1.142857142857142821e-01 1.301587301587301682e-01 9.523809523809523281e-02 1.047619047619047644e-01 1.301587301587301682e-01 6.802721088435374763e-02 9.977324263038549190e-02 6.349206349206348854e-02 7.256235827664399285e-02 7.709750566893423807e-02 4.444444444444444614e-02 5.396825396825397081e-02 5.714285714285714107e-02 4.444444444444444614e-02 5.714285714285714107e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.814058956916099821e-02 7.482993197278911546e-02 8.843537414965986498e-02 1.020408163265306145e-01 1.156462585034013640e-01 9.523809523809523281e-02 7.936507936507936067e-02 8.390022675736961977e-02 1.156462585034013640e-01 8.843537414965986498e-02 1.292517006802721136e-01 1.088435374149659823e-01 9.977324263038549190e-02 8.503401360544217413e-02 7.142857142857142461e-02 9.863945578231292366e-02 5.952380952380952051e-02 7.993197278911565173e-02 5.952380952380952051e-02 7.482993197278911546e-02 6.972789115646257918e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.895691609977324332e-02 5.895691609977324332e-02 4.761904761904761640e-02 4.988662131519274595e-02 4.535147392290249380e-02 5.442176870748299117e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.571428571428571230e-02 3.401360544217687382e-02 3.741496598639455773e-02 2.040816326530612082e-02 2.040816326530612082e-02 6.802721088435374763e-02 7.653061224489796088e-02 8.843537414965986498e-02 9.523809523809523281e-02 9.013605442176871041e-02 7.993197278911565173e-02 1.156462585034013640e-01 9.183673469387755584e-02 1.275510204081632681e-01 9.183673469387755584e-02 1.156462585034013640e-01 9.863945578231292366e-02 1.037414965986394599e-01 6.084656084656084318e-02 9.523809523809523281e-02 7.804232804232803800e-02 8.201058201058200603e-02 6.878306878306877925e-02 5.555555555555555247e-02 6.216931216931216586e-02 6.349206349206348854e-02 6.216931216931216586e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.591836734693877792e-02 5.102040816326530726e-02 4.591836734693877792e-02 2.380952380952380820e-02 7.804232804232803800e-02 7.804232804232803800e-02 7.936507936507936067e-02 8.862433862433861942e-02 9.523809523809523281e-02 9.656084656084655549e-02 7.407407407407406996e-02 8.068783068783068335e-02 1.071428571428571369e-01 1.256613756613756683e-01 1.150793650793650730e-01 6.216931216931216586e-02 9.391534391534391013e-02 6.137566137566137503e-02 8.253968253968253788e-02 7.195767195767195645e-02 5.185185185185185036e-02 7.301587301587302015e-02 6.243386243386243178e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.629629629629629373e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.629629629629629373e-02 5.026455026455026176e-02 3.597883597883597823e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.539682539682539680e-02 7.407407407407406996e-02 6.243386243386243178e-02 6.984126984126984294e-02 8.571428571428571508e-02 6.984126984126984294e-02 6.560846560846560205e-02 7.195767195767195645e-02 6.349206349206348854e-02 5.714285714285714107e-02 6.137566137566137503e-02 7.407407407407406996e-02 6.243386243386243178e-02 8.359788359788360157e-02 5.800865800865800948e-02 6.060606060606060774e-02 5.714285714285714107e-02 6.926406926406926401e-02 7.965367965367965708e-02 6.580086580086580428e-02 7.619047619047619735e-02 4.444444444444444614e-02 4.761904761904761640e-02 2.943722943722943894e-02 2.597402597402597574e-02 5.714285714285714107e-02 7.792207792207792028e-02 8.744588744588745188e-02 7.878787878787878174e-02 7.993197278911565173e-02 6.320346320346320601e-02 6.320346320346320601e-02 6.666666666666666574e-02 6.233766233766233761e-02 6.320346320346320601e-02 5.800865800865800948e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.502164502164502508e-02 4.415584415584415667e-02 4.502164502164502508e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.523809523809523281e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 9.047619047619047394e-02 1.047619047619047644e-01 5.079365079365079361e-02 6.031746031746031828e-02 5.442176870748299117e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.015873015873015872e-01 8.571428571428571508e-02 9.206349206349206948e-02 9.841269841269841001e-02 1.015873015873015872e-01 1.111111111111111049e-01 5.442176870748299117e-02 7.482993197278911546e-02 7.029478458049887024e-02 6.122448979591836593e-02 7.029478458049887024e-02 4.761904761904761640e-02 6.349206349206348854e-02 6.666666666666666574e-02 4.761904761904761640e-02 6.666666666666666574e-02 3.401360544217687382e-02 3.401360544217687382e-02 1.814058956916099821e-02 9.297052154195011020e-02 8.616780045351474238e-02 7.936507936507936067e-02 8.390022675736961977e-02 8.616780045351474238e-02 1.043083900226757371e-01 1.020408163265306145e-01 8.843537414965986498e-02 1.020408163265306145e-01 8.843537414965986498e-02 1.065759637188208597e-01 1.020408163265306145e-01 5.782312925170068202e-02 6.972789115646257918e-02 6.802721088435374763e-02 7.482993197278911546e-02 5.952380952380952051e-02 7.142857142857142461e-02 5.782312925170068202e-02 7.653061224489796088e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 7.256235827664399285e-02 7.256235827664399285e-02 5.442176870748299117e-02 5.668934240362812071e-02 4.988662131519274595e-02 6.802721088435374763e-02 3.741496598639455773e-02 3.741496598639455773e-02 4.081632653061224164e-02 3.741496598639455773e-02 4.251700680272108707e-02 2.040816326530612082e-02 2.040816326530612082e-02 8.673469387755101956e-02 8.333333333333332871e-02 7.823129251700680631e-02 8.163265306122448328e-02 7.823129251700680631e-02 9.013605442176871041e-02 8.503401360544217413e-02 8.843537414965986498e-02 8.843537414965986498e-02 8.673469387755101956e-02 9.183673469387755584e-02 8.843537414965986498e-02 8.503401360544217413e-02 5.291005291005290712e-02 6.216931216931216586e-02 5.555555555555555247e-02 5.687830687830687515e-02 5.952380952380952051e-02 6.878306878306877925e-02 6.481481481481481122e-02 7.275132275132274728e-02 7.275132275132274728e-02 6.802721088435374763e-02 5.272108843537415268e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.442176870748299117e-02 5.272108843537415268e-02 6.122448979591836593e-02 5.272108843537415268e-02 2.513227513227513088e-02 7.539682539682539264e-02 8.068783068783068335e-02 7.539682539682539264e-02 7.671957671957671532e-02 8.068783068783068335e-02 7.671957671957671532e-02 9.391534391534391013e-02 7.539682539682539264e-02 8.068783068783068335e-02 8.597883597883597406e-02 8.465608465608465139e-02 8.068783068783068335e-02 7.671957671957671532e-02 6.137566137566137503e-02 5.714285714285714107e-02 5.291005291005290712e-02 6.560846560846560205e-02 8.253968253968253788e-02 5.925925925925926152e-02 5.291005291005290712e-02 5.555555555555555247e-02 5.423280423280422979e-02 5.555555555555555247e-02 5.291005291005290712e-02 5.291005291005290712e-02 5.423280423280422979e-02 6.613756613756613389e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.751322751322751378e-02 9.523809523809523281e-02 7.195767195767195645e-02 7.619047619047619735e-02 7.936507936507936067e-02 7.089947089947089276e-02 8.148148148148148806e-02 7.619047619047619735e-02 7.830687830687831086e-02 7.513227513227513366e-02 8.465608465608465139e-02 8.783068783068782859e-02 8.677248677248677877e-02 8.888888888888889228e-02 6.060606060606060774e-02 7.359307359307359908e-02 5.887445887445887788e-02 5.108225108225108307e-02 5.454545454545454281e-02 5.281385281385281294e-02 5.454545454545454281e-02 5.714285714285714107e-02 6.349206349206348854e-02 3.463203463203463200e-02 2.857142857142857054e-02 6.753246753246752720e-02 8.917748917748917481e-02 7.012987012987012547e-02 7.186147186147186228e-02 9.863945578231292366e-02 7.532467532467532201e-02 6.666666666666666574e-02 6.753246753246752720e-02 6.580086580086580428e-02 7.099567099567100081e-02 7.272727272727272374e-02 5.541125541125541121e-02 5.194805194805195148e-02 5.541125541125541121e-02 5.541125541125541121e-02 6.060606060606060774e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.349206349206349131e-01 5.714285714285714107e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.047619047619047394e-02 1.571428571428571397e-01 1.476190476190476220e-01 6.349206349206348854e-02 6.984126984126984294e-02 7.709750566893423807e-02 9.523809523809523281e-02 6.984126984126984294e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 7.936507936507936067e-02 1.238095238095238138e-01 1.619047619047619124e-01 8.571428571428571508e-02 1.047619047619047644e-01 1.492063492063492036e-01 7.709750566893423807e-02 1.179138321995464866e-01 6.349206349206348854e-02 8.616780045351474238e-02 8.163265306122448328e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.714285714285714107e-02 4.444444444444444614e-02 5.714285714285714107e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.814058956916099821e-02 7.029478458049887024e-02 9.297052154195011020e-02 1.020408163265306145e-01 1.292517006802721136e-01 9.750566893424036929e-02 7.482993197278911546e-02 7.482993197278911546e-02 1.292517006802721136e-01 8.843537414965986498e-02 1.655328798185941030e-01 1.111111111111111049e-01 9.297052154195011020e-02 1.054421768707483054e-01 6.122448979591836593e-02 1.224489795918367319e-01 5.782312925170068202e-02 8.843537414965986498e-02 5.782312925170068202e-02 9.183673469387755584e-02 7.142857142857142461e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.988662131519274595e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 2.040816326530612082e-02 2.040816326530612082e-02 6.292517006802721136e-02 7.993197278911565173e-02 8.673469387755101956e-02 1.071428571428571369e-01 8.333333333333332871e-02 8.333333333333332871e-02 1.309523809523809590e-01 9.013605442176871041e-02 1.649659863945578120e-01 1.003401360544217691e-01 1.309523809523809590e-01 1.037414965986394599e-01 1.071428571428571369e-01 6.084656084656084318e-02 1.216931216931216864e-01 8.994708994708994210e-02 8.994708994708994210e-02 7.936507936507936067e-02 5.291005291005290712e-02 6.613756613756613389e-02 6.349206349206348854e-02 5.820105820105819783e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.421768707482993249e-02 2.380952380952380820e-02 7.539682539682539264e-02 7.804232804232803800e-02 7.275132275132274728e-02 9.126984126984126477e-02 1.097883597883597823e-01 9.391534391534391013e-02 7.010582010582010193e-02 7.010582010582010193e-02 1.124338624338624276e-01 1.653439153439153486e-01 1.362433862433862497e-01 5.687830687830687515e-02 9.126984126984126477e-02 6.772486772486772943e-02 1.037037037037037007e-01 7.830687830687831086e-02 4.867724867724868010e-02 7.619047619047619735e-02 6.984126984126984294e-02 4.232804232804232569e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 3.386243386243386472e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.539682539682539680e-02 6.878306878306877925e-02 6.243386243386243178e-02 6.878306878306877925e-02 9.629629629629629650e-02 6.666666666666666574e-02 6.243386243386243178e-02 6.666666666666666574e-02 6.031746031746031828e-02 5.185185185185185036e-02 5.396825396825397081e-02 7.301587301587302015e-02 5.608465608465608432e-02 9.206349206349206948e-02 6.060606060606060774e-02 5.021645021645021467e-02 6.233766233766233761e-02 6.926406926406926401e-02 9.004329004329005015e-02 6.926406926406926401e-02 9.004329004329005015e-02 4.021164021164021218e-02 4.232804232804232569e-02 2.770562770562770560e-02 2.597402597402597574e-02 5.627705627705627961e-02 8.398268398268397827e-02 8.398268398268397827e-02 8.398268398268397827e-02 7.482993197278911546e-02 6.320346320346320601e-02 5.974025974025973934e-02 7.012987012987012547e-02 5.974025974025973934e-02 6.147186147186146921e-02 5.454545454545454281e-02 4.155844155844155841e-02 3.809523809523809867e-02 4.155844155844155841e-02 4.155844155844155841e-02 3.982683982683982854e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.111111111111111049e-01 5.238095238095238221e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.095238095238095233e-01 1.095238095238095233e-01 1.285714285714285587e-01 5.714285714285714107e-02 6.984126984126984294e-02 6.349206349206348854e-02 7.619047619047619735e-02 6.984126984126984294e-02 5.714285714285714107e-02 3.809523809523809867e-02 5.714285714285714107e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.015873015873015872e-01 1.047619047619047644e-01 1.142857142857142821e-01 1.174603174603174593e-01 1.206349206349206366e-01 1.396825396825396859e-01 6.349206349206348854e-02 9.523809523809523281e-02 7.256235827664399285e-02 7.029478458049887024e-02 8.390022675736961977e-02 5.079365079365079361e-02 6.984126984126984294e-02 7.619047619047619735e-02 5.079365079365079361e-02 7.619047619047619735e-02 3.628117913832199642e-02 3.628117913832199642e-02 1.814058956916099821e-02 9.523809523809523281e-02 9.523809523809523281e-02 9.750566893424036929e-02 1.043083900226757371e-01 1.043083900226757371e-01 1.043083900226757371e-01 1.088435374149659823e-01 1.088435374149659823e-01 1.065759637188208597e-01 1.111111111111111049e-01 1.292517006802721136e-01 1.224489795918367319e-01 7.142857142857142461e-02 8.503401360544217413e-02 8.673469387755101956e-02 7.142857142857142461e-02 7.312925170068027003e-02 7.142857142857142461e-02 6.802721088435374763e-02 7.993197278911565173e-02 7.256235827664399285e-02 5.895691609977324332e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.349206349206348854e-02 8.163265306122448328e-02 8.163265306122448328e-02 5.895691609977324332e-02 6.349206349206348854e-02 5.442176870748299117e-02 7.256235827664399285e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.421768707482993249e-02 4.081632653061224164e-02 4.761904761904761640e-02 2.040816326530612082e-02 2.040816326530612082e-02 8.843537414965986498e-02 8.673469387755101956e-02 9.013605442176871041e-02 9.523809523809523281e-02 9.523809523809523281e-02 9.353741496598640126e-02 1.071428571428571369e-01 1.088435374149659823e-01 1.122448979591836732e-01 9.693877551020407823e-02 1.139455782312925186e-01 1.071428571428571369e-01 1.020408163265306145e-01 6.349206349206348854e-02 7.936507936507936067e-02 6.878306878306877925e-02 7.142857142857142461e-02 6.613756613756613389e-02 6.878306878306877925e-02 6.746031746031745657e-02 7.539682539682539264e-02 7.671957671957671532e-02 7.142857142857142461e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.122448979591836593e-02 5.782312925170068202e-02 6.802721088435374763e-02 5.782312925170068202e-02 2.645502645502645356e-02 8.333333333333332871e-02 8.730158730158729674e-02 8.730158730158729674e-02 8.994708994708994210e-02 9.656084656084655549e-02 9.523809523809523281e-02 9.656084656084655549e-02 9.126984126984126477e-02 1.018518518518518462e-01 1.097883597883597823e-01 1.058201058201058142e-01 8.201058201058200603e-02 9.259259259259258745e-02 7.407407407407406996e-02 7.195767195767195645e-02 6.560846560846560205e-02 6.560846560846560205e-02 8.571428571428571508e-02 6.349206349206348854e-02 5.820105820105819783e-02 6.349206349206348854e-02 6.084656084656084318e-02 6.349206349206348854e-02 5.820105820105819783e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.878306878306877925e-02 4.656084656084655965e-02 5.079365079365079361e-02 5.079365079365079361e-02 2.962962962962963076e-02 9.841269841269841001e-02 7.513227513227513366e-02 8.042328042328042437e-02 9.206349206349206948e-02 7.724867724867724716e-02 8.571428571428571508e-02 8.359788359788360157e-02 8.148148148148148806e-02 7.619047619047619735e-02 8.359788359788360157e-02 9.523809523809523281e-02 8.677248677248677877e-02 9.417989417989418299e-02 7.186147186147186228e-02 7.965367965367965708e-02 6.060606060606060774e-02 6.406926406926406747e-02 6.926406926406926401e-02 6.233766233766233761e-02 6.926406926406926401e-02 5.925925925925926152e-02 6.560846560846560205e-02 3.809523809523809867e-02 3.116883116883116880e-02 7.012987012987012547e-02 9.264069264069263454e-02 8.398268398268397827e-02 8.051948051948051854e-02 1.037414965986394599e-01 7.705627705627705881e-02 7.186147186147186228e-02 7.445887445887446054e-02 7.186147186147186228e-02 7.445887445887446054e-02 7.532467532467532201e-02 6.233766233766233761e-02 5.541125541125541121e-02 6.233766233766233761e-02 6.060606060606060774e-02 6.233766233766233761e-02
-7.936507936507936067e-02 3.174603174603174427e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.523809523809523281e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.142857142857142821e-01 9.047619047619047394e-02 1.095238095238095233e-01 5.079365079365079361e-02 6.349206349206348854e-02 5.442176870748299117e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.714285714285714107e-02 3.809523809523809867e-02 5.714285714285714107e-02 2.539682539682539680e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.015873015873015872e-01 9.841269841269841001e-02 9.523809523809523281e-02 1.269841269841269771e-01 1.174603174603174593e-01 1.206349206349206366e-01 5.442176870748299117e-02 7.709750566893423807e-02 7.256235827664399285e-02 6.122448979591836593e-02 7.936507936507936067e-02 5.079365079365079361e-02 6.984126984126984294e-02 7.619047619047619735e-02 5.079365079365079361e-02 7.619047619047619735e-02 3.628117913832199642e-02 3.628117913832199642e-02 1.814058956916099821e-02 9.523809523809523281e-02 9.297052154195011020e-02 9.750566893424036929e-02 9.523809523809523281e-02 1.065759637188208597e-01 1.043083900226757371e-01 1.133786848072562414e-01 9.977324263038549190e-02 1.020408163265306145e-01 9.297052154195011020e-02 1.224489795918367319e-01 1.247165532879818545e-01 5.782312925170068202e-02 9.523809523809523281e-02 6.972789115646257918e-02 7.142857142857142461e-02 6.632653061224490221e-02 7.142857142857142461e-02 5.782312925170068202e-02 7.653061224489796088e-02 7.256235827664399285e-02 5.895691609977324332e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.349206349206348854e-02 8.163265306122448328e-02 8.163265306122448328e-02 5.895691609977324332e-02 6.349206349206348854e-02 5.442176870748299117e-02 7.256235827664399285e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.421768707482993249e-02 4.081632653061224164e-02 4.761904761904761640e-02 2.040816326530612082e-02 2.040816326530612082e-02 8.843537414965986498e-02 8.333333333333332871e-02 9.013605442176871041e-02 8.843537414965986498e-02 1.003401360544217691e-01 9.013605442176871041e-02 9.693877551020407823e-02 1.139455782312925186e-01 9.523809523809523281e-02 9.183673469387755584e-02 1.037414965986394599e-01 1.054421768707483054e-01 9.863945578231292366e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.084656084656084318e-02 6.613756613756613389e-02 6.084656084656084318e-02 6.878306878306877925e-02 6.481481481481481122e-02 7.275132275132274728e-02 7.804232804232803800e-02 7.142857142857142461e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.122448979591836593e-02 5.782312925170068202e-02 6.802721088435374763e-02 5.782312925170068202e-02 2.645502645502645356e-02 8.333333333333332871e-02 8.597883597883597406e-02 9.126984126984126477e-02 8.730158730158729674e-02 8.862433862433861942e-02 9.523809523809523281e-02 9.391534391534391013e-02 9.920634920634920084e-02 9.656084656084655549e-02 9.391534391534391013e-02 9.523809523809523281e-02 8.201058201058200603e-02 9.259259259259258745e-02 6.772486772486772943e-02 5.925925925925926152e-02 6.137566137566137503e-02 6.560846560846560205e-02 7.936507936507936067e-02 5.925925925925926152e-02 5.820105820105819783e-02 6.349206349206348854e-02 6.084656084656084318e-02 6.349206349206348854e-02 5.820105820105819783e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.878306878306877925e-02 4.656084656084655965e-02 5.079365079365079361e-02 5.079365079365079361e-02 2.962962962962963076e-02 9.735449735449736020e-02 7.301587301587302015e-02 7.936507936507936067e-02 8.571428571428571508e-02 7.724867724867724716e-02 8.359788359788360157e-02 8.571428571428571508e-02 7.936507936507936067e-02 7.619047619047619735e-02 8.465608465608465139e-02 9.100529100529100579e-02 8.677248677248677877e-02 8.783068783068782859e-02 6.753246753246752720e-02 8.658008658008657654e-02 5.714285714285714107e-02 6.406926406926406747e-02 6.233766233766233761e-02 6.060606060606060774e-02 6.233766233766233761e-02 5.925925925925926152e-02 6.560846560846560205e-02 3.809523809523809867e-02 3.116883116883116880e-02 6.839826839826840255e-02 8.744588744588745188e-02 8.398268398268397827e-02 7.705627705627705881e-02 1.020408163265306145e-01 7.532467532467532201e-02 7.186147186147186228e-02 7.099567099567100081e-02 7.186147186147186228e-02 7.359307359307359908e-02 7.359307359307359908e-02 6.233766233766233761e-02 5.541125541125541121e-02 6.233766233766233761e-02 6.060606060606060774e-02 6.233766233766233761e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.250000000000000000e-01 5.357142857142856845e-02 9.285714285714286031e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 7.142857142857142461e-02 1.214285714285714274e-01 1.142857142857142821e-01 5.238095238095238221e-02 5.238095238095238221e-02 9.693877551020407823e-02 1.071428571428571369e-01 7.380952380952381098e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 5.476190476190476164e-02 8.809523809523810145e-02 1.119047619047619097e-01 5.476190476190476164e-02 7.380952380952381098e-02 1.023809523809523780e-01 9.693877551020407823e-02 1.088435374149659823e-01 5.782312925170068202e-02 7.482993197278911546e-02 7.142857142857142461e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.020408163265306041e-02 4.421768707482993249e-02 6.122448979591836593e-02 6.802721088435374763e-02 8.503401360544217413e-02 6.122448979591836593e-02 4.421768707482993249e-02 4.421768707482993249e-02 8.503401360544217413e-02 5.782312925170068202e-02 1.054421768707483054e-01 7.142857142857142461e-02 5.782312925170068202e-02 1.020408163265306145e-01 5.484693877551020252e-02 1.058673469387755167e-01 5.484693877551020252e-02 8.035714285714286309e-02 4.719387755102040505e-02 7.270408163265305868e-02 5.739795918367347066e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.147959183673469448e-02 1.147959183673469448e-02 3.698979591836734637e-02 4.974489795918367319e-02 5.484693877551020252e-02 6.760204081632653628e-02 4.974489795918367319e-02 4.974489795918367319e-02 8.035714285714286309e-02 4.974489795918367319e-02 9.821428571428571230e-02 6.250000000000000000e-02 8.035714285714286309e-02 6.250000000000000000e-02 6.760204081632653628e-02 6.250000000000000000e-02 1.021825396825396803e-01 8.234126984126984017e-02 8.234126984126984017e-02 5.952380952380952051e-02 3.968253968253968034e-02 4.960317460317460042e-02 4.761904761904761640e-02 4.365079365079364837e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.190476190476190410e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.166666666666666435e-02 5.555555555555555247e-02 6.547619047619047949e-02 5.555555555555555247e-02 3.968253968253968034e-02 3.769841269841269632e-02 6.547619047619047949e-02 9.325396825396825573e-02 7.936507936507936067e-02 3.174603174603174427e-02 5.555555555555555247e-02 3.968253968253968034e-02 9.285714285714286031e-02 6.825396825396824740e-02 3.888888888888888951e-02 5.317460317460317304e-02 5.000000000000000278e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.190476190476190410e-02 3.730158730158730090e-02 3.571428571428571230e-02 3.888888888888888951e-02 5.476190476190476164e-02 3.888888888888888951e-02 3.412698412698412370e-02 3.571428571428571230e-02 3.412698412698412370e-02 2.777777777777777624e-02 2.777777777777777624e-02 4.047619047619047811e-02 2.777777777777777624e-02 5.158730158730158444e-02 3.441558441558441317e-02 3.376623376623376360e-02 5.259740259740259410e-02 5.779220779220779064e-02 7.792207792207792028e-02 4.805194805194805407e-02 6.103896103896103847e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.116883116883116880e-02 4.415584415584415667e-02 4.805194805194805407e-02 4.805194805194805407e-02 3.826530612244898044e-02 3.376623376623376360e-02 3.376623376623376360e-02 4.025974025974025927e-02 3.376623376623376360e-02 3.376623376623376360e-02 2.987012987012986967e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.928571428571428770e-02 4.642857142857143016e-02 9.285714285714286031e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 1.107142857142857095e-01 7.142857142857142461e-02 8.214285714285714246e-02 4.285714285714285754e-02 5.000000000000000278e-02 6.122448979591836593e-02 7.857142857142856984e-02 7.380952380952381098e-02 3.214285714285713969e-02 2.142857142857142877e-02 3.214285714285713969e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 7.857142857142856984e-02 8.095238095238095621e-02 6.666666666666666574e-02 1.142857142857142821e-01 9.285714285714286031e-02 8.095238095238095621e-02 6.122448979591836593e-02 7.312925170068027003e-02 7.653061224489796088e-02 6.122448979591836593e-02 7.993197278911565173e-02 2.857142857142857054e-02 3.571428571428571230e-02 4.285714285714285754e-02 2.857142857142857054e-02 4.285714285714285754e-02 2.040816326530612082e-02 2.040816326530612082e-02 1.020408163265306041e-02 6.632653061224490221e-02 7.142857142857142461e-02 8.163265306122448328e-02 7.142857142857142461e-02 9.013605442176871041e-02 7.142857142857142461e-02 8.503401360544217413e-02 7.142857142857142461e-02 6.972789115646257918e-02 6.122448979591836593e-02 8.503401360544217413e-02 9.523809523809523281e-02 5.484693877551020252e-02 1.071428571428571369e-01 6.122448979591836593e-02 7.270408163265305868e-02 6.632653061224490221e-02 6.505102040816326814e-02 5.229591836734694132e-02 6.760204081632653628e-02 3.571428571428571230e-02 3.061224489795918297e-02 3.061224489795918297e-02 3.061224489795918297e-02 3.571428571428571230e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.061224489795918297e-02 3.571428571428571230e-02 3.061224489795918297e-02 3.571428571428571230e-02 2.295918367346938896e-02 2.295918367346938896e-02 2.295918367346938896e-02 2.295918367346938896e-02 2.678571428571428423e-02 1.147959183673469448e-02 1.147959183673469448e-02 5.739795918367347066e-02 5.612244897959183659e-02 6.887755102040815647e-02 6.122448979591836593e-02 8.545918367346938549e-02 5.994897959183673186e-02 6.760204081632653628e-02 9.311224489795918990e-02 5.994897959183673186e-02 6.377551020408163407e-02 6.760204081632653628e-02 7.780612244897959495e-02 7.142857142857142461e-02 6.349206349206348854e-02 5.257936507936507992e-02 5.654761904761904795e-02 6.448412698412699096e-02 5.456349206349206393e-02 5.654761904761904795e-02 5.555555555555555247e-02 5.853174603174603197e-02 6.845238095238095899e-02 3.443877551020407823e-02 3.061224489795918297e-02 3.443877551020407823e-02 3.443877551020407823e-02 3.443877551020407823e-02 3.443877551020407823e-02 3.061224489795918297e-02 3.443877551020407823e-02 3.061224489795918297e-02 1.488095238095238013e-02 5.952380952380952051e-02 5.853174603174603197e-02 7.242063492063492702e-02 6.150793650793650452e-02 5.853174603174603197e-02 7.242063492063492702e-02 5.555555555555555247e-02 8.531746031746031966e-02 6.944444444444444753e-02 5.753968253968253649e-02 6.051587301587301598e-02 5.059523809523809590e-02 6.944444444444444753e-02 4.444444444444444614e-02 4.682539682539682557e-02 5.634920634920635024e-02 5.476190476190476164e-02 5.714285714285714107e-02 4.841269841269841417e-02 2.976190476190476025e-02 3.571428571428571230e-02 3.273809523809523975e-02 3.571428571428571230e-02 2.976190476190476025e-02 2.976190476190476025e-02 3.273809523809523975e-02 3.273809523809523975e-02 2.380952380952380820e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.666666666666666644e-02 6.111111111111110911e-02 4.444444444444444614e-02 5.158730158730158444e-02 5.396825396825397081e-02 5.238095238095238221e-02 4.920634920634920501e-02 6.190476190476190688e-02 4.682539682539682557e-02 4.523809523809523697e-02 5.079365079365079361e-02 5.317460317460317304e-02 5.000000000000000278e-02 5.238095238095238221e-02 4.285714285714285754e-02 7.532467532467532201e-02 4.740259740259740451e-02 6.038961038961038891e-02 5.259740259740259410e-02 5.129870129870130191e-02 4.870129870129870364e-02 2.857142857142857054e-02 3.095238095238095344e-02 1.948051948051948007e-02 1.753246753246753137e-02 4.025974025974025927e-02 5.064935064935065234e-02 5.974025974025973934e-02 4.935064935064935321e-02 5.739795918367347066e-02 4.545454545454545581e-02 4.675324675324675494e-02 4.285714285714285754e-02 4.675324675324675494e-02 4.610389610389610537e-02 4.220779220779220797e-02 3.116883116883116880e-02 2.727272727272727140e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.922077922077922010e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.369047619047619180e-01 5.714285714285714107e-02 9.285714285714286031e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 7.857142857142856984e-02 1.357142857142857040e-01 1.321428571428571452e-01 5.714285714285714107e-02 5.952380952380952051e-02 9.523809523809523281e-02 1.166666666666666685e-01 7.857142857142856984e-02 2.500000000000000139e-02 2.142857142857142877e-02 2.500000000000000139e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 6.190476190476190688e-02 1.000000000000000056e-01 1.285714285714285587e-01 6.428571428571427937e-02 8.571428571428571508e-02 1.238095238095238138e-01 9.523809523809523281e-02 1.241496598639455773e-01 6.292517006802721136e-02 8.163265306122448328e-02 7.993197278911565173e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.857142857142857054e-02 2.380952380952380820e-02 2.857142857142857054e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.020408163265306041e-02 5.102040816326530726e-02 6.972789115646257918e-02 7.823129251700680631e-02 9.863945578231292366e-02 7.142857142857142461e-02 5.272108843537415268e-02 5.272108843537415268e-02 9.863945578231292366e-02 6.802721088435374763e-02 1.224489795918367319e-01 8.673469387755101956e-02 6.972789115646257918e-02 1.058673469387755167e-01 6.122448979591836593e-02 1.173469387755102095e-01 5.994897959183673186e-02 8.801020408163265363e-02 5.229591836734694132e-02 8.035714285714286309e-02 6.505102040816326814e-02 2.551020408163265363e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.551020408163265363e-02 2.721088435374149558e-02 2.721088435374149558e-02 2.380952380952380820e-02 2.551020408163265363e-02 2.380952380952380820e-02 2.551020408163265363e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.913265306122449022e-02 1.147959183673469448e-02 1.147959183673469448e-02 4.336734693877550978e-02 5.739795918367347066e-02 6.377551020408163407e-02 7.908163265306122902e-02 5.867346938775510473e-02 5.867346938775510473e-02 9.566326530612244416e-02 6.122448979591836593e-02 1.160714285714285754e-01 7.270408163265305868e-02 9.566326530612244416e-02 7.397959183673469274e-02 7.908163265306122902e-02 6.349206349206348854e-02 1.101190476190476164e-01 8.630952380952380820e-02 8.630952380952380820e-02 6.646825396825396803e-02 4.464285714285714385e-02 5.555555555555555247e-02 5.456349206349206393e-02 4.960317460317460042e-02 2.423469387755101956e-02 2.295918367346938896e-02 2.423469387755101956e-02 2.423469387755101956e-02 2.423469387755101956e-02 2.423469387755101956e-02 2.295918367346938896e-02 2.423469387755101956e-02 2.295918367346938896e-02 1.289682539682539611e-02 5.357142857142856845e-02 5.456349206349206393e-02 4.960317460317460042e-02 6.547619047619047949e-02 7.837301587301587213e-02 6.646825396825396803e-02 4.861111111111111188e-02 4.563492063492063239e-02 7.936507936507936067e-02 1.111111111111111049e-01 9.424603174603174427e-02 3.769841269841269632e-02 6.547619047619047949e-02 4.920634920634920501e-02 9.444444444444444198e-02 7.222222222222221544e-02 4.365079365079364837e-02 6.349206349206348854e-02 5.634920634920635024e-02 2.182539682539682419e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.182539682539682419e-02 2.182539682539682419e-02 2.281746031746031619e-02 2.281746031746031619e-02 1.746031746031746074e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.349206349206349270e-02 4.682539682539682557e-02 4.206349206349206671e-02 4.682539682539682557e-02 6.666666666666666574e-02 4.603174603174603474e-02 4.206349206349206671e-02 4.365079365079364837e-02 4.126984126984126894e-02 3.333333333333333287e-02 3.412698412698412370e-02 5.079365079365079361e-02 3.492063492063492147e-02 6.269841269841269771e-02 4.285714285714285754e-02 4.025974025974025927e-02 5.519480519480519237e-02 6.168831168831168804e-02 7.987012987012986898e-02 5.519480519480519237e-02 7.077922077922077504e-02 2.063492063492063447e-02 2.142857142857142877e-02 1.428571428571428527e-02 1.363636363636363570e-02 3.701298701298701144e-02 5.519480519480519237e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.719387755102040505e-02 4.155844155844155841e-02 4.025974025974025927e-02 4.805194805194805407e-02 4.025974025974025927e-02 4.090909090909090884e-02 3.636363636363636187e-02 2.077922077922077920e-02 1.948051948051948007e-02 2.077922077922077920e-02 2.077922077922077920e-02 2.012987012987012964e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.928571428571428770e-02 4.642857142857143016e-02 9.285714285714286031e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 8.571428571428571508e-02 7.142857142857142461e-02 7.857142857142856984e-02 4.285714285714285754e-02 4.761904761904761640e-02 6.122448979591836593e-02 7.857142857142856984e-02 7.380952380952381098e-02 2.857142857142857054e-02 2.142857142857142877e-02 2.857142857142857054e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 8.571428571428571508e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.857142857142856984e-02 7.619047619047619735e-02 7.380952380952381098e-02 6.122448979591836593e-02 7.142857142857142461e-02 7.482993197278911546e-02 6.122448979591836593e-02 6.802721088435374763e-02 2.619047619047619110e-02 3.333333333333333287e-02 3.571428571428571230e-02 2.619047619047619110e-02 3.571428571428571230e-02 1.870748299319727886e-02 1.870748299319727886e-02 1.020408163265306041e-02 6.802721088435374763e-02 6.632653061224490221e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.462585034013605678e-02 8.163265306122448328e-02 7.823129251700680631e-02 5.952380952380952051e-02 7.482993197278911546e-02 5.782312925170068202e-02 7.142857142857142461e-02 7.142857142857142461e-02 5.484693877551020252e-02 7.270408163265305868e-02 5.994897959183673186e-02 8.163265306122448328e-02 5.739795918367347066e-02 6.887755102040815647e-02 5.229591836734694132e-02 7.142857142857142461e-02 3.401360544217687382e-02 2.891156462585034101e-02 2.721088435374149558e-02 2.721088435374149558e-02 3.061224489795918297e-02 3.741496598639455773e-02 3.741496598639455773e-02 2.891156462585034101e-02 3.061224489795918297e-02 2.721088435374149558e-02 3.401360544217687382e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.168367346938775489e-02 2.040816326530612082e-02 2.295918367346938896e-02 1.147959183673469448e-02 1.147959183673469448e-02 5.867346938775510473e-02 5.994897959183673186e-02 5.612244897959183659e-02 5.612244897959183659e-02 5.739795918367347066e-02 6.760204081632653628e-02 5.484693877551020252e-02 6.250000000000000000e-02 5.484693877551020252e-02 5.994897959183673186e-02 5.739795918367347066e-02 5.994897959183673186e-02 5.484693877551020252e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.357142857142856845e-02 5.952380952380952051e-02 5.853174603174603197e-02 6.150793650793650452e-02 6.349206349206348854e-02 3.316326530612245110e-02 2.806122448979591830e-02 2.933673469387755237e-02 2.933673469387755237e-02 3.188775510204081703e-02 2.933673469387755237e-02 2.806122448979591830e-02 3.188775510204081703e-02 2.806122448979591830e-02 1.388888888888888812e-02 5.357142857142856845e-02 5.753968253968253649e-02 5.456349206349206393e-02 5.158730158730158444e-02 5.257936507936507992e-02 5.059523809523809590e-02 6.051587301587301598e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.960317460317460042e-02 3.968253968253968034e-02 4.523809523809523697e-02 4.523809523809523697e-02 5.714285714285714107e-02 6.507936507936508408e-02 5.079365079365079361e-02 2.777777777777777624e-02 2.976190476190476025e-02 2.876984126984126824e-02 2.976190476190476025e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.876984126984126824e-02 3.174603174603174427e-02 2.222222222222222307e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.507936507936507957e-02 6.111111111111110911e-02 4.523809523809523697e-02 5.396825396825397081e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.920634920634920501e-02 5.476190476190476164e-02 4.761904761904761640e-02 4.603174603174603474e-02 5.476190476190476164e-02 5.238095238095238221e-02 5.317460317460317304e-02 5.634920634920635024e-02 3.831168831168831057e-02 5.974025974025973934e-02 5.194805194805195148e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.155844155844155841e-02 3.896103896103896014e-02 2.777777777777777624e-02 3.015873015873015914e-02 1.818181818181818094e-02 1.558441558441558440e-02 4.090909090909090884e-02 5.519480519480519237e-02 4.285714285714285754e-02 4.545454545454545581e-02 5.612244897959183659e-02 5.324675324675324367e-02 4.285714285714285754e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.805194805194805407e-02 4.285714285714285754e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.792207792207792097e-02 2.857142857142857054e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.130952380952380959e-01 5.000000000000000278e-02 9.285714285714286031e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 7.857142857142856984e-02 1.035714285714285782e-01 1.035714285714285782e-01 4.761904761904761640e-02 5.000000000000000278e-02 7.823129251700680631e-02 9.761904761904761918e-02 6.904761904761905211e-02 2.500000000000000139e-02 2.142857142857142877e-02 2.500000000000000139e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 5.952380952380952051e-02 8.571428571428571508e-02 9.523809523809523281e-02 6.904761904761905211e-02 7.619047619047619735e-02 9.523809523809523281e-02 7.823129251700680631e-02 9.693877551020407823e-02 5.952380952380952051e-02 6.632653061224490221e-02 6.972789115646257918e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.857142857142857054e-02 2.380952380952380820e-02 2.857142857142857054e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.020408163265306041e-02 4.931972789115646183e-02 6.122448979591836593e-02 7.312925170068027003e-02 8.163265306122448328e-02 6.632653061224490221e-02 5.102040816326530726e-02 5.442176870748299117e-02 7.993197278911565173e-02 5.952380952380952051e-02 8.843537414965986498e-02 7.312925170068027003e-02 6.632653061224490221e-02 8.035714285714286309e-02 6.632653061224490221e-02 8.801020408163265363e-02 5.739795918367347066e-02 7.908163265306122902e-02 4.974489795918367319e-02 6.250000000000000000e-02 5.739795918367347066e-02 2.551020408163265363e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.551020408163265363e-02 2.721088435374149558e-02 2.721088435374149558e-02 2.380952380952380820e-02 2.551020408163265363e-02 2.380952380952380820e-02 2.551020408163265363e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.913265306122449022e-02 1.147959183673469448e-02 1.147959183673469448e-02 4.209183673469387571e-02 4.974489795918367319e-02 5.994897959183673186e-02 6.377551020408163407e-02 6.122448979591836593e-02 5.102040816326530726e-02 7.780612244897959495e-02 5.994897959183673186e-02 8.290816326530611735e-02 5.994897959183673186e-02 7.525510204081632681e-02 6.377551020408163407e-02 6.887755102040815647e-02 6.150793650793650452e-02 8.035714285714286309e-02 7.142857142857142461e-02 7.242063492063492702e-02 5.456349206349206393e-02 4.265873015873015983e-02 4.861111111111111188e-02 4.861111111111111188e-02 4.761904761904761640e-02 2.423469387755101956e-02 2.295918367346938896e-02 2.423469387755101956e-02 2.423469387755101956e-02 2.423469387755101956e-02 2.423469387755101956e-02 2.295918367346938896e-02 2.423469387755101956e-02 2.295918367346938896e-02 1.289682539682539611e-02 5.059523809523809590e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.753968253968253649e-02 6.150793650793650452e-02 6.349206349206348854e-02 4.365079365079364837e-02 5.257936507936507992e-02 7.043650793650793607e-02 7.837301587301587213e-02 7.242063492063492702e-02 3.670634920634920778e-02 6.150793650793650452e-02 3.968253968253968034e-02 6.904761904761905211e-02 6.428571428571427937e-02 4.206349206349206671e-02 5.238095238095238221e-02 4.682539682539682557e-02 2.182539682539682419e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.182539682539682419e-02 2.182539682539682419e-02 2.281746031746031619e-02 2.281746031746031619e-02 1.746031746031746074e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.349206349206349270e-02 4.285714285714285754e-02 3.730158730158730090e-02 4.285714285714285754e-02 5.317460317460317304e-02 4.365079365079364837e-02 3.809523809523809867e-02 4.444444444444444614e-02 3.730158730158730090e-02 3.253968253968254204e-02 3.412698412698412370e-02 4.365079365079364837e-02 3.412698412698412370e-02 5.000000000000000278e-02 3.571428571428571230e-02 4.415584415584415667e-02 4.610389610389610537e-02 5.909090909090908977e-02 6.428571428571427937e-02 4.870129870129870364e-02 5.519480519480519237e-02 2.063492063492063447e-02 2.142857142857142877e-02 1.428571428571428527e-02 1.363636363636363570e-02 3.311688311688311404e-02 4.480519480519480624e-02 5.454545454545454281e-02 4.805194805194805407e-02 4.336734693877550978e-02 3.701298701298701144e-02 3.831168831168831057e-02 4.025974025974025927e-02 3.766233766233766100e-02 3.766233766233766100e-02 3.311688311688311404e-02 2.077922077922077920e-02 1.948051948051948007e-02 2.077922077922077920e-02 2.077922077922077920e-02 2.012987012987012964e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.928571428571428770e-02 4.642857142857143016e-02 7.142857142857142461e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 8.571428571428571508e-02 7.142857142857142461e-02 7.857142857142856984e-02 4.285714285714285754e-02 4.761904761904761640e-02 5.102040816326530726e-02 6.428571428571427937e-02 7.380952380952381098e-02 2.857142857142857054e-02 2.142857142857142877e-02 2.857142857142857054e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 8.095238095238095621e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.857142857142856984e-02 7.619047619047619735e-02 7.380952380952381098e-02 5.102040816326530726e-02 6.122448979591836593e-02 7.482993197278911546e-02 6.122448979591836593e-02 6.802721088435374763e-02 2.619047619047619110e-02 3.333333333333333287e-02 3.571428571428571230e-02 2.619047619047619110e-02 3.571428571428571230e-02 1.870748299319727886e-02 1.870748299319727886e-02 1.020408163265306041e-02 7.142857142857142461e-02 6.292517006802721136e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.462585034013605678e-02 7.482993197278911546e-02 7.482993197278911546e-02 5.952380952380952051e-02 7.142857142857142461e-02 5.782312925170068202e-02 7.142857142857142461e-02 7.142857142857142461e-02 4.719387755102040505e-02 6.505102040816326814e-02 5.229591836734694132e-02 6.887755102040815647e-02 4.974489795918367319e-02 8.163265306122448328e-02 5.229591836734694132e-02 6.887755102040815647e-02 3.401360544217687382e-02 2.891156462585034101e-02 2.721088435374149558e-02 2.721088435374149558e-02 3.061224489795918297e-02 3.741496598639455773e-02 3.741496598639455773e-02 2.891156462585034101e-02 3.061224489795918297e-02 2.721088435374149558e-02 3.401360544217687382e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.168367346938775489e-02 2.040816326530612082e-02 2.295918367346938896e-02 1.147959183673469448e-02 1.147959183673469448e-02 6.122448979591836593e-02 5.739795918367347066e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.739795918367347066e-02 6.250000000000000000e-02 5.484693877551020252e-02 6.250000000000000000e-02 5.484693877551020252e-02 5.739795918367347066e-02 5.739795918367347066e-02 5.994897959183673186e-02 5.484693877551020252e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.563492063492063239e-02 5.357142857142856845e-02 6.349206349206348854e-02 6.845238095238095899e-02 7.142857142857142461e-02 6.547619047619047949e-02 3.316326530612245110e-02 2.806122448979591830e-02 2.933673469387755237e-02 2.933673469387755237e-02 3.188775510204081703e-02 2.933673469387755237e-02 2.806122448979591830e-02 3.188775510204081703e-02 2.806122448979591830e-02 1.388888888888888812e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.257936507936507992e-02 4.960317460317460042e-02 5.059523809523809590e-02 5.059523809523809590e-02 6.051587301587301598e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.357142857142856845e-02 4.960317460317460042e-02 3.968253968253968034e-02 4.047619047619047811e-02 4.047619047619047811e-02 5.555555555555555247e-02 6.031746031746031828e-02 5.873015873015872967e-02 2.777777777777777624e-02 2.976190476190476025e-02 2.876984126984126824e-02 2.976190476190476025e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.876984126984126824e-02 3.174603174603174427e-02 2.222222222222222307e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.507936507936507957e-02 5.793650793650793884e-02 4.682539682539682557e-02 4.920634920634920501e-02 4.841269841269841417e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.158730158730158444e-02 4.920634920634920501e-02 4.761904761904761640e-02 5.476190476190476164e-02 5.396825396825397081e-02 5.317460317460317304e-02 5.317460317460317304e-02 3.831168831168831057e-02 5.844155844155844021e-02 4.545454545454545581e-02 3.896103896103896014e-02 3.896103896103896014e-02 4.155844155844155841e-02 3.896103896103896014e-02 2.777777777777777624e-02 3.015873015873015914e-02 1.818181818181818094e-02 1.558441558441558440e-02 4.220779220779220797e-02 5.129870129870130191e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.612244897959183659e-02 4.805194805194805407e-02 4.025974025974025927e-02 3.896103896103896014e-02 3.896103896103896014e-02 4.415584415584415667e-02 4.415584415584415667e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.792207792207792097e-02 2.857142857142857054e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.130952380952380959e-01 5.357142857142856845e-02 7.142857142857142461e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 7.857142857142856984e-02 1.071428571428571369e-01 1.000000000000000056e-01 5.238095238095238221e-02 5.238095238095238221e-02 6.462585034013605678e-02 8.333333333333332871e-02 9.761904761904761918e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 5.952380952380952051e-02 7.857142857142856984e-02 9.761904761904761918e-02 5.952380952380952051e-02 7.380952380952381098e-02 8.809523809523810145e-02 6.462585034013605678e-02 8.503401360544217413e-02 6.462585034013605678e-02 9.523809523809523281e-02 8.503401360544217413e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.020408163265306041e-02 4.761904761904761640e-02 6.122448979591836593e-02 6.122448979591836593e-02 7.482993197278911546e-02 6.122448979591836593e-02 4.761904761904761640e-02 4.761904761904761640e-02 7.482993197278911546e-02 5.782312925170068202e-02 9.183673469387755584e-02 6.802721088435374763e-02 5.782312925170068202e-02 7.270408163265305868e-02 5.229591836734694132e-02 8.035714285714286309e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 9.183673469387755584e-02 5.994897959183673186e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.530612244897959148e-02 1.147959183673469448e-02 1.147959183673469448e-02 3.954081632653061451e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.994897959183673186e-02 4.974489795918367319e-02 4.974489795918367319e-02 7.015306122448979054e-02 4.974489795918367319e-02 8.545918367346938549e-02 5.994897959183673186e-02 7.015306122448979054e-02 5.994897959183673186e-02 5.994897959183673186e-02 4.365079365079364837e-02 7.539682539682539264e-02 5.952380952380952051e-02 5.952380952380952051e-02 5.952380952380952051e-02 4.365079365079364837e-02 5.158730158730158444e-02 4.960317460317460042e-02 4.960317460317460042e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.190476190476190410e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.960317460317460042e-02 5.753968253968253649e-02 4.960317460317460042e-02 3.968253968253968034e-02 4.166666666666666435e-02 5.753968253968253649e-02 8.134920634920635163e-02 6.944444444444444753e-02 3.373015873015872829e-02 4.960317460317460042e-02 3.968253968253968034e-02 6.269841269841269771e-02 5.000000000000000278e-02 3.730158730158730090e-02 5.158730158730158444e-02 5.000000000000000278e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.190476190476190410e-02 3.888888888888888951e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.841269841269841417e-02 3.571428571428571230e-02 3.412698412698412370e-02 3.571428571428571230e-02 3.412698412698412370e-02 2.936507936507936484e-02 2.936507936507936484e-02 3.888888888888888951e-02 2.936507936507936484e-02 4.841269841269841417e-02 3.441558441558441317e-02 3.246753246753246447e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.324675324675324367e-02 4.285714285714285754e-02 5.324675324675324367e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.116883116883116880e-02 4.155844155844155841e-02 4.285714285714285754e-02 4.285714285714285754e-02 3.826530612244898044e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.636363636363636187e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.987012987012986967e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02
-1.190476190476190410e-01 4.761904761904761640e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.011904761904761918e-01 5.000000000000000278e-02 7.142857142857142461e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 8.928571428571428770e-02 8.571428571428571508e-02 9.285714285714286031e-02 4.761904761904761640e-02 5.238095238095238221e-02 5.782312925170068202e-02 7.380952380952381098e-02 7.857142857142856984e-02 2.857142857142857054e-02 2.142857142857142877e-02 2.857142857142857054e-02 1.428571428571428527e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142634e-03 8.333333333333332871e-02 7.380952380952381098e-02 7.857142857142856984e-02 8.095238095238095621e-02 8.333333333333332871e-02 8.809523809523810145e-02 5.782312925170068202e-02 7.482993197278911546e-02 8.843537414965986498e-02 6.802721088435374763e-02 7.482993197278911546e-02 2.619047619047619110e-02 3.333333333333333287e-02 3.571428571428571230e-02 2.619047619047619110e-02 3.571428571428571230e-02 1.870748299319727886e-02 1.870748299319727886e-02 1.020408163265306041e-02 6.802721088435374763e-02 6.802721088435374763e-02 6.462585034013605678e-02 6.802721088435374763e-02 6.972789115646257918e-02 7.653061224489796088e-02 7.653061224489796088e-02 6.972789115646257918e-02 7.653061224489796088e-02 7.142857142857142461e-02 7.993197278911565173e-02 7.653061224489796088e-02 5.739795918367347066e-02 6.760204081632653628e-02 6.505102040816326814e-02 7.142857142857142461e-02 5.739795918367347066e-02 6.887755102040815647e-02 5.994897959183673186e-02 8.163265306122448328e-02 3.401360544217687382e-02 2.891156462585034101e-02 2.721088435374149558e-02 2.721088435374149558e-02 3.061224489795918297e-02 3.741496598639455773e-02 3.741496598639455773e-02 2.891156462585034101e-02 3.061224489795918297e-02 2.721088435374149558e-02 3.401360544217687382e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.168367346938775489e-02 2.040816326530612082e-02 2.295918367346938896e-02 1.147959183673469448e-02 1.147959183673469448e-02 5.867346938775510473e-02 6.122448979591836593e-02 5.867346938775510473e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.632653061224490221e-02 6.505102040816326814e-02 6.632653061224490221e-02 6.760204081632653628e-02 6.377551020408163407e-02 6.760204081632653628e-02 6.632653061224490221e-02 6.250000000000000000e-02 4.761904761904761640e-02 5.753968253968253649e-02 5.158730158730158444e-02 5.357142857142856845e-02 6.547619047619047949e-02 5.952380952380952051e-02 6.051587301587301598e-02 6.349206349206348854e-02 7.142857142857142461e-02 3.316326530612245110e-02 2.806122448979591830e-02 2.933673469387755237e-02 2.933673469387755237e-02 3.188775510204081703e-02 2.933673469387755237e-02 2.806122448979591830e-02 3.188775510204081703e-02 2.806122448979591830e-02 1.388888888888888812e-02 5.357142857142856845e-02 5.753968253968253649e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.853174603174603197e-02 5.654761904761904795e-02 6.051587301587301598e-02 5.753968253968253649e-02 5.952380952380952051e-02 6.349206349206348854e-02 6.150793650793650452e-02 5.158730158730158444e-02 5.555555555555555247e-02 4.444444444444444614e-02 5.000000000000000278e-02 4.682539682539682557e-02 5.238095238095238221e-02 7.142857142857142461e-02 5.396825396825397081e-02 2.777777777777777624e-02 2.976190476190476025e-02 2.876984126984126824e-02 2.976190476190476025e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.876984126984126824e-02 3.174603174603174427e-02 2.222222222222222307e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.507936507936507957e-02 6.111111111111110911e-02 4.682539682539682557e-02 5.238095238095238221e-02 5.476190476190476164e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.396825396825397081e-02 4.920634920634920501e-02 4.603174603174603474e-02 5.317460317460317304e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.873015873015872967e-02 4.220779220779220797e-02 5.584415584415584194e-02 4.935064935064935321e-02 4.415584415584415667e-02 4.675324675324675494e-02 5.064935064935065234e-02 5.064935064935065234e-02 2.777777777777777624e-02 3.015873015873015914e-02 1.818181818181818094e-02 1.558441558441558440e-02 4.220779220779220797e-02 5.584415584415584194e-02 4.805194805194805407e-02 4.805194805194805407e-02 5.739795918367347066e-02 5.064935064935065234e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.415584415584415667e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.792207792207792097e-02 2.857142857142857054e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 7.936507936507936067e-02 4.285714285714285754e-02 4.761904761904761640e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 3.492063492063492147e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.047619047619047394e-02 7.142857142857142461e-02 8.571428571428571508e-02 4.444444444444444614e-02 5.079365079365079361e-02 4.761904761904761640e-02 5.714285714285714107e-02 6.349206349206348854e-02 4.761904761904761640e-02 4.761904761904761640e-02 6.666666666666666574e-02 6.349206349206348854e-02 6.666666666666666574e-02 1.814058956916099821e-02 2.494331065759637298e-02 2.494331065759637298e-02 1.814058956916099821e-02 2.494331065759637298e-02 7.301587301587302015e-02 9.523809523809523281e-02 9.523809523809523281e-02 7.301587301587302015e-02 9.206349206349206948e-02 4.988662131519274595e-02 4.988662131519274595e-02 4.535147392290249380e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 7.256235827664399285e-02 7.256235827664399285e-02 5.442176870748299117e-02 6.802721088435374763e-02 4.988662131519274595e-02 7.256235827664399285e-02 7.256235827664399285e-02 2.040816326530612082e-02 3.571428571428571230e-02 2.551020408163265363e-02 3.401360544217687382e-02 2.551020408163265363e-02 3.401360544217687382e-02 2.040816326530612082e-02 3.401360544217687382e-02 9.750566893424036929e-02 7.709750566893423807e-02 7.029478458049887024e-02 7.029478458049887024e-02 7.936507936507936067e-02 9.750566893424036929e-02 9.750566893424036929e-02 7.482993197278911546e-02 7.709750566893423807e-02 7.029478458049887024e-02 9.297052154195011020e-02 5.102040816326530726e-02 5.102040816326530726e-02 5.442176870748299117e-02 5.102040816326530726e-02 5.612244897959183659e-02 4.421768707482993249e-02 4.761904761904761640e-02 6.802721088435374763e-02 5.782312925170068202e-02 5.272108843537415268e-02 5.272108843537415268e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.442176870748299117e-02 6.462585034013605678e-02 5.442176870748299117e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 5.782312925170068202e-02 2.910052910052909891e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 3.968253968253968034e-02 3.174603174603174427e-02 3.968253968253968034e-02 3.968253968253968034e-02 8.843537414965986498e-02 6.972789115646257918e-02 7.312925170068027003e-02 7.142857142857142461e-02 7.993197278911565173e-02 7.142857142857142461e-02 6.972789115646257918e-02 8.163265306122448328e-02 6.972789115646257918e-02 5.158730158730158444e-02 5.291005291005290712e-02 5.687830687830687515e-02 5.423280423280422979e-02 5.291005291005290712e-02 5.423280423280422979e-02 5.423280423280422979e-02 7.275132275132274728e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.687830687830687515e-02 6.613756613756613389e-02 5.423280423280422979e-02 4.444444444444444614e-02 2.751322751322751378e-02 2.751322751322751378e-02 4.232804232804232569e-02 4.550264550264550290e-02 2.962962962962963076e-02 6.746031746031745657e-02 7.275132275132274728e-02 7.010582010582010193e-02 7.010582010582010193e-02 6.746031746031745657e-02 6.746031746031745657e-02 6.878306878306877925e-02 8.201058201058200603e-02 5.291005291005290712e-02 5.502645502645502756e-02 5.502645502645502756e-02 4.444444444444444614e-02 7.301587301587302015e-02 5.714285714285714107e-02 5.502645502645502756e-02 5.608465608465608432e-02 5.291005291005290712e-02 6.666666666666666574e-02 5.608465608465608432e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.878306878306877925e-02 6.984126984126984294e-02 7.301587301587302015e-02 6.455026455026455223e-02 4.588744588744588654e-02 5.108225108225108307e-02 3.290043290043290214e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 7.089947089947089276e-02 7.619047619047619735e-02 4.848484848484848481e-02 4.415584415584415667e-02 5.541125541125541121e-02 6.753246753246752720e-02 5.194805194805195148e-02 5.194805194805195148e-02 8.503401360544217413e-02 5.541125541125541121e-02 5.194805194805195148e-02 5.281385281385281294e-02 5.281385281385281294e-02 5.367965367965368134e-02 6.060606060606060774e-02 6.666666666666666574e-02 6.493506493506492894e-02 6.839826839826840255e-02 7.012987012987012547e-02 7.099567099567100081e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 3.174603174603174427e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.000000000000000056e-01 5.079365079365079361e-02 6.349206349206348854e-02 5.396825396825397081e-02 6.349206349206348854e-02 5.396825396825397081e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 5.396825396825397081e-02 5.714285714285714107e-02 1.814058956916099821e-02 2.267573696145124690e-02 2.267573696145124690e-02 1.814058956916099821e-02 2.267573696145124690e-02 9.523809523809523281e-02 8.888888888888889228e-02 9.523809523809523281e-02 8.888888888888889228e-02 1.015873015873015872e-01 5.215419501133786856e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.895691609977324332e-02 5.895691609977324332e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.535147392290249380e-02 5.895691609977324332e-02 5.895691609977324332e-02 2.040816326530612082e-02 3.061224489795918297e-02 2.380952380952380820e-02 2.891156462585034101e-02 2.380952380952380820e-02 2.891156462585034101e-02 2.040816326530612082e-02 2.891156462585034101e-02 7.709750566893423807e-02 8.843537414965986498e-02 8.163265306122448328e-02 8.616780045351474238e-02 9.070294784580498759e-02 8.843537414965986498e-02 8.843537414965986498e-02 8.843537414965986498e-02 9.523809523809523281e-02 9.297052154195011020e-02 8.843537414965986498e-02 5.102040816326530726e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.272108843537415268e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 5.272108843537415268e-02 4.761904761904761640e-02 4.591836734693877792e-02 4.591836734693877792e-02 4.761904761904761640e-02 5.102040816326530726e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.102040816326530726e-02 5.102040816326530726e-02 4.761904761904761640e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.174603174603174427e-02 2.777777777777777624e-02 3.174603174603174427e-02 3.174603174603174427e-02 6.972789115646257918e-02 7.823129251700680631e-02 7.823129251700680631e-02 8.163265306122448328e-02 8.673469387755101956e-02 8.503401360544217413e-02 8.503401360544217413e-02 8.163265306122448328e-02 8.673469387755101956e-02 6.084656084656084318e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.629629629629629373e-02 4.629629629629629373e-02 5.423280423280422979e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.026455026455026176e-02 4.497354497354497105e-02 3.809523809523809867e-02 2.539682539682539680e-02 2.539682539682539680e-02 3.280423280423280102e-02 3.492063492063492147e-02 2.645502645502645356e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.407407407407406996e-02 8.994708994708994210e-02 7.804232804232803800e-02 7.804232804232803800e-02 7.539682539682539264e-02 6.349206349206348854e-02 4.867724867724868010e-02 5.079365079365079361e-02 5.502645502645502756e-02 5.925925925925926152e-02 5.291005291005290712e-02 4.444444444444444614e-02 4.550264550264550290e-02 4.656084656084655965e-02 4.338624338624338939e-02 4.973544973544973685e-02 4.656084656084655965e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.185185185185185036e-02 5.291005291005290712e-02 4.867724867724868010e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.770562770562770560e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 6.031746031746031828e-02 5.820105820105819783e-02 5.021645021645021467e-02 5.714285714285714107e-02 4.242424242424242681e-02 4.935064935064935321e-02 4.155844155844155841e-02 4.155844155844155841e-02 6.462585034013605678e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.329004329004328827e-02 4.502164502164502508e-02 7.619047619047619735e-02 6.060606060606060774e-02 6.926406926406926401e-02 6.147186147186146921e-02 5.367965367965368134e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.031746031746031689e-01 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 3.174603174603174427e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.000000000000000056e-01 1.047619047619047644e-01 1.095238095238095233e-01 5.079365079365079361e-02 6.984126984126984294e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 5.079365079365079361e-02 5.714285714285714107e-02 1.814058956916099821e-02 2.267573696145124690e-02 2.267573696145124690e-02 1.814058956916099821e-02 2.267573696145124690e-02 1.047619047619047644e-01 7.936507936507936067e-02 1.047619047619047644e-01 1.079365079365079416e-01 1.111111111111111049e-01 5.442176870748299117e-02 5.442176870748299117e-02 5.895691609977324332e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.988662131519274595e-02 4.535147392290249380e-02 5.442176870748299117e-02 5.442176870748299117e-02 2.040816326530612082e-02 3.061224489795918297e-02 2.380952380952380820e-02 2.721088435374149558e-02 2.380952380952380820e-02 2.721088435374149558e-02 2.040816326530612082e-02 2.721088435374149558e-02 7.029478458049887024e-02 8.163265306122448328e-02 1.065759637188208597e-01 1.065759637188208597e-01 9.523809523809523281e-02 7.482993197278911546e-02 8.390022675736961977e-02 1.020408163265306145e-01 1.043083900226757371e-01 1.020408163265306145e-01 8.843537414965986498e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.782312925170068202e-02 6.802721088435374763e-02 6.122448979591836593e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.421768707482993249e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.910052910052909891e-02 2.645502645502645356e-02 2.910052910052909891e-02 2.910052910052909891e-02 6.292517006802721136e-02 9.013605442176871041e-02 1.020408163265306145e-01 1.071428571428571369e-01 8.163265306122448328e-02 1.054421768707483054e-01 1.003401360544217691e-01 7.142857142857142461e-02 8.333333333333332871e-02 6.084656084656084318e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.232804232804232569e-02 3.809523809523809867e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.962962962962963076e-02 3.174603174603174427e-02 2.539682539682539680e-02 7.804232804232803800e-02 9.788359788359787816e-02 8.730158730158729674e-02 9.788359788359787816e-02 9.788359788359787816e-02 8.465608465608465139e-02 1.018518518518518462e-01 5.687830687830687515e-02 4.656084656084655965e-02 5.502645502645502756e-02 5.502645502645502756e-02 6.560846560846560205e-02 4.656084656084655965e-02 4.021164021164021218e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.021164021164021218e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.656084656084655965e-02 4.656084656084655965e-02 4.444444444444444614e-02 3.636363636363636187e-02 3.463203463203463200e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 5.502645502645502756e-02 5.185185185185185036e-02 5.021645021645021467e-02 7.272727272727272374e-02 3.809523809523809867e-02 4.502164502164502508e-02 3.809523809523809867e-02 3.809523809523809867e-02 5.782312925170068202e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.982683982683982854e-02 3.982683982683982854e-02 9.264069264069263454e-02 6.147186147186146921e-02 5.800865800865800948e-02 6.926406926406926401e-02 4.761904761904761640e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.190476190476190410e-01 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 3.174603174603174427e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809523281e-02 1.285714285714285587e-01 1.285714285714285587e-01 5.714285714285714107e-02 8.253968253968253788e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 5.079365079365079361e-02 5.714285714285714107e-02 1.814058956916099821e-02 2.267573696145124690e-02 2.267573696145124690e-02 1.814058956916099821e-02 2.267573696145124690e-02 1.301587301587301682e-01 7.936507936507936067e-02 9.523809523809523281e-02 1.174603174603174593e-01 1.301587301587301682e-01 5.668934240362812071e-02 6.122448979591836593e-02 6.802721088435374763e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.988662131519274595e-02 4.535147392290249380e-02 5.442176870748299117e-02 5.442176870748299117e-02 2.040816326530612082e-02 3.061224489795918297e-02 2.380952380952380820e-02 2.721088435374149558e-02 2.380952380952380820e-02 2.721088435374149558e-02 2.040816326530612082e-02 2.721088435374149558e-02 7.029478458049887024e-02 8.616780045351474238e-02 1.065759637188208597e-01 1.201814058956916093e-01 9.523809523809523281e-02 7.482993197278911546e-02 7.936507936507936067e-02 1.156462585034013640e-01 1.292517006802721136e-01 1.292517006802721136e-01 8.390022675736961977e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.462585034013605678e-02 8.503401360544217413e-02 7.482993197278911546e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.421768707482993249e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.910052910052909891e-02 2.645502645502645356e-02 2.910052910052909891e-02 2.910052910052909891e-02 6.292517006802721136e-02 9.013605442176871041e-02 9.523809523809523281e-02 1.139455782312925186e-01 8.163265306122448328e-02 1.207482993197278864e-01 1.173469387755102095e-01 7.653061224489796088e-02 9.353741496598640126e-02 6.878306878306877925e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.232804232804232569e-02 3.809523809523809867e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.962962962962963076e-02 3.174603174603174427e-02 2.539682539682539680e-02 7.804232804232803800e-02 8.597883597883597406e-02 8.201058201058200603e-02 1.256613756613756683e-01 1.071428571428571369e-01 8.862433862433861942e-02 1.018518518518518462e-01 5.687830687830687515e-02 4.656084656084655965e-02 5.291005291005290712e-02 6.137566137566137503e-02 8.253968253968253788e-02 4.656084656084655965e-02 4.021164021164021218e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.021164021164021218e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.656084656084655965e-02 4.656084656084655965e-02 4.444444444444444614e-02 3.636363636363636187e-02 3.463203463203463200e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 5.820105820105819783e-02 5.185185185185185036e-02 5.021645021645021467e-02 8.311688311688311681e-02 3.809523809523809867e-02 4.502164502164502508e-02 3.809523809523809867e-02 3.809523809523809867e-02 5.782312925170068202e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.982683982683982854e-02 3.982683982683982854e-02 1.064935064935064873e-01 6.147186147186146921e-02 5.974025974025973934e-02 6.580086580086580428e-02 4.761904761904761640e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 9.523809523809523281e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 3.492063492063492147e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.095238095238095233e-01 9.523809523809523281e-02 1.047619047619047644e-01 5.079365079365079361e-02 6.349206349206348854e-02 5.396825396825397081e-02 6.349206349206348854e-02 5.714285714285714107e-02 4.761904761904761640e-02 4.761904761904761640e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 1.814058956916099821e-02 2.494331065759637298e-02 2.494331065759637298e-02 1.814058956916099821e-02 2.494331065759637298e-02 9.841269841269841001e-02 8.888888888888889228e-02 1.174603174603174593e-01 9.841269841269841001e-02 1.111111111111111049e-01 5.668934240362812071e-02 5.668934240362812071e-02 5.442176870748299117e-02 5.668934240362812071e-02 4.988662131519274595e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.988662131519274595e-02 5.668934240362812071e-02 4.988662131519274595e-02 6.349206349206348854e-02 6.349206349206348854e-02 2.040816326530612082e-02 3.571428571428571230e-02 2.551020408163265363e-02 3.061224489795918297e-02 2.551020408163265363e-02 3.061224489795918297e-02 2.040816326530612082e-02 3.061224489795918297e-02 7.936507936507936067e-02 9.070294784580498759e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.111111111111111049e-01 8.616780045351474238e-02 9.523809523809523281e-02 9.523809523809523281e-02 1.020408163265306145e-01 9.750566893424036929e-02 9.977324263038549190e-02 5.782312925170068202e-02 5.782312925170068202e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.292517006802721136e-02 6.122448979591836593e-02 6.122448979591836593e-02 5.442176870748299117e-02 4.931972789115646183e-02 4.931972789115646183e-02 4.931972789115646183e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.462585034013605678e-02 5.442176870748299117e-02 4.931972789115646183e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.931972789115646183e-02 2.910052910052909891e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 3.306878306878306695e-02 2.910052910052909891e-02 3.306878306878306695e-02 3.306878306878306695e-02 7.142857142857142461e-02 8.333333333333332871e-02 9.523809523809523281e-02 9.523809523809523281e-02 9.523809523809523281e-02 9.523809523809523281e-02 9.183673469387755584e-02 8.163265306122448328e-02 9.013605442176871041e-02 6.746031746031745657e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.444444444444444614e-02 2.751322751322751378e-02 2.751322751322751378e-02 3.386243386243386472e-02 3.703703703703703498e-02 2.751322751322751378e-02 7.407407407407406996e-02 9.259259259259258745e-02 8.333333333333332871e-02 9.788359788359787816e-02 8.597883597883597406e-02 8.201058201058200603e-02 8.862433862433861942e-02 6.481481481481481122e-02 5.079365079365079361e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 5.502645502645502756e-02 4.550264550264550290e-02 4.867724867724868010e-02 5.185185185185185036e-02 4.550264550264550290e-02 5.185185185185185036e-02 5.185185185185185036e-02 4.867724867724868010e-02 4.867724867724868010e-02 5.185185185185185036e-02 5.502645502645502756e-02 5.502645502645502756e-02 5.185185185185185036e-02 4.242424242424242681e-02 4.155844155844155841e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 6.243386243386243178e-02 5.925925925925926152e-02 5.194805194805195148e-02 6.320346320346320601e-02 4.329004329004328827e-02 5.367965367965368134e-02 4.329004329004328827e-02 4.329004329004328827e-02 6.632653061224490221e-02 4.848484848484848481e-02 4.329004329004328827e-02 4.329004329004328827e-02 4.329004329004328827e-02 4.588744588744588654e-02 4.588744588744588654e-02 8.311688311688311681e-02 6.060606060606060774e-02 6.580086580086580428e-02 6.926406926406926401e-02 5.454545454545454281e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 7.936507936507936067e-02 4.285714285714285754e-02 4.761904761904761640e-02 5.714285714285714107e-02 3.809523809523809867e-02 5.714285714285714107e-02 2.539682539682539680e-02 3.809523809523809867e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809523281e-02 7.142857142857142461e-02 9.047619047619047394e-02 4.444444444444444614e-02 5.079365079365079361e-02 4.761904761904761640e-02 5.714285714285714107e-02 6.984126984126984294e-02 5.079365079365079361e-02 5.079365079365079361e-02 7.619047619047619735e-02 6.984126984126984294e-02 7.619047619047619735e-02 1.814058956916099821e-02 2.721088435374149558e-02 2.721088435374149558e-02 1.814058956916099821e-02 2.721088435374149558e-02 7.619047619047619735e-02 1.111111111111111049e-01 1.047619047619047644e-01 7.619047619047619735e-02 1.015873015873015872e-01 5.215419501133786856e-02 5.215419501133786856e-02 4.535147392290249380e-02 7.256235827664399285e-02 5.895691609977324332e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.349206349206348854e-02 8.163265306122448328e-02 8.163265306122448328e-02 5.895691609977324332e-02 7.256235827664399285e-02 5.442176870748299117e-02 8.163265306122448328e-02 8.163265306122448328e-02 2.040816326530612082e-02 4.081632653061224164e-02 2.721088435374149558e-02 3.741496598639455773e-02 2.721088435374149558e-02 3.741496598639455773e-02 2.040816326530612082e-02 3.741496598639455773e-02 9.750566893424036929e-02 8.843537414965986498e-02 7.482993197278911546e-02 7.482993197278911546e-02 8.616780045351474238e-02 1.201814058956916093e-01 1.133786848072562414e-01 7.936507936507936067e-02 8.390022675736961977e-02 7.482993197278911546e-02 9.750566893424036929e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.782312925170068202e-02 5.442176870748299117e-02 6.122448979591836593e-02 4.421768707482993249e-02 4.761904761904761640e-02 7.142857142857142461e-02 6.122448979591836593e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.122448979591836593e-02 7.482993197278911546e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.802721088435374763e-02 6.122448979591836593e-02 3.174603174603174427e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.645502645502645356e-02 4.232804232804232569e-02 3.439153439153438963e-02 4.232804232804232569e-02 4.232804232804232569e-02 9.013605442176871041e-02 7.993197278911565173e-02 7.993197278911565173e-02 7.823129251700680631e-02 9.183673469387755584e-02 7.823129251700680631e-02 7.482993197278911546e-02 9.863945578231292366e-02 7.993197278911565173e-02 5.291005291005290712e-02 5.820105820105819783e-02 6.349206349206348854e-02 6.084656084656084318e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.084656084656084318e-02 7.671957671957671532e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.878306878306877925e-02 5.820105820105819783e-02 5.079365079365079361e-02 2.962962962962963076e-02 2.962962962962963076e-02 4.444444444444444614e-02 4.867724867724868010e-02 3.174603174603174427e-02 8.068783068783068335e-02 8.068783068783068335e-02 8.068783068783068335e-02 7.804232804232803800e-02 7.275132275132274728e-02 7.671957671957671532e-02 7.539682539682539264e-02 8.333333333333332871e-02 5.925925925925926152e-02 6.137566137566137503e-02 6.137566137566137503e-02 4.656084656084655965e-02 7.619047619047619735e-02 5.925925925925926152e-02 6.137566137566137503e-02 6.349206349206348854e-02 5.714285714285714107e-02 6.984126984126984294e-02 6.349206349206348854e-02 6.560846560846560205e-02 6.560846560846560205e-02 7.195767195767195645e-02 7.407407407407406996e-02 7.619047619047619735e-02 6.772486772486772943e-02 5.194805194805195148e-02 5.541125541125541121e-02 3.463203463203463200e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.116883116883116880e-02 7.195767195767195645e-02 7.724867724867724716e-02 5.714285714285714107e-02 4.675324675324675494e-02 5.714285714285714107e-02 7.099567099567100081e-02 5.541125541125541121e-02 5.541125541125541121e-02 8.843537414965986498e-02 6.233766233766233761e-02 5.541125541125541121e-02 5.541125541125541121e-02 5.541125541125541121e-02 5.887445887445887788e-02 6.233766233766233761e-02 7.359307359307359908e-02 7.012987012987012547e-02 8.571428571428571508e-02 7.186147186147186228e-02 7.186147186147186228e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 7.936507936507936067e-02 4.285714285714285754e-02 4.761904761904761640e-02 5.714285714285714107e-02 3.809523809523809867e-02 5.714285714285714107e-02 2.539682539682539680e-02 3.809523809523809867e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.047619047619047644e-01 7.142857142857142461e-02 9.047619047619047394e-02 4.444444444444444614e-02 5.079365079365079361e-02 4.761904761904761640e-02 5.714285714285714107e-02 6.984126984126984294e-02 5.079365079365079361e-02 5.079365079365079361e-02 7.619047619047619735e-02 6.984126984126984294e-02 7.619047619047619735e-02 1.814058956916099821e-02 2.721088435374149558e-02 2.721088435374149558e-02 1.814058956916099821e-02 2.721088435374149558e-02 7.619047619047619735e-02 1.079365079365079416e-01 1.174603174603174593e-01 8.253968253968253788e-02 1.015873015873015872e-01 5.442176870748299117e-02 5.215419501133786856e-02 4.535147392290249380e-02 7.256235827664399285e-02 5.895691609977324332e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.349206349206348854e-02 8.163265306122448328e-02 8.163265306122448328e-02 5.895691609977324332e-02 7.256235827664399285e-02 5.442176870748299117e-02 8.163265306122448328e-02 8.163265306122448328e-02 2.040816326530612082e-02 4.081632653061224164e-02 2.721088435374149558e-02 3.741496598639455773e-02 2.721088435374149558e-02 3.741496598639455773e-02 2.040816326530612082e-02 3.741496598639455773e-02 9.750566893424036929e-02 8.843537414965986498e-02 8.390022675736961977e-02 7.936507936507936067e-02 9.523809523809523281e-02 1.133786848072562414e-01 1.156462585034013640e-01 8.390022675736961977e-02 8.390022675736961977e-02 7.482993197278911546e-02 1.065759637188208597e-01 5.782312925170068202e-02 5.442176870748299117e-02 5.952380952380952051e-02 5.612244897959183659e-02 6.122448979591836593e-02 4.421768707482993249e-02 4.761904761904761640e-02 7.142857142857142461e-02 6.122448979591836593e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.122448979591836593e-02 7.482993197278911546e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.802721088435374763e-02 6.802721088435374763e-02 6.122448979591836593e-02 3.174603174603174427e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.645502645502645356e-02 4.232804232804232569e-02 3.439153439153438963e-02 4.232804232804232569e-02 4.232804232804232569e-02 9.013605442176871041e-02 8.333333333333332871e-02 9.013605442176871041e-02 8.503401360544217413e-02 9.523809523809523281e-02 8.163265306122448328e-02 7.823129251700680631e-02 9.353741496598640126e-02 7.993197278911565173e-02 5.555555555555555247e-02 5.820105820105819783e-02 6.349206349206348854e-02 6.084656084656084318e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.084656084656084318e-02 7.671957671957671532e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.878306878306877925e-02 5.820105820105819783e-02 5.079365079365079361e-02 2.962962962962963076e-02 2.962962962962963076e-02 4.444444444444444614e-02 4.867724867724868010e-02 3.174603174603174427e-02 8.068783068783068335e-02 9.126984126984126477e-02 8.597883597883597406e-02 7.804232804232803800e-02 7.804232804232803800e-02 7.936507936507936067e-02 8.333333333333332871e-02 8.333333333333332871e-02 5.925925925925926152e-02 6.560846560846560205e-02 6.137566137566137503e-02 4.656084656084655965e-02 7.619047619047619735e-02 5.925925925925926152e-02 6.137566137566137503e-02 6.349206349206348854e-02 5.714285714285714107e-02 6.984126984126984294e-02 6.349206349206348854e-02 6.560846560846560205e-02 6.560846560846560205e-02 7.195767195767195645e-02 7.407407407407406996e-02 7.619047619047619735e-02 6.772486772486772943e-02 5.194805194805195148e-02 5.541125541125541121e-02 3.463203463203463200e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.116883116883116880e-02 7.195767195767195645e-02 7.724867724867724716e-02 5.714285714285714107e-02 5.021645021645021467e-02 5.714285714285714107e-02 7.099567099567100081e-02 5.541125541125541121e-02 5.541125541125541121e-02 8.843537414965986498e-02 6.233766233766233761e-02 5.541125541125541121e-02 5.541125541125541121e-02 5.541125541125541121e-02 5.887445887445887788e-02 6.233766233766233761e-02 7.705627705627705881e-02 7.012987012987012547e-02 8.051948051948051854e-02 7.619047619047619735e-02 7.186147186147186228e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.190476190476190410e-01 5.238095238095238221e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 3.174603174603174427e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809523281e-02 1.285714285714285587e-01 1.285714285714285587e-01 5.714285714285714107e-02 8.253968253968253788e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.396825396825397081e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 5.396825396825397081e-02 5.714285714285714107e-02 1.814058956916099821e-02 2.267573696145124690e-02 2.267573696145124690e-02 1.814058956916099821e-02 2.267573696145124690e-02 1.301587301587301682e-01 8.253968253968253788e-02 9.523809523809523281e-02 1.142857142857142821e-01 1.301587301587301682e-01 5.668934240362812071e-02 6.122448979591836593e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.895691609977324332e-02 5.895691609977324332e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.535147392290249380e-02 5.895691609977324332e-02 5.895691609977324332e-02 2.040816326530612082e-02 3.061224489795918297e-02 2.380952380952380820e-02 2.891156462585034101e-02 2.380952380952380820e-02 2.891156462585034101e-02 2.040816326530612082e-02 2.891156462585034101e-02 7.482993197278911546e-02 8.843537414965986498e-02 1.020408163265306145e-01 1.156462585034013640e-01 9.523809523809523281e-02 7.936507936507936067e-02 8.390022675736961977e-02 1.156462585034013640e-01 1.292517006802721136e-01 1.292517006802721136e-01 8.843537414965986498e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.952380952380952051e-02 5.782312925170068202e-02 6.462585034013605678e-02 8.503401360544217413e-02 7.482993197278911546e-02 5.272108843537415268e-02 4.761904761904761640e-02 4.591836734693877792e-02 4.591836734693877792e-02 4.761904761904761640e-02 5.102040816326530726e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.102040816326530726e-02 5.102040816326530726e-02 4.761904761904761640e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.174603174603174427e-02 2.777777777777777624e-02 3.174603174603174427e-02 3.174603174603174427e-02 6.802721088435374763e-02 8.843537414965986498e-02 9.013605442176871041e-02 1.071428571428571369e-01 8.503401360544217413e-02 1.156462585034013640e-01 1.156462585034013640e-01 7.993197278911565173e-02 9.523809523809523281e-02 6.878306878306877925e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.629629629629629373e-02 4.629629629629629373e-02 5.423280423280422979e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.026455026455026176e-02 4.497354497354497105e-02 3.809523809523809867e-02 2.539682539682539680e-02 2.539682539682539680e-02 3.280423280423280102e-02 3.492063492063492147e-02 2.645502645502645356e-02 7.804232804232803800e-02 8.068783068783068335e-02 7.936507936507936067e-02 1.256613756613756683e-01 1.044973544973544916e-01 8.862433862433861942e-02 9.656084656084655549e-02 6.216931216931216586e-02 4.867724867724868010e-02 5.291005291005290712e-02 6.137566137566137503e-02 8.253968253968253788e-02 5.291005291005290712e-02 4.444444444444444614e-02 4.550264550264550290e-02 4.656084656084655965e-02 4.338624338624338939e-02 4.973544973544973685e-02 4.656084656084655965e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.079365079365079361e-02 5.185185185185185036e-02 5.291005291005290712e-02 4.867724867724868010e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.770562770562770560e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 6.243386243386243178e-02 5.714285714285714107e-02 5.194805194805195148e-02 7.965367965367965708e-02 4.242424242424242681e-02 4.935064935064935321e-02 4.155844155844155841e-02 4.155844155844155841e-02 6.462585034013605678e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.329004329004328827e-02 4.502164502164502508e-02 1.047619047619047644e-01 6.320346320346320601e-02 6.320346320346320601e-02 6.580086580086580428e-02 5.281385281385281294e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.349206349206349131e-01 5.714285714285714107e-02 4.761904761904761640e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 3.492063492063492147e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.523809523809523281e-02 1.523809523809523947e-01 1.523809523809523947e-01 6.349206349206348854e-02 9.523809523809523281e-02 6.984126984126984294e-02 6.984126984126984294e-02 5.714285714285714107e-02 4.761904761904761640e-02 4.761904761904761640e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 1.814058956916099821e-02 2.494331065759637298e-02 2.494331065759637298e-02 1.814058956916099821e-02 2.494331065759637298e-02 1.587301587301587213e-01 8.571428571428571508e-02 9.523809523809523281e-02 1.238095238095238138e-01 1.587301587301587213e-01 6.122448979591836593e-02 7.029478458049887024e-02 7.709750566893423807e-02 5.668934240362812071e-02 4.988662131519274595e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.988662131519274595e-02 5.668934240362812071e-02 4.988662131519274595e-02 6.349206349206348854e-02 6.349206349206348854e-02 2.040816326530612082e-02 3.571428571428571230e-02 2.551020408163265363e-02 3.061224489795918297e-02 2.551020408163265363e-02 3.061224489795918297e-02 2.040816326530612082e-02 3.061224489795918297e-02 7.709750566893423807e-02 9.523809523809523281e-02 1.043083900226757371e-01 1.292517006802721136e-01 1.020408163265306145e-01 8.390022675736961977e-02 8.390022675736961977e-02 1.292517006802721136e-01 1.609977324263038578e-01 1.609977324263038578e-01 8.616780045351474238e-02 5.782312925170068202e-02 7.142857142857142461e-02 6.462585034013605678e-02 6.462585034013605678e-02 7.653061224489796088e-02 1.020408163265306145e-01 8.843537414965986498e-02 5.442176870748299117e-02 4.931972789115646183e-02 4.931972789115646183e-02 4.931972789115646183e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.462585034013605678e-02 5.442176870748299117e-02 4.931972789115646183e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.931972789115646183e-02 2.910052910052909891e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 3.306878306878306695e-02 2.910052910052909891e-02 3.306878306878306695e-02 3.306878306878306695e-02 6.972789115646257918e-02 9.013605442176871041e-02 8.843537414965986498e-02 1.139455782312925186e-01 8.843537414965986498e-02 1.326530612244898044e-01 1.326530612244898044e-01 8.843537414965986498e-02 1.088435374149659823e-01 7.804232804232803800e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.444444444444444614e-02 2.751322751322751378e-02 2.751322751322751378e-02 3.386243386243386472e-02 3.703703703703703498e-02 2.751322751322751378e-02 7.936507936507936067e-02 7.671957671957671532e-02 7.804232804232803800e-02 1.613756613756613667e-01 1.124338624338624276e-01 9.391534391534391013e-02 9.788359788359787816e-02 6.349206349206348854e-02 5.079365079365079361e-02 5.714285714285714107e-02 7.407407407407406996e-02 1.015873015873015872e-01 5.502645502645502756e-02 4.550264550264550290e-02 4.867724867724868010e-02 5.185185185185185036e-02 4.550264550264550290e-02 5.185185185185185036e-02 5.185185185185185036e-02 4.867724867724868010e-02 4.867724867724868010e-02 5.185185185185185036e-02 5.502645502645502756e-02 5.502645502645502756e-02 5.185185185185185036e-02 4.242424242424242681e-02 4.155844155844155841e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 6.666666666666666574e-02 5.820105820105819783e-02 5.367965367965368134e-02 8.917748917748917481e-02 4.329004329004328827e-02 5.367965367965368134e-02 4.329004329004328827e-02 4.329004329004328827e-02 6.632653061224490221e-02 4.848484848484848481e-02 4.329004329004328827e-02 4.329004329004328827e-02 4.329004329004328827e-02 4.588744588744588654e-02 4.588744588744588654e-02 1.177489177489177558e-01 6.406926406926406747e-02 6.926406926406926401e-02 6.580086580086580428e-02 5.367965367965368134e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 1.349206349206349131e-01 5.714285714285714107e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 2.539682539682539680e-02 3.174603174603174427e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 9.047619047619047394e-02 1.571428571428571397e-01 1.476190476190476220e-01 6.349206349206348854e-02 9.523809523809523281e-02 6.984126984126984294e-02 6.984126984126984294e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.714285714285714107e-02 5.079365079365079361e-02 5.714285714285714107e-02 1.814058956916099821e-02 2.267573696145124690e-02 2.267573696145124690e-02 1.814058956916099821e-02 2.267573696145124690e-02 1.619047619047619124e-01 7.936507936507936067e-02 8.571428571428571508e-02 1.238095238095238138e-01 1.492063492063492036e-01 5.895691609977324332e-02 6.802721088435374763e-02 7.709750566893423807e-02 4.988662131519274595e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.535147392290249380e-02 4.988662131519274595e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.535147392290249380e-02 4.988662131519274595e-02 4.535147392290249380e-02 5.442176870748299117e-02 5.442176870748299117e-02 2.040816326530612082e-02 3.061224489795918297e-02 2.380952380952380820e-02 2.721088435374149558e-02 2.380952380952380820e-02 2.721088435374149558e-02 2.040816326530612082e-02 2.721088435374149558e-02 7.029478458049887024e-02 9.297052154195011020e-02 1.020408163265306145e-01 1.292517006802721136e-01 9.750566893424036929e-02 7.482993197278911546e-02 7.482993197278911546e-02 1.292517006802721136e-01 1.609977324263038578e-01 1.655328798185941030e-01 7.936507936507936067e-02 5.442176870748299117e-02 6.802721088435374763e-02 6.122448979591836593e-02 6.122448979591836593e-02 7.142857142857142461e-02 1.054421768707483054e-01 9.183673469387755584e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.421768707482993249e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.910052910052909891e-02 2.645502645502645356e-02 2.910052910052909891e-02 2.910052910052909891e-02 6.292517006802721136e-02 8.673469387755101956e-02 8.333333333333332871e-02 1.105442176870748278e-01 8.333333333333332871e-02 1.309523809523809590e-01 1.343537414965986498e-01 8.333333333333332871e-02 1.071428571428571369e-01 7.936507936507936067e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 4.232804232804232569e-02 3.809523809523809867e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.962962962962963076e-02 3.174603174603174427e-02 2.539682539682539680e-02 7.539682539682539264e-02 7.010582010582010193e-02 7.275132275132274728e-02 1.653439153439153486e-01 1.124338624338624276e-01 9.126984126984126477e-02 9.391534391534391013e-02 5.687830687830687515e-02 4.656084656084655965e-02 5.079365079365079361e-02 6.772486772486772943e-02 1.037037037037037007e-01 4.656084656084655965e-02 4.021164021164021218e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.021164021164021218e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.444444444444444614e-02 4.656084656084655965e-02 4.656084656084655965e-02 4.444444444444444614e-02 3.636363636363636187e-02 3.463203463203463200e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 6.243386243386243178e-02 5.185185185185185036e-02 5.021645021645021467e-02 9.004329004329005015e-02 3.809523809523809867e-02 4.502164502164502508e-02 3.809523809523809867e-02 3.809523809523809867e-02 5.782312925170068202e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.982683982683982854e-02 3.982683982683982854e-02 1.186147186147186172e-01 5.974025974025973934e-02 6.320346320346320601e-02 5.974025974025973934e-02 4.761904761904761640e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.936507936507936067e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.904761904761904934e-02 0.000000000000000000e+00 8.730158730158729674e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.238095238095238221e-02 3.809523809523809867e-02 5.238095238095238221e-02 2.539682539682539680e-02 3.492063492063492147e-02 1.814058956916099821e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.142857142857142821e-01 8.095238095238095621e-02 9.523809523809523281e-02 5.079365079365079361e-02 5.714285714285714107e-02 5.079365079365079361e-02 6.984126984126984294e-02 6.349206349206348854e-02 4.761904761904761640e-02 4.761904761904761640e-02 6.666666666666666574e-02 6.349206349206348854e-02 6.666666666666666574e-02 1.814058956916099821e-02 2.494331065759637298e-02 2.494331065759637298e-02 1.814058956916099821e-02 2.494331065759637298e-02 8.253968253968253788e-02 9.841269841269841001e-02 1.206349206349206366e-01 8.888888888888889228e-02 1.015873015873015872e-01 5.668934240362812071e-02 5.668934240362812071e-02 4.988662131519274595e-02 6.802721088435374763e-02 5.442176870748299117e-02 4.988662131519274595e-02 4.988662131519274595e-02 5.668934240362812071e-02 7.256235827664399285e-02 7.256235827664399285e-02 5.442176870748299117e-02 6.802721088435374763e-02 4.988662131519274595e-02 7.256235827664399285e-02 7.256235827664399285e-02 2.040816326530612082e-02 3.571428571428571230e-02 2.551020408163265363e-02 3.401360544217687382e-02 2.551020408163265363e-02 3.401360544217687382e-02 2.040816326530612082e-02 3.401360544217687382e-02 9.297052154195011020e-02 8.843537414965986498e-02 8.843537414965986498e-02 8.390022675736961977e-02 9.977324263038549190e-02 9.750566893424036929e-02 1.065759637188208597e-01 8.843537414965986498e-02 8.616780045351474238e-02 7.936507936507936067e-02 1.156462585034013640e-01 5.782312925170068202e-02 5.782312925170068202e-02 6.122448979591836593e-02 5.782312925170068202e-02 6.292517006802721136e-02 5.102040816326530726e-02 5.782312925170068202e-02 6.802721088435374763e-02 5.782312925170068202e-02 5.272108843537415268e-02 5.272108843537415268e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.442176870748299117e-02 6.462585034013605678e-02 5.442176870748299117e-02 5.782312925170068202e-02 6.122448979591836593e-02 6.122448979591836593e-02 5.782312925170068202e-02 2.910052910052909891e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 2.513227513227513088e-02 3.968253968253968034e-02 3.174603174603174427e-02 3.968253968253968034e-02 3.968253968253968034e-02 8.673469387755101956e-02 8.163265306122448328e-02 9.353741496598640126e-02 8.673469387755101956e-02 9.353741496598640126e-02 8.333333333333332871e-02 8.163265306122448328e-02 8.333333333333332871e-02 7.823129251700680631e-02 5.952380952380952051e-02 5.291005291005290712e-02 5.687830687830687515e-02 5.423280423280422979e-02 5.291005291005290712e-02 5.423280423280422979e-02 5.423280423280422979e-02 7.275132275132274728e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.687830687830687515e-02 6.613756613756613389e-02 5.423280423280422979e-02 4.444444444444444614e-02 2.751322751322751378e-02 2.751322751322751378e-02 4.232804232804232569e-02 4.550264550264550290e-02 2.962962962962963076e-02 7.539682539682539264e-02 9.391534391534391013e-02 8.465608465608465139e-02 7.804232804232803800e-02 8.068783068783068335e-02 7.804232804232803800e-02 8.465608465608465139e-02 8.068783068783068335e-02 5.502645502645502756e-02 6.137566137566137503e-02 6.137566137566137503e-02 5.079365079365079361e-02 7.301587301587302015e-02 5.714285714285714107e-02 5.502645502645502756e-02 5.608465608465608432e-02 5.291005291005290712e-02 6.666666666666666574e-02 5.608465608465608432e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.878306878306877925e-02 6.984126984126984294e-02 7.301587301587302015e-02 6.455026455026455223e-02 4.588744588744588654e-02 5.108225108225108307e-02 3.290043290043290214e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 7.089947089947089276e-02 7.513227513227513366e-02 5.367965367965368134e-02 5.454545454545454281e-02 5.541125541125541121e-02 6.753246753246752720e-02 5.194805194805195148e-02 5.194805194805195148e-02 8.503401360544217413e-02 5.541125541125541121e-02 5.194805194805195148e-02 5.281385281385281294e-02 5.281385281385281294e-02 5.367965367965368134e-02 6.060606060606060774e-02 7.705627705627705881e-02 6.666666666666666574e-02 6.666666666666666574e-02 7.532467532467532201e-02 7.012987012987012547e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 5.952380952380952051e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 5.952380952380952051e-02 7.142857142857142461e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.857142857142857054e-02 3.571428571428571230e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 7.380952380952381098e-02 3.809523809523809867e-02 3.333333333333333287e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.333333333333333287e-02 3.333333333333333287e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 1.360544217687074779e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.360544217687074779e-02 1.700680272108843691e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.666666666666666574e-02 5.714285714285714107e-02 6.666666666666666574e-02 7.653061224489796088e-02 7.482993197278911546e-02 3.401360544217687382e-02 3.741496598639455773e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.401360544217687382e-02 3.741496598639455773e-02 3.401360544217687382e-02 4.081632653061224164e-02 4.081632653061224164e-02 1.530612244897959148e-02 2.295918367346938896e-02 1.785714285714285615e-02 2.040816326530612082e-02 1.785714285714285615e-02 2.040816326530612082e-02 1.530612244897959148e-02 2.040816326530612082e-02 5.102040816326530726e-02 5.102040816326530726e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.782312925170068202e-02 5.442176870748299117e-02 5.782312925170068202e-02 5.442176870748299117e-02 5.782312925170068202e-02 5.442176870748299117e-02 5.782312925170068202e-02 7.653061224489796088e-02 7.397959183673469274e-02 7.397959183673469274e-02 7.653061224489796088e-02 7.525510204081632681e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.081632653061224164e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.316326530612245110e-02 1.984126984126984017e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 2.182539682539682419e-02 1.984126984126984017e-02 2.182539682539682419e-02 2.182539682539682419e-02 4.591836734693877792e-02 4.846938775510203912e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.102040816326530726e-02 5.357142857142856845e-02 5.102040816326530726e-02 4.846938775510203912e-02 4.846938775510203912e-02 3.769841269841269632e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.174603174603174427e-02 2.857142857142857054e-02 1.904761904761904934e-02 1.904761904761904934e-02 2.222222222222222307e-02 2.380952380952380820e-02 1.904761904761904934e-02 4.365079365079364837e-02 5.158730158730158444e-02 4.761904761904761640e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.166666666666666435e-02 5.714285714285714107e-02 7.222222222222221544e-02 7.222222222222221544e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.015873015873015914e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.015873015873015914e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.333333333333333287e-02 2.727272727272727140e-02 2.597402597402597574e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.116883116883116880e-02 3.506493506493506274e-02 2.857142857142857054e-02 3.376623376623376360e-02 2.857142857142857054e-02 2.857142857142857054e-02 4.336734693877550978e-02 3.116883116883116880e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.987012987012986967e-02 2.987012987012986967e-02 4.415584415584415667e-02 3.636363636363636187e-02 3.896103896103896014e-02 4.025974025974025927e-02 3.506493506493506274e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 5.952380952380952051e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 7.142857142857142461e-02 9.285714285714286031e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.857142857142857054e-02 3.571428571428571230e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.428571428571427937e-02 7.142857142857142461e-02 7.857142857142856984e-02 1.095238095238095233e-01 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 3.809523809523809867e-02 3.333333333333333287e-02 3.333333333333333287e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 1.360544217687074779e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.360544217687074779e-02 1.700680272108843691e-02 7.142857142857142461e-02 5.714285714285714107e-02 6.190476190476190688e-02 6.190476190476190688e-02 8.095238095238095621e-02 8.843537414965986498e-02 1.156462585034013640e-01 4.081632653061224164e-02 3.741496598639455773e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.401360544217687382e-02 3.741496598639455773e-02 3.401360544217687382e-02 4.081632653061224164e-02 4.081632653061224164e-02 1.530612244897959148e-02 2.295918367346938896e-02 1.785714285714285615e-02 2.040816326530612082e-02 1.785714285714285615e-02 2.040816326530612082e-02 1.530612244897959148e-02 2.040816326530612082e-02 5.102040816326530726e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.122448979591836593e-02 5.782312925170068202e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.122448979591836593e-02 7.142857142857142461e-02 6.802721088435374763e-02 5.782312925170068202e-02 7.397959183673469274e-02 1.198979591836734637e-01 9.438775510204081010e-02 9.438775510204081010e-02 1.173469387755102095e-01 4.591836734693877792e-02 4.591836734693877792e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.081632653061224164e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.316326530612245110e-02 1.984126984126984017e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 2.182539682539682419e-02 1.984126984126984017e-02 2.182539682539682419e-02 2.182539682539682419e-02 4.591836734693877792e-02 4.846938775510203912e-02 5.102040816326530726e-02 5.612244897959183659e-02 5.102040816326530726e-02 6.122448979591836593e-02 5.867346938775510473e-02 5.102040816326530726e-02 5.357142857142856845e-02 4.166666666666666435e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.174603174603174427e-02 2.857142857142857054e-02 1.904761904761904934e-02 1.904761904761904934e-02 2.222222222222222307e-02 2.380952380952380820e-02 1.904761904761904934e-02 4.365079365079364837e-02 4.761904761904761640e-02 4.563492063492063239e-02 6.349206349206348854e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.960317460317460042e-02 4.166666666666666435e-02 5.555555555555555247e-02 5.238095238095238221e-02 1.222222222222222182e-01 4.444444444444444614e-02 3.492063492063492147e-02 3.015873015873015914e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.015873015873015914e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.333333333333333287e-02 2.727272727272727140e-02 2.597402597402597574e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 3.968253968253968034e-02 3.809523809523809867e-02 3.116883116883116880e-02 4.025974025974025927e-02 2.857142857142857054e-02 3.376623376623376360e-02 2.857142857142857054e-02 2.857142857142857054e-02 4.336734693877550978e-02 3.116883116883116880e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.987012987012986967e-02 2.987012987012986967e-02 4.935064935064935321e-02 3.636363636363636187e-02 3.896103896103896014e-02 3.896103896103896014e-02 3.506493506493506274e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 5.952380952380952051e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 6.547619047619047949e-02 8.214285714285714246e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.857142857142857054e-02 3.571428571428571230e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.428571428571427937e-02 6.428571428571427937e-02 7.142857142857142461e-02 9.047619047619047394e-02 4.285714285714285754e-02 3.571428571428571230e-02 4.285714285714285754e-02 4.047619047619047811e-02 3.333333333333333287e-02 3.333333333333333287e-02 4.285714285714285754e-02 4.047619047619047811e-02 4.285714285714285754e-02 1.360544217687074779e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.360544217687074779e-02 1.700680272108843691e-02 6.428571428571427937e-02 5.952380952380952051e-02 6.428571428571427937e-02 5.952380952380952051e-02 7.380952380952381098e-02 8.163265306122448328e-02 9.353741496598640126e-02 3.741496598639455773e-02 4.081632653061224164e-02 3.571428571428571230e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 4.421768707482993249e-02 4.421768707482993249e-02 3.571428571428571230e-02 4.081632653061224164e-02 3.401360544217687382e-02 4.421768707482993249e-02 4.421768707482993249e-02 1.530612244897959148e-02 2.295918367346938896e-02 1.785714285714285615e-02 2.168367346938775489e-02 1.785714285714285615e-02 2.168367346938775489e-02 1.530612244897959148e-02 2.168367346938775489e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.782312925170068202e-02 5.782312925170068202e-02 5.782312925170068202e-02 5.952380952380952051e-02 5.952380952380952051e-02 6.462585034013605678e-02 6.122448979591836593e-02 6.122448979591836593e-02 7.397959183673469274e-02 9.438775510204081010e-02 8.418367346938775142e-02 8.418367346938775142e-02 9.438775510204081010e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.954081632653061451e-02 3.571428571428571230e-02 3.443877551020407823e-02 3.443877551020407823e-02 3.571428571428571230e-02 3.826530612244898044e-02 3.571428571428571230e-02 4.081632653061224164e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.826530612244898044e-02 3.826530612244898044e-02 3.571428571428571230e-02 1.984126984126984017e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 2.380952380952380820e-02 2.083333333333333218e-02 2.380952380952380820e-02 2.380952380952380820e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.229591836734694132e-02 5.484693877551020252e-02 5.357142857142856845e-02 5.739795918367347066e-02 5.612244897959183659e-02 5.229591836734694132e-02 5.229591836734694132e-02 3.968253968253968034e-02 3.373015873015872829e-02 3.571428571428571230e-02 3.472222222222222376e-02 3.373015873015872829e-02 3.472222222222222376e-02 3.472222222222222376e-02 4.067460317460317581e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.769841269841269632e-02 3.373015873015872829e-02 2.857142857142857054e-02 1.904761904761904934e-02 1.904761904761904934e-02 2.460317460317460250e-02 2.619047619047619110e-02 1.984126984126984017e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.761904761904761640e-02 5.753968253968253649e-02 5.158730158730158444e-02 4.861111111111111188e-02 5.059523809523809590e-02 4.563492063492063239e-02 5.714285714285714107e-02 6.031746031746031828e-02 9.365079365079365115e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.333333333333333287e-02 3.412698412698412370e-02 3.492063492063492147e-02 3.253968253968254204e-02 3.730158730158730090e-02 3.492063492063492147e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.809523809523809867e-02 3.888888888888888951e-02 3.968253968253968034e-02 3.650793650793651007e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.077922077922077920e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 4.206349206349206671e-02 4.206349206349206671e-02 3.246753246753246447e-02 3.766233766233766100e-02 3.181818181818181490e-02 3.701298701298701144e-02 3.116883116883116880e-02 3.116883116883116880e-02 4.846938775510203912e-02 3.376623376623376360e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.246753246753246447e-02 3.376623376623376360e-02 4.935064935064935321e-02 3.896103896103896014e-02 4.155844155844155841e-02 4.155844155844155841e-02 3.896103896103896014e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 5.952380952380952051e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 6.547619047619047949e-02 8.214285714285714246e-02 2.857142857142857054e-02 3.571428571428571230e-02 2.857142857142857054e-02 3.571428571428571230e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.428571428571427937e-02 6.428571428571427937e-02 7.142857142857142461e-02 9.047619047619047394e-02 4.285714285714285754e-02 3.571428571428571230e-02 4.285714285714285754e-02 3.809523809523809867e-02 3.333333333333333287e-02 3.333333333333333287e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 1.360544217687074779e-02 1.700680272108843691e-02 1.700680272108843691e-02 1.360544217687074779e-02 1.700680272108843691e-02 6.428571428571427937e-02 5.714285714285714107e-02 6.428571428571427937e-02 5.952380952380952051e-02 7.380952380952381098e-02 8.333333333333332871e-02 9.353741496598640126e-02 3.741496598639455773e-02 3.741496598639455773e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.401360544217687382e-02 3.741496598639455773e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.401360544217687382e-02 3.741496598639455773e-02 3.401360544217687382e-02 4.081632653061224164e-02 4.081632653061224164e-02 1.530612244897959148e-02 2.295918367346938896e-02 1.785714285714285615e-02 2.040816326530612082e-02 1.785714285714285615e-02 2.040816326530612082e-02 1.530612244897959148e-02 2.040816326530612082e-02 5.102040816326530726e-02 5.272108843537415268e-02 5.442176870748299117e-02 5.782312925170068202e-02 5.782312925170068202e-02 5.442176870748299117e-02 5.612244897959183659e-02 5.782312925170068202e-02 6.462585034013605678e-02 6.122448979591836593e-02 5.782312925170068202e-02 7.653061224489796088e-02 9.438775510204081010e-02 8.418367346938775142e-02 8.673469387755101956e-02 9.438775510204081010e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.081632653061224164e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.316326530612245110e-02 1.984126984126984017e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 2.182539682539682419e-02 1.984126984126984017e-02 2.182539682539682419e-02 2.182539682539682419e-02 4.591836734693877792e-02 4.846938775510203912e-02 5.229591836734694132e-02 5.484693877551020252e-02 5.102040816326530726e-02 5.739795918367347066e-02 5.484693877551020252e-02 4.974489795918367319e-02 5.102040816326530726e-02 3.968253968253968034e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.174603174603174427e-02 2.857142857142857054e-02 1.904761904761904934e-02 1.904761904761904934e-02 2.222222222222222307e-02 2.380952380952380820e-02 1.904761904761904934e-02 4.365079365079364837e-02 4.960317460317460042e-02 4.662698412698412787e-02 5.753968253968253649e-02 4.960317460317460042e-02 4.662698412698412787e-02 4.960317460317460042e-02 4.166666666666666435e-02 5.714285714285714107e-02 6.349206349206348854e-02 9.365079365079365115e-02 3.968253968253968034e-02 3.492063492063492147e-02 3.015873015873015914e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.015873015873015914e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.333333333333333287e-02 2.727272727272727140e-02 2.597402597402597574e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 3.888888888888888951e-02 3.809523809523809867e-02 3.116883116883116880e-02 3.766233766233766100e-02 2.857142857142857054e-02 3.376623376623376360e-02 2.857142857142857054e-02 2.857142857142857054e-02 4.336734693877550978e-02 3.116883116883116880e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.987012987012986967e-02 2.987012987012986967e-02 4.675324675324675494e-02 3.636363636363636187e-02 3.896103896103896014e-02 3.961038961038960970e-02 3.506493506493506274e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.761904761904761640e-02 5.952380952380952051e-02 2.380952380952380820e-02 1.428571428571428527e-02 0.000000000000000000e+00 7.142857142857142461e-02 9.285714285714286031e-02 2.857142857142857054e-02 3.928571428571428492e-02 2.857142857142857054e-02 3.928571428571428492e-02 1.904761904761904934e-02 2.619047619047619110e-02 1.360544217687074779e-02 9.523809523809524669e-03 9.523809523809524669e-03 6.785714285714285199e-02 7.142857142857142461e-02 8.214285714285714246e-02 1.071428571428571369e-01 4.761904761904761640e-02 3.809523809523809867e-02 4.761904761904761640e-02 4.285714285714285754e-02 3.571428571428571230e-02 3.571428571428571230e-02 5.000000000000000278e-02 4.285714285714285754e-02 5.000000000000000278e-02 1.360544217687074779e-02 1.870748299319727886e-02 1.870748299319727886e-02 1.360544217687074779e-02 1.870748299319727886e-02 7.380952380952381098e-02 6.190476190476190688e-02 6.904761904761905211e-02 6.428571428571427937e-02 8.809523809523810145e-02 8.843537414965986498e-02 1.139455782312925186e-01 4.081632653061224164e-02 4.251700680272108707e-02 3.741496598639455773e-02 3.741496598639455773e-02 3.741496598639455773e-02 4.251700680272108707e-02 4.761904761904761640e-02 4.761904761904761640e-02 3.741496598639455773e-02 4.251700680272108707e-02 3.741496598639455773e-02 4.761904761904761640e-02 4.761904761904761640e-02 1.530612244897959148e-02 2.678571428571428423e-02 1.913265306122449022e-02 2.295918367346938896e-02 1.913265306122449022e-02 2.295918367346938896e-02 1.530612244897959148e-02 2.295918367346938896e-02 5.612244897959183659e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.462585034013605678e-02 6.292517006802721136e-02 6.122448979591836593e-02 6.122448979591836593e-02 6.462585034013605678e-02 7.653061224489796088e-02 7.142857142857142461e-02 6.292517006802721136e-02 7.525510204081632681e-02 1.173469387755102095e-01 9.438775510204081010e-02 9.438775510204081010e-02 1.173469387755102095e-01 4.591836734693877792e-02 4.591836734693877792e-02 4.081632653061224164e-02 3.698979591836734637e-02 3.698979591836734637e-02 3.698979591836734637e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.846938775510203912e-02 4.081632653061224164e-02 3.698979591836734637e-02 4.081632653061224164e-02 4.081632653061224164e-02 3.698979591836734637e-02 2.182539682539682419e-02 1.884920634920634816e-02 1.884920634920634816e-02 1.884920634920634816e-02 1.884920634920634816e-02 2.480158730158730021e-02 2.182539682539682419e-02 2.480158730158730021e-02 2.480158730158730021e-02 5.102040816326530726e-02 5.229591836734694132e-02 5.612244897959183659e-02 6.122448979591836593e-02 5.612244897959183659e-02 6.632653061224490221e-02 6.250000000000000000e-02 5.612244897959183659e-02 5.739795918367347066e-02 4.265873015873015983e-02 3.571428571428571230e-02 3.869047619047619180e-02 3.869047619047619180e-02 3.571428571428571230e-02 3.869047619047619180e-02 3.869047619047619180e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.166666666666666435e-02 3.869047619047619180e-02 3.869047619047619180e-02 3.571428571428571230e-02 3.333333333333333287e-02 2.063492063492063447e-02 2.063492063492063447e-02 2.539682539682539680e-02 2.777777777777777624e-02 2.063492063492063447e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.059523809523809590e-02 6.944444444444444753e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.456349206349206393e-02 4.662698412698412787e-02 5.793650793650793884e-02 5.634920634920635024e-02 1.198412698412698457e-01 4.603174603174603474e-02 4.126984126984126894e-02 3.412698412698412370e-02 3.650793650793651007e-02 3.888888888888888951e-02 3.412698412698412370e-02 3.888888888888888951e-02 3.888888888888888951e-02 3.650793650793651007e-02 3.650793650793651007e-02 3.888888888888888951e-02 4.126984126984126894e-02 4.126984126984126894e-02 3.888888888888888951e-02 3.181818181818181490e-02 3.116883116883116880e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 4.365079365079364837e-02 4.285714285714285754e-02 3.376623376623376360e-02 4.220779220779220797e-02 3.246753246753246447e-02 4.025974025974025927e-02 3.246753246753246447e-02 3.246753246753246447e-02 4.974489795918367319e-02 3.636363636363636187e-02 3.246753246753246447e-02 3.246753246753246447e-02 3.246753246753246447e-02 3.441558441558441317e-02 3.441558441558441317e-02 5.454545454545454281e-02 4.025974025974025927e-02 4.415584415584415667e-02 4.415584415584415667e-02 3.961038961038960970e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.190476190476190410e-01 4.761904761904761640e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 1.250000000000000000e-01 5.357142857142856845e-02 9.285714285714286031e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.020408163265306041e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.142857142857142461e-02 1.214285714285714274e-01 1.142857142857142821e-01 5.238095238095238221e-02 1.071428571428571369e-01 9.761904761904761918e-02 7.380952380952381098e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.119047619047619097e-01 5.476190476190476164e-02 5.476190476190476164e-02 8.809523809523810145e-02 1.023809523809523780e-01 4.251700680272108707e-02 4.931972789115646183e-02 9.693877551020407823e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 4.421768707482993249e-02 6.122448979591836593e-02 6.802721088435374763e-02 8.503401360544217413e-02 6.122448979591836593e-02 4.421768707482993249e-02 4.421768707482993249e-02 8.503401360544217413e-02 1.020408163265306145e-01 1.054421768707483054e-01 5.102040816326530726e-02 3.571428571428571230e-02 4.591836734693877792e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.591836734693877792e-02 1.020408163265306145e-01 7.270408163265305868e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.698979591836734637e-02 5.484693877551020252e-02 4.974489795918367319e-02 6.760204081632653628e-02 4.974489795918367319e-02 8.035714285714286309e-02 8.290816326530611735e-02 4.974489795918367319e-02 6.760204081632653628e-02 5.952380952380952051e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.428571428571428527e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 4.563492063492063239e-02 3.769841269841269632e-02 4.166666666666666435e-02 9.325396825396825573e-02 6.746031746031745657e-02 5.555555555555555247e-02 5.555555555555555247e-02 3.174603174603174427e-02 2.698412698412698540e-02 2.698412698412698540e-02 3.968253968253968034e-02 9.285714285714286031e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.363636363636363570e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.571428571428571230e-02 2.777777777777777624e-02 4.480519480519480624e-02 7.077922077922077504e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 2.295918367346938896e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.493506493506492894e-02 3.376623376623376360e-02 3.376623376623376360e-02 3.116883116883116880e-02 2.467532467532467660e-02
-0.000000000000000000e+00 0.000000000000000000e+00 1.190476190476190410e-01 4.761904761904761640e-02 1.785714285714285615e-02 1.071428571428571438e-02 0.000000000000000000e+00 1.130952380952380959e-01 5.357142857142856845e-02 7.142857142857142461e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.020408163265306041e-02 7.142857142857142634e-03 7.142857142857142634e-03 7.857142857142856984e-02 1.071428571428571369e-01 1.000000000000000056e-01 5.238095238095238221e-02 8.333333333333332871e-02 6.904761904761905211e-02 9.761904761904761918e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 2.142857142857142877e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 1.020408163265306041e-02 9.761904761904761918e-02 5.952380952380952051e-02 5.952380952380952051e-02 7.857142857142856984e-02 8.809523809523810145e-02 4.251700680272108707e-02 4.931972789115646183e-02 6.462585034013605678e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 2.040816326530612082e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 1.147959183673469448e-02 4.761904761904761640e-02 6.122448979591836593e-02 6.122448979591836593e-02 7.482993197278911546e-02 6.122448979591836593e-02 4.761904761904761640e-02 4.761904761904761640e-02 7.482993197278911546e-02 8.843537414965986498e-02 9.183673469387755584e-02 5.782312925170068202e-02 3.571428571428571230e-02 4.591836734693877792e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.591836734693877792e-02 7.270408163265305868e-02 9.183673469387755584e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.913265306122449022e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.954081632653061451e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.994897959183673186e-02 4.974489795918367319e-02 7.015306122448979054e-02 7.270408163265305868e-02 4.974489795918367319e-02 5.994897959183673186e-02 5.952380952380952051e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.428571428571428527e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.166666666666666435e-02 8.134920634920635163e-02 5.952380952380952051e-02 4.960317460317460042e-02 4.960317460317460042e-02 3.373015873015872829e-02 2.698412698412698540e-02 2.698412698412698540e-02 3.968253968253968034e-02 6.269841269841269771e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.666666666666666644e-02 1.363636363636363570e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.571428571428571230e-02 2.936507936507936484e-02 3.246753246753246447e-02 5.324675324675324367e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 2.295918367346938896e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 1.558441558441558440e-02 5.714285714285714107e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.597402597402597574e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.547619047619047949e-02 3.571428571428571230e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 7.857142857142856984e-02 6.071428571428571369e-02 7.499999999999999722e-02 3.809523809523809867e-02 4.761904761904761640e-02 3.741496598639455773e-02 4.047619047619047811e-02 4.523809523809523697e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.571428571428571508e-02 6.428571428571427937e-02 6.428571428571427937e-02 8.571428571428571508e-02 8.333333333333332871e-02 8.333333333333332871e-02 3.741496598639455773e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.251700680272108707e-02 5.102040816326530726e-02 4.523809523809523697e-02 6.190476190476190688e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 8.843537414965986498e-02 6.972789115646257918e-02 6.292517006802721136e-02 6.292517006802721136e-02 7.142857142857142461e-02 9.013605442176871041e-02 9.013605442176871041e-02 6.802721088435374763e-02 8.673469387755101956e-02 6.292517006802721136e-02 8.843537414965986498e-02 8.843537414965986498e-02 3.698979591836734637e-02 5.739795918367347066e-02 4.336734693877550978e-02 5.867346938775510473e-02 4.209183673469387571e-02 6.122448979591836593e-02 3.954081632653061451e-02 5.867346938775510473e-02 6.802721088435374763e-02 5.272108843537415268e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 7.142857142857142461e-02 7.142857142857142461e-02 5.272108843537415268e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.802721088435374763e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.954081632653061451e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 8.928571428571428770e-02 7.270408163265305868e-02 6.377551020408163407e-02 6.377551020408163407e-02 6.632653061224490221e-02 7.525510204081632681e-02 6.505102040816326814e-02 7.653061224489796088e-02 6.505102040816326814e-02 7.142857142857142461e-02 7.270408163265305868e-02 7.397959183673469274e-02 7.015306122448979054e-02 4.166666666666666435e-02 3.968253968253968034e-02 3.869047619047619180e-02 3.968253968253968034e-02 4.365079365079364837e-02 6.547619047619047949e-02 5.357142857142856845e-02 6.349206349206348854e-02 6.349206349206348854e-02 7.015306122448979054e-02 5.102040816326530726e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.994897959183673186e-02 5.229591836734694132e-02 5.102040816326530726e-02 5.994897959183673186e-02 5.102040816326530726e-02 2.380952380952380820e-02 6.250000000000000000e-02 6.746031746031745657e-02 6.448412698412699096e-02 6.250000000000000000e-02 6.349206349206348854e-02 6.349206349206348854e-02 8.928571428571428770e-02 6.646825396825396803e-02 6.448412698412699096e-02 6.448412698412699096e-02 6.646825396825396803e-02 8.333333333333332871e-02 6.448412698412699096e-02 5.079365079365079361e-02 3.888888888888888951e-02 3.888888888888888951e-02 6.746031746031745657e-02 6.507936507936508408e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.357142857142856845e-02 5.257936507936507992e-02 5.357142857142856845e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.257936507936507992e-02 6.944444444444444753e-02 4.126984126984126894e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 8.888888888888889228e-02 7.222222222222221544e-02 6.428571428571427937e-02 6.428571428571427937e-02 6.190476190476190688e-02 8.492063492063492425e-02 6.587301587301587491e-02 7.936507936507936067e-02 7.857142857142856984e-02 8.730158730158729674e-02 8.650793650793650591e-02 9.126984126984126477e-02 7.698412698412698818e-02 5.194805194805195148e-02 7.012987012987012547e-02 4.805194805194805407e-02 3.896103896103896014e-02 3.896103896103896014e-02 4.025974025974025927e-02 3.896103896103896014e-02 5.952380952380952051e-02 6.746031746031745657e-02 3.376623376623376360e-02 2.727272727272727140e-02 6.818181818181817677e-02 7.922077922077921941e-02 6.038961038961038891e-02 6.038961038961038891e-02 9.948979591836734637e-02 6.428571428571427937e-02 6.038961038961038891e-02 6.298701298701298024e-02 6.298701298701298024e-02 6.233766233766233761e-02 7.467532467532467244e-02 5.454545454545454281e-02 5.194805194805195148e-02 5.454545454545454281e-02 5.519480519480519237e-02 6.493506493506492894e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 7.857142857142856984e-02 7.857142857142856984e-02 8.571428571428571508e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.421768707482993249e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.333333333333332871e-02 7.380952380952381098e-02 8.095238095238095621e-02 8.095238095238095621e-02 8.333333333333332871e-02 9.047619047619047394e-02 4.421768707482993249e-02 5.952380952380952051e-02 5.612244897959183659e-02 5.102040816326530726e-02 5.612244897959183659e-02 4.285714285714285754e-02 5.476190476190476164e-02 5.714285714285714107e-02 4.285714285714285754e-02 5.714285714285714107e-02 3.061224489795918297e-02 3.061224489795918297e-02 1.700680272108843691e-02 7.993197278911565173e-02 7.482993197278911546e-02 6.802721088435374763e-02 7.312925170068027003e-02 7.653061224489796088e-02 8.503401360544217413e-02 8.333333333333332871e-02 7.653061224489796088e-02 8.333333333333332871e-02 7.993197278911565173e-02 8.673469387755101956e-02 8.333333333333332871e-02 4.974489795918367319e-02 5.612244897959183659e-02 5.739795918367347066e-02 5.994897959183673186e-02 4.974489795918367319e-02 5.739795918367347066e-02 4.974489795918367319e-02 6.122448979591836593e-02 5.782312925170068202e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 4.931972789115646183e-02 4.421768707482993249e-02 5.782312925170068202e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.698979591836734637e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.270408163265305868e-02 7.653061224489796088e-02 6.632653061224490221e-02 7.270408163265305868e-02 6.632653061224490221e-02 7.908163265306122902e-02 7.397959183673469274e-02 7.780612244897959495e-02 8.035714285714286309e-02 7.525510204081632681e-02 7.908163265306122902e-02 7.780612244897959495e-02 7.270408163265305868e-02 4.365079365079364837e-02 5.456349206349206393e-02 4.761904761904761640e-02 4.861111111111111188e-02 5.257936507936507992e-02 5.753968253968253649e-02 5.555555555555555247e-02 6.051587301587301598e-02 6.051587301587301598e-02 5.739795918367347066e-02 4.591836734693877792e-02 4.719387755102040505e-02 4.719387755102040505e-02 5.229591836734694132e-02 4.719387755102040505e-02 4.591836734693877792e-02 5.229591836734694132e-02 4.591836734693877792e-02 2.281746031746031619e-02 6.349206349206348854e-02 6.845238095238095899e-02 6.349206349206348854e-02 6.646825396825396803e-02 7.242063492063492702e-02 6.547619047619047949e-02 7.837301587301587213e-02 6.349206349206348854e-02 6.944444444444444753e-02 7.936507936507936067e-02 7.539682539682539264e-02 6.746031746031745657e-02 6.547619047619047949e-02 5.079365079365079361e-02 5.000000000000000278e-02 4.523809523809523697e-02 5.476190476190476164e-02 6.587301587301587491e-02 5.317460317460317304e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.662698412698412787e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.662698412698412787e-02 5.555555555555555247e-02 3.650793650793651007e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.460317460317460250e-02 7.698412698412698818e-02 6.269841269841269771e-02 6.428571428571427937e-02 6.984126984126984294e-02 6.190476190476190688e-02 6.746031746031745657e-02 6.349206349206348854e-02 6.507936507936508408e-02 6.269841269841269771e-02 6.984126984126984294e-02 7.460317460317460181e-02 7.142857142857142461e-02 7.698412698412698818e-02 5.000000000000000278e-02 6.168831168831168804e-02 5.584415584415584194e-02 4.350649350649350711e-02 4.740259740259740451e-02 4.610389610389610537e-02 5.000000000000000278e-02 4.841269841269841417e-02 5.317460317460317304e-02 2.987012987012986967e-02 2.532467532467532617e-02 5.844155844155844021e-02 7.662337662337662114e-02 6.038961038961038891e-02 6.298701298701298024e-02 8.290816326530611735e-02 6.363636363636362980e-02 5.974025974025973934e-02 6.103896103896103847e-02 5.779220779220779064e-02 6.168831168831168804e-02 6.038961038961038891e-02 4.675324675324675494e-02 4.415584415584415667e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.064935064935065234e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.333333333333332871e-02 3.928571428571428492e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.571428571428571508e-02 8.571428571428571508e-02 9.285714285714286031e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.761904761904761640e-02 5.476190476190476164e-02 4.523809523809523697e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.857142857142856984e-02 9.047619047619047394e-02 8.809523809523810145e-02 9.285714285714286031e-02 8.809523809523810145e-02 9.761904761904761918e-02 4.761904761904761640e-02 6.632653061224490221e-02 5.272108843537415268e-02 4.931972789115646183e-02 5.952380952380952051e-02 4.285714285714285754e-02 5.238095238095238221e-02 5.714285714285714107e-02 4.285714285714285754e-02 5.714285714285714107e-02 3.061224489795918297e-02 3.061224489795918297e-02 1.700680272108843691e-02 6.972789115646257918e-02 7.823129251700680631e-02 9.013605442176871041e-02 9.013605442176871041e-02 8.333333333333332871e-02 7.993197278911565173e-02 8.333333333333332871e-02 8.843537414965986498e-02 7.823129251700680631e-02 8.673469387755101956e-02 9.013605442176871041e-02 9.013605442176871041e-02 5.484693877551020252e-02 6.887755102040815647e-02 6.377551020408163407e-02 5.612244897959183659e-02 5.994897959183673186e-02 5.357142857142856845e-02 4.974489795918367319e-02 5.867346938775510473e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 5.782312925170068202e-02 5.782312925170068202e-02 4.591836734693877792e-02 4.931972789115646183e-02 4.421768707482993249e-02 5.272108843537415268e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.443877551020407823e-02 3.316326530612245110e-02 3.698979591836734637e-02 1.913265306122449022e-02 1.913265306122449022e-02 6.377551020408163407e-02 6.632653061224490221e-02 8.418367346938775142e-02 7.908163265306122902e-02 8.801020408163265363e-02 7.270408163265305868e-02 9.056122448979592177e-02 8.545918367346938549e-02 8.673469387755101956e-02 7.653061224489796088e-02 8.801020408163265363e-02 8.163265306122448328e-02 8.545918367346938549e-02 5.158730158730158444e-02 6.051587301587301598e-02 5.654761904761904795e-02 6.250000000000000000e-02 5.059523809523809590e-02 5.059523809523809590e-02 5.158730158730158444e-02 5.456349206349206393e-02 5.555555555555555247e-02 5.102040816326530726e-02 4.464285714285714385e-02 4.719387755102040505e-02 4.719387755102040505e-02 4.974489795918367319e-02 4.719387755102040505e-02 4.464285714285714385e-02 4.974489795918367319e-02 4.464285714285714385e-02 2.281746031746031619e-02 7.837301587301587213e-02 7.539682539682539264e-02 8.234126984126984017e-02 8.035714285714286309e-02 7.837301587301587213e-02 8.829365079365079916e-02 6.845238095238095899e-02 8.531746031746031966e-02 9.126984126984126477e-02 8.531746031746031966e-02 8.531746031746031966e-02 5.853174603174603197e-02 8.432539682539683112e-02 5.079365079365079361e-02 5.476190476190476164e-02 5.634920634920635024e-02 4.761904761904761640e-02 6.269841269841269771e-02 4.920634920634920501e-02 4.365079365079364837e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.861111111111111188e-02 3.492063492063492147e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.460317460317460250e-02 6.984126984126984294e-02 5.476190476190476164e-02 7.222222222222221544e-02 7.619047619047619735e-02 6.984126984126984294e-02 5.952380952380952051e-02 7.777777777777777901e-02 5.714285714285714107e-02 5.396825396825397081e-02 6.031746031746031828e-02 6.507936507936508408e-02 6.111111111111110911e-02 6.984126984126984294e-02 4.935064935064935321e-02 6.103896103896103847e-02 4.740259740259740451e-02 6.038961038961038891e-02 6.038961038961038891e-02 5.519480519480519237e-02 5.519480519480519237e-02 4.285714285714285754e-02 4.603174603174603474e-02 2.857142857142857054e-02 2.532467532467532617e-02 5.064935064935065234e-02 6.818181818181817677e-02 7.987012987012986898e-02 7.142857142857142461e-02 7.397959183673469274e-02 6.688311688311687764e-02 6.298701298701298024e-02 5.649350649350649151e-02 5.779220779220779064e-02 6.493506493506492894e-02 5.259740259740259410e-02 4.415584415584415667e-02 4.025974025974025927e-02 4.415584415584415667e-02 4.350649350649350711e-02 4.350649350649350711e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.523809523809523281e-02 4.285714285714285754e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.214285714285714246e-02 1.035714285714285782e-01 1.071428571428571369e-01 4.761904761904761640e-02 5.476190476190476164e-02 5.442176870748299117e-02 6.428571428571427937e-02 5.000000000000000278e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.857142857142856984e-02 9.285714285714286031e-02 1.071428571428571369e-01 8.571428571428571508e-02 9.047619047619047394e-02 1.119047619047619097e-01 5.442176870748299117e-02 7.993197278911565173e-02 5.272108843537415268e-02 5.782312925170068202e-02 6.292517006802721136e-02 4.285714285714285754e-02 5.238095238095238221e-02 5.714285714285714107e-02 4.285714285714285754e-02 5.714285714285714107e-02 3.061224489795918297e-02 3.061224489795918297e-02 1.700680272108843691e-02 6.972789115646257918e-02 8.673469387755101956e-02 8.333333333333332871e-02 9.353741496598640126e-02 9.013605442176871041e-02 7.993197278911565173e-02 7.993197278911565173e-02 9.523809523809523281e-02 8.163265306122448328e-02 1.071428571428571369e-01 9.523809523809523281e-02 8.843537414965986498e-02 6.760204081632653628e-02 6.122448979591836593e-02 7.908163265306122902e-02 5.612244897959183659e-02 6.377551020408163407e-02 5.357142857142856845e-02 5.994897959183673186e-02 6.122448979591836593e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 5.782312925170068202e-02 5.782312925170068202e-02 4.591836734693877792e-02 4.931972789115646183e-02 4.421768707482993249e-02 5.272108843537415268e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.443877551020407823e-02 3.316326530612245110e-02 3.698979591836734637e-02 1.913265306122449022e-02 1.913265306122449022e-02 6.377551020408163407e-02 7.270408163265305868e-02 7.908163265306122902e-02 9.438775510204081010e-02 7.525510204081632681e-02 7.908163265306122902e-02 9.438775510204081010e-02 9.056122448979592177e-02 1.071428571428571369e-01 8.673469387755101956e-02 9.693877551020407823e-02 9.056122448979592177e-02 8.545918367346938549e-02 5.158730158730158444e-02 7.638888888888889506e-02 6.250000000000000000e-02 6.448412698412699096e-02 6.051587301587301598e-02 5.059523809523809590e-02 5.654761904761904795e-02 5.654761904761904795e-02 5.456349206349206393e-02 5.102040816326530726e-02 4.464285714285714385e-02 4.719387755102040505e-02 4.719387755102040505e-02 4.974489795918367319e-02 4.719387755102040505e-02 4.464285714285714385e-02 4.974489795918367319e-02 4.464285714285714385e-02 2.281746031746031619e-02 7.440476190476190410e-02 7.837301587301587213e-02 7.142857142857142461e-02 8.234126984126984017e-02 9.623015873015873523e-02 7.837301587301587213e-02 7.043650793650793607e-02 6.845238095238095899e-02 8.630952380952380820e-02 1.061507936507936484e-01 9.523809523809523281e-02 5.853174603174603197e-02 7.638888888888889506e-02 5.555555555555555247e-02 6.746031746031745657e-02 5.793650793650793884e-02 4.761904761904761640e-02 6.746031746031745657e-02 5.714285714285714107e-02 4.365079365079364837e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.861111111111111188e-02 3.492063492063492147e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.460317460317460250e-02 7.063492063492063378e-02 5.873015873015872967e-02 7.301587301587302015e-02 9.285714285714286031e-02 6.666666666666666574e-02 6.111111111111110911e-02 6.904761904761905211e-02 5.873015873015872967e-02 5.396825396825397081e-02 5.952380952380952051e-02 6.825396825396824740e-02 6.111111111111110911e-02 7.857142857142856984e-02 5.259740259740259410e-02 5.584415584415584194e-02 5.389610389610389324e-02 5.519480519480519237e-02 6.298701298701298024e-02 5.649350649350649151e-02 6.818181818181817677e-02 4.285714285714285754e-02 4.603174603174603474e-02 2.857142857142857054e-02 2.532467532467532617e-02 5.389610389610389324e-02 7.532467532467532201e-02 6.948051948051947591e-02 7.727272727272727071e-02 7.525510204081632681e-02 7.077922077922077504e-02 6.038961038961038891e-02 6.298701298701298024e-02 5.779220779220779064e-02 6.558441558441557850e-02 5.389610389610389324e-02 4.415584415584415667e-02 4.025974025974025927e-02 4.415584415584415667e-02 4.350649350649350711e-02 4.350649350649350711e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 1.000000000000000056e-01 7.857142857142856984e-02 8.928571428571428770e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.421768707482993249e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.095238095238095621e-02 9.523809523809523281e-02 8.333333333333332871e-02 1.119047619047619097e-01 9.523809523809523281e-02 9.761904761904761918e-02 4.421768707482993249e-02 6.122448979591836593e-02 6.122448979591836593e-02 5.102040816326530726e-02 6.632653061224490221e-02 4.523809523809523697e-02 5.476190476190476164e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 7.312925170068027003e-02 7.823129251700680631e-02 1.020408163265306145e-01 9.523809523809523281e-02 9.523809523809523281e-02 7.993197278911565173e-02 9.013605442176871041e-02 9.013605442176871041e-02 7.823129251700680631e-02 8.333333333333332871e-02 9.523809523809523281e-02 1.003401360544217691e-01 4.974489795918367319e-02 8.545918367346938549e-02 5.867346938775510473e-02 5.739795918367347066e-02 6.122448979591836593e-02 5.739795918367347066e-02 4.974489795918367319e-02 6.122448979591836593e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 5.442176870748299117e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 6.632653061224490221e-02 6.632653061224490221e-02 8.801020408163265363e-02 7.525510204081632681e-02 1.071428571428571369e-01 7.142857142857142461e-02 9.693877551020407823e-02 1.007653061224489804e-01 8.545918367346938549e-02 7.525510204081632681e-02 8.928571428571428770e-02 8.801020408163265363e-02 9.056122448979592177e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.654761904761904795e-02 6.547619047619047949e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.654761904761904795e-02 6.250000000000000000e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 4.719387755102040505e-02 2.380952380952380820e-02 7.738095238095238360e-02 7.539682539682539264e-02 9.226190476190476719e-02 8.134920634920635163e-02 7.539682539682539264e-02 1.021825396825396803e-01 6.746031746031745657e-02 1.091269841269841279e-01 1.031746031746031689e-01 8.531746031746031966e-02 8.730158730158729674e-02 6.051587301587301598e-02 9.523809523809523281e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.952380952380952051e-02 5.000000000000000278e-02 6.031746031746031828e-02 5.000000000000000278e-02 4.563492063492063239e-02 5.357142857142856845e-02 4.960317460317460042e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 3.650793650793651007e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 7.142857142857142461e-02 5.555555555555555247e-02 6.746031746031745657e-02 7.063492063492063378e-02 6.904761904761905211e-02 6.111111111111110911e-02 8.174603174603174705e-02 5.793650793650793884e-02 5.555555555555555247e-02 6.111111111111110911e-02 6.666666666666666574e-02 6.190476190476190688e-02 6.825396825396824740e-02 5.324675324675324367e-02 7.012987012987012547e-02 4.675324675324675494e-02 6.883116883116882634e-02 6.363636363636362980e-02 6.103896103896103847e-02 5.584415584415584194e-02 4.365079365079364837e-02 4.682539682539682557e-02 2.987012987012986967e-02 2.727272727272727140e-02 5.129870129870130191e-02 6.688311688311687764e-02 9.155844155844156118e-02 6.818181818181817677e-02 7.525510204081632681e-02 5.974025974025973934e-02 6.233766233766233761e-02 5.584415584415584194e-02 6.038961038961038891e-02 6.103896103896103847e-02 5.324675324675324367e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.415584415584415667e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.214285714285714246e-02 7.857142857142856984e-02 8.928571428571428770e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.421768707482993249e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 9.285714285714286031e-02 7.619047619047619735e-02 8.333333333333332871e-02 8.809523809523810145e-02 8.809523809523810145e-02 9.761904761904761918e-02 4.421768707482993249e-02 6.122448979591836593e-02 5.782312925170068202e-02 5.102040816326530726e-02 5.782312925170068202e-02 4.523809523809523697e-02 5.952380952380952051e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 8.163265306122448328e-02 8.163265306122448328e-02 7.142857142857142461e-02 7.653061224489796088e-02 8.163265306122448328e-02 9.863945578231292366e-02 9.353741496598640126e-02 7.993197278911565173e-02 9.013605442176871041e-02 8.333333333333332871e-02 9.353741496598640126e-02 9.013605442176871041e-02 4.974489795918367319e-02 5.994897959183673186e-02 5.867346938775510473e-02 6.760204081632653628e-02 5.102040816326530726e-02 6.250000000000000000e-02 4.974489795918367319e-02 6.632653061224490221e-02 6.122448979591836593e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.802721088435374763e-02 6.802721088435374763e-02 5.102040816326530726e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.122448979591836593e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.826530612244898044e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.525510204081632681e-02 7.908163265306122902e-02 7.270408163265305868e-02 7.908163265306122902e-02 7.142857142857142461e-02 9.183673469387755584e-02 7.908163265306122902e-02 8.545918367346938549e-02 8.545918367346938549e-02 8.035714285714286309e-02 8.418367346938775142e-02 8.290816326530611735e-02 7.525510204081632681e-02 4.563492063492063239e-02 5.555555555555555247e-02 4.861111111111111188e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.952380952380952051e-02 6.051587301587301598e-02 6.250000000000000000e-02 6.150793650793650452e-02 5.994897959183673186e-02 4.974489795918367319e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.229591836734694132e-02 4.974489795918367319e-02 5.739795918367347066e-02 4.974489795918367319e-02 2.380952380952380820e-02 7.142857142857142461e-02 7.738095238095238360e-02 7.043650793650793607e-02 7.242063492063492702e-02 7.936507936507936067e-02 7.043650793650793607e-02 8.234126984126984017e-02 6.944444444444444753e-02 7.539682539682539264e-02 8.531746031746031966e-02 8.035714285714286309e-02 6.944444444444444753e-02 6.845238095238095899e-02 5.555555555555555247e-02 5.158730158730158444e-02 4.682539682539682557e-02 5.634920634920635024e-02 7.301587301587302015e-02 5.793650793650793884e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.158730158730158444e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.753968253968253649e-02 3.968253968253968034e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 8.253968253968253788e-02 6.507936507936508408e-02 7.380952380952381098e-02 7.857142857142856984e-02 6.587301587301587491e-02 6.984126984126984294e-02 7.222222222222221544e-02 6.666666666666666574e-02 6.428571428571427937e-02 7.380952380952381098e-02 7.539682539682539264e-02 7.539682539682539264e-02 8.412698412698413342e-02 5.454545454545454281e-02 6.363636363636362980e-02 5.714285714285714107e-02 4.545454545454545581e-02 4.935064935064935321e-02 4.805194805194805407e-02 5.194805194805195148e-02 5.000000000000000278e-02 5.476190476190476164e-02 3.246753246753246447e-02 2.727272727272727140e-02 6.038961038961038891e-02 8.506493506493506551e-02 6.298701298701298024e-02 6.818181818181817677e-02 8.545918367346938549e-02 7.402597402597402287e-02 6.103896103896103847e-02 6.428571428571427937e-02 6.038961038961038891e-02 6.753246753246752720e-02 6.168831168831168804e-02 5.194805194805195148e-02 4.675324675324675494e-02 5.194805194805195148e-02 5.064935064935065234e-02 5.194805194805195148e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.071428571428571369e-01 4.642857142857143016e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.928571428571428770e-02 1.214285714285714274e-01 1.250000000000000000e-01 5.238095238095238221e-02 6.190476190476190688e-02 6.122448979591836593e-02 7.380952380952381098e-02 5.476190476190476164e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.857142857142856984e-02 1.142857142857142821e-01 1.285714285714285587e-01 9.523809523809523281e-02 1.000000000000000056e-01 1.333333333333333315e-01 6.122448979591836593e-02 9.523809523809523281e-02 5.782312925170068202e-02 6.632653061224490221e-02 7.142857142857142461e-02 4.523809523809523697e-02 5.476190476190476164e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 7.142857142857142461e-02 8.503401360544217413e-02 1.054421768707483054e-01 1.207482993197278864e-01 9.523809523809523281e-02 7.823129251700680631e-02 8.163265306122448328e-02 1.156462585034013640e-01 8.503401360544217413e-02 1.309523809523809590e-01 1.071428571428571369e-01 9.693877551020407823e-02 8.035714285714286309e-02 6.760204081632653628e-02 9.566326530612244416e-02 5.484693877551020252e-02 7.780612244897959495e-02 5.484693877551020252e-02 7.015306122448979054e-02 6.505102040816326814e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 5.442176870748299117e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 6.505102040816326814e-02 7.397959183673469274e-02 9.056122448979592177e-02 9.438775510204081010e-02 9.693877551020407823e-02 7.908163265306122902e-02 1.250000000000000000e-01 9.566326530612244416e-02 1.326530612244898044e-01 8.928571428571428770e-02 1.173469387755102095e-01 9.693877551020407823e-02 1.045918367346938826e-01 5.753968253968253649e-02 9.325396825396825573e-02 7.638888888888889506e-02 8.134920634920635163e-02 6.547619047619047949e-02 5.158730158730158444e-02 5.853174603174603197e-02 5.952380952380952051e-02 5.753968253968253649e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 4.719387755102040505e-02 2.380952380952380820e-02 7.936507936507936067e-02 7.936507936507936067e-02 8.432539682539683112e-02 9.027777777777777624e-02 9.722222222222222376e-02 1.051587301587301598e-01 7.142857142857142461e-02 8.928571428571428770e-02 1.210317460317460320e-01 1.329365079365079361e-01 1.200396825396825434e-01 5.952380952380952051e-02 9.821428571428571230e-02 6.507936507936508408e-02 8.174603174603174705e-02 7.222222222222221544e-02 4.841269841269841417e-02 7.063492063492063378e-02 5.952380952380952051e-02 4.563492063492063239e-02 5.357142857142856845e-02 4.960317460317460042e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 3.650793650793651007e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 7.222222222222221544e-02 6.031746031746031828e-02 7.063492063492063378e-02 8.809523809523810145e-02 7.063492063492063378e-02 6.428571428571427937e-02 7.698412698412698818e-02 6.111111111111110911e-02 5.476190476190476164e-02 5.873015873015872967e-02 7.380952380952381098e-02 6.111111111111110911e-02 8.412698412698413342e-02 5.974025974025973934e-02 5.844155844155844021e-02 5.454545454545454281e-02 7.142857142857142461e-02 8.311688311688311681e-02 6.623376623376622807e-02 7.532467532467532201e-02 4.365079365079364837e-02 4.682539682539682557e-02 2.987012987012986967e-02 2.727272727272727140e-02 5.519480519480519237e-02 7.987012987012986898e-02 9.415584415584415945e-02 7.987012987012986898e-02 7.780612244897959495e-02 6.363636363636362980e-02 6.363636363636362980e-02 6.558441558441557850e-02 6.168831168831168804e-02 6.363636363636362980e-02 5.584415584415584194e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.415584415584415667e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 1.071428571428571369e-01 7.857142857142856984e-02 9.642857142857143293e-02 4.285714285714285754e-02 5.714285714285714107e-02 4.421768707482993249e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.714285714285714107e-02 3.571428571428571230e-02 5.714285714285714107e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 9.047619047619047394e-02 9.285714285714286031e-02 8.809523809523810145e-02 1.261904761904761862e-01 1.047619047619047644e-01 1.119047619047619097e-01 4.421768707482993249e-02 6.462585034013605678e-02 6.462585034013605678e-02 5.102040816326530726e-02 6.972789115646257918e-02 5.000000000000000278e-02 6.428571428571427937e-02 7.857142857142856984e-02 5.000000000000000278e-02 7.857142857142856984e-02 3.571428571428571230e-02 3.571428571428571230e-02 1.700680272108843691e-02 8.333333333333332871e-02 9.013605442176871041e-02 9.523809523809523281e-02 9.183673469387755584e-02 1.156462585034013640e-01 9.353741496598640126e-02 1.037414965986394599e-01 9.183673469387755584e-02 8.843537414965986498e-02 9.013605442176871041e-02 1.088435374149659823e-01 1.139455782312925186e-01 4.974489795918367319e-02 9.311224489795918990e-02 6.122448979591836593e-02 6.250000000000000000e-02 5.994897959183673186e-02 6.250000000000000000e-02 4.974489795918367319e-02 6.632653061224490221e-02 6.462585034013605678e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 6.462585034013605678e-02 7.482993197278911546e-02 7.482993197278911546e-02 5.442176870748299117e-02 6.462585034013605678e-02 5.442176870748299117e-02 6.462585034013605678e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.081632653061224164e-02 4.846938775510203912e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.653061224489796088e-02 7.780612244897959495e-02 8.545918367346938549e-02 9.056122448979592177e-02 1.007653061224489804e-01 8.545918367346938549e-02 9.566326530612244416e-02 1.275510204081632681e-01 9.566326530612244416e-02 8.673469387755101956e-02 9.566326530612244416e-02 1.058673469387755167e-01 9.056122448979592177e-02 5.952380952380952051e-02 5.753968253968253649e-02 5.555555555555555247e-02 6.150793650793650452e-02 6.150793650793650452e-02 5.952380952380952051e-02 6.051587301587301598e-02 6.250000000000000000e-02 6.845238095238095899e-02 6.250000000000000000e-02 5.484693877551020252e-02 6.250000000000000000e-02 6.250000000000000000e-02 6.250000000000000000e-02 6.250000000000000000e-02 5.484693877551020252e-02 6.250000000000000000e-02 5.484693877551020252e-02 2.579365079365079222e-02 7.738095238095238360e-02 8.333333333333332871e-02 8.928571428571428770e-02 8.432539682539683112e-02 9.424603174603174427e-02 9.325396825396825573e-02 7.936507936507936067e-02 1.011904761904761918e-01 9.722222222222222376e-02 9.722222222222222376e-02 9.126984126984126477e-02 7.043650793650793607e-02 8.730158730158729674e-02 6.507936507936508408e-02 5.476190476190476164e-02 5.793650793650793884e-02 5.634920634920635024e-02 6.825396825396824740e-02 5.793650793650793884e-02 5.357142857142856845e-02 6.547619047619047949e-02 5.952380952380952051e-02 6.547619047619047949e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.952380952380952051e-02 5.952380952380952051e-02 4.285714285714285754e-02 5.238095238095238221e-02 5.238095238095238221e-02 2.936507936507936484e-02 8.412698412698413342e-02 6.587301587301587491e-02 7.539682539682539264e-02 8.888888888888889228e-02 7.063492063492063378e-02 7.222222222222221544e-02 8.253968253968253788e-02 6.746031746031745657e-02 6.507936507936508408e-02 7.222222222222221544e-02 7.936507936507936067e-02 7.460317460317460181e-02 8.174603174603174705e-02 6.233766233766233761e-02 8.051948051948051854e-02 5.454545454545454281e-02 6.233766233766233761e-02 5.974025974025973934e-02 6.233766233766233761e-02 6.753246753246752720e-02 5.158730158730158444e-02 5.634920634920635024e-02 3.506493506493506274e-02 3.116883116883116880e-02 6.103896103896103847e-02 8.181818181818181768e-02 7.857142857142856984e-02 7.467532467532467244e-02 8.801020408163265363e-02 7.272727272727272374e-02 6.493506493506492894e-02 6.753246753246752720e-02 6.688311688311687764e-02 6.883116883116882634e-02 6.233766233766233761e-02 5.714285714285714107e-02 4.935064935064935321e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.324675324675324367e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.190476190476190410e-01 5.000000000000000278e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.571428571428571508e-02 1.464285714285714357e-01 1.392857142857142905e-01 5.714285714285714107e-02 6.666666666666666574e-02 6.802721088435374763e-02 8.333333333333332871e-02 5.952380952380952051e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.857142857142856984e-02 1.190476190476190410e-01 1.571428571428571397e-01 8.809523809523810145e-02 1.023809523809523780e-01 1.476190476190476220e-01 6.802721088435374763e-02 1.088435374149659823e-01 5.782312925170068202e-02 7.823129251700680631e-02 7.482993197278911546e-02 4.523809523809523697e-02 5.476190476190476164e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 7.142857142857142461e-02 9.183673469387755584e-02 1.003401360544217691e-01 1.275510204081632681e-01 9.863945578231292366e-02 7.823129251700680631e-02 7.823129251700680631e-02 1.275510204081632681e-01 8.843537414965986498e-02 1.649659863945578120e-01 1.122448979591836732e-01 9.523809523809523281e-02 9.821428571428571230e-02 5.994897959183673186e-02 1.160714285714285754e-01 5.484693877551020252e-02 8.290816326530611735e-02 5.484693877551020252e-02 8.545918367346938549e-02 6.760204081632653628e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 5.442176870748299117e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 6.505102040816326814e-02 8.035714285714286309e-02 8.673469387755101956e-02 1.071428571428571369e-01 8.545918367346938549e-02 8.545918367346938549e-02 1.326530612244898044e-01 9.566326530612244416e-02 1.683673469387755028e-01 1.007653061224489804e-01 1.326530612244898044e-01 1.058673469387755167e-01 1.071428571428571369e-01 5.753968253968253649e-02 1.170634920634920639e-01 8.531746031746031966e-02 8.531746031746031966e-02 7.539682539682539264e-02 5.158730158730158444e-02 6.349206349206348854e-02 6.150793650793650452e-02 5.654761904761904795e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 4.719387755102040505e-02 2.380952380952380820e-02 7.638888888888889506e-02 8.035714285714286309e-02 7.539682539682539264e-02 9.226190476190476719e-02 1.121031746031746074e-01 9.623015873015873523e-02 7.341269841269841556e-02 7.440476190476190410e-02 1.160714285714285754e-01 1.716269841269841279e-01 1.398809523809523836e-01 5.952380952380952051e-02 9.226190476190476719e-02 6.984126984126984294e-02 1.007936507936507964e-01 7.539682539682539264e-02 4.841269841269841417e-02 7.539682539682539264e-02 6.746031746031745657e-02 4.563492063492063239e-02 5.357142857142856845e-02 4.960317460317460042e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 3.650793650793651007e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 7.301587301587302015e-02 6.428571428571427937e-02 7.142857142857142461e-02 1.000000000000000056e-01 6.825396825396824740e-02 6.587301587301587491e-02 7.063492063492063378e-02 6.269841269841269771e-02 5.476190476190476164e-02 5.793650793650793884e-02 7.698412698412698818e-02 6.111111111111110911e-02 9.603174603174603752e-02 6.298701298701298024e-02 5.324675324675324367e-02 6.103896103896103847e-02 6.753246753246752720e-02 8.831168831168831335e-02 6.753246753246752720e-02 8.831168831168831335e-02 4.365079365079364837e-02 4.682539682539682557e-02 2.987012987012986967e-02 2.727272727272727140e-02 5.844155844155844021e-02 8.961038961038961248e-02 8.636363636363636465e-02 8.636363636363636465e-02 7.908163265306122902e-02 6.688311688311687764e-02 6.168831168831168804e-02 7.207792207792207417e-02 6.168831168831168804e-02 6.428571428571427937e-02 5.714285714285714107e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.415584415584415667e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.928571428571428770e-02 4.285714285714285754e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.571428571428571508e-02 9.642857142857143293e-02 1.000000000000000056e-01 4.761904761904761640e-02 5.476190476190476164e-02 5.102040816326530726e-02 5.952380952380952051e-02 5.476190476190476164e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.333333333333332871e-02 8.809523809523810145e-02 1.000000000000000056e-01 8.809523809523810145e-02 9.285714285714286031e-02 1.047619047619047644e-01 5.102040816326530726e-02 7.312925170068027003e-02 5.612244897959183659e-02 5.952380952380952051e-02 6.462585034013605678e-02 4.285714285714285754e-02 5.476190476190476164e-02 5.714285714285714107e-02 4.285714285714285754e-02 5.714285714285714107e-02 3.061224489795918297e-02 3.061224489795918297e-02 1.700680272108843691e-02 7.653061224489796088e-02 8.503401360544217413e-02 7.993197278911565173e-02 8.843537414965986498e-02 8.673469387755101956e-02 8.503401360544217413e-02 8.503401360544217413e-02 9.183673469387755584e-02 8.673469387755101956e-02 1.003401360544217691e-01 9.693877551020407823e-02 9.183673469387755584e-02 6.250000000000000000e-02 6.377551020408163407e-02 7.270408163265305868e-02 5.994897959183673186e-02 5.994897959183673186e-02 5.739795918367347066e-02 5.994897959183673186e-02 6.377551020408163407e-02 5.782312925170068202e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 4.931972789115646183e-02 4.421768707482993249e-02 5.782312925170068202e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.698979591836734637e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.142857142857142461e-02 7.525510204081632681e-02 7.653061224489796088e-02 8.673469387755101956e-02 7.525510204081632681e-02 8.035714285714286309e-02 8.928571428571428770e-02 8.673469387755101956e-02 1.007653061224489804e-01 8.673469387755101956e-02 9.438775510204081010e-02 8.928571428571428770e-02 8.545918367346938549e-02 4.960317460317460042e-02 7.043650793650793607e-02 5.853174603174603197e-02 6.051587301587301598e-02 5.853174603174603197e-02 5.555555555555555247e-02 5.753968253968253649e-02 6.051587301587301598e-02 6.051587301587301598e-02 5.739795918367347066e-02 4.591836734693877792e-02 4.719387755102040505e-02 4.719387755102040505e-02 5.229591836734694132e-02 4.719387755102040505e-02 4.591836734693877792e-02 5.229591836734694132e-02 4.591836734693877792e-02 2.281746031746031619e-02 7.242063492063492702e-02 7.539682539682539264e-02 7.142857142857142461e-02 7.837301587301587213e-02 8.730158730158729674e-02 7.638888888888889506e-02 7.837301587301587213e-02 7.043650793650793607e-02 8.234126984126984017e-02 1.001984126984127033e-01 9.226190476190476719e-02 6.646825396825396803e-02 7.638888888888889506e-02 5.555555555555555247e-02 6.269841269841269771e-02 5.476190476190476164e-02 5.317460317460317304e-02 7.063492063492063378e-02 5.634920634920635024e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.662698412698412787e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.662698412698412787e-02 5.555555555555555247e-02 3.650793650793651007e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.460317460317460250e-02 8.015873015873016538e-02 6.349206349206348854e-02 7.063492063492063378e-02 8.412698412698413342e-02 6.666666666666666574e-02 6.825396825396824740e-02 6.904761904761905211e-02 6.587301587301587491e-02 6.190476190476190688e-02 6.825396825396824740e-02 7.460317460317460181e-02 7.063492063492063378e-02 8.253968253968253788e-02 5.389610389610389324e-02 6.168831168831168804e-02 5.454545454545454281e-02 5.259740259740259410e-02 5.909090909090908977e-02 5.259740259740259410e-02 6.168831168831168804e-02 4.841269841269841417e-02 5.317460317460317304e-02 2.987012987012986967e-02 2.532467532467532617e-02 5.909090909090908977e-02 8.051948051948051854e-02 7.077922077922077504e-02 7.467532467532467244e-02 8.418367346938775142e-02 6.818181818181817677e-02 6.168831168831168804e-02 6.428571428571427937e-02 6.038961038961038891e-02 6.493506493506492894e-02 6.103896103896103847e-02 4.675324675324675494e-02 4.415584415584415667e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.064935064935065234e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.071428571428571369e-01 4.642857142857143016e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.928571428571428770e-02 1.214285714285714274e-01 1.250000000000000000e-01 5.238095238095238221e-02 6.190476190476190688e-02 6.122448979591836593e-02 7.380952380952381098e-02 5.476190476190476164e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.333333333333332871e-02 1.095238095238095233e-01 1.285714285714285587e-01 9.523809523809523281e-02 1.047619047619047644e-01 1.333333333333333315e-01 6.122448979591836593e-02 9.523809523809523281e-02 5.782312925170068202e-02 6.632653061224490221e-02 7.142857142857142461e-02 4.523809523809523697e-02 5.952380952380952051e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 7.823129251700680631e-02 8.843537414965986498e-02 9.863945578231292366e-02 1.139455782312925186e-01 9.523809523809523281e-02 8.503401360544217413e-02 8.843537414965986498e-02 1.156462585034013640e-01 9.183673469387755584e-02 1.309523809523809590e-01 1.139455782312925186e-01 1.037414965986394599e-01 8.035714285714286309e-02 6.760204081632653628e-02 9.566326530612244416e-02 5.739795918367347066e-02 7.525510204081632681e-02 5.739795918367347066e-02 7.015306122448979054e-02 6.760204081632653628e-02 6.122448979591836593e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.802721088435374763e-02 6.802721088435374763e-02 5.102040816326530726e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.122448979591836593e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.826530612244898044e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.270408163265305868e-02 7.908163265306122902e-02 8.801020408163265363e-02 9.693877551020407823e-02 8.928571428571428770e-02 8.418367346938775142e-02 1.173469387755102095e-01 9.566326530612244416e-02 1.326530612244898044e-01 9.438775510204081010e-02 1.198979591836734637e-01 1.020408163265306145e-01 1.045918367346938826e-01 5.753968253968253649e-02 9.325396825396825573e-02 7.440476190476190410e-02 7.738095238095238360e-02 6.547619047619047949e-02 5.555555555555555247e-02 6.051587301587301598e-02 6.349206349206348854e-02 6.150793650793650452e-02 5.994897959183673186e-02 4.974489795918367319e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.229591836734694132e-02 4.974489795918367319e-02 5.739795918367347066e-02 4.974489795918367319e-02 2.380952380952380820e-02 7.936507936507936067e-02 8.134920634920635163e-02 8.035714285714286309e-02 9.027777777777777624e-02 9.920634920634920084e-02 9.722222222222222376e-02 8.134920634920635163e-02 8.134920634920635163e-02 1.091269841269841279e-01 1.329365079365079361e-01 1.200396825396825434e-01 6.746031746031745657e-02 9.424603174603174427e-02 6.507936507936508408e-02 8.174603174603174705e-02 6.904761904761905211e-02 5.317460317460317304e-02 7.539682539682539264e-02 6.111111111111110911e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.158730158730158444e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.753968253968253649e-02 3.968253968253968034e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 8.174603174603174705e-02 6.666666666666666574e-02 7.380952380952381098e-02 9.126984126984126477e-02 7.222222222222221544e-02 7.222222222222221544e-02 7.539682539682539264e-02 6.904761904761905211e-02 6.269841269841269771e-02 6.825396825396824740e-02 8.174603174603174705e-02 7.063492063492063378e-02 9.047619047619047394e-02 6.233766233766233761e-02 6.363636363636362980e-02 5.714285714285714107e-02 6.623376623376622807e-02 7.792207792207792028e-02 6.363636363636362980e-02 7.532467532467532201e-02 5.000000000000000278e-02 5.476190476190476164e-02 3.246753246753246447e-02 2.727272727272727140e-02 6.168831168831168804e-02 8.636363636363636465e-02 8.896103896103896291e-02 8.246753246753246724e-02 8.801020408163265363e-02 6.883116883116882634e-02 6.623376623376622807e-02 7.077922077922077504e-02 6.558441558441557850e-02 6.753246753246752720e-02 6.363636363636362980e-02 5.194805194805195148e-02 4.675324675324675494e-02 5.194805194805195148e-02 5.064935064935065234e-02 5.194805194805195148e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.928571428571428770e-02 4.285714285714285754e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 9.642857142857143293e-02 9.642857142857143293e-02 1.035714285714285782e-01 4.761904761904761640e-02 5.714285714285714107e-02 5.102040816326530726e-02 5.952380952380952051e-02 5.476190476190476164e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.571428571428571508e-02 9.523809523809523281e-02 1.023809523809523780e-01 1.047619047619047644e-01 1.023809523809523780e-01 1.119047619047619097e-01 5.102040816326530726e-02 7.482993197278911546e-02 6.122448979591836593e-02 5.952380952380952051e-02 6.972789115646257918e-02 4.523809523809523697e-02 5.952380952380952051e-02 6.428571428571427937e-02 4.523809523809523697e-02 6.428571428571427937e-02 3.231292517006802839e-02 3.231292517006802839e-02 1.700680272108843691e-02 7.993197278911565173e-02 8.843537414965986498e-02 9.013605442176871041e-02 9.523809523809523281e-02 1.020408163265306145e-01 8.673469387755101956e-02 9.353741496598640126e-02 9.863945578231292366e-02 8.843537414965986498e-02 1.037414965986394599e-01 1.071428571428571369e-01 1.054421768707483054e-01 6.250000000000000000e-02 7.780612244897959495e-02 7.397959183673469274e-02 5.994897959183673186e-02 6.377551020408163407e-02 5.994897959183673186e-02 5.994897959183673186e-02 6.632653061224490221e-02 6.122448979591836593e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.802721088435374763e-02 6.802721088435374763e-02 5.102040816326530726e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.122448979591836593e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.826530612244898044e-02 3.571428571428571230e-02 4.081632653061224164e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.397959183673469274e-02 7.780612244897959495e-02 8.163265306122448328e-02 9.056122448979592177e-02 8.801020408163265363e-02 8.290816326530611735e-02 9.693877551020407823e-02 1.058673469387755167e-01 1.058673469387755167e-01 8.928571428571428770e-02 1.020408163265306145e-01 1.020408163265306145e-01 9.311224489795918990e-02 5.555555555555555247e-02 7.142857142857142461e-02 6.150793650793650452e-02 6.547619047619047949e-02 6.349206349206348854e-02 5.753968253968253649e-02 6.051587301587301598e-02 6.250000000000000000e-02 6.547619047619047949e-02 5.994897959183673186e-02 4.974489795918367319e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.229591836734694132e-02 4.974489795918367319e-02 5.739795918367347066e-02 4.974489795918367319e-02 2.380952380952380820e-02 7.440476190476190410e-02 7.837301587301587213e-02 7.936507936507936067e-02 8.333333333333332871e-02 9.226190476190476719e-02 8.730158730158729674e-02 7.936507936507936067e-02 8.432539682539683112e-02 9.226190476190476719e-02 1.061507936507936484e-01 9.920634920634920084e-02 6.845238095238095899e-02 8.531746031746031966e-02 6.031746031746031828e-02 6.428571428571427937e-02 5.952380952380952051e-02 5.476190476190476164e-02 6.984126984126984294e-02 5.952380952380952051e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.158730158730158444e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.753968253968253649e-02 3.968253968253968034e-02 4.285714285714285754e-02 4.285714285714285754e-02 2.619047619047619110e-02 8.174603174603174705e-02 6.587301587301587491e-02 7.142857142857142461e-02 8.571428571428571508e-02 6.825396825396824740e-02 7.063492063492063378e-02 7.380952380952381098e-02 6.746031746031745657e-02 6.349206349206348854e-02 6.984126984126984294e-02 7.777777777777777901e-02 7.142857142857142461e-02 8.333333333333332871e-02 5.909090909090908977e-02 7.012987012987012547e-02 5.584415584415584194e-02 5.974025974025973934e-02 6.363636363636362980e-02 5.974025974025973934e-02 6.883116883116882634e-02 5.000000000000000278e-02 5.476190476190476164e-02 3.246753246753246447e-02 2.727272727272727140e-02 6.103896103896103847e-02 8.051948051948051854e-02 7.857142857142856984e-02 7.597402597402597157e-02 8.673469387755101956e-02 6.818181818181817677e-02 6.298701298701298024e-02 6.753246753246752720e-02 6.428571428571427937e-02 6.558441558441557850e-02 6.233766233766233761e-02 5.194805194805195148e-02 4.675324675324675494e-02 5.194805194805195148e-02 5.064935064935065234e-02 5.194805194805195148e-02
-5.952380952380952051e-02 2.380952380952380820e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.523809523809523281e-02 4.285714285714285754e-02 3.571428571428571230e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.928571428571428770e-02 1.035714285714285782e-01 1.071428571428571369e-01 4.761904761904761640e-02 5.476190476190476164e-02 5.442176870748299117e-02 6.428571428571427937e-02 5.000000000000000278e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.857142857142856984e-02 1.023809523809523780e-01 1.071428571428571369e-01 9.523809523809523281e-02 9.761904761904761918e-02 1.119047619047619097e-01 5.442176870748299117e-02 7.993197278911565173e-02 5.612244897959183659e-02 5.782312925170068202e-02 6.632653061224490221e-02 4.285714285714285754e-02 5.476190476190476164e-02 5.714285714285714107e-02 4.285714285714285754e-02 5.714285714285714107e-02 3.061224489795918297e-02 3.061224489795918297e-02 1.700680272108843691e-02 7.482993197278911546e-02 8.163265306122448328e-02 9.693877551020407823e-02 1.037414965986394599e-01 9.013605442176871041e-02 7.823129251700680631e-02 8.503401360544217413e-02 1.037414965986394599e-01 8.503401360544217413e-02 1.071428571428571369e-01 1.020408163265306145e-01 9.863945578231292366e-02 6.760204081632653628e-02 7.142857142857142461e-02 7.908163265306122902e-02 5.484693877551020252e-02 6.887755102040815647e-02 5.484693877551020252e-02 5.994897959183673186e-02 6.250000000000000000e-02 5.782312925170068202e-02 4.761904761904761640e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 4.931972789115646183e-02 4.421768707482993249e-02 5.782312925170068202e-02 3.316326530612245110e-02 3.316326530612245110e-02 3.571428571428571230e-02 3.316326530612245110e-02 3.698979591836734637e-02 1.913265306122449022e-02 1.913265306122449022e-02 7.015306122448979054e-02 7.270408163265305868e-02 8.545918367346938549e-02 8.545918367346938549e-02 9.056122448979592177e-02 7.525510204081632681e-02 1.045918367346938826e-01 9.056122448979592177e-02 1.071428571428571369e-01 8.545918367346938549e-02 1.045918367346938826e-01 9.311224489795918990e-02 9.948979591836734637e-02 5.555555555555555247e-02 7.638888888888889506e-02 6.646825396825396803e-02 7.242063492063492702e-02 5.853174603174603197e-02 5.357142857142856845e-02 5.555555555555555247e-02 5.952380952380952051e-02 6.051587301587301598e-02 5.739795918367347066e-02 4.591836734693877792e-02 4.719387755102040505e-02 4.719387755102040505e-02 5.229591836734694132e-02 4.719387755102040505e-02 4.591836734693877792e-02 5.229591836734694132e-02 4.591836734693877792e-02 2.281746031746031619e-02 7.638888888888889506e-02 7.539682539682539264e-02 8.035714285714286309e-02 8.432539682539683112e-02 8.531746031746031966e-02 9.523809523809523281e-02 7.638888888888889506e-02 8.432539682539683112e-02 1.021825396825396803e-01 1.061507936507936484e-01 1.031746031746031689e-01 6.547619047619047949e-02 9.325396825396825573e-02 5.555555555555555247e-02 6.746031746031745657e-02 6.428571428571427937e-02 5.158730158730158444e-02 6.825396825396824740e-02 5.476190476190476164e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.662698412698412787e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.662698412698412787e-02 5.555555555555555247e-02 3.650793650793651007e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.460317460317460250e-02 7.857142857142856984e-02 6.269841269841269771e-02 6.825396825396824740e-02 7.857142857142856984e-02 6.984126984126984294e-02 6.825396825396824740e-02 7.301587301587302015e-02 6.587301587301587491e-02 6.111111111111110911e-02 6.666666666666666574e-02 7.539682539682539264e-02 6.825396825396824740e-02 8.095238095238095621e-02 5.454545454545454281e-02 6.558441558441557850e-02 5.194805194805195148e-02 6.558441558441557850e-02 7.077922077922077504e-02 6.038961038961038891e-02 6.558441558441557850e-02 4.841269841269841417e-02 5.317460317460317304e-02 2.987012987012986967e-02 2.532467532467532617e-02 5.844155844155844021e-02 7.792207792207792028e-02 8.896103896103896291e-02 7.597402597402597157e-02 8.418367346938775142e-02 6.168831168831168804e-02 6.428571428571427937e-02 6.493506493506492894e-02 6.363636363636362980e-02 6.298701298701298024e-02 6.103896103896103847e-02 4.675324675324675494e-02 4.415584415584415667e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.064935064935065234e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.333333333333332871e-02 3.888888888888888951e-02 7.222222222222221544e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.222222222222221544e-02 7.222222222222221544e-02 7.777777777777777901e-02 3.703703703703703498e-02 4.074074074074074403e-02 6.746031746031745657e-02 7.037037037037037479e-02 5.185185185185185036e-02 2.777777777777777624e-02 2.222222222222222307e-02 2.777777777777777624e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 5.555555555555555247e-02 6.666666666666666574e-02 6.666666666666666574e-02 7.037037037037037479e-02 6.666666666666666574e-02 7.407407407407406996e-02 6.746031746031745657e-02 6.878306878306877925e-02 5.026455026455026176e-02 4.761904761904761640e-02 5.555555555555555247e-02 2.592592592592592518e-02 2.962962962962963076e-02 3.333333333333333287e-02 2.592592592592592518e-02 3.333333333333333287e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.058201058201058142e-02 4.761904761904761640e-02 5.291005291005290712e-02 6.084656084656084318e-02 6.084656084656084318e-02 6.084656084656084318e-02 5.026455026455026176e-02 5.555555555555555247e-02 6.084656084656084318e-02 5.291005291005290712e-02 6.084656084656084318e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.250000000000000000e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.960317460317460042e-02 6.150793650793650452e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.761904761904761640e-02 2.910052910052909891e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.910052910052909891e-02 3.174603174603174427e-02 3.174603174603174427e-02 2.645502645502645356e-02 2.910052910052909891e-02 2.645502645502645356e-02 2.910052910052909891e-02 1.984126984126984017e-02 1.984126984126984017e-02 1.984126984126984017e-02 1.984126984126984017e-02 2.182539682539682419e-02 1.190476190476190410e-02 1.190476190476190410e-02 4.166666666666666435e-02 4.365079365079364837e-02 5.158730158730158444e-02 5.158730158730158444e-02 5.555555555555555247e-02 4.563492063492063239e-02 5.753968253968253649e-02 5.952380952380952051e-02 5.753968253968253649e-02 4.960317460317460042e-02 5.753968253968253649e-02 5.555555555555555247e-02 5.555555555555555247e-02 6.481481481481481122e-02 5.941358024691358042e-02 6.095679012345679021e-02 5.941358024691358042e-02 4.166666666666666435e-02 3.858024691358024477e-02 4.012345679012345456e-02 4.166666666666666435e-02 4.320987654320987414e-02 2.777777777777777624e-02 2.579365079365079222e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.579365079365079222e-02 2.777777777777777624e-02 2.579365079365079222e-02 1.388888888888888812e-02 4.475308641975308394e-02 4.475308641975308394e-02 4.783950617283950352e-02 4.783950617283950352e-02 4.938271604938271331e-02 5.246913580246913289e-02 4.166666666666666435e-02 5.092592592592592310e-02 5.401234567901234268e-02 5.401234567901234268e-02 5.246913580246913289e-02 3.703703703703703498e-02 5.092592592592592310e-02 3.456790123456789793e-02 5.802469135802468952e-02 5.925925925925926152e-02 3.827160493827160698e-02 4.320987654320987414e-02 3.703703703703703498e-02 2.469135802469135665e-02 2.777777777777777624e-02 2.623456790123456644e-02 2.777777777777777624e-02 2.469135802469135665e-02 2.469135802469135665e-02 2.623456790123456644e-02 2.623456790123456644e-02 1.975308641975308602e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.481481481481481538e-02 4.197530864197530909e-02 3.456790123456789793e-02 3.950617283950617203e-02 4.444444444444444614e-02 3.950617283950617203e-02 3.703703703703703498e-02 4.197530864197530909e-02 3.580246913580246992e-02 3.333333333333333287e-02 3.580246913580246992e-02 4.074074074074074403e-02 3.580246913580246992e-02 4.197530864197530909e-02 3.232323232323232320e-02 4.545454545454545581e-02 4.090909090909090884e-02 5.555555555555555247e-02 5.454545454545454281e-02 3.939393939393939087e-02 4.141414141414141714e-02 2.345679012345679160e-02 2.469135802469135665e-02 1.616161616161616160e-02 1.515151515151515194e-02 3.131313131313131354e-02 3.939393939393939087e-02 4.343434343434343647e-02 3.939393939393939087e-02 4.365079365079364837e-02 3.535353535353535220e-02 3.535353535353535220e-02 3.535353535353535220e-02 3.535353535353535220e-02 3.535353535353535220e-02 3.232323232323232320e-02 2.424242424242424240e-02 2.222222222222222307e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.323232323232323274e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.203703703703703637e-01 5.000000000000000278e-02 7.222222222222221544e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 6.944444444444444753e-02 1.277777777777777679e-01 1.194444444444444503e-01 5.185185185185185036e-02 5.370370370370370489e-02 8.862433862433861942e-02 1.000000000000000056e-01 6.666666666666666574e-02 2.500000000000000139e-02 2.222222222222222307e-02 2.500000000000000139e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 5.555555555555555247e-02 9.444444444444444198e-02 1.240740740740740727e-01 5.740740740740740700e-02 7.777777777777777901e-02 1.129629629629629595e-01 8.862433862433861942e-02 1.097883597883597823e-01 5.423280423280422979e-02 7.407407407407406996e-02 7.010582010582010193e-02 2.407407407407407413e-02 2.592592592592592518e-02 2.777777777777777624e-02 2.407407407407407413e-02 2.777777777777777624e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.058201058201058142e-02 4.629629629629629373e-02 6.613756613756613389e-02 7.407407407407406996e-02 9.523809523809523281e-02 6.746031746031745657e-02 4.761904761904761640e-02 4.761904761904761640e-02 9.523809523809523281e-02 6.216931216931216586e-02 1.216931216931216864e-01 7.936507936507936067e-02 6.349206349206348854e-02 1.021825396825396803e-01 5.257936507936507992e-02 1.101190476190476164e-01 5.158730158730158444e-02 8.035714285714286309e-02 4.563492063492063239e-02 7.539682539682539264e-02 5.753968253968253649e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.513227513227513088e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.884920634920634816e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.968253968253968034e-02 5.456349206349206393e-02 6.051587301587301598e-02 7.638888888888889506e-02 5.555555555555555247e-02 5.555555555555555247e-02 9.325396825396825573e-02 5.753968253968253649e-02 1.170634920634920639e-01 7.043650793650793607e-02 9.325396825396825573e-02 7.142857142857142461e-02 7.638888888888889506e-02 5.941358024691358042e-02 1.080246913580246854e-01 8.333333333333332871e-02 8.333333333333332871e-02 6.250000000000000000e-02 3.935185185185185314e-02 5.092592592592592310e-02 4.861111111111111188e-02 4.398148148148148251e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.281746031746031619e-02 1.311728395061728322e-02 5.092592592592592310e-02 5.169753086419753146e-02 4.706790123456790209e-02 6.327160493827160836e-02 7.638888888888889506e-02 6.404320987654321673e-02 4.475308641975308394e-02 4.320987654320987414e-02 7.716049382716048954e-02 1.141975308641975245e-01 9.490740740740741255e-02 3.472222222222222376e-02 6.327160493827160836e-02 4.567901234567901120e-02 9.629629629629629650e-02 6.975308641975308532e-02 3.827160493827160698e-02 5.740740740740740700e-02 5.308641975308642236e-02 2.160493827160493707e-02 2.314814814814814686e-02 2.237654320987654197e-02 2.314814814814814686e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.237654320987654197e-02 2.237654320987654197e-02 1.728395061728394896e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.358024691358024685e-02 4.320987654320987414e-02 4.012345679012345456e-02 4.444444444444444614e-02 6.481481481481481122e-02 4.382716049382716361e-02 3.888888888888888951e-02 4.135802469135802656e-02 3.827160493827160698e-02 3.086419753086419582e-02 3.148148148148147835e-02 4.691358024691358319e-02 3.209876543209876781e-02 6.111111111111110911e-02 3.989898989898989917e-02 3.535353535353535220e-02 5.353535353535353314e-02 5.959595959595959808e-02 8.131313131313130937e-02 5.202020202020202211e-02 6.818181818181817677e-02 2.037037037037037202e-02 2.098765432098765454e-02 1.414141414141414227e-02 1.363636363636363570e-02 3.535353535353535220e-02 5.353535353535353314e-02 5.656565656565656908e-02 5.656565656565656908e-02 4.464285714285714385e-02 3.939393939393939087e-02 3.838383838383838120e-02 4.646464646464646547e-02 3.838383838383838120e-02 3.888888888888888951e-02 3.383838383838384117e-02 2.020202020202020374e-02 1.919191919191919060e-02 2.020202020202020374e-02 2.020202020202020374e-02 1.969696969696969543e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.018518518518518462e-01 4.444444444444444614e-02 7.222222222222221544e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 6.944444444444444753e-02 1.000000000000000056e-01 9.722222222222222376e-02 4.444444444444444614e-02 4.629629629629629373e-02 7.804232804232803800e-02 8.518518518518518323e-02 5.925925925925926152e-02 2.500000000000000139e-02 2.222222222222222307e-02 2.500000000000000139e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 5.370370370370370489e-02 8.148148148148148806e-02 9.444444444444444198e-02 6.111111111111110911e-02 7.037037037037037479e-02 9.074074074074074681e-02 7.804232804232803800e-02 8.862433862433861942e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.216931216931216586e-02 2.407407407407407413e-02 2.592592592592592518e-02 2.777777777777777624e-02 2.407407407407407413e-02 2.777777777777777624e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.058201058201058142e-02 4.497354497354497105e-02 5.820105820105819783e-02 6.878306878306877925e-02 7.936507936507936067e-02 6.216931216931216586e-02 4.629629629629629373e-02 4.894179894179893908e-02 7.804232804232803800e-02 5.555555555555555247e-02 8.994708994708994210e-02 6.878306878306877925e-02 6.084656084656084318e-02 8.234126984126984017e-02 5.654761904761904795e-02 8.630952380952380820e-02 4.960317460317460042e-02 7.142857142857142461e-02 4.365079365079364837e-02 5.952380952380952051e-02 5.158730158730158444e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.513227513227513088e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.884920634920634816e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.869047619047619180e-02 4.761904761904761640e-02 5.654761904761904795e-02 6.250000000000000000e-02 5.654761904761904795e-02 4.861111111111111188e-02 7.638888888888889506e-02 5.555555555555555247e-02 8.531746031746031966e-02 5.853174603174603197e-02 7.440476190476190410e-02 6.150793650793650452e-02 6.646825396825396803e-02 6.095679012345679021e-02 8.333333333333332871e-02 7.253086419753086711e-02 7.253086419753086711e-02 5.169753086419753146e-02 3.780864197530864335e-02 4.475308641975308394e-02 4.398148148148148251e-02 4.243827160493827272e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.281746031746031619e-02 1.311728395061728322e-02 4.783950617283950352e-02 4.706790123456790209e-02 4.783950617283950352e-02 5.555555555555555247e-02 6.095679012345679021e-02 6.018518518518518184e-02 4.089506172839506293e-02 4.783950617283950352e-02 6.790123456790123080e-02 8.179012345679012586e-02 7.330246913580247548e-02 3.395061728395061540e-02 5.864197530864197205e-02 3.827160493827160698e-02 7.654320987654321395e-02 6.481481481481481122e-02 3.703703703703703498e-02 4.876543209876543078e-02 4.444444444444444614e-02 2.160493827160493707e-02 2.314814814814814686e-02 2.237654320987654197e-02 2.314814814814814686e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.237654320987654197e-02 2.237654320987654197e-02 1.728395061728394896e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.358024691358024685e-02 4.012345679012345456e-02 3.580246913580246992e-02 4.074074074074074403e-02 5.246913580246913289e-02 4.135802469135802656e-02 3.580246913580246992e-02 4.135802469135802656e-02 3.518518518518518740e-02 3.024691358024691329e-02 3.148148148148147835e-02 4.135802469135802656e-02 3.148148148148147835e-02 4.938271604938271331e-02 3.434343434343434254e-02 3.838383838383838120e-02 4.646464646464646547e-02 5.858585858585858841e-02 6.919191919191919338e-02 4.595959595959595717e-02 5.404040404040404144e-02 2.037037037037037202e-02 2.098765432098765454e-02 1.414141414141414227e-02 1.363636363636363570e-02 3.181818181818181490e-02 4.393939393939393784e-02 5.252525252525252347e-02 4.747474747474747514e-02 4.166666666666666435e-02 3.535353535353535220e-02 3.636363636363636187e-02 3.939393939393939087e-02 3.585858585858586051e-02 3.585858585858586051e-02 3.131313131313131354e-02 2.020202020202020374e-02 1.919191919191919060e-02 2.020202020202020374e-02 2.020202020202020374e-02 1.969696969696969543e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.018518518518518462e-01 4.444444444444444614e-02 7.222222222222221544e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.499999999999999722e-02 1.000000000000000056e-01 9.722222222222222376e-02 4.444444444444444614e-02 4.629629629629629373e-02 7.804232804232803800e-02 8.518518518518518323e-02 5.925925925925926152e-02 2.500000000000000139e-02 2.222222222222222307e-02 2.500000000000000139e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 5.555555555555555247e-02 8.703703703703703776e-02 9.444444444444444198e-02 6.851851851851852027e-02 7.407407407407406996e-02 9.074074074074074681e-02 7.804232804232803800e-02 8.862433862433861942e-02 5.423280423280422979e-02 6.084656084656084318e-02 6.481481481481481122e-02 2.407407407407407413e-02 2.592592592592592518e-02 2.777777777777777624e-02 2.407407407407407413e-02 2.777777777777777624e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.058201058201058142e-02 4.629629629629629373e-02 6.084656084656084318e-02 7.671957671957671532e-02 8.465608465608465139e-02 6.746031746031745657e-02 4.761904761904761640e-02 5.291005291005290712e-02 8.201058201058200603e-02 5.687830687830687515e-02 8.994708994708994210e-02 7.142857142857142461e-02 6.613756613756613389e-02 8.234126984126984017e-02 6.448412698412699096e-02 8.630952380952380820e-02 5.158730158730158444e-02 7.242063492063492702e-02 4.563492063492063239e-02 5.952380952380952051e-02 5.357142857142856845e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.513227513227513088e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.884920634920634816e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.968253968253968034e-02 4.861111111111111188e-02 6.250000000000000000e-02 6.448412698412699096e-02 6.547619047619047949e-02 4.960317460317460042e-02 8.134920634920635163e-02 6.150793650793650452e-02 8.531746031746031966e-02 6.051587301587301598e-02 7.738095238095238360e-02 6.547619047619047949e-02 7.242063492063492702e-02 5.941358024691358042e-02 8.333333333333332871e-02 7.253086419753086711e-02 7.716049382716048954e-02 5.324074074074074125e-02 3.935185185185185314e-02 4.629629629629629373e-02 4.552469135802469230e-02 4.552469135802469230e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.281746031746031619e-02 1.311728395061728322e-02 5.246913580246913289e-02 5.015432098765432167e-02 5.478395061728395105e-02 6.018518518518518184e-02 6.250000000000000000e-02 6.867283950617283916e-02 4.166666666666666435e-02 5.709876543209876226e-02 7.561728395061728669e-02 8.179012345679012586e-02 7.638888888888889506e-02 3.472222222222222376e-02 6.635802469135802795e-02 3.827160493827160698e-02 7.654320987654321395e-02 6.481481481481481122e-02 3.827160493827160698e-02 5.000000000000000278e-02 4.567901234567901120e-02 2.160493827160493707e-02 2.314814814814814686e-02 2.237654320987654197e-02 2.314814814814814686e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.237654320987654197e-02 2.237654320987654197e-02 1.728395061728394896e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.358024691358024685e-02 4.197530864197530909e-02 3.641975308641975245e-02 4.320987654320987414e-02 5.370370370370370489e-02 4.506172839506172867e-02 3.641975308641975245e-02 4.629629629629629373e-02 3.580246913580246992e-02 3.086419753086419582e-02 3.271604938271605034e-02 4.197530864197530909e-02 3.209876543209876781e-02 5.000000000000000278e-02 3.484848484848485084e-02 4.343434343434343647e-02 4.747474747474747514e-02 6.161616161616161741e-02 7.323232323232323204e-02 5.000000000000000278e-02 5.606060606060606077e-02 2.037037037037037202e-02 2.098765432098765454e-02 1.414141414141414227e-02 1.363636363636363570e-02 3.232323232323232320e-02 4.444444444444444614e-02 6.060606060606060774e-02 5.050505050505050414e-02 4.265873015873015983e-02 3.636363636363636187e-02 3.939393939393939087e-02 4.040404040404040747e-02 3.838383838383838120e-02 3.787878787878787984e-02 3.181818181818181490e-02 2.020202020202020374e-02 1.919191919191919060e-02 2.020202020202020374e-02 2.020202020202020374e-02 1.969696969696969543e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.259259259259258745e-02 4.444444444444444614e-02 5.555555555555555247e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.499999999999999722e-02 8.888888888888889228e-02 8.611111111111111049e-02 4.444444444444444614e-02 4.629629629629629373e-02 5.291005291005290712e-02 6.666666666666666574e-02 6.666666666666666574e-02 2.500000000000000139e-02 2.222222222222222307e-02 2.500000000000000139e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 6.296296296296295669e-02 7.222222222222221544e-02 8.333333333333332871e-02 6.481481481481481122e-02 7.037037037037037479e-02 7.962962962962963354e-02 5.291005291005290712e-02 7.010582010582010193e-02 7.275132275132274728e-02 6.349206349206348854e-02 6.481481481481481122e-02 2.407407407407407413e-02 2.592592592592592518e-02 2.777777777777777624e-02 2.407407407407407413e-02 2.777777777777777624e-02 1.719576719576719481e-02 1.719576719576719481e-02 1.058201058201058142e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 6.878306878306877925e-02 6.746031746031745657e-02 5.291005291005290712e-02 5.555555555555555247e-02 6.878306878306877925e-02 5.952380952380952051e-02 7.936507936507936067e-02 6.613756613756613389e-02 6.084656084656084318e-02 5.952380952380952051e-02 5.456349206349206393e-02 6.646825396825396803e-02 5.357142857142856845e-02 5.456349206349206393e-02 5.357142857142856845e-02 5.952380952380952051e-02 6.547619047619047949e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.513227513227513088e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.785714285714285615e-02 1.884920634920634816e-02 1.190476190476190410e-02 1.190476190476190410e-02 4.365079365079364837e-02 5.257936507936507992e-02 5.059523809523809590e-02 6.051587301587301598e-02 5.357142857142856845e-02 5.357142857142856845e-02 6.547619047619047949e-02 6.150793650793650452e-02 7.539682539682539264e-02 5.853174603174603197e-02 6.547619047619047949e-02 6.349206349206348854e-02 5.853174603174603197e-02 4.166666666666666435e-02 6.250000000000000000e-02 5.169753086419753146e-02 5.324074074074074125e-02 7.098765432098765038e-02 4.552469135802469230e-02 5.246913580246913289e-02 5.015432098765432167e-02 5.632716049382716084e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.281746031746031619e-02 1.311728395061728322e-02 4.320987654320987414e-02 4.706790123456790209e-02 4.552469135802469230e-02 5.092592592592592310e-02 5.941358024691358042e-02 5.169753086419753146e-02 4.320987654320987414e-02 4.783950617283950352e-02 5.709876543209876226e-02 7.253086419753086711e-02 6.404320987654321673e-02 3.780864197530864335e-02 5.092592592592592310e-02 3.827160493827160698e-02 5.308641975308642236e-02 4.567901234567901120e-02 3.950617283950617203e-02 5.493827160493826994e-02 5.061728395061728530e-02 2.160493827160493707e-02 2.314814814814814686e-02 2.237654320987654197e-02 2.314814814814814686e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.237654320987654197e-02 2.237654320987654197e-02 1.728395061728394896e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.358024691358024685e-02 4.320987654320987414e-02 3.888888888888888951e-02 4.074074074074074403e-02 5.123456790123456783e-02 3.765432098765432445e-02 3.765432098765432445e-02 4.135802469135802656e-02 3.703703703703703498e-02 3.333333333333333287e-02 3.641975308641975245e-02 4.197530864197530909e-02 3.456790123456789793e-02 5.000000000000000278e-02 3.484848484848485084e-02 4.040404040404040747e-02 4.393939393939393784e-02 4.191919191919191851e-02 4.797979797979797650e-02 5.353535353535353314e-02 6.515151515151515471e-02 2.037037037037037202e-02 2.098765432098765454e-02 1.414141414141414227e-02 1.363636363636363570e-02 3.434343434343434254e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.265873015873015983e-02 3.838383838383838120e-02 3.333333333333333287e-02 3.838383838383838120e-02 3.434343434343434254e-02 3.585858585858586051e-02 3.282828282828283151e-02 2.020202020202020374e-02 1.919191919191919060e-02 2.020202020202020374e-02 2.020202020202020374e-02 1.969696969696969543e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.407407407407406996e-02 3.888888888888888951e-02 5.555555555555555247e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.499999999999999722e-02 6.111111111111110911e-02 6.944444444444444753e-02 3.703703703703703498e-02 4.259259259259259162e-02 4.232804232804232569e-02 5.185185185185185036e-02 5.925925925925926152e-02 3.055555555555555455e-02 2.222222222222222307e-02 3.055555555555555455e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407406996e-02 5.740740740740740700e-02 5.740740740740740700e-02 7.222222222222221544e-02 7.037037037037037479e-02 6.851851851851852027e-02 4.232804232804232569e-02 5.158730158730158444e-02 6.216931216931216586e-02 5.026455026455026176e-02 5.687830687830687515e-02 2.777777777777777624e-02 3.703703703703703498e-02 3.888888888888888951e-02 2.777777777777777624e-02 3.888888888888888951e-02 1.984126984126984017e-02 1.984126984126984017e-02 1.058201058201058142e-02 7.142857142857142461e-02 5.820105820105819783e-02 5.291005291005290712e-02 5.291005291005290712e-02 5.952380952380952051e-02 7.142857142857142461e-02 7.142857142857142461e-02 5.555555555555555247e-02 6.878306878306877925e-02 5.291005291005290712e-02 6.878306878306877925e-02 6.878306878306877925e-02 3.968253968253968034e-02 5.654761904761904795e-02 4.464285714285714385e-02 5.952380952380952051e-02 4.265873015873015983e-02 6.349206349206348854e-02 4.365079365079364837e-02 5.952380952380952051e-02 3.968253968253968034e-02 3.174603174603174427e-02 2.910052910052909891e-02 2.910052910052909891e-02 3.306878306878306695e-02 4.232804232804232569e-02 4.232804232804232569e-02 3.174603174603174427e-02 3.306878306878306695e-02 2.910052910052909891e-02 3.968253968253968034e-02 2.182539682539682419e-02 2.182539682539682419e-02 2.380952380952380820e-02 2.182539682539682419e-02 2.480158730158730021e-02 1.190476190476190410e-02 1.190476190476190410e-02 6.547619047619047949e-02 5.753968253968253649e-02 5.059523809523809590e-02 5.059523809523809590e-02 5.357142857142856845e-02 5.952380952380952051e-02 5.158730158730158444e-02 5.952380952380952051e-02 5.158730158730158444e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.753968253968253649e-02 5.357142857142856845e-02 3.858024691358024477e-02 3.935185185185185314e-02 3.780864197530864335e-02 3.935185185185185314e-02 4.552469135802469230e-02 6.790123456790123080e-02 5.401234567901234268e-02 6.018518518518518184e-02 6.018518518518518184e-02 3.968253968253968034e-02 3.075396825396825226e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.571428571428571230e-02 3.174603174603174427e-02 3.075396825396825226e-02 3.571428571428571230e-02 3.075396825396825226e-02 1.466049382716049301e-02 4.783950617283950352e-02 5.169753086419753146e-02 5.015432098765432167e-02 4.783950617283950352e-02 4.861111111111111188e-02 4.861111111111111188e-02 6.404320987654321673e-02 5.246913580246913289e-02 4.938271604938271331e-02 4.938271604938271331e-02 5.015432098765432167e-02 5.864197530864197205e-02 4.861111111111111188e-02 3.827160493827160698e-02 3.580246913580246992e-02 3.580246913580246992e-02 5.679012345679012447e-02 5.617283950617284194e-02 4.691358024691358319e-02 3.086419753086419582e-02 3.240740740740740561e-02 3.163580246913580418e-02 3.240740740740740561e-02 3.086419753086419582e-02 3.086419753086419582e-02 3.163580246913580418e-02 3.858024691358024477e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.592592592592592518e-02 1.604938271604938391e-02 6.111111111111110911e-02 5.061728395061728530e-02 4.814814814814814825e-02 4.753086419753086572e-02 4.567901234567901120e-02 5.617283950617284194e-02 5.000000000000000278e-02 5.432098765432098741e-02 5.308641975308642236e-02 5.987654320987654405e-02 5.925925925925926152e-02 5.987654320987654405e-02 5.493827160493826994e-02 3.787878787878787984e-02 5.808080808080808011e-02 4.343434343434343647e-02 3.484848484848485084e-02 3.484848484848485084e-02 3.686868686868687017e-02 3.484848484848485084e-02 3.333333333333333287e-02 3.703703703703703498e-02 2.020202020202020374e-02 1.666666666666666644e-02 4.646464646464646547e-02 5.454545454545454281e-02 4.343434343434343647e-02 4.343434343434343647e-02 6.349206349206348854e-02 4.747474747474747514e-02 4.343434343434343647e-02 4.191919191919191851e-02 4.191919191919191851e-02 4.545454545454545581e-02 4.949494949494949447e-02 3.232323232323232320e-02 3.030303030303030387e-02 3.232323232323232320e-02 3.232323232323232320e-02 3.535353535353535220e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.333333333333332871e-02 4.166666666666666435e-02 5.555555555555555247e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.499999999999999722e-02 7.499999999999999722e-02 7.777777777777777901e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.761904761904761640e-02 5.925925925925926152e-02 6.296296296296295669e-02 2.777777777777777624e-02 2.222222222222222307e-02 2.777777777777777624e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.037037037037037479e-02 6.481481481481481122e-02 7.037037037037037479e-02 6.851851851851852027e-02 7.037037037037037479e-02 7.407407407407406996e-02 4.761904761904761640e-02 6.084656084656084318e-02 6.349206349206348854e-02 5.687830687830687515e-02 6.084656084656084318e-02 2.592592592592592518e-02 3.148148148148147835e-02 3.333333333333333287e-02 2.592592592592592518e-02 3.333333333333333287e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.058201058201058142e-02 6.216931216931216586e-02 6.084656084656084318e-02 5.687830687830687515e-02 6.084656084656084318e-02 6.349206349206348854e-02 6.481481481481481122e-02 6.481481481481481122e-02 6.216931216931216586e-02 6.481481481481481122e-02 6.613756613756613389e-02 6.746031746031745657e-02 6.481481481481481122e-02 4.960317460317460042e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.853174603174603197e-02 4.861111111111111188e-02 6.845238095238095899e-02 5.158730158730158444e-02 6.051587301587301598e-02 3.174603174603174427e-02 2.777777777777777624e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.910052910052909891e-02 3.439153439153438963e-02 3.439153439153438963e-02 2.777777777777777624e-02 2.910052910052909891e-02 2.645502645502645356e-02 3.174603174603174427e-02 1.984126984126984017e-02 1.984126984126984017e-02 2.083333333333333218e-02 1.984126984126984017e-02 2.182539682539682419e-02 1.190476190476190410e-02 1.190476190476190410e-02 5.357142857142856845e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.654761904761904795e-02 5.357142857142856845e-02 6.051587301587301598e-02 5.853174603174603197e-02 6.051587301587301598e-02 6.349206349206348854e-02 5.753968253968253649e-02 6.051587301587301598e-02 6.051587301587301598e-02 5.555555555555555247e-02 4.012345679012345456e-02 5.092592592592592310e-02 4.475308641975308394e-02 4.629629629629629373e-02 5.246913580246913289e-02 5.401234567901234268e-02 6.327160493827160836e-02 6.172839506172839164e-02 5.632716049382716084e-02 3.075396825396825226e-02 2.678571428571428423e-02 2.777777777777777624e-02 2.777777777777777624e-02 2.976190476190476025e-02 2.777777777777777624e-02 2.678571428571428423e-02 2.976190476190476025e-02 2.678571428571428423e-02 1.388888888888888812e-02 4.706790123456790209e-02 5.092592592592592310e-02 4.861111111111111188e-02 5.015432098765432167e-02 5.478395061728395105e-02 5.015432098765432167e-02 5.401234567901234268e-02 5.015432098765432167e-02 5.324074074074074125e-02 6.095679012345679021e-02 5.709876543209876226e-02 4.706790123456790209e-02 4.938271604938271331e-02 3.827160493827160698e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.753086419753086572e-02 5.493827160493826994e-02 5.864197530864197205e-02 2.623456790123456644e-02 2.777777777777777624e-02 2.700617283950617134e-02 2.777777777777777624e-02 2.623456790123456644e-02 2.623456790123456644e-02 2.700617283950617134e-02 2.932098765432098603e-02 2.098765432098765454e-02 2.222222222222222307e-02 2.222222222222222307e-02 1.481481481481481538e-02 5.185185185185185036e-02 4.444444444444444614e-02 4.629629629629629373e-02 5.061728395061728530e-02 4.197530864197530909e-02 4.567901234567901120e-02 4.691358024691358319e-02 4.444444444444444614e-02 4.197530864197530909e-02 4.753086419753086572e-02 4.938271604938271331e-02 4.629629629629629373e-02 5.370370370370370489e-02 3.636363636363636187e-02 4.949494949494949447e-02 4.444444444444444614e-02 3.838383838383838120e-02 4.141414141414141714e-02 4.141414141414141714e-02 4.343434343434343647e-02 2.592592592592592518e-02 2.777777777777777624e-02 1.717171717171717127e-02 1.515151515151515194e-02 3.989898989898989917e-02 5.101010101010101244e-02 4.343434343434343647e-02 4.444444444444444614e-02 5.158730158730158444e-02 4.494949494949494750e-02 3.787878787878787984e-02 4.040404040404040747e-02 3.787878787878787984e-02 4.141414141414141714e-02 3.989898989898989917e-02 2.626262626262626174e-02 2.424242424242424240e-02 2.626262626262626174e-02 2.575757575757575690e-02 2.626262626262626174e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.333333333333332871e-02 4.166666666666666435e-02 5.555555555555555247e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.777777777777777901e-02 7.222222222222221544e-02 8.055555555555556080e-02 4.074074074074074403e-02 4.629629629629629373e-02 4.761904761904761640e-02 5.925925925925926152e-02 6.296296296296295669e-02 3.055555555555555455e-02 2.222222222222222307e-02 3.055555555555555455e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.592592592592592449e-02 6.481481481481481122e-02 6.851851851851852027e-02 7.407407407407406996e-02 7.592592592592592449e-02 7.962962962962963354e-02 4.761904761904761640e-02 6.216931216931216586e-02 6.481481481481481122e-02 5.555555555555555247e-02 6.216931216931216586e-02 2.777777777777777624e-02 3.703703703703703498e-02 3.888888888888888951e-02 2.777777777777777624e-02 3.888888888888888951e-02 1.984126984126984017e-02 1.984126984126984017e-02 1.058201058201058142e-02 7.275132275132274728e-02 6.216931216931216586e-02 5.820105820105819783e-02 6.084656084656084318e-02 6.349206349206348854e-02 7.275132275132274728e-02 7.275132275132274728e-02 6.349206349206348854e-02 7.275132275132274728e-02 6.349206349206348854e-02 7.539682539682539264e-02 7.275132275132274728e-02 4.761904761904761640e-02 5.853174603174603197e-02 5.456349206349206393e-02 6.150793650793650452e-02 4.861111111111111188e-02 7.142857142857142461e-02 4.960317460317460042e-02 6.349206349206348854e-02 3.968253968253968034e-02 3.174603174603174427e-02 2.910052910052909891e-02 2.910052910052909891e-02 3.306878306878306695e-02 4.232804232804232569e-02 4.232804232804232569e-02 3.174603174603174427e-02 3.306878306878306695e-02 2.910052910052909891e-02 3.968253968253968034e-02 2.182539682539682419e-02 2.182539682539682419e-02 2.380952380952380820e-02 2.182539682539682419e-02 2.480158730158730021e-02 1.190476190476190410e-02 1.190476190476190410e-02 6.349206349206348854e-02 6.051587301587301598e-02 5.456349206349206393e-02 5.654761904761904795e-02 5.654761904761904795e-02 6.250000000000000000e-02 5.952380952380952051e-02 6.250000000000000000e-02 6.150793650793650452e-02 6.051587301587301598e-02 6.349206349206348854e-02 6.250000000000000000e-02 5.952380952380952051e-02 4.166666666666666435e-02 4.861111111111111188e-02 4.398148148148148251e-02 4.552469135802469230e-02 5.015432098765432167e-02 6.018518518518518184e-02 6.172839506172839164e-02 6.790123456790123080e-02 6.250000000000000000e-02 3.968253968253968034e-02 3.075396825396825226e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.571428571428571230e-02 3.174603174603174427e-02 3.075396825396825226e-02 3.571428571428571230e-02 3.075396825396825226e-02 1.466049382716049301e-02 5.092592592592592310e-02 5.478395061728395105e-02 5.246913580246913289e-02 5.246913580246913289e-02 5.478395061728395105e-02 5.324074074074074125e-02 6.635802469135802795e-02 5.401234567901234268e-02 5.555555555555555247e-02 5.864197530864197205e-02 5.787037037037037063e-02 5.709876543209876226e-02 5.324074074074074125e-02 4.197530864197530909e-02 4.320987654320987414e-02 4.074074074074074403e-02 5.432098765432098741e-02 6.111111111111110911e-02 5.432098765432098741e-02 3.086419753086419582e-02 3.240740740740740561e-02 3.163580246913580418e-02 3.240740740740740561e-02 3.086419753086419582e-02 3.086419753086419582e-02 3.163580246913580418e-02 3.858024691358024477e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.592592592592592518e-02 1.604938271604938391e-02 6.358024691358024616e-02 5.061728395061728530e-02 5.061728395061728530e-02 5.246913580246913289e-02 4.814814814814814825e-02 5.617283950617284194e-02 5.185185185185185036e-02 5.432098765432098741e-02 5.185185185185185036e-02 5.864197530864197205e-02 6.234567901234568110e-02 5.864197530864197205e-02 5.925925925925926152e-02 4.090909090909090884e-02 5.909090909090908977e-02 4.646464646464646547e-02 3.888888888888888951e-02 4.090909090909090884e-02 4.090909090909090884e-02 4.090909090909090884e-02 3.333333333333333287e-02 3.703703703703703498e-02 2.020202020202020374e-02 1.666666666666666644e-02 4.646464646464646547e-02 5.808080808080808011e-02 4.747474747474747514e-02 4.747474747474747514e-02 6.448412698412699096e-02 4.949494949494949447e-02 4.545454545454545581e-02 4.494949494949494750e-02 4.393939393939393784e-02 4.747474747474747514e-02 4.949494949494949447e-02 3.232323232323232320e-02 3.030303030303030387e-02 3.232323232323232320e-02 3.232323232323232320e-02 3.535353535353535220e-02
-9.259259259259258745e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.870370370370370627e-02 4.166666666666666435e-02 5.555555555555555247e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 8.611111111111111049e-02 6.666666666666666574e-02 7.499999999999999722e-02 4.074074074074074403e-02 4.629629629629629373e-02 4.497354497354497105e-02 5.555555555555555247e-02 6.666666666666666574e-02 3.055555555555555455e-02 2.222222222222222307e-02 3.055555555555555455e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 7.777777777777777901e-02 6.481481481481481122e-02 6.296296296296295669e-02 8.333333333333332871e-02 7.962962962962963354e-02 7.407407407407406996e-02 4.497354497354497105e-02 5.687830687830687515e-02 7.804232804232803800e-02 5.687830687830687515e-02 6.613756613756613389e-02 2.777777777777777624e-02 3.703703703703703498e-02 3.888888888888888951e-02 2.777777777777777624e-02 3.888888888888888951e-02 1.984126984126984017e-02 1.984126984126984017e-02 1.058201058201058142e-02 7.275132275132274728e-02 6.349206349206348854e-02 6.084656084656084318e-02 5.952380952380952051e-02 6.878306878306877925e-02 7.275132275132274728e-02 7.671957671957671532e-02 6.216931216931216586e-02 7.275132275132274728e-02 5.820105820105819783e-02 7.671957671957671532e-02 7.804232804232803800e-02 4.365079365079364837e-02 6.845238095238095899e-02 4.960317460317460042e-02 6.349206349206348854e-02 4.761904761904761640e-02 6.547619047619047949e-02 4.960317460317460042e-02 7.142857142857142461e-02 3.968253968253968034e-02 3.174603174603174427e-02 2.910052910052909891e-02 2.910052910052909891e-02 3.306878306878306695e-02 4.232804232804232569e-02 4.232804232804232569e-02 3.174603174603174427e-02 3.306878306878306695e-02 2.910052910052909891e-02 3.968253968253968034e-02 2.182539682539682419e-02 2.182539682539682419e-02 2.380952380952380820e-02 2.182539682539682419e-02 2.480158730158730021e-02 1.190476190476190410e-02 1.190476190476190410e-02 6.349206349206348854e-02 6.051587301587301598e-02 5.555555555555555247e-02 5.456349206349206393e-02 6.250000000000000000e-02 6.150793650793650452e-02 5.753968253968253649e-02 6.845238095238095899e-02 5.654761904761904795e-02 6.051587301587301598e-02 6.150793650793650452e-02 6.547619047619047949e-02 6.051587301587301598e-02 4.320987654320987414e-02 4.398148148148148251e-02 4.243827160493827272e-02 4.552469135802469230e-02 5.632716049382716084e-02 6.018518518518518184e-02 5.632716049382716084e-02 6.250000000000000000e-02 7.098765432098765038e-02 3.968253968253968034e-02 3.075396825396825226e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.571428571428571230e-02 3.174603174603174427e-02 3.075396825396825226e-02 3.571428571428571230e-02 3.075396825396825226e-02 1.466049382716049301e-02 5.092592592592592310e-02 5.401234567901234268e-02 5.632716049382716084e-02 5.246913580246913289e-02 5.246913580246913289e-02 5.555555555555555247e-02 6.481481481481481122e-02 6.172839506172839164e-02 5.555555555555555247e-02 5.401234567901234268e-02 5.555555555555555247e-02 5.709876543209876226e-02 5.555555555555555247e-02 4.197530864197530909e-02 3.950617283950617203e-02 4.074074074074074403e-02 5.432098765432098741e-02 6.358024691358024616e-02 4.938271604938271331e-02 3.086419753086419582e-02 3.240740740740740561e-02 3.163580246913580418e-02 3.240740740740740561e-02 3.086419753086419582e-02 3.086419753086419582e-02 3.163580246913580418e-02 3.858024691358024477e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.592592592592592518e-02 1.604938271604938391e-02 6.481481481481481122e-02 5.000000000000000278e-02 4.938271604938271331e-02 5.000000000000000278e-02 4.814814814814814825e-02 5.555555555555555247e-02 5.370370370370370489e-02 5.370370370370370489e-02 5.185185185185185036e-02 5.864197530864197205e-02 6.111111111111110911e-02 5.802469135802468952e-02 5.740740740740740700e-02 4.141414141414141714e-02 6.313131313131313538e-02 4.646464646464646547e-02 4.090909090909090884e-02 3.989898989898989917e-02 4.494949494949494750e-02 4.292929292929292817e-02 3.333333333333333287e-02 3.703703703703703498e-02 2.020202020202020374e-02 1.666666666666666644e-02 4.595959595959595717e-02 5.606060606060606077e-02 4.949494949494949447e-02 4.646464646464646547e-02 6.448412698412699096e-02 4.747474747474747514e-02 4.545454545454545581e-02 4.393939393939393784e-02 4.444444444444444614e-02 4.646464646464646547e-02 4.898989898989899311e-02 3.232323232323232320e-02 3.030303030303030387e-02 3.232323232323232320e-02 3.232323232323232320e-02 3.535353535353535220e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 6.547619047619047949e-02 3.571428571428571230e-02 3.571428571428571230e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 3.333333333333333287e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 7.857142857142856984e-02 6.071428571428571369e-02 7.499999999999999722e-02 3.809523809523809867e-02 4.047619047619047811e-02 3.809523809523809867e-02 4.523809523809523697e-02 6.190476190476190688e-02 4.523809523809523697e-02 4.523809523809523697e-02 6.428571428571427937e-02 6.190476190476190688e-02 6.428571428571427937e-02 1.700680272108843691e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.700680272108843691e-02 2.380952380952380820e-02 6.428571428571427937e-02 8.571428571428571508e-02 8.571428571428571508e-02 6.428571428571427937e-02 8.333333333333332871e-02 4.421768707482993249e-02 4.421768707482993249e-02 3.741496598639455773e-02 6.802721088435374763e-02 5.272108843537415268e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 7.142857142857142461e-02 7.142857142857142461e-02 5.272108843537415268e-02 6.802721088435374763e-02 4.761904761904761640e-02 7.142857142857142461e-02 7.142857142857142461e-02 1.913265306122449022e-02 3.443877551020407823e-02 2.423469387755101956e-02 3.316326530612245110e-02 2.423469387755101956e-02 3.316326530612245110e-02 1.913265306122449022e-02 3.316326530612245110e-02 8.843537414965986498e-02 6.972789115646257918e-02 6.292517006802721136e-02 6.292517006802721136e-02 7.142857142857142461e-02 9.013605442176871041e-02 9.013605442176871041e-02 6.802721088435374763e-02 6.972789115646257918e-02 6.292517006802721136e-02 8.673469387755101956e-02 4.591836734693877792e-02 4.591836734693877792e-02 4.974489795918367319e-02 4.591836734693877792e-02 5.102040816326530726e-02 3.698979591836734637e-02 3.954081632653061451e-02 7.015306122448979054e-02 5.739795918367347066e-02 5.102040816326530726e-02 5.102040816326530726e-02 5.229591836734694132e-02 5.994897959183673186e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.994897959183673186e-02 5.994897959183673186e-02 5.739795918367347066e-02 2.777777777777777624e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.968253968253968034e-02 3.075396825396825226e-02 3.968253968253968034e-02 3.968253968253968034e-02 8.928571428571428770e-02 6.377551020408163407e-02 6.632653061224490221e-02 6.505102040816326814e-02 7.397959183673469274e-02 6.505102040816326814e-02 6.377551020408163407e-02 7.525510204081632681e-02 6.377551020408163407e-02 4.365079365079364837e-02 5.158730158730158444e-02 5.555555555555555247e-02 5.257936507936507992e-02 5.158730158730158444e-02 5.257936507936507992e-02 5.257936507936507992e-02 7.539682539682539264e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.555555555555555247e-02 6.944444444444444753e-02 5.357142857142856845e-02 4.285714285714285754e-02 2.619047619047619110e-02 2.619047619047619110e-02 4.365079365079364837e-02 4.603174603174603474e-02 2.857142857142857054e-02 6.250000000000000000e-02 6.646825396825396803e-02 6.448412698412699096e-02 6.448412698412699096e-02 6.250000000000000000e-02 6.250000000000000000e-02 6.349206349206348854e-02 8.333333333333332871e-02 4.920634920634920501e-02 5.079365079365079361e-02 5.079365079365079361e-02 3.888888888888888951e-02 7.698412698412698818e-02 5.952380952380952051e-02 5.396825396825397081e-02 5.476190476190476164e-02 5.238095238095238221e-02 7.063492063492063378e-02 5.476190476190476164e-02 6.746031746031745657e-02 6.746031746031745657e-02 7.301587301587302015e-02 7.460317460317460181e-02 7.698412698412698818e-02 6.587301587301587491e-02 4.480519480519480624e-02 5.194805194805195148e-02 3.246753246753246447e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 7.222222222222221544e-02 7.857142857142856984e-02 4.415584415584415667e-02 3.896103896103896014e-02 5.844155844155844021e-02 6.948051948051947591e-02 5.194805194805195148e-02 5.194805194805195148e-02 8.928571428571428770e-02 5.454545454545454281e-02 5.194805194805195148e-02 5.454545454545454281e-02 5.454545454545454281e-02 5.324675324675324367e-02 6.493506493506492894e-02 6.298701298701298024e-02 6.038961038961038891e-02 6.428571428571427937e-02 6.493506493506492894e-02 7.402597402597402287e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 8.333333333333332871e-02 3.928571428571428492e-02 3.571428571428571230e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 3.095238095238095344e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.571428571428571508e-02 8.571428571428571508e-02 9.285714285714286031e-02 4.285714285714285754e-02 5.476190476190476164e-02 4.523809523809523697e-02 4.523809523809523697e-02 5.238095238095238221e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.714285714285714107e-02 5.238095238095238221e-02 5.714285714285714107e-02 1.700680272108843691e-02 2.210884353741496625e-02 2.210884353741496625e-02 1.700680272108843691e-02 2.210884353741496625e-02 8.809523809523810145e-02 7.857142857142856984e-02 9.285714285714286031e-02 9.047619047619047394e-02 9.761904761904761918e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 5.782312925170068202e-02 5.782312925170068202e-02 4.591836734693877792e-02 5.272108843537415268e-02 4.421768707482993249e-02 5.782312925170068202e-02 5.782312925170068202e-02 1.913265306122449022e-02 3.061224489795918297e-02 2.295918367346938896e-02 2.806122448979591830e-02 2.295918367346938896e-02 2.806122448979591830e-02 1.913265306122449022e-02 2.806122448979591830e-02 6.972789115646257918e-02 7.823129251700680631e-02 9.013605442176871041e-02 9.013605442176871041e-02 8.333333333333332871e-02 7.993197278911565173e-02 8.333333333333332871e-02 8.843537414965986498e-02 9.013605442176871041e-02 8.673469387755101956e-02 8.163265306122448328e-02 4.846938775510203912e-02 4.846938775510203912e-02 4.974489795918367319e-02 4.846938775510203912e-02 5.229591836734694132e-02 5.484693877551020252e-02 4.974489795918367319e-02 5.102040816326530726e-02 4.591836734693877792e-02 4.464285714285714385e-02 4.464285714285714385e-02 4.719387755102040505e-02 4.974489795918367319e-02 4.719387755102040505e-02 5.484693877551020252e-02 4.719387755102040505e-02 4.591836734693877792e-02 4.974489795918367319e-02 4.974489795918367319e-02 4.591836734693877792e-02 2.579365079365079222e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 3.075396825396825226e-02 2.678571428571428423e-02 3.075396825396825226e-02 3.075396825396825226e-02 6.377551020408163407e-02 8.418367346938775142e-02 8.801020408163265363e-02 9.183673469387755584e-02 7.908163265306122902e-02 9.056122448979592177e-02 8.673469387755101956e-02 7.270408163265305868e-02 7.908163265306122902e-02 5.059523809523809590e-02 4.365079365079364837e-02 4.662698412698412787e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.563492063492063239e-02 5.257936507936507992e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.662698412698412787e-02 4.861111111111111188e-02 4.365079365079364837e-02 3.809523809523809867e-02 2.460317460317460250e-02 2.460317460317460250e-02 3.174603174603174427e-02 3.412698412698412370e-02 2.539682539682539680e-02 7.837301587301587213e-02 8.531746031746031966e-02 8.234126984126984017e-02 8.531746031746031966e-02 8.531746031746031966e-02 8.035714285714286309e-02 8.829365079365079916e-02 5.853174603174603197e-02 4.603174603174603474e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.476190476190476164e-02 5.158730158730158444e-02 4.285714285714285754e-02 4.444444444444444614e-02 4.603174603174603474e-02 4.206349206349206671e-02 4.841269841269841417e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.920634920634920501e-02 5.079365079365079361e-02 5.158730158730158444e-02 4.761904761904761640e-02 3.766233766233766100e-02 3.766233766233766100e-02 2.662337662337662184e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 5.476190476190476164e-02 5.396825396825397081e-02 4.805194805194805407e-02 6.038961038961038891e-02 4.090909090909090884e-02 4.870129870129870364e-02 4.025974025974025927e-02 4.025974025974025927e-02 6.250000000000000000e-02 4.415584415584415667e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.220779220779220797e-02 4.350649350649350711e-02 8.246753246753246724e-02 6.298701298701298024e-02 6.688311688311687764e-02 6.558441558441557850e-02 5.000000000000000278e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 3.333333333333333287e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.000000000000000056e-01 7.857142857142856984e-02 8.928571428571428770e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.285714285714285754e-02 5.000000000000000278e-02 5.476190476190476164e-02 4.523809523809523697e-02 4.523809523809523697e-02 6.428571428571427937e-02 5.476190476190476164e-02 6.428571428571427937e-02 1.700680272108843691e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.700680272108843691e-02 2.380952380952380820e-02 8.333333333333332871e-02 8.095238095238095621e-02 1.119047619047619097e-01 9.523809523809523281e-02 9.761904761904761918e-02 5.102040816326530726e-02 4.931972789115646183e-02 4.421768707482993249e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.122448979591836593e-02 6.122448979591836593e-02 1.913265306122449022e-02 3.443877551020407823e-02 2.423469387755101956e-02 2.933673469387755237e-02 2.423469387755101956e-02 2.933673469387755237e-02 1.913265306122449022e-02 2.933673469387755237e-02 7.312925170068027003e-02 7.823129251700680631e-02 1.020408163265306145e-01 9.523809523809523281e-02 9.523809523809523281e-02 7.993197278911565173e-02 9.013605442176871041e-02 9.013605442176871041e-02 8.843537414965986498e-02 8.333333333333332871e-02 9.353741496598640126e-02 5.357142857142856845e-02 5.102040816326530726e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.612244897959183659e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.229591836734694132e-02 4.719387755102040505e-02 4.719387755102040505e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 4.719387755102040505e-02 2.777777777777777624e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.174603174603174427e-02 2.777777777777777624e-02 3.174603174603174427e-02 3.174603174603174427e-02 6.632653061224490221e-02 8.801020408163265363e-02 1.071428571428571369e-01 1.033163265306122486e-01 8.290816326530611735e-02 9.693877551020407823e-02 8.928571428571428770e-02 7.142857142857142461e-02 7.525510204081632681e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.285714285714285754e-02 2.619047619047619110e-02 2.619047619047619110e-02 3.253968253968254204e-02 3.571428571428571230e-02 2.619047619047619110e-02 7.738095238095238360e-02 1.091269841269841279e-01 9.226190476190476719e-02 8.531746031746031966e-02 9.226190476190476719e-02 8.134920634920635163e-02 1.021825396825396803e-01 6.051587301587301598e-02 4.761904761904761640e-02 5.873015873015872967e-02 5.555555555555555247e-02 5.158730158730158444e-02 5.317460317460317304e-02 4.365079365079364837e-02 4.682539682539682557e-02 5.000000000000000278e-02 4.365079365079364837e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.682539682539682557e-02 4.682539682539682557e-02 5.000000000000000278e-02 5.317460317460317304e-02 5.317460317460317304e-02 5.000000000000000278e-02 4.090909090909090884e-02 4.025974025974025927e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 5.555555555555555247e-02 5.555555555555555247e-02 4.805194805194805407e-02 6.363636363636362980e-02 4.155844155844155841e-02 5.194805194805195148e-02 4.155844155844155841e-02 4.155844155844155841e-02 6.377551020408163407e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.415584415584415667e-02 4.415584415584415667e-02 8.831168831168831335e-02 6.233766233766233761e-02 5.974025974025973934e-02 7.467532467532467244e-02 5.129870129870130191e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 9.523809523809523281e-02 4.285714285714285754e-02 3.571428571428571230e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 3.333333333333333287e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 9.285714285714286031e-02 1.035714285714285782e-01 1.107142857142857095e-01 4.761904761904761640e-02 6.428571428571427937e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.476190476190476164e-02 4.523809523809523697e-02 4.523809523809523697e-02 6.428571428571427937e-02 5.476190476190476164e-02 6.428571428571427937e-02 1.700680272108843691e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.700680272108843691e-02 2.380952380952380820e-02 1.095238095238095233e-01 7.857142857142856984e-02 1.023809523809523780e-01 1.095238095238095233e-01 1.190476190476190410e-01 5.272108843537415268e-02 5.442176870748299117e-02 5.442176870748299117e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.122448979591836593e-02 6.122448979591836593e-02 1.913265306122449022e-02 3.443877551020407823e-02 2.423469387755101956e-02 2.933673469387755237e-02 2.423469387755101956e-02 2.933673469387755237e-02 1.913265306122449022e-02 2.933673469387755237e-02 7.142857142857142461e-02 8.163265306122448328e-02 1.071428571428571369e-01 1.139455782312925186e-01 9.523809523809523281e-02 7.823129251700680631e-02 8.503401360544217413e-02 1.071428571428571369e-01 1.139455782312925186e-01 1.105442176870748278e-01 8.673469387755101956e-02 5.357142857142856845e-02 5.612244897959183659e-02 5.484693877551020252e-02 5.484693877551020252e-02 6.122448979591836593e-02 6.760204081632653628e-02 5.994897959183673186e-02 5.229591836734694132e-02 4.719387755102040505e-02 4.719387755102040505e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 4.719387755102040505e-02 2.777777777777777624e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.174603174603174427e-02 2.777777777777777624e-02 3.174603174603174427e-02 3.174603174603174427e-02 6.505102040816326814e-02 9.183673469387755584e-02 1.033163265306122486e-01 1.173469387755102095e-01 8.290816326530611735e-02 1.173469387755102095e-01 1.096938775510204050e-01 7.525510204081632681e-02 8.673469387755101956e-02 5.952380952380952051e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.285714285714285754e-02 2.619047619047619110e-02 2.619047619047619110e-02 3.253968253968254204e-02 3.571428571428571230e-02 2.619047619047619110e-02 8.035714285714286309e-02 9.821428571428571230e-02 8.928571428571428770e-02 1.121031746031746074e-01 1.061507936507936484e-01 8.829365079365079916e-02 1.081349206349206393e-01 5.952380952380952051e-02 4.761904761904761640e-02 5.714285714285714107e-02 6.031746031746031828e-02 6.904761904761905211e-02 5.317460317460317304e-02 4.365079365079364837e-02 4.682539682539682557e-02 5.000000000000000278e-02 4.365079365079364837e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.682539682539682557e-02 4.682539682539682557e-02 5.000000000000000278e-02 5.317460317460317304e-02 5.317460317460317304e-02 5.000000000000000278e-02 4.090909090909090884e-02 4.025974025974025927e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 5.793650793650793884e-02 5.476190476190476164e-02 4.935064935064935321e-02 7.792207792207792028e-02 4.155844155844155841e-02 5.194805194805195148e-02 4.155844155844155841e-02 4.155844155844155841e-02 6.377551020408163407e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.415584415584415667e-02 4.415584415584415667e-02 1.058441558441558378e-01 6.428571428571427937e-02 6.168831168831168804e-02 7.272727272727272374e-02 5.064935064935065234e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 3.333333333333333287e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 9.285714285714286031e-02 7.857142857142856984e-02 8.928571428571428770e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.285714285714285754e-02 5.000000000000000278e-02 5.952380952380952051e-02 4.523809523809523697e-02 4.523809523809523697e-02 6.428571428571427937e-02 5.952380952380952051e-02 6.428571428571427937e-02 1.700680272108843691e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.700680272108843691e-02 2.380952380952380820e-02 8.333333333333332871e-02 8.809523809523810145e-02 1.023809523809523780e-01 8.333333333333332871e-02 9.761904761904761918e-02 4.931972789115646183e-02 4.931972789115646183e-02 4.421768707482993249e-02 6.122448979591836593e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.802721088435374763e-02 6.802721088435374763e-02 5.102040816326530726e-02 6.122448979591836593e-02 4.761904761904761640e-02 6.802721088435374763e-02 6.802721088435374763e-02 1.913265306122449022e-02 3.443877551020407823e-02 2.423469387755101956e-02 3.188775510204081703e-02 2.423469387755101956e-02 3.188775510204081703e-02 1.913265306122449022e-02 3.188775510204081703e-02 7.993197278911565173e-02 8.673469387755101956e-02 8.163265306122448328e-02 8.163265306122448328e-02 9.523809523809523281e-02 9.183673469387755584e-02 9.523809523809523281e-02 8.503401360544217413e-02 8.843537414965986498e-02 8.333333333333332871e-02 9.353741496598640126e-02 5.102040816326530726e-02 5.102040816326530726e-02 5.357142857142856845e-02 5.102040816326530726e-02 5.612244897959183659e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.994897959183673186e-02 5.229591836734694132e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.739795918367347066e-02 5.229591836734694132e-02 2.777777777777777624e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.571428571428571230e-02 2.976190476190476025e-02 3.571428571428571230e-02 3.571428571428571230e-02 7.397959183673469274e-02 7.908163265306122902e-02 8.290816326530611735e-02 8.290816326530611735e-02 9.183673469387755584e-02 8.290816326530611735e-02 8.163265306122448328e-02 8.290816326530611735e-02 8.545918367346938549e-02 5.555555555555555247e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.158730158730158444e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.158730158730158444e-02 6.349206349206348854e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.753968253968253649e-02 4.960317460317460042e-02 4.285714285714285754e-02 2.619047619047619110e-02 2.619047619047619110e-02 3.730158730158730090e-02 4.047619047619047811e-02 2.777777777777777624e-02 7.539682539682539264e-02 8.134920634920635163e-02 7.837301587301587213e-02 8.531746031746031966e-02 7.837301587301587213e-02 7.936507936507936067e-02 7.936507936507936067e-02 6.845238095238095899e-02 5.079365079365079361e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.158730158730158444e-02 6.269841269841269771e-02 5.000000000000000278e-02 5.158730158730158444e-02 5.317460317460317304e-02 4.841269841269841417e-02 5.793650793650793884e-02 5.317460317460317304e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.952380952380952051e-02 6.111111111111110911e-02 6.269841269841269771e-02 5.634920634920635024e-02 4.350649350649350711e-02 4.545454545454545581e-02 2.987012987012986967e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 6.349206349206348854e-02 6.349206349206348854e-02 5.064935064935065234e-02 5.324675324675324367e-02 4.805194805194805407e-02 5.844155844155844021e-02 4.675324675324675494e-02 4.675324675324675494e-02 7.397959183673469274e-02 5.194805194805195148e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.935064935064935321e-02 5.194805194805195148e-02 7.792207792207792028e-02 6.363636363636362980e-02 7.272727272727272374e-02 6.753246753246752720e-02 5.909090909090908977e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 1.071428571428571369e-01 4.642857142857143016e-02 3.571428571428571230e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 3.333333333333333287e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.928571428571428770e-02 1.214285714285714274e-01 1.250000000000000000e-01 5.238095238095238221e-02 7.380952380952381098e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.476190476190476164e-02 4.523809523809523697e-02 4.523809523809523697e-02 6.428571428571427937e-02 5.476190476190476164e-02 6.428571428571427937e-02 1.700680272108843691e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.700680272108843691e-02 2.380952380952380820e-02 1.285714285714285587e-01 7.857142857142856984e-02 9.523809523809523281e-02 1.142857142857142821e-01 1.333333333333333315e-01 5.442176870748299117e-02 5.952380952380952051e-02 6.122448979591836593e-02 5.442176870748299117e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.122448979591836593e-02 6.122448979591836593e-02 4.761904761904761640e-02 5.442176870748299117e-02 4.761904761904761640e-02 6.122448979591836593e-02 6.122448979591836593e-02 1.913265306122449022e-02 3.443877551020407823e-02 2.423469387755101956e-02 2.933673469387755237e-02 2.423469387755101956e-02 2.933673469387755237e-02 1.913265306122449022e-02 2.933673469387755237e-02 7.142857142857142461e-02 8.503401360544217413e-02 1.054421768707483054e-01 1.207482993197278864e-01 9.523809523809523281e-02 7.823129251700680631e-02 8.163265306122448328e-02 1.156462585034013640e-01 1.326530612244898044e-01 1.309523809523809590e-01 8.333333333333332871e-02 5.357142857142856845e-02 6.122448979591836593e-02 5.739795918367347066e-02 5.739795918367347066e-02 6.632653061224490221e-02 8.035714285714286309e-02 7.015306122448979054e-02 5.229591836734694132e-02 4.719387755102040505e-02 4.719387755102040505e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 4.719387755102040505e-02 5.229591836734694132e-02 5.229591836734694132e-02 4.719387755102040505e-02 2.777777777777777624e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.174603174603174427e-02 2.777777777777777624e-02 3.174603174603174427e-02 3.174603174603174427e-02 6.505102040816326814e-02 9.056122448979592177e-02 9.693877551020407823e-02 1.173469387755102095e-01 8.290816326530611735e-02 1.250000000000000000e-01 1.198979591836734637e-01 7.908163265306122902e-02 9.438775510204081010e-02 6.547619047619047949e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.285714285714285754e-02 2.619047619047619110e-02 2.619047619047619110e-02 3.253968253968254204e-02 3.571428571428571230e-02 2.619047619047619110e-02 7.936507936507936067e-02 8.928571428571428770e-02 8.432539682539683112e-02 1.329365079365079361e-01 1.101190476190476164e-01 9.027777777777777624e-02 1.051587301587301598e-01 5.952380952380952051e-02 4.761904761904761640e-02 5.555555555555555247e-02 6.507936507936508408e-02 8.174603174603174705e-02 5.317460317460317304e-02 4.365079365079364837e-02 4.682539682539682557e-02 5.000000000000000278e-02 4.365079365079364837e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.682539682539682557e-02 4.682539682539682557e-02 5.000000000000000278e-02 5.317460317460317304e-02 5.317460317460317304e-02 5.000000000000000278e-02 4.090909090909090884e-02 4.025974025974025927e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 6.031746031746031828e-02 5.476190476190476164e-02 4.935064935064935321e-02 8.311688311688311681e-02 4.155844155844155841e-02 5.194805194805195148e-02 4.155844155844155841e-02 4.155844155844155841e-02 6.377551020408163407e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.415584415584415667e-02 4.415584415584415667e-02 1.129870129870129830e-01 6.363636363636362980e-02 6.363636363636362980e-02 6.948051948051947591e-02 5.064935064935065234e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 1.071428571428571369e-01 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 3.095238095238095344e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.571428571428571508e-02 1.250000000000000000e-01 1.214285714285714274e-01 5.238095238095238221e-02 7.380952380952381098e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.238095238095238221e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.714285714285714107e-02 5.238095238095238221e-02 5.714285714285714107e-02 1.700680272108843691e-02 2.210884353741496625e-02 2.210884353741496625e-02 1.700680272108843691e-02 2.210884353741496625e-02 1.309523809523809590e-01 7.619047619047619735e-02 8.809523809523810145e-02 1.119047619047619097e-01 1.261904761904761862e-01 5.272108843537415268e-02 5.782312925170068202e-02 6.122448979591836593e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 5.782312925170068202e-02 5.782312925170068202e-02 4.591836734693877792e-02 5.272108843537415268e-02 4.421768707482993249e-02 5.782312925170068202e-02 5.782312925170068202e-02 1.913265306122449022e-02 3.061224489795918297e-02 2.295918367346938896e-02 2.806122448979591830e-02 2.295918367346938896e-02 2.806122448979591830e-02 1.913265306122449022e-02 2.806122448979591830e-02 6.972789115646257918e-02 8.503401360544217413e-02 1.003401360544217691e-01 1.173469387755102095e-01 9.183673469387755584e-02 7.482993197278911546e-02 7.823129251700680631e-02 1.156462585034013640e-01 1.326530612244898044e-01 1.343537414965986498e-01 8.163265306122448328e-02 5.102040816326530726e-02 5.867346938775510473e-02 5.612244897959183659e-02 5.484693877551020252e-02 6.250000000000000000e-02 8.290816326530611735e-02 7.270408163265305868e-02 5.102040816326530726e-02 4.591836734693877792e-02 4.464285714285714385e-02 4.464285714285714385e-02 4.719387755102040505e-02 4.974489795918367319e-02 4.719387755102040505e-02 5.484693877551020252e-02 4.719387755102040505e-02 4.591836734693877792e-02 4.974489795918367319e-02 4.974489795918367319e-02 4.591836734693877792e-02 2.579365079365079222e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 3.075396825396825226e-02 2.678571428571428423e-02 3.075396825396825226e-02 3.075396825396825226e-02 6.377551020408163407e-02 8.673469387755101956e-02 8.928571428571428770e-02 1.096938775510204050e-01 8.163265306122448328e-02 1.198979591836734637e-01 1.198979591836734637e-01 7.780612244897959495e-02 9.438775510204081010e-02 6.646825396825396803e-02 4.365079365079364837e-02 4.662698412698412787e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.563492063492063239e-02 5.257936507936507992e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.662698412698412787e-02 4.861111111111111188e-02 4.365079365079364837e-02 3.809523809523809867e-02 2.460317460317460250e-02 2.460317460317460250e-02 3.174603174603174427e-02 3.412698412698412370e-02 2.539682539682539680e-02 7.638888888888889506e-02 8.035714285714286309e-02 7.837301587301587213e-02 1.359126984126984017e-01 1.081349206349206393e-01 8.829365079365079916e-02 9.821428571428571230e-02 5.853174603174603197e-02 4.603174603174603474e-02 5.079365079365079361e-02 6.031746031746031828e-02 8.333333333333332871e-02 5.158730158730158444e-02 4.285714285714285754e-02 4.444444444444444614e-02 4.603174603174603474e-02 4.206349206349206671e-02 4.841269841269841417e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.920634920634920501e-02 5.079365079365079361e-02 5.158730158730158444e-02 4.761904761904761640e-02 3.766233766233766100e-02 3.766233766233766100e-02 2.662337662337662184e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 6.031746031746031828e-02 5.396825396825397081e-02 4.805194805194805407e-02 8.116883116883116811e-02 4.090909090909090884e-02 4.870129870129870364e-02 4.025974025974025927e-02 4.025974025974025927e-02 6.250000000000000000e-02 4.415584415584415667e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.220779220779220797e-02 4.350649350649350711e-02 1.123376623376623334e-01 6.168831168831168804e-02 6.168831168831168804e-02 6.493506493506492894e-02 5.000000000000000278e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 7.738095238095238360e-02 3.928571428571428492e-02 3.571428571428571230e-02 5.000000000000000278e-02 3.571428571428571230e-02 5.000000000000000278e-02 2.380952380952380820e-02 3.333333333333333287e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.214285714285714246e-02 7.857142857142856984e-02 8.928571428571428770e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.285714285714285754e-02 5.000000000000000278e-02 5.952380952380952051e-02 4.523809523809523697e-02 4.523809523809523697e-02 6.428571428571427937e-02 5.952380952380952051e-02 6.428571428571427937e-02 1.700680272108843691e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.700680272108843691e-02 2.380952380952380820e-02 8.333333333333332871e-02 9.285714285714286031e-02 8.809523809523810145e-02 7.619047619047619735e-02 9.761904761904761918e-02 4.761904761904761640e-02 4.931972789115646183e-02 4.421768707482993249e-02 6.122448979591836593e-02 5.102040816326530726e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.442176870748299117e-02 6.802721088435374763e-02 6.802721088435374763e-02 5.102040816326530726e-02 6.122448979591836593e-02 4.761904761904761640e-02 6.802721088435374763e-02 6.802721088435374763e-02 1.913265306122449022e-02 3.443877551020407823e-02 2.423469387755101956e-02 3.188775510204081703e-02 2.423469387755101956e-02 3.188775510204081703e-02 1.913265306122449022e-02 3.188775510204081703e-02 8.163265306122448328e-02 8.163265306122448328e-02 7.142857142857142461e-02 7.653061224489796088e-02 8.163265306122448328e-02 9.863945578231292366e-02 9.353741496598640126e-02 7.993197278911565173e-02 8.843537414965986498e-02 8.333333333333332871e-02 8.333333333333332871e-02 4.846938775510203912e-02 5.102040816326530726e-02 5.229591836734694132e-02 4.974489795918367319e-02 5.612244897959183659e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.994897959183673186e-02 5.229591836734694132e-02 4.974489795918367319e-02 4.974489795918367319e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.229591836734694132e-02 6.250000000000000000e-02 5.229591836734694132e-02 5.229591836734694132e-02 5.739795918367347066e-02 5.739795918367347066e-02 5.229591836734694132e-02 2.777777777777777624e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 3.571428571428571230e-02 2.976190476190476025e-02 3.571428571428571230e-02 3.571428571428571230e-02 7.525510204081632681e-02 7.270408163265305868e-02 7.142857142857142461e-02 7.525510204081632681e-02 8.290816326530611735e-02 7.908163265306122902e-02 7.780612244897959495e-02 9.183673469387755584e-02 7.908163265306122902e-02 5.357142857142856845e-02 4.960317460317460042e-02 5.357142857142856845e-02 5.158730158730158444e-02 4.960317460317460042e-02 5.158730158730158444e-02 5.158730158730158444e-02 6.349206349206348854e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.753968253968253649e-02 4.960317460317460042e-02 4.285714285714285754e-02 2.619047619047619110e-02 2.619047619047619110e-02 3.730158730158730090e-02 4.047619047619047811e-02 2.777777777777777624e-02 7.142857142857142461e-02 6.944444444444444753e-02 7.043650793650793607e-02 8.531746031746031966e-02 7.242063492063492702e-02 7.242063492063492702e-02 7.043650793650793607e-02 6.944444444444444753e-02 5.079365079365079361e-02 5.238095238095238221e-02 5.555555555555555247e-02 5.158730158730158444e-02 6.269841269841269771e-02 5.000000000000000278e-02 5.158730158730158444e-02 5.317460317460317304e-02 4.841269841269841417e-02 5.793650793650793884e-02 5.317460317460317304e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.952380952380952051e-02 6.111111111111110911e-02 6.269841269841269771e-02 5.634920634920635024e-02 4.350649350649350711e-02 4.545454545454545581e-02 2.987012987012986967e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 6.507936507936508408e-02 6.428571428571427937e-02 4.935064935064935321e-02 4.935064935064935321e-02 4.805194805194805407e-02 5.844155844155844021e-02 4.675324675324675494e-02 4.675324675324675494e-02 7.397959183673469274e-02 5.194805194805195148e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.935064935064935321e-02 5.194805194805195148e-02 7.402597402597402287e-02 6.103896103896103847e-02 7.402597402597402287e-02 6.168831168831168804e-02 5.974025974025973934e-02
-0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 0.000000000000000000e+00 9.523809523809523281e-02 4.285714285714285754e-02 3.571428571428571230e-02 4.642857142857143016e-02 3.571428571428571230e-02 4.642857142857143016e-02 2.380952380952380820e-02 3.095238095238095344e-02 1.700680272108843691e-02 1.190476190476190410e-02 1.190476190476190410e-02 8.214285714285714246e-02 1.035714285714285782e-01 1.071428571428571369e-01 4.761904761904761640e-02 6.428571428571427937e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.238095238095238221e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.714285714285714107e-02 5.238095238095238221e-02 5.714285714285714107e-02 1.700680272108843691e-02 2.210884353741496625e-02 2.210884353741496625e-02 1.700680272108843691e-02 2.210884353741496625e-02 1.071428571428571369e-01 7.857142857142856984e-02 8.571428571428571508e-02 9.285714285714286031e-02 1.119047619047619097e-01 4.931972789115646183e-02 5.272108843537415268e-02 5.442176870748299117e-02 5.272108843537415268e-02 4.591836734693877792e-02 4.421768707482993249e-02 4.421768707482993249e-02 4.931972789115646183e-02 5.782312925170068202e-02 5.782312925170068202e-02 4.591836734693877792e-02 5.272108843537415268e-02 4.421768707482993249e-02 5.782312925170068202e-02 5.782312925170068202e-02 1.913265306122449022e-02 3.061224489795918297e-02 2.295918367346938896e-02 2.806122448979591830e-02 2.295918367346938896e-02 2.806122448979591830e-02 1.913265306122449022e-02 2.806122448979591830e-02 6.972789115646257918e-02 8.673469387755101956e-02 8.333333333333332871e-02 9.353741496598640126e-02 9.013605442176871041e-02 7.993197278911565173e-02 7.993197278911565173e-02 9.523809523809523281e-02 1.088435374149659823e-01 1.071428571428571369e-01 7.823129251700680631e-02 4.846938775510203912e-02 5.357142857142856845e-02 5.229591836734694132e-02 5.102040816326530726e-02 5.739795918367347066e-02 6.760204081632653628e-02 5.994897959183673186e-02 5.102040816326530726e-02 4.591836734693877792e-02 4.464285714285714385e-02 4.464285714285714385e-02 4.719387755102040505e-02 4.974489795918367319e-02 4.719387755102040505e-02 5.484693877551020252e-02 4.719387755102040505e-02 4.591836734693877792e-02 4.974489795918367319e-02 4.974489795918367319e-02 4.591836734693877792e-02 2.579365079365079222e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 3.075396825396825226e-02 2.678571428571428423e-02 3.075396825396825226e-02 3.075396825396825226e-02 6.377551020408163407e-02 7.908163265306122902e-02 7.525510204081632681e-02 8.673469387755101956e-02 8.545918367346938549e-02 9.438775510204081010e-02 9.438775510204081010e-02 7.908163265306122902e-02 9.438775510204081010e-02 6.051587301587301598e-02 4.365079365079364837e-02 4.662698412698412787e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.563492063492063239e-02 5.257936507936507992e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.662698412698412787e-02 4.861111111111111188e-02 4.365079365079364837e-02 3.809523809523809867e-02 2.460317460317460250e-02 2.460317460317460250e-02 3.174603174603174427e-02 3.412698412698412370e-02 2.539682539682539680e-02 7.440476190476190410e-02 6.845238095238095899e-02 7.142857142857142461e-02 1.061507936507936484e-01 8.432539682539683112e-02 8.234126984126984017e-02 7.837301587301587213e-02 5.853174603174603197e-02 4.603174603174603474e-02 4.920634920634920501e-02 5.555555555555555247e-02 6.746031746031745657e-02 5.158730158730158444e-02 4.285714285714285754e-02 4.444444444444444614e-02 4.603174603174603474e-02 4.206349206349206671e-02 4.841269841269841417e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.920634920634920501e-02 5.079365079365079361e-02 5.158730158730158444e-02 4.761904761904761640e-02 3.766233766233766100e-02 3.766233766233766100e-02 2.662337662337662184e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 5.873015873015872967e-02 5.396825396825397081e-02 4.805194805194805407e-02 6.298701298701298024e-02 4.090909090909090884e-02 4.870129870129870364e-02 4.025974025974025927e-02 4.025974025974025927e-02 6.250000000000000000e-02 4.415584415584415667e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.220779220779220797e-02 4.350649350649350711e-02 8.506493506493506551e-02 6.038961038961038891e-02 7.077922077922077504e-02 5.974025974025973934e-02 5.000000000000000278e-02
-0.000000000000000000e+00 0.000000000000000000e+00 9.259259259259258745e-02 3.703703703703703498e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 9.259259259259258745e-02 4.444444444444444614e-02 5.555555555555555247e-02 2.500000000000000139e-02 2.222222222222222307e-02 2.500000000000000139e-02 1.481481481481481538e-02 1.666666666666666644e-02 1.058201058201058142e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.499999999999999722e-02 8.888888888888889228e-02 8.611111111111111049e-02 4.444444444444444614e-02 6.666666666666666574e-02 5.555555555555555247e-02 6.666666666666666574e-02 2.592592592592592518e-02 2.407407407407407413e-02 2.407407407407407413e-02 2.777777777777777624e-02 2.592592592592592518e-02 2.777777777777777624e-02 1.058201058201058142e-02 1.190476190476190410e-02 1.190476190476190410e-02 1.058201058201058142e-02 1.190476190476190410e-02 8.333333333333332871e-02 6.296296296296295669e-02 6.481481481481481122e-02 7.222222222222221544e-02 7.962962962962963354e-02 4.100529100529100301e-02 4.365079365079364837e-02 5.291005291005290712e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.645502645502645356e-02 2.645502645502645356e-02 2.380952380952380820e-02 2.513227513227513088e-02 2.380952380952380820e-02 2.645502645502645356e-02 2.645502645502645356e-02 1.190476190476190410e-02 1.488095238095238013e-02 1.289682539682539611e-02 1.388888888888888812e-02 1.289682539682539611e-02 1.388888888888888812e-02 1.190476190476190410e-02 1.388888888888888812e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 6.878306878306877925e-02 6.746031746031745657e-02 5.291005291005290712e-02 5.555555555555555247e-02 6.878306878306877925e-02 7.804232804232803800e-02 7.936507936507936067e-02 5.952380952380952051e-02 3.769841269841269632e-02 4.166666666666666435e-02 3.968253968253968034e-02 3.968253968253968034e-02 4.265873015873015983e-02 5.952380952380952051e-02 5.952380952380952051e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.579365079365079222e-02 2.380952380952380820e-02 2.281746031746031619e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.281746031746031619e-02 1.388888888888888812e-02 1.311728395061728322e-02 1.311728395061728322e-02 1.311728395061728322e-02 1.311728395061728322e-02 1.466049382716049301e-02 1.388888888888888812e-02 1.466049382716049301e-02 1.466049382716049301e-02 4.365079365079364837e-02 5.059523809523809590e-02 5.357142857142856845e-02 5.952380952380952051e-02 5.555555555555555247e-02 6.547619047619047949e-02 6.646825396825396803e-02 5.357142857142856845e-02 6.051587301587301598e-02 7.098765432098765038e-02 2.160493827160493707e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.160493827160493707e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.160493827160493707e-02 1.851851851851851749e-02 1.358024691358024685e-02 1.358024691358024685e-02 1.481481481481481538e-02 1.543209876543209791e-02 1.358024691358024685e-02 4.320987654320987414e-02 4.783950617283950352e-02 4.552469135802469230e-02 7.253086419753086711e-02 5.709876543209876226e-02 5.092592592592592310e-02 5.169753086419753146e-02 3.780864197530864335e-02 2.962962962962963076e-02 3.333333333333333287e-02 3.827160493827160698e-02 5.308641975308642236e-02 2.222222222222222307e-02 2.037037037037037202e-02 2.098765432098765454e-02 2.160493827160493707e-02 2.037037037037037202e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.098765432098765454e-02 2.098765432098765454e-02 2.160493827160493707e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.160493827160493707e-02 1.767676767676767610e-02 1.616161616161616160e-02 1.363636363636363570e-02 1.363636363636363570e-02 1.363636363636363570e-02 1.363636363636363570e-02 1.363636363636363570e-02 3.888888888888888951e-02 3.333333333333333287e-02 3.232323232323232320e-02 4.797979797979797650e-02 1.919191919191919060e-02 2.121212121212121340e-02 1.919191919191919060e-02 1.919191919191919060e-02 2.876984126984126824e-02 2.020202020202020374e-02 1.919191919191919060e-02 1.919191919191919060e-02 1.919191919191919060e-02 1.969696969696969543e-02 1.969696969696969543e-02 5.454545454545454281e-02 3.333333333333333287e-02 3.838383838383838120e-02 3.535353535353535220e-02 2.979797979797979904e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.944444444444444753e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.499999999999999722e-02 7.222222222222221544e-02 8.055555555555556080e-02 3.703703703703703498e-02 4.444444444444444614e-02 3.968253968253968034e-02 4.444444444444444614e-02 3.703703703703703498e-02 4.444444444444444614e-02 3.333333333333333287e-02 4.444444444444444614e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.592592592592592449e-02 7.777777777777777901e-02 7.592592592592592449e-02 8.333333333333332871e-02 7.962962962962963354e-02 8.703703703703703776e-02 3.968253968253968034e-02 5.555555555555555247e-02 4.497354497354497105e-02 4.100529100529100301e-02 5.026455026455026176e-02 4.074074074074074403e-02 5.185185185185185036e-02 5.555555555555555247e-02 4.074074074074074403e-02 5.555555555555555247e-02 2.910052910052909891e-02 2.910052910052909891e-02 1.587301587301587213e-02 6.746031746031745657e-02 7.407407407407406996e-02 7.804232804232803800e-02 7.804232804232803800e-02 7.407407407407406996e-02 8.068783068783068335e-02 8.068783068783068335e-02 7.804232804232803800e-02 7.539682539682539264e-02 7.539682539682539264e-02 8.333333333333332871e-02 8.333333333333332871e-02 4.563492063492063239e-02 5.952380952380952051e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.059523809523809590e-02 4.960317460317460042e-02 4.166666666666666435e-02 5.357142857142856845e-02 5.291005291005290712e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.820105820105819783e-02 5.820105820105819783e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.232804232804232569e-02 5.291005291005290712e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.174603174603174427e-02 3.571428571428571230e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.250000000000000000e-02 6.349206349206348854e-02 7.837301587301587213e-02 7.440476190476190410e-02 7.738095238095238360e-02 7.142857142857142461e-02 7.936507936507936067e-02 7.738095238095238360e-02 7.638888888888889506e-02 7.242063492063492702e-02 7.936507936507936067e-02 7.440476190476190410e-02 7.638888888888889506e-02 4.475308641975308394e-02 5.092592592592592310e-02 4.783950617283950352e-02 5.246913580246913289e-02 4.320987654320987414e-02 4.783950617283950352e-02 4.706790123456790209e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.158730158730158444e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.960317460317460042e-02 4.365079365079364837e-02 2.160493827160493707e-02 7.716049382716048954e-02 7.330246913580247548e-02 7.716049382716048954e-02 7.561728395061728669e-02 7.407407407407406996e-02 7.793209876543209791e-02 6.867283950617283916e-02 7.561728395061728669e-02 8.024691358024690913e-02 7.561728395061728669e-02 7.638888888888889506e-02 5.787037037037037063e-02 7.484567901234567833e-02 4.691358024691358319e-02 4.691358024691358319e-02 4.814814814814814825e-02 4.567901234567901120e-02 6.049382716049382658e-02 4.444444444444444614e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.475308641975308394e-02 4.629629629629629373e-02 4.320987654320987414e-02 4.320987654320987414e-02 4.475308641975308394e-02 4.938271604938271331e-02 3.456790123456789793e-02 3.703703703703703498e-02 3.703703703703703498e-02 2.345679012345679160e-02 6.975308641975308532e-02 5.308641975308642236e-02 7.469135802469135943e-02 7.530864197530864890e-02 6.913580246913579586e-02 5.864197530864197205e-02 7.654320987654321395e-02 5.617283950617284194e-02 5.370370370370370489e-02 6.111111111111110911e-02 6.358024691358024616e-02 6.234567901234568110e-02 6.790123456790123080e-02 4.646464646464646547e-02 5.757575757575757874e-02 4.444444444444444614e-02 5.151515151515151381e-02 5.151515151515151381e-02 4.747474747474747514e-02 4.747474747474747514e-02 4.320987654320987414e-02 4.691358024691358319e-02 2.828282828282828454e-02 2.424242424242424240e-02 4.949494949494949447e-02 6.767676767676768235e-02 7.121212121212121271e-02 7.020202020202019610e-02 7.341269841269841556e-02 7.222222222222221544e-02 6.262626262626262708e-02 5.404040404040404144e-02 5.505050505050505111e-02 6.717171717171717404e-02 5.202020202020202211e-02 4.444444444444444614e-02 4.040404040404040747e-02 4.444444444444444614e-02 4.343434343434343647e-02 4.444444444444444614e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.944444444444444753e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.499999999999999722e-02 7.222222222222221544e-02 8.333333333333332871e-02 3.703703703703703498e-02 4.629629629629629373e-02 3.968253968253968034e-02 4.444444444444444614e-02 3.703703703703703498e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.148148148148148806e-02 7.592592592592592449e-02 7.777777777777777901e-02 8.518518518518518323e-02 8.148148148148148806e-02 9.259259259259258745e-02 3.968253968253968034e-02 5.687830687830687515e-02 4.894179894179893908e-02 4.100529100529100301e-02 5.026455026455026176e-02 4.259259259259259162e-02 5.555555555555555247e-02 6.111111111111110911e-02 4.259259259259259162e-02 6.111111111111110911e-02 3.042328042328042159e-02 3.042328042328042159e-02 1.587301587301587213e-02 7.275132275132274728e-02 7.671957671957671532e-02 7.539682539682539264e-02 7.671957671957671532e-02 7.804232804232803800e-02 8.730158730158729674e-02 8.597883597883597406e-02 7.804232804232803800e-02 8.068783068783068335e-02 7.804232804232803800e-02 8.730158730158729674e-02 8.597883597883597406e-02 4.563492063492063239e-02 5.853174603174603197e-02 5.456349206349206393e-02 5.753968253968253649e-02 4.960317460317460042e-02 5.357142857142856845e-02 4.166666666666666435e-02 5.753968253968253649e-02 5.687830687830687515e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.158730158730158444e-02 4.497354497354497105e-02 5.687830687830687515e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.571428571428571230e-02 3.373015873015872829e-02 3.869047619047619180e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.746031746031745657e-02 6.845238095238095899e-02 7.539682539682539264e-02 7.837301587301587213e-02 7.539682539682539264e-02 7.738095238095238360e-02 7.936507936507936067e-02 8.333333333333332871e-02 8.035714285714286309e-02 7.539682539682539264e-02 8.134920634920635163e-02 7.837301587301587213e-02 7.539682539682539264e-02 4.475308641975308394e-02 5.169753086419753146e-02 4.706790123456790209e-02 5.015432098765432167e-02 4.706790123456790209e-02 5.169753086419753146e-02 5.092592592592592310e-02 5.478395061728395105e-02 5.401234567901234268e-02 5.555555555555555247e-02 4.662698412698412787e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.662698412698412787e-02 5.357142857142856845e-02 4.662698412698412787e-02 2.237654320987654197e-02 7.330246913580247548e-02 8.024691358024690913e-02 7.330246913580247548e-02 7.484567901234567833e-02 7.947530864197531464e-02 7.561728395061728669e-02 7.407407407407406996e-02 7.407407407407406996e-02 7.870370370370370627e-02 8.024691358024690913e-02 7.793209876543209791e-02 6.250000000000000000e-02 7.253086419753086711e-02 5.061728395061728530e-02 4.814814814814814825e-02 4.691358024691358319e-02 4.938271604938271331e-02 6.481481481481481122e-02 4.814814814814814825e-02 4.629629629629629373e-02 5.092592592592592310e-02 4.861111111111111188e-02 5.092592592592592310e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.861111111111111188e-02 5.324074074074074125e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.469135802469135665e-02 7.469135802469135943e-02 5.740740740740740700e-02 7.345679012345679437e-02 8.086419753086419859e-02 6.666666666666666574e-02 6.358024691358024616e-02 7.345679012345679437e-02 6.049382716049382658e-02 5.802469135802468952e-02 6.666666666666666574e-02 6.913580246913579586e-02 6.790123456790123080e-02 7.345679012345679437e-02 5.000000000000000278e-02 6.060606060606060774e-02 4.797979797979797650e-02 4.898989898989899311e-02 5.000000000000000278e-02 4.898989898989899311e-02 5.000000000000000278e-02 4.629629629629629373e-02 5.061728395061728530e-02 3.030303030303030387e-02 2.575757575757575690e-02 5.353535353535353314e-02 7.373737373737374035e-02 6.818181818181817677e-02 6.969696969696970168e-02 7.837301587301587213e-02 7.222222222222221544e-02 6.111111111111110911e-02 5.757575757575757874e-02 5.808080808080808011e-02 6.666666666666666574e-02 5.606060606060606077e-02 4.848484848484848481e-02 4.343434343434343647e-02 4.848484848484848481e-02 4.747474747474747514e-02 4.797979797979797650e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.481481481481481122e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.611111111111111049e-02 6.666666666666666574e-02 7.777777777777777901e-02 3.703703703703703498e-02 4.629629629629629373e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.777777777777777901e-02 8.148148148148148806e-02 7.222222222222221544e-02 9.814814814814815103e-02 8.518518518518518323e-02 8.703703703703703776e-02 3.703703703703703498e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.232804232804232569e-02 5.555555555555555247e-02 4.259259259259259162e-02 5.370370370370370489e-02 6.111111111111110911e-02 4.259259259259259162e-02 6.111111111111110911e-02 3.042328042328042159e-02 3.042328042328042159e-02 1.587301587301587213e-02 7.010582010582010193e-02 7.407407407407406996e-02 8.730158730158729674e-02 8.201058201058200603e-02 8.333333333333332871e-02 8.068783068783068335e-02 8.597883597883597406e-02 7.936507936507936067e-02 7.539682539682539264e-02 7.275132275132274728e-02 8.730158730158729674e-02 9.126984126984126477e-02 4.166666666666666435e-02 7.242063492063492702e-02 4.960317460317460042e-02 5.456349206349206393e-02 5.158730158730158444e-02 5.257936507936507992e-02 4.166666666666666435e-02 5.555555555555555247e-02 5.423280423280422979e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 4.629629629629629373e-02 5.158730158730158444e-02 4.497354497354497105e-02 5.423280423280422979e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.472222222222222376e-02 3.373015873015872829e-02 3.869047619047619180e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.448412698412699096e-02 6.349206349206348854e-02 8.234126984126984017e-02 7.142857142857142461e-02 9.226190476190476719e-02 7.043650793650793607e-02 8.432539682539683112e-02 8.928571428571428770e-02 7.539682539682539264e-02 7.142857142857142461e-02 8.035714285714286309e-02 7.936507936507936067e-02 8.035714285714286309e-02 4.783950617283950352e-02 4.706790123456790209e-02 4.783950617283950352e-02 5.478395061728395105e-02 4.552469135802469230e-02 5.015432098765432167e-02 4.861111111111111188e-02 5.246913580246913289e-02 5.632716049382716084e-02 5.257936507936507992e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.960317460317460042e-02 4.563492063492063239e-02 5.158730158730158444e-02 4.563492063492063239e-02 2.237654320987654197e-02 7.716049382716048954e-02 7.330246913580247548e-02 8.641975308641974829e-02 7.716049382716048954e-02 7.175925925925925875e-02 8.873456790123457338e-02 6.790123456790123080e-02 9.413580246913580418e-02 8.950617283950616787e-02 7.561728395061728669e-02 7.793209876543209791e-02 5.941358024691358042e-02 8.333333333333332871e-02 5.061728395061728530e-02 4.444444444444444614e-02 5.061728395061728530e-02 4.753086419753086572e-02 5.864197530864197205e-02 4.506172839506172867e-02 4.475308641975308394e-02 5.092592592592592310e-02 4.783950617283950352e-02 5.092592592592592310e-02 4.475308641975308394e-02 4.475308641975308394e-02 4.783950617283950352e-02 5.015432098765432167e-02 3.580246913580246992e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.469135802469135665e-02 7.098765432098765038e-02 5.370370370370370489e-02 7.160493827160493985e-02 7.098765432098765038e-02 6.913580246913579586e-02 5.987654320987654405e-02 8.209876543209876365e-02 5.679012345679012447e-02 5.493827160493826994e-02 6.172839506172839164e-02 6.481481481481481122e-02 6.296296296296295669e-02 6.666666666666666574e-02 4.949494949494949447e-02 6.464646464646464641e-02 4.393939393939393784e-02 5.808080808080808011e-02 5.404040404040404144e-02 5.202020202020202211e-02 4.797979797979797650e-02 4.382716049382716361e-02 4.753086419753086572e-02 2.929292929292929420e-02 2.575757575757575690e-02 5.000000000000000278e-02 6.666666666666666574e-02 8.030303030303030665e-02 6.818181818181817677e-02 7.440476190476190410e-02 6.666666666666666574e-02 6.262626262626262708e-02 5.353535353535353314e-02 5.707070707070707044e-02 6.464646464646464641e-02 5.252525252525252347e-02 4.646464646464646547e-02 4.141414141414141714e-02 4.646464646464646547e-02 4.595959595959595717e-02 4.494949494949494750e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.870370370370370627e-02 3.611111111111110772e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.777777777777777901e-02 8.611111111111111049e-02 9.166666666666666019e-02 4.074074074074074403e-02 4.814814814814814825e-02 4.497354497354497105e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.444444444444444614e-02 3.333333333333333287e-02 4.444444444444444614e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.407407407407406996e-02 8.703703703703703776e-02 9.074074074074074681e-02 8.518518518518518323e-02 8.518518518518518323e-02 9.814814814814815103e-02 4.497354497354497105e-02 6.613756613756613389e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 4.074074074074074403e-02 5.185185185185185036e-02 5.555555555555555247e-02 4.074074074074074403e-02 5.555555555555555247e-02 2.910052910052909891e-02 2.910052910052909891e-02 1.587301587301587213e-02 6.746031746031745657e-02 7.804232804232803800e-02 8.465608465608465139e-02 8.862433862433861942e-02 8.201058201058200603e-02 7.671957671957671532e-02 7.936507936507936067e-02 8.862433862433861942e-02 7.671957671957671532e-02 9.126984126984126477e-02 8.994708994708994210e-02 8.730158730158729674e-02 5.555555555555555247e-02 6.150793650793650452e-02 6.547619047619047949e-02 5.158730158730158444e-02 5.753968253968253649e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.555555555555555247e-02 5.291005291005290712e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.820105820105819783e-02 5.820105820105819783e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.232804232804232569e-02 5.291005291005290712e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.174603174603174427e-02 3.571428571428571230e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.250000000000000000e-02 6.646825396825396803e-02 8.035714285714286309e-02 8.234126984126984017e-02 8.134920634920635163e-02 7.242063492063492702e-02 9.027777777777777624e-02 8.432539682539683112e-02 9.226190476190476719e-02 7.837301587301587213e-02 9.027777777777777624e-02 8.333333333333332871e-02 8.432539682539683112e-02 4.783950617283950352e-02 6.327160493827160836e-02 5.555555555555555247e-02 6.018518518518518184e-02 5.092592592592592310e-02 4.783950617283950352e-02 5.015432098765432167e-02 5.246913580246913289e-02 5.246913580246913289e-02 5.158730158730158444e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.960317460317460042e-02 4.365079365079364837e-02 2.160493827160493707e-02 7.561728395061728669e-02 7.484567901234567833e-02 7.716049382716048954e-02 8.024691358024690913e-02 8.333333333333332871e-02 8.410493827160493707e-02 6.867283950617283916e-02 7.793209876543209791e-02 8.873456790123457338e-02 9.182098765432099297e-02 8.873456790123457338e-02 5.787037037037037063e-02 8.101851851851851749e-02 5.061728395061728530e-02 5.679012345679012447e-02 5.432098765432098741e-02 4.567901234567901120e-02 6.172839506172839164e-02 4.938271604938271331e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.475308641975308394e-02 4.629629629629629373e-02 4.320987654320987414e-02 4.320987654320987414e-02 4.475308641975308394e-02 4.938271604938271331e-02 3.456790123456789793e-02 3.703703703703703498e-02 3.703703703703703498e-02 2.345679012345679160e-02 6.975308641975308532e-02 5.555555555555555247e-02 7.160493827160493985e-02 8.148148148148148806e-02 6.790123456790123080e-02 5.987654320987654405e-02 7.407407407407406996e-02 5.740740740740740700e-02 5.370370370370370489e-02 5.987654320987654405e-02 6.604938271604937627e-02 6.111111111111110911e-02 7.222222222222221544e-02 4.949494949494949447e-02 5.757575757575757874e-02 4.747474747474747514e-02 5.656565656565656908e-02 5.959595959595959808e-02 5.353535353535353314e-02 5.757575757575757874e-02 4.320987654320987414e-02 4.691358024691358319e-02 2.828282828282828454e-02 2.424242424242424240e-02 5.151515151515151381e-02 7.020202020202019610e-02 7.727272727272727071e-02 7.373737373737374035e-02 7.440476190476190410e-02 6.767676767676768235e-02 6.161616161616161741e-02 5.808080808080808011e-02 5.707070707070707044e-02 6.464646464646464641e-02 5.303030303030303177e-02 4.444444444444444614e-02 4.040404040404040747e-02 4.444444444444444614e-02 4.343434343434343647e-02 4.444444444444444614e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.796296296296296502e-02 3.888888888888888951e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.777777777777777901e-02 1.000000000000000056e-01 1.055555555555555552e-01 4.444444444444444614e-02 5.370370370370370489e-02 5.026455026455026176e-02 5.925925925925926152e-02 4.444444444444444614e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.592592592592592449e-02 9.074074074074074681e-02 1.074074074074074098e-01 8.518518518518518323e-02 8.888888888888889228e-02 1.148148148148148140e-01 5.026455026455026176e-02 7.804232804232803800e-02 4.894179894179893908e-02 5.423280423280422979e-02 5.952380952380952051e-02 4.259259259259259162e-02 5.370370370370370489e-02 6.111111111111110911e-02 4.259259259259259162e-02 6.111111111111110911e-02 3.042328042328042159e-02 3.042328042328042159e-02 1.587301587301587213e-02 6.878306878306877925e-02 8.597883597883597406e-02 8.201058201058200603e-02 9.391534391534391013e-02 9.126984126984126477e-02 7.936507936507936067e-02 7.936507936507936067e-02 9.523809523809523281e-02 8.068783068783068335e-02 1.097883597883597823e-01 9.656084656084655549e-02 8.862433862433861942e-02 6.547619047619047949e-02 5.853174603174603197e-02 7.837301587301587213e-02 5.257936507936507992e-02 6.150793650793650452e-02 5.059523809523809590e-02 5.753968253968253649e-02 5.853174603174603197e-02 5.423280423280422979e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 4.629629629629629373e-02 5.158730158730158444e-02 4.497354497354497105e-02 5.423280423280422979e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.472222222222222376e-02 3.373015873015872829e-02 3.869047619047619180e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.349206349206348854e-02 7.242063492063492702e-02 7.837301587301587213e-02 9.623015873015873523e-02 7.539682539682539264e-02 7.936507936507936067e-02 9.722222222222222376e-02 9.424603174603174427e-02 1.121031746031746074e-01 8.730158730158729674e-02 9.920634920634920084e-02 9.226190476190476719e-02 8.531746031746031966e-02 4.938271604938271331e-02 7.638888888888889506e-02 6.095679012345679021e-02 6.250000000000000000e-02 5.941358024691358042e-02 4.861111111111111188e-02 5.478395061728395105e-02 5.478395061728395105e-02 5.246913580246913289e-02 5.257936507936507992e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.960317460317460042e-02 4.563492063492063239e-02 5.158730158730158444e-02 4.563492063492063239e-02 2.237654320987654197e-02 7.407407407407406996e-02 7.947530864197531464e-02 7.175925925925925875e-02 8.333333333333332871e-02 1.003086419753086433e-01 7.947530864197531464e-02 7.098765432098765038e-02 6.944444444444444753e-02 8.950617283950616787e-02 1.126543209876543217e-01 9.876543209876542662e-02 5.864197530864197205e-02 7.638888888888889506e-02 5.802469135802468952e-02 6.790123456790123080e-02 5.679012345679012447e-02 4.629629629629629373e-02 6.666666666666666574e-02 5.617283950617284194e-02 4.475308641975308394e-02 5.092592592592592310e-02 4.783950617283950352e-02 5.092592592592592310e-02 4.475308641975308394e-02 4.475308641975308394e-02 4.783950617283950352e-02 5.015432098765432167e-02 3.580246913580246992e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.469135802469135665e-02 7.160493827160493985e-02 5.925925925925926152e-02 7.407407407407406996e-02 9.753086419753086156e-02 6.666666666666666574e-02 6.234567901234568110e-02 7.037037037037037479e-02 5.925925925925926152e-02 5.432098765432098741e-02 5.987654320987654405e-02 7.037037037037037479e-02 6.234567901234568110e-02 8.086419753086419859e-02 5.454545454545454281e-02 5.555555555555555247e-02 5.303030303030303177e-02 5.404040404040404144e-02 6.313131313131313538e-02 5.606060606060606077e-02 6.919191919191919338e-02 4.382716049382716361e-02 4.753086419753086572e-02 2.929292929292929420e-02 2.575757575757575690e-02 5.454545454545454281e-02 7.828282828282828731e-02 7.020202020202019610e-02 7.929292929292929004e-02 7.638888888888889506e-02 7.272727272727272374e-02 6.060606060606060774e-02 6.414141414141413811e-02 5.808080808080808011e-02 6.666666666666666574e-02 5.454545454545454281e-02 4.646464646464646547e-02 4.141414141414141714e-02 4.646464646464646547e-02 4.595959595959595717e-02 4.494949494949494750e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.870370370370370627e-02 3.611111111111110772e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.611111111111111049e-02 8.611111111111111049e-02 9.444444444444444198e-02 4.074074074074074403e-02 5.000000000000000278e-02 4.497354497354497105e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.407407407407406996e-02 9.814814814814815103e-02 9.259259259259258745e-02 9.814814814814815103e-02 9.074074074074074681e-02 1.037037037037037007e-01 4.497354497354497105e-02 6.746031746031745657e-02 5.158730158730158444e-02 4.761904761904761640e-02 5.952380952380952051e-02 4.259259259259259162e-02 5.370370370370370489e-02 6.111111111111110911e-02 4.259259259259259162e-02 6.111111111111110911e-02 3.042328042328042159e-02 3.042328042328042159e-02 1.587301587301587213e-02 6.878306878306877925e-02 7.539682539682539264e-02 1.018518518518518462e-01 1.018518518518518462e-01 8.862433862433861942e-02 7.539682539682539264e-02 8.333333333333332871e-02 9.656084656084655549e-02 7.671957671957671532e-02 9.391534391534391013e-02 9.523809523809523281e-02 9.523809523809523281e-02 5.555555555555555247e-02 7.242063492063492702e-02 6.646825396825396803e-02 5.059523809523809590e-02 6.349206349206348854e-02 5.059523809523809590e-02 4.960317460317460042e-02 5.654761904761904795e-02 5.423280423280422979e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 4.629629629629629373e-02 5.158730158730158444e-02 4.497354497354497105e-02 5.423280423280422979e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.472222222222222376e-02 3.373015873015872829e-02 3.869047619047619180e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.349206349206348854e-02 6.547619047619047949e-02 8.829365079365079916e-02 7.837301587301587213e-02 1.021825396825396803e-01 7.043650793650793607e-02 1.051587301587301598e-01 9.325396825396825573e-02 9.623015873015873523e-02 7.638888888888889506e-02 9.722222222222222376e-02 8.730158730158729674e-02 9.523809523809523281e-02 5.246913580246913289e-02 6.404320987654321673e-02 6.018518518518518184e-02 6.867283950617283916e-02 5.169753086419753146e-02 4.861111111111111188e-02 5.015432098765432167e-02 5.324074074074074125e-02 5.555555555555555247e-02 5.257936507936507992e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.960317460317460042e-02 4.563492063492063239e-02 5.158730158730158444e-02 4.563492063492063239e-02 2.237654320987654197e-02 7.793209876543209791e-02 7.561728395061728669e-02 8.873456790123457338e-02 8.410493827160493707e-02 7.947530864197531464e-02 1.049382716049382658e-01 6.790123456790123080e-02 1.010802469135802517e-01 1.103395061728395105e-01 9.645061728395061540e-02 9.722222222222222376e-02 5.864197530864197205e-02 9.799382716049383213e-02 5.432098765432098741e-02 5.802469135802468952e-02 6.172839506172839164e-02 4.629629629629629373e-02 6.049382716049382658e-02 4.876543209876543078e-02 4.475308641975308394e-02 5.092592592592592310e-02 4.783950617283950352e-02 5.092592592592592310e-02 4.475308641975308394e-02 4.475308641975308394e-02 4.783950617283950352e-02 5.015432098765432167e-02 3.580246913580246992e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.469135802469135665e-02 7.037037037037037479e-02 5.555555555555555247e-02 6.790123456790123080e-02 7.407407407407406996e-02 6.975308641975308532e-02 6.111111111111110911e-02 7.962962962962963354e-02 5.802469135802468952e-02 5.432098765432098741e-02 5.987654320987654405e-02 6.790123456790123080e-02 6.111111111111110911e-02 7.160493827160493985e-02 5.252525252525252347e-02 6.363636363636362980e-02 4.595959595959595717e-02 6.919191919191919338e-02 6.919191919191919338e-02 6.111111111111110911e-02 5.909090909090908977e-02 4.382716049382716361e-02 4.753086419753086572e-02 2.929292929292929420e-02 2.575757575757575690e-02 5.151515151515151381e-02 6.969696969696970168e-02 9.646464646464646131e-02 7.222222222222221544e-02 7.539682539682539264e-02 6.010101010101009944e-02 6.313131313131313538e-02 5.808080808080808011e-02 6.060606060606060774e-02 6.161616161616161741e-02 5.353535353535353314e-02 4.646464646464646547e-02 4.141414141414141714e-02 4.646464646464646547e-02 4.595959595959595717e-02 4.494949494949494750e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.481481481481481122e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.499999999999999722e-02 6.388888888888888395e-02 8.055555555555556080e-02 3.703703703703703498e-02 4.814814814814814825e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 5.000000000000000278e-02 3.333333333333333287e-02 5.000000000000000278e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.888888888888889228e-02 6.666666666666666574e-02 7.037037037037037479e-02 8.518518518518518323e-02 8.703703703703703776e-02 9.259259259259258745e-02 3.703703703703703498e-02 5.291005291005290712e-02 5.026455026455026176e-02 4.100529100529100301e-02 5.026455026455026176e-02 4.444444444444444614e-02 6.481481481481481122e-02 6.666666666666666574e-02 4.444444444444444614e-02 6.666666666666666574e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.587301587301587213e-02 9.259259259259258745e-02 7.407407407407406996e-02 6.481481481481481122e-02 6.746031746031745657e-02 7.275132275132274728e-02 9.788359788359787816e-02 9.523809523809523281e-02 7.407407407407406996e-02 9.391534391534391013e-02 7.010582010582010193e-02 9.656084656084655549e-02 9.391534391534391013e-02 3.968253968253968034e-02 5.555555555555555247e-02 4.861111111111111188e-02 6.051587301587301598e-02 4.365079365079364837e-02 6.051587301587301598e-02 3.968253968253968034e-02 6.051587301587301598e-02 7.275132275132274728e-02 5.423280423280422979e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 7.671957671957671532e-02 7.671957671957671532e-02 5.423280423280422979e-02 5.555555555555555247e-02 4.761904761904761640e-02 7.275132275132274728e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.067460317460317581e-02 3.571428571428571230e-02 4.166666666666666435e-02 1.785714285714285615e-02 1.785714285714285615e-02 8.928571428571428770e-02 7.837301587301587213e-02 6.845238095238095899e-02 7.043650793650793607e-02 6.746031746031745657e-02 8.234126984126984017e-02 7.142857142857142461e-02 7.936507936507936067e-02 7.341269841269841556e-02 7.837301587301587213e-02 8.134920634920635163e-02 7.936507936507936067e-02 7.638888888888889506e-02 4.166666666666666435e-02 4.475308641975308394e-02 4.089506172839506293e-02 4.166666666666666435e-02 4.320987654320987414e-02 6.404320987654321673e-02 5.401234567901234268e-02 6.635802469135802795e-02 6.481481481481481122e-02 7.539682539682539264e-02 5.257936507936507992e-02 5.357142857142856845e-02 5.357142857142856845e-02 6.349206349206348854e-02 5.357142857142856845e-02 5.257936507936507992e-02 6.349206349206348854e-02 5.257936507936507992e-02 2.314814814814814686e-02 6.867283950617283916e-02 7.407407407407406996e-02 6.790123456790123080e-02 6.867283950617283916e-02 7.098765432098765038e-02 6.790123456790123080e-02 9.876543209876542662e-02 6.712962962962963631e-02 7.021604938271605589e-02 7.330246913580247548e-02 7.484567901234567833e-02 8.564814814814815380e-02 6.944444444444444753e-02 5.432098765432098741e-02 4.320987654320987414e-02 4.074074074074074403e-02 6.419753086419753563e-02 7.345679012345679437e-02 4.876543209876543078e-02 5.401234567901234268e-02 5.555555555555555247e-02 5.478395061728395105e-02 5.555555555555555247e-02 5.401234567901234268e-02 5.401234567901234268e-02 5.478395061728395105e-02 7.484567901234567833e-02 4.320987654320987414e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.592592592592592518e-02 9.876543209876542662e-02 7.407407407407406996e-02 7.160493827160493985e-02 7.283950617283950490e-02 6.851851851851852027e-02 8.703703703703703776e-02 7.098765432098765038e-02 8.333333333333332871e-02 8.148148148148148806e-02 9.135802469135802240e-02 9.506172839506173144e-02 9.506172839506173144e-02 8.703703703703703776e-02 5.606060606060606077e-02 7.424242424242424865e-02 5.202020202020202211e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.242424242424242681e-02 6.358024691358024616e-02 7.283950617283950490e-02 3.535353535353535220e-02 2.727272727272727140e-02 7.121212121212121271e-02 9.040404040404040331e-02 6.616161616161615744e-02 6.818181818181817677e-02 1.061507936507936484e-01 7.222222222222221544e-02 6.717171717171717404e-02 6.818181818181817677e-02 6.717171717171717404e-02 6.969696969696970168e-02 7.878787878787878174e-02 5.858585858585858841e-02 5.555555555555555247e-02 5.858585858585858841e-02 5.909090909090908977e-02 7.020202020202019610e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.018518518518518184e-02 3.333333333333333287e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 9.722222222222222376e-02 6.111111111111110911e-02 7.499999999999999722e-02 3.703703703703703498e-02 4.814814814814814825e-02 3.439153439153438963e-02 3.703703703703703498e-02 4.444444444444444614e-02 5.000000000000000278e-02 3.333333333333333287e-02 5.000000000000000278e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.962962962962963354e-02 8.518518518518518323e-02 6.851851851851852027e-02 1.129629629629629595e-01 9.074074074074074681e-02 8.703703703703703776e-02 3.439153439153438963e-02 4.761904761904761640e-02 5.820105820105819783e-02 4.365079365079364837e-02 6.084656084656084318e-02 4.444444444444444614e-02 5.555555555555555247e-02 6.666666666666666574e-02 4.444444444444444614e-02 6.666666666666666574e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.587301587301587213e-02 7.275132275132274728e-02 7.407407407407406996e-02 9.788359788359787816e-02 8.597883597883597406e-02 9.259259259259258745e-02 8.068783068783068335e-02 9.126984126984126477e-02 8.068783068783068335e-02 7.539682539682539264e-02 7.010582010582010193e-02 9.126984126984126477e-02 9.920634920634920084e-02 3.769841269841269632e-02 8.531746031746031966e-02 4.563492063492063239e-02 5.555555555555555247e-02 5.257936507936507992e-02 5.555555555555555247e-02 4.166666666666666435e-02 5.753968253968253649e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.555555555555555247e-02 4.761904761904761640e-02 5.555555555555555247e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.166666666666666435e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.646825396825396803e-02 6.349206349206348854e-02 8.531746031746031966e-02 6.845238095238095899e-02 1.091269841269841279e-01 6.944444444444444753e-02 8.928571428571428770e-02 1.011904761904761918e-01 7.440476190476190410e-02 7.043650793650793607e-02 8.134920634920635163e-02 8.432539682539683112e-02 8.432539682539683112e-02 5.092592592592592310e-02 4.320987654320987414e-02 4.783950617283950352e-02 5.709876543209876226e-02 4.783950617283950352e-02 5.246913580246913289e-02 5.015432098765432167e-02 5.401234567901234268e-02 6.172839506172839164e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 4.761904761904761640e-02 2.314814814814814686e-02 7.561728395061728669e-02 7.407407407407406996e-02 9.413580246913580418e-02 7.793209876543209791e-02 6.944444444444444753e-02 1.010802469135802517e-01 6.712962962962963631e-02 1.157407407407407413e-01 9.876543209876542662e-02 7.561728395061728669e-02 7.947530864197531464e-02 6.095679012345679021e-02 9.336419753086419582e-02 5.432098765432098741e-02 4.197530864197530909e-02 5.308641975308642236e-02 4.938271604938271331e-02 5.679012345679012447e-02 4.567901234567901120e-02 4.629629629629629373e-02 5.555555555555555247e-02 5.092592592592592310e-02 5.555555555555555247e-02 4.629629629629629373e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 3.703703703703703498e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.592592592592592518e-02 7.222222222222221544e-02 5.432098765432098741e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.790123456790123080e-02 6.111111111111110911e-02 8.395061728395061817e-02 5.740740740740740700e-02 5.617283950617284194e-02 6.234567901234568110e-02 6.604938271604937627e-02 6.358024691358024616e-02 6.543209876543210068e-02 5.252525252525252347e-02 7.171717171717172101e-02 4.343434343434343647e-02 6.565656565656566301e-02 5.656565656565656908e-02 5.757575757575757874e-02 4.848484848484848481e-02 4.444444444444444614e-02 4.814814814814814825e-02 3.030303030303030387e-02 2.727272727272727140e-02 5.050505050505050414e-02 6.565656565656566301e-02 9.141414141414141992e-02 6.515151515151515471e-02 7.539682539682539264e-02 5.959595959595959808e-02 6.161616161616161741e-02 5.303030303030303177e-02 5.959595959595959808e-02 6.060606060606060774e-02 5.303030303030303177e-02 4.848484848484848481e-02 4.242424242424242681e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.545454545454545581e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.796296296296296502e-02 3.888888888888888951e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.611111111111111049e-02 1.000000000000000056e-01 1.083333333333333370e-01 4.444444444444444614e-02 5.555555555555555247e-02 5.026455026455026176e-02 5.925925925925926152e-02 4.444444444444444614e-02 5.000000000000000278e-02 3.333333333333333287e-02 5.000000000000000278e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.592592592592592449e-02 1.074074074074074098e-01 1.092592592592592643e-01 9.814814814814815103e-02 9.444444444444444198e-02 1.203703703703703637e-01 5.026455026455026176e-02 7.936507936507936067e-02 5.291005291005290712e-02 5.423280423280422979e-02 6.349206349206348854e-02 4.444444444444444614e-02 5.555555555555555247e-02 6.666666666666666574e-02 4.444444444444444614e-02 6.666666666666666574e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.587301587301587213e-02 7.010582010582010193e-02 7.936507936507936067e-02 1.058201058201058142e-01 1.150793650793650730e-01 9.259259259259258745e-02 7.804232804232803800e-02 8.333333333333332871e-02 1.071428571428571369e-01 8.068783068783068335e-02 1.124338624338624276e-01 1.018518518518518462e-01 9.656084656084655549e-02 6.547619047619047949e-02 6.944444444444444753e-02 7.936507936507936067e-02 5.158730158730158444e-02 7.043650793650793607e-02 5.158730158730158444e-02 5.753968253968253649e-02 5.952380952380952051e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.555555555555555247e-02 4.761904761904761640e-02 5.555555555555555247e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.166666666666666435e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.448412698412699096e-02 6.944444444444444753e-02 9.126984126984126477e-02 8.630952380952380820e-02 1.031746031746031689e-01 7.539682539682539264e-02 1.210317460317460320e-01 9.722222222222222376e-02 1.160714285714285754e-01 8.234126984126984017e-02 1.091269841269841279e-01 9.226190476190476719e-02 1.021825396825396803e-01 5.401234567901234268e-02 7.716049382716048954e-02 6.790123456790123080e-02 7.561728395061728669e-02 5.709876543209876226e-02 4.938271604938271331e-02 5.324074074074074125e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 4.761904761904761640e-02 2.314814814814814686e-02 8.024691358024690913e-02 7.870370370370370627e-02 8.950617283950616787e-02 8.873456790123457338e-02 8.950617283950616787e-02 1.103395061728395105e-01 7.021604938271605589e-02 9.876543209876542662e-02 1.250000000000000000e-01 1.172839506172839441e-01 1.118827160493827133e-01 5.941358024691358042e-02 1.010802469135802517e-01 6.172839506172839164e-02 6.913580246913579586e-02 6.790123456790123080e-02 4.691358024691358319e-02 6.543209876543210068e-02 5.308641975308642236e-02 4.629629629629629373e-02 5.555555555555555247e-02 5.092592592592592310e-02 5.555555555555555247e-02 4.629629629629629373e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 3.703703703703703498e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.592592592592592518e-02 7.222222222222221544e-02 5.802469135802468952e-02 7.037037037037037479e-02 8.271604938271605312e-02 7.160493827160493985e-02 6.358024691358024616e-02 8.148148148148148806e-02 5.987654320987654405e-02 5.493827160493826994e-02 5.987654320987654405e-02 7.222222222222221544e-02 6.234567901234568110e-02 7.901234567901234407e-02 5.757575757575757874e-02 6.161616161616161741e-02 4.949494949494949447e-02 7.171717171717172101e-02 7.878787878787878174e-02 6.363636363636362980e-02 6.666666666666666574e-02 4.444444444444444614e-02 4.814814814814814825e-02 3.030303030303030387e-02 2.727272727272727140e-02 5.353535353535353314e-02 7.676767676767676241e-02 9.949494949494949725e-02 7.727272727272727071e-02 7.738095238095238360e-02 6.262626262626262708e-02 6.464646464646464641e-02 6.212121212121211877e-02 6.161616161616161741e-02 6.363636363636362980e-02 5.505050505050505111e-02 4.848484848484848481e-02 4.242424242424242681e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.545454545454545581e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.064814814814814825e-01 4.444444444444444614e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.055555555555556080e-02 1.388888888888888951e-01 1.305555555555555636e-01 5.185185185185185036e-02 6.296296296296295669e-02 6.084656084656084318e-02 7.407407407407406996e-02 5.185185185185185036e-02 5.000000000000000278e-02 3.333333333333333287e-02 5.000000000000000278e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.592592592592592449e-02 1.148148148148148140e-01 1.537037037037037035e-01 8.703703703703703776e-02 9.814814814814815103e-02 1.425925925925926041e-01 6.084656084656084318e-02 1.005291005291005235e-01 5.291005291005290712e-02 7.275132275132274728e-02 6.878306878306877925e-02 4.444444444444444614e-02 5.555555555555555247e-02 6.666666666666666574e-02 4.444444444444444614e-02 6.666666666666666574e-02 3.174603174603174427e-02 3.174603174603174427e-02 1.587301587301587213e-02 7.010582010582010193e-02 8.994708994708994210e-02 9.788359788359787816e-02 1.256613756613756683e-01 9.788359788359787816e-02 7.804232804232803800e-02 7.804232804232803800e-02 1.256613756613756683e-01 8.597883597883597406e-02 1.653439153439153486e-01 1.097883597883597823e-01 9.391534391534391013e-02 9.325396825396825573e-02 5.753968253968253649e-02 1.111111111111111049e-01 5.158730158730158444e-02 7.837301587301587213e-02 5.158730158730158444e-02 8.134920634920635163e-02 6.349206349206348854e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.555555555555555247e-02 4.761904761904761640e-02 5.555555555555555247e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.166666666666666435e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.448412698412699096e-02 7.936507936507936067e-02 8.531746031746031966e-02 1.061507936507936484e-01 8.531746031746031966e-02 8.531746031746031966e-02 1.329365079365079361e-01 9.722222222222222376e-02 1.716269841269841279e-01 1.001984126984127033e-01 1.329365079365079361e-01 1.061507936507936484e-01 1.061507936507936484e-01 5.401234567901234268e-02 1.141975308641975245e-01 8.179012345679012586e-02 8.179012345679012586e-02 7.253086419753086711e-02 4.938271604938271331e-02 6.095679012345679021e-02 5.864197530864197205e-02 5.401234567901234268e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 4.761904761904761640e-02 2.314814814814814686e-02 7.561728395061728669e-02 8.024691358024690913e-02 7.561728395061728669e-02 9.182098765432099297e-02 1.126543209876543217e-01 9.645061728395061540e-02 7.330246913580247548e-02 7.561728395061728669e-02 1.172839506172839441e-01 1.774691358024691468e-01 1.427469135802469091e-01 5.941358024691358042e-02 9.182098765432099297e-02 6.913580246913579586e-02 9.876543209876542662e-02 7.283950617283950490e-02 4.691358024691358319e-02 7.283950617283950490e-02 6.543209876543210068e-02 4.629629629629629373e-02 5.555555555555555247e-02 5.092592592592592310e-02 5.555555555555555247e-02 4.629629629629629373e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 3.703703703703703498e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.592592592592592518e-02 7.345679012345679437e-02 6.419753086419753563e-02 7.160493827160493985e-02 1.012345679012345706e-01 6.790123456790123080e-02 6.604938271604937627e-02 7.160493827160493985e-02 6.234567901234568110e-02 5.493827160493826994e-02 5.864197530864197205e-02 7.716049382716048954e-02 6.234567901234568110e-02 9.753086419753086156e-02 6.262626262626262708e-02 5.353535353535353314e-02 5.959595959595959808e-02 6.565656565656566301e-02 8.686868686868687295e-02 6.565656565656566301e-02 8.686868686868687295e-02 4.444444444444444614e-02 4.814814814814814825e-02 3.030303030303030387e-02 2.727272727272727140e-02 5.858585858585858841e-02 9.191919191919191434e-02 8.737373737373736737e-02 8.737373737373736737e-02 7.936507936507936067e-02 6.767676767676768235e-02 6.161616161616161741e-02 7.222222222222221544e-02 6.161616161616161741e-02 6.464646464646464641e-02 5.707070707070707044e-02 4.848484848484848481e-02 4.242424242424242681e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.545454545454545581e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 9.722222222222222376e-02 4.166666666666666435e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.055555555555556080e-02 1.194444444444444503e-01 1.166666666666666685e-01 4.814814814814814825e-02 5.740740740740740700e-02 5.555555555555555247e-02 6.666666666666666574e-02 4.814814814814814825e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.592592592592592449e-02 1.074074074074074098e-01 1.296296296296296224e-01 8.703703703703703776e-02 9.629629629629629650e-02 1.259259259259259134e-01 5.555555555555555247e-02 8.862433862433861942e-02 5.158730158730158444e-02 6.349206349206348854e-02 6.481481481481481122e-02 4.259259259259259162e-02 5.555555555555555247e-02 6.111111111111110911e-02 4.259259259259259162e-02 6.111111111111110911e-02 3.042328042328042159e-02 3.042328042328042159e-02 1.587301587301587213e-02 7.142857142857142461e-02 8.465608465608465139e-02 9.656084656084655549e-02 1.150793650793650730e-01 9.126984126984126477e-02 7.804232804232803800e-02 8.068783068783068335e-02 1.150793650793650730e-01 8.465608465608465139e-02 1.362433862433862497e-01 1.058201058201058142e-01 9.523809523809523281e-02 7.936507936507936067e-02 6.051587301587301598e-02 9.424603174603174427e-02 5.158730158730158444e-02 7.242063492063492702e-02 5.158730158730158444e-02 6.944444444444444753e-02 6.150793650793650452e-02 5.687830687830687515e-02 4.761904761904761640e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.158730158730158444e-02 4.497354497354497105e-02 5.687830687830687515e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.571428571428571230e-02 3.373015873015872829e-02 3.869047619047619180e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.646825396825396803e-02 7.539682539682539264e-02 8.531746031746031966e-02 9.523809523809523281e-02 8.730158730158729674e-02 8.035714285714286309e-02 1.200396825396825434e-01 9.126984126984126477e-02 1.398809523809523836e-01 9.226190476190476719e-02 1.200396825396825434e-01 9.920634920634920084e-02 1.031746031746031689e-01 5.246913580246913289e-02 9.490740740740741255e-02 7.330246913580247548e-02 7.638888888888889506e-02 6.404320987654321673e-02 5.015432098765432167e-02 5.709876543209876226e-02 5.787037037037037063e-02 5.555555555555555247e-02 5.555555555555555247e-02 4.662698412698412787e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.357142857142856845e-02 4.960317460317460042e-02 4.662698412698412787e-02 5.357142857142856845e-02 4.662698412698412787e-02 2.237654320987654197e-02 7.638888888888889506e-02 7.793209876543209791e-02 7.793209876543209791e-02 8.873456790123457338e-02 9.876543209876542662e-02 9.722222222222222376e-02 7.484567901234567833e-02 7.947530864197531464e-02 1.118827160493827133e-01 1.427469135802469091e-01 1.250000000000000000e-01 6.172839506172839164e-02 9.336419753086419582e-02 6.172839506172839164e-02 8.271604938271605312e-02 6.790123456790123080e-02 4.814814814814814825e-02 6.975308641975308532e-02 5.925925925925926152e-02 4.629629629629629373e-02 5.092592592592592310e-02 4.861111111111111188e-02 5.092592592592592310e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.861111111111111188e-02 5.324074074074074125e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.469135802469135665e-02 7.530864197530864890e-02 6.296296296296295669e-02 7.037037037037037479e-02 9.012345679012345734e-02 6.913580246913579586e-02 6.666666666666666574e-02 7.283950617283950490e-02 6.358024691358024616e-02 5.740740740740740700e-02 6.234567901234568110e-02 7.592592592592592449e-02 6.481481481481481122e-02 8.888888888888889228e-02 5.858585858585858841e-02 5.757575757575757874e-02 5.505050505050505111e-02 6.515151515151515471e-02 7.929292929292929004e-02 6.212121212121211877e-02 7.525252525252525138e-02 4.629629629629629373e-02 5.061728395061728530e-02 3.030303030303030387e-02 2.575757575757575690e-02 5.808080808080808011e-02 8.434343434343434531e-02 9.040404040404040331e-02 8.232323232323232598e-02 8.134920634920635163e-02 6.515151515151515471e-02 6.313131313131313538e-02 6.818181818181817677e-02 6.212121212121211877e-02 6.414141414141413811e-02 5.858585858585858841e-02 4.848484848484848481e-02 4.343434343434343647e-02 4.848484848484848481e-02 4.747474747474747514e-02 4.797979797979797650e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.555555555555555247e-02 3.055555555555555455e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 6.944444444444444753e-02 5.277777777777777762e-02 6.666666666666666574e-02 3.333333333333333287e-02 4.259259259259259162e-02 3.174603174603174427e-02 3.333333333333333287e-02 3.703703703703703498e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.777777777777777901e-02 5.740740740740740700e-02 5.740740740740740700e-02 7.777777777777777901e-02 7.592592592592592449e-02 7.592592592592592449e-02 3.174603174603174427e-02 4.100529100529100301e-02 4.629629629629629373e-02 3.571428571428571230e-02 4.365079365079364837e-02 4.259259259259259162e-02 5.925925925925926152e-02 6.111111111111110911e-02 4.259259259259259162e-02 6.111111111111110911e-02 3.042328042328042159e-02 3.042328042328042159e-02 1.587301587301587213e-02 8.201058201058200603e-02 6.349206349206348854e-02 5.687830687830687515e-02 5.687830687830687515e-02 6.481481481481481122e-02 8.333333333333332871e-02 8.333333333333332871e-02 6.216931216931216586e-02 8.068783068783068335e-02 5.687830687830687515e-02 8.201058201058200603e-02 8.201058201058200603e-02 3.174603174603174427e-02 5.059523809523809590e-02 3.769841269841269632e-02 5.158730158730158444e-02 3.670634920634920778e-02 5.357142857142856845e-02 3.373015873015872829e-02 5.158730158730158444e-02 6.613756613756613389e-02 5.026455026455026176e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.878306878306877925e-02 6.878306878306877925e-02 5.026455026455026176e-02 5.158730158730158444e-02 4.497354497354497105e-02 6.613756613756613389e-02 3.373015873015872829e-02 3.373015873015872829e-02 3.769841269841269632e-02 3.373015873015872829e-02 3.869047619047619180e-02 1.785714285714285615e-02 1.785714285714285615e-02 8.333333333333332871e-02 6.746031746031745657e-02 5.853174603174603197e-02 5.853174603174603197e-02 6.051587301587301598e-02 6.944444444444444753e-02 5.952380952380952051e-02 7.043650793650793607e-02 5.952380952380952051e-02 6.646825396825396803e-02 6.746031746031745657e-02 6.845238095238095899e-02 6.547619047619047949e-02 3.703703703703703498e-02 3.472222222222222376e-02 3.395061728395061540e-02 3.472222222222222376e-02 3.780864197530864335e-02 5.864197530864197205e-02 4.706790123456790209e-02 5.709876543209876226e-02 5.709876543209876226e-02 6.944444444444444753e-02 4.861111111111111188e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.753968253968253649e-02 4.960317460317460042e-02 4.861111111111111188e-02 5.753968253968253649e-02 4.861111111111111188e-02 2.237654320987654197e-02 5.787037037037037063e-02 6.250000000000000000e-02 5.941358024691358042e-02 5.787037037037037063e-02 5.864197530864197205e-02 5.864197530864197205e-02 8.564814814814815380e-02 6.095679012345679021e-02 5.941358024691358042e-02 5.941358024691358042e-02 6.172839506172839164e-02 8.333333333333332871e-02 6.018518518518518184e-02 4.691358024691358319e-02 3.456790123456789793e-02 3.456790123456789793e-02 6.049382716049382658e-02 5.987654320987654405e-02 4.197530864197530909e-02 4.938271604938271331e-02 5.092592592592592310e-02 5.015432098765432167e-02 5.092592592592592310e-02 4.938271604938271331e-02 4.938271604938271331e-02 5.015432098765432167e-02 7.021604938271605589e-02 3.950617283950617203e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.469135802469135665e-02 8.641975308641974829e-02 7.037037037037037479e-02 5.987654320987654405e-02 5.987654320987654405e-02 5.802469135802468952e-02 8.209876543209876365e-02 6.111111111111110911e-02 8.024691358024690913e-02 7.901234567901234407e-02 8.456790123456790764e-02 8.456790123456790764e-02 8.703703703703703776e-02 7.345679012345679437e-02 4.848484848484848481e-02 6.515151515151515471e-02 4.343434343434343647e-02 3.484848484848485084e-02 3.484848484848485084e-02 3.585858585858586051e-02 3.484848484848485084e-02 5.987654320987654405e-02 6.913580246913579586e-02 3.232323232323232320e-02 2.575757575757575690e-02 6.868686868686868507e-02 7.626262626262626798e-02 5.707070707070707044e-02 5.707070707070707044e-02 9.821428571428571230e-02 6.010101010101009944e-02 5.707070707070707044e-02 6.161616161616161741e-02 6.161616161616161741e-02 5.858585858585858841e-02 7.575757575757575968e-02 5.252525252525252347e-02 5.050505050505050414e-02 5.252525252525252347e-02 5.353535353535353314e-02 6.717171717171717404e-02
-4.629629629629629373e-02 1.851851851851851749e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.870370370370370627e-02 3.611111111111110772e-02 2.777777777777777624e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.333333333333332871e-02 8.611111111111111049e-02 9.166666666666666019e-02 4.074074074074074403e-02 4.814814814814814825e-02 4.497354497354497105e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.444444444444444614e-02 3.333333333333333287e-02 4.444444444444444614e-02 2.222222222222222307e-02 1.111111111111111154e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.222222222222221544e-02 9.444444444444444198e-02 9.074074074074074681e-02 9.259259259259258745e-02 8.888888888888889228e-02 9.814814814814815103e-02 4.497354497354497105e-02 6.613756613756613389e-02 5.026455026455026176e-02 4.761904761904761640e-02 5.820105820105819783e-02 4.074074074074074403e-02 5.185185185185185036e-02 5.555555555555555247e-02 4.074074074074074403e-02 5.555555555555555247e-02 2.910052910052909891e-02 2.910052910052909891e-02 1.587301587301587213e-02 6.878306878306877925e-02 7.407407407407406996e-02 9.656084656084655549e-02 9.656084656084655549e-02 8.465608465608465139e-02 7.275132275132274728e-02 8.068783068783068335e-02 9.391534391534391013e-02 7.671957671957671532e-02 9.126984126984126477e-02 9.259259259259258745e-02 9.259259259259258745e-02 5.555555555555555247e-02 6.944444444444444753e-02 6.547619047619047949e-02 4.960317460317460042e-02 6.150793650793650452e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.555555555555555247e-02 5.423280423280422979e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.820105820105819783e-02 5.820105820105819783e-02 4.497354497354497105e-02 4.761904761904761640e-02 4.232804232804232569e-02 5.423280423280422979e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.373015873015872829e-02 3.174603174603174427e-02 3.571428571428571230e-02 1.785714285714285615e-02 1.785714285714285615e-02 6.448412698412699096e-02 6.547619047619047949e-02 8.432539682539683112e-02 7.638888888888889506e-02 9.523809523809523281e-02 6.845238095238095899e-02 9.821428571428571230e-02 8.730158730158729674e-02 9.226190476190476719e-02 7.638888888888889506e-02 9.424603174603174427e-02 8.531746031746031966e-02 9.325396825396825573e-02 5.092592592592592310e-02 6.327160493827160836e-02 5.864197530864197205e-02 6.635802469135802795e-02 5.092592592592592310e-02 4.861111111111111188e-02 4.938271604938271331e-02 5.324074074074074125e-02 5.555555555555555247e-02 5.357142857142856845e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.960317460317460042e-02 4.365079365079364837e-02 2.160493827160493707e-02 7.484567901234567833e-02 7.253086419753086711e-02 8.333333333333332871e-02 8.101851851851851749e-02 7.638888888888889506e-02 9.799382716049383213e-02 6.944444444444444753e-02 9.336419753086419582e-02 1.010802469135802517e-01 9.182098765432099297e-02 9.336419753086419582e-02 6.018518518518518184e-02 9.413580246913580418e-02 5.061728395061728530e-02 5.679012345679012447e-02 5.925925925925926152e-02 4.691358024691358319e-02 6.049382716049382658e-02 4.814814814814814825e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.475308641975308394e-02 4.629629629629629373e-02 4.320987654320987414e-02 4.320987654320987414e-02 4.475308641975308394e-02 5.169753086419753146e-02 3.456790123456789793e-02 3.703703703703703498e-02 3.703703703703703498e-02 2.345679012345679160e-02 7.222222222222221544e-02 5.679012345679012447e-02 6.543209876543210068e-02 7.098765432098765038e-02 6.790123456790123080e-02 6.234567901234568110e-02 7.469135802469135943e-02 5.987654320987654405e-02 5.617283950617284194e-02 6.172839506172839164e-02 6.851851851851852027e-02 6.296296296296295669e-02 7.222222222222221544e-02 5.000000000000000278e-02 6.313131313131313538e-02 4.595959595959595717e-02 6.565656565656566301e-02 6.565656565656566301e-02 5.858585858585858841e-02 5.757575757575757874e-02 4.506172839506172867e-02 4.938271604938271331e-02 2.828282828282828454e-02 2.424242424242424240e-02 5.303030303030303177e-02 7.020202020202019610e-02 9.242424242424242264e-02 7.121212121212121271e-02 7.738095238095238360e-02 5.808080808080808011e-02 6.212121212121211877e-02 5.858585858585858841e-02 6.060606060606060774e-02 6.010101010101009944e-02 5.555555555555555247e-02 4.444444444444444614e-02 4.141414141414141714e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.696969696969696684e-02
-2.962962962962963076e-02 3.703703703703703498e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.925925925925926152e-02 7.555555555555555636e-02 1.777777777777777776e-02 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 6.222222222222222043e-02 6.222222222222222043e-02 7.555555555555555636e-02 9.925925925925925541e-02 9.481481481481481011e-02 3.386243386243386472e-02 3.851851851851852138e-02 3.851851851851852138e-02 4.000000000000000083e-02 2.666666666666666838e-02 4.000000000000000083e-02 1.777777777777777776e-02 8.888888888888888881e-03 8.888888888888888881e-03 8.888888888888888881e-03 5.925925925925926152e-02 5.925925925925926152e-02 6.814814814814815214e-02 6.814814814814815214e-02 6.814814814814815214e-02 8.592592592592591949e-02 3.386243386243386472e-02 5.291005291005290712e-02 4.021164021164021218e-02 4.021164021164021218e-02 4.656084656084655965e-02 3.555555555555555552e-02 4.444444444444444614e-02 5.333333333333333676e-02 3.555555555555555552e-02 5.333333333333333676e-02 2.539682539682539680e-02 2.539682539682539680e-02 1.269841269841269840e-02 5.502645502645502756e-02 5.502645502645502756e-02 5.502645502645502756e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.772486772486772943e-02 7.407407407407406996e-02 6.772486772486772943e-02 3.968253968253968034e-02 4.444444444444444614e-02 4.920634920634920501e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.968253968253968034e-02 4.444444444444444614e-02 4.444444444444444614e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 3.809523809523809867e-02 4.444444444444444614e-02 3.809523809523809867e-02 4.444444444444444614e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.333333333333333287e-02 1.428571428571428527e-02 1.428571428571428527e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 6.507936507936508408e-02 6.507936507936508408e-02 6.984126984126984294e-02 5.555555555555555247e-02 6.507936507936508408e-02 6.031746031746031828e-02 5.555555555555555247e-02 3.456790123456789793e-02 4.567901234567901120e-02 3.827160493827160698e-02 3.827160493827160698e-02 3.827160493827160698e-02 3.827160493827160698e-02 3.827160493827160698e-02 4.197530864197530909e-02 4.197530864197530909e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 3.809523809523809867e-02 1.851851851851851749e-02 4.691358024691358319e-02 5.061728395061728530e-02 5.061728395061728530e-02 5.061728395061728530e-02 5.802469135802468952e-02 5.432098765432098741e-02 5.432098765432098741e-02 5.432098765432098741e-02 6.172839506172839164e-02 6.913580246913579586e-02 6.172839506172839164e-02 4.691358024691358319e-02 5.061728395061728530e-02 1.338271604938271742e-01 4.246913580246913789e-02 3.654320987654321312e-02 3.654320987654321312e-02 4.839506172839506265e-02 3.654320987654321312e-02 3.703703703703703498e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.444444444444444614e-02 3.703703703703703498e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.962962962962963076e-02 3.555555555555555552e-02 3.555555555555555552e-02 2.074074074074074014e-02 5.530864197530864501e-02 4.345679012345678854e-02 4.641975308641975440e-02 5.530864197530864501e-02 4.345679012345678854e-02 4.938271604938271331e-02 4.938271604938271331e-02 4.641975308641975440e-02 4.345679012345678854e-02 4.641975308641975440e-02 5.530864197530864501e-02 4.938271604938271331e-02 5.530864197530864501e-02 1.143434343434343442e-01 4.202020202020202017e-02 3.474747474747474918e-02 3.474747474747474918e-02 3.959595959595959419e-02 3.474747474747474918e-02 3.959595959595959419e-02 3.555555555555555552e-02 3.851851851851852138e-02 2.424242424242424240e-02 2.181818181818181990e-02 4.040404040404040747e-02 5.494949494949494945e-02 4.525252525252525249e-02 4.525252525252525249e-02 6.349206349206348854e-02 4.525252525252525249e-02 4.040404040404040747e-02 4.282828282828282651e-02 4.040404040404040747e-02 4.282828282828282651e-02 4.282828282828282651e-02 3.878787878787878785e-02 3.393939393939394283e-02 3.878787878787878785e-02 3.878787878787878785e-02 3.636363636363636187e-02
-7.407407407407406996e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000056e-01 4.222222222222222349e-02 5.777777777777777513e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 6.222222222222222043e-02 1.066666666666666735e-01 1.022222222222222282e-01 4.444444444444444614e-02 4.740740740740740505e-02 8.465608465608465139e-02 8.148148148148148806e-02 5.481481481481481621e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.666666666666666838e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 5.185185185185185036e-02 8.148148148148148806e-02 1.051851851851851871e-01 5.481481481481481621e-02 6.962962962962962465e-02 9.925925925925925541e-02 8.465608465608465139e-02 9.100529100529100579e-02 4.656084656084655965e-02 6.137566137566137503e-02 5.925925925925926152e-02 2.518518518518518545e-02 2.814814814814814783e-02 3.111111111111111022e-02 2.518518518518518545e-02 3.111111111111111022e-02 1.798941798941798911e-02 1.798941798941798911e-02 1.058201058201058142e-02 4.444444444444444614e-02 5.925925925925926152e-02 6.560846560846560205e-02 8.253968253968253788e-02 6.137566137566137503e-02 4.656084656084655965e-02 4.656084656084655965e-02 8.253968253968253788e-02 5.714285714285714107e-02 1.037037037037037007e-01 7.195767195767195645e-02 5.925925925925926152e-02 9.285714285714286031e-02 4.682539682539682557e-02 9.444444444444444198e-02 4.523809523809523697e-02 6.904761904761905211e-02 4.047619047619047811e-02 6.269841269841269771e-02 5.000000000000000278e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.751322751322751378e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.539682539682539680e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.751322751322751378e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 2.063492063492063447e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.888888888888888951e-02 5.000000000000000278e-02 5.476190476190476164e-02 6.746031746031745657e-02 5.158730158730158444e-02 5.158730158730158444e-02 8.174603174603174705e-02 5.476190476190476164e-02 1.007936507936507964e-01 6.269841269841269771e-02 8.174603174603174705e-02 6.428571428571427937e-02 6.746031746031745657e-02 5.802469135802468952e-02 9.629629629629629650e-02 7.654320987654321395e-02 7.654320987654321395e-02 5.308641975308642236e-02 3.580246913580246992e-02 4.444444444444444614e-02 4.320987654320987414e-02 3.950617283950617203e-02 2.619047619047619110e-02 2.460317460317460250e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.460317460317460250e-02 2.619047619047619110e-02 2.460317460317460250e-02 1.358024691358024685e-02 4.691358024691358319e-02 4.814814814814814825e-02 4.444444444444444614e-02 5.679012345679012447e-02 6.790123456790123080e-02 5.802469135802468952e-02 4.320987654320987414e-02 4.197530864197530909e-02 6.913580246913579586e-02 9.876543209876542662e-02 8.271604938271605312e-02 3.456790123456789793e-02 5.679012345679012447e-02 4.246913580246913789e-02 9.432098765432098131e-02 6.469135802469136443e-02 3.506172839506172673e-02 5.086419753086419970e-02 4.592592592592592560e-02 2.345679012345679160e-02 2.592592592592592518e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.345679012345679160e-02 2.345679012345679160e-02 2.469135802469135665e-02 2.469135802469135665e-02 1.876543209876543189e-02 2.074074074074074014e-02 2.074074074074074014e-02 1.432098765432098832e-02 4.197530864197530909e-02 3.802469135802469258e-02 4.197530864197530909e-02 5.876543209876543272e-02 4.098765432098765149e-02 3.802469135802469258e-02 4.000000000000000083e-02 3.703703703703703498e-02 3.111111111111111022e-02 3.209876543209876781e-02 4.493827160493827494e-02 3.308641975308641847e-02 5.580246913580246687e-02 3.757575757575757486e-02 3.393939393939394283e-02 5.090909090909091078e-02 5.575757575757575579e-02 8.000000000000000167e-02 4.525252525252525249e-02 5.818181818181818177e-02 2.222222222222222307e-02 2.320987654320987720e-02 1.535353535353535352e-02 1.454545454545454544e-02 3.393939393939394283e-02 5.010101010101010444e-02 5.090909090909091078e-02 5.090909090909091078e-02 4.444444444444444614e-02 3.797979797979798150e-02 3.636363636363636187e-02 4.282828282828282651e-02 3.636363636363636187e-02 3.717171717171717515e-02 3.313131313131312955e-02 2.262626262626262624e-02 2.101010101010101008e-02 2.262626262626262624e-02 2.262626262626262624e-02 2.181818181818181990e-02
-7.407407407407406996e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.518518518518518323e-02 3.777777777777777818e-02 5.777777777777777513e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 6.666666666666666574e-02 8.444444444444444697e-02 8.444444444444444697e-02 3.851851851851852138e-02 4.148148148148148029e-02 6.455026455026455223e-02 6.962962962962962465e-02 4.888888888888889145e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.666666666666666838e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 5.185185185185185036e-02 7.555555555555555636e-02 8.148148148148148806e-02 6.370370370370370683e-02 6.666666666666666574e-02 8.148148148148148806e-02 6.455026455026455223e-02 7.407407407407406996e-02 4.656084656084655965e-02 5.079365079365079361e-02 5.502645502645502756e-02 2.518518518518518545e-02 2.814814814814814783e-02 3.111111111111111022e-02 2.518518518518518545e-02 3.111111111111111022e-02 1.798941798941798911e-02 1.798941798941798911e-02 1.058201058201058142e-02 4.444444444444444614e-02 5.502645502645502756e-02 6.772486772486772943e-02 7.407407407407406996e-02 6.137566137566137503e-02 4.656084656084655965e-02 5.079365079365079361e-02 7.195767195767195645e-02 5.291005291005290712e-02 7.830687830687831086e-02 6.560846560846560205e-02 6.137566137566137503e-02 6.825396825396824740e-02 5.634920634920635024e-02 7.222222222222221544e-02 4.523809523809523697e-02 6.428571428571427937e-02 4.047619047619047811e-02 5.000000000000000278e-02 4.682539682539682557e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.751322751322751378e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.539682539682539680e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.751322751322751378e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.904761904761904934e-02 2.063492063492063447e-02 1.190476190476190410e-02 1.190476190476190410e-02 3.888888888888888951e-02 4.523809523809523697e-02 5.634920634920635024e-02 5.793650793650793884e-02 5.952380952380952051e-02 4.682539682539682557e-02 7.222222222222221544e-02 5.793650793650793884e-02 7.539682539682539264e-02 5.476190476190476164e-02 6.904761904761905211e-02 5.952380952380952051e-02 6.428571428571427937e-02 5.925925925925926152e-02 6.975308641975308532e-02 6.481481481481481122e-02 6.481481481481481122e-02 4.567901234567901120e-02 3.580246913580246992e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.074074074074074403e-02 2.619047619047619110e-02 2.460317460317460250e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.460317460317460250e-02 2.619047619047619110e-02 2.460317460317460250e-02 1.358024691358024685e-02 4.814814814814814825e-02 4.691358024691358319e-02 5.061728395061728530e-02 5.432098765432098741e-02 5.679012345679012447e-02 6.172839506172839164e-02 4.074074074074074403e-02 5.308641975308642236e-02 6.790123456790123080e-02 7.283950617283950490e-02 6.790123456790123080e-02 3.456790123456789793e-02 5.925925925925926152e-02 3.654320987654321312e-02 6.469135802469136443e-02 6.172839506172839164e-02 3.506172839506172673e-02 4.493827160493827494e-02 4.000000000000000083e-02 2.345679012345679160e-02 2.592592592592592518e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.345679012345679160e-02 2.345679012345679160e-02 2.469135802469135665e-02 2.469135802469135665e-02 1.876543209876543189e-02 2.074074074074074014e-02 2.074074074074074014e-02 1.432098765432098832e-02 4.098765432098765149e-02 3.506172839506172673e-02 4.098765432098765149e-02 4.987654320987654211e-02 4.197530864197530909e-02 3.604938271604938432e-02 4.395061728395061734e-02 3.506172839506172673e-02 3.111111111111111022e-02 3.308641975308641847e-02 4.098765432098765149e-02 3.308641975308641847e-02 4.691358024691358319e-02 3.353535353535353619e-02 4.040404040404040747e-02 4.161616161616161352e-02 5.818181818181818177e-02 6.222222222222222043e-02 4.363636363636363980e-02 4.848484848484848481e-02 2.222222222222222307e-02 2.320987654320987720e-02 1.535353535353535352e-02 1.454545454545454544e-02 3.151515151515151686e-02 4.282828282828282651e-02 5.414141414141414310e-02 4.606060606060605883e-02 4.285714285714285754e-02 3.555555555555555552e-02 3.717171717171717515e-02 3.797979797979798150e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.151515151515151686e-02 2.262626262626262624e-02 2.101010101010101008e-02 2.262626262626262624e-02 2.262626262626262624e-02 2.181818181818181990e-02
-7.407407407407406996e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.296296296296295669e-02 3.333333333333333287e-02 5.777777777777777513e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 6.666666666666666574e-02 5.333333333333333676e-02 6.222222222222222043e-02 3.259259259259258967e-02 3.851851851851852138e-02 4.232804232804232569e-02 5.185185185185185036e-02 4.888888888888889145e-02 3.111111111111111022e-02 2.222222222222222307e-02 3.111111111111111022e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 6.814814814814815214e-02 5.185185185185185036e-02 5.185185185185185036e-02 6.666666666666666574e-02 6.518518518518517935e-02 6.370370370370370683e-02 4.232804232804232569e-02 5.079365079365079361e-02 5.291005291005290712e-02 4.232804232804232569e-02 4.867724867724868010e-02 2.814814814814814783e-02 3.851851851851852138e-02 4.000000000000000083e-02 2.814814814814814783e-02 4.000000000000000083e-02 2.010582010582010609e-02 2.010582010582010609e-02 1.058201058201058142e-02 6.772486772486772943e-02 5.396825396825397081e-02 4.867724867724868010e-02 4.867724867724868010e-02 5.502645502645502756e-02 6.772486772486772943e-02 6.772486772486772943e-02 5.185185185185185036e-02 6.560846560846560205e-02 4.867724867724868010e-02 6.560846560846560205e-02 6.560846560846560205e-02 3.888888888888888951e-02 5.476190476190476164e-02 4.365079365079364837e-02 5.714285714285714107e-02 4.206349206349206671e-02 5.555555555555555247e-02 3.730158730158730090e-02 5.238095238095238221e-02 4.232804232804232569e-02 3.280423280423280102e-02 2.962962962962963076e-02 2.962962962962963076e-02 3.386243386243386472e-02 4.444444444444444614e-02 4.444444444444444614e-02 3.280423280423280102e-02 3.386243386243386472e-02 2.962962962962963076e-02 4.232804232804232569e-02 2.222222222222222307e-02 2.222222222222222307e-02 2.460317460317460250e-02 2.222222222222222307e-02 2.539682539682539680e-02 1.190476190476190410e-02 1.190476190476190410e-02 6.746031746031745657e-02 5.476190476190476164e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.000000000000000278e-02 5.634920634920635024e-02 4.841269841269841417e-02 5.634920634920635024e-02 4.841269841269841417e-02 5.317460317460317304e-02 5.317460317460317304e-02 5.476190476190476164e-02 5.158730158730158444e-02 3.827160493827160698e-02 3.827160493827160698e-02 3.703703703703703498e-02 3.827160493827160698e-02 3.950617283950617203e-02 5.679012345679012447e-02 4.753086419753086572e-02 5.432098765432098741e-02 5.432098765432098741e-02 4.365079365079364837e-02 3.174603174603174427e-02 3.253968253968254204e-02 3.253968253968254204e-02 3.730158730158730090e-02 3.253968253968254204e-02 3.174603174603174427e-02 3.730158730158730090e-02 3.174603174603174427e-02 1.481481481481481538e-02 4.567901234567901120e-02 4.938271604938271331e-02 4.753086419753086572e-02 4.567901234567901120e-02 4.629629629629629373e-02 4.629629629629629373e-02 6.419753086419753563e-02 4.938271604938271331e-02 4.691358024691358319e-02 4.691358024691358319e-02 4.814814814814814825e-02 6.049382716049382658e-02 4.691358024691358319e-02 3.654320987654321312e-02 3.506172839506172673e-02 3.506172839506172673e-02 5.975308641975308338e-02 5.234567901234567916e-02 4.148148148148148029e-02 3.209876543209876781e-02 3.333333333333333287e-02 3.271604938271605034e-02 3.333333333333333287e-02 3.209876543209876781e-02 3.209876543209876781e-02 3.271604938271605034e-02 4.320987654320987414e-02 2.567901234567901078e-02 2.666666666666666838e-02 2.666666666666666838e-02 1.629629629629629484e-02 6.271604938271604923e-02 5.283950617283950796e-02 4.641975308641975440e-02 4.592592592592592560e-02 4.444444444444444614e-02 6.172839506172839164e-02 4.790123456790123385e-02 5.679012345679012447e-02 5.580246913580246687e-02 6.320987654320987803e-02 6.123456790123456978e-02 6.567901234567900814e-02 5.481481481481481621e-02 3.676767676767676851e-02 5.737373737373737542e-02 4.202020202020202017e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.313131313131312955e-02 3.151515151515151686e-02 3.703703703703703498e-02 4.197530864197530909e-02 2.101010101010101008e-02 1.696969696969697142e-02 4.848484848484848481e-02 5.535353535353535609e-02 4.282828282828282651e-02 4.282828282828282651e-02 6.825396825396824740e-02 4.606060606060605883e-02 4.282828282828282651e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.252525252525252347e-02 3.393939393939394283e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.434343434343434254e-02 4.040404040404040747e-02
-7.407407407407406996e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.518518518518518323e-02 4.000000000000000083e-02 4.444444444444444614e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 7.555555555555555636e-02 8.000000000000000167e-02 9.111111111111111494e-02 4.148148148148148029e-02 4.888888888888889145e-02 4.867724867724868010e-02 6.074074074074074098e-02 5.777777777777777513e-02 3.333333333333333287e-02 2.222222222222222307e-02 3.333333333333333287e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 8.148148148148148806e-02 7.111111111111111105e-02 8.000000000000000167e-02 7.555555555555555636e-02 8.148148148148148806e-02 9.481481481481481011e-02 4.867724867724868010e-02 7.089947089947089276e-02 6.666666666666666574e-02 5.502645502645502756e-02 6.243386243386243178e-02 2.962962962962963076e-02 4.148148148148148029e-02 4.444444444444444614e-02 2.962962962962963076e-02 4.444444444444444614e-02 2.116402116402116285e-02 2.116402116402116285e-02 1.058201058201058142e-02 7.089947089947089276e-02 6.984126984126984294e-02 6.349206349206348854e-02 6.984126984126984294e-02 6.772486772486772943e-02 8.359788359788360157e-02 7.936507936507936067e-02 7.301587301587302015e-02 8.253968253968253788e-02 7.619047619047619735e-02 8.571428571428571508e-02 7.936507936507936067e-02 5.317460317460317304e-02 5.714285714285714107e-02 6.349206349206348854e-02 6.507936507936508408e-02 5.238095238095238221e-02 6.031746031746031828e-02 5.158730158730158444e-02 7.142857142857142461e-02 4.550264550264550290e-02 3.492063492063492147e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.703703703703703498e-02 4.867724867724868010e-02 4.867724867724868010e-02 3.492063492063492147e-02 3.703703703703703498e-02 3.174603174603174427e-02 4.550264550264550290e-02 2.380952380952380820e-02 2.380952380952380820e-02 2.619047619047619110e-02 2.380952380952380820e-02 2.777777777777777624e-02 1.190476190476190410e-02 1.190476190476190410e-02 6.507936507936508408e-02 6.587301587301587491e-02 6.269841269841269771e-02 6.746031746031745657e-02 6.031746031746031828e-02 7.301587301587302015e-02 7.063492063492063378e-02 6.825396825396824740e-02 7.539682539682539264e-02 7.063492063492063378e-02 7.539682539682539264e-02 6.984126984126984294e-02 6.825396825396824740e-02 4.320987654320987414e-02 5.740740740740740700e-02 4.876543209876543078e-02 5.000000000000000278e-02 5.493827160493826994e-02 5.617283950617284194e-02 5.493827160493826994e-02 6.111111111111110911e-02 6.358024691358024616e-02 4.603174603174603474e-02 3.412698412698412370e-02 3.571428571428571230e-02 3.571428571428571230e-02 4.047619047619047811e-02 3.571428571428571230e-02 3.412698412698412370e-02 4.047619047619047811e-02 3.412698412698412370e-02 1.543209876543209791e-02 6.049382716049382658e-02 6.481481481481481122e-02 5.864197530864197205e-02 6.172839506172839164e-02 6.666666666666666574e-02 6.049382716049382658e-02 7.345679012345679437e-02 5.679012345679012447e-02 6.543209876543210068e-02 7.283950617283950490e-02 6.975308641975308532e-02 5.987654320987654405e-02 6.049382716049382658e-02 4.839506172839506265e-02 5.086419753086419970e-02 4.493827160493827494e-02 5.234567901234567916e-02 7.555555555555555636e-02 5.037037037037037090e-02 3.456790123456789793e-02 3.703703703703703498e-02 3.580246913580246992e-02 3.703703703703703498e-02 3.456790123456789793e-02 3.456790123456789793e-02 3.580246913580246992e-02 4.506172839506172867e-02 2.765432098765432251e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.728395061728394896e-02 7.555555555555555636e-02 5.432098765432098741e-02 6.123456790123456978e-02 6.518518518518517935e-02 5.580246913580246687e-02 6.172839506172839164e-02 5.975308641975308338e-02 5.925925925925926152e-02 5.530864197530864501e-02 6.419753086419753563e-02 6.814814814814815214e-02 6.567901234567900814e-02 7.259259259259259744e-02 4.686868686868687212e-02 5.818181818181818177e-02 4.969696969696969779e-02 4.242424242424242681e-02 4.727272727272727182e-02 4.646464646464646547e-02 4.969696969696969779e-02 3.851851851851852138e-02 4.345679012345678854e-02 2.262626262626262624e-02 1.818181818181818094e-02 5.050505050505050414e-02 7.272727272727272374e-02 5.494949494949494945e-02 5.818181818181818177e-02 7.301587301587302015e-02 6.060606060606060774e-02 5.171717171717171713e-02 5.252525252525252347e-02 5.010101010101010444e-02 5.616161616161616243e-02 5.454545454545454281e-02 3.717171717171717515e-02 3.474747474747474918e-02 3.717171717171717515e-02 3.757575757575757486e-02 4.161616161616161352e-02
-7.407407407407406996e-02 2.962962962962963076e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.777777777777777901e-02 3.777777777777777818e-02 4.444444444444444614e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 6.666666666666666574e-02 7.555555555555555636e-02 7.555555555555555636e-02 3.851851851851852138e-02 4.148148148148148029e-02 4.444444444444444614e-02 5.481481481481481621e-02 5.481481481481481621e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.666666666666666838e-02 1.481481481481481538e-02 7.407407407407407690e-03 7.407407407407407690e-03 7.407407407407407690e-03 6.222222222222222043e-02 6.370370370370370683e-02 7.259259259259259744e-02 6.074074074074074098e-02 6.518518518518517935e-02 7.259259259259259744e-02 4.444444444444444614e-02 5.925925925925926152e-02 5.502645502645502756e-02 5.291005291005290712e-02 5.502645502645502756e-02 2.518518518518518545e-02 2.962962962962963076e-02 3.111111111111111022e-02 2.518518518518518545e-02 3.111111111111111022e-02 1.798941798941798911e-02 1.798941798941798911e-02 1.058201058201058142e-02 5.502645502645502756e-02 5.820105820105819783e-02 5.502645502645502756e-02 6.137566137566137503e-02 6.137566137566137503e-02 5.714285714285714107e-02 5.714285714285714107e-02 6.243386243386243178e-02 5.925925925925926152e-02 6.984126984126984294e-02 6.349206349206348854e-02 5.925925925925926152e-02 5.000000000000000278e-02 4.841269841269841417e-02 5.634920634920635024e-02 5.079365079365079361e-02 4.682539682539682557e-02 5.873015873015872967e-02 5.000000000000000278e-02 5.396825396825397081e-02 2.962962962962963076e-02 2.645502645502645356e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.751322751322751378e-02 3.174603174603174427e-02 3.174603174603174427e-02 2.645502645502645356e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.962962962962963076e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.984126984126984017e-02 1.904761904761904934e-02 2.063492063492063447e-02 1.190476190476190410e-02 1.190476190476190410e-02 4.761904761904761640e-02 5.317460317460317304e-02 4.920634920634920501e-02 5.714285714285714107e-02 5.000000000000000278e-02 5.793650793650793884e-02 5.952380952380952051e-02 5.793650793650793884e-02 6.746031746031745657e-02 5.634920634920635024e-02 6.111111111111110911e-02 5.952380952380952051e-02 5.476190476190476164e-02 3.703703703703703498e-02 5.308641975308642236e-02 4.444444444444444614e-02 4.567901234567901120e-02 5.061728395061728530e-02 4.691358024691358319e-02 5.864197530864197205e-02 5.432098765432098741e-02 4.938271604938271331e-02 2.857142857142857054e-02 2.539682539682539680e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.777777777777777624e-02 2.619047619047619110e-02 2.539682539682539680e-02 2.777777777777777624e-02 2.539682539682539680e-02 1.358024691358024685e-02 4.444444444444444614e-02 4.814814814814814825e-02 4.506172839506172867e-02 4.938271604938271331e-02 5.617283950617284194e-02 4.876543209876543078e-02 4.876543209876543078e-02 4.567901234567901120e-02 5.308641975308642236e-02 6.543209876543210068e-02 5.925925925925926152e-02 4.197530864197530909e-02 4.814814814814814825e-02 3.654320987654321312e-02 4.592592592592592560e-02 4.000000000000000083e-02 4.148148148148148029e-02 5.037037037037037090e-02 5.777777777777777513e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.530864197530864265e-02 2.592592592592592518e-02 2.469135802469135665e-02 2.469135802469135665e-02 2.530864197530864265e-02 2.716049382716049371e-02 1.975308641975308602e-02 2.074074074074074014e-02 2.074074074074074014e-02 1.432098765432098832e-02 4.691358024691358319e-02 4.197530864197530909e-02 4.345679012345678854e-02 5.086419753086419970e-02 3.950617283950617203e-02 4.148148148148148029e-02 4.296296296296295975e-02 4.049382716049382963e-02 3.753086419753086378e-02 4.197530864197530909e-02 4.543209876543209680e-02 4.098765432098765149e-02 5.283950617283950796e-02 3.434343434343434254e-02 4.282828282828282651e-02 4.282828282828282651e-02 3.717171717171717515e-02 4.202020202020202017e-02 4.040404040404040747e-02 4.525252525252525249e-02 2.419753086419753133e-02 2.567901234567901078e-02 1.616161616161616160e-02 1.454545454545454544e-02 3.757575757575757486e-02 4.969696969696969779e-02 4.282828282828282651e-02 4.444444444444444614e-02 4.761904761904761640e-02 4.202020202020202017e-02 3.555555555555555552e-02 4.040404040404040747e-02 3.636363636363636187e-02 3.878787878787878785e-02 3.636363636363636187e-02 2.424242424242424240e-02 2.262626262626262624e-02 2.424242424242424240e-02 2.383838383838383923e-02 2.424242424242424240e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 6.944444444444444753e-02 3.333333333333333287e-02 2.777777777777777624e-02 4.444444444444444614e-02 3.333333333333333287e-02 4.444444444444444614e-02 2.222222222222222307e-02 2.962962962962963076e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.499999999999999722e-02 7.222222222222221544e-02 8.055555555555556080e-02 3.703703703703703498e-02 4.444444444444444614e-02 3.703703703703703498e-02 3.703703703703703498e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.074074074074074403e-02 5.555555555555555247e-02 5.185185185185185036e-02 5.555555555555555247e-02 1.587301587301587213e-02 2.116402116402116285e-02 2.116402116402116285e-02 1.587301587301587213e-02 2.116402116402116285e-02 7.592592592592592449e-02 7.592592592592592449e-02 8.333333333333332871e-02 7.777777777777777901e-02 8.703703703703703776e-02 4.232804232804232569e-02 4.232804232804232569e-02 3.968253968253968034e-02 5.291005291005290712e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.820105820105819783e-02 5.820105820105819783e-02 4.497354497354497105e-02 5.291005291005290712e-02 4.232804232804232569e-02 5.820105820105819783e-02 5.820105820105819783e-02 1.785714285714285615e-02 2.976190476190476025e-02 2.182539682539682419e-02 2.777777777777777624e-02 2.182539682539682419e-02 2.777777777777777624e-02 1.785714285714285615e-02 2.777777777777777624e-02 6.746031746031745657e-02 7.407407407407406996e-02 7.804232804232803800e-02 7.804232804232803800e-02 7.407407407407406996e-02 8.068783068783068335e-02 8.068783068783068335e-02 7.804232804232803800e-02 7.936507936507936067e-02 7.539682539682539264e-02 7.539682539682539264e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.166666666666666435e-02 5.158730158730158444e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.563492063492063239e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 2.469135802469135665e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.160493827160493707e-02 3.086419753086419582e-02 2.623456790123456644e-02 3.086419753086419582e-02 3.086419753086419582e-02 6.250000000000000000e-02 7.837301587301587213e-02 7.738095238095238360e-02 8.035714285714286309e-02 7.539682539682539264e-02 7.936507936507936067e-02 7.638888888888889506e-02 7.142857142857142461e-02 7.440476190476190410e-02 4.320987654320987414e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.475308641975308394e-02 4.320987654320987414e-02 4.475308641975308394e-02 4.475308641975308394e-02 5.401234567901234268e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.938271604938271331e-02 4.320987654320987414e-02 3.703703703703703498e-02 2.345679012345679160e-02 2.345679012345679160e-02 3.209876543209876781e-02 3.456790123456789793e-02 2.469135802469135665e-02 7.716049382716048954e-02 7.561728395061728669e-02 7.716049382716048954e-02 7.561728395061728669e-02 7.561728395061728669e-02 7.561728395061728669e-02 7.793209876543209791e-02 5.787037037037037063e-02 4.444444444444444614e-02 4.691358024691358319e-02 4.691358024691358319e-02 4.691358024691358319e-02 5.308641975308642236e-02 4.320987654320987414e-02 4.444444444444444614e-02 4.567901234567901120e-02 4.197530864197530909e-02 4.938271604938271331e-02 4.567901234567901120e-02 4.691358024691358319e-02 4.691358024691358319e-02 5.061728395061728530e-02 5.185185185185185036e-02 5.308641975308642236e-02 4.814814814814814825e-02 3.737373737373737154e-02 3.838383838383838120e-02 2.626262626262626174e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 5.308641975308642236e-02 5.370370370370370489e-02 4.545454545454545581e-02 5.151515151515151381e-02 4.141414141414141714e-02 4.949494949494949447e-02 4.040404040404040747e-02 4.040404040404040747e-02 6.349206349206348854e-02 4.444444444444444614e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.444444444444444614e-02 7.424242424242424865e-02 6.262626262626262708e-02 7.222222222222221544e-02 6.161616161616161741e-02 5.000000000000000278e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 6.018518518518518184e-02 3.333333333333333287e-02 2.777777777777777624e-02 5.000000000000000278e-02 3.333333333333333287e-02 5.000000000000000278e-02 2.222222222222222307e-02 3.333333333333333287e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 9.722222222222222376e-02 6.111111111111110911e-02 7.499999999999999722e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.333333333333333287e-02 4.444444444444444614e-02 5.555555555555555247e-02 4.444444444444444614e-02 4.444444444444444614e-02 6.666666666666666574e-02 5.555555555555555247e-02 6.666666666666666574e-02 1.587301587301587213e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.587301587301587213e-02 2.380952380952380820e-02 6.851851851851852027e-02 7.962962962962963354e-02 1.129629629629629595e-01 8.518518518518518323e-02 8.703703703703703776e-02 4.761904761904761640e-02 4.497354497354497105e-02 3.439153439153438963e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.555555555555555247e-02 4.761904761904761640e-02 6.349206349206348854e-02 6.349206349206348854e-02 1.785714285714285615e-02 3.571428571428571230e-02 2.380952380952380820e-02 2.976190476190476025e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 2.976190476190476025e-02 7.275132275132274728e-02 7.407407407407406996e-02 9.788359788359787816e-02 8.597883597883597406e-02 9.259259259259258745e-02 8.068783068783068335e-02 9.126984126984126477e-02 8.068783068783068335e-02 7.671957671957671532e-02 7.010582010582010193e-02 9.391534391534391013e-02 5.158730158730158444e-02 4.761904761904761640e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.357142857142856845e-02 3.769841269841269632e-02 4.166666666666666435e-02 5.357142857142856845e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 6.547619047619047949e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.761904761904761640e-02 2.777777777777777624e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.314814814814814686e-02 3.240740740740740561e-02 2.777777777777777624e-02 3.240740740740740561e-02 3.240740740740740561e-02 6.646825396825396803e-02 8.531746031746031966e-02 1.091269841269841279e-01 9.821428571428571230e-02 8.134920634920635163e-02 8.928571428571428770e-02 8.035714285714286309e-02 6.944444444444444753e-02 6.845238095238095899e-02 4.783950617283950352e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.092592592592592310e-02 5.092592592592592310e-02 4.629629629629629373e-02 4.444444444444444614e-02 2.592592592592592518e-02 2.592592592592592518e-02 3.333333333333333287e-02 3.703703703703703498e-02 2.592592592592592518e-02 7.561728395061728669e-02 1.157407407407407413e-01 9.413580246913580418e-02 7.561728395061728669e-02 8.719135802469135665e-02 7.793209876543209791e-02 1.010802469135802517e-01 6.095679012345679021e-02 4.691358024691358319e-02 5.925925925925926152e-02 5.432098765432098741e-02 4.197530864197530909e-02 5.555555555555555247e-02 4.444444444444444614e-02 4.814814814814814825e-02 5.185185185185185036e-02 4.444444444444444614e-02 5.185185185185185036e-02 5.185185185185185036e-02 4.814814814814814825e-02 4.814814814814814825e-02 5.185185185185185036e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.185185185185185036e-02 4.242424242424242681e-02 4.242424242424242681e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 5.432098765432098741e-02 5.617283950617284194e-02 4.545454545454545581e-02 5.656565656565656908e-02 4.242424242424242681e-02 5.454545454545454281e-02 4.242424242424242681e-02 4.242424242424242681e-02 6.547619047619047949e-02 4.848484848484848481e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.545454545454545581e-02 4.545454545454545581e-02 8.383838383838383701e-02 6.161616161616161741e-02 5.959595959595959808e-02 7.676767676767676241e-02 5.202020202020202211e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 6.481481481481481122e-02 3.333333333333333287e-02 2.777777777777777624e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 3.148148148148147835e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.611111111111111049e-02 6.666666666666666574e-02 7.777777777777777901e-02 3.703703703703703498e-02 4.074074074074074403e-02 3.518518518518518740e-02 4.074074074074074403e-02 5.370370370370370489e-02 4.259259259259259162e-02 4.259259259259259162e-02 6.111111111111110911e-02 5.370370370370370489e-02 6.111111111111110911e-02 1.587301587301587213e-02 2.248677248677248552e-02 2.248677248677248552e-02 1.587301587301587213e-02 2.248677248677248552e-02 7.222222222222221544e-02 7.777777777777777901e-02 9.814814814814815103e-02 8.148148148148148806e-02 8.703703703703703776e-02 4.497354497354497105e-02 4.365079365079364837e-02 3.703703703703703498e-02 5.423280423280422979e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 4.629629629629629373e-02 5.423280423280422979e-02 4.497354497354497105e-02 6.084656084656084318e-02 6.084656084656084318e-02 1.785714285714285615e-02 3.273809523809523975e-02 2.281746031746031619e-02 2.876984126984126824e-02 2.281746031746031619e-02 2.876984126984126824e-02 1.785714285714285615e-02 2.876984126984126824e-02 7.010582010582010193e-02 7.407407407407406996e-02 8.730158730158729674e-02 8.201058201058200603e-02 8.333333333333332871e-02 8.068783068783068335e-02 8.597883597883597406e-02 7.936507936507936067e-02 7.804232804232803800e-02 7.275132275132274728e-02 8.465608465608465139e-02 4.761904761904761640e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.662698412698412787e-02 5.059523809523809590e-02 4.166666666666666435e-02 4.166666666666666435e-02 5.257936507936507992e-02 4.662698412698412787e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.960317460317460042e-02 5.952380952380952051e-02 4.960317460317460042e-02 4.662698412698412787e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.662698412698412787e-02 2.623456790123456644e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.237654320987654197e-02 3.163580246913580418e-02 2.700617283950617134e-02 3.163580246913580418e-02 3.163580246913580418e-02 6.448412698412699096e-02 8.234126984126984017e-02 9.226190476190476719e-02 8.928571428571428770e-02 7.837301587301587213e-02 8.432539682539683112e-02 7.837301587301587213e-02 7.043650793650793607e-02 7.142857142857142461e-02 4.552469135802469230e-02 4.475308641975308394e-02 4.861111111111111188e-02 4.783950617283950352e-02 4.475308641975308394e-02 4.783950617283950352e-02 4.783950617283950352e-02 5.478395061728395105e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.092592592592592310e-02 4.861111111111111188e-02 5.015432098765432167e-02 4.475308641975308394e-02 4.074074074074074403e-02 2.469135802469135665e-02 2.469135802469135665e-02 3.271604938271605034e-02 3.580246913580246992e-02 2.530864197530864265e-02 7.716049382716048954e-02 9.413580246913580418e-02 8.641975308641974829e-02 7.561728395061728669e-02 8.101851851851851749e-02 7.716049382716048954e-02 8.873456790123457338e-02 5.941358024691358042e-02 4.567901234567901120e-02 5.308641975308642236e-02 5.061728395061728530e-02 4.444444444444444614e-02 5.432098765432098741e-02 4.382716049382716361e-02 4.629629629629629373e-02 4.876543209876543078e-02 4.320987654320987414e-02 5.061728395061728530e-02 4.876543209876543078e-02 4.753086419753086572e-02 4.753086419753086572e-02 5.123456790123456783e-02 5.370370370370370489e-02 5.432098765432098741e-02 5.000000000000000278e-02 3.989898989898989917e-02 4.040404040404040747e-02 2.676767676767676657e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.575757575757575690e-02 5.370370370370370489e-02 5.493827160493826994e-02 4.545454545454545581e-02 5.404040404040404144e-02 4.191919191919191851e-02 5.202020202020202211e-02 4.141414141414141714e-02 4.141414141414141714e-02 6.448412698412699096e-02 4.646464646464646547e-02 4.141414141414141714e-02 4.141414141414141714e-02 4.141414141414141714e-02 4.393939393939393784e-02 4.494949494949494750e-02 7.878787878787878174e-02 6.262626262626262708e-02 6.666666666666666574e-02 6.868686868686868507e-02 5.101010101010101244e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 1.064814814814814825e-01 4.444444444444444614e-02 2.777777777777777624e-02 5.000000000000000278e-02 3.333333333333333287e-02 5.000000000000000278e-02 2.222222222222222307e-02 3.333333333333333287e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.055555555555556080e-02 1.388888888888888951e-01 1.305555555555555636e-01 5.185185185185185036e-02 7.407407407407406996e-02 5.185185185185185036e-02 5.185185185185185036e-02 5.555555555555555247e-02 4.444444444444444614e-02 4.444444444444444614e-02 6.666666666666666574e-02 5.555555555555555247e-02 6.666666666666666574e-02 1.587301587301587213e-02 2.380952380952380820e-02 2.380952380952380820e-02 1.587301587301587213e-02 2.380952380952380820e-02 1.537037037037037035e-01 7.592592592592592449e-02 8.703703703703703776e-02 1.148148148148148140e-01 1.425925925925926041e-01 5.291005291005290712e-02 6.084656084656084318e-02 6.084656084656084318e-02 5.555555555555555247e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.555555555555555247e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.761904761904761640e-02 5.555555555555555247e-02 4.761904761904761640e-02 6.349206349206348854e-02 6.349206349206348854e-02 1.785714285714285615e-02 3.571428571428571230e-02 2.380952380952380820e-02 2.976190476190476025e-02 2.380952380952380820e-02 2.976190476190476025e-02 1.785714285714285615e-02 2.976190476190476025e-02 7.010582010582010193e-02 8.994708994708994210e-02 9.788359788359787816e-02 1.256613756613756683e-01 9.788359788359787816e-02 7.804232804232803800e-02 7.804232804232803800e-02 1.256613756613756683e-01 1.613756613756613667e-01 1.653439153439153486e-01 7.804232804232803800e-02 5.158730158730158444e-02 6.349206349206348854e-02 5.753968253968253649e-02 5.753968253968253649e-02 6.944444444444444753e-02 9.325396825396825573e-02 8.134920634920635163e-02 5.357142857142856845e-02 4.761904761904761640e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 5.357142857142856845e-02 6.547619047619047949e-02 5.357142857142856845e-02 4.761904761904761640e-02 5.357142857142856845e-02 5.357142857142856845e-02 4.761904761904761640e-02 2.777777777777777624e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.314814814814814686e-02 2.314814814814814686e-02 3.240740740740740561e-02 2.777777777777777624e-02 3.240740740740740561e-02 3.240740740740740561e-02 6.448412698412699096e-02 8.531746031746031966e-02 8.531746031746031966e-02 1.121031746031746074e-01 8.531746031746031966e-02 1.329365079365079361e-01 1.359126984126984017e-01 8.531746031746031966e-02 1.061507936507936484e-01 7.253086419753086711e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 4.629629629629629373e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.092592592592592310e-02 5.092592592592592310e-02 4.629629629629629373e-02 4.444444444444444614e-02 2.592592592592592518e-02 2.592592592592592518e-02 3.333333333333333287e-02 3.703703703703703498e-02 2.592592592592592518e-02 7.561728395061728669e-02 7.561728395061728669e-02 7.561728395061728669e-02 1.774691358024691468e-01 1.149691358024691329e-01 9.182098765432099297e-02 9.645061728395061540e-02 5.941358024691358042e-02 4.691358024691358319e-02 5.432098765432098741e-02 6.913580246913579586e-02 9.876543209876542662e-02 5.555555555555555247e-02 4.444444444444444614e-02 4.814814814814814825e-02 5.185185185185185036e-02 4.444444444444444614e-02 5.185185185185185036e-02 5.185185185185185036e-02 4.814814814814814825e-02 4.814814814814814825e-02 5.185185185185185036e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.185185185185185036e-02 4.242424242424242681e-02 4.242424242424242681e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 6.419753086419753563e-02 5.493827160493826994e-02 4.747474747474747514e-02 8.686868686868687295e-02 4.242424242424242681e-02 5.454545454545454281e-02 4.242424242424242681e-02 4.242424242424242681e-02 6.547619047619047949e-02 4.848484848484848481e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.545454545454545581e-02 4.545454545454545581e-02 1.282828282828282762e-01 6.161616161616161741e-02 6.767676767676768235e-02 6.464646464646464641e-02 5.101010101010101244e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 8.796296296296296502e-02 3.888888888888888951e-02 2.777777777777777624e-02 4.444444444444444614e-02 3.333333333333333287e-02 4.444444444444444614e-02 2.222222222222222307e-02 2.962962962962963076e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.055555555555556080e-02 1.027777777777777735e-01 1.027777777777777735e-01 4.444444444444444614e-02 5.925925925925926152e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.074074074074074403e-02 5.555555555555555247e-02 5.185185185185185036e-02 5.555555555555555247e-02 1.587301587301587213e-02 2.116402116402116285e-02 2.116402116402116285e-02 1.587301587301587213e-02 2.116402116402116285e-02 1.092592592592592643e-01 7.222222222222221544e-02 8.703703703703703776e-02 1.018518518518518462e-01 1.092592592592592643e-01 4.761904761904761640e-02 5.026455026455026176e-02 5.026455026455026176e-02 5.291005291005290712e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.820105820105819783e-02 5.820105820105819783e-02 4.497354497354497105e-02 5.291005291005290712e-02 4.232804232804232569e-02 5.820105820105819783e-02 5.820105820105819783e-02 1.785714285714285615e-02 2.976190476190476025e-02 2.182539682539682419e-02 2.777777777777777624e-02 2.182539682539682419e-02 2.777777777777777624e-02 1.785714285714285615e-02 2.777777777777777624e-02 6.746031746031745657e-02 7.804232804232803800e-02 9.788359788359787816e-02 1.071428571428571369e-01 8.597883597883597406e-02 7.275132275132274728e-02 7.804232804232803800e-02 1.044973544973544916e-01 1.124338624338624276e-01 1.124338624338624276e-01 8.068783068783068335e-02 4.761904761904761640e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.960317460317460042e-02 5.555555555555555247e-02 6.746031746031745657e-02 5.952380952380952051e-02 5.158730158730158444e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.563492063492063239e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 2.469135802469135665e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.160493827160493707e-02 3.086419753086419582e-02 2.623456790123456644e-02 3.086419753086419582e-02 3.086419753086419582e-02 6.250000000000000000e-02 8.531746031746031966e-02 9.226190476190476719e-02 1.061507936507936484e-01 7.837301587301587213e-02 1.101190476190476164e-01 1.081349206349206393e-01 7.242063492063492702e-02 8.432539682539683112e-02 5.709876543209876226e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.475308641975308394e-02 4.320987654320987414e-02 4.475308641975308394e-02 4.475308641975308394e-02 5.401234567901234268e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.938271604938271331e-02 4.320987654320987414e-02 3.703703703703703498e-02 2.345679012345679160e-02 2.345679012345679160e-02 3.209876543209876781e-02 3.456790123456789793e-02 2.469135802469135665e-02 7.561728395061728669e-02 8.719135802469135665e-02 8.101851851851851749e-02 1.149691358024691329e-01 1.033950617283950629e-01 8.487654320987654544e-02 9.953703703703703498e-02 5.787037037037037063e-02 4.444444444444444614e-02 4.938271604938271331e-02 5.432098765432098741e-02 6.913580246913579586e-02 5.308641975308642236e-02 4.320987654320987414e-02 4.444444444444444614e-02 4.567901234567901120e-02 4.197530864197530909e-02 4.938271604938271331e-02 4.567901234567901120e-02 4.691358024691358319e-02 4.691358024691358319e-02 5.061728395061728530e-02 5.185185185185185036e-02 5.308641975308642236e-02 4.814814814814814825e-02 3.737373737373737154e-02 3.838383838383838120e-02 2.626262626262626174e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 5.740740740740740700e-02 5.370370370370370489e-02 4.545454545454545581e-02 7.373737373737374035e-02 4.141414141414141714e-02 4.949494949494949447e-02 4.040404040404040747e-02 4.040404040404040747e-02 6.349206349206348854e-02 4.444444444444444614e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.444444444444444614e-02 1.060606060606060635e-01 6.161616161616161741e-02 5.959595959595959808e-02 6.717171717171717404e-02 5.000000000000000278e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.870370370370370627e-02 3.611111111111110772e-02 2.777777777777777624e-02 4.444444444444444614e-02 3.333333333333333287e-02 4.444444444444444614e-02 2.222222222222222307e-02 2.962962962962963076e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 7.777777777777777901e-02 8.611111111111111049e-02 9.166666666666666019e-02 4.074074074074074403e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.074074074074074403e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.074074074074074403e-02 5.555555555555555247e-02 5.185185185185185036e-02 5.555555555555555247e-02 1.587301587301587213e-02 2.116402116402116285e-02 2.116402116402116285e-02 1.587301587301587213e-02 2.116402116402116285e-02 9.074074074074074681e-02 7.407407407407406996e-02 8.518518518518518323e-02 8.703703703703703776e-02 9.814814814814815103e-02 4.497354497354497105e-02 4.629629629629629373e-02 4.497354497354497105e-02 5.291005291005290712e-02 4.497354497354497105e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.761904761904761640e-02 5.820105820105819783e-02 5.820105820105819783e-02 4.497354497354497105e-02 5.291005291005290712e-02 4.232804232804232569e-02 5.820105820105819783e-02 5.820105820105819783e-02 1.785714285714285615e-02 2.976190476190476025e-02 2.182539682539682419e-02 2.777777777777777624e-02 2.182539682539682419e-02 2.777777777777777624e-02 1.785714285714285615e-02 2.777777777777777624e-02 6.746031746031745657e-02 7.804232804232803800e-02 8.465608465608465139e-02 8.862433862433861942e-02 8.201058201058200603e-02 7.671957671957671532e-02 7.936507936507936067e-02 8.862433862433861942e-02 9.391534391534391013e-02 9.126984126984126477e-02 7.804232804232803800e-02 4.563492063492063239e-02 4.761904761904761640e-02 4.861111111111111188e-02 4.662698412698412787e-02 5.158730158730158444e-02 5.555555555555555247e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.563492063492063239e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.563492063492063239e-02 5.357142857142856845e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 4.960317460317460042e-02 4.563492063492063239e-02 2.469135802469135665e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.160493827160493707e-02 2.160493827160493707e-02 3.086419753086419582e-02 2.623456790123456644e-02 3.086419753086419582e-02 3.086419753086419582e-02 6.250000000000000000e-02 8.035714285714286309e-02 8.134920634920635163e-02 8.829365079365079916e-02 7.936507936507936067e-02 9.027777777777777624e-02 8.829365079365079916e-02 7.242063492063492702e-02 8.234126984126984017e-02 5.092592592592592310e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.475308641975308394e-02 4.320987654320987414e-02 4.475308641975308394e-02 4.475308641975308394e-02 5.401234567901234268e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.629629629629629373e-02 4.938271604938271331e-02 4.320987654320987414e-02 3.703703703703703498e-02 2.345679012345679160e-02 2.345679012345679160e-02 3.209876543209876781e-02 3.456790123456789793e-02 2.469135802469135665e-02 7.561728395061728669e-02 7.793209876543209791e-02 7.716049382716048954e-02 9.182098765432099297e-02 8.487654320987654544e-02 8.024691358024690913e-02 8.410493827160493707e-02 5.787037037037037063e-02 4.444444444444444614e-02 4.814814814814814825e-02 5.061728395061728530e-02 5.679012345679012447e-02 5.308641975308642236e-02 4.320987654320987414e-02 4.444444444444444614e-02 4.567901234567901120e-02 4.197530864197530909e-02 4.938271604938271331e-02 4.567901234567901120e-02 4.691358024691358319e-02 4.691358024691358319e-02 5.061728395061728530e-02 5.185185185185185036e-02 5.308641975308642236e-02 4.814814814814814825e-02 3.737373737373737154e-02 3.838383838383838120e-02 2.626262626262626174e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 5.555555555555555247e-02 5.370370370370370489e-02 4.545454545454545581e-02 5.959595959595959808e-02 4.141414141414141714e-02 4.949494949494949447e-02 4.040404040404040747e-02 4.040404040404040747e-02 6.349206349206348854e-02 4.444444444444444614e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.444444444444444614e-02 8.484848484848485362e-02 6.161616161616161741e-02 6.767676767676768235e-02 6.313131313131313538e-02 5.000000000000000278e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 7.870370370370370627e-02 3.611111111111110772e-02 2.777777777777777624e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 3.148148148148147835e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 8.611111111111111049e-02 8.611111111111111049e-02 9.444444444444444198e-02 4.074074074074074403e-02 5.185185185185185036e-02 4.074074074074074403e-02 4.074074074074074403e-02 5.370370370370370489e-02 4.259259259259259162e-02 4.259259259259259162e-02 6.111111111111110911e-02 5.370370370370370489e-02 6.111111111111110911e-02 1.587301587301587213e-02 2.248677248677248552e-02 2.248677248677248552e-02 1.587301587301587213e-02 2.248677248677248552e-02 9.259259259259258745e-02 7.407407407407406996e-02 9.814814814814815103e-02 9.814814814814815103e-02 1.037037037037037007e-01 4.761904761904761640e-02 4.761904761904761640e-02 4.497354497354497105e-02 5.423280423280422979e-02 4.629629629629629373e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.084656084656084318e-02 6.084656084656084318e-02 4.629629629629629373e-02 5.423280423280422979e-02 4.497354497354497105e-02 6.084656084656084318e-02 6.084656084656084318e-02 1.785714285714285615e-02 3.273809523809523975e-02 2.281746031746031619e-02 2.876984126984126824e-02 2.281746031746031619e-02 2.876984126984126824e-02 1.785714285714285615e-02 2.876984126984126824e-02 6.878306878306877925e-02 7.539682539682539264e-02 1.018518518518518462e-01 1.018518518518518462e-01 8.862433862433861942e-02 7.539682539682539264e-02 8.333333333333332871e-02 9.656084656084655549e-02 9.788359788359787816e-02 9.391534391534391013e-02 8.465608465608465139e-02 4.960317460317460042e-02 4.960317460317460042e-02 5.059523809523809590e-02 4.960317460317460042e-02 5.456349206349206393e-02 5.555555555555555247e-02 4.960317460317460042e-02 5.257936507936507992e-02 4.662698412698412787e-02 4.563492063492063239e-02 4.563492063492063239e-02 4.960317460317460042e-02 5.158730158730158444e-02 4.960317460317460042e-02 5.952380952380952051e-02 4.960317460317460042e-02 4.662698412698412787e-02 5.158730158730158444e-02 5.158730158730158444e-02 4.662698412698412787e-02 2.623456790123456644e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.237654320987654197e-02 3.163580246913580418e-02 2.700617283950617134e-02 3.163580246913580418e-02 3.163580246913580418e-02 6.349206349206348854e-02 8.829365079365079916e-02 1.021825396825396803e-01 1.081349206349206393e-01 7.936507936507936067e-02 1.051587301587301598e-01 9.821428571428571230e-02 7.043650793650793607e-02 7.837301587301587213e-02 5.169753086419753146e-02 4.475308641975308394e-02 4.861111111111111188e-02 4.783950617283950352e-02 4.475308641975308394e-02 4.783950617283950352e-02 4.783950617283950352e-02 5.478395061728395105e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.092592592592592310e-02 4.861111111111111188e-02 5.015432098765432167e-02 4.475308641975308394e-02 4.074074074074074403e-02 2.469135802469135665e-02 2.469135802469135665e-02 3.271604938271605034e-02 3.580246913580246992e-02 2.530864197530864265e-02 7.793209876543209791e-02 1.010802469135802517e-01 8.873456790123457338e-02 9.645061728395061540e-02 9.953703703703703498e-02 8.410493827160493707e-02 1.049382716049382658e-01 5.864197530864197205e-02 4.567901234567901120e-02 5.432098765432098741e-02 5.432098765432098741e-02 5.802469135802468952e-02 5.432098765432098741e-02 4.382716049382716361e-02 4.629629629629629373e-02 4.876543209876543078e-02 4.320987654320987414e-02 5.061728395061728530e-02 4.876543209876543078e-02 4.753086419753086572e-02 4.753086419753086572e-02 5.123456790123456783e-02 5.370370370370370489e-02 5.432098765432098741e-02 5.000000000000000278e-02 3.989898989898989917e-02 4.040404040404040747e-02 2.676767676767676657e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.575757575757575690e-02 5.555555555555555247e-02 5.432098765432098741e-02 4.646464646464646547e-02 6.919191919191919338e-02 4.191919191919191851e-02 5.202020202020202211e-02 4.141414141414141714e-02 4.141414141414141714e-02 6.448412698412699096e-02 4.646464646464646547e-02 4.141414141414141714e-02 4.141414141414141714e-02 4.141414141414141714e-02 4.393939393939393784e-02 4.494949494949494750e-02 9.848484848484848064e-02 6.313131313131313538e-02 6.010101010101009944e-02 7.272727272727272374e-02 5.050505050505050414e-02
-0.000000000000000000e+00 0.000000000000000000e+00 4.629629629629629373e-02 1.851851851851851749e-02 2.777777777777777624e-02 1.666666666666666644e-02 0.000000000000000000e+00 5.555555555555555247e-02 3.055555555555555455e-02 2.777777777777777624e-02 4.722222222222222099e-02 3.333333333333333287e-02 4.722222222222222099e-02 2.222222222222222307e-02 3.148148148148147835e-02 1.587301587301587213e-02 1.111111111111111154e-02 1.111111111111111154e-02 6.944444444444444753e-02 5.277777777777777762e-02 6.666666666666666574e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.148148148148147835e-02 3.703703703703703498e-02 5.925925925925926152e-02 4.259259259259259162e-02 4.259259259259259162e-02 6.111111111111110911e-02 5.925925925925926152e-02 6.111111111111110911e-02 1.587301587301587213e-02 2.248677248677248552e-02 2.248677248677248552e-02 1.587301587301587213e-02 2.248677248677248552e-02 5.740740740740740700e-02 7.777777777777777901e-02 7.777777777777777901e-02 5.740740740740740700e-02 7.592592592592592449e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.174603174603174427e-02 6.613756613756613389e-02 5.026455026455026176e-02 4.497354497354497105e-02 4.497354497354497105e-02 5.158730158730158444e-02 6.878306878306877925e-02 6.878306878306877925e-02 5.026455026455026176e-02 6.613756613756613389e-02 4.497354497354497105e-02 6.878306878306877925e-02 6.878306878306877925e-02 1.785714285714285615e-02 3.273809523809523975e-02 2.281746031746031619e-02 3.174603174603174427e-02 2.281746031746031619e-02 3.174603174603174427e-02 1.785714285714285615e-02 3.174603174603174427e-02 8.201058201058200603e-02 6.349206349206348854e-02 5.687830687830687515e-02 5.687830687830687515e-02 6.481481481481481122e-02 8.333333333333332871e-02 8.333333333333332871e-02 6.216931216931216586e-02 6.349206349206348854e-02 5.687830687830687515e-02 8.068783068783068335e-02 4.166666666666666435e-02 4.166666666666666435e-02 4.563492063492063239e-02 4.166666666666666435e-02 4.662698412698412787e-02 3.174603174603174427e-02 3.373015873015872829e-02 6.944444444444444753e-02 5.555555555555555247e-02 4.861111111111111188e-02 4.861111111111111188e-02 4.960317460317460042e-02 5.753968253968253649e-02 4.960317460317460042e-02 5.952380952380952051e-02 4.960317460317460042e-02 5.555555555555555247e-02 5.753968253968253649e-02 5.753968253968253649e-02 5.555555555555555247e-02 2.623456790123456644e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.237654320987654197e-02 2.237654320987654197e-02 3.858024691358024477e-02 2.932098765432098603e-02 3.858024691358024477e-02 3.858024691358024477e-02 8.333333333333332871e-02 5.853174603174603197e-02 6.051587301587301598e-02 5.952380952380952051e-02 6.845238095238095899e-02 5.952380952380952051e-02 5.853174603174603197e-02 6.944444444444444753e-02 5.853174603174603197e-02 3.780864197530864335e-02 4.938271604938271331e-02 5.324074074074074125e-02 5.015432098765432167e-02 4.938271604938271331e-02 5.015432098765432167e-02 5.015432098765432167e-02 7.484567901234567833e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.092592592592592310e-02 5.324074074074074125e-02 7.021604938271605589e-02 5.169753086419753146e-02 4.074074074074074403e-02 2.469135802469135665e-02 2.469135802469135665e-02 4.320987654320987414e-02 4.506172839506172867e-02 2.716049382716049371e-02 5.787037037037037063e-02 6.095679012345679021e-02 5.941358024691358042e-02 5.941358024691358042e-02 5.787037037037037063e-02 5.787037037037037063e-02 5.864197530864197205e-02 8.333333333333332871e-02 4.567901234567901120e-02 4.691358024691358319e-02 4.691358024691358319e-02 3.456790123456789793e-02 7.716049382716048954e-02 5.987654320987654405e-02 5.185185185185185036e-02 5.246913580246913289e-02 5.061728395061728530e-02 7.222222222222221544e-02 5.246913580246913289e-02 6.913580246913579586e-02 6.913580246913579586e-02 7.407407407407406996e-02 7.530864197530864890e-02 7.716049382716048954e-02 6.481481481481481122e-02 4.292929292929292817e-02 5.101010101010101244e-02 3.131313131313131354e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.575757575757575690e-02 7.037037037037037479e-02 7.901234567901234407e-02 4.040404040404040747e-02 3.484848484848485084e-02 5.959595959595959808e-02 6.868686868686868507e-02 5.050505050505050414e-02 5.050505050505050414e-02 9.027777777777777624e-02 5.252525252525252347e-02 5.050505050505050414e-02 5.454545454545454281e-02 5.454545454545454281e-02 5.151515151515151381e-02 6.717171717171717404e-02 5.909090909090908977e-02 5.707070707070707044e-02 6.010101010101009944e-02 6.111111111111110911e-02 7.525252525252525138e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.962962962962963076e-02 3.703703703703703498e-02 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 4.444444444444444614e-02 4.888888888888889145e-02 1.777777777777777776e-02 3.555555555555555552e-02 2.666666666666666838e-02 3.555555555555555552e-02 1.777777777777777776e-02 2.370370370370370253e-02 1.269841269841269840e-02 8.888888888888888881e-03 8.888888888888888881e-03 5.333333333333333676e-02 4.444444444444444614e-02 5.333333333333333676e-02 5.185185185185185036e-02 2.666666666666666838e-02 2.370370370370370253e-02 2.666666666666666838e-02 4.148148148148148029e-02 3.259259259259258967e-02 3.259259259259258967e-02 4.444444444444444614e-02 4.148148148148148029e-02 4.444444444444444614e-02 1.269841269841269840e-02 1.693121693121693236e-02 1.693121693121693236e-02 1.269841269841269840e-02 1.693121693121693236e-02 4.740740740740740505e-02 5.629629629629629567e-02 5.925925925925926152e-02 4.740740740740740505e-02 5.925925925925926152e-02 5.608465608465608432e-02 5.502645502645502756e-02 2.539682539682539680e-02 4.232804232804232569e-02 3.597883597883597823e-02 3.386243386243386472e-02 3.386243386243386472e-02 3.809523809523809867e-02 4.656084656084655965e-02 4.656084656084655965e-02 3.597883597883597823e-02 4.232804232804232569e-02 3.386243386243386472e-02 4.656084656084655965e-02 4.656084656084655965e-02 1.428571428571428527e-02 2.380952380952380820e-02 1.746031746031746074e-02 2.222222222222222307e-02 1.746031746031746074e-02 2.222222222222222307e-02 1.428571428571428527e-02 2.222222222222222307e-02 5.291005291005290712e-02 4.867724867724868010e-02 4.656084656084655965e-02 4.656084656084655965e-02 5.079365079365079361e-02 5.925925925925926152e-02 5.925925925925926152e-02 4.867724867724868010e-02 5.079365079365079361e-02 4.656084656084655965e-02 5.502645502645502756e-02 5.714285714285714107e-02 5.555555555555555247e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.793650793650793884e-02 2.698412698412698540e-02 2.698412698412698540e-02 4.126984126984126894e-02 3.650793650793651007e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.650793650793651007e-02 3.968253968253968034e-02 3.650793650793651007e-02 4.285714285714285754e-02 3.650793650793651007e-02 3.650793650793651007e-02 3.968253968253968034e-02 3.968253968253968034e-02 3.650793650793651007e-02 1.975308641975308602e-02 1.728395061728394896e-02 1.728395061728394896e-02 1.728395061728394896e-02 1.728395061728394896e-02 2.469135802469135665e-02 2.098765432098765454e-02 2.469135802469135665e-02 2.469135802469135665e-02 4.920634920634920501e-02 4.603174603174603474e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.079365079365079361e-02 4.761904761904761640e-02 4.603174603174603474e-02 5.079365079365079361e-02 4.603174603174603474e-02 2.962962962962963076e-02 3.456790123456789793e-02 3.703703703703703498e-02 3.580246913580246992e-02 3.456790123456789793e-02 3.580246913580246992e-02 3.580246913580246992e-02 4.320987654320987414e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.703703703703703498e-02 3.950617283950617203e-02 3.456790123456789793e-02 2.962962962962963076e-02 1.876543209876543189e-02 1.876543209876543189e-02 2.567901234567901078e-02 2.765432098765432251e-02 1.975308641975308602e-02 4.444444444444444614e-02 4.691358024691358319e-02 4.567901234567901120e-02 4.691358024691358319e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.567901234567901120e-02 4.567901234567901120e-02 5.777777777777777513e-02 5.679012345679012447e-02 5.679012345679012447e-02 2.864197530864197663e-02 4.246913580246913789e-02 3.456790123456789793e-02 3.555555555555555552e-02 3.654320987654321312e-02 3.358024691358024727e-02 3.950617283950617203e-02 3.654320987654321312e-02 3.753086419753086378e-02 3.753086419753086378e-02 4.049382716049382963e-02 4.148148148148148029e-02 4.246913580246913789e-02 3.851851851851852138e-02 2.989898989898990070e-02 3.070707070707070704e-02 2.101010101010101008e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 1.939393939393939392e-02 4.049382716049382963e-02 4.246913580246913789e-02 3.070707070707070704e-02 2.909090909090909088e-02 3.313131313131312955e-02 3.959595959595959419e-02 3.232323232323232320e-02 3.232323232323232320e-02 5.079365079365079361e-02 3.555555555555555552e-02 3.232323232323232320e-02 3.232323232323232320e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.555555555555555552e-02 4.363636363636363980e-02 3.878787878787878785e-02 4.363636363636363980e-02 4.121212121212121382e-02 3.959595959595959419e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.962962962962963076e-02 3.703703703703703498e-02 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 4.444444444444444614e-02 4.444444444444444614e-02 1.777777777777777776e-02 4.000000000000000083e-02 2.666666666666666838e-02 4.000000000000000083e-02 1.777777777777777776e-02 2.666666666666666838e-02 1.269841269841269840e-02 8.888888888888888881e-03 8.888888888888888881e-03 6.222222222222222043e-02 4.444444444444444614e-02 5.777777777777777513e-02 4.592592592592592560e-02 2.666666666666666838e-02 2.370370370370370253e-02 2.666666666666666838e-02 4.444444444444444614e-02 3.555555555555555552e-02 3.555555555555555552e-02 5.333333333333333676e-02 4.444444444444444614e-02 5.333333333333333676e-02 1.269841269841269840e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 1.904761904761904934e-02 5.037037037037037090e-02 5.925925925925926152e-02 7.407407407407406996e-02 5.333333333333333676e-02 6.814814814814815214e-02 6.243386243386243178e-02 5.079365079365079361e-02 2.539682539682539680e-02 4.444444444444444614e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 3.809523809523809867e-02 4.444444444444444614e-02 3.809523809523809867e-02 5.079365079365079361e-02 5.079365079365079361e-02 1.428571428571428527e-02 2.857142857142857054e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.428571428571428527e-02 2.380952380952380820e-02 5.502645502645502756e-02 5.079365079365079361e-02 5.502645502645502756e-02 5.291005291005290712e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.560846560846560205e-02 5.291005291005290712e-02 5.714285714285714107e-02 5.079365079365079361e-02 6.137566137566137503e-02 7.222222222222221544e-02 5.238095238095238221e-02 6.031746031746031828e-02 6.349206349206348854e-02 5.634920634920635024e-02 2.698412698412698540e-02 2.698412698412698540e-02 4.285714285714285754e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 3.809523809523809867e-02 2.222222222222222307e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 2.592592592592592518e-02 2.222222222222222307e-02 2.592592592592592518e-02 2.592592592592592518e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.873015873015872967e-02 5.714285714285714107e-02 5.555555555555555247e-02 5.555555555555555247e-02 5.079365079365079361e-02 5.238095238095238221e-02 4.920634920634920501e-02 3.333333333333333287e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.703703703703703498e-02 3.555555555555555552e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.666666666666666838e-02 2.962962962962963076e-02 2.074074074074074014e-02 4.691358024691358319e-02 5.925925925925926152e-02 5.308641975308642236e-02 5.432098765432098741e-02 4.938271604938271331e-02 4.814814814814814825e-02 5.432098765432098741e-02 4.691358024691358319e-02 5.679012345679012447e-02 8.641975308641974829e-02 5.679012345679012447e-02 3.061728395061728489e-02 4.444444444444444614e-02 3.555555555555555552e-02 3.851851851851852138e-02 4.148148148148148029e-02 3.555555555555555552e-02 4.148148148148148029e-02 4.148148148148148029e-02 3.851851851851852138e-02 3.851851851851852138e-02 4.148148148148148029e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.148148148148148029e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 4.148148148148148029e-02 4.345679012345678854e-02 3.232323232323232320e-02 3.313131313131312955e-02 3.393939393939394283e-02 4.363636363636363980e-02 3.393939393939394283e-02 3.393939393939394283e-02 5.238095238095238221e-02 3.878787878787878785e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.636363636363636187e-02 3.636363636363636187e-02 4.848484848484848481e-02 4.040404040404040747e-02 4.525252525252525249e-02 4.686868686868687212e-02 4.040404040404040747e-02
-0.000000000000000000e+00 0.000000000000000000e+00 2.962962962962963076e-02 3.703703703703703498e-02 2.222222222222222307e-02 1.333333333333333419e-02 0.000000000000000000e+00 5.925925925925926152e-02 7.555555555555555636e-02 1.777777777777777776e-02 4.000000000000000083e-02 2.666666666666666838e-02 4.000000000000000083e-02 1.777777777777777776e-02 2.666666666666666838e-02 1.269841269841269840e-02 8.888888888888888881e-03 8.888888888888888881e-03 6.222222222222222043e-02 6.222222222222222043e-02 7.555555555555555636e-02 9.925925925925925541e-02 3.851851851851852138e-02 2.962962962962963076e-02 3.851851851851852138e-02 4.444444444444444614e-02 3.555555555555555552e-02 3.555555555555555552e-02 5.333333333333333676e-02 4.444444444444444614e-02 5.333333333333333676e-02 1.269841269841269840e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.269841269841269840e-02 1.904761904761904934e-02 6.814814814814815214e-02 5.925925925925926152e-02 6.814814814814815214e-02 5.925925925925926152e-02 8.592592592592591949e-02 8.359788359788360157e-02 1.121693121693121686e-01 3.386243386243386472e-02 4.444444444444444614e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.444444444444444614e-02 5.079365079365079361e-02 5.079365079365079361e-02 3.809523809523809867e-02 4.444444444444444614e-02 3.809523809523809867e-02 5.079365079365079361e-02 5.079365079365079361e-02 1.428571428571428527e-02 2.857142857142857054e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.904761904761904934e-02 2.380952380952380820e-02 1.428571428571428527e-02 2.380952380952380820e-02 5.502645502645502756e-02 5.502645502645502756e-02 5.502645502645502756e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 6.137566137566137503e-02 7.407407407407406996e-02 6.772486772486772943e-02 6.137566137566137503e-02 7.222222222222221544e-02 1.222222222222222182e-01 9.365079365079365115e-02 9.365079365079365115e-02 1.198412698412698457e-01 3.968253968253968034e-02 3.968253968253968034e-02 4.285714285714285754e-02 3.809523809523809867e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.285714285714285754e-02 5.238095238095238221e-02 4.285714285714285754e-02 3.809523809523809867e-02 4.285714285714285754e-02 4.285714285714285754e-02 3.809523809523809867e-02 2.222222222222222307e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 1.851851851851851749e-02 2.592592592592592518e-02 2.222222222222222307e-02 2.592592592592592518e-02 2.592592592592592518e-02 5.079365079365079361e-02 5.079365079365079361e-02 5.555555555555555247e-02 6.031746031746031828e-02 5.555555555555555247e-02 6.507936507936508408e-02 6.031746031746031828e-02 5.555555555555555247e-02 5.555555555555555247e-02 3.827160493827160698e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.703703703703703498e-02 4.074074074074074403e-02 4.074074074074074403e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.074074074074074403e-02 3.703703703703703498e-02 3.555555555555555552e-02 2.074074074074074014e-02 2.074074074074074014e-02 2.666666666666666838e-02 2.962962962962963076e-02 2.074074074074074014e-02 4.691358024691358319e-02 5.432098765432098741e-02 5.061728395061728530e-02 6.913580246913579586e-02 5.432098765432098741e-02 5.061728395061728530e-02 5.432098765432098741e-02 4.691358024691358319e-02 5.679012345679012447e-02 5.679012345679012447e-02 1.338271604938271742e-01 4.246913580246913789e-02 4.444444444444444614e-02 3.555555555555555552e-02 3.851851851851852138e-02 4.148148148148148029e-02 3.555555555555555552e-02 4.148148148148148029e-02 4.148148148148148029e-02 3.851851851851852138e-02 3.851851851851852138e-02 4.148148148148148029e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.148148148148148029e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 4.345679012345678854e-02 4.345679012345678854e-02 3.232323232323232320e-02 3.959595959595959419e-02 3.393939393939394283e-02 4.363636363636363980e-02 3.393939393939394283e-02 3.393939393939394283e-02 5.238095238095238221e-02 3.878787878787878785e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.636363636363636187e-02 3.636363636363636187e-02 5.494949494949494945e-02 4.040404040404040747e-02 4.525252525252525249e-02 4.525252525252525249e-02 4.040404040404040747e-02
-0.000000000000000000e+00 0.000000000000000000e+00 7.407407407407406996e-02 2.962962962962963076e-02 1.851851851851851749e-02 1.111111111111111154e-02 0.000000000000000000e+00 1.000000000000000056e-01 4.222222222222222349e-02 5.777777777777777513e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.666666666666666838e-02 1.481481481481481538e-02 1.777777777777777776e-02 1.058201058201058142e-02 7.407407407407407690e-03 7.407407407407407690e-03 6.222222222222222043e-02 1.066666666666666735e-01 1.022222222222222282e-01 4.444444444444444614e-02 8.148148148148148806e-02 7.555555555555555636e-02 5.481481481481481621e-02 2.814814814814814783e-02 2.518518518518518545e-02 2.518518518518518545e-02 3.111111111111111022e-02 2.814814814814814783e-02 3.111111111111111022e-02 1.058201058201058142e-02 1.269841269841269840e-02 1.269841269841269840e-02 1.058201058201058142e-02 1.269841269841269840e-02 1.051851851851851871e-01 5.185185185185185036e-02 5.481481481481481621e-02 8.148148148148148806e-02 9.925925925925925541e-02 3.915343915343915543e-02 4.550264550264550290e-02 8.465608465608465139e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.539682539682539680e-02 2.751322751322751378e-02 2.962962962962963076e-02 2.962962962962963076e-02 2.539682539682539680e-02 2.751322751322751378e-02 2.539682539682539680e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.190476190476190410e-02 1.666666666666666644e-02 1.349206349206349270e-02 1.507936507936507957e-02 1.349206349206349270e-02 1.507936507936507957e-02 1.190476190476190410e-02 1.507936507936507957e-02 4.444444444444444614e-02 5.925925925925926152e-02 6.560846560846560205e-02 8.253968253968253788e-02 6.137566137566137503e-02 4.656084656084655965e-02 4.656084656084655965e-02 8.253968253968253788e-02 1.015873015873015872e-01 1.037037037037037007e-01 5.079365079365079361e-02 3.492063492063492147e-02 4.444444444444444614e-02 3.968253968253968034e-02 3.968253968253968034e-02 4.603174603174603474e-02 9.285714285714286031e-02 6.269841269841269771e-02 2.619047619047619110e-02 2.460317460317460250e-02 2.460317460317460250e-02 2.460317460317460250e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.936507936507936484e-02 2.619047619047619110e-02 2.460317460317460250e-02 2.619047619047619110e-02 2.619047619047619110e-02 2.460317460317460250e-02 1.481481481481481538e-02 1.358024691358024685e-02 1.358024691358024685e-02 1.358024691358024685e-02 1.358024691358024685e-02 1.604938271604938391e-02 1.481481481481481538e-02 1.604938271604938391e-02 1.604938271604938391e-02 3.888888888888888951e-02 5.476190476190476164e-02 5.158730158730158444e-02 6.904761904761905211e-02 5.158730158730158444e-02 8.174603174603174705e-02 8.333333333333332871e-02 5.158730158730158444e-02 6.746031746031745657e-02 5.308641975308642236e-02 2.345679012345679160e-02 2.469135802469135665e-02 2.469135802469135665e-02 2.345679012345679160e-02 2.469135802469135665e-02 2.469135802469135665e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.592592592592592518e-02 2.469135802469135665e-02 2.469135802469135665e-02 2.345679012345679160e-02 2.074074074074074014e-02 1.432098765432098832e-02 1.432098765432098832e-02 1.629629629629629484e-02 1.728395061728394896e-02 1.432098765432098832e-02 4.691358024691358319e-02 4.197530864197530909e-02 4.444444444444444614e-02 9.876543209876542662e-02 6.913580246913579586e-02 5.679012345679012447e-02 5.802469135802468952e-02 3.456790123456789793e-02 2.864197530864197663e-02 3.061728395061728489e-02 4.246913580246913789e-02 9.432098765432098131e-02 2.518518518518518545e-02 2.222222222222222307e-02 2.320987654320987720e-02 2.419753086419753133e-02 2.222222222222222307e-02 2.419753086419753133e-02 2.419753086419753133e-02 2.320987654320987720e-02 2.320987654320987720e-02 2.419753086419753133e-02 2.518518518518518545e-02 2.518518518518518545e-02 2.419753086419753133e-02 1.979797979797979710e-02 1.858585858585858758e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.454545454545454544e-02 3.802469135802469258e-02 3.111111111111111022e-02 4.363636363636363980e-02 6.868686868686868507e-02 2.101010101010101008e-02 2.424242424242424240e-02 2.101010101010101008e-02 2.101010101010101008e-02 3.174603174603174427e-02 2.262626262626262624e-02 2.101010101010101008e-02 2.101010101010101008e-02 2.101010101010101008e-02 2.181818181818181990e-02 2.181818181818181990e-02 7.030303030303029777e-02 3.636363636363636187e-02 3.797979797979798150e-02 3.555555555555555552e-02 2.828282828282828454e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.925925925925926152e-02 3.111111111111111022e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 7.777777777777777901e-02 5.999999999999999778e-02 7.777777777777777901e-02 3.555555555555555552e-02 4.740740740740740505e-02 3.386243386243386472e-02 3.703703703703703498e-02 4.000000000000000083e-02 4.888888888888889145e-02 3.111111111111111022e-02 4.888888888888889145e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.888888888888889228e-02 6.666666666666666574e-02 6.814814814814815214e-02 9.037037037037036480e-02 8.888888888888889228e-02 9.185185185185185119e-02 3.386243386243386472e-02 5.079365079365079361e-02 5.079365079365079361e-02 4.021164021164021218e-02 5.185185185185185036e-02 4.296296296296295975e-02 6.370370370370370683e-02 6.666666666666666574e-02 4.296296296296295975e-02 6.666666666666666574e-02 3.068783068783068751e-02 3.068783068783068751e-02 1.481481481481481538e-02 8.888888888888889228e-02 7.513227513227513366e-02 6.666666666666666574e-02 6.772486772486772943e-02 7.619047619047619735e-02 9.841269841269841001e-02 9.735449735449736020e-02 7.407407407407406996e-02 9.523809523809523281e-02 6.878306878306877925e-02 9.841269841269841001e-02 9.735449735449736020e-02 3.730158730158730090e-02 6.111111111111110911e-02 4.682539682539682557e-02 6.111111111111110911e-02 4.285714285714285754e-02 5.793650793650793884e-02 3.888888888888888951e-02 6.111111111111110911e-02 7.301587301587302015e-02 5.291005291005290712e-02 4.656084656084655965e-02 4.656084656084655965e-02 5.502645502645502756e-02 7.619047619047619735e-02 7.619047619047619735e-02 5.291005291005290712e-02 5.502645502645502756e-02 4.656084656084655965e-02 7.301587301587302015e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.968253968253968034e-02 3.492063492063492147e-02 4.126984126984126894e-02 1.666666666666666644e-02 1.666666666666666644e-02 8.888888888888889228e-02 7.698412698412698818e-02 6.984126984126984294e-02 7.063492063492063378e-02 7.142857142857142461e-02 8.253968253968253788e-02 7.222222222222221544e-02 8.412698412698413342e-02 7.301587301587302015e-02 8.015873015873016538e-02 8.174603174603174705e-02 8.174603174603174705e-02 7.857142857142856984e-02 4.197530864197530909e-02 4.320987654320987414e-02 4.012345679012345456e-02 4.197530864197530909e-02 4.320987654320987414e-02 6.111111111111110911e-02 5.185185185185185036e-02 6.358024691358024616e-02 6.481481481481481122e-02 7.698412698412698818e-02 5.158730158730158444e-02 5.317460317460317304e-02 5.317460317460317304e-02 6.269841269841269771e-02 5.317460317460317304e-02 5.158730158730158444e-02 6.269841269841269771e-02 5.158730158730158444e-02 2.222222222222222307e-02 6.975308641975308532e-02 7.469135802469135943e-02 7.098765432098765038e-02 6.975308641975308532e-02 7.160493827160493985e-02 7.037037037037037479e-02 9.876543209876542662e-02 7.222222222222221544e-02 7.222222222222221544e-02 7.345679012345679437e-02 7.530864197530864890e-02 8.641975308641974829e-02 7.222222222222221544e-02 5.530864197530864501e-02 4.197530864197530909e-02 4.098765432098765149e-02 6.271604938271604923e-02 7.555555555555555636e-02 4.691358024691358319e-02 5.308641975308642236e-02 5.555555555555555247e-02 5.432098765432098741e-02 5.555555555555555247e-02 5.308641975308642236e-02 5.308641975308642236e-02 5.432098765432098741e-02 7.716049382716048954e-02 4.246913580246913789e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.518518518518518545e-02 1.041975308641975295e-01 7.456790123456789876e-02 7.209876543209876865e-02 7.358024691358024116e-02 6.864197530864198094e-02 8.888888888888889228e-02 7.308641975308642624e-02 8.493827160493827577e-02 8.296296296296296058e-02 9.333333333333333759e-02 9.580246913580246770e-02 9.728395061728395410e-02 8.987654320987654988e-02 5.696969696969696878e-02 7.555555555555555636e-02 5.050505050505050414e-02 4.202020202020202017e-02 4.282828282828282651e-02 4.202020202020202017e-02 4.282828282828282651e-02 6.518518518518517935e-02 7.555555555555555636e-02 3.474747474747474918e-02 2.666666666666666838e-02 7.232323232323231710e-02 9.414141414141413700e-02 6.909090909090909172e-02 6.989898989898990500e-02 1.087301587301587324e-01 7.232323232323231710e-02 6.666666666666666574e-02 6.909090909090909172e-02 6.868686868686868507e-02 6.949494949494949836e-02 8.080808080808081495e-02 5.818181818181818177e-02 5.575757575757575579e-02 5.818181818181818177e-02 5.979797979797980140e-02 7.313131313131313038e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.555555555555555247e-02 2.888888888888888756e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.222222222222222043e-02 5.777777777777777513e-02 6.666666666666666574e-02 3.259259259259258967e-02 4.000000000000000083e-02 3.174603174603174427e-02 3.407407407407407607e-02 3.407407407407407607e-02 4.222222222222222349e-02 3.111111111111111022e-02 4.222222222222222349e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 6.814814814814815214e-02 5.777777777777777513e-02 6.222222222222222043e-02 6.814814814814815214e-02 6.962962962962962465e-02 7.407407407407406996e-02 3.174603174603174427e-02 4.338624338624338939e-02 4.126984126984126894e-02 3.597883597883597823e-02 4.126984126984126894e-02 3.851851851851852138e-02 5.185185185185185036e-02 5.333333333333333676e-02 3.851851851851852138e-02 5.333333333333333676e-02 2.751322751322751378e-02 2.751322751322751378e-02 1.481481481481481538e-02 7.089947089947089276e-02 6.031746031746031828e-02 5.502645502645502756e-02 5.820105820105819783e-02 6.243386243386243178e-02 7.195767195767195645e-02 7.195767195767195645e-02 6.243386243386243178e-02 7.195767195767195645e-02 6.243386243386243178e-02 7.513227513227513366e-02 7.301587301587302015e-02 3.571428571428571230e-02 4.444444444444444614e-02 4.206349206349206671e-02 4.523809523809523697e-02 3.730158730158730090e-02 4.682539682539682557e-02 3.571428571428571230e-02 4.682539682539682557e-02 5.714285714285714107e-02 4.444444444444444614e-02 4.021164021164021218e-02 4.021164021164021218e-02 4.550264550264550290e-02 5.925925925925926152e-02 5.925925925925926152e-02 4.444444444444444614e-02 4.550264550264550290e-02 4.021164021164021218e-02 5.714285714285714107e-02 3.015873015873015914e-02 3.015873015873015914e-02 3.333333333333333287e-02 3.015873015873015914e-02 3.412698412698412370e-02 1.666666666666666644e-02 1.666666666666666644e-02 7.222222222222221544e-02 6.269841269841269771e-02 5.476190476190476164e-02 5.873015873015872967e-02 5.555555555555555247e-02 6.507936507936508408e-02 6.031746031746031828e-02 6.587301587301587491e-02 6.428571428571427937e-02 6.349206349206348854e-02 6.666666666666666574e-02 6.587301587301587491e-02 6.269841269841269771e-02 3.456790123456789793e-02 4.012345679012345456e-02 3.580246913580246992e-02 3.641975308641975245e-02 3.888888888888888951e-02 5.061728395061728530e-02 4.444444444444444614e-02 5.061728395061728530e-02 5.000000000000000278e-02 5.952380952380952051e-02 4.285714285714285754e-02 4.365079365079364837e-02 4.365079365079364837e-02 5.000000000000000278e-02 4.365079365079364837e-02 4.285714285714285754e-02 5.000000000000000278e-02 4.285714285714285754e-02 2.037037037037037202e-02 5.308641975308642236e-02 5.740740740740740700e-02 5.370370370370370489e-02 5.555555555555555247e-02 5.925925925925926152e-02 5.555555555555555247e-02 7.407407407407406996e-02 5.432098765432098741e-02 5.802469135802468952e-02 6.419753086419753563e-02 6.296296296296295669e-02 7.037037037037037479e-02 5.679012345679012447e-02 4.345679012345678854e-02 3.802469135802469258e-02 3.506172839506172673e-02 5.283950617283950796e-02 5.432098765432098741e-02 4.197530864197530909e-02 4.320987654320987414e-02 4.444444444444444614e-02 4.382716049382716361e-02 4.444444444444444614e-02 4.320987654320987414e-02 4.320987654320987414e-02 4.382716049382716361e-02 5.987654320987654405e-02 3.456790123456789793e-02 3.555555555555555552e-02 3.555555555555555552e-02 2.222222222222222307e-02 7.456790123456789876e-02 6.469135802469136443e-02 5.432098765432098741e-02 5.827160493827160392e-02 5.234567901234567916e-02 7.160493827160493985e-02 5.432098765432098741e-02 6.864197530864198094e-02 6.666666666666666574e-02 7.209876543209876865e-02 7.358024691358024116e-02 7.456790123456789876e-02 6.864197530864198094e-02 4.404040404040403950e-02 5.535353535353535609e-02 4.202020202020202017e-02 3.434343434343434254e-02 3.676767676767676851e-02 3.595959595959596217e-02 3.838383838383838120e-02 5.135802469135802156e-02 5.876543209876543272e-02 2.828282828282828454e-02 2.303030303030302942e-02 6.101010101010100745e-02 6.989898989898990500e-02 5.373737373737373646e-02 5.454545454545454281e-02 8.492063492063492425e-02 5.414141414141414310e-02 5.090909090909091078e-02 6.020202020202020110e-02 5.656565656565656908e-02 5.252525252525252347e-02 6.464646464646464641e-02 4.525252525252525249e-02 4.363636363636363980e-02 4.525252525252525249e-02 4.606060606060605883e-02 5.696969696969696878e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.925925925925926152e-02 2.888888888888888756e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.666666666666666574e-02 6.222222222222222043e-02 7.333333333333333370e-02 3.259259259259258967e-02 4.148148148148148029e-02 3.386243386243386472e-02 3.703703703703703498e-02 3.111111111111111022e-02 4.444444444444444614e-02 3.111111111111111022e-02 4.444444444444444614e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.703703703703704275e-02 6.666666666666666574e-02 6.814814814814815214e-02 7.703703703703704275e-02 7.407407407407406996e-02 8.296296296296296058e-02 3.386243386243386472e-02 4.867724867724868010e-02 4.232804232804232569e-02 3.492063492063492147e-02 4.338624338624338939e-02 4.000000000000000083e-02 5.333333333333333676e-02 5.777777777777777513e-02 4.000000000000000083e-02 5.777777777777777513e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.481481481481481538e-02 6.878306878306877925e-02 7.195767195767195645e-02 6.666666666666666574e-02 6.772486772486772943e-02 6.984126984126984294e-02 8.465608465608465139e-02 8.148148148148148806e-02 6.984126984126984294e-02 7.619047619047619735e-02 6.878306878306877925e-02 8.042328042328042437e-02 7.936507936507936067e-02 3.888888888888888951e-02 5.158730158730158444e-02 4.682539682539682557e-02 5.396825396825397081e-02 4.285714285714285754e-02 4.920634920634920501e-02 3.571428571428571230e-02 5.238095238095238221e-02 5.502645502645502756e-02 4.550264550264550290e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.867724867724868010e-02 6.137566137566137503e-02 6.137566137566137503e-02 4.550264550264550290e-02 4.867724867724868010e-02 4.232804232804232569e-02 5.502645502645502756e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.412698412698412370e-02 3.174603174603174427e-02 3.650793650793651007e-02 1.666666666666666644e-02 1.666666666666666644e-02 6.428571428571427937e-02 6.428571428571427937e-02 7.222222222222221544e-02 7.301587301587302015e-02 6.746031746031745657e-02 7.380952380952381098e-02 7.063492063492063378e-02 7.539682539682539264e-02 7.142857142857142461e-02 7.063492063492063378e-02 7.380952380952381098e-02 7.142857142857142461e-02 6.825396825396824740e-02 3.950617283950617203e-02 4.444444444444444614e-02 4.074074074074074403e-02 4.320987654320987414e-02 4.074074074074074403e-02 4.814814814814814825e-02 4.629629629629629373e-02 5.061728395061728530e-02 4.938271604938271331e-02 5.396825396825397081e-02 4.444444444444444614e-02 4.682539682539682557e-02 4.682539682539682557e-02 5.158730158730158444e-02 4.682539682539682557e-02 4.444444444444444614e-02 5.158730158730158444e-02 4.444444444444444614e-02 2.098765432098765454e-02 7.469135802469135943e-02 7.345679012345679437e-02 7.160493827160493985e-02 7.160493827160493985e-02 7.407407407407406996e-02 6.790123456790123080e-02 7.160493827160493985e-02 6.666666666666666574e-02 7.037037037037037479e-02 7.160493827160493985e-02 7.037037037037037479e-02 5.987654320987654405e-02 6.543209876543210068e-02 4.641975308641975440e-02 4.197530864197530909e-02 4.098765432098765149e-02 4.641975308641975440e-02 6.123456790123456978e-02 4.345679012345678854e-02 4.444444444444444614e-02 4.814814814814814825e-02 4.629629629629629373e-02 4.814814814814814825e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.629629629629629373e-02 5.185185185185185036e-02 3.555555555555555552e-02 3.851851851851852138e-02 3.851851851851852138e-02 2.320987654320987720e-02 7.209876543209876865e-02 5.432098765432098741e-02 7.753086419753085767e-02 7.753086419753085767e-02 6.765432098765432334e-02 6.074074074074074098e-02 7.555555555555555636e-02 5.777777777777777513e-02 5.580246913580246687e-02 6.469135802469136443e-02 6.567901234567900814e-02 6.617283950617283694e-02 6.962962962962962465e-02 4.646464646464646547e-02 5.656565656565656908e-02 4.444444444444444614e-02 4.282828282828282651e-02 4.363636363636363980e-02 4.282828282828282651e-02 4.363636363636363980e-02 4.493827160493827494e-02 4.938271604938271331e-02 2.909090909090909088e-02 2.424242424242424240e-02 5.090909090909091078e-02 7.070707070707070441e-02 6.181818181818182073e-02 6.949494949494949836e-02 7.539682539682539264e-02 7.878787878787878174e-02 6.181818181818182073e-02 5.414141414141414310e-02 5.373737373737373646e-02 6.989898989898990500e-02 5.373737373737373646e-02 4.686868686868687212e-02 4.202020202020202017e-02 4.686868686868687212e-02 4.565656565656565913e-02 4.686868686868687212e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.407407407407406996e-02 3.333333333333333287e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 7.111111111111111105e-02 8.444444444444444697e-02 9.333333333333333759e-02 3.851851851851852138e-02 4.888888888888889145e-02 4.232804232804232569e-02 4.888888888888889145e-02 3.703703703703703498e-02 4.666666666666666879e-02 3.111111111111111022e-02 4.666666666666666879e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.555555555555555636e-02 8.000000000000000167e-02 9.333333333333333759e-02 8.148148148148148806e-02 8.296296296296296058e-02 1.051851851851851871e-01 4.232804232804232569e-02 6.666666666666666574e-02 4.338624338624338939e-02 4.550264550264550290e-02 5.185185185185185036e-02 4.148148148148148029e-02 5.481481481481481621e-02 6.222222222222222043e-02 4.148148148148148029e-02 6.222222222222222043e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.481481481481481538e-02 6.878306878306877925e-02 8.359788359788360157e-02 7.407407407407406996e-02 8.359788359788360157e-02 8.359788359788360157e-02 8.253968253968253788e-02 8.042328042328042437e-02 8.571428571428571508e-02 7.936507936507936067e-02 9.629629629629629650e-02 9.206349206349206948e-02 8.571428571428571508e-02 5.476190476190476164e-02 5.396825396825397081e-02 6.666666666666666574e-02 5.158730158730158444e-02 5.317460317460317304e-02 4.841269841269841417e-02 4.841269841269841417e-02 5.476190476190476164e-02 5.608465608465608432e-02 4.656084656084655965e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.656084656084655965e-02 5.185185185185185036e-02 4.444444444444444614e-02 5.608465608465608432e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.492063492063492147e-02 3.333333333333333287e-02 3.888888888888888951e-02 1.666666666666666644e-02 1.666666666666666644e-02 6.428571428571427937e-02 6.984126984126984294e-02 7.619047619047619735e-02 9.285714285714286031e-02 7.063492063492063378e-02 7.857142857142856984e-02 8.809523809523810145e-02 8.888888888888889228e-02 1.000000000000000056e-01 8.412698412698413342e-02 9.126984126984126477e-02 8.571428571428571508e-02 7.857142857142856984e-02 4.444444444444444614e-02 6.481481481481481122e-02 5.246913580246913289e-02 5.370370370370370489e-02 5.123456790123456783e-02 4.753086419753086572e-02 5.061728395061728530e-02 5.246913580246913289e-02 5.000000000000000278e-02 5.476190476190476164e-02 4.603174603174603474e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.317460317460317304e-02 5.000000000000000278e-02 4.603174603174603474e-02 5.317460317460317304e-02 4.603174603174603474e-02 2.160493827160493707e-02 7.530864197530864890e-02 8.086419753086419859e-02 7.098765432098765038e-02 8.148148148148148806e-02 9.753086419753086156e-02 7.407407407407406996e-02 7.283950617283950490e-02 6.666666666666666574e-02 8.271604938271605312e-02 1.012345679012345706e-01 9.012345679012345734e-02 5.987654320987654405e-02 7.098765432098765038e-02 5.530864197530864501e-02 5.876543209876543272e-02 4.987654320987654211e-02 4.592592592592592560e-02 6.518518518518517935e-02 5.086419753086419970e-02 4.567901234567901120e-02 5.185185185185185036e-02 4.876543209876543078e-02 5.185185185185185036e-02 4.567901234567901120e-02 4.567901234567901120e-02 4.876543209876543078e-02 5.246913580246913289e-02 3.654320987654321312e-02 4.148148148148148029e-02 4.148148148148148029e-02 2.419753086419753133e-02 7.358024691358024116e-02 5.827160493827160392e-02 7.753086419753085767e-02 1.002469135802469130e-01 6.814814814814815214e-02 6.320987654320987803e-02 7.259259259259259744e-02 5.975308641975308338e-02 5.580246913580246687e-02 6.271604938271604923e-02 7.061728395061728225e-02 6.567901234567900814e-02 7.901234567901234407e-02 5.292929292929293011e-02 5.575757575757575579e-02 4.969696969696969779e-02 4.808080808080807816e-02 5.535353535353535609e-02 4.969696969696969779e-02 6.020202020202020110e-02 4.543209876543209680e-02 4.987654320987654211e-02 2.989898989898990070e-02 2.545454545454545539e-02 5.414141414141414310e-02 7.838383838383838897e-02 6.585858585858585246e-02 7.959595959595959502e-02 7.777777777777777901e-02 7.757575757575757569e-02 6.222222222222222043e-02 6.181818181818182073e-02 5.696969696969696878e-02 6.989898989898990500e-02 5.535353535353535609e-02 4.848484848484848481e-02 4.282828282828282651e-02 4.848484848484848481e-02 4.767676767676767846e-02 4.727272727272727182e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.925925925925926152e-02 2.888888888888888756e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.666666666666666574e-02 6.222222222222222043e-02 7.111111111111111105e-02 3.259259259259258967e-02 4.000000000000000083e-02 3.386243386243386472e-02 3.703703703703703498e-02 3.111111111111111022e-02 4.222222222222222349e-02 3.111111111111111022e-02 4.222222222222222349e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 6.962962962962962465e-02 6.814814814814815214e-02 6.666666666666666574e-02 7.555555555555555636e-02 7.259259259259259744e-02 7.851851851851851527e-02 3.386243386243386472e-02 4.761904761904761640e-02 3.915343915343915543e-02 3.492063492063492147e-02 4.338624338624338939e-02 3.851851851851852138e-02 5.037037037037037090e-02 5.333333333333333676e-02 3.851851851851852138e-02 5.333333333333333676e-02 2.751322751322751378e-02 2.751322751322751378e-02 1.481481481481481538e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.878306878306877925e-02 6.878306878306877925e-02 6.666666666666666574e-02 7.513227513227513366e-02 7.513227513227513366e-02 6.984126984126984294e-02 7.089947089947089276e-02 6.666666666666666574e-02 7.724867724867724716e-02 7.724867724867724716e-02 3.888888888888888951e-02 5.238095238095238221e-02 4.603174603174603474e-02 4.761904761904761640e-02 4.365079365079364837e-02 4.444444444444444614e-02 3.571428571428571230e-02 4.761904761904761640e-02 5.291005291005290712e-02 4.338624338624338939e-02 4.021164021164021218e-02 4.021164021164021218e-02 4.550264550264550290e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.338624338624338939e-02 4.550264550264550290e-02 4.021164021164021218e-02 5.291005291005290712e-02 3.015873015873015914e-02 3.015873015873015914e-02 3.253968253968254204e-02 3.015873015873015914e-02 3.412698412698412370e-02 1.666666666666666644e-02 1.666666666666666644e-02 6.190476190476190688e-02 6.190476190476190688e-02 6.984126984126984294e-02 6.666666666666666574e-02 6.904761904761905211e-02 6.587301587301587491e-02 7.063492063492063378e-02 7.063492063492063378e-02 6.825396825396824740e-02 6.666666666666666574e-02 7.222222222222221544e-02 6.825396825396824740e-02 6.984126984126984294e-02 3.950617283950617203e-02 4.382716049382716361e-02 4.135802469135802656e-02 4.506172839506172867e-02 3.765432098765432445e-02 4.567901234567901120e-02 4.197530864197530909e-02 4.814814814814814825e-02 4.814814814814814825e-02 5.238095238095238221e-02 4.206349206349206671e-02 4.365079365079364837e-02 4.365079365079364837e-02 4.841269841269841417e-02 4.365079365079364837e-02 4.206349206349206671e-02 4.841269841269841417e-02 4.206349206349206671e-02 2.037037037037037202e-02 6.913580246913579586e-02 6.666666666666666574e-02 6.913580246913579586e-02 6.790123456790123080e-02 6.666666666666666574e-02 6.975308641975308532e-02 6.851851851851852027e-02 6.790123456790123080e-02 7.160493827160493985e-02 6.790123456790123080e-02 6.913580246913579586e-02 5.802469135802468952e-02 6.790123456790123080e-02 4.345679012345678854e-02 4.098765432098765149e-02 4.197530864197530909e-02 4.444444444444444614e-02 5.580246913580246687e-02 3.950617283950617203e-02 4.197530864197530909e-02 4.444444444444444614e-02 4.320987654320987414e-02 4.444444444444444614e-02 4.197530864197530909e-02 4.197530864197530909e-02 4.320987654320987414e-02 5.061728395061728530e-02 3.358024691358024727e-02 3.555555555555555552e-02 3.555555555555555552e-02 2.222222222222222307e-02 6.864197530864198094e-02 5.234567901234567916e-02 6.765432098765432334e-02 6.814814814814815214e-02 6.567901234567900814e-02 5.876543209876543272e-02 6.913580246913579586e-02 5.629629629629629567e-02 5.432098765432098741e-02 6.123456790123456978e-02 6.469135802469136443e-02 6.320987654320987803e-02 6.518518518518517935e-02 4.363636363636363980e-02 5.616161616161616243e-02 4.282828282828282651e-02 4.484848484848484584e-02 4.484848484848484584e-02 4.161616161616161352e-02 4.161616161616161352e-02 4.395061728395061734e-02 4.839506172839506265e-02 2.747474747474747472e-02 2.303030303030302942e-02 4.929292929292929115e-02 6.585858585858585246e-02 6.505050505050505305e-02 6.424242424242423977e-02 7.380952380952381098e-02 6.585858585858585246e-02 6.222222222222222043e-02 5.252525252525252347e-02 5.333333333333333676e-02 6.424242424242423977e-02 5.252525252525252347e-02 4.363636363636363980e-02 4.040404040404040747e-02 4.363636363636363980e-02 4.323232323232323315e-02 4.606060606060605883e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.555555555555555247e-02 2.888888888888888756e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.666666666666666574e-02 5.555555555555555247e-02 7.111111111111111105e-02 3.259259259259258967e-02 4.296296296296295975e-02 3.174603174603174427e-02 3.407407407407407607e-02 3.407407407407407607e-02 4.666666666666666879e-02 3.111111111111111022e-02 4.666666666666666879e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.555555555555555636e-02 5.925925925925926152e-02 6.222222222222222043e-02 7.703703703703704275e-02 7.703703703703704275e-02 8.296296296296296058e-02 3.174603174603174427e-02 4.550264550264550290e-02 4.338624338624338939e-02 3.492063492063492147e-02 4.338624338624338939e-02 4.148148148148148029e-02 5.925925925925926152e-02 6.222222222222222043e-02 4.148148148148148029e-02 6.222222222222222043e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.481481481481481538e-02 8.042328042328042437e-02 6.349206349206348854e-02 5.820105820105819783e-02 6.031746031746031828e-02 6.560846560846560205e-02 8.253968253968253788e-02 8.253968253968253788e-02 6.560846560846560205e-02 8.148148148148148806e-02 6.243386243386243178e-02 8.571428571428571508e-02 8.359788359788360157e-02 3.412698412698412370e-02 4.920634920634920501e-02 4.206349206349206671e-02 4.920634920634920501e-02 3.809523809523809867e-02 5.079365079365079361e-02 3.412698412698412370e-02 5.079365079365079361e-02 6.666666666666666574e-02 4.973544973544973685e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 6.984126984126984294e-02 6.984126984126984294e-02 4.973544973544973685e-02 5.185185185185185036e-02 4.444444444444444614e-02 6.666666666666666574e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.730158730158730090e-02 3.333333333333333287e-02 3.888888888888888951e-02 1.666666666666666644e-02 1.666666666666666644e-02 8.492063492063492425e-02 6.746031746031745657e-02 5.952380952380952051e-02 6.111111111111110911e-02 6.111111111111110911e-02 6.984126984126984294e-02 6.428571428571427937e-02 7.222222222222221544e-02 6.587301587301587491e-02 6.825396825396824740e-02 7.222222222222221544e-02 7.063492063492063378e-02 6.825396825396824740e-02 3.703703703703703498e-02 3.888888888888888951e-02 3.580246913580246992e-02 3.641975308641975245e-02 3.765432098765432445e-02 5.617283950617284194e-02 4.567901234567901120e-02 5.617283950617284194e-02 5.555555555555555247e-02 7.063492063492063378e-02 4.841269841269841417e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.793650793650793884e-02 5.000000000000000278e-02 4.841269841269841417e-02 5.793650793650793884e-02 4.841269841269841417e-02 2.160493827160493707e-02 5.864197530864197205e-02 6.358024691358024616e-02 5.987654320987654405e-02 5.987654320987654405e-02 6.234567901234568110e-02 6.111111111111110911e-02 8.703703703703703776e-02 6.111111111111110911e-02 6.358024691358024616e-02 6.604938271604937627e-02 6.666666666666666574e-02 8.209876543209876365e-02 6.234567901234568110e-02 4.938271604938271331e-02 3.802469135802469258e-02 3.604938271604938432e-02 6.172839506172839164e-02 6.172839506172839164e-02 4.148148148148148029e-02 4.938271604938271331e-02 5.185185185185185036e-02 5.061728395061728530e-02 5.185185185185185036e-02 4.938271604938271331e-02 4.938271604938271331e-02 5.061728395061728530e-02 7.222222222222221544e-02 3.950617283950617203e-02 4.148148148148148029e-02 4.148148148148148029e-02 2.419753086419753133e-02 8.888888888888889228e-02 7.160493827160493985e-02 6.074074074074074098e-02 6.320987654320987803e-02 5.876543209876543272e-02 8.740740740740740589e-02 6.172839506172839164e-02 8.098765432098765926e-02 7.950617283950617287e-02 8.691358024691357709e-02 8.790123456790123468e-02 9.086419753086419360e-02 7.654320987654321395e-02 5.050505050505050414e-02 6.464646464646464641e-02 4.323232323232323315e-02 3.595959595959596217e-02 3.757575757575757486e-02 3.676767676767676851e-02 3.757575757575757486e-02 6.123456790123456978e-02 7.160493827160493985e-02 3.232323232323232320e-02 2.545454545454545539e-02 6.909090909090909172e-02 7.959595959595959502e-02 5.939393939393939476e-02 5.939393939393939476e-02 1.015873015873015872e-01 6.101010101010100745e-02 5.777777777777777513e-02 6.424242424242423977e-02 6.343434343434344036e-02 5.939393939393939476e-02 7.757575757575757569e-02 5.333333333333333676e-02 5.090909090909091078e-02 5.333333333333333676e-02 5.454545454545454281e-02 6.989898989898990500e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.555555555555555247e-02 2.888888888888888756e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 7.555555555555555636e-02 5.777777777777777513e-02 7.111111111111111105e-02 3.259259259259258967e-02 4.296296296296295975e-02 3.174603174603174427e-02 3.407407407407407607e-02 3.407407407407407607e-02 4.666666666666666879e-02 3.111111111111111022e-02 4.666666666666666879e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.851851851851851527e-02 7.111111111111111105e-02 6.518518518518517935e-02 8.888888888888889228e-02 7.851851851851851527e-02 8.296296296296296058e-02 3.174603174603174427e-02 4.550264550264550290e-02 4.761904761904761640e-02 3.597883597883597823e-02 4.761904761904761640e-02 4.148148148148148029e-02 5.481481481481481621e-02 6.222222222222222043e-02 4.148148148148148029e-02 6.222222222222222043e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.481481481481481538e-02 7.089947089947089276e-02 7.089947089947089276e-02 7.619047619047619735e-02 7.301587301587302015e-02 7.513227513227513366e-02 8.465608465608465139e-02 8.571428571428571508e-02 7.195767195767195645e-02 7.619047619047619735e-02 6.666666666666666574e-02 8.359788359788360157e-02 8.571428571428571508e-02 3.571428571428571230e-02 6.190476190476190688e-02 4.365079365079364837e-02 5.476190476190476164e-02 4.444444444444444614e-02 5.158730158730158444e-02 3.571428571428571230e-02 5.396825396825397081e-02 5.608465608465608432e-02 4.656084656084655965e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 6.349206349206348854e-02 6.349206349206348854e-02 4.656084656084655965e-02 5.185185185185185036e-02 4.444444444444444614e-02 5.608465608465608432e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.492063492063492147e-02 3.333333333333333287e-02 3.888888888888888951e-02 1.666666666666666644e-02 1.666666666666666644e-02 6.587301587301587491e-02 6.349206349206348854e-02 7.777777777777777901e-02 6.904761904761905211e-02 8.174603174603174705e-02 7.222222222222221544e-02 7.698412698412698818e-02 8.253968253968253788e-02 7.063492063492063378e-02 6.904761904761905211e-02 7.539682539682539264e-02 7.380952380952381098e-02 7.301587301587302015e-02 4.197530864197530909e-02 4.135802469135802656e-02 4.135802469135802656e-02 4.629629629629629373e-02 4.135802469135802656e-02 5.000000000000000278e-02 4.691358024691358319e-02 5.185185185185185036e-02 5.370370370370370489e-02 5.476190476190476164e-02 4.603174603174603474e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.317460317460317304e-02 5.000000000000000278e-02 4.603174603174603474e-02 5.317460317460317304e-02 4.603174603174603474e-02 2.160493827160493707e-02 7.654320987654321395e-02 7.345679012345679437e-02 8.209876543209876365e-02 7.407407407407406996e-02 7.037037037037037479e-02 7.962962962962963354e-02 7.098765432098765038e-02 8.395061728395061817e-02 8.148148148148148806e-02 7.160493827160493985e-02 7.283950617283950490e-02 6.111111111111110911e-02 7.469135802469135943e-02 4.938271604938271331e-02 4.000000000000000083e-02 4.395061728395061734e-02 4.790123456790123385e-02 5.975308641975308338e-02 4.296296296296295975e-02 4.567901234567901120e-02 5.185185185185185036e-02 4.876543209876543078e-02 5.185185185185185036e-02 4.567901234567901120e-02 4.567901234567901120e-02 4.876543209876543078e-02 5.246913580246913289e-02 3.654320987654321312e-02 4.148148148148148029e-02 4.148148148148148029e-02 2.419753086419753133e-02 7.308641975308642624e-02 5.432098765432098741e-02 7.555555555555555636e-02 7.259259259259259744e-02 6.913580246913579586e-02 6.172839506172839164e-02 8.345679012345678938e-02 5.827160493827160392e-02 5.679012345679012447e-02 6.518518518518517935e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.814814814814815214e-02 4.888888888888889145e-02 6.222222222222222043e-02 4.323232323232323315e-02 4.969696969696969779e-02 4.727272727272727182e-02 4.646464646464646547e-02 4.242424242424242681e-02 4.543209876543209680e-02 4.987654320987654211e-02 2.989898989898990070e-02 2.545454545454545539e-02 5.090909090909091078e-02 6.949494949494949836e-02 7.232323232323231710e-02 6.828282828282827843e-02 7.619047619047619735e-02 7.353535353535353702e-02 6.303030303030303372e-02 5.292929292929293011e-02 5.535353535353535609e-02 6.828282828282827843e-02 5.414141414141414310e-02 4.848484848484848481e-02 4.282828282828282651e-02 4.848484848484848481e-02 4.767676767676767846e-02 4.727272727272727182e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.555555555555555247e-02 2.888888888888888756e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.444444444444444309e-02 5.555555555555555247e-02 6.888888888888888840e-02 3.259259259259258967e-02 4.148148148148148029e-02 3.174603174603174427e-02 3.407407407407407607e-02 3.407407407407407607e-02 4.444444444444444614e-02 3.111111111111111022e-02 4.444444444444444614e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.259259259259259744e-02 5.777777777777777513e-02 6.074074074074074098e-02 7.259259259259259744e-02 7.407407407407406996e-02 7.851851851851851527e-02 3.174603174603174427e-02 4.444444444444444614e-02 4.232804232804232569e-02 3.492063492063492147e-02 4.232804232804232569e-02 4.000000000000000083e-02 5.629629629629629567e-02 5.777777777777777513e-02 4.000000000000000083e-02 5.777777777777777513e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.481481481481481538e-02 7.724867724867724716e-02 6.137566137566137503e-02 5.608465608465608432e-02 5.820105820105819783e-02 6.243386243386243178e-02 7.830687830687831086e-02 7.830687830687831086e-02 6.349206349206348854e-02 7.830687830687831086e-02 6.031746031746031828e-02 8.148148148148148806e-02 7.936507936507936067e-02 3.412698412698412370e-02 4.682539682539682557e-02 4.126984126984126894e-02 4.761904761904761640e-02 3.730158730158730090e-02 4.920634920634920501e-02 3.412698412698412370e-02 4.920634920634920501e-02 6.349206349206348854e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.867724867724868010e-02 6.560846560846560205e-02 6.560846560846560205e-02 4.761904761904761640e-02 4.867724867724868010e-02 4.232804232804232569e-02 6.349206349206348854e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.571428571428571230e-02 3.174603174603174427e-02 3.650793650793651007e-02 1.666666666666666644e-02 1.666666666666666644e-02 7.936507936507936067e-02 6.507936507936508408e-02 5.714285714285714107e-02 5.873015873015872967e-02 5.793650793650793884e-02 6.666666666666666574e-02 6.111111111111110911e-02 6.746031746031745657e-02 6.269841269841269771e-02 6.587301587301587491e-02 6.904761904761905211e-02 6.746031746031745657e-02 6.587301587301587491e-02 3.580246913580246992e-02 3.827160493827160698e-02 3.518518518518518740e-02 3.580246913580246992e-02 3.703703703703703498e-02 5.432098765432098741e-02 4.444444444444444614e-02 5.432098765432098741e-02 5.370370370370370489e-02 6.746031746031745657e-02 4.603174603174603474e-02 4.682539682539682557e-02 4.682539682539682557e-02 5.476190476190476164e-02 4.682539682539682557e-02 4.603174603174603474e-02 5.476190476190476164e-02 4.603174603174603474e-02 2.098765432098765454e-02 5.617283950617284194e-02 6.049382716049382658e-02 5.679012345679012447e-02 5.740740740740740700e-02 5.925925925925926152e-02 5.802469135802468952e-02 8.333333333333332871e-02 5.740740740740740700e-02 5.987654320987654405e-02 6.234567901234568110e-02 6.358024691358024616e-02 8.024691358024690913e-02 5.987654320987654405e-02 4.641975308641975440e-02 3.703703703703703498e-02 3.506172839506172673e-02 5.679012345679012447e-02 5.925925925925926152e-02 4.049382716049382963e-02 4.691358024691358319e-02 4.814814814814814825e-02 4.753086419753086572e-02 4.814814814814814825e-02 4.691358024691358319e-02 4.691358024691358319e-02 4.753086419753086572e-02 6.913580246913579586e-02 3.753086419753086378e-02 3.851851851851852138e-02 3.851851851851852138e-02 2.320987654320987720e-02 8.493827160493827577e-02 6.864197530864198094e-02 5.777777777777777513e-02 5.975308641975308338e-02 5.629629629629629567e-02 8.098765432098765926e-02 5.827160493827160392e-02 7.950617283950617287e-02 7.753086419753085767e-02 8.197530864197530298e-02 8.395061728395061817e-02 8.395061728395061817e-02 7.308641975308642624e-02 4.767676767676767846e-02 6.141414141414141409e-02 4.202020202020202017e-02 3.474747474747474918e-02 3.636363636363636187e-02 3.555555555555555552e-02 3.636363636363636187e-02 5.876543209876543272e-02 6.913580246913579586e-02 3.070707070707070704e-02 2.424242424242424240e-02 6.787878787878788567e-02 7.555555555555555636e-02 5.696969696969696878e-02 5.696969696969696878e-02 9.682539682539682835e-02 5.777777777777777513e-02 5.535353535353535609e-02 6.141414141414141409e-02 6.060606060606060774e-02 5.656565656565656908e-02 7.595959595959596300e-02 5.010101010101010444e-02 4.848484848484848481e-02 5.010101010101010444e-02 5.131313131313131048e-02 6.787878787878788567e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 4.814814814814814825e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.222222222222222043e-02 4.666666666666666879e-02 5.999999999999999778e-02 2.962962962962963076e-02 3.851851851851852138e-02 2.751322751322751378e-02 2.814814814814814783e-02 3.111111111111111022e-02 4.444444444444444614e-02 3.111111111111111022e-02 4.444444444444444614e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 7.111111111111111105e-02 5.185185185185185036e-02 5.185185185185185036e-02 7.111111111111111105e-02 6.962962962962962465e-02 6.962962962962962465e-02 2.751322751322751378e-02 3.597883597883597823e-02 4.021164021164021218e-02 3.068783068783068751e-02 3.809523809523809867e-02 4.000000000000000083e-02 5.629629629629629567e-02 5.777777777777777513e-02 4.000000000000000083e-02 5.777777777777777513e-02 2.857142857142857054e-02 2.857142857142857054e-02 1.481481481481481538e-02 7.619047619047619735e-02 5.820105820105819783e-02 5.185185185185185036e-02 5.185185185185185036e-02 5.925925925925926152e-02 7.724867724867724716e-02 7.724867724867724716e-02 5.714285714285714107e-02 7.513227513227513366e-02 5.185185185185185036e-02 7.619047619047619735e-02 7.619047619047619735e-02 2.777777777777777624e-02 4.523809523809523697e-02 3.333333333333333287e-02 4.603174603174603474e-02 3.253968253968254204e-02 4.761904761904761640e-02 2.936507936507936484e-02 4.603174603174603474e-02 6.349206349206348854e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.867724867724868010e-02 6.560846560846560205e-02 6.560846560846560205e-02 4.761904761904761640e-02 4.867724867724868010e-02 4.232804232804232569e-02 6.349206349206348854e-02 3.174603174603174427e-02 3.174603174603174427e-02 3.571428571428571230e-02 3.174603174603174427e-02 3.650793650793651007e-02 1.666666666666666644e-02 1.666666666666666644e-02 7.857142857142856984e-02 6.269841269841269771e-02 5.396825396825397081e-02 5.396825396825397081e-02 5.555555555555555247e-02 6.428571428571427937e-02 5.476190476190476164e-02 6.507936507936508408e-02 5.476190476190476164e-02 6.190476190476190688e-02 6.269841269841269771e-02 6.349206349206348854e-02 6.111111111111110911e-02 3.333333333333333287e-02 3.086419753086419582e-02 3.024691358024691329e-02 3.086419753086419582e-02 3.333333333333333287e-02 5.308641975308642236e-02 4.197530864197530909e-02 5.185185185185185036e-02 5.185185185185185036e-02 6.746031746031745657e-02 4.603174603174603474e-02 4.682539682539682557e-02 4.682539682539682557e-02 5.476190476190476164e-02 4.682539682539682557e-02 4.603174603174603474e-02 5.476190476190476164e-02 4.603174603174603474e-02 2.098765432098765454e-02 5.370370370370370489e-02 5.802469135802468952e-02 5.493827160493826994e-02 5.370370370370370489e-02 5.432098765432098741e-02 5.432098765432098741e-02 8.148148148148148806e-02 5.617283950617284194e-02 5.493827160493826994e-02 5.493827160493826994e-02 5.740740740740740700e-02 7.901234567901234407e-02 5.617283950617284194e-02 4.345679012345678854e-02 3.111111111111111022e-02 3.111111111111111022e-02 5.580246913580246687e-02 5.530864197530864501e-02 3.753086419753086378e-02 4.691358024691358319e-02 4.814814814814814825e-02 4.753086419753086572e-02 4.814814814814814825e-02 4.691358024691358319e-02 4.691358024691358319e-02 4.753086419753086572e-02 6.913580246913579586e-02 3.753086419753086378e-02 3.851851851851852138e-02 3.851851851851852138e-02 2.320987654320987720e-02 8.296296296296296058e-02 6.666666666666666574e-02 5.580246913580246687e-02 5.580246913580246687e-02 5.432098765432098741e-02 7.950617283950617287e-02 5.679012345679012447e-02 7.753086419753085767e-02 7.851851851851851527e-02 8.148148148148148806e-02 8.148148148148148806e-02 8.345679012345678938e-02 6.962962962962962465e-02 4.525252525252525249e-02 6.060606060606060774e-02 3.959595959595959419e-02 3.151515151515151686e-02 3.151515151515151686e-02 3.232323232323232320e-02 3.151515151515151686e-02 5.876543209876543272e-02 6.913580246913579586e-02 3.070707070707070704e-02 2.424242424242424240e-02 6.707070707070707238e-02 7.272727272727272374e-02 5.373737373737373646e-02 5.373737373737373646e-02 9.523809523809523281e-02 5.616161616161616243e-02 5.373737373737373646e-02 5.858585858585858841e-02 5.858585858585858841e-02 5.494949494949494945e-02 7.595959595959596300e-02 5.010101010101010444e-02 4.848484848484848481e-02 5.010101010101010444e-02 5.131313131313131048e-02 6.787878787878788567e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 4.814814814814814825e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.666666666666666574e-02 4.666666666666666879e-02 6.222222222222222043e-02 2.962962962962963076e-02 4.000000000000000083e-02 2.751322751322751378e-02 2.814814814814814783e-02 3.111111111111111022e-02 4.666666666666666879e-02 3.111111111111111022e-02 4.666666666666666879e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.148148148148148806e-02 5.481481481481481621e-02 5.333333333333333676e-02 7.851851851851851527e-02 7.555555555555555636e-02 7.407407407407406996e-02 2.751322751322751378e-02 3.703703703703703498e-02 4.550264550264550290e-02 3.068783068783068751e-02 4.021164021164021218e-02 4.148148148148148029e-02 6.074074074074074098e-02 6.222222222222222043e-02 4.148148148148148029e-02 6.222222222222222043e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.481481481481481538e-02 8.465608465608465139e-02 6.455026455026455223e-02 5.608465608465608432e-02 5.502645502645502756e-02 6.455026455026455223e-02 8.994708994708994210e-02 8.888888888888889228e-02 6.137566137566137503e-02 8.465608465608465139e-02 5.396825396825397081e-02 8.359788359788360157e-02 8.465608465608465139e-02 2.777777777777777624e-02 5.079365079365079361e-02 3.412698412698412370e-02 5.476190476190476164e-02 3.412698412698412370e-02 5.476190476190476164e-02 2.936507936507936484e-02 5.317460317460317304e-02 6.878306878306877925e-02 5.079365079365079361e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 7.195767195767195645e-02 7.195767195767195645e-02 5.079365079365079361e-02 5.185185185185185036e-02 4.444444444444444614e-02 6.878306878306877925e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.809523809523809867e-02 3.333333333333333287e-02 3.888888888888888951e-02 1.666666666666666644e-02 1.666666666666666644e-02 8.730158730158729674e-02 6.984126984126984294e-02 6.031746031746031828e-02 5.952380952380952051e-02 6.111111111111110911e-02 7.380952380952381098e-02 5.873015873015872967e-02 7.222222222222221544e-02 5.793650793650793884e-02 6.825396825396824740e-02 6.825396825396824740e-02 6.984126984126984294e-02 6.666666666666666574e-02 3.580246913580246992e-02 3.148148148148147835e-02 3.148148148148147835e-02 3.271604938271605034e-02 3.641975308641975245e-02 5.987654320987654405e-02 4.753086419753086572e-02 5.864197530864197205e-02 5.864197530864197205e-02 7.301587301587302015e-02 4.920634920634920501e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.952380952380952051e-02 5.000000000000000278e-02 4.920634920634920501e-02 5.952380952380952051e-02 4.920634920634920501e-02 2.160493827160493707e-02 6.111111111111110911e-02 6.666666666666666574e-02 6.172839506172839164e-02 5.987654320987654405e-02 5.987654320987654405e-02 5.987654320987654405e-02 9.135802469135802240e-02 6.234567901234568110e-02 5.987654320987654405e-02 5.864197530864197205e-02 6.234567901234568110e-02 8.456790123456790764e-02 6.172839506172839164e-02 4.641975308641975440e-02 3.209876543209876781e-02 3.308641975308641847e-02 6.320987654320987803e-02 6.419753086419753563e-02 4.197530864197530909e-02 5.061728395061728530e-02 5.185185185185185036e-02 5.123456790123456783e-02 5.185185185185185036e-02 5.061728395061728530e-02 5.061728395061728530e-02 5.123456790123456783e-02 7.407407407407406996e-02 4.049382716049382963e-02 4.148148148148148029e-02 4.148148148148148029e-02 2.419753086419753133e-02 9.333333333333333759e-02 7.209876543209876865e-02 6.469135802469136443e-02 6.271604938271604923e-02 6.123456790123456978e-02 8.691358024691357709e-02 6.518518518518517935e-02 8.197530864197530298e-02 8.148148148148148806e-02 9.135802469135802240e-02 8.938271604938272108e-02 9.382716049382716639e-02 7.802469135802468647e-02 4.929292929292929115e-02 6.949494949494949836e-02 4.484848484848484584e-02 3.434343434343434254e-02 3.353535353535353619e-02 3.595959595959596217e-02 3.353535353535353619e-02 6.271604938271604923e-02 7.308641975308642624e-02 3.313131313131312955e-02 2.545454545454545539e-02 6.989898989898990500e-02 8.202020202020202100e-02 5.939393939393939476e-02 6.020202020202020110e-02 1.031746031746031689e-01 6.585858585858585246e-02 6.020202020202020110e-02 6.343434343434344036e-02 6.383838383838383312e-02 6.303030303030303372e-02 7.838383838383838897e-02 5.494949494949494945e-02 5.252525252525252347e-02 5.494949494949494945e-02 5.575757575757575579e-02 7.111111111111111105e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.296296296296295669e-02 3.111111111111111022e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 7.111111111111111105e-02 6.444444444444444309e-02 8.222222222222222432e-02 3.555555555555555552e-02 4.740740740740740505e-02 3.597883597883597823e-02 4.000000000000000083e-02 3.703703703703703498e-02 4.888888888888889145e-02 3.111111111111111022e-02 4.888888888888889145e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.000000000000000167e-02 6.666666666666666574e-02 7.259259259259259744e-02 8.296296296296296058e-02 8.444444444444444697e-02 9.629629629629629650e-02 3.597883597883597823e-02 5.502645502645502756e-02 4.656084656084655965e-02 3.915343915343915543e-02 4.867724867724868010e-02 4.296296296296295975e-02 6.222222222222222043e-02 6.666666666666666574e-02 4.296296296296295975e-02 6.666666666666666574e-02 3.068783068783068751e-02 3.068783068783068751e-02 1.481481481481481538e-02 8.783068783068782859e-02 6.878306878306877925e-02 6.455026455026455223e-02 6.878306878306877925e-02 7.195767195767195645e-02 8.783068783068782859e-02 8.783068783068782859e-02 7.407407407407406996e-02 8.783068783068782859e-02 7.301587301587302015e-02 9.523809523809523281e-02 9.100529100529100579e-02 4.047619047619047811e-02 5.317460317460317304e-02 5.079365079365079361e-02 5.238095238095238221e-02 4.365079365079364837e-02 5.396825396825397081e-02 3.888888888888888951e-02 5.555555555555555247e-02 6.984126984126984294e-02 5.185185185185185036e-02 4.656084656084655965e-02 4.656084656084655965e-02 5.502645502645502756e-02 7.407407407407406996e-02 7.407407407407406996e-02 5.185185185185185036e-02 5.502645502645502756e-02 4.656084656084655965e-02 6.984126984126984294e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.888888888888888951e-02 3.492063492063492147e-02 4.126984126984126894e-02 1.666666666666666644e-02 1.666666666666666644e-02 8.650793650793650591e-02 7.460317460317460181e-02 6.507936507936508408e-02 6.825396825396824740e-02 6.666666666666666574e-02 7.539682539682539264e-02 7.380952380952381098e-02 7.936507936507936067e-02 7.698412698412698818e-02 7.460317460317460181e-02 8.174603174603174705e-02 7.777777777777777901e-02 7.539682539682539264e-02 4.074074074074074403e-02 4.691358024691358319e-02 4.135802469135802656e-02 4.197530864197530909e-02 4.197530864197530909e-02 5.925925925925926152e-02 4.938271604938271331e-02 6.234567901234568110e-02 6.111111111111110911e-02 7.460317460317460181e-02 5.079365079365079361e-02 5.317460317460317304e-02 5.317460317460317304e-02 6.111111111111110911e-02 5.317460317460317304e-02 5.079365079365079361e-02 6.111111111111110911e-02 5.079365079365079361e-02 2.222222222222222307e-02 6.358024691358024616e-02 6.913580246913579586e-02 6.481481481481481122e-02 6.604938271604937627e-02 7.037037037037037479e-02 6.790123456790123080e-02 9.506172839506173144e-02 6.604938271604937627e-02 7.222222222222221544e-02 7.716049382716048954e-02 7.592592592592592449e-02 8.456790123456790764e-02 6.851851851851852027e-02 5.530864197530864501e-02 4.493827160493827494e-02 4.098765432098765149e-02 6.123456790123456978e-02 6.814814814814815214e-02 4.543209876543209680e-02 5.185185185185185036e-02 5.555555555555555247e-02 5.370370370370370489e-02 5.555555555555555247e-02 5.185185185185185036e-02 5.185185185185185036e-02 5.370370370370370489e-02 7.530864197530864890e-02 4.148148148148148029e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.518518518518518545e-02 9.580246913580246770e-02 7.358024691358024116e-02 6.567901234567900814e-02 7.061728395061728225e-02 6.469135802469136443e-02 8.790123456790123468e-02 6.666666666666666574e-02 8.395061728395061817e-02 8.148148148148148806e-02 8.938271604938272108e-02 9.925925925925925541e-02 9.234567901234567999e-02 8.345679012345678938e-02 5.575757575757575579e-02 7.111111111111111105e-02 4.929292929292929115e-02 4.040404040404040747e-02 4.363636363636363980e-02 4.121212121212121382e-02 4.363636363636363980e-02 6.370370370370370683e-02 7.407407407407406996e-02 3.393939393939394283e-02 2.666666666666666838e-02 7.151515151515151769e-02 8.646464646464646631e-02 6.505050505050505305e-02 6.505050505050505305e-02 1.063492063492063461e-01 6.585858585858585246e-02 6.424242424242423977e-02 6.868686868686868507e-02 6.707070707070707238e-02 6.505050505050505305e-02 8.000000000000000167e-02 5.656565656565656908e-02 5.333333333333333676e-02 5.656565656565656908e-02 5.777777777777777513e-02 7.191919191919192433e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 4.814814814814814825e-02 2.666666666666666838e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 6.666666666666666574e-02 4.666666666666666879e-02 6.444444444444444309e-02 2.962962962962963076e-02 4.148148148148148029e-02 2.751322751322751378e-02 2.814814814814814783e-02 3.111111111111111022e-02 4.888888888888889145e-02 3.111111111111111022e-02 4.888888888888889145e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.148148148148148806e-02 5.481481481481481621e-02 5.481481481481481621e-02 8.000000000000000167e-02 7.703703703703704275e-02 7.851851851851851527e-02 2.751322751322751378e-02 3.809523809523809867e-02 4.232804232804232569e-02 3.068783068783068751e-02 4.021164021164021218e-02 4.296296296296295975e-02 6.370370370370370683e-02 6.666666666666666574e-02 4.296296296296295975e-02 6.666666666666666574e-02 3.068783068783068751e-02 3.068783068783068751e-02 1.481481481481481538e-02 8.677248677248677877e-02 6.560846560846560205e-02 5.608465608465608432e-02 5.608465608465608432e-02 6.560846560846560205e-02 9.206349206349206948e-02 8.994708994708994210e-02 6.243386243386243178e-02 8.677248677248677877e-02 5.608465608465608432e-02 8.677248677248677877e-02 8.677248677248677877e-02 2.777777777777777624e-02 5.000000000000000278e-02 3.492063492063492147e-02 5.317460317460317304e-02 3.412698412698412370e-02 5.317460317460317304e-02 2.936507936507936484e-02 5.158730158730158444e-02 7.301587301587302015e-02 5.291005291005290712e-02 4.656084656084655965e-02 4.656084656084655965e-02 5.502645502645502756e-02 7.619047619047619735e-02 7.619047619047619735e-02 5.291005291005290712e-02 5.502645502645502756e-02 4.656084656084655965e-02 7.301587301587302015e-02 3.492063492063492147e-02 3.492063492063492147e-02 3.968253968253968034e-02 3.492063492063492147e-02 4.126984126984126894e-02 1.666666666666666644e-02 1.666666666666666644e-02 9.126984126984126477e-02 7.142857142857142461e-02 6.111111111111110911e-02 6.111111111111110911e-02 6.190476190476190688e-02 7.539682539682539264e-02 6.111111111111110911e-02 7.460317460317460181e-02 6.111111111111110911e-02 7.063492063492063378e-02 7.063492063492063378e-02 7.142857142857142461e-02 6.825396825396824740e-02 3.580246913580246992e-02 3.209876543209876781e-02 3.148148148148147835e-02 3.209876543209876781e-02 3.456790123456789793e-02 5.987654320987654405e-02 4.629629629629629373e-02 5.864197530864197205e-02 5.802469135802468952e-02 7.698412698412698818e-02 5.158730158730158444e-02 5.317460317460317304e-02 5.317460317460317304e-02 6.269841269841269771e-02 5.317460317460317304e-02 5.158730158730158444e-02 6.269841269841269771e-02 5.158730158730158444e-02 2.222222222222222307e-02 6.234567901234568110e-02 6.790123456790123080e-02 6.296296296296295669e-02 6.111111111111110911e-02 6.234567901234568110e-02 6.111111111111110911e-02 9.506172839506173144e-02 6.358024691358024616e-02 6.234567901234568110e-02 6.234567901234568110e-02 6.481481481481481122e-02 8.703703703703703776e-02 6.296296296296295669e-02 4.938271604938271331e-02 3.308641975308641847e-02 3.308641975308641847e-02 6.567901234567900814e-02 6.567901234567900814e-02 4.098765432098765149e-02 5.308641975308642236e-02 5.555555555555555247e-02 5.432098765432098741e-02 5.555555555555555247e-02 5.308641975308642236e-02 5.308641975308642236e-02 5.432098765432098741e-02 7.716049382716048954e-02 4.246913580246913789e-02 4.444444444444444614e-02 4.444444444444444614e-02 2.518518518518518545e-02 9.728395061728395410e-02 7.456790123456789876e-02 6.617283950617283694e-02 6.567901234567900814e-02 6.320987654320987803e-02 9.086419753086419360e-02 6.666666666666666574e-02 8.395061728395061817e-02 8.345679012345678938e-02 9.382716049382716639e-02 9.234567901234567999e-02 1.002469135802469130e-01 8.246913580246913178e-02 5.171717171717171713e-02 7.070707070707070441e-02 4.484848484848484584e-02 3.393939393939394283e-02 3.393939393939394283e-02 3.474747474747474918e-02 3.393939393939394283e-02 6.518518518518517935e-02 7.555555555555555636e-02 3.474747474747474918e-02 2.666666666666666838e-02 7.151515151515151769e-02 8.767676767676767235e-02 6.101010101010100745e-02 6.262626262626262708e-02 1.071428571428571369e-01 6.747474747474747903e-02 6.262626262626262708e-02 6.626262626262625910e-02 6.626262626262625910e-02 6.505050505050505305e-02 8.000000000000000167e-02 5.818181818181818177e-02 5.575757575757575579e-02 5.818181818181818177e-02 5.979797979797980140e-02 7.313131313131313038e-02
-3.703703703703703498e-02 1.481481481481481538e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.037037037037037479e-02 3.333333333333333287e-02 2.222222222222222307e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 7.111111111111111105e-02 8.000000000000000167e-02 8.888888888888889228e-02 3.851851851851852138e-02 4.888888888888889145e-02 4.021164021164021218e-02 4.592592592592592560e-02 4.000000000000000083e-02 4.666666666666666879e-02 3.111111111111111022e-02 4.666666666666666879e-02 2.074074074074074014e-02 1.037037037037037007e-02 1.037037037037037007e-02 1.037037037037037007e-02 8.148148148148148806e-02 7.555555555555555636e-02 8.888888888888889228e-02 8.000000000000000167e-02 8.444444444444444697e-02 1.007407407407407418e-01 4.021164021164021218e-02 6.243386243386243178e-02 4.761904761904761640e-02 4.656084656084655965e-02 5.185185185185185036e-02 4.148148148148148029e-02 5.777777777777777513e-02 6.222222222222222043e-02 4.148148148148148029e-02 6.222222222222222043e-02 2.962962962962963076e-02 2.962962962962963076e-02 1.481481481481481538e-02 7.936507936507936067e-02 7.619047619047619735e-02 6.984126984126984294e-02 7.936507936507936067e-02 7.724867724867724716e-02 8.888888888888889228e-02 8.571428571428571508e-02 8.359788359788360157e-02 8.888888888888889228e-02 9.206349206349206948e-02 9.417989417989418299e-02 8.783068783068782859e-02 5.158730158730158444e-02 5.238095238095238221e-02 6.269841269841269771e-02 5.634920634920635024e-02 5.000000000000000278e-02 5.317460317460317304e-02 4.841269841269841417e-02 5.873015873015872967e-02 6.455026455026455223e-02 4.867724867724868010e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.185185185185185036e-02 6.772486772486772943e-02 6.772486772486772943e-02 4.867724867724868010e-02 5.185185185185185036e-02 4.444444444444444614e-02 6.455026455026455223e-02 3.333333333333333287e-02 3.333333333333333287e-02 3.650793650793651007e-02 3.333333333333333287e-02 3.888888888888888951e-02 1.666666666666666644e-02 1.666666666666666644e-02 7.698412698412698818e-02 7.698412698412698818e-02 6.984126984126984294e-02 7.857142857142856984e-02 6.825396825396824740e-02 8.412698412698413342e-02 8.412698412698413342e-02 8.174603174603174705e-02 9.603174603174603752e-02 8.253968253968253788e-02 9.047619047619047394e-02 8.333333333333332871e-02 8.095238095238095621e-02 4.197530864197530909e-02 6.111111111111110911e-02 4.938271604938271331e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.493827160493826994e-02 5.370370370370370489e-02 5.925925925925926152e-02 5.740740740740740700e-02 6.587301587301587491e-02 4.761904761904761640e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.634920634920635024e-02 5.000000000000000278e-02 4.761904761904761640e-02 5.634920634920635024e-02 4.761904761904761640e-02 2.160493827160493707e-02 6.790123456790123080e-02 7.345679012345679437e-02 6.666666666666666574e-02 7.222222222222221544e-02 8.086419753086419859e-02 7.160493827160493985e-02 8.703703703703703776e-02 6.543209876543210068e-02 7.901234567901234407e-02 9.753086419753086156e-02 8.888888888888889228e-02 7.345679012345679437e-02 7.222222222222221544e-02 5.530864197530864501e-02 5.580246913580246687e-02 4.691358024691358319e-02 5.481481481481481621e-02 7.259259259259259744e-02 5.283950617283950796e-02 4.814814814814814825e-02 5.185185185185185036e-02 5.000000000000000278e-02 5.185185185185185036e-02 4.814814814814814825e-02 4.814814814814814825e-02 5.000000000000000278e-02 6.481481481481481122e-02 3.851851851851852138e-02 4.148148148148148029e-02 4.148148148148148029e-02 2.419753086419753133e-02 8.987654320987654988e-02 6.864197530864198094e-02 6.962962962962962465e-02 7.901234567901234407e-02 6.518518518518517935e-02 7.654320987654321395e-02 6.814814814814815214e-02 7.308641975308642624e-02 6.962962962962962465e-02 7.802469135802468647e-02 8.345679012345678938e-02 8.246913580246913178e-02 9.135802469135802240e-02 5.454545454545454281e-02 6.383838383838383312e-02 5.373737373737373646e-02 4.484848484848484584e-02 5.212121212121212377e-02 4.646464646464646547e-02 5.373737373737373646e-02 5.530864197530864501e-02 6.271604938271604923e-02 3.151515151515151686e-02 2.545454545454545539e-02 6.505050505050505305e-02 9.333333333333333759e-02 6.909090909090909172e-02 7.232323232323231710e-02 9.365079365079365115e-02 6.949494949494949836e-02 6.222222222222222043e-02 6.828282828282827843e-02 6.343434343434344036e-02 6.585858585858585246e-02 6.868686868686868507e-02 5.171717171717171713e-02 4.929292929292929115e-02 5.171717171717171713e-02 5.333333333333333676e-02 6.020202020202020110e-02
-2.424242424242424240e-02 3.030303030303030387e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.151515151515151381e-02 6.363636363636362980e-02 1.454545454545454544e-02 2.121212121212121340e-02 1.272727272727272770e-02 0.000000000000000000e+00 5.818181818181818177e-02 5.454545454545454281e-02 6.727272727272727570e-02 8.363636363636363369e-02 8.121212121212120771e-02 2.943722943722943894e-02 3.272727272727272985e-02 3.272727272727272985e-02 3.818181818181818482e-02 2.545454545454545539e-02 3.818181818181818482e-02 1.696969696969697142e-02 8.484848484848485708e-03 8.484848484848485708e-03 8.484848484848485708e-03 5.818181818181818177e-02 5.454545454545454281e-02 6.060606060606060774e-02 6.545454545454545969e-02 6.545454545454545969e-02 7.757575757575757569e-02 2.943722943722943894e-02 4.588744588744588654e-02 3.722943722943723027e-02 3.463203463203463200e-02 4.155844155844155841e-02 3.393939393939394283e-02 4.484848484848484584e-02 5.090909090909091078e-02 3.393939393939394283e-02 5.090909090909091078e-02 2.424242424242424240e-02 2.424242424242424240e-02 1.212121212121212120e-02 5.541125541125541121e-02 5.281385281385281294e-02 5.194805194805195148e-02 5.627705627705627961e-02 5.800865800865800948e-02 6.147186147186146921e-02 6.233766233766233761e-02 5.800865800865800948e-02 6.060606060606060774e-02 6.060606060606060774e-02 7.186147186147186228e-02 6.753246753246752720e-02 3.441558441558441317e-02 4.285714285714285754e-02 4.285714285714285754e-02 3.831168831168831057e-02 3.571428571428571230e-02 3.831168831168831057e-02 3.441558441558441317e-02 4.220779220779220797e-02 4.588744588744588654e-02 3.809523809523809867e-02 3.636363636363636187e-02 3.636363636363636187e-02 4.242424242424242681e-02 5.194805194805195148e-02 5.194805194805195148e-02 3.809523809523809867e-02 4.242424242424242681e-02 3.636363636363636187e-02 4.588744588744588654e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.857142857142857054e-02 2.727272727272727140e-02 3.181818181818181490e-02 1.363636363636363570e-02 1.363636363636363570e-02 5.194805194805195148e-02 5.000000000000000278e-02 4.935064935064935321e-02 5.259740259740259410e-02 5.324675324675324367e-02 5.454545454545454281e-02 5.974025974025973934e-02 6.233766233766233761e-02 6.298701298701298024e-02 5.389610389610389324e-02 6.233766233766233761e-02 5.909090909090908977e-02 5.454545454545454281e-02 3.232323232323232320e-02 3.989898989898989917e-02 3.434343434343434254e-02 3.484848484848485084e-02 3.484848484848485084e-02 3.787878787878787984e-02 3.636363636363636187e-02 4.090909090909090884e-02 4.141414141414141714e-02 4.480519480519480624e-02 3.766233766233766100e-02 4.090909090909090884e-02 4.090909090909090884e-02 4.350649350649350711e-02 4.090909090909090884e-02 3.766233766233766100e-02 4.350649350649350711e-02 3.766233766233766100e-02 1.767676767676767610e-02 4.646464646464646547e-02 5.000000000000000278e-02 4.949494949494949447e-02 4.949494949494949447e-02 5.454545454545454281e-02 5.252525252525252347e-02 5.606060606060606077e-02 5.252525252525252347e-02 5.757575757575757874e-02 6.262626262626262708e-02 5.858585858585858841e-02 4.848484848484848481e-02 5.000000000000000278e-02 1.143434343434343442e-01 3.757575757575757486e-02 3.353535353535353619e-02 3.676767676767676851e-02 4.686868686868687212e-02 3.434343434343434254e-02 3.737373737373737154e-02 4.242424242424242681e-02 3.989898989898989917e-02 4.242424242424242681e-02 3.737373737373737154e-02 3.737373737373737154e-02 3.989898989898989917e-02 4.292929292929292817e-02 2.989898989898990070e-02 3.393939393939394283e-02 3.393939393939394283e-02 1.979797979797979710e-02 5.696969696969696878e-02 4.404040404040403950e-02 4.646464646464646547e-02 5.292929292929293011e-02 4.363636363636363980e-02 5.050505050505050414e-02 4.888888888888889145e-02 4.767676767676767846e-02 4.525252525252525249e-02 4.929292929292929115e-02 5.575757575757575579e-02 5.171717171717171713e-02 5.454545454545454281e-02 1.034710743801652844e-01 4.363636363636363980e-02 3.338842975206611496e-02 3.272727272727272985e-02 3.603305785123966931e-02 3.272727272727272985e-02 3.603305785123966931e-02 3.717171717171717515e-02 4.080808080808080718e-02 2.446280991735537078e-02 2.082644628099173528e-02 4.132231404958677801e-02 5.454545454545454281e-02 4.561983471074380209e-02 4.495867768595041003e-02 6.493506493506492894e-02 4.561983471074380209e-02 4.099173553719008545e-02 4.264462809917355518e-02 4.099173553719008545e-02 4.330578512396694030e-02 4.429752066115702491e-02 3.966942148760330827e-02 3.504132231404958470e-02 3.966942148760330827e-02 3.900826446280991622e-02 3.867768595041322366e-02
-6.060606060606060774e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.454545454545454281e-02 2.909090909090909088e-02 4.727272727272727182e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 8.000000000000000167e-02 4.727272727272727182e-02 6.181818181818182073e-02 2.909090909090909088e-02 3.878787878787878785e-02 3.636363636363636187e-02 4.363636363636363980e-02 4.121212121212121382e-02 3.636363636363636187e-02 2.181818181818181990e-02 3.636363636363636187e-02 1.454545454545454544e-02 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 7.515151515151514972e-02 6.060606060606060774e-02 5.090909090909091078e-02 9.212121212121211766e-02 7.757575757575757569e-02 7.030303030303029777e-02 3.636363636363636187e-02 4.675324675324675494e-02 5.541125541125541121e-02 3.636363636363636187e-02 5.194805194805195148e-02 3.151515151515151686e-02 4.606060606060605883e-02 5.090909090909091078e-02 3.151515151515151686e-02 5.090909090909091078e-02 2.251082251082251254e-02 2.251082251082251254e-02 1.038961038961038960e-02 7.705627705627705881e-02 6.233766233766233761e-02 6.406926406926406747e-02 5.714285714285714107e-02 7.272727272727272374e-02 7.792207792207792028e-02 8.484848484848485362e-02 6.060606060606060774e-02 7.359307359307359908e-02 5.021645021645021467e-02 7.965367965367965708e-02 8.658008658008657654e-02 3.376623376623376360e-02 7.532467532467532201e-02 4.025974025974025927e-02 5.974025974025973934e-02 4.415584415584415667e-02 5.844155844155844021e-02 3.246753246753246447e-02 5.584415584415584194e-02 5.108225108225108307e-02 3.809523809523809867e-02 3.463203463203463200e-02 3.463203463203463200e-02 4.155844155844155841e-02 5.541125541125541121e-02 5.541125541125541121e-02 3.809523809523809867e-02 4.155844155844155841e-02 3.463203463203463200e-02 5.108225108225108307e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.597402597402597574e-02 3.116883116883116880e-02 1.168831168831168874e-02 1.168831168831168874e-02 7.012987012987012547e-02 6.168831168831168804e-02 6.103896103896103847e-02 5.584415584415584194e-02 7.012987012987012547e-02 6.363636363636362980e-02 5.844155844155844021e-02 8.051948051948051854e-02 5.324675324675324367e-02 6.168831168831168804e-02 6.363636363636362980e-02 7.012987012987012547e-02 6.558441558441557850e-02 4.545454545454545581e-02 3.535353535353535220e-02 3.838383838383838120e-02 4.343434343434343647e-02 4.040404040404040747e-02 5.808080808080808011e-02 4.949494949494949447e-02 5.909090909090908977e-02 6.313131313131313538e-02 5.194805194805195148e-02 3.766233766233766100e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.545454545454545581e-02 4.025974025974025927e-02 3.766233766233766100e-02 4.545454545454545581e-02 3.766233766233766100e-02 1.616161616161616160e-02 5.757575757575757874e-02 6.060606060606060774e-02 6.464646464646464641e-02 5.757575757575757874e-02 5.555555555555555247e-02 6.363636363636362980e-02 7.424242424242424865e-02 7.171717171717172101e-02 6.161616161616161741e-02 5.353535353535353314e-02 5.757575757575757874e-02 6.515151515151515471e-02 6.313131313131313538e-02 4.202020202020202017e-02 3.393939393939394283e-02 4.040404040404040747e-02 5.737373737373737542e-02 5.818181818181818177e-02 4.282828282828282651e-02 3.838383838383838120e-02 4.242424242424242681e-02 4.040404040404040747e-02 4.242424242424242681e-02 3.838383838383838120e-02 3.838383838383838120e-02 4.040404040404040747e-02 5.101010101010101244e-02 3.070707070707070704e-02 3.393939393939394283e-02 3.393939393939394283e-02 1.858585858585858758e-02 7.555555555555555636e-02 5.535353535353535609e-02 5.656565656565656908e-02 5.575757575757575579e-02 5.616161616161616243e-02 6.464646464646464641e-02 6.222222222222222043e-02 6.141414141414141409e-02 6.060606060606060774e-02 6.949494949494949836e-02 7.111111111111111105e-02 7.070707070707070441e-02 6.383838383838383312e-02 4.363636363636363980e-02 7.636363636363636964e-02 4.528925619834710953e-02 4.429752066115702491e-02 3.900826446280991622e-02 4.099173553719008545e-02 3.702479338842975393e-02 4.323232323232323315e-02 4.929292929292929115e-02 2.512396694214875936e-02 1.983471074380165414e-02 5.223140495867768796e-02 6.611570247933884481e-02 5.818181818181818177e-02 5.289256198347107307e-02 7.727272727272727071e-02 5.487603305785124230e-02 5.421487603305785025e-02 5.057851239669421822e-02 5.322314049586776563e-02 5.454545454545454281e-02 5.719008264462809715e-02 4.231404958677686262e-02 3.900826446280991622e-02 4.231404958677686262e-02 4.297520661157024774e-02 4.727272727272727182e-02
-6.060606060606060774e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.666666666666666574e-02 3.272727272727272985e-02 4.727272727272727182e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 5.999999999999999778e-02 6.545454545454545969e-02 6.727272727272727570e-02 3.393939393939394283e-02 3.757575757575757486e-02 5.108225108225108307e-02 5.333333333333333676e-02 4.606060606060605883e-02 2.727272727272727140e-02 2.181818181818181990e-02 2.727272727272727140e-02 1.454545454545454544e-02 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 6.060606060606060774e-02 5.696969696969696878e-02 6.424242424242423977e-02 5.696969696969696878e-02 6.060606060606060774e-02 6.666666666666666574e-02 5.108225108225108307e-02 5.627705627705627961e-02 4.761904761904761640e-02 4.502164502164502508e-02 4.761904761904761640e-02 2.545454545454545539e-02 3.151515151515151686e-02 3.272727272727272985e-02 2.545454545454545539e-02 3.272727272727272985e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.038961038961038960e-02 5.541125541125541121e-02 5.541125541125541121e-02 5.021645021645021467e-02 5.541125541125541121e-02 5.627705627705627961e-02 5.887445887445887788e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.887445887445887788e-02 6.233766233766233761e-02 6.060606060606060774e-02 5.714285714285714107e-02 5.259740259740259410e-02 4.740259740259740451e-02 5.519480519480519237e-02 5.194805194805195148e-02 4.610389610389610537e-02 4.545454545454545581e-02 4.285714285714285754e-02 4.935064935064935321e-02 3.290043290043290214e-02 2.770562770562770560e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 3.463203463203463200e-02 3.463203463203463200e-02 2.770562770562770560e-02 2.857142857142857054e-02 2.597402597402597574e-02 3.290043290043290214e-02 1.948051948051948007e-02 1.948051948051948007e-02 2.077922077922077920e-02 1.948051948051948007e-02 2.142857142857142877e-02 1.168831168831168874e-02 1.168831168831168874e-02 4.805194805194805407e-02 5.584415584415584194e-02 4.740259740259740451e-02 5.389610389610389324e-02 4.675324675324675494e-02 5.714285714285714107e-02 5.454545454545454281e-02 5.454545454545454281e-02 6.103896103896103847e-02 5.454545454545454281e-02 5.714285714285714107e-02 5.584415584415584194e-02 5.194805194805195148e-02 4.090909090909090884e-02 5.353535353535353314e-02 4.646464646464646547e-02 4.747474747474747514e-02 4.393939393939393784e-02 4.343434343434343647e-02 4.444444444444444614e-02 4.646464646464646547e-02 4.646464646464646547e-02 3.246753246753246447e-02 2.662337662337662184e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.987012987012986967e-02 2.727272727272727140e-02 2.662337662337662184e-02 2.987012987012986967e-02 2.662337662337662184e-02 1.363636363636363570e-02 4.444444444444444614e-02 4.797979797979797650e-02 4.393939393939393784e-02 4.747474747474747514e-02 5.303030303030303177e-02 4.595959595959595717e-02 5.202020202020202211e-02 4.343434343434343647e-02 4.949494949494949447e-02 5.959595959595959808e-02 5.505050505050505111e-02 4.343434343434343647e-02 4.595959595959595717e-02 3.474747474747474918e-02 5.090909090909091078e-02 4.161616161616161352e-02 4.202020202020202017e-02 4.969696969696969779e-02 4.282828282828282651e-02 2.626262626262626174e-02 2.727272727272727140e-02 2.676767676767676657e-02 2.727272727272727140e-02 2.626262626262626174e-02 2.626262626262626174e-02 2.676767676767676657e-02 3.131313131313131354e-02 2.101010101010101008e-02 2.181818181818181990e-02 2.181818181818181990e-02 1.454545454545454544e-02 5.050505050505050414e-02 4.202020202020202017e-02 4.444444444444444614e-02 4.969696969696969779e-02 4.282828282828282651e-02 4.323232323232323315e-02 4.323232323232323315e-02 4.202020202020202017e-02 3.959595959595959419e-02 4.484848484848484584e-02 4.929292929292929115e-02 4.484848484848484584e-02 5.373737373737373646e-02 3.338842975206611496e-02 4.528925619834710953e-02 4.925619834710744105e-02 3.834710743801653110e-02 4.595041322314049465e-02 3.603305785123966931e-02 4.000000000000000083e-02 2.747474747474747472e-02 2.989898989898990070e-02 1.719008264462809979e-02 1.487603305785123974e-02 3.834710743801653110e-02 5.223140495867768796e-02 4.165289256198347057e-02 4.429752066115702491e-02 5.194805194805195148e-02 4.363636363636363980e-02 4.099173553719008545e-02 4.198347107438016312e-02 3.867768595041322366e-02 4.231404958677686262e-02 3.834710743801653110e-02 2.644628099173553654e-02 2.512396694214875936e-02 2.644628099173553654e-02 2.644628099173553654e-02 2.842975206611570230e-02
-6.060606060606060774e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.272727272727272374e-02 3.272727272727272985e-02 4.727272727272727182e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 6.727272727272727570e-02 7.272727272727272374e-02 7.454545454545453975e-02 3.393939393939394283e-02 3.757575757575757486e-02 5.454545454545454281e-02 5.818181818181818177e-02 4.121212121212121382e-02 2.727272727272727140e-02 2.181818181818181990e-02 2.727272727272727140e-02 1.454545454545454544e-02 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 5.090909090909091078e-02 7.393939393939394367e-02 7.151515151515151769e-02 6.909090909090909172e-02 6.545454545454545969e-02 7.393939393939394367e-02 5.454545454545454281e-02 6.320346320346320601e-02 4.415584415584415667e-02 4.329004329004328827e-02 5.108225108225108307e-02 2.545454545454545539e-02 2.909090909090909088e-02 3.272727272727272985e-02 2.545454545454545539e-02 3.272727272727272985e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.038961038961038960e-02 4.415584415584415667e-02 5.367965367965368134e-02 7.272727272727272374e-02 7.272727272727272374e-02 6.320346320346320601e-02 4.675324675324675494e-02 5.367965367965368134e-02 6.926406926406926401e-02 5.108225108225108307e-02 6.926406926406926401e-02 6.406926406926406747e-02 6.406926406926406747e-02 5.779220779220779064e-02 6.038961038961038891e-02 6.168831168831168804e-02 4.285714285714285754e-02 5.909090909090908977e-02 3.896103896103896014e-02 4.285714285714285754e-02 4.415584415584415667e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 2.142857142857142877e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.896103896103896014e-02 4.350649350649350711e-02 6.038961038961038891e-02 5.519480519480519237e-02 6.883116883116882634e-02 4.545454545454545581e-02 7.142857142857142461e-02 6.233766233766233761e-02 6.753246753246752720e-02 5.259740259740259410e-02 6.623376623376622807e-02 5.974025974025973934e-02 6.558441558441557850e-02 5.555555555555555247e-02 5.959595959595959808e-02 5.858585858585858841e-02 6.161616161616161741e-02 4.191919191919191851e-02 3.484848484848485084e-02 3.838383838383838120e-02 3.888888888888888951e-02 4.090909090909090884e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.532467532467532617e-02 1.363636363636363570e-02 5.151515151515151381e-02 4.898989898989899311e-02 5.808080808080808011e-02 5.656565656565656908e-02 5.404040404040404144e-02 6.919191919191919338e-02 4.040404040404040747e-02 6.565656565656566301e-02 7.171717171717172101e-02 6.565656565656566301e-02 6.515151515151515471e-02 3.484848484848485084e-02 6.565656565656566301e-02 3.474747474747474918e-02 5.575757575757575579e-02 5.818181818181818177e-02 3.393939393939394283e-02 4.242424242424242681e-02 3.717171717171717515e-02 2.424242424242424240e-02 2.727272727272727140e-02 2.575757575757575690e-02 2.727272727272727140e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.575757575757575690e-02 2.575757575757575690e-02 1.939393939393939392e-02 2.181818181818181990e-02 2.181818181818181990e-02 1.454545454545454544e-02 4.202020202020202017e-02 3.434343434343434254e-02 4.282828282828282651e-02 4.808080808080807816e-02 4.484848484848484584e-02 3.595959595959596217e-02 4.969696969696969779e-02 3.474747474747474918e-02 3.151515151515151686e-02 3.434343434343434254e-02 4.040404040404040747e-02 3.393939393939394283e-02 4.484848484848484584e-02 3.272727272727272985e-02 4.429752066115702491e-02 3.834710743801653110e-02 5.983471074380165150e-02 5.917355371900826638e-02 4.528925619834710953e-02 4.528925619834710953e-02 2.303030303030302942e-02 2.424242424242424240e-02 1.586776859504132262e-02 1.487603305785123974e-02 3.107438016528925664e-02 4.165289256198347057e-02 6.214876033057851329e-02 4.694214876033057926e-02 4.350649350649350711e-02 3.636363636363636187e-02 3.966942148760330827e-02 3.702479338842975393e-02 3.834710743801653110e-02 3.801652892561983160e-02 3.140495867768595267e-02 2.380165289256198219e-02 2.181818181818181990e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.280991735537190104e-02
-6.060606060606060774e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.484848484848485362e-02 3.636363636363636187e-02 4.727272727272727182e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 6.363636363636362980e-02 9.090909090909091161e-02 8.909090909090909560e-02 3.878787878787878785e-02 4.242424242424242681e-02 7.099567099567100081e-02 6.787878787878788567e-02 4.606060606060605883e-02 2.727272727272727140e-02 2.181818181818181990e-02 2.727272727272727140e-02 1.454545454545454544e-02 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 5.090909090909091078e-02 8.121212121212120771e-02 9.090909090909091161e-02 6.181818181818182073e-02 6.787878787878788567e-02 8.848484848484848564e-02 7.099567099567100081e-02 7.705627705627705881e-02 4.415584415584415667e-02 5.194805194805195148e-02 5.454545454545454281e-02 2.545454545454545539e-02 2.909090909090909088e-02 3.272727272727272985e-02 2.545454545454545539e-02 3.272727272727272985e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.038961038961038960e-02 4.415584415584415667e-02 5.714285714285714107e-02 7.272727272727272374e-02 8.311688311688311681e-02 6.320346320346320601e-02 4.675324675324675494e-02 5.021645021645021467e-02 7.965367965367965708e-02 5.454545454545454281e-02 9.004329004329005015e-02 6.926406926406926401e-02 6.233766233766233761e-02 7.792207792207792028e-02 5.259740259740259410e-02 7.987012987012986898e-02 4.285714285714285754e-02 6.428571428571427937e-02 3.896103896103896014e-02 5.324675324675324367e-02 4.675324675324675494e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 2.142857142857142877e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.896103896103896014e-02 4.740259740259740451e-02 6.038961038961038891e-02 6.298701298701298024e-02 6.363636363636362980e-02 4.935064935064935321e-02 8.311688311688311681e-02 5.974025974025973934e-02 8.831168831168831335e-02 5.909090909090908977e-02 7.792207792207792028e-02 6.363636363636362980e-02 7.077922077922077504e-02 5.454545454545454281e-02 8.131313131313130937e-02 6.919191919191919338e-02 7.323232323232323204e-02 4.797979797979797650e-02 3.484848484848485084e-02 4.141414141414141714e-02 4.090909090909090884e-02 3.989898989898989917e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.532467532467532617e-02 1.363636363636363570e-02 5.151515151515151381e-02 5.000000000000000278e-02 5.404040404040404144e-02 5.959595959595959808e-02 6.313131313131313538e-02 6.919191919191919338e-02 4.242424242424242681e-02 5.656565656565656908e-02 7.878787878787878174e-02 8.686868686868687295e-02 7.929292929292929004e-02 3.484848484848485084e-02 6.565656565656566301e-02 3.959595959595959419e-02 8.000000000000000167e-02 6.222222222222222043e-02 3.393939393939394283e-02 4.727272727272727182e-02 4.202020202020202017e-02 2.424242424242424240e-02 2.727272727272727140e-02 2.575757575757575690e-02 2.727272727272727140e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.575757575757575690e-02 2.575757575757575690e-02 1.939393939393939392e-02 2.181818181818181990e-02 2.181818181818181990e-02 1.454545454545454544e-02 4.282828282828282651e-02 3.676767676767676851e-02 4.363636363636363980e-02 5.535353535353535609e-02 4.484848484848484584e-02 3.757575757575757486e-02 4.727272727272727182e-02 3.636363636363636187e-02 3.151515151515151686e-02 3.353535353535353619e-02 4.363636363636363980e-02 3.393939393939394283e-02 5.212121212121212377e-02 3.603305785123966931e-02 3.900826446280991622e-02 4.595041322314049465e-02 5.917355371900826638e-02 7.636363636363636964e-02 4.661157024793388670e-02 5.322314049586776563e-02 2.303030303030302942e-02 2.424242424242424240e-02 1.586776859504132262e-02 1.487603305785123974e-02 3.305785123966942241e-02 4.760330578512396438e-02 6.214876033057851329e-02 5.157024793388429590e-02 4.480519480519480624e-02 3.768595041322313904e-02 3.966942148760330827e-02 4.099173553719008545e-02 3.834710743801653110e-02 3.867768595041322366e-02 3.272727272727272985e-02 2.380165289256198219e-02 2.181818181818181990e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.280991735537190104e-02
-6.060606060606060774e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 7.272727272727272374e-02 3.272727272727272985e-02 3.636363636363636187e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 6.363636363636362980e-02 7.272727272727272374e-02 7.454545454545453975e-02 3.393939393939394283e-02 3.757575757575757486e-02 4.155844155844155841e-02 5.090909090909091078e-02 4.121212121212121382e-02 2.727272727272727140e-02 2.181818181818181990e-02 2.727272727272727140e-02 1.454545454545454544e-02 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 5.333333333333333676e-02 6.909090909090909172e-02 7.151515151515151769e-02 6.424242424242423977e-02 6.303030303030303372e-02 7.393939393939394367e-02 4.155844155844155841e-02 5.800865800865800948e-02 5.281385281385281294e-02 4.329004329004328827e-02 4.935064935064935321e-02 2.545454545454545539e-02 2.909090909090909088e-02 3.272727272727272985e-02 2.545454545454545539e-02 3.272727272727272985e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.038961038961038960e-02 4.588744588744588654e-02 5.367965367965368134e-02 6.580086580086580428e-02 6.753246753246752720e-02 6.320346320346320601e-02 4.848484848484848481e-02 5.367965367965368134e-02 6.580086580086580428e-02 5.281385281385281294e-02 6.926406926406926401e-02 6.233766233766233761e-02 6.060606060606060774e-02 4.805194805194805407e-02 5.129870129870130191e-02 5.519480519480519237e-02 4.155844155844155841e-02 4.870129870129870364e-02 4.155844155844155841e-02 4.285714285714285754e-02 5.064935064935065234e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 2.142857142857142877e-02 1.168831168831168874e-02 1.168831168831168874e-02 4.025974025974025927e-02 4.610389610389610537e-02 5.519480519480519237e-02 5.649350649350649151e-02 6.103896103896103847e-02 4.805194805194805407e-02 6.623376623376622807e-02 6.233766233766233761e-02 6.753246753246752720e-02 5.259740259740259410e-02 6.363636363636362980e-02 5.974025974025973934e-02 6.038961038961038891e-02 3.939393939393939087e-02 5.202020202020202211e-02 4.595959595959595717e-02 5.000000000000000278e-02 5.353535353535353314e-02 3.686868686868687017e-02 4.141414141414141714e-02 4.090909090909090884e-02 4.494949494949494750e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.532467532467532617e-02 1.363636363636363570e-02 4.747474747474747514e-02 4.898989898989899311e-02 5.202020202020202211e-02 5.353535353535353314e-02 5.606060606060606077e-02 6.111111111111110911e-02 4.141414141414141714e-02 5.757575757575757874e-02 6.363636363636362980e-02 6.565656565656566301e-02 6.212121212121211877e-02 3.585858585858586051e-02 5.858585858585858841e-02 3.474747474747474918e-02 4.525252525252525249e-02 4.363636363636363980e-02 3.313131313131312955e-02 4.646464646464646547e-02 4.040404040404040747e-02 2.424242424242424240e-02 2.727272727272727140e-02 2.575757575757575690e-02 2.727272727272727140e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.575757575757575690e-02 2.575757575757575690e-02 1.939393939393939392e-02 2.181818181818181990e-02 2.181818181818181990e-02 1.454545454545454544e-02 4.202020202020202017e-02 3.595959595959596217e-02 4.282828282828282651e-02 4.969696969696969779e-02 4.161616161616161352e-02 3.676767676767676851e-02 4.646464646464646547e-02 3.555555555555555552e-02 3.232323232323232320e-02 3.595959595959596217e-02 4.121212121212121382e-02 3.474747474747474918e-02 4.646464646464646547e-02 3.272727272727272985e-02 4.099173553719008545e-02 3.603305785123966931e-02 4.528925619834710953e-02 4.661157024793388670e-02 5.454545454545454281e-02 5.520661157024793486e-02 2.303030303030302942e-02 2.424242424242424240e-02 1.586776859504132262e-02 1.487603305785123974e-02 3.239669421487603035e-02 4.297520661157024774e-02 5.421487603305785025e-02 4.561983471074380209e-02 4.350649350649350711e-02 3.900826446280991622e-02 3.702479338842975393e-02 3.768595041322313904e-02 3.702479338842975393e-02 3.801652892561983160e-02 3.206611570247933779e-02 2.380165289256198219e-02 2.181818181818181990e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.280991735537190104e-02
-6.060606060606060774e-02 2.424242424242424240e-02 0.000000000000000000e+00 0.000000000000000000e+00 8.484848484848485362e-02 3.636363636363636187e-02 3.636363636363636187e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 6.363636363636362980e-02 9.090909090909091161e-02 8.909090909090909560e-02 3.878787878787878785e-02 4.242424242424242681e-02 4.848484848484848481e-02 6.060606060606060774e-02 4.606060606060605883e-02 2.727272727272727140e-02 2.181818181818181990e-02 2.727272727272727140e-02 1.454545454545454544e-02 7.272727272727272721e-03 7.272727272727272721e-03 7.272727272727272721e-03 5.090909090909091078e-02 7.636363636363636964e-02 9.090909090909091161e-02 6.181818181818182073e-02 6.787878787878788567e-02 8.848484848484848564e-02 4.848484848484848481e-02 7.186147186147186228e-02 4.935064935064935321e-02 5.194805194805195148e-02 5.454545454545454281e-02 2.545454545454545539e-02 2.909090909090909088e-02 3.272727272727272985e-02 2.545454545454545539e-02 3.272727272727272985e-02 1.818181818181818094e-02 1.818181818181818094e-02 1.038961038961038960e-02 4.415584415584415667e-02 6.060606060606060774e-02 6.580086580086580428e-02 7.619047619047619735e-02 7.012987012987012547e-02 4.675324675324675494e-02 5.021645021645021467e-02 7.619047619047619735e-02 5.454545454545454281e-02 9.004329004329005015e-02 6.926406926406926401e-02 6.233766233766233761e-02 6.103896103896103847e-02 4.870129870129870364e-02 7.077922077922077504e-02 3.896103896103896014e-02 5.519480519480519237e-02 3.896103896103896014e-02 5.324675324675324367e-02 5.064935064935065234e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.857142857142857054e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 1.948051948051948007e-02 2.142857142857142877e-02 1.168831168831168874e-02 1.168831168831168874e-02 3.896103896103896014e-02 5.000000000000000278e-02 5.519480519480519237e-02 6.818181818181817677e-02 5.584415584415584194e-02 5.194805194805195148e-02 7.532467532467532201e-02 6.753246753246752720e-02 8.831168831168831335e-02 6.168831168831168804e-02 7.532467532467532201e-02 6.883116883116882634e-02 6.558441558441557850e-02 4.141414141414141714e-02 6.818181818181817677e-02 5.404040404040404144e-02 5.606060606060606077e-02 6.515151515151515471e-02 3.484848484848485084e-02 4.343434343434343647e-02 4.090909090909090884e-02 4.292929292929292817e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.532467532467532617e-02 1.363636363636363570e-02 4.747474747474747514e-02 5.000000000000000278e-02 4.797979797979797650e-02 5.757575757575757874e-02 6.919191919191919338e-02 5.909090909090908977e-02 4.242424242424242681e-02 4.848484848484848481e-02 6.666666666666666574e-02 8.686868686868687295e-02 7.525252525252525138e-02 3.484848484848485084e-02 5.757575757575757874e-02 3.959595959595959419e-02 5.818181818181818177e-02 4.848484848484848481e-02 3.151515151515151686e-02 4.969696969696969779e-02 4.525252525252525249e-02 2.424242424242424240e-02 2.727272727272727140e-02 2.575757575757575690e-02 2.727272727272727140e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.575757575757575690e-02 2.575757575757575690e-02 1.939393939393939392e-02 2.181818181818181990e-02 2.181818181818181990e-02 1.454545454545454544e-02 4.282828282828282651e-02 3.838383838383838120e-02 4.363636363636363980e-02 6.020202020202020110e-02 4.161616161616161352e-02 3.757575757575757486e-02 4.242424242424242681e-02 3.636363636363636187e-02 3.151515151515151686e-02 3.353535353535353619e-02 4.363636363636363980e-02 3.393939393939394283e-02 5.373737373737373646e-02 3.603305785123966931e-02 3.702479338842975393e-02 4.000000000000000083e-02 4.528925619834710953e-02 5.322314049586776563e-02 5.520661157024793486e-02 7.636363636363636964e-02 2.303030303030302942e-02 2.424242424242424240e-02 1.586776859504132262e-02 1.487603305785123974e-02 3.438016528925619958e-02 4.892561983471074155e-02 5.157024793388429590e-02 5.157024793388429590e-02 4.480519480519480624e-02 4.033057851239669339e-02 3.702479338842975393e-02 4.363636363636363980e-02 3.834710743801653110e-02 3.867768595041322366e-02 3.272727272727272985e-02 2.380165289256198219e-02 2.181818181818181990e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.280991735537190104e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.703703703703703498e-02 1.481481481481481538e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 5.555555555555555247e-02 2.888888888888888756e-02 2.222222222222222307e-02 4.222222222222222349e-02 3.111111111111111022e-02 4.222222222222222349e-02 2.074074074074074014e-02 2.814814814814814783e-02 1.481481481481481538e-02 1.037037037037037007e-02 1.037037037037037007e-02 6.222222222222222043e-02 5.777777777777777513e-02 6.666666666666666574e-02 3.259259259259258967e-02 3.407407407407407607e-02 2.962962962962963076e-02 3.407407407407407607e-02 5.185185185185185036e-02 3.851851851851852138e-02 3.851851851851852138e-02 5.333333333333333676e-02 5.185185185185185036e-02 5.333333333333333676e-02 1.481481481481481538e-02 2.010582010582010609e-02 2.010582010582010609e-02 1.481481481481481538e-02 2.010582010582010609e-02 6.222222222222222043e-02 6.814814814814815214e-02 6.814814814814815214e-02 5.777777777777777513e-02 7.407407407407406996e-02 3.703703703703703498e-02 3.809523809523809867e-02 3.174603174603174427e-02 5.714285714285714107e-02 4.444444444444444614e-02 4.021164021164021218e-02 4.021164021164021218e-02 4.550264550264550290e-02 5.925925925925926152e-02 5.925925925925926152e-02 4.444444444444444614e-02 5.714285714285714107e-02 4.021164021164021218e-02 5.925925925925926152e-02 5.925925925925926152e-02 1.666666666666666644e-02 2.857142857142857054e-02 2.063492063492063447e-02 2.777777777777777624e-02 2.063492063492063447e-02 2.777777777777777624e-02 1.666666666666666644e-02 2.777777777777777624e-02 7.089947089947089276e-02 6.031746031746031828e-02 5.502645502645502756e-02 5.820105820105819783e-02 6.243386243386243178e-02 7.195767195767195645e-02 7.195767195767195645e-02 6.243386243386243178e-02 6.666666666666666574e-02 6.243386243386243178e-02 7.089947089947089276e-02 3.809523809523809867e-02 3.968253968253968034e-02 4.206349206349206671e-02 3.888888888888888951e-02 4.365079365079364837e-02 3.571428571428571230e-02 3.571428571428571230e-02 5.952380952380952051e-02 4.841269841269841417e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.365079365079364837e-02 5.000000000000000278e-02 4.365079365079364837e-02 5.158730158730158444e-02 4.365079365079364837e-02 4.841269841269841417e-02 5.000000000000000278e-02 5.000000000000000278e-02 4.841269841269841417e-02 2.345679012345679160e-02 2.037037037037037202e-02 2.037037037037037202e-02 2.037037037037037202e-02 2.037037037037037202e-02 3.333333333333333287e-02 2.592592592592592518e-02 3.333333333333333287e-02 3.333333333333333287e-02 7.222222222222221544e-02 5.476190476190476164e-02 5.555555555555555247e-02 5.793650793650793884e-02 6.349206349206348854e-02 6.031746031746031828e-02 6.031746031746031828e-02 6.507936507936508408e-02 5.873015873015872967e-02 3.888888888888888951e-02 4.320987654320987414e-02 4.629629629629629373e-02 4.382716049382716361e-02 4.320987654320987414e-02 4.382716049382716361e-02 4.382716049382716361e-02 6.358024691358024616e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.629629629629629373e-02 5.987654320987654405e-02 4.506172839506172867e-02 3.555555555555555552e-02 2.222222222222222307e-02 2.222222222222222307e-02 3.703703703703703498e-02 3.851851851851852138e-02 2.419753086419753133e-02 5.308641975308642236e-02 5.432098765432098741e-02 5.370370370370370489e-02 6.419753086419753563e-02 5.740740740740740700e-02 5.555555555555555247e-02 5.555555555555555247e-02 7.037037037037037479e-02 4.049382716049382963e-02 4.148148148148148029e-02 4.345679012345678854e-02 3.802469135802469258e-02 6.518518518518517935e-02 5.135802469135802156e-02 4.493827160493827494e-02 4.543209876543209680e-02 4.395061728395061734e-02 6.123456790123456978e-02 4.543209876543209680e-02 5.876543209876543272e-02 5.876543209876543272e-02 6.271604938271604923e-02 6.370370370370370683e-02 6.518518518518517935e-02 5.530864197530864501e-02 3.717171717171717515e-02 4.323232323232323315e-02 2.747474747474747472e-02 2.303030303030302942e-02 2.303030303030302942e-02 2.303030303030302942e-02 2.303030303030302942e-02 6.469135802469136443e-02 6.666666666666666574e-02 3.636363636363636187e-02 3.676767676767676851e-02 5.090909090909091078e-02 5.818181818181818177e-02 4.363636363636363980e-02 4.363636363636363980e-02 7.698412698412698818e-02 4.525252525252525249e-02 4.363636363636363980e-02 4.686868686868687212e-02 4.686868686868687212e-02 4.444444444444444614e-02 5.696969696969696878e-02 5.898989898989898811e-02 5.090909090909091078e-02 5.414141414141414310e-02 5.373737373737373646e-02 6.343434343434344036e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.703703703703703498e-02 1.481481481481481538e-02 2.592592592592592518e-02 1.555555555555555511e-02 0.000000000000000000e+00 4.814814814814814825e-02 2.666666666666666838e-02 2.222222222222222307e-02 4.444444444444444614e-02 3.111111111111111022e-02 4.444444444444444614e-02 2.074074074074074014e-02 2.962962962962963076e-02 1.481481481481481538e-02 1.037037037037037007e-02 1.037037037037037007e-02 6.222222222222222043e-02 4.666666666666666879e-02 5.999999999999999778e-02 2.962962962962963076e-02 2.814814814814814783e-02 2.666666666666666838e-02 3.111111111111111022e-02 5.629629629629629567e-02 4.000000000000000083e-02 4.000000000000000083e-02 5.777777777777777513e-02 5.629629629629629567e-02 5.777777777777777513e-02 1.481481481481481538e-02 2.116402116402116285e-02 2.116402116402116285e-02 1.481481481481481538e-02 2.116402116402116285e-02 5.185185185185185036e-02 7.111111111111111105e-02 7.111111111111111105e-02 5.185185185185185036e-02 6.962962962962962465e-02 3.597883597883597823e-02 3.597883597883597823e-02 2.751322751322751378e-02 6.349206349206348854e-02 4.761904761904761640e-02 4.232804232804232569e-02 4.232804232804232569e-02 4.867724867724868010e-02 6.560846560846560205e-02 6.560846560846560205e-02 4.761904761904761640e-02 6.349206349206348854e-02 4.232804232804232569e-02 6.560846560846560205e-02 6.560846560846560205e-02 1.666666666666666644e-02 3.095238095238095344e-02 2.142857142857142877e-02 3.015873015873015914e-02 2.142857142857142877e-02 3.015873015873015914e-02 1.666666666666666644e-02 3.015873015873015914e-02 7.619047619047619735e-02 5.820105820105819783e-02 5.185185185185185036e-02 5.185185185185185036e-02 5.925925925925926152e-02 7.724867724867724716e-02 7.724867724867724716e-02 5.714285714285714107e-02 5.820105820105819783e-02 5.185185185185185036e-02 7.513227513227513366e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.206349206349206671e-02 3.809523809523809867e-02 4.285714285714285754e-02 2.777777777777777624e-02 2.936507936507936484e-02 6.746031746031745657e-02 5.317460317460317304e-02 4.603174603174603474e-02 4.603174603174603474e-02 4.682539682539682557e-02 5.476190476190476164e-02 4.682539682539682557e-02 5.634920634920635024e-02 4.682539682539682557e-02 5.317460317460317304e-02 5.476190476190476164e-02 5.476190476190476164e-02 5.317460317460317304e-02 2.469135802469135665e-02 2.098765432098765454e-02 2.098765432098765454e-02 2.098765432098765454e-02 2.098765432098765454e-02 3.703703703703703498e-02 2.777777777777777624e-02 3.703703703703703498e-02 3.703703703703703498e-02 7.857142857142856984e-02 5.396825396825397081e-02 5.555555555555555247e-02 5.476190476190476164e-02 6.349206349206348854e-02 5.476190476190476164e-02 5.396825396825397081e-02 6.428571428571427937e-02 5.396825396825397081e-02 3.333333333333333287e-02 4.691358024691358319e-02 5.061728395061728530e-02 4.753086419753086572e-02 4.691358024691358319e-02 4.753086419753086572e-02 4.753086419753086572e-02 7.283950617283950490e-02 4.814814814814814825e-02 4.814814814814814825e-02 4.814814814814814825e-02 5.061728395061728530e-02 6.913580246913579586e-02 4.938271604938271331e-02 3.851851851851852138e-02 2.320987654320987720e-02 2.320987654320987720e-02 4.197530864197530909e-02 4.345679012345678854e-02 2.567901234567901078e-02 5.370370370370370489e-02 5.617283950617284194e-02 5.493827160493826994e-02 5.493827160493826994e-02 5.370370370370370489e-02 5.370370370370370489e-02 5.432098765432098741e-02 7.901234567901234407e-02 4.246913580246913789e-02 4.345679012345678854e-02 4.345679012345678854e-02 3.111111111111111022e-02 7.555555555555555636e-02 5.876543209876543272e-02 4.938271604938271331e-02 4.987654320987654211e-02 4.839506172839506265e-02 7.160493827160493985e-02 4.987654320987654211e-02 6.913580246913579586e-02 6.913580246913579586e-02 7.308641975308642624e-02 7.407407407407406996e-02 7.555555555555555636e-02 6.271604938271604923e-02 4.080808080808080718e-02 4.929292929292929115e-02 2.989898989898990070e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.424242424242424240e-02 6.666666666666666574e-02 7.851851851851851527e-02 3.717171717171717515e-02 3.151515151515151686e-02 5.939393939393939476e-02 6.666666666666666574e-02 4.848484848484848481e-02 4.848484848484848481e-02 8.888888888888889228e-02 5.010101010101010444e-02 4.848484848484848481e-02 5.333333333333333676e-02 5.333333333333333676e-02 4.929292929292929115e-02 6.787878787878788567e-02 5.535353535353535609e-02 5.373737373737373646e-02 5.616161616161616243e-02 5.737373737373737542e-02 7.515151515151514972e-02
-0.000000000000000000e+00 0.000000000000000000e+00 6.060606060606060774e-02 2.424242424242424240e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 6.060606060606060774e-02 2.909090909090909088e-02 4.727272727272727182e-02 2.909090909090909088e-02 2.181818181818181990e-02 2.909090909090909088e-02 1.454545454545454544e-02 1.939393939393939392e-02 1.038961038961038960e-02 7.272727272727272721e-03 7.272727272727272721e-03 5.818181818181818177e-02 5.454545454545454281e-02 6.181818181818182073e-02 2.909090909090909088e-02 4.848484848484848481e-02 4.848484848484848481e-02 3.636363636363636187e-02 3.393939393939394283e-02 2.666666666666666838e-02 2.666666666666666838e-02 3.636363636363636187e-02 3.393939393939394283e-02 3.636363636363636187e-02 1.038961038961038960e-02 1.385281385281385280e-02 1.385281385281385280e-02 1.038961038961038960e-02 1.385281385281385280e-02 5.333333333333333676e-02 5.575757575757575579e-02 6.060606060606060774e-02 5.333333333333333676e-02 6.303030303030303372e-02 3.116883116883116880e-02 3.116883116883116880e-02 4.761904761904761640e-02 3.463203463203463200e-02 2.943722943722943894e-02 2.770562770562770560e-02 2.770562770562770560e-02 3.116883116883116880e-02 3.809523809523809867e-02 3.809523809523809867e-02 2.943722943722943894e-02 3.463203463203463200e-02 2.770562770562770560e-02 3.809523809523809867e-02 3.809523809523809867e-02 1.168831168831168874e-02 1.948051948051948007e-02 1.428571428571428527e-02 1.818181818181818094e-02 1.428571428571428527e-02 1.818181818181818094e-02 1.168831168831168874e-02 1.818181818181818094e-02 4.848484848484848481e-02 5.021645021645021467e-02 5.021645021645021467e-02 5.021645021645021467e-02 5.194805194805195148e-02 5.714285714285714107e-02 5.714285714285714107e-02 5.194805194805195148e-02 5.367965367965368134e-02 5.021645021645021467e-02 5.367965367965368134e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.246753246753246447e-02 3.116883116883116880e-02 3.376623376623376360e-02 4.480519480519480624e-02 3.246753246753246447e-02 3.376623376623376360e-02 2.987012987012986967e-02 2.857142857142857054e-02 2.857142857142857054e-02 2.987012987012986967e-02 3.246753246753246447e-02 2.987012987012986967e-02 3.506493506493506274e-02 2.987012987012986967e-02 2.987012987012986967e-02 3.246753246753246447e-02 3.246753246753246447e-02 2.987012987012986967e-02 1.616161616161616160e-02 1.414141414141414227e-02 1.414141414141414227e-02 1.414141414141414227e-02 1.414141414141414227e-02 2.020202020202020374e-02 1.717171717171717127e-02 2.020202020202020374e-02 2.020202020202020374e-02 4.415584415584415667e-02 4.805194805194805407e-02 4.805194805194805407e-02 4.935064935064935321e-02 5.064935064935065234e-02 4.935064935064935321e-02 4.805194805194805407e-02 4.935064935064935321e-02 4.805194805194805407e-02 3.232323232323232320e-02 2.828282828282828454e-02 3.030303030303030387e-02 2.929292929292929420e-02 2.828282828282828454e-02 2.929292929292929420e-02 2.929292929292929420e-02 3.535353535353535220e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.030303030303030387e-02 3.232323232323232320e-02 2.828282828282828454e-02 2.424242424242424240e-02 1.535353535353535352e-02 1.535353535353535352e-02 2.101010101010101008e-02 2.262626262626262624e-02 1.616161616161616160e-02 4.545454545454545581e-02 4.545454545454545581e-02 4.545454545454545581e-02 4.747474747474747514e-02 4.545454545454545581e-02 4.545454545454545581e-02 4.646464646464646547e-02 4.040404040404040747e-02 3.070707070707070704e-02 3.232323232323232320e-02 3.232323232323232320e-02 4.363636363636363980e-02 3.474747474747474918e-02 2.828282828282828454e-02 2.909090909090909088e-02 2.989898989898990070e-02 2.747474747474747472e-02 3.232323232323232320e-02 2.989898989898990070e-02 3.070707070707070704e-02 3.070707070707070704e-02 3.313131313131312955e-02 3.393939393939394283e-02 3.474747474747474918e-02 3.151515151515151686e-02 2.446280991735537078e-02 2.512396694214875936e-02 1.719008264462809979e-02 1.586776859504132262e-02 1.586776859504132262e-02 1.586776859504132262e-02 1.586776859504132262e-02 3.636363636363636187e-02 3.717171717171717515e-02 4.793388429752066388e-02 4.231404958677686262e-02 2.710743801652892512e-02 3.239669421487603035e-02 2.644628099173553654e-02 2.644628099173553654e-02 4.155844155844155841e-02 2.909090909090909088e-02 2.644628099173553654e-02 2.644628099173553654e-02 2.644628099173553654e-02 2.776859504132231371e-02 2.909090909090909088e-02 4.297520661157024774e-02 3.768595041322313904e-02 4.165289256198347057e-02 3.834710743801653110e-02 3.438016528925619958e-02
-0.000000000000000000e+00 0.000000000000000000e+00 6.060606060606060774e-02 2.424242424242424240e-02 1.818181818181818094e-02 1.090909090909090995e-02 0.000000000000000000e+00 8.484848484848485362e-02 3.636363636363636187e-02 4.727272727272727182e-02 2.727272727272727140e-02 2.181818181818181990e-02 2.727272727272727140e-02 1.454545454545454544e-02 1.818181818181818094e-02 1.038961038961038960e-02 7.272727272727272721e-03 7.272727272727272721e-03 6.363636363636362980e-02 9.090909090909091161e-02 8.909090909090909560e-02 3.878787878787878785e-02 6.787878787878788567e-02 5.818181818181818177e-02 4.606060606060605883e-02 2.909090909090909088e-02 2.545454545454545539e-02 2.545454545454545539e-02 3.272727272727272985e-02 2.909090909090909088e-02 3.272727272727272985e-02 1.038961038961038960e-02 1.298701298701298787e-02 1.298701298701298787e-02 1.038961038961038960e-02 1.298701298701298787e-02 9.090909090909091161e-02 5.090909090909091078e-02 6.181818181818182073e-02 8.121212121212120771e-02 8.848484848484848564e-02 3.722943722943723027e-02 4.069264069264069000e-02 6.147186147186146921e-02 2.857142857142857054e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.597402597402597574e-02 2.857142857142857054e-02 3.116883116883116880e-02 3.116883116883116880e-02 2.597402597402597574e-02 2.857142857142857054e-02 2.597402597402597574e-02 3.116883116883116880e-02 3.116883116883116880e-02 1.168831168831168874e-02 1.753246753246753137e-02 1.363636363636363570e-02 1.558441558441558440e-02 1.363636363636363570e-02 1.558441558441558440e-02 1.168831168831168874e-02 1.558441558441558440e-02 4.415584415584415667e-02 5.714285714285714107e-02 7.272727272727272374e-02 8.311688311688311681e-02 6.320346320346320601e-02 4.675324675324675494e-02 5.021645021645021467e-02 7.965367965367965708e-02 8.917748917748917481e-02 9.004329004329005015e-02 5.454545454545454281e-02 3.506493506493506274e-02 4.025974025974025927e-02 3.766233766233766100e-02 3.766233766233766100e-02 4.220779220779220797e-02 7.077922077922077504e-02 5.324675324675324367e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 3.116883116883116880e-02 2.727272727272727140e-02 2.532467532467532617e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.532467532467532617e-02 1.515151515151515194e-02 1.363636363636363570e-02 1.363636363636363570e-02 1.363636363636363570e-02 1.363636363636363570e-02 1.666666666666666644e-02 1.515151515151515194e-02 1.666666666666666644e-02 1.666666666666666644e-02 3.896103896103896014e-02 6.038961038961038891e-02 6.363636363636362980e-02 7.792207792207792028e-02 5.324675324675324367e-02 8.311688311688311681e-02 8.116883116883116811e-02 4.935064935064935321e-02 6.298701298701298024e-02 4.797979797979797650e-02 2.424242424242424240e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.424242424242424240e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.727272727272727140e-02 2.575757575757575690e-02 2.575757575757575690e-02 2.424242424242424240e-02 2.181818181818181990e-02 1.454545454545454544e-02 1.454545454545454544e-02 1.696969696969697142e-02 1.818181818181818094e-02 1.454545454545454544e-02 5.151515151515151381e-02 5.656565656565656908e-02 5.404040404040404144e-02 8.686868686868687295e-02 7.373737373737374035e-02 5.959595959595959808e-02 6.919191919191919338e-02 3.484848484848485084e-02 2.909090909090909088e-02 3.313131313131312955e-02 3.959595959595959419e-02 6.868686868686868507e-02 2.666666666666666838e-02 2.303030303030302942e-02 2.424242424242424240e-02 2.545454545454545539e-02 2.303030303030302942e-02 2.545454545454545539e-02 2.545454545454545539e-02 2.424242424242424240e-02 2.424242424242424240e-02 2.545454545454545539e-02 2.666666666666666838e-02 2.666666666666666838e-02 2.545454545454545539e-02 2.082644628099173528e-02 1.983471074380165414e-02 1.487603305785123974e-02 1.487603305785123974e-02 1.487603305785123974e-02 1.487603305785123974e-02 1.487603305785123974e-02 3.676767676767676851e-02 3.151515151515151686e-02 4.231404958677686262e-02 6.776859504132230760e-02 2.181818181818181990e-02 2.578512396694214795e-02 2.181818181818181990e-02 2.181818181818181990e-02 3.311688311688311404e-02 2.380165289256198219e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.280991735537190104e-02 2.280991735537190104e-02 7.338842975206610886e-02 3.966942148760330827e-02 3.768595041322313904e-02 4.231404958677686262e-02 2.876033057851239833e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 4.848484848484848481e-02 2.545454545454545539e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.636363636363636576e-02 5.090909090909091078e-02 5.999999999999999778e-02 2.909090909090909088e-02 3.636363636363636187e-02 2.770562770562770560e-02 2.909090909090909088e-02 2.909090909090909088e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.303030303030303372e-02 5.212121212121212377e-02 5.575757575757575579e-02 6.303030303030303372e-02 6.424242424242423977e-02 6.787878787878788567e-02 2.770562770562770560e-02 3.809523809523809867e-02 3.636363636363636187e-02 3.116883116883116880e-02 3.636363636363636187e-02 3.636363636363636187e-02 4.969696969696969779e-02 5.090909090909091078e-02 3.636363636363636187e-02 5.090909090909091078e-02 2.597402597402597574e-02 2.597402597402597574e-02 1.385281385281385280e-02 6.666666666666666574e-02 5.541125541125541121e-02 5.021645021645021467e-02 5.281385281385281294e-02 5.714285714285714107e-02 6.753246753246752720e-02 6.753246753246752720e-02 5.714285714285714107e-02 6.753246753246752720e-02 5.627705627705627961e-02 7.012987012987012547e-02 6.839826839826840255e-02 3.116883116883116880e-02 4.025974025974025927e-02 3.701298701298701144e-02 4.090909090909090884e-02 3.311688311688311404e-02 4.220779220779220797e-02 3.116883116883116880e-02 4.220779220779220797e-02 5.541125541125541121e-02 4.242424242424242681e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.714285714285714107e-02 5.714285714285714107e-02 4.242424242424242681e-02 4.329004329004328827e-02 3.809523809523809867e-02 5.541125541125541121e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.181818181818181490e-02 2.857142857142857054e-02 3.246753246753246447e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.818181818181817677e-02 5.844155844155844021e-02 5.064935064935065234e-02 5.389610389610389324e-02 5.129870129870130191e-02 6.038961038961038891e-02 5.519480519480519237e-02 6.103896103896103847e-02 5.844155844155844021e-02 5.909090909090908977e-02 6.168831168831168804e-02 6.103896103896103847e-02 5.844155844155844021e-02 3.131313131313131354e-02 3.535353535353535220e-02 3.181818181818181490e-02 3.232323232323232320e-02 3.434343434343434254e-02 4.646464646464646547e-02 3.989898989898989917e-02 4.646464646464646547e-02 4.595959595959595717e-02 5.844155844155844021e-02 4.090909090909090884e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.805194805194805407e-02 4.155844155844155841e-02 4.090909090909090884e-02 4.805194805194805407e-02 4.090909090909090884e-02 1.919191919191919060e-02 4.949494949494949447e-02 5.353535353535353314e-02 5.000000000000000278e-02 5.151515151515151381e-02 5.454545454545454281e-02 5.151515151515151381e-02 7.121212121212121271e-02 5.050505050505050414e-02 5.353535353535353314e-02 5.858585858585858841e-02 5.808080808080808011e-02 6.868686868686868507e-02 5.303030303030303177e-02 4.040404040404040747e-02 3.393939393939394283e-02 3.151515151515151686e-02 4.848484848484848481e-02 5.050505050505050414e-02 3.757575757575757486e-02 4.141414141414141714e-02 4.242424242424242681e-02 4.191919191919191851e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.141414141414141714e-02 4.191919191919191851e-02 5.959595959595959808e-02 3.313131313131312955e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.101010101010101008e-02 7.232323232323231710e-02 6.101010101010100745e-02 5.090909090909091078e-02 5.414141414141414310e-02 4.929292929292929115e-02 6.909090909090909172e-02 5.090909090909091078e-02 6.787878787878788567e-02 6.707070707070707238e-02 6.989898989898990500e-02 7.151515151515151769e-02 7.151515151515151769e-02 6.505050505050505305e-02 4.132231404958677801e-02 5.223140495867768796e-02 3.834710743801653110e-02 3.107438016528925664e-02 3.305785123966942241e-02 3.239669421487603035e-02 3.438016528925619958e-02 5.090909090909091078e-02 5.939393939393939476e-02 2.710743801652892512e-02 2.181818181818181990e-02 6.115702479338842867e-02 6.677685950413222993e-02 5.057851239669421822e-02 5.123966942148760334e-02 8.311688311688311681e-02 5.090909090909091078e-02 4.826446280991735643e-02 5.619834710743801948e-02 5.355371900826446513e-02 4.958677685950413361e-02 6.545454545454545969e-02 4.363636363636363980e-02 4.231404958677686262e-02 4.363636363636363980e-02 4.462809917355371747e-02 5.818181818181818177e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.060606060606060774e-02 2.909090909090909088e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 6.727272727272727570e-02 6.909090909090909172e-02 8.181818181818181768e-02 3.393939393939394283e-02 4.606060606060605883e-02 3.463203463203463200e-02 3.878787878787878785e-02 3.393939393939394283e-02 4.727272727272727182e-02 2.909090909090909088e-02 4.727272727272727182e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 8.121212121212120771e-02 6.909090909090909172e-02 8.000000000000000167e-02 8.000000000000000167e-02 8.121212121212120771e-02 9.696969696969696961e-02 3.463203463203463200e-02 5.541125541125541121e-02 4.329004329004328827e-02 3.982683982683982854e-02 4.675324675324675494e-02 4.121212121212121382e-02 5.939393939393939476e-02 6.545454545454545969e-02 4.121212121212121382e-02 6.545454545454545969e-02 2.943722943722943894e-02 2.943722943722943894e-02 1.385281385281385280e-02 8.051948051948051854e-02 7.359307359307359908e-02 6.580086580086580428e-02 7.359307359307359908e-02 7.445887445887446054e-02 9.177489177489177308e-02 8.744588744588745188e-02 7.792207792207792028e-02 8.917748917748917481e-02 8.398268398268397827e-02 9.264069264069263454e-02 8.744588744588745188e-02 4.415584415584415667e-02 5.064935064935065234e-02 5.519480519480519237e-02 5.519480519480519237e-02 4.480519480519480624e-02 5.129870129870130191e-02 4.155844155844155841e-02 5.584415584415584194e-02 6.753246753246752720e-02 4.935064935064935321e-02 4.502164502164502508e-02 4.502164502164502508e-02 5.367965367965368134e-02 7.099567099567100081e-02 7.099567099567100081e-02 4.935064935064935321e-02 5.367965367965368134e-02 4.502164502164502508e-02 6.753246753246752720e-02 3.376623376623376360e-02 3.376623376623376360e-02 3.701298701298701144e-02 3.376623376623376360e-02 4.025974025974025927e-02 1.558441558441558440e-02 1.558441558441558440e-02 7.922077922077921941e-02 7.662337662337662114e-02 6.818181818181817677e-02 7.532467532467532201e-02 6.688311688311687764e-02 8.506493506493506551e-02 7.987012987012986898e-02 8.181818181818181768e-02 8.961038961038961248e-02 8.051948051948051854e-02 8.636363636363636465e-02 8.051948051948051854e-02 7.792207792207792028e-02 3.939393939393939087e-02 5.353535353535353314e-02 4.393939393939393784e-02 4.444444444444444614e-02 4.444444444444444614e-02 5.454545454545454281e-02 5.101010101010101244e-02 5.808080808080808011e-02 5.606060606060606077e-02 6.948051948051947591e-02 4.870129870129870364e-02 5.194805194805195148e-02 5.194805194805195148e-02 5.844155844155844021e-02 5.194805194805195148e-02 4.870129870129870364e-02 5.844155844155844021e-02 4.870129870129870364e-02 2.121212121212121340e-02 6.767676767676768235e-02 7.373737373737374035e-02 6.666666666666666574e-02 7.020202020202019610e-02 7.828282828282828731e-02 6.969696969696970168e-02 9.040404040404040331e-02 6.565656565656566301e-02 7.676767676767676241e-02 9.191919191919191434e-02 8.434343434343434531e-02 7.626262626262626798e-02 7.020202020202019610e-02 5.494949494949494945e-02 5.010101010101010444e-02 4.282828282828282651e-02 5.535353535353535609e-02 7.272727272727272374e-02 4.969696969696969779e-02 4.949494949494949447e-02 5.454545454545454281e-02 5.202020202020202211e-02 5.454545454545454281e-02 4.949494949494949447e-02 4.949494949494949447e-02 5.202020202020202211e-02 6.868686868686868507e-02 3.959595959595959419e-02 4.363636363636363980e-02 4.363636363636363980e-02 2.424242424242424240e-02 9.414141414141413700e-02 6.989898989898990500e-02 7.070707070707070441e-02 7.838383838383838897e-02 6.585858585858585246e-02 7.959595959595959502e-02 6.949494949494949836e-02 7.555555555555555636e-02 7.272727272727272374e-02 8.202020202020202100e-02 8.646464646464646631e-02 8.767676767676767235e-02 9.333333333333333759e-02 5.454545454545454281e-02 6.611570247933884481e-02 5.223140495867768796e-02 4.165289256198347057e-02 4.760330578512396438e-02 4.297520661157024774e-02 4.892561983471074155e-02 5.818181818181818177e-02 6.666666666666666574e-02 3.239669421487603035e-02 2.578512396694214795e-02 6.677685950413222993e-02 9.752066115702479054e-02 6.776859504132230760e-02 7.173553719008264606e-02 9.740259740259740728e-02 7.140495867768595351e-02 6.347107438016529046e-02 6.909090909090909172e-02 6.479338842975206070e-02 6.743801652892561505e-02 7.140495867768595351e-02 5.421487603305785025e-02 5.157024793388429590e-02 5.421487603305785025e-02 5.652892561983471204e-02 6.413223140495867558e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.363636363636362980e-02 2.909090909090909088e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 7.090909090909090773e-02 7.454545454545453975e-02 7.818181818181818565e-02 3.393939393939394283e-02 4.121212121212121382e-02 3.636363636363636187e-02 4.121212121212121382e-02 3.151515151515151686e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.424242424242423977e-02 8.606060606060605966e-02 8.121212121212120771e-02 8.121212121212120771e-02 7.878787878787878174e-02 8.606060606060605966e-02 3.636363636363636187e-02 5.541125541125541121e-02 4.155844155844155841e-02 3.982683982683982854e-02 4.848484848484848481e-02 3.636363636363636187e-02 4.848484848484848481e-02 5.090909090909091078e-02 3.636363636363636187e-02 5.090909090909091078e-02 2.597402597402597574e-02 2.597402597402597574e-02 1.385281385281385280e-02 6.320346320346320601e-02 6.666666666666666574e-02 9.090909090909091161e-02 9.090909090909091161e-02 7.532467532467532201e-02 6.666666666666666574e-02 7.359307359307359908e-02 8.744588744588745188e-02 7.012987012987012547e-02 8.398268398268397827e-02 8.398268398268397827e-02 8.398268398268397827e-02 4.805194805194805407e-02 5.974025974025973934e-02 5.714285714285714107e-02 4.285714285714285754e-02 5.454545454545454281e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.805194805194805407e-02 5.194805194805195148e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.155844155844155841e-02 4.329004329004328827e-02 3.809523809523809867e-02 5.194805194805195148e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.116883116883116880e-02 2.857142857142857054e-02 3.246753246753246447e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.038961038961038891e-02 6.038961038961038891e-02 7.987012987012986898e-02 6.948051948051947591e-02 9.155844155844156118e-02 6.298701298701298024e-02 9.415584415584415945e-02 7.857142857142856984e-02 8.636363636363636465e-02 7.077922077922077504e-02 8.896103896103896291e-02 7.857142857142856984e-02 8.896103896103896291e-02 4.343434343434343647e-02 5.656565656565656908e-02 5.252525252525252347e-02 6.060606060606060774e-02 4.444444444444444614e-02 4.343434343434343647e-02 4.343434343434343647e-02 4.747474747474747514e-02 4.949494949494949447e-02 5.194805194805195148e-02 4.025974025974025927e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.025974025974025927e-02 4.675324675324675494e-02 4.025974025974025927e-02 1.919191919191919060e-02 7.121212121212121271e-02 6.818181818181817677e-02 8.030303030303030665e-02 7.727272727272727071e-02 7.020202020202019610e-02 9.646464646464646131e-02 6.616161616161615744e-02 9.141414141414141992e-02 9.949494949494949725e-02 8.737373737373736737e-02 9.040404040404040331e-02 5.707070707070707044e-02 9.242424242424242264e-02 4.525252525252525249e-02 5.090909090909091078e-02 5.414141414141414310e-02 4.282828282828282651e-02 5.494949494949494945e-02 4.282828282828282651e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.242424242424242681e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.141414141414141714e-02 5.050505050505050414e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.101010101010101008e-02 6.909090909090909172e-02 5.373737373737373646e-02 6.181818181818182073e-02 6.585858585858585246e-02 6.505050505050505305e-02 5.939393939393939476e-02 7.232323232323231710e-02 5.696969696969696878e-02 5.373737373737373646e-02 5.939393939393939476e-02 6.505050505050505305e-02 6.101010101010100745e-02 6.909090909090909172e-02 4.561983471074380209e-02 5.818181818181818177e-02 4.165289256198347057e-02 6.214876033057851329e-02 6.214876033057851329e-02 5.421487603305785025e-02 5.157024793388429590e-02 4.363636363636363980e-02 4.848484848484848481e-02 2.644628099173553654e-02 2.181818181818181990e-02 5.057851239669421822e-02 6.776859504132230760e-02 9.421487603305785108e-02 6.909090909090909172e-02 7.402597402597402287e-02 5.454545454545454281e-02 5.983471074380165150e-02 5.520661157024793486e-02 5.785123966942148921e-02 5.719008264462809715e-02 5.322314049586776563e-02 4.231404958677686262e-02 3.966942148760330827e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.628099173553718720e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 6.363636363636362980e-02 2.909090909090909088e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 6.363636363636362980e-02 7.454545454545453975e-02 7.818181818181818565e-02 3.393939393939394283e-02 4.121212121212121382e-02 3.636363636363636187e-02 4.121212121212121382e-02 3.151515151515151686e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.666666666666666574e-02 7.393939393939394367e-02 8.121212121212120771e-02 7.151515151515151769e-02 7.393939393939394367e-02 8.606060606060605966e-02 3.636363636363636187e-02 5.541125541125541121e-02 3.809523809523809867e-02 3.982683982683982854e-02 4.502164502164502508e-02 3.636363636363636187e-02 4.848484848484848481e-02 5.090909090909091078e-02 3.636363636363636187e-02 5.090909090909091078e-02 2.597402597402597574e-02 2.597402597402597574e-02 1.385281385281385280e-02 6.320346320346320601e-02 7.186147186147186228e-02 7.099567099567100081e-02 7.705627705627705881e-02 7.186147186147186228e-02 7.186147186147186228e-02 7.186147186147186228e-02 7.878787878787878174e-02 7.186147186147186228e-02 8.398268398268397827e-02 8.051948051948051854e-02 7.705627705627705881e-02 4.805194805194805407e-02 4.935064935064935321e-02 5.714285714285714107e-02 4.545454545454545581e-02 4.805194805194805407e-02 4.285714285714285754e-02 4.285714285714285754e-02 4.805194805194805407e-02 5.194805194805195148e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.155844155844155841e-02 4.329004329004328827e-02 3.809523809523809867e-02 5.194805194805195148e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.116883116883116880e-02 2.857142857142857054e-02 3.246753246753246447e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.038961038961038891e-02 6.298701298701298024e-02 7.142857142857142461e-02 7.727272727272727071e-02 6.818181818181817677e-02 6.818181818181817677e-02 7.987012987012986898e-02 7.467532467532467244e-02 8.636363636363636465e-02 7.467532467532467244e-02 8.246753246753246724e-02 7.597402597402597157e-02 7.597402597402597157e-02 3.939393939393939087e-02 5.656565656565656908e-02 4.747474747474747514e-02 5.050505050505050414e-02 4.444444444444444614e-02 4.343434343434343647e-02 4.444444444444444614e-02 4.747474747474747514e-02 4.646464646464646547e-02 5.194805194805195148e-02 4.025974025974025927e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.025974025974025927e-02 4.675324675324675494e-02 4.025974025974025927e-02 1.919191919191919060e-02 7.020202020202019610e-02 6.969696969696970168e-02 6.818181818181817677e-02 7.373737373737374035e-02 7.929292929292929004e-02 7.222222222222221544e-02 6.818181818181817677e-02 6.515151515151515471e-02 7.727272727272727071e-02 8.737373737373736737e-02 8.232323232323232598e-02 5.707070707070707044e-02 7.121212121212121271e-02 4.525252525252525249e-02 5.090909090909091078e-02 4.606060606060605883e-02 4.282828282828282651e-02 5.818181818181818177e-02 4.444444444444444614e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.242424242424242681e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.141414141414141714e-02 5.050505050505050414e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.101010101010101008e-02 6.989898989898990500e-02 5.454545454545454281e-02 6.949494949494949836e-02 7.959595959595959502e-02 6.424242424242423977e-02 5.939393939393939476e-02 6.828282828282827843e-02 5.696969696969696878e-02 5.373737373737373646e-02 6.020202020202020110e-02 6.505050505050505305e-02 6.262626262626262708e-02 7.232323232323231710e-02 4.495867768595041003e-02 5.289256198347107307e-02 4.429752066115702491e-02 4.694214876033057926e-02 5.157024793388429590e-02 4.561983471074380209e-02 5.157024793388429590e-02 4.363636363636363980e-02 4.848484848484848481e-02 2.644628099173553654e-02 2.181818181818181990e-02 5.123966942148760334e-02 7.173553719008264606e-02 6.909090909090909172e-02 7.239669421487603118e-02 7.402597402597402287e-02 6.809917355371901404e-02 5.917355371900826638e-02 5.652892561983471204e-02 5.421487603305785025e-02 6.347107438016529046e-02 5.322314049586776563e-02 4.231404958677686262e-02 3.966942148760330827e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.628099173553718720e-02
-2.380952380952380820e-02 1.190476190476190410e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.952380952380952051e-02 3.571428571428571230e-02 1.428571428571428527e-02 3.571428571428571230e-02 2.142857142857142877e-02 0.000000000000000000e+00 7.857142857142856984e-02 6.428571428571427937e-02 8.214285714285714246e-02 4.285714285714285754e-02 5.476190476190476164e-02 3.401360544217687382e-02 3.333333333333333287e-02 3.333333333333333287e-02 6.071428571428571369e-02 4.285714285714285754e-02 6.071428571428571369e-02 2.857142857142857054e-02 1.428571428571428527e-02 1.428571428571428527e-02 1.428571428571428527e-02 9.047619047619047394e-02 7.142857142857142461e-02 7.380952380952381098e-02 9.285714285714286031e-02 9.285714285714286031e-02 9.761904761904761918e-02 3.401360544217687382e-02 4.931972789115646183e-02 4.591836734693877792e-02 3.741496598639455773e-02 4.761904761904761640e-02 5.476190476190476164e-02 7.619047619047619735e-02 7.857142857142856984e-02 5.476190476190476164e-02 7.857142857142856984e-02 3.911564625850340315e-02 3.911564625850340315e-02 2.040816326530612082e-02 9.693877551020407823e-02 7.823129251700680631e-02 7.142857142857142461e-02 7.312925170068027003e-02 7.993197278911565173e-02 1.003401360544217691e-01 1.003401360544217691e-01 7.993197278911565173e-02 9.863945578231292366e-02 7.482993197278911546e-02 1.037414965986394599e-01 1.020408163265306145e-01 3.826530612244898044e-02 5.739795918367347066e-02 4.719387755102040505e-02 5.612244897959183659e-02 4.336734693877550978e-02 5.612244897959183659e-02 3.826530612244898044e-02 5.739795918367347066e-02 8.503401360544217413e-02 6.462585034013605678e-02 5.782312925170068202e-02 5.782312925170068202e-02 6.632653061224490221e-02 8.843537414965986498e-02 8.843537414965986498e-02 6.462585034013605678e-02 6.632653061224490221e-02 5.782312925170068202e-02 8.503401360544217413e-02 4.336734693877550978e-02 4.336734693877550978e-02 4.846938775510203912e-02 4.336734693877550978e-02 4.974489795918367319e-02 2.295918367346938896e-02 2.295918367346938896e-02 9.948979591836734637e-02 8.290816326530611735e-02 7.397959183673469274e-02 7.525510204081632681e-02 7.525510204081632681e-02 8.545918367346938549e-02 7.780612244897959495e-02 8.801020408163265363e-02 7.908163265306122902e-02 8.418367346938775142e-02 8.801020408163265363e-02 8.673469387755101956e-02 8.418367346938775142e-02 4.365079365079364837e-02 4.464285714285714385e-02 4.166666666666666435e-02 4.265873015873015983e-02 4.265873015873015983e-02 6.349206349206348854e-02 5.158730158730158444e-02 6.448412698412699096e-02 6.448412698412699096e-02 8.928571428571428770e-02 6.250000000000000000e-02 6.377551020408163407e-02 6.377551020408163407e-02 7.397959183673469274e-02 6.377551020408163407e-02 6.250000000000000000e-02 7.397959183673469274e-02 6.250000000000000000e-02 2.876984126984126824e-02 7.341269841269841556e-02 7.837301587301587213e-02 7.440476190476190410e-02 7.440476190476190410e-02 7.638888888888889506e-02 7.539682539682539264e-02 1.061507936507936484e-01 7.539682539682539264e-02 7.738095238095238360e-02 7.936507936507936067e-02 8.134920634920635163e-02 9.821428571428571230e-02 7.738095238095238360e-02 6.349206349206348854e-02 4.444444444444444614e-02 4.285714285714285754e-02 6.825396825396824740e-02 7.301587301587302015e-02 4.761904761904761640e-02 6.349206349206348854e-02 6.547619047619047949e-02 6.448412698412699096e-02 6.547619047619047949e-02 6.349206349206348854e-02 6.349206349206348854e-02 6.448412698412699096e-02 9.027777777777777624e-02 5.079365079365079361e-02 5.238095238095238221e-02 5.238095238095238221e-02 3.174603174603174427e-02 1.087301587301587324e-01 8.492063492063492425e-02 7.539682539682539264e-02 7.777777777777777901e-02 7.380952380952381098e-02 1.015873015873015872e-01 7.619047619047619735e-02 9.682539682539682835e-02 9.523809523809523281e-02 1.031746031746031689e-01 1.063492063492063461e-01 1.071428571428571369e-01 9.365079365079365115e-02 6.493506493506492894e-02 7.727272727272727071e-02 5.194805194805195148e-02 4.350649350649350711e-02 4.480519480519480624e-02 4.350649350649350711e-02 4.480519480519480624e-02 7.698412698412698818e-02 8.888888888888889228e-02 4.155844155844155841e-02 3.311688311688311404e-02 8.311688311688311681e-02 9.740259740259740728e-02 7.402597402597402287e-02 7.402597402597402287e-02 1.275510204081632681e-01 7.532467532467532201e-02 7.272727272727272374e-02 7.792207792207792028e-02 7.727272727272727071e-02 7.402597402597402287e-02 9.285714285714286031e-02 6.753246753246752720e-02 6.493506493506492894e-02 6.753246753246752720e-02 6.883116883116882634e-02 8.636363636363636465e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.151515151515151381e-02 2.545454545454545539e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.999999999999999778e-02 5.454545454545454281e-02 6.727272727272727570e-02 2.909090909090909088e-02 3.878787878787878785e-02 2.943722943722943894e-02 3.151515151515151686e-02 2.666666666666666838e-02 4.363636363636363980e-02 2.909090909090909088e-02 4.363636363636363980e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 7.636363636363636964e-02 5.818181818181818177e-02 6.181818181818182073e-02 7.151515151515151769e-02 6.909090909090909172e-02 7.878787878787878174e-02 2.943722943722943894e-02 4.329004329004328827e-02 3.982683982683982854e-02 3.030303030303030387e-02 3.809523809523809867e-02 3.878787878787878785e-02 5.333333333333333676e-02 5.818181818181818177e-02 3.878787878787878785e-02 5.818181818181818177e-02 2.770562770562770560e-02 2.770562770562770560e-02 1.385281385281385280e-02 6.839826839826840255e-02 6.926406926406926401e-02 5.800865800865800948e-02 5.974025974025973934e-02 6.580086580086580428e-02 8.571428571428571508e-02 8.051948051948051854e-02 6.320346320346320601e-02 7.532467532467532201e-02 6.320346320346320601e-02 7.705627705627705881e-02 7.532467532467532201e-02 3.376623376623376360e-02 4.545454545454545581e-02 4.155844155844155841e-02 5.324675324675324367e-02 3.701298701298701144e-02 4.805194805194805407e-02 3.116883116883116880e-02 5.064935064935065234e-02 5.541125541125541121e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.848484848484848481e-02 6.233766233766233761e-02 6.233766233766233761e-02 4.502164502164502508e-02 4.848484848484848481e-02 4.155844155844155841e-02 5.541125541125541121e-02 3.116883116883116880e-02 3.116883116883116880e-02 3.376623376623376360e-02 3.116883116883116880e-02 3.636363636363636187e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.428571428571427937e-02 6.363636363636362980e-02 6.688311688311687764e-02 7.077922077922077504e-02 5.974025974025973934e-02 7.402597402597402287e-02 6.363636363636362980e-02 7.272727272727272374e-02 6.688311688311687764e-02 6.818181818181817677e-02 6.883116883116882634e-02 6.818181818181817677e-02 6.168831168831168804e-02 3.535353535353535220e-02 3.939393939393939087e-02 3.535353535353535220e-02 3.636363636363636187e-02 3.838383838383838120e-02 4.747474747474747514e-02 4.494949494949494750e-02 4.949494949494949447e-02 4.747474747474747514e-02 5.454545454545454281e-02 4.415584415584415667e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.194805194805195148e-02 4.675324675324675494e-02 4.415584415584415667e-02 5.194805194805195148e-02 4.415584415584415667e-02 2.020202020202020374e-02 7.222222222222221544e-02 7.222222222222221544e-02 6.666666666666666574e-02 6.767676767676768235e-02 7.272727272727272374e-02 6.010101010101009944e-02 7.222222222222221544e-02 5.959595959595959808e-02 6.262626262626262708e-02 6.767676767676768235e-02 6.515151515151515471e-02 6.010101010101009944e-02 5.808080808080808011e-02 4.525252525252525249e-02 3.797979797979798150e-02 3.555555555555555552e-02 4.606060606060605883e-02 6.060606060606060774e-02 4.202020202020202017e-02 4.444444444444444614e-02 4.848484848484848481e-02 4.646464646464646547e-02 4.848484848484848481e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.646464646464646547e-02 5.252525252525252347e-02 3.555555555555555552e-02 3.878787878787878785e-02 3.878787878787878785e-02 2.262626262626262624e-02 7.232323232323231710e-02 5.414141414141414310e-02 7.878787878787878174e-02 7.757575757575757569e-02 6.585858585858585246e-02 6.101010101010100745e-02 7.353535353535353702e-02 5.777777777777777513e-02 5.616161616161616243e-02 6.585858585858585246e-02 6.585858585858585246e-02 6.747474747474747903e-02 6.949494949494949836e-02 4.561983471074380209e-02 5.487603305785124230e-02 4.363636363636363980e-02 3.636363636363636187e-02 3.768595041322313904e-02 3.900826446280991622e-02 4.033057851239669339e-02 4.525252525252525249e-02 5.010101010101010444e-02 2.909090909090909088e-02 2.380165289256198219e-02 5.090909090909091078e-02 7.140495867768595351e-02 5.454545454545454281e-02 6.809917355371901404e-02 7.532467532467532201e-02 8.297520661157024857e-02 6.049586776859504356e-02 5.322314049586776563e-02 5.190082644628098846e-02 7.107438016528926095e-02 5.388429752066115769e-02 4.760330578512396438e-02 4.231404958677686262e-02 4.760330578512396438e-02 4.628099173553718720e-02 4.760330578512396438e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.151515151515151381e-02 2.545454545454545539e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.999999999999999778e-02 5.454545454545454281e-02 6.363636363636362980e-02 2.909090909090909088e-02 3.636363636363636187e-02 2.943722943722943894e-02 3.151515151515151686e-02 2.666666666666666838e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.424242424242423977e-02 6.060606060606060774e-02 5.939393939393939476e-02 6.909090909090909172e-02 6.666666666666666574e-02 7.151515151515151769e-02 2.943722943722943894e-02 4.155844155844155841e-02 3.463203463203463200e-02 3.030303030303030387e-02 3.809523809523809867e-02 3.636363636363636187e-02 4.848484848484848481e-02 5.090909090909091078e-02 3.636363636363636187e-02 5.090909090909091078e-02 2.597402597402597574e-02 2.597402597402597574e-02 1.385281385281385280e-02 6.493506493506492894e-02 6.060606060606060774e-02 6.147186147186146921e-02 6.147186147186146921e-02 6.060606060606060774e-02 7.012987012987012547e-02 7.012987012987012547e-02 6.320346320346320601e-02 6.666666666666666574e-02 5.974025974025973934e-02 7.186147186147186228e-02 7.186147186147186228e-02 3.376623376623376360e-02 4.675324675324675494e-02 4.025974025974025927e-02 4.285714285714285754e-02 3.831168831168831057e-02 4.025974025974025927e-02 3.116883116883116880e-02 4.285714285714285754e-02 5.194805194805195148e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.155844155844155841e-02 4.329004329004328827e-02 3.809523809523809867e-02 5.194805194805195148e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.116883116883116880e-02 2.857142857142857054e-02 3.246753246753246447e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.038961038961038891e-02 5.974025974025973934e-02 6.298701298701298024e-02 6.038961038961038891e-02 6.233766233766233761e-02 6.103896103896103847e-02 6.363636363636362980e-02 6.493506493506492894e-02 6.168831168831168804e-02 6.168831168831168804e-02 6.623376623376622807e-02 6.298701298701298024e-02 6.428571428571427937e-02 3.535353535353535220e-02 3.838383838383838120e-02 3.636363636363636187e-02 3.939393939393939087e-02 3.333333333333333287e-02 4.343434343434343647e-02 3.787878787878787984e-02 4.545454545454545581e-02 4.545454545454545581e-02 5.194805194805195148e-02 4.025974025974025927e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.025974025974025927e-02 4.675324675324675494e-02 4.025974025974025927e-02 1.919191919191919060e-02 6.262626262626262708e-02 6.111111111111110911e-02 6.262626262626262708e-02 6.161616161616161741e-02 6.060606060606060774e-02 6.313131313131313538e-02 6.717171717171717404e-02 6.161616161616161741e-02 6.464646464646464641e-02 6.161616161616161741e-02 6.313131313131313538e-02 5.707070707070707044e-02 6.212121212121211877e-02 4.040404040404040747e-02 3.636363636363636187e-02 3.717171717171717515e-02 4.282828282828282651e-02 5.171717171717171713e-02 3.555555555555555552e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.242424242424242681e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.141414141414141714e-02 5.050505050505050414e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.101010101010101008e-02 6.666666666666666574e-02 5.090909090909091078e-02 6.181818181818182073e-02 6.222222222222222043e-02 6.222222222222222043e-02 5.777777777777777513e-02 6.303030303030303372e-02 5.535353535353535609e-02 5.373737373737373646e-02 6.020202020202020110e-02 6.424242424242423977e-02 6.262626262626262708e-02 6.222222222222222043e-02 4.099173553719008545e-02 5.421487603305785025e-02 4.099173553719008545e-02 3.966942148760330827e-02 3.966942148760330827e-02 3.702479338842975393e-02 3.702479338842975393e-02 4.363636363636363980e-02 4.848484848484848481e-02 2.644628099173553654e-02 2.181818181818181990e-02 4.826446280991735643e-02 6.347107438016529046e-02 5.983471074380165150e-02 5.917355371900826638e-02 7.272727272727272374e-02 6.049586776859504356e-02 6.115702479338842867e-02 5.057851239669421822e-02 5.123966942148760334e-02 6.115702479338842867e-02 5.190082644628098846e-02 4.231404958677686262e-02 3.966942148760330827e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.628099173553718720e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.757575757575757874e-02 2.727272727272727140e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.818181818181818177e-02 6.363636363636362980e-02 7.090909090909090773e-02 3.151515151515151686e-02 3.878787878787878785e-02 3.290043290043290214e-02 3.636363636363636187e-02 2.909090909090909088e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.181818181818182073e-02 6.181818181818182073e-02 6.909090909090909172e-02 6.545454545454545969e-02 6.787878787878788567e-02 7.878787878787878174e-02 3.290043290043290214e-02 4.848484848484848481e-02 3.463203463203463200e-02 3.463203463203463200e-02 3.982683982683982854e-02 3.636363636363636187e-02 4.848484848484848481e-02 5.090909090909091078e-02 3.636363636363636187e-02 5.090909090909091078e-02 2.597402597402597574e-02 2.597402597402597574e-02 1.385281385281385280e-02 6.320346320346320601e-02 6.060606060606060774e-02 5.800865800865800948e-02 6.320346320346320601e-02 6.406926406926406747e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.666666666666666574e-02 6.753246753246752720e-02 7.012987012987012547e-02 7.445887445887446054e-02 7.099567099567100081e-02 4.025974025974025927e-02 4.285714285714285754e-02 4.805194805194805407e-02 4.025974025974025927e-02 4.025974025974025927e-02 3.896103896103896014e-02 3.636363636363636187e-02 4.285714285714285754e-02 5.281385281385281294e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.155844155844155841e-02 4.329004329004328827e-02 3.809523809523809867e-02 5.281385281385281294e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.116883116883116880e-02 2.857142857142857054e-02 3.246753246753246447e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.298701298701298024e-02 6.103896103896103847e-02 5.649350649350649151e-02 6.298701298701298024e-02 5.584415584415584194e-02 6.428571428571427937e-02 6.558441558441557850e-02 6.753246753246752720e-02 7.207792207792207417e-02 6.428571428571427937e-02 7.077922077922077504e-02 6.753246753246752720e-02 6.493506493506492894e-02 3.535353535353535220e-02 4.646464646464646547e-02 3.939393939393939087e-02 4.040404040404040747e-02 3.838383838383838120e-02 4.191919191919191851e-02 4.040404040404040747e-02 4.494949494949494750e-02 4.393939393939393784e-02 5.454545454545454281e-02 4.025974025974025927e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.025974025974025927e-02 4.675324675324675494e-02 4.025974025974025927e-02 1.919191919191919060e-02 5.404040404040404144e-02 5.757575757575757874e-02 5.353535353535353314e-02 5.808080808080808011e-02 6.414141414141413811e-02 5.808080808080808011e-02 6.818181818181817677e-02 5.303030303030303177e-02 6.212121212121211877e-02 7.222222222222221544e-02 6.818181818181817677e-02 6.161616161616161741e-02 5.858585858585858841e-02 4.282828282828282651e-02 4.282828282828282651e-02 3.797979797979798150e-02 4.444444444444444614e-02 5.252525252525252347e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.242424242424242681e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.141414141414141714e-02 5.454545454545454281e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.101010101010101008e-02 6.909090909090909172e-02 6.020202020202020110e-02 5.414141414141414310e-02 6.181818181818182073e-02 5.252525252525252347e-02 6.424242424242423977e-02 5.292929292929293011e-02 6.141414141414141409e-02 5.858585858585858841e-02 6.343434343434344036e-02 6.868686868686868507e-02 6.626262626262625910e-02 6.828282828282827843e-02 4.264462809917355518e-02 5.057851239669421822e-02 4.198347107438016312e-02 3.702479338842975393e-02 4.099173553719008545e-02 3.768595041322313904e-02 4.363636363636363980e-02 4.686868686868687212e-02 5.333333333333333676e-02 2.644628099173553654e-02 2.181818181818181990e-02 5.619834710743801948e-02 6.909090909090909172e-02 5.520661157024793486e-02 5.652892561983471204e-02 7.792207792207792028e-02 5.322314049586776563e-02 5.057851239669421822e-02 6.181818181818182073e-02 5.586776859504131998e-02 5.190082644628098846e-02 5.785123966942148921e-02 4.231404958677686262e-02 4.033057851239669339e-02 4.231404958677686262e-02 4.297520661157024774e-02 5.157024793388429590e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.151515151515151381e-02 2.545454545454545539e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.999999999999999778e-02 5.454545454545454281e-02 6.363636363636362980e-02 2.909090909090909088e-02 3.636363636363636187e-02 2.943722943722943894e-02 3.151515151515151686e-02 2.666666666666666838e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.181818181818182073e-02 5.939393939393939476e-02 5.939393939393939476e-02 6.909090909090909172e-02 6.666666666666666574e-02 7.151515151515151769e-02 2.943722943722943894e-02 4.155844155844155841e-02 3.463203463203463200e-02 3.030303030303030387e-02 3.809523809523809867e-02 3.636363636363636187e-02 4.848484848484848481e-02 5.090909090909091078e-02 3.636363636363636187e-02 5.090909090909091078e-02 2.597402597402597574e-02 2.597402597402597574e-02 1.385281385281385280e-02 6.320346320346320601e-02 5.800865800865800948e-02 5.974025974025973934e-02 5.974025974025973934e-02 6.233766233766233761e-02 6.666666666666666574e-02 6.839826839826840255e-02 6.233766233766233761e-02 6.580086580086580428e-02 5.974025974025973934e-02 7.186147186147186228e-02 7.186147186147186228e-02 3.376623376623376360e-02 4.675324675324675494e-02 4.025974025974025927e-02 4.025974025974025927e-02 3.766233766233766100e-02 3.896103896103896014e-02 3.116883116883116880e-02 4.155844155844155841e-02 5.281385281385281294e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.155844155844155841e-02 4.329004329004328827e-02 3.809523809523809867e-02 5.281385281385281294e-02 2.857142857142857054e-02 2.857142857142857054e-02 3.116883116883116880e-02 2.857142857142857054e-02 3.246753246753246447e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.298701298701298024e-02 5.779220779220779064e-02 5.779220779220779064e-02 5.779220779220779064e-02 6.038961038961038891e-02 6.038961038961038891e-02 6.168831168831168804e-02 6.688311688311687764e-02 6.168831168831168804e-02 6.038961038961038891e-02 6.558441558441557850e-02 6.428571428571427937e-02 6.363636363636362980e-02 3.535353535353535220e-02 3.838383838383838120e-02 3.585858585858586051e-02 3.838383838383838120e-02 3.434343434343434254e-02 4.191919191919191851e-02 3.787878787878787984e-02 4.393939393939393784e-02 4.444444444444444614e-02 5.454545454545454281e-02 4.025974025974025927e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.025974025974025927e-02 4.675324675324675494e-02 4.025974025974025927e-02 1.919191919191919060e-02 5.505050505050505111e-02 5.808080808080808011e-02 5.707070707070707044e-02 5.707070707070707044e-02 5.808080808080808011e-02 6.060606060606060774e-02 6.717171717171717404e-02 5.959595959595959808e-02 6.161616161616161741e-02 6.161616161616161741e-02 6.212121212121211877e-02 6.161616161616161741e-02 6.060606060606060774e-02 4.040404040404040747e-02 3.636363636363636187e-02 3.636363636363636187e-02 4.444444444444444614e-02 5.010101010101010444e-02 3.636363636363636187e-02 4.040404040404040747e-02 4.242424242424242681e-02 4.141414141414141714e-02 4.242424242424242681e-02 4.040404040404040747e-02 4.040404040404040747e-02 4.141414141414141714e-02 5.454545454545454281e-02 3.232323232323232320e-02 3.393939393939394283e-02 3.393939393939394283e-02 2.101010101010101008e-02 6.868686868686868507e-02 5.656565656565656908e-02 5.373737373737373646e-02 5.696969696969696878e-02 5.333333333333333676e-02 6.343434343434344036e-02 5.535353535353535609e-02 6.060606060606060774e-02 5.858585858585858841e-02 6.383838383838383312e-02 6.707070707070707238e-02 6.626262626262625910e-02 6.343434343434344036e-02 4.099173553719008545e-02 5.322314049586776563e-02 3.867768595041322366e-02 3.834710743801653110e-02 3.834710743801653110e-02 3.702479338842975393e-02 3.834710743801653110e-02 4.686868686868687212e-02 5.333333333333333676e-02 2.644628099173553654e-02 2.181818181818181990e-02 5.355371900826446513e-02 6.479338842975206070e-02 5.785123966942148921e-02 5.421487603305785025e-02 7.727272727272727071e-02 5.190082644628098846e-02 5.123966942148760334e-02 5.586776859504131998e-02 5.652892561983471204e-02 5.157024793388429590e-02 5.719008264462809715e-02 4.231404958677686262e-02 4.033057851239669339e-02 4.231404958677686262e-02 4.297520661157024774e-02 5.157024793388429590e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 5.151515151515151381e-02 2.545454545454545539e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.999999999999999778e-02 5.454545454545454281e-02 6.545454545454545969e-02 2.909090909090909088e-02 3.757575757575757486e-02 2.943722943722943894e-02 3.151515151515151686e-02 2.666666666666666838e-02 4.181818181818181684e-02 2.909090909090909088e-02 4.181818181818181684e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 7.030303030303029777e-02 5.939393939393939476e-02 6.060606060606060774e-02 7.030303030303029777e-02 6.787878787878788567e-02 7.515151515151514972e-02 2.943722943722943894e-02 4.242424242424242681e-02 3.722943722943723027e-02 3.030303030303030387e-02 3.809523809523809867e-02 3.757575757575757486e-02 5.090909090909091078e-02 5.454545454545454281e-02 3.757575757575757486e-02 5.454545454545454281e-02 2.683982683982684067e-02 2.683982683982684067e-02 1.385281385281385280e-02 6.666666666666666574e-02 6.493506493506492894e-02 5.974025974025973934e-02 6.060606060606060774e-02 6.320346320346320601e-02 7.792207792207792028e-02 7.532467532467532201e-02 6.320346320346320601e-02 7.099567099567100081e-02 6.147186147186146921e-02 7.445887445887446054e-02 7.359307359307359908e-02 3.376623376623376360e-02 4.610389610389610537e-02 4.090909090909090884e-02 4.805194805194805407e-02 3.766233766233766100e-02 4.415584415584415667e-02 3.116883116883116880e-02 4.675324675324675494e-02 5.367965367965368134e-02 4.329004329004328827e-02 3.982683982683982854e-02 3.982683982683982854e-02 4.588744588744588654e-02 5.887445887445887788e-02 5.887445887445887788e-02 4.329004329004328827e-02 4.588744588744588654e-02 3.982683982683982854e-02 5.367965367965368134e-02 2.987012987012986967e-02 2.987012987012986967e-02 3.246753246753246447e-02 2.987012987012986967e-02 3.441558441558441317e-02 1.558441558441558440e-02 1.558441558441558440e-02 6.233766233766233761e-02 6.168831168831168804e-02 6.493506493506492894e-02 6.558441558441557850e-02 6.103896103896103847e-02 6.753246753246752720e-02 6.363636363636362980e-02 6.883116883116882634e-02 6.428571428571427937e-02 6.493506493506492894e-02 6.753246753246752720e-02 6.558441558441557850e-02 6.298701298701298024e-02 3.535353535353535220e-02 3.888888888888888951e-02 3.585858585858586051e-02 3.787878787878787984e-02 3.585858585858586051e-02 4.545454545454545581e-02 4.141414141414141714e-02 4.747474747474747514e-02 4.646464646464646547e-02 5.324675324675324367e-02 4.220779220779220797e-02 4.415584415584415667e-02 4.415584415584415667e-02 4.935064935064935321e-02 4.415584415584415667e-02 4.220779220779220797e-02 4.935064935064935321e-02 4.220779220779220797e-02 1.969696969696969543e-02 6.717171717171717404e-02 6.666666666666666574e-02 6.464646464646464641e-02 6.464646464646464641e-02 6.666666666666666574e-02 6.161616161616161741e-02 6.969696969696970168e-02 6.060606060606060774e-02 6.363636363636362980e-02 6.464646464646464641e-02 6.414141414141413811e-02 5.858585858585858841e-02 6.010101010101009944e-02 4.282828282828282651e-02 3.717171717171717515e-02 3.636363636363636187e-02 4.444444444444444614e-02 5.616161616161616243e-02 3.878787878787878785e-02 4.242424242424242681e-02 4.545454545454545581e-02 4.393939393939393784e-02 4.545454545454545581e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.393939393939393784e-02 5.151515151515151381e-02 3.393939393939394283e-02 3.636363636363636187e-02 3.636363636363636187e-02 2.181818181818181990e-02 6.949494949494949836e-02 5.252525252525252347e-02 6.989898989898990500e-02 6.989898989898990500e-02 6.424242424242423977e-02 5.939393939393939476e-02 6.828282828282827843e-02 5.656565656565656908e-02 5.494949494949494945e-02 6.303030303030303372e-02 6.505050505050505305e-02 6.505050505050505305e-02 6.585858585858585246e-02 4.330578512396694030e-02 5.454545454545454281e-02 4.231404958677686262e-02 3.801652892561983160e-02 3.867768595041322366e-02 3.801652892561983160e-02 3.867768595041322366e-02 4.444444444444444614e-02 4.929292929292929115e-02 2.776859504132231371e-02 2.280991735537190104e-02 4.958677685950413361e-02 6.743801652892561505e-02 5.719008264462809715e-02 6.347107438016529046e-02 7.402597402597402287e-02 7.107438016528926095e-02 6.115702479338842867e-02 5.190082644628098846e-02 5.157024793388429590e-02 6.644628099173553737e-02 5.289256198347107307e-02 4.495867768595041003e-02 4.099173553719008545e-02 4.495867768595041003e-02 4.429752066115702491e-02 4.694214876033057926e-02
-3.030303030303030387e-02 1.212121212121212120e-02 0.000000000000000000e+00 0.000000000000000000e+00 4.848484848484848481e-02 2.545454545454545539e-02 1.818181818181818094e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.818181818181818177e-02 4.909090909090908783e-02 6.181818181818182073e-02 2.909090909090909088e-02 3.757575757575757486e-02 2.770562770562770560e-02 2.909090909090909088e-02 2.909090909090909088e-02 4.181818181818181684e-02 2.909090909090909088e-02 4.181818181818181684e-02 1.939393939393939392e-02 9.696969696969696961e-03 9.696969696969696961e-03 9.696969696969696961e-03 6.666666666666666574e-02 5.212121212121212377e-02 5.454545454545454281e-02 6.666666666666666574e-02 6.787878787878788567e-02 7.151515151515151769e-02 2.770562770562770560e-02 3.896103896103896014e-02 3.722943722943723027e-02 3.030303030303030387e-02 3.722943722943723027e-02 3.757575757575757486e-02 5.333333333333333676e-02 5.454545454545454281e-02 3.757575757575757486e-02 5.454545454545454281e-02 2.683982683982684067e-02 2.683982683982684067e-02 1.385281385281385280e-02 7.186147186147186228e-02 5.627705627705627961e-02 5.108225108225108307e-02 5.281385281385281294e-02 5.714285714285714107e-02 7.272727272727272374e-02 7.272727272727272374e-02 5.800865800865800948e-02 7.272727272727272374e-02 5.454545454545454281e-02 7.532467532467532201e-02 7.359307359307359908e-02 2.987012987012986967e-02 4.220779220779220797e-02 3.636363636363636187e-02 4.285714285714285754e-02 3.311688311688311404e-02 4.415584415584415667e-02 2.987012987012986967e-02 4.415584415584415667e-02 6.060606060606060774e-02 4.502164502164502508e-02 3.982683982683982854e-02 3.982683982683982854e-02 4.588744588744588654e-02 6.233766233766233761e-02 6.233766233766233761e-02 4.502164502164502508e-02 4.588744588744588654e-02 3.982683982683982854e-02 6.060606060606060774e-02 2.987012987012986967e-02 2.987012987012986967e-02 3.376623376623376360e-02 2.987012987012986967e-02 3.441558441558441317e-02 1.558441558441558440e-02 1.558441558441558440e-02 7.467532467532467244e-02 6.038961038961038891e-02 5.259740259740259410e-02 5.389610389610389324e-02 5.324675324675324367e-02 6.168831168831168804e-02 5.584415584415584194e-02 6.233766233766233761e-02 5.714285714285714107e-02 6.103896103896103847e-02 6.363636363636362980e-02 6.233766233766233761e-02 6.103896103896103847e-02 3.232323232323232320e-02 3.383838383838384117e-02 3.131313131313131354e-02 3.181818181818181490e-02 3.282828282828283151e-02 4.949494949494949447e-02 3.989898989898989917e-02 4.949494949494949447e-02 4.898989898989899311e-02 6.493506493506492894e-02 4.350649350649350711e-02 4.415584415584415667e-02 4.415584415584415667e-02 5.194805194805195148e-02 4.415584415584415667e-02 4.350649350649350711e-02 5.194805194805195148e-02 4.350649350649350711e-02 1.969696969696969543e-02 5.202020202020202211e-02 5.606060606060606077e-02 5.252525252525252347e-02 5.303030303030303177e-02 5.454545454545454281e-02 5.353535353535353314e-02 7.878787878787878174e-02 5.303030303030303177e-02 5.505050505050505111e-02 5.707070707070707044e-02 5.858585858585858841e-02 7.575757575757575968e-02 5.555555555555555247e-02 4.282828282828282651e-02 3.313131313131312955e-02 3.151515151515151686e-02 5.252525252525252347e-02 5.454545454545454281e-02 3.636363636363636187e-02 4.444444444444444614e-02 4.545454545454545581e-02 4.494949494949494750e-02 4.545454545454545581e-02 4.444444444444444614e-02 4.444444444444444614e-02 4.494949494949494750e-02 6.717171717171717404e-02 3.555555555555555552e-02 3.636363636363636187e-02 3.636363636363636187e-02 2.181818181818181990e-02 8.080808080808081495e-02 6.464646464646464641e-02 5.373737373737373646e-02 5.535353535353535609e-02 5.252525252525252347e-02 7.757575757575757569e-02 5.414141414141414310e-02 7.595959595959596300e-02 7.595959595959596300e-02 7.838383838383838897e-02 8.000000000000000167e-02 8.000000000000000167e-02 6.868686868686868507e-02 4.429752066115702491e-02 5.719008264462809715e-02 3.834710743801653110e-02 3.140495867768595267e-02 3.272727272727272985e-02 3.206611570247933779e-02 3.272727272727272985e-02 5.696969696969696878e-02 6.787878787878788567e-02 2.909090909090909088e-02 2.280991735537190104e-02 6.545454545454545969e-02 7.140495867768595351e-02 5.322314049586776563e-02 5.322314049586776563e-02 9.285714285714286031e-02 5.388429752066115769e-02 5.190082644628098846e-02 5.785123966942148921e-02 5.719008264462809715e-02 5.289256198347107307e-02 7.504132231404958553e-02 4.760330578512396438e-02 4.628099173553718720e-02 4.760330578512396438e-02 4.892561983471074155e-02 6.743801652892561505e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 1.212121212121212120e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 7.575757575757575968e-02 3.272727272727272985e-02 1.818181818181818094e-02 4.363636363636363980e-02 2.909090909090909088e-02 4.363636363636363980e-02 1.939393939393939392e-02 2.909090909090909088e-02 1.385281385281385280e-02 9.696969696969696961e-03 9.696969696969696961e-03 7.090909090909090773e-02 9.818181818181817566e-02 9.636363636363635965e-02 3.878787878787878785e-02 5.090909090909091078e-02 3.636363636363636187e-02 3.636363636363636187e-02 5.333333333333333676e-02 3.878787878787878785e-02 3.878787878787878785e-02 5.818181818181818177e-02 5.333333333333333676e-02 5.818181818181818177e-02 1.385281385281385280e-02 2.077922077922077920e-02 2.077922077922077920e-02 1.385281385281385280e-02 2.077922077922077920e-02 1.103030303030303055e-01 6.909090909090909172e-02 8.121212121212120771e-02 9.696969696969696961e-02 1.078787878787878796e-01 4.329004329004328827e-02 4.675324675324675494e-02 4.329004329004328827e-02 5.541125541125541121e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.848484848484848481e-02 6.233766233766233761e-02 6.233766233766233761e-02 4.502164502164502508e-02 5.541125541125541121e-02 4.155844155844155841e-02 6.233766233766233761e-02 6.233766233766233761e-02 1.558441558441558440e-02 3.116883116883116880e-02 2.077922077922077920e-02 2.857142857142857054e-02 2.077922077922077920e-02 2.857142857142857054e-02 1.558441558441558440e-02 2.857142857142857054e-02 6.666666666666666574e-02 7.619047619047619735e-02 9.264069264069263454e-02 1.064935064935064873e-01 8.311688311688311681e-02 7.359307359307359908e-02 7.705627705627705881e-02 1.047619047619047644e-01 1.177489177489177558e-01 1.186147186147186172e-01 7.705627705627705881e-02 4.415584415584415667e-02 4.935064935064935321e-02 4.935064935064935321e-02 4.675324675324675494e-02 5.454545454545454281e-02 6.493506493506492894e-02 5.714285714285714107e-02 5.454545454545454281e-02 4.675324675324675494e-02 4.415584415584415667e-02 4.415584415584415667e-02 4.675324675324675494e-02 5.194805194805195148e-02 4.675324675324675494e-02 5.714285714285714107e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.194805194805195148e-02 5.194805194805195148e-02 4.675324675324675494e-02 2.424242424242424240e-02 2.020202020202020374e-02 2.020202020202020374e-02 2.020202020202020374e-02 2.020202020202020374e-02 3.232323232323232320e-02 2.626262626262626174e-02 3.232323232323232320e-02 3.232323232323232320e-02 6.298701298701298024e-02 8.246753246753246724e-02 8.831168831168831335e-02 1.058441558441558378e-01 7.792207792207792028e-02 1.129870129870129830e-01 1.123376623376623334e-01 7.402597402597402287e-02 8.506493506493506551e-02 5.454545454545454281e-02 4.444444444444444614e-02 4.848484848484848481e-02 4.646464646464646547e-02 4.444444444444444614e-02 4.646464646464646547e-02 4.646464646464646547e-02 5.858585858585858841e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.848484848484848481e-02 5.252525252525252347e-02 4.444444444444444614e-02 3.878787878787878785e-02 2.262626262626262624e-02 2.262626262626262624e-02 3.393939393939394283e-02 3.717171717171717515e-02 2.424242424242424240e-02 7.424242424242424865e-02 8.383838383838383701e-02 7.878787878787878174e-02 1.282828282828282762e-01 1.060606060606060635e-01 8.484848484848485362e-02 9.848484848484848064e-02 5.909090909090908977e-02 4.363636363636363980e-02 4.848484848484848481e-02 5.494949494949494945e-02 7.030303030303029777e-02 5.818181818181818177e-02 4.525252525252525249e-02 4.686868686868687212e-02 4.848484848484848481e-02 4.363636363636363980e-02 5.333333333333333676e-02 4.848484848484848481e-02 5.010101010101010444e-02 5.010101010101010444e-02 5.494949494949494945e-02 5.656565656565656908e-02 5.818181818181818177e-02 5.171717171717171713e-02 3.966942148760330827e-02 4.231404958677686262e-02 2.644628099173553654e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.380165289256198219e-02 5.898989898989898811e-02 5.535353535353535609e-02 4.297520661157024774e-02 7.338842975206610886e-02 4.363636363636363980e-02 5.421487603305785025e-02 4.231404958677686262e-02 4.231404958677686262e-02 6.753246753246752720e-02 4.760330578512396438e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.495867768595041003e-02 4.760330578512396438e-02 1.147107438016528869e-01 6.181818181818182073e-02 6.181818181818182073e-02 6.743801652892561505e-02 5.190082644628098846e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 1.212121212121212120e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.151515151515151381e-02 2.545454545454545539e-02 1.818181818181818094e-02 4.000000000000000083e-02 2.909090909090909088e-02 4.000000000000000083e-02 1.939393939393939392e-02 2.666666666666666838e-02 1.385281385281385280e-02 9.696969696969696961e-03 9.696969696969696961e-03 5.999999999999999778e-02 5.454545454545454281e-02 6.363636363636362980e-02 2.909090909090909088e-02 3.151515151515151686e-02 2.666666666666666838e-02 2.666666666666666838e-02 4.848484848484848481e-02 3.636363636363636187e-02 3.636363636363636187e-02 5.090909090909091078e-02 4.848484848484848481e-02 5.090909090909091078e-02 1.385281385281385280e-02 1.904761904761904934e-02 1.904761904761904934e-02 1.385281385281385280e-02 1.904761904761904934e-02 5.939393939393939476e-02 6.424242424242423977e-02 6.909090909090909172e-02 6.060606060606060774e-02 7.151515151515151769e-02 3.463203463203463200e-02 3.463203463203463200e-02 2.943722943722943894e-02 5.194805194805195148e-02 4.155844155844155841e-02 3.809523809523809867e-02 3.809523809523809867e-02 4.329004329004328827e-02 5.541125541125541121e-02 5.541125541125541121e-02 4.155844155844155841e-02 5.194805194805195148e-02 3.809523809523809867e-02 5.541125541125541121e-02 5.541125541125541121e-02 1.558441558441558440e-02 2.727272727272727140e-02 1.948051948051948007e-02 2.597402597402597574e-02 1.948051948051948007e-02 2.597402597402597574e-02 1.558441558441558440e-02 2.597402597402597574e-02 6.493506493506492894e-02 6.060606060606060774e-02 6.147186147186146921e-02 6.147186147186146921e-02 6.060606060606060774e-02 7.012987012987012547e-02 7.012987012987012547e-02 6.320346320346320601e-02 6.406926406926406747e-02 5.974025974025973934e-02 6.666666666666666574e-02 3.636363636363636187e-02 3.636363636363636187e-02 3.896103896103896014e-02 3.636363636363636187e-02 4.025974025974025927e-02 3.376623376623376360e-02 3.116883116883116880e-02 5.194805194805195148e-02 4.415584415584415667e-02 4.025974025974025927e-02 4.025974025974025927e-02 4.155844155844155841e-02 4.675324675324675494e-02 4.155844155844155841e-02 4.935064935064935321e-02 4.155844155844155841e-02 4.415584415584415667e-02 4.675324675324675494e-02 4.675324675324675494e-02 4.415584415584415667e-02 2.222222222222222307e-02 1.919191919191919060e-02 1.919191919191919060e-02 1.919191919191919060e-02 1.919191919191919060e-02 3.030303030303030387e-02 2.424242424242424240e-02 3.030303030303030387e-02 3.030303030303030387e-02 6.038961038961038891e-02 6.298701298701298024e-02 6.233766233766233761e-02 6.428571428571427937e-02 6.363636363636362980e-02 6.363636363636362980e-02 6.168831168831168804e-02 6.103896103896103847e-02 6.038961038961038891e-02 3.333333333333333287e-02 4.040404040404040747e-02 4.343434343434343647e-02 4.141414141414141714e-02 4.040404040404040747e-02 4.141414141414141714e-02 4.141414141414141714e-02 5.555555555555555247e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.242424242424242681e-02 4.343434343434343647e-02 5.050505050505050414e-02 4.141414141414141714e-02 3.393939393939394283e-02 2.101010101010101008e-02 2.101010101010101008e-02 3.232323232323232320e-02 3.474747474747474918e-02 2.262626262626262624e-02 6.262626262626262708e-02 6.161616161616161741e-02 6.262626262626262708e-02 6.161616161616161741e-02 6.161616161616161741e-02 6.161616161616161741e-02 6.313131313131313538e-02 5.707070707070707044e-02 3.878787878787878785e-02 4.040404040404040747e-02 4.040404040404040747e-02 3.636363636363636187e-02 5.575757575757575579e-02 4.363636363636363980e-02 4.202020202020202017e-02 4.282828282828282651e-02 4.040404040404040747e-02 5.090909090909091078e-02 4.282828282828282651e-02 4.848484848484848481e-02 4.848484848484848481e-02 5.252525252525252347e-02 5.333333333333333676e-02 5.575757575757575579e-02 4.929292929292929115e-02 3.504132231404958470e-02 3.900826446280991622e-02 2.512396694214875936e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 2.181818181818181990e-02 5.090909090909091078e-02 5.373737373737373646e-02 3.768595041322313904e-02 3.966942148760330827e-02 4.231404958677686262e-02 5.157024793388429590e-02 3.966942148760330827e-02 3.966942148760330827e-02 6.493506493506492894e-02 4.231404958677686262e-02 3.966942148760330827e-02 4.033057851239669339e-02 4.033057851239669339e-02 4.099173553719008545e-02 4.628099173553718720e-02 6.181818181818182073e-02 6.115702479338842867e-02 6.049586776859504356e-02 6.082644628099173612e-02 5.057851239669421822e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 1.212121212121212120e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 5.151515151515151381e-02 2.545454545454545539e-02 1.818181818181818094e-02 4.363636363636363980e-02 2.909090909090909088e-02 4.363636363636363980e-02 1.939393939393939392e-02 2.909090909090909088e-02 1.385281385281385280e-02 9.696969696969696961e-03 9.696969696969696961e-03 5.999999999999999778e-02 5.454545454545454281e-02 6.727272727272727570e-02 2.909090909090909088e-02 3.151515151515151686e-02 2.666666666666666838e-02 2.666666666666666838e-02 5.333333333333333676e-02 3.878787878787878785e-02 3.878787878787878785e-02 5.818181818181818177e-02 5.333333333333333676e-02 5.818181818181818177e-02 1.385281385281385280e-02 2.077922077922077920e-02 2.077922077922077920e-02 1.385281385281385280e-02 2.077922077922077920e-02 6.181818181818182073e-02 7.636363636363636964e-02 7.151515151515151769e-02 5.818181818181818177e-02 7.878787878787878174e-02 3.636363636363636187e-02 3.636363636363636187e-02 2.943722943722943894e-02 5.541125541125541121e-02 4.502164502164502508e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.848484848484848481e-02 6.233766233766233761e-02 6.233766233766233761e-02 4.502164502164502508e-02 5.541125541125541121e-02 4.155844155844155841e-02 6.233766233766233761e-02 6.233766233766233761e-02 1.558441558441558440e-02 3.116883116883116880e-02 2.077922077922077920e-02 2.857142857142857054e-02 2.077922077922077920e-02 2.857142857142857054e-02 1.558441558441558440e-02 2.857142857142857054e-02 6.839826839826840255e-02 6.926406926406926401e-02 5.800865800865800948e-02 5.974025974025973934e-02 6.580086580086580428e-02 8.571428571428571508e-02 8.051948051948051854e-02 6.320346320346320601e-02 6.926406926406926401e-02 6.320346320346320601e-02 6.666666666666666574e-02 3.896103896103896014e-02 3.896103896103896014e-02 4.155844155844155841e-02 3.896103896103896014e-02 4.415584415584415667e-02 3.376623376623376360e-02 3.116883116883116880e-02 5.454545454545454281e-02 4.675324675324675494e-02 4.415584415584415667e-02 4.415584415584415667e-02 4.675324675324675494e-02 5.194805194805195148e-02 4.675324675324675494e-02 5.714285714285714107e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.194805194805195148e-02 5.194805194805195148e-02 4.675324675324675494e-02 2.424242424242424240e-02 2.020202020202020374e-02 2.020202020202020374e-02 2.020202020202020374e-02 2.020202020202020374e-02 3.232323232323232320e-02 2.626262626262626174e-02 3.232323232323232320e-02 3.232323232323232320e-02 6.428571428571427937e-02 6.688311688311687764e-02 5.974025974025973934e-02 6.168831168831168804e-02 7.272727272727272374e-02 6.363636363636362980e-02 6.168831168831168804e-02 7.402597402597402287e-02 7.077922077922077504e-02 3.838383838383838120e-02 4.444444444444444614e-02 4.848484848484848481e-02 4.646464646464646547e-02 4.444444444444444614e-02 4.646464646464646547e-02 4.646464646464646547e-02 5.858585858585858841e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.848484848484848481e-02 5.252525252525252347e-02 4.444444444444444614e-02 3.878787878787878785e-02 2.262626262626262624e-02 2.262626262626262624e-02 3.393939393939394283e-02 3.717171717171717515e-02 2.424242424242424240e-02 7.222222222222221544e-02 5.959595959595959808e-02 6.666666666666666574e-02 6.767676767676768235e-02 5.959595959595959808e-02 6.767676767676768235e-02 6.010101010101009944e-02 6.010101010101009944e-02 4.363636363636363980e-02 4.525252525252525249e-02 4.525252525252525249e-02 3.797979797979798150e-02 5.818181818181818177e-02 4.525252525252525249e-02 4.686868686868687212e-02 4.848484848484848481e-02 4.363636363636363980e-02 5.333333333333333676e-02 4.848484848484848481e-02 5.010101010101010444e-02 5.010101010101010444e-02 5.494949494949494945e-02 5.656565656565656908e-02 5.818181818181818177e-02 5.171717171717171713e-02 3.966942148760330827e-02 4.231404958677686262e-02 2.644628099173553654e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.380165289256198219e-02 5.414141414141414310e-02 5.616161616161616243e-02 4.165289256198347057e-02 3.768595041322313904e-02 4.363636363636363980e-02 5.421487603305785025e-02 4.231404958677686262e-02 4.231404958677686262e-02 6.753246753246752720e-02 4.760330578512396438e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.231404958677686262e-02 4.495867768595041003e-02 4.760330578512396438e-02 6.181818181818182073e-02 6.049586776859504356e-02 8.297520661157024857e-02 5.553719008264462742e-02 5.256198347107438051e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 1.212121212121212120e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 4.848484848484848481e-02 2.545454545454545539e-02 1.818181818181818094e-02 4.363636363636363980e-02 2.909090909090909088e-02 4.363636363636363980e-02 1.939393939393939392e-02 2.909090909090909088e-02 1.385281385281385280e-02 9.696969696969696961e-03 9.696969696969696961e-03 6.909090909090909172e-02 5.090909090909091078e-02 6.363636363636362980e-02 2.909090909090909088e-02 2.909090909090909088e-02 2.545454545454545539e-02 2.909090909090909088e-02 5.212121212121212377e-02 3.878787878787878785e-02 3.878787878787878785e-02 5.818181818181818177e-02 5.212121212121212377e-02 5.818181818181818177e-02 1.385281385281385280e-02 2.077922077922077920e-02 2.077922077922077920e-02 1.385281385281385280e-02 2.077922077922077920e-02 5.818181818181818177e-02 6.666666666666666574e-02 8.242424242424242764e-02 6.424242424242423977e-02 7.515151515151514972e-02 3.722943722943723027e-02 3.636363636363636187e-02 2.770562770562770560e-02 5.541125541125541121e-02 4.415584415584415667e-02 4.155844155844155841e-02 4.155844155844155841e-02 4.848484848484848481e-02 6.060606060606060774e-02 6.060606060606060774e-02 4.415584415584415667e-02 5.541125541125541121e-02 4.155844155844155841e-02 6.060606060606060774e-02 6.060606060606060774e-02 1.558441558441558440e-02 3.116883116883116880e-02 2.077922077922077920e-02 2.792207792207792097e-02 2.077922077922077920e-02 2.792207792207792097e-02 1.558441558441558440e-02 2.792207792207792097e-02 7.012987012987012547e-02 6.147186147186146921e-02 6.926406926406926401e-02 6.580086580086580428e-02 6.926406926406926401e-02 7.186147186147186228e-02 7.619047619047619735e-02 6.580086580086580428e-02 6.580086580086580428e-02 5.974025974025973934e-02 7.532467532467532201e-02 4.025974025974025927e-02 3.896103896103896014e-02 4.155844155844155841e-02 3.961038961038960970e-02 4.415584415584415667e-02 3.116883116883116880e-02 3.116883116883116880e-02 5.519480519480519237e-02 4.675324675324675494e-02 4.350649350649350711e-02 4.350649350649350711e-02 4.675324675324675494e-02 5.064935064935065234e-02 4.675324675324675494e-02 5.714285714285714107e-02 4.675324675324675494e-02 4.675324675324675494e-02 5.064935064935065234e-02 5.064935064935065234e-02 4.675324675324675494e-02 2.424242424242424240e-02 2.020202020202020374e-02 2.020202020202020374e-02 2.020202020202020374e-02 2.020202020202020374e-02 3.232323232323232320e-02 2.575757575757575690e-02 3.232323232323232320e-02 3.232323232323232320e-02 6.493506493506492894e-02 6.558441558441557850e-02 7.467532467532467244e-02 7.272727272727272374e-02 6.753246753246752720e-02 6.948051948051947591e-02 6.493506493506492894e-02 6.168831168831168804e-02 5.974025974025973934e-02 3.535353535353535220e-02 4.343434343434343647e-02 4.747474747474747514e-02 4.595959595959595717e-02 4.343434343434343647e-02 4.595959595959595717e-02 4.595959595959595717e-02 5.909090909090908977e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.848484848484848481e-02 4.747474747474747514e-02 5.353535353535353314e-02 4.444444444444444614e-02 3.878787878787878785e-02 2.262626262626262624e-02 2.262626262626262624e-02 3.434343434343434254e-02 3.757575757575757486e-02 2.383838383838383923e-02 6.161616161616161741e-02 7.676767676767676241e-02 6.868686868686868507e-02 6.464646464646464641e-02 6.717171717171717404e-02 6.313131313131313538e-02 7.272727272727272374e-02 6.111111111111110911e-02 4.121212121212121382e-02 4.686868686868687212e-02 4.525252525252525249e-02 3.555555555555555552e-02 5.979797979797980140e-02 4.606060606060605883e-02 4.565656565656565913e-02 4.767676767676767846e-02 4.323232323232323315e-02 5.454545454545454281e-02 4.767676767676767846e-02 5.131313131313131048e-02 5.131313131313131048e-02 5.575757575757575579e-02 5.777777777777777513e-02 5.979797979797980140e-02 5.333333333333333676e-02 3.900826446280991622e-02 4.297520661157024774e-02 2.644628099173553654e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.380165289256198219e-02 2.380165289256198219e-02 5.373737373737373646e-02 5.737373737373737542e-02 3.834710743801653110e-02 4.231404958677686262e-02 4.462809917355371747e-02 5.652892561983471204e-02 4.231404958677686262e-02 4.231404958677686262e-02 6.883116883116882634e-02 4.628099173553718720e-02 4.231404958677686262e-02 4.297520661157024774e-02 4.297520661157024774e-02 4.429752066115702491e-02 4.892561983471074155e-02 6.743801652892561505e-02 6.082644628099173612e-02 5.553719008264462742e-02 7.239669421487603118e-02 5.388429752066115769e-02
-0.000000000000000000e+00 0.000000000000000000e+00 3.030303030303030387e-02 1.212121212121212120e-02 2.424242424242424240e-02 1.454545454545454544e-02 0.000000000000000000e+00 4.242424242424242681e-02 2.363636363636363591e-02 1.818181818181818094e-02 4.181818181818181684e-02 2.909090909090909088e-02 4.181818181818181684e-02 1.939393939393939392e-02 2.787878787878787790e-02 1.385281385281385280e-02 9.696969696969696961e-03 9.696969696969696961e-03 5.636363636363636576e-02 4.181818181818181684e-02 5.454545454545454281e-02 2.666666666666666838e-02 2.424242424242424240e-02 2.303030303030302942e-02 2.666666666666666838e-02 5.333333333333333676e-02 3.757575757575757486e-02 3.757575757575757486e-02 5.454545454545454281e-02 5.333333333333333676e-02 5.454545454545454281e-02 1.385281385281385280e-02 1.991341991341991427e-02 1.991341991341991427e-02 1.385281385281385280e-02 1.991341991341991427e-02 4.727272727272727182e-02 6.545454545454545969e-02 6.545454545454545969e-02 4.727272727272727182e-02 6.424242424242423977e-02 3.290043290043290214e-02 3.290043290043290214e-02 2.424242424242424240e-02 6.060606060606060774e-02 4.502164502164502508e-02 3.982683982683982854e-02 3.982683982683982854e-02 4.588744588744588654e-02 6.233766233766233761e-02 6.233766233766233761e-02 4.502164502164502508e-02 6.060606060606060774e-02 3.982683982683982854e-02 6.233766233766233761e-02 6.233766233766233761e-02 1.558441558441558440e-02 2.922077922077922010e-02 2.012987012987012964e-02 2.857142857142857054e-02 2.012987012987012964e-02 2.857142857142857054e-02 1.558441558441558440e-02 2.857142857142857054e-02 7.099567099567100081e-02 5.367965367965368134e-02 4.761904761904761640e-02 4.761904761904761640e-02 5.454545454545454281e-02 7.186147186147186228e-02 7.186147186147186228e-02 5.281385281385281294e-02 5.367965367965368134e-02 4.761904761904761640e-02 7.012987012987012547e-02 3.506493506493506274e-02 3.506493506493506274e-02 3.896103896103896014e-02 3.506493506493506274e-02 3.961038961038960970e-02 2.467532467532467660e-02 2.597402597402597574e-02 6.493506493506492894e-02 5.064935064935065234e-02 4.350649350649350711e-02 4.350649350649350711e-02 4.415584415584415667e-02 5.194805194805195148e-02 4.415584415584415667e-02 5.324675324675324367e-02 4.415584415584415667e-02 5.064935064935065234e-02 5.194805194805195148e-02 5.194805194805195148e-02 5.064935064935065234e-02 2.323232323232323274e-02 1.969696969696969543e-02 1.969696969696969543e-02 1.969696969696969543e-02 1.969696969696969543e-02 3.535353535353535220e-02 2.626262626262626174e-02 3.535353535353535220e-02 3.535353535353535220e-02 7.402597402597402287e-02 5.000000000000000278e-02 5.129870129870130191e-02 5.064935064935065234e-02 5.909090909090908977e-02 5.064935064935065234e-02 5.000000000000000278e-02 5.974025974025973934e-02 5.000000000000000278e-02 2.979797979797979904e-02 4.444444444444444614e-02 4.797979797979797650e-02 4.494949494949494750e-02 4.444444444444444614e-02 4.494949494949494750e-02 4.494949494949494750e-02 7.020202020202019610e-02 4.545454545454545581e-02 4.545454545454545581e-02 4.545454545454545581e-02 4.797979797979797650e-02 6.717171717171717404e-02 4.696969696969696684e-02 3.636363636363636187e-02 2.181818181818181990e-02 2.181818181818181990e-02 4.040404040404040747e-02 4.161616161616161352e-02 2.424242424242424240e-02 5.000000000000000278e-02 5.202020202020202211e-02 5.101010101010101244e-02 5.101010101010101244e-02 5.000000000000000278e-02 5.000000000000000278e-02 5.050505050505050414e-02 7.525252525252525138e-02 3.959595959595959419e-02 4.040404040404040747e-02 4.040404040404040747e-02 2.828282828282828454e-02 7.313131313131313038e-02 5.696969696969696878e-02 4.686868686868687212e-02 4.727272727272727182e-02 4.606060606060605883e-02 6.989898989898990500e-02 4.727272727272727182e-02 6.787878787878788567e-02 6.787878787878788567e-02 7.111111111111111105e-02 7.191919191919192433e-02 7.313131313131313038e-02 6.020202020202020110e-02 3.867768595041322366e-02 4.727272727272727182e-02 2.842975206611570230e-02 2.280991735537190104e-02 2.280991735537190104e-02 2.280991735537190104e-02 2.280991735537190104e-02 6.343434343434344036e-02 7.515151515151514972e-02 3.438016528925619958e-02 2.876033057851239833e-02 5.818181818181818177e-02 6.413223140495867558e-02 4.628099173553718720e-02 4.628099173553718720e-02 8.636363636363636465e-02 4.760330578512396438e-02 4.628099173553718720e-02 5.157024793388429590e-02 5.157024793388429590e-02 4.694214876033057926e-02 6.743801652892561505e-02 5.190082644628098846e-02 5.057851239669421822e-02 5.256198347107438051e-02 5.388429752066115769e-02 7.438016528925620041e-02
diff --git a/notebooks/run_WeisfeilerLehmankernel_acyclic.ipynb b/notebooks/run_WeisfeilerLehmankernel_acyclic.ipynb
deleted file mode 100644
index 4b7d560..0000000
--- a/notebooks/run_WeisfeilerLehmankernel_acyclic.ipynb
+++ /dev/null
@@ -1,1893 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'O', 'C'}\n",
-      "{'O', 'C'}\n",
-      "--- shortest path kernel built in 0.0002582073211669922 seconds ---\n",
-      "3\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xt4VPW97/H3l3soGlFiK6KgPlxqBaQNeANESIrgpWAVQa2gaATC3uo+9qlusFq794Huavc5rUJBRPBOBQWsoCSAiBQ8CaLcrIJsQZS7GBUSDOF3/lgz4xAmmUBmZs3l83qePMys9VtrvqzAZ61Zl9/PnHOIiEhmaeB3ASIikngKfxGRDKTwFxHJQAp/EZEMpPAXEclACn8RkQyk8BcRyUAKfxGRDKTwFxHJQI38LqAmrVq1cu3atfO7DBGRlLJ69eq9zrmcaO2SNvzbtWtHaWmp32WIiKQUM9tal3Y67SMikoEU/iIiGUjhLyKSgRT+IiIZSOEvIpKBFP4iIhlI4S8ikoEU/iIiGSgmD3mZ2ZXA/wUaAtOccxMjtBkCPAw44APn3E2x+OyUt3s3zJgBa9dCWRlkZ0OXLnDbbZAT9SE9EZETYvUdwN3MGgIfA/nAdqAEGOac2xjWpj3wN6Cvc26/mZ3unNtd23pzc3NdWj/hW1ICEybAwoXe+4qK7+dlZYFzMGAAPPAAdO/uT40iknLMbLVzLjdau1ic9ukBbHbObXHOfQe8BPyiWps7gSecc/sBogV/2ps8Gfr0gblzvdAPD36A8nJv2ty5XrvJk/2oUkTSWCzC/0zgs7D32wPTwnUAOpjZCjNbFThNdAwzKzCzUjMr3bNnTwxKS0KTJ8N998HBg97RfW2c89rdd592ACISU4m64NsIaA/0AYYBT5rZKdUbOeemOudynXO5Oel4vruk5PvgD/M4kAs0BUZEWi64A0jn02AiklCxCP/PgbPC3rcJTAu3HZjvnKt0zv0P3jWC9jH47NQyYYJ3Sqea1sB44Pbali0v95YXEYmBWIR/CdDezM4xsybAUGB+tTZz8Y76MbNWeKeBtsTgs1PH7t3exd0Ip3quAwYBp9W2vHOwYAGk6+kwEUmoeoe/c+4wMBZ4E/gQ+JtzboOZPWJm1waavQnsM7ONwFLg1865ffX97JQyY0b912EWm/WISMaLyX3+zrkFwIJq034b9toB/xb4yUxr1x57V8/xKi+HdetiU4+IZDQ94ZsoZWWxWc/+/bFZj4hkNIV/omRnx2Y9LVvGZj0iktEU/onSpQs0axZx1mGgAqgK/FQEph0jKws6d45XhSKSQRT+iTJiRI2z/gPIAiYCzwVe/0ekhs7Vuh4RkbpS+CfK6ad7ffWYHTPrYbze7sJ/Hq7eyAwGDlRnbyISEwr/RHrgAe/UzYnIyvKWFxGJAYV/InXvDo8+Cs2bH99yzZt7y+VG7ahPRKROYnKfvxyH0aO9P++7z7tvv5bO3ZwZlpXlBX9wORGRGNCRvx9Gj4Zly2DwYO8OoOqngrKyOGTGjosu8top+EUkxnTk75fcXJgzx+urZ8YM78nd/fu9+/g7d2Ze8+Y8NX8+b+pUj4jEQb1H8oqXtB/JK4qKigratm3L8uXL6dChg9/liEiKSORIXhIHzZo1Y+TIkUyaNMnvUkQkDSn8k9ioUaN49tln+fbbb/0uRUTSjMI/iZ199tn06tWL559/3u9SRCTNKPyT3NixY3niiSdI1mszIpKaFP5Jrl+/fnz33Xe88847fpciImlE4Z/kzIwxY8bw+OOP+12KiKQRhX8KGD58OIsWLWLHjh1+lyIiaULhnwKys7MZOnQoU6dO9bsUEUkTCv8UUVhYyNSpU6msrPS7FBFJAwr/FHHBBRfQvn17Xn31Vb9LEZE0oPBPIYWFhTzxxBN+lyEiaUDhn0IGDRrE5s2bWbdund+liEiKU/inkMaNG1NQUKCjfxGpN4V/iikoKGDWrFmUlZX5XYqIpDCFf4o544wz6N+/PzNnzvS7FBFJYQr/FBS88HvkyBG/SxGRFKXwT0E9e/akWbNmLF682O9SRCRFKfxTkJnptk8RqReFf4q6+eabWb58OVu3bvW7FBFJQQr/FPWDH/yAW2+9lb/+9a9+lyIiKUjhn8LGjBnD9OnTqaio8LsUEUkxMQl/M7vSzD4ys81mdn8t7X5pZs7Moo4sL9G1b9+eCy+8kJdfftnvUkQkxdQ7/M2sIfAEMAA4HxhmZudHaHcScDfwbn0/U743duxYDfQiIsctFkf+PYDNzrktzrnvgJeAX0Ro93vgD4DOUcTQwIED2bVrF6WlpX6XIiIpJBbhfybwWdj77YFpIWb2U+As59zrta3IzArMrNTMSvfs2ROD0tJfw4YNGT16tG77FJHjEvcLvmbWAPgT8L+itXXOTXXO5TrncnNycuJdWtoYOXIkr776Knv37vW7FBFJEbEI/8+Bs8LetwlMCzoJuAB4y8w+BS4G5uuib+y0atWKQYMGMX36dL9LEZEUEYvwLwHam9k5ZtYEGArMD850zpU551o559o559oBq4BrnXM6SR1DhYWFTJ48maqqKr9LEZEUUO/wd84dBsYCbwIfAn9zzm0ws0fM7Nr6rl/qpnv37uTk5LBgwQK/SxGRFGDOOb9riCg3N9fpDpbj88wzz/DCCy/wxhtv+F2KiPjEzFY756KeVtcTvmlkyJAhvPfee2zatMnvUkQkySn800izZs24/fbbmTx5st+liEiSU/inmVGjRjFz5kwOHDjgdykiksQU/mmmXbt29OzZkxdeeMHvUkQkiSn801BwoJdkvZgvIv5T+KehvLw8Dh48yIoVK/wuRUSSlMI/DTVo0EDDPIpIrRT+aWr48OG88cYb7Nixw+9SRCQJKfzT1CmnnMKQIUN48skn/S5FRJKQwj+NFRYWMnXqVCorK/0uRUSSjMI/jXXp0oVzzz2XefPm+V2KiCQZhX+a04VfEYlE4Z/mBg8ezEcffcT69ev9LkVEkojCP801adKEgoICJk2a5HcpIpJEFP4ZoKCggJdeeomysjK/SxGRJKHwzwCtW7cmLy+PZ555xu9SRCRJKPwzxNixY5k0aZL6+xERQOGfMXr16kWjRo1YsmSJ36WISBJQ+GcIM6OwsJDHH3/c71JEJAko/DPILbfcwttvv822bdv8LkVEfKbwzyAtWrTglltuYcqUKX6XIiI+U/hnmDFjxjBt2jQOHTrkdyki4iOFf4bp2LEjXbt25eWXX/a7FBHxkcI/A6m/HxFR+Gegq6++mi+++ILVq1f7XYqI+EThn4EaNmzI6NGjdfQvksEU/hlq5MiRvPrqq+zbt8/vUkTEBwr/DJWTk8M111zD008/7XcpIuIDhX8GKywsZNKkSVRVVfldiogkmMI/g/Xo0YPTTjuNN954w+9SRCTBFP4ZLNjfjy78imSemIS/mV1pZh+Z2WYzuz/C/H8zs41mttbMFptZ21h8rtTfjTfeSElJCZs3b/a7FBFJoHqHv5k1BJ4ABgDnA8PM7PxqzdYAuc65LsBs4L/q+7kSG1lZWdx+++1MnjzZ71JEJIFiceTfA9jsnNvinPsOeAn4RXgD59xS59zBwNtVQJsYfK7EyKhRo5g5cyYHDx6M3lhE0kIswv9M4LOw99sD02oyElgYg8+VGDnnnHO45JJLePHFF/0uRUQSJKEXfM3sFiAX+GMN8wvMrNTMSvfs2ZPI0jLe2LFjefzxxzXMo0iGiEX4fw6cFfa+TWDaUcwsDxgHXOuci9ifsHNuqnMu1zmXm5OTE4PSpK7y8/P59ttvWblypd+liEgCxCL8S4D2ZnaOmTUBhgLzwxuYWTdgCl7w747BZ0qMNWjQgDFjxui2T5EMUe/wd84dBsYCbwIfAn9zzm0ws0fM7NpAsz8CLYCXzex9M5tfw+rERyNGjGDBggXs2rXL71JEJM4sWc/x5ubmutLSUr/LyDgFBQWcffbZjB8/3u9SROQEmNlq51xutHZ6wleOUlhYyJQpUzh8+LDfpYhIHCn85Shdu3albdu2zJs3z+9SRCSOFP5yjLFjx+rCr0iaU/jLMa677jo+/PBDNm7c6HcpIhInCn85RpMmTbjzzjuZNGmS36WISJwo/CWiu+66ixdeeIGvv/7a71JEJA4U/hLRmWeeSb9+/Xj22Wf9LkVE4kDhLzUKDvSSrM+CiMiJU/hLjS6//HIaNGjA0qVL/S5FRGJM4S810jCPIulL4S+1uuWWW1i6dCmfffZZ9MYikjIU/lKrk046iZtvvpkpU6b4XYqIxJDCX6IqLCxk2rRpHDoUcRgGEUlBCn+JqlOnTlxwwQXMmTPH71JEJEYU/lInuvArkl4U/lIn11xzDZ999hlr1qzxuxQRiQGFv9RJo0aNGDVqlI7+RdKERvKSOtu9ezcdO3Zky6pVtJw3D9auhbIyyM6GLl3gttsgJ8fvMkUyWl1H8mqUiGIkPZy+dSuLWrTgpAsugEaNoKLi+5mvvAIPPQQDBsADD0D37v4VKiJR6bSP1M3kydCnD7mff06jw4ePDn6A8nJv2ty50KeP115EkpaO/CW6yZPhvvvg4EEsWlvn4OBBrz3A6NHxrk5EToCO/KV2JSWh4A93C3AGcDLQAZhWfbngDkDXbUSSksJfajdhgndKp5oHgE+Br4H5wHhgdfVG5eXe8iKSdBT+UrPdu2HhQu9UTjU/AZoGXlvg55PqjZyDBQtgz564likix0/hLzWbMaPW2WOA5kAnvFNAAyM1Mou6HhFJPIW/1Gzt2mPv6gkzCfgGWA5cx/ffBI5SXg7r1sWlPBE5cQp/qVlZWdQmDYGewHagxps79++PXU0iEhMKf6lZdnadmx4mwjn/gCPHsR4RSQyFv9SsSxdo1uyYybuBl4BvgSrgTeBFoF+EVZSb8dCcOQwePJgnnniCjz/+WAPCiyQBhb/UbMSIiJMN7xRPG6AlcB/wf4BrI7TNatqUfykt5YYbbqC0tJS+ffvSrl077rjjDmbNmsUe3Qkk4gt17Ca1u+46r8uGE/l3YgaDB0PYIDDOOf75z39SXFxMUVERy5Yt47zzziM/P5/8/Hx69uxJswjfNkSkburasZvCX2pXUkLlZZfRuLLy+Jdt3hyWLYPcmv8dVlZW8u6771JUVERxcTFr167l4osvDu0MunbtSoMG+oIqUld1Df+Y/K8ysyvN7CMz22xm90eY39TMZgXmv2tm7WLxuRJ/Excv5pGTT+ZIVtbxLdi8OTz6aK3BD9C4cWN69uzJ7373O1asWMH27dsZO3Ys27ZtY+jQofzoRz9i2LBhPPXUU2zbtq0efxMRCVfvI38zawh8DOTj3fFXAgxzzm0MazMG6OKcG2VmQ4HBzrkba1uvjvz95ZzjwQcfZM6cORQXF3Pm/PleXz3l5bWfAjKDrCwv+GPQqdu2bdtCp4gWL15My5YtQ98K+vTpQ7buJBI5SsJO+5jZJcDDzrn+gfcPADjnJoS1eTPQZqWZNQJ2Ajmulg9X+PvHOce9997LsmXLWLRoETnBAVpKS72+ehYs8EI+vM+frCxvpzBwoNeff5Qj/hNx5MgRPvjgg9DOYOXKlXTu3Dm0M7joooto3LhxzD9XJJUkMvyvB650zt0ReP8r4CLn3NiwNusDbbYH3n8SaLO3pvUq/P1RVVXFqFGjWL9+PQsXLuSUU045ttGePV6XDevWeQ9wtWwJnTt7dwclcCSv8vJyVqxYEbpe8Mknn9C7d2/y8vLIz8+nU6dOmEXthFokraTkSF5mVgAUAJx99tk+V5N5KisrGT58ODt37qSoqIgWLVpEbpiTA7/+dWKLiyArK4u8vDzy8vIA2Lt3L4sXL6aoqIjHHnuMqqqq0LeCfv368cMf/tDnikWSRywu+H4OnBX2vk1gWsQ2gdM+2cC+6ityzk11zuU653JzNBZsQlVUVHDDDTfw9ddf8/rrr9cc/EmsVatW3HjjjUybNo1PP/2UpUuX0r17d15++WU6depE165due+++3jzzTc5WG18ApFME4vTPo3wLvj2wwv5EuAm59yGsDaFQOewC77XOeeG1LZenfZJnAMHDjB48GCys7N5/vnnadKkid8lxdzhw4cpKSkJnSJas2YN3bt3D30z6NatGw0bNvS7TJF6S+h9/mY2EO8hz4bAdOfcf5rZI0Cpc26+mTUDngW6AV8CQ51zW2pbp8I/McrKyrj66qs577zzmDZtGo0aJdWZwLj55ptvWLZsWWhnsGvXLvr27Ru6XnDOOef4XaLICdFDXhLVvn37uPLKK+nRowd/+ctfMvphqs8//zx0F1FxcTEtWrQIfSu44ooraNmypd8litSJwl9qtXPnTvLz8xk4cCATJ07UXTFhnHOsW7cutDNYsWIFP/7xj0M7g0suuSQtT41JelD4S422bdtGXl4et956K+PGjVPwR3Ho0CH+8Y9/hL4V/POf/6Rnz56hncFPfvITbUNJGgp/iWjz5s3k5eVx9913c++99/pdTkr68ssvWbJkCUVFRRQVFVFRURG65TQvL4/WrVv7XaJkMIW/HGPDhg3079+f3/72txQUFPhdTtrYsmVLaEewZMkSWrduHfpW0Lt375S8bVZSl8JfjrJ69WquuuoqHnvsMW6++Wa/y0lbVVVVrF69OnSKqLS0lJ/+9KehnUFubq5uKZW4UvhLyIoVKxg8eDBTpkxh8ODBfpeTUQ4cOMDbb78d2hls376dK664InRL6XnnnRf/6wW7d3vdcaxd643LnJ3tjdJ2220J7Y5DEkPhLwAsXryYYcOG8eyzz9K/f3+/y8l4O3fuDN1FVFRURNOmTUPfCvr27ctpp50Wuw8rKfE64lu40HtfUfH9vGBHfAMGeB3xde8eu88VXyn8hddee42RI0cye/Zsevfu7Xc5Uo1zjo0bN4Z2BsuXL6d9+/ahncFll11G06ZNT2zlkyf70gW3+E/hn+FmzZrF3XffzWuvvUZ3HdWlhO+++45Vq1aFThGtX7+eSy+9NLQz6Ny5c90exAsG//H0XxQcfEc7gJSn8M9gTz/9NOPGjeONN96gS5cufpcjJ+irr75i6dKloVNEX3/9deh20vz8fNq0aXPsQiUl0KdPjcG/CegMXA88V31mHYbdlOSn8M9Qf/nLX/jjH/9IUVERHTt29LsciaFPP/30qFHNcnJyjhrV7KSTToLrroO5c2s81fNzoBxoS4TwN4PBg2HOnPj+RSSuFP4ZaOLEiTz55JMsXryYdu3a+V2OxNGRI0dYs2ZNaGfw7rvvcvmPf8yra9bQ+PDhiMu8BLwCnA9sJkL4AzRrBtu26S6gFJbQAdzFX845xo8fzzPPPMPy5csV/BmgQYMG/OxnP+M3v/lNqFfSx7p0qfGI/2vgt8Cfoq3YzLstVNJeZvTfm8aC4+2+/fbbLFu2DA2Ck5maN29Ox4oKqKqKOP9BYCTeSEu1Ki/3hueUtKfwT2FVVVXcddddbNy4kSVLlkQeb1cyR1lZxMnvA8XAmrquZ//+GBUkyUzhn6IqKyu59dZb2bVrF4sWLVL/MeI9uRvBW8CnQHBU7G+BKmAj8F6E9keys3U+OAPod5yCKioquP766/nmm29SdrxdiYMuXbwLttUUAJ/gfQN4HxgFXAW8GWEV5WY8/Mor3HDDDUydOpUtW2odcE9SmMI/xRw4cIBrr72Wpk2b8sorr5CVleV3SZIsRoyIOLk58KOwnxZAMyDS1aGspk0ZvWoV11xzDcuXL+eyyy7jvPPO46677mL27Nl8+eWXcSpeEk23eqaQTB1vV45DlPv8axXhPn/nHBs2bAg9dbx8+XI6duxIfn4+eXl59euCQuJC9/mnmX379tG/f38uvvhi/vznP2f0eLtSiyhP+NaqDk/4hndBUVRUxIYNG0JdUOTl5dGlSxf92/SZwj+NBMfbveqqq5gwYYKGDJTaJbBvn2AXFMGHzb766qujuqA466yzjrN4qS+Ff5oIjrc7fPhw/v3f/13BL3XjU6+eW7dupbi4OPRz6qmnhr4VXHHFFWTXcEeSxI7CPw1s2rSJ/Px87rnnHu655x6/y5FUU1rq9ee/YIEX8uXl388L9uc/cKDXn38cOnM7cuQIH3zwQehbwcqVK+ncuXPoW8FFF11EkyZNYv65mU7hn+LWr19P//79efjhh7nzzjv9LkdS2Z49XpcN69Z5D3C1bAmdO3t3ByXwifCKigpWrFgR2hls2rSJXr16hXYG559/vr7ZxoDCP4UFx9v905/+xE033eR3OSJxsXfv3qO6rD506FBoR9CvXz9at27td4kpSeGfooLj7U6dOpVBgwb5XY5IwnzyySehbwVLliyhdevWoZ1B7969vS6rJSqFfwoqLi7mpptu4rnnnuPnP/+53+WI+Kaqqor33nsv9HxBSUkJ3bp1C+0MunfvrudcaqDwTzHB8XbnzJlDr169/C5HJKkcOHCAd955J7Qz+PTTT+nTp0/oTqIOHTroekGAwj+FzJo1i3/913/l73//u8bbFamDXbt2sWTJktD1AjM76nrB6aef7neJvlH4p4jp06czfvx4jbcrcoKcc3z88cehbwVvvfUW7dq1C30r6NWrF82bN/e7zIRR+KeA4Hi7xcXFdOjQwe9yRNLC4cOHKSkpCX0rWLNmDT169AiNd9ytWzcaNmwYvwJ27/ZurV271htjITvb63H1ttsScmutwj/JTZgwgaeeeori4mINuygSR9988w3Lli0L3Um0c+dO+vbtGzpNdO6558bmg0pKvIfqFi703ldUfD8v+FDdgAHeQ3VxPL2bkPA3s1OBWUA7vPEihjjn9ldrcyEwGTgZbwyJ/3TOzYq27nQN/+B4u3PnzqWoqEj3Mosk2BdffBHaERQXF5OVlRU6RdS3b19OO+2041+pT91pRP6IxIT/fwFfOucmmtn9QEvn3G+qtekAOOfcJjNrDawGfuyc+6q2dadj+B85coR7772X5cuXs2jRIlq1auV3SSIZLdhldXBnEOyyOvit4NJLL6VZhAFyjpLAjvTqIlHh/xHQxzm3w8zOAN5yznWMsswHwPXOuU21tUu38K+qqqKgoIAPP/yQBQsWaLxdkSQU7LI6uDNYv349l156aWhncEyX1RG60D4EjMEbN/lL4DxgAjCg+ofVoQvtE5Go8P/KOXdK4LUB+4Pva2jfA5gJ/MQ5d6S2dadT+FdWVvKrX/2KPXv2MG/ePA27KJIivvrqK956663QKaL9+/fTr1+/0M7g7HvuOWbwnAPAH4EReOMmLwCGAevwzo+HRBg8JxZiFv5mVow3+lt144CZ4WFvZvudcy1rWM8ZeGNJD3fOraqhTQHekKOcffbZP9u6dWu0+pNeRUUFQ4YM4ciRI8yePTv6V0gRSVrbtm0LfSt4f9Ei3t+/n6Z1OIDuAjwE/LL6jGbNYNu2mN4FlFSnfczsZLzg/9/Oudl1WXc6HPkfOHCAQYMG0bJlS5577jl1XyuSRo784Q/w0EM0OHSo1na7gLbA+0Cn6jOzsuB3v4Nf/zpmddU1/Os73tp8YHjg9XBgXoRCmgCvAs/UNfjTQVlZGf3796dNmza8+OKLCn6RNNNg3bqowV8J3IwXjscEP3h3B61bF/vi6qC+4T8RyDezTUBe4D1mlmtm0wJthgC9gRFm9n7g58J6fm5S27t3L/369aNbt2489dRT8X2gRET8UVZW6+wjwK+AJsDjtTXcv7+2uXFTr27xnHP7gH4RppcCdwRePwc8V5/PSSU7duwgPz+fq6++WuPtiqSzWoakdMBIvFM+C4DGta2nZcTLpHFX3yN/CbN161Z69+7NsGHDFPwi6a5LF++CbQSjgQ+B14Cs2taRleWNquYDhX+MbNq0icsvv5zCwkLGjRun4BdJdyNGRJy8FZiCd4H3R0CLwM/zkRo7V+N64k3hHwPr16+nT58+jB8/XgOti2SK00/3+uqpdqDXFu+0TwXwbdjPzdWXN4OBAxM6jnI4hX89rV69mry8PB599FHuuOMOv8sRkUR64AHv1M2JyMrylveJwr8e3nnnHQYMGMCUKVMYNmyY3+WISKJ17+710XO84wUE+/aJcdcOx0ODYJ6g4Hi7zz//PPn5+X6XIyJ+CXbOliS9etaVjvxPwPz587npppuYM2eOgl9EvCBftszrq6dZs2NPBWVledMHD/ba+Rz8oCP/4/bSSy9xzz338Prrr2u8XRH5Xm6u10nbnj3eSF7r1nkPcLVs6d3OOWKEbxd3I1H4Q52HXZs+fToPPvggRUVFdPbp3lwRSXI5OTHtqydeMjv8axt27ZVX4KGHQsOu/XnlSh577DGWLl2q8XZFJOVlbvhHG3atvNz7c+5cKv/+d/ZkZ/N2aSlt27ZNbJ0iInGQmRd8w4ddi9altXM0rqzkdwcO0HbBgsTUJyISZ5kX/iUlEcfb/BIYDPwA7wm9F6ot1qC83FsuxccYEBGBTAz/CRO+P6UTphCv69VdeH1wjAY2VG9UXu4tLyKS4jIr/Hfv9i7uVjvVcwCYA/werwOmnsC1wLPVl3cOFizwbuUSEUlhmRX+M2ZEnPwx3pXv8Ht4uhLhyB+8J/RqWI+ISKrIrPBfu/bo2zkDvgVOrjYtG/gm0jp8HHZNRCRWMiv8axh2rQXwdbVpXwMn1bQen4ZdExGJlcwK/xqGXesAHAY2hU37APhJTevxadg1EZFYyazwr2HYtR8A1wG/xbv4uwKYhzf48jF8HHZNRCRWMiv8axkubRJQDpwODAMmU8ORv4/DromIxEpmhX8Nw64BnArMxTvy3wbcFGl5n4ddExGJlcwKf0jpYddERGIl88I/hYddExGJlczs1TNFh10TEYmVzDvyD0rBYddERGIlM4/8g1Js2DURkVjJ7PAPSpFh10REYiVzT/uIiGQwhb+ISAZS+IuIZCCFv4hIBqpX+JvZqWZWZGabAn/W2N2lmZ1sZtvN7PH6fKaIiNRffY/87wcWO+faA4sD72vye+Dten6eiIjEQH3D/xfAzMDrmcCgSI3M7GfAD4FF9fw8ERGJgfqG/w+dczsCr3fiBfxRzKwB8BhwX7SVmVmBmZWaWekeDZIuIhI3UR/yMrNi4EcRZo05U6GgAAAGFklEQVQLf+Occ2YWqZOcMcAC59x2i9CVcrV1TAWmBj53j5ltjVbfcWoF7I3xOmNNNdZfstcHqjEWkr0+8KfGtnVpFDX8nXN5Nc0zs11mdoZzboeZnQHsjtDsEqCXmY3BGy63iZl965yr7foAzrmY96tgZqXOuaTullM11l+y1weqMRaSvT5I7hrr273DfGA4MDHw57zqDZxzNwdfm9kIIDda8IuISHzV95z/RCDfzDYBeYH3mFmumU2rb3EiIhIf9Tryd87tA/pFmF4K3BFh+gxgRn0+s56m+vjZdaUa6y/Z6wPVGAvJXh8kcY3mahvIRERE0pK6dxARyUBpF/516XLCzK4ws/fDfirMbFBg3gwz+5+weRf6UWOgXVVYHfPDpp9jZu+a2WYzm2VmTRJdn5ldaGYrzWyDma01sxvD5sVtG5rZlWb2UeDvfsyNA2bWNLBNNge2UbuweQ8Epn9kZv1jVdMJ1PhvZrYxsN0Wm1nbsHkRf+cJrm9E4FbrYB13hM0bHvh3scnMhsejvjrW+N9h9X1sZl+FzUvENpxuZrvNbH0N883M/hyof62Z/TRsXkK2YVTOubT6Af4LuD/w+n7gD1Hanwp8CTQPvJ8BXJ8MNQLf1jD9b8DQwOu/AqMTXR/QAWgfeN0a2AGcEs9tCDQEPgHOBZoAHwDnV2szBvhr4PVQYFbg9fmB9k2BcwLraehTjVeE/XsbHayxtt95gusbATweYdlTgS2BP1sGXrf0o8Zq7f8FmJ6obRj4jN7AT4H1NcwfCCwEDLgYeDeR27AuP2l35E8du5wIcz2w0Dl3MK5VHe14awwxMwP6ArNPZPk6ilqfc+5j59ymwOsv8J7xiPeYlz2Azc65Lc6574CXArWGC699NtAvsM1+AbzknDvknPsfYHNgfQmv0Tm3NOzf2yqgTRzqOOH6atEfKHLOfemc2w8UAVcmQY3DgBfjUEeNnHNv4x001uQXwDPOswo4xbxnoRK1DaNKx/CP2uVENUM59h/Ofwa+qv23mTWNeYV1r7GZed1drAqelgJOA75yzh0OvN8OnOlTfQCYWQ+8I7RPwibHYxueCXwW9j7S3z3UJrCNyvC2WV2WTVSN4UbiHSEGRfqd+1HfLwO/v9lmdtZxLpuoGgmcMjsHWBI2Od7bsC5q+jskahtGlZJj+Fr9u5wIrucMoDPwZtjkB/ACrwnebVq/AR7xqca2zrnPzexcYImZrcMLs3qL8TZ8FhjunDsSmByTbZjuzOwWIBe4PGzyMb9z59wnkdcQN68BLzrnDpnZXXjfpPomuIa6GgrMds5VhU1Lhm2Y9FIy/F39u5wIGgK86pyrDFt38Ij3kJk9TR06pItXjc65zwN/bjGzt4BuwBy8r5CNAke2bYDP/ajPzE4GXgfGBb7aBtcdk20YwefAWWHvI/3dg222m1kjIBvYV8dlE1UjZpaHt6O93Dl3KDi9ht95LIMran3Oe34naBreNaDgsn2qLftWDGsLOp7f1VCgMHxCArZhXdT0d0jUNowqHU/7BLucgBq6nAhzzLnCQNgFz60PAiJezY93jWbWMni6xMxaAZcBG5131Wgp3rWKGpdPQH1NgFfxzmvOrjYvXtuwBGhv3t1OTfD+41e/myO89uuBJYFtNh8Yat7dQOcA7YH/F6O6jqtGM+sGTAGudc7tDpse8XfuQ31nhL29Fvgw8PpN4OeBOlsCP+fob80JqzFQZye8i6Yrw6YlYhvWxXzg1sBdPxcDZYGDokRtw+j8uMoczx+887uLgU1AMXBqYHouMC2sXTu8vXCDassvAdbhBdZzQAs/agQuDdTxQeDPkWHLn4sXXJuBl4GmPtR3C1AJvB/2c2G8tyHeXRQf4x3JjQtMewQvSAGaBbbJ5sA2Ojds2XGB5T4CBsTx32C0GouBXWHbbX6033mC65sAbAjUsRToFLbs7YFtuxm4za9tGHj/MDCx2nKJ2oYv4t3hVol33n4kMAoYFZhvwBOB+tfh9WmW0G0Y7UdP+IqIZKB0PO0jIiJRKPxFRDKQwl9EJAMp/EVEMpDCX0QkAyn8RUQykMJfRCQDKfxFRDLQ/wdyCo1UI9GkmAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773eab40b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'C'}), (1, {'label': 'C'}), (2, {'label': 'C'}), (3, {'label': 'C'}), (4, {'label': 'O'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773ca1cc88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'CC'}), (1, {'label': 'CC'}), (2, {'label': 'CO'}), (3, {'label': 'CCCO'}), (4, {'label': 'OCC'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773c9a44e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': '0'}), (1, {'label': '0'}), (2, {'label': '3'}), (3, {'label': '1'}), (4, {'label': '2'})]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773c9957b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'C'}), (1, {'label': 'C'}), (2, {'label': 'C'}), (3, {'label': 'C'}), (4, {'label': 'C'}), (5, {'label': 'C'}), (6, {'label': 'O'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7788e0e390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': 'CC'}), (1, {'label': 'CC'}), (2, {'label': 'CC'}), (3, {'label': 'CO'}), (4, {'label': 'CCCC'}), (5, {'label': 'CCCO'}), (6, {'label': 'OCC'})]\n",
-      " -> \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f773c95a5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, {'label': '0'}), (1, {'label': '0'}), (2, {'label': '0'}), (3, {'label': '3'}), (4, {'label': '4'}), (5, {'label': '1'}), (6, {'label': '2'})]\n",
-      "--- shortest path kernel built in 0.00026607513427734375 seconds ---\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "import networkx as nx\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "def weisfeilerlehman_test(G):\n",
-    "    '''\n",
-    "    Weisfeiler-Lehman test of graph isomorphism.\n",
-    "    '''\n",
-    "\n",
-    "    nx.draw_networkx(G)\n",
-    "    plt.show()\n",
-    "    nx.draw_networkx_labels(G, nx.spring_layout(G), labels = nx.get_node_attributes(G,'label'))\n",
-    "    print(G.nodes(data = True))\n",
-    "    \n",
-    "    set_multisets = []\n",
-    "    for node in G.nodes(data = True):\n",
-    "        # Multiset-label determination.\n",
-    "        multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
-    "        # sorting each multiset\n",
-    "        multiset.sort()\n",
-    "        multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
-    "        set_multisets.append(multiset)\n",
-    "        \n",
-    "    # label compression\n",
-    "#     set_multisets.sort() # this is unnecessary\n",
-    "    set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
-    "    set_compressed = { value : str(set_unique.index(value)) for value in set_unique } # assign indices as the new labels\n",
-    "#     print(set_compressed)\n",
-    "#     print(set_multisets)\n",
-    "    \n",
-    "    # relabel nodes with multisets\n",
-    "    for node in G.nodes(data = True):\n",
-    "        node[1]['label'] = set_multisets[node[0]]\n",
-    "    print(' -> ')\n",
-    "    nx.draw_networkx(G)\n",
-    "    plt.show()\n",
-    "    print(G.nodes(data = True))\n",
-    "\n",
-    "    \n",
-    "    # relabel nodes\n",
-    "    for node in G.nodes(data = True):\n",
-    "        node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
-    "        \n",
-    "    print(' -> ')\n",
-    "    nx.draw_networkx(G)\n",
-    "    plt.show()\n",
-    "    print(G.nodes(data = True))\n",
-    "\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "G1 = dataset[12]\n",
-    "G2 = dataset[55]\n",
-    "\n",
-    "# init.\n",
-    "kernel = 0 # init kernel\n",
-    "num_nodes1 = G1.number_of_nodes()\n",
-    "num_nodes2 = G2.number_of_nodes()\n",
-    "\n",
-    "# the first iteration.\n",
-    "labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "print(labelset1)\n",
-    "print(labelset2)\n",
-    "kernel += spkernel(G1, G2)\n",
-    "print(kernel)\n",
-    "\n",
-    "\n",
-    "\n",
-    "for height in range(0, min(num_nodes1, num_nodes2)): #Q how to determine the upper bound of the height?\n",
-    "    if labelset1 != labelset2:\n",
-    "        break\n",
-    "        \n",
-    "    # Weisfeiler-Lehman test of graph isomorphism.\n",
-    "    weisfeilerlehman_test(G1)\n",
-    "    weisfeilerlehman_test(G2)\n",
-    "    \n",
-    "    # calculate kernel\n",
-    "    kernel += spkernel(G1, G2)\n",
-    "    \n",
-    "    # get label sets of both graphs\n",
-    "    labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "    labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "#     print(labelset1)\n",
-    "#     print(labelset2)\n",
-    "\n",
-    "print(kernel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'O', 6: 'O'}\n",
-      "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'C', 6: 'S', 7: 'S'}\n",
-      "\n",
-      " --- height = 0 --- \n",
-      "\n",
-      " --- for graph 0 --- \n",
-      "\n",
-      "labels_ori: ['C', 'C', 'C', 'C', 'C', 'O', 'O']\n",
-      "all_labels_ori: {'C', 'O'}\n",
-      "num_of_each_label: {'C': 5, 'O': 2}\n",
-      "all_num_of_each_label: [{'C': 5, 'O': 2}]\n",
-      "num_of_labels: 2\n",
-      "all_labels_ori: {'C', 'O'}\n",
-      "\n",
-      " --- for graph 1 --- \n",
-      "\n",
-      "labels_ori: ['C', 'C', 'C', 'C', 'C', 'C', 'S', 'S']\n",
-      "all_labels_ori: {'C', 'O', 'S'}\n",
-      "num_of_each_label: {'C': 6, 'S': 2}\n",
-      "all_num_of_each_label: [{'C': 5, 'O': 2}, {'C': 6, 'S': 2}]\n",
-      "num_of_labels: 2\n",
-      "all_labels_ori: {'C', 'O', 'S'}\n",
-      "\n",
-      " all_num_of_labels_occured: 3\n",
-      "\n",
-      " --- calculating kernel matrix ---\n",
-      "\n",
-      " labels: {'C', 'O'}\n",
-      "vector1: [[5 2]]\n",
-      "vector2: [[5 2]]\n",
-      "Kmatrix: [[ 29.   0.]\n",
-      " [  0.   0.]]\n",
-      "\n",
-      " labels: {'C', 'O', 'S'}\n",
-      "vector1: [[5 2 0]]\n",
-      "vector2: [[6 0 2]]\n",
-      "Kmatrix: [[ 29.  30.]\n",
-      " [ 30.   0.]]\n",
-      "\n",
-      " labels: {'C', 'S'}\n",
-      "vector1: [[6 2]]\n",
-      "vector2: [[6 2]]\n",
-      "Kmatrix: [[ 29.  30.]\n",
-      " [ 30.  40.]]\n",
-      "\n",
-      " --- height = 1 --- \n",
-      "\n",
-      " --- for graph 0 --- \n",
-      "\n",
-      "multiset: ['CC', 'CC', 'CCO', 'CCO', 'COO', 'OCC', 'OCC']\n",
-      "set_unique: ['OCC', 'COO', 'CCO', 'CC']\n",
-      "set_compressed: {'OCC': '4', 'COO': '5', 'CCO': '6', 'CC': '7'}\n",
-      "all_set_compressed: {'OCC': '4', 'COO': '5', 'CCO': '6', 'CC': '7'}\n",
-      "num_of_labels_occured: 7\n",
-      "\n",
-      " compressed labels: {0: '7', 1: '7', 2: '6', 3: '6', 4: '5', 5: '4', 6: '4'}\n",
-      "labels_comp: ['7', '7', '6', '6', '5', '4', '4']\n",
-      "all_labels_ori: {'5', '4', '6', '7'}\n",
-      "num_of_each_label: {'5': 1, '4': 2, '6': 2, '7': 2}\n",
-      "all_num_of_each_label: [{'5': 1, '4': 2, '6': 2, '7': 2}]\n",
-      "\n",
-      " --- for graph 1 --- \n",
-      "\n",
-      "multiset: ['CC', 'CC', 'CC', 'CCS', 'CCS', 'CCSS', 'SCC', 'SCC']\n",
-      "set_unique: ['SCC', 'CC', 'CCS', 'CCSS']\n",
-      "set_compressed: {'SCC': '8', 'CC': '7', 'CCS': '9', 'CCSS': '10'}\n",
-      "all_set_compressed: {'SCC': '8', 'COO': '5', 'CCS': '9', 'OCC': '4', 'CCO': '6', 'CCSS': '10', 'CC': '7'}\n",
-      "num_of_labels_occured: 10\n",
-      "\n",
-      " compressed labels: {0: '7', 1: '7', 2: '7', 3: '9', 4: '9', 5: '10', 6: '8', 7: '8'}\n",
-      "labels_comp: ['7', '7', '7', '9', '9', '10', '8', '8']\n",
-      "all_labels_ori: {'10', '4', '7', '9', '6', '5', '8'}\n",
-      "num_of_each_label: {'10': 1, '9': 2, '7': 3, '8': 2}\n",
-      "all_num_of_each_label: [{'5': 1, '4': 2, '6': 2, '7': 2}, {'10': 1, '9': 2, '7': 3, '8': 2}]\n",
-      "\n",
-      " all_num_of_labels_occured: 10\n",
-      "\n",
-      " --- calculating kernel matrix ---\n",
-      "\n",
-      " labels: {'5', '4', '6', '7'}\n",
-      "vector1: [[1 2 2 2]]\n",
-      "vector2: [[1 2 2 2]]\n",
-      "\n",
-      " labels: {'10', '4', '7', '9', '6', '5', '8'}\n",
-      "vector1: [[0 2 2 0 2 1 0]]\n",
-      "vector2: [[1 0 3 2 0 0 2]]\n",
-      "\n",
-      " labels: {'8', '10', '7', '9'}\n",
-      "vector1: [[2 1 3 2]]\n",
-      "vector2: [[2 1 3 2]]\n",
-      "\n",
-      " Kmatrix: [[ 42.  36.]\n",
-      " [ 36.  58.]]\n",
-      "\n",
-      " --- height = 2 --- \n",
-      "\n",
-      " --- for graph 0 --- \n",
-      "\n",
-      "multiset: ['76', '76', '647', '647', '544', '456', '456']\n",
-      "set_unique: ['647', '76', '456', '544']\n",
-      "set_compressed: {'647': '11', '76': '12', '544': '14', '456': '13'}\n",
-      "all_set_compressed: {'647': '11', '76': '12', '456': '13', '544': '14'}\n",
-      "num_of_labels_occured: 14\n",
-      "\n",
-      " compressed labels: {0: '12', 1: '12', 2: '11', 3: '11', 4: '14', 5: '13', 6: '13'}\n",
-      "labels_comp: ['12', '12', '11', '11', '14', '13', '13']\n",
-      "all_labels_ori: {'14', '12', '11', '13'}\n",
-      "num_of_each_label: {'14': 1, '13': 2, '12': 2, '11': 2}\n",
-      "all_num_of_each_label: [{'14': 1, '13': 2, '12': 2, '11': 2}]\n",
-      "\n",
-      " --- for graph 1 --- \n",
-      "\n",
-      "multiset: ['79', '79', '710', '978', '978', '10788', '8109', '8109']\n",
-      "set_unique: ['710', '8109', '79', '10788', '978']\n",
-      "set_compressed: {'710': '15', '79': '17', '8109': '16', '978': '19', '10788': '18'}\n",
-      "all_set_compressed: {'710': '15', '79': '17', '978': '19', '10788': '18', '8109': '16', '456': '13', '544': '14', '647': '11', '76': '12'}\n",
-      "num_of_labels_occured: 19\n",
-      "\n",
-      " compressed labels: {0: '17', 1: '17', 2: '15', 3: '19', 4: '19', 5: '18', 6: '16', 7: '16'}\n",
-      "labels_comp: ['17', '17', '15', '19', '19', '18', '16', '16']\n",
-      "all_labels_ori: {'18', '19', '12', '13', '17', '11', '14', '16', '15'}\n",
-      "num_of_each_label: {'15': 1, '17': 2, '19': 2, '16': 2, '18': 1}\n",
-      "all_num_of_each_label: [{'14': 1, '13': 2, '12': 2, '11': 2}, {'15': 1, '17': 2, '19': 2, '16': 2, '18': 1}]\n",
-      "\n",
-      " all_num_of_labels_occured: 19\n",
-      "\n",
-      " --- calculating kernel matrix ---\n",
-      "\n",
-      " labels: {'14', '12', '11', '13'}\n",
-      "vector1: [[1 2 2 2]]\n",
-      "vector2: [[1 2 2 2]]\n",
-      "\n",
-      " labels: {'18', '19', '12', '13', '17', '11', '14', '16', '15'}\n",
-      "vector1: [[0 0 2 2 0 2 1 0 0]]\n",
-      "vector2: [[1 2 0 0 2 0 0 2 1]]\n",
-      "\n",
-      " labels: {'18', '17', '15', '16', '19'}\n",
-      "vector1: [[1 2 1 2 2]]\n",
-      "vector2: [[1 2 1 2 2]]\n",
-      "\n",
-      " Kmatrix: [[ 55.  36.]\n",
-      " [ 36.  72.]]\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel built in 0.0034377574920654297 seconds ---\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 55.,  36.],\n",
-       "       [ 36.,  72.]])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# test of WL subtree kernel on many graphs\n",
-    "\n",
-    "import sys\n",
-    "import pathlib\n",
-    "from collections import Counter\n",
-    "sys.path.insert(0, \"../\")\n",
-    "\n",
-    "import networkx as nx\n",
-    "import numpy as np\n",
-    "import time\n",
-    "\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "from pygraph.kernels.pathKernel import pathkernel\n",
-    "\n",
-    "def weisfeilerlehmankernel(*args, height = 0, base_kernel = 'subtree'):\n",
-    "    \"\"\"Calculate Weisfeiler-Lehman kernels between graphs.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    Gn : List of NetworkX graph\n",
-    "        List of graphs between which the kernels are calculated.\n",
-    "    /\n",
-    "    G1, G2 : NetworkX graphs\n",
-    "        2 graphs between which the kernel is calculated.\n",
-    "        \n",
-    "    height : subtree height\n",
-    "    \n",
-    "    base_kernel : base kernel used in each iteration of WL kernel\n",
-    "        the default base kernel is subtree kernel\n",
-    "        \n",
-    "    Return\n",
-    "    ------\n",
-    "    Kmatrix/Kernel : Numpy matrix/int\n",
-    "        Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. / Weisfeiler-Lehman Kernel between 2 graphs.\n",
-    "        \n",
-    "    Notes\n",
-    "    -----\n",
-    "    This function now supports WL subtree kernel and WL shortest path kernel.\n",
-    "    \n",
-    "    References\n",
-    "    ----------\n",
-    "    [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. 2011;12(Sep):2539-61.\n",
-    "    \"\"\"\n",
-    "    if len(args) == 1: # for a list of graphs\n",
-    "\n",
-    "#         print(args)\n",
-    "        start_time = time.time()\n",
-    "        \n",
-    "        # for WL subtree kernel\n",
-    "        if base_kernel == 'subtree':           \n",
-    "            Kmatrix = _wl_subtreekernel_do(args[0], height = height, base_kernel = 'subtree')\n",
-    "            \n",
-    "        # for WL edge kernel\n",
-    "        elif base_kernel == 'edge':\n",
-    "            print('edge')\n",
-    "            \n",
-    "        # for WL shortest path kernel\n",
-    "        elif base_kernel == 'sp':\n",
-    "            Gn = args[0]\n",
-    "            Kmatrix = np.zeros((len(Gn), len(Gn)))\n",
-    "            \n",
-    "            for i in range(0, len(Gn)):\n",
-    "                for j in range(i, len(Gn)):\n",
-    "                    Kmatrix[i][j] = _weisfeilerlehmankernel_do(Gn[i], Gn[j])\n",
-    "                    Kmatrix[j][i] = Kmatrix[i][j]\n",
-    "\n",
-    "        print(\"\\n --- Weisfeiler-Lehman %s kernel matrix of size %d built in %s seconds ---\" % (base_kernel, len(args[0]), (time.time() - start_time)))\n",
-    "        \n",
-    "        return Kmatrix\n",
-    "        \n",
-    "    else: # for only 2 graphs\n",
-    "        \n",
-    "        start_time = time.time()\n",
-    "        \n",
-    "        # for WL subtree kernel\n",
-    "        if base_kernel == 'subtree':\n",
-    "            \n",
-    "            args = [args[0], args[1]]\n",
-    "#             print(args)\n",
-    "            kernel = _wl_subtreekernel_do(args, height = height, base_kernel = 'subtree')\n",
-    "            \n",
-    "        # for WL edge kernel\n",
-    "        elif base_kernel == 'edge':\n",
-    "            print('edge')\n",
-    "            \n",
-    "        # for WL shortest path kernel\n",
-    "        elif base_kernel == 'sp':\n",
-    "            \n",
-    "\n",
-    "            kernel = _pathkernel_do(args[0], args[1])\n",
-    "\n",
-    "        print(\"\\n --- Weisfeiler-Lehman %s kernel built in %s seconds ---\" % (base_kernel, time.time() - start_time))\n",
-    "        \n",
-    "        return kernel\n",
-    "    \n",
-    "    \n",
-    "def _weisfeilerlehmankernel_do(G1, G2):\n",
-    "    \"\"\"Calculate Weisfeiler-Lehman kernels between 2 graphs. This kernel use shortest path kernel to calculate kernel between two graphs in each iteration.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    G1, G2 : NetworkX graphs\n",
-    "        2 graphs between which the kernel is calculated.\n",
-    "        \n",
-    "    Return\n",
-    "    ------\n",
-    "    Kernel : int\n",
-    "        Weisfeiler-Lehman Kernel between 2 graphs.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    # init.\n",
-    "    kernel = 0 # init kernel\n",
-    "    num_nodes1 = G1.number_of_nodes()\n",
-    "    num_nodes2 = G2.number_of_nodes()\n",
-    "    height = 12 #min(num_nodes1, num_nodes2)) #Q how to determine the upper bound of the height?\n",
-    "    \n",
-    "    # the first iteration.\n",
-    "    labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "    labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "    kernel += pathkernel(G1, G2) # change your base kernel here (and one more below)\n",
-    "    \n",
-    "    for h in range(0, height):\n",
-    "#         if labelset1 != labelset2:\n",
-    "#             break\n",
-    "\n",
-    "        # Weisfeiler-Lehman test of graph isomorphism.\n",
-    "        relabel(G1)\n",
-    "        relabel(G2)\n",
-    "\n",
-    "        # calculate kernel\n",
-    "        kernel += pathkernel(G1, G2) # change your base kernel here (and one more before)\n",
-    "\n",
-    "        # get label sets of both graphs\n",
-    "        labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
-    "        labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
-    "    \n",
-    "    return kernel\n",
-    "\n",
-    "\n",
-    "def relabel(G):\n",
-    "    '''\n",
-    "    Relabel nodes in graph G in one iteration of the 1-dim. WL test of graph isomorphism.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    G : NetworkX graph\n",
-    "        The graphs whose nodes are relabeled.\n",
-    "    '''\n",
-    "    \n",
-    "    # get the set of original labels\n",
-    "    labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
-    "    print(labels_ori)\n",
-    "    num_of_each_label = dict(Counter(labels_ori))\n",
-    "    print(num_of_each_label)\n",
-    "    num_of_labels = len(num_of_each_label)\n",
-    "    print(num_of_labels)\n",
-    "    \n",
-    "    set_multisets = []\n",
-    "    for node in G.nodes(data = True):\n",
-    "        # Multiset-label determination.\n",
-    "        multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
-    "        # sorting each multiset\n",
-    "        multiset.sort()\n",
-    "        multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
-    "        set_multisets.append(multiset)\n",
-    "    print(set_multisets)\n",
-    "        \n",
-    "    # label compression\n",
-    "#     set_multisets.sort() # this is unnecessary\n",
-    "    set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
-    "    print(set_unique)\n",
-    "    set_compressed = { value : str(set_unique.index(value) + num_of_labels + 1) for value in set_unique } # assign new labels\n",
-    "    print(set_compressed)\n",
-    "    \n",
-    "    # relabel nodes\n",
-    "#     nx.relabel_nodes(G, set_compressed, copy = False)\n",
-    "    for node in G.nodes(data = True):\n",
-    "        node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
-    "    print(nx.get_node_attributes(G, 'label'))\n",
-    "\n",
-    "    # get the set of compressed labels\n",
-    "    labels_comp = list(nx.get_node_attributes(G, 'label').values())\n",
-    "    print(labels_comp)\n",
-    "    num_of_each_label.update(dict(Counter(labels_comp)))\n",
-    "    print(num_of_each_label)\n",
-    "    \n",
-    "    \n",
-    "def _wl_subtreekernel_do(*args, height = 0, base_kernel = 'subtree'):\n",
-    "    \"\"\"Calculate Weisfeiler-Lehman subtree kernels between graphs.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    Gn : List of NetworkX graph\n",
-    "        List of graphs between which the kernels are calculated.\n",
-    "        \n",
-    "    Return\n",
-    "    ------\n",
-    "    Kmatrix/Kernel : Numpy matrix/int\n",
-    "        Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "#     print(args)\n",
-    "    Gn = args[0]\n",
-    "#     print(Gn)\n",
-    "\n",
-    "    Kmatrix = np.zeros((len(Gn), len(Gn)))\n",
-    "    all_num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs\n",
-    "    \n",
-    "    # initial for height = 0\n",
-    "    print('\\n --- height = 0 --- ')\n",
-    "    all_labels_ori = set() # all unique orignal labels in all graphs in this iteration\n",
-    "    all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration\n",
-    "    all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration\n",
-    "    num_of_labels_occured = all_num_of_labels_occured # number of the set of letters that occur before as node labels at least once in all graphs\n",
-    "\n",
-    "    # for each graph\n",
-    "    for idx, G in enumerate(Gn):\n",
-    "        # get the set of original labels\n",
-    "        print('\\n --- for graph %d --- \\n' % (idx))\n",
-    "        labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
-    "        print('labels_ori: %s' % (labels_ori))\n",
-    "        all_labels_ori.update(labels_ori)\n",
-    "        print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "        num_of_each_label = dict(Counter(labels_ori)) # number of occurence of each label in graph\n",
-    "        print('num_of_each_label: %s' % (num_of_each_label))\n",
-    "        all_num_of_each_label.append(num_of_each_label)\n",
-    "        print('all_num_of_each_label: %s' % (all_num_of_each_label))\n",
-    "        num_of_labels = len(num_of_each_label) # number of all unique labels\n",
-    "        print('num_of_labels: %s' % (num_of_labels))\n",
-    "        \n",
-    "\n",
-    "        all_labels_ori.update(labels_ori)\n",
-    "        print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "        \n",
-    "    all_num_of_labels_occured += len(all_labels_ori)\n",
-    "    print('\\n all_num_of_labels_occured: %s' % (all_num_of_labels_occured))\n",
-    "        \n",
-    "    # calculate subtree kernel with the 0th iteration and add it to the final kernel\n",
-    "    print('\\n --- calculating kernel matrix ---')\n",
-    "    for i in range(0, len(Gn)):\n",
-    "        for j in range(i, len(Gn)):\n",
-    "            labels = set(list(all_num_of_each_label[i].keys()) + list(all_num_of_each_label[j].keys()))\n",
-    "            print('\\n labels: %s' % (labels))\n",
-    "            vector1 = np.matrix([ (all_num_of_each_label[i][label] if (label in all_num_of_each_label[i].keys()) else 0) for label in labels ])\n",
-    "            vector2 = np.matrix([ (all_num_of_each_label[j][label] if (label in all_num_of_each_label[j].keys()) else 0) for label in labels ])\n",
-    "            print('vector1: %s' % (vector1))\n",
-    "            print('vector2: %s' % (vector2))\n",
-    "            Kmatrix[i][j] += np.dot(vector1, vector2.transpose())\n",
-    "            Kmatrix[j][i] = Kmatrix[i][j]\n",
-    "            print('Kmatrix: %s' % (Kmatrix))\n",
-    "\n",
-    "    \n",
-    "    # iterate each height\n",
-    "    for h in range(1, height + 1):\n",
-    "        print('\\n --- height = %d --- ' % (h))\n",
-    "        all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration\n",
-    "        num_of_labels_occured = all_num_of_labels_occured # number of the set of letters that occur before as node labels at least once in all graphs\n",
-    "        all_labels_ori = set()\n",
-    "        all_num_of_each_label = []\n",
-    "        \n",
-    "        # for each graph\n",
-    "        for idx, G in enumerate(Gn):\n",
-    "#             # get the set of original labels\n",
-    "            print('\\n --- for graph %d --- \\n' % (idx))\n",
-    "#             labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
-    "#             print('labels_ori: %s' % (labels_ori))\n",
-    "#             num_of_each_label = dict(Counter(labels_ori)) # number of occurence of each label in graph\n",
-    "#             print('num_of_each_label: %s' % (num_of_each_label))\n",
-    "#             num_of_labels = len(num_of_each_label) # number of all unique labels\n",
-    "#             print('num_of_labels: %s' % (num_of_labels))\n",
-    "            \n",
-    "#             all_labels_ori.update(labels_ori)\n",
-    "#             print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "#             #             num_of_labels_occured += num_of_labels #@todo not precise\n",
-    "#             num_of_labels_occured = all_num_of_labels_occured + len(all_labels_ori) + len(all_set_compressed)\n",
-    "#             print('num_of_labels_occured: %s' % (num_of_labels_occured))\n",
-    "            \n",
-    "            set_multisets = []\n",
-    "            for node in G.nodes(data = True):\n",
-    "                # Multiset-label determination.\n",
-    "                multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
-    "                # sorting each multiset\n",
-    "                multiset.sort()\n",
-    "                multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
-    "                set_multisets.append(multiset)\n",
-    "            print('multiset: %s' % (set_multisets))\n",
-    "\n",
-    "            # label compression\n",
-    "        #     set_multisets.sort() # this is unnecessary\n",
-    "            set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
-    "            print('set_unique: %s' % (set_unique))\n",
-    "            # a dictionary mapping original labels to new ones. \n",
-    "            set_compressed = {}\n",
-    "            # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label \n",
-    "            for value in set_unique:\n",
-    "                if value in all_set_compressed.keys():\n",
-    "                    set_compressed.update({ value : all_set_compressed[value] })\n",
-    "                else:\n",
-    "                    set_compressed.update({ value : str(num_of_labels_occured + 1) })\n",
-    "                    num_of_labels_occured += 1\n",
-    "#             set_compressed = { value : (all_set_compressed[value] if value in all_set_compressed.keys() else str(set_unique.index(value) + num_of_labels_occured + 1)) for value in set_unique }\n",
-    "            print('set_compressed: %s' % (set_compressed))\n",
-    "            \n",
-    "            all_set_compressed.update(set_compressed)\n",
-    "            print('all_set_compressed: %s' % (all_set_compressed))\n",
-    "#             num_of_labels_occured += len(set_compressed) #@todo not precise\n",
-    "            print('num_of_labels_occured: %s' % (num_of_labels_occured))\n",
-    "            \n",
-    "            # relabel nodes\n",
-    "        #     nx.relabel_nodes(G, set_compressed, copy = False)\n",
-    "            for node in G.nodes(data = True):\n",
-    "                node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
-    "            print('\\n compressed labels: %s' % (nx.get_node_attributes(G, 'label')))\n",
-    "\n",
-    "            # get the set of compressed labels\n",
-    "            labels_comp = list(nx.get_node_attributes(G, 'label').values())\n",
-    "            print('labels_comp: %s' % (labels_comp))\n",
-    "            all_labels_ori.update(labels_comp)\n",
-    "            print('all_labels_ori: %s' % (all_labels_ori))\n",
-    "            num_of_each_label = dict(Counter(labels_comp))\n",
-    "            print('num_of_each_label: %s' % (num_of_each_label))\n",
-    "            all_num_of_each_label.append(num_of_each_label)\n",
-    "            print('all_num_of_each_label: %s' % (all_num_of_each_label))\n",
-    "                    \n",
-    "        all_num_of_labels_occured += len(all_labels_ori)\n",
-    "        print('\\n all_num_of_labels_occured: %s' % (all_num_of_labels_occured))\n",
-    "        \n",
-    "        # calculate subtree kernel with h iterations and add it to the final kernel\n",
-    "        print('\\n --- calculating kernel matrix ---')\n",
-    "        for i in range(0, len(Gn)):\n",
-    "            for j in range(i, len(Gn)):\n",
-    "                labels = set(list(all_num_of_each_label[i].keys()) + list(all_num_of_each_label[j].keys()))\n",
-    "                print('\\n labels: %s' % (labels))\n",
-    "                vector1 = np.matrix([ (all_num_of_each_label[i][label] if (label in all_num_of_each_label[i].keys()) else 0) for label in labels ])\n",
-    "                vector2 = np.matrix([ (all_num_of_each_label[j][label] if (label in all_num_of_each_label[j].keys()) else 0) for label in labels ])\n",
-    "                print('vector1: %s' % (vector1))\n",
-    "                print('vector2: %s' % (vector2))\n",
-    "                Kmatrix[i][j] += np.dot(vector1, vector2.transpose())\n",
-    "                Kmatrix[j][i] = Kmatrix[i][j]\n",
-    "                    \n",
-    "        print('\\n Kmatrix: %s' % (Kmatrix))\n",
-    "\n",
-    "    return Kmatrix\n",
-    "\n",
-    "    \n",
-    "# main\n",
-    "import sys\n",
-    "from collections import Counter\n",
-    "import networkx as nx\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "G1 = dataset[15]\n",
-    "print(nx.get_node_attributes(G1, 'label'))\n",
-    "G2 = dataset[80]\n",
-    "print(nx.get_node_attributes(G2, 'label'))\n",
-    "\n",
-    "weisfeilerlehmankernel(G1, G2, height = 2)\n",
-    "# Kmatrix = weisfeilerlehmankernel(G1, G2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "- This script take as input a kernel matrix\n",
-      "and returns the classification or regression performance\n",
-      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-      "correspond to the average of the performances on the test sets. \n",
-      "\n",
-      "@references\n",
-      "    Elisabetta Ghisu, https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-      "\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 0 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 0.3845643997192383 seconds ---\n",
-      "[[   5.    6.    4. ...,   20.   20.   20.]\n",
-      " [   6.    8.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.    5. ...,   21.   21.   21.]\n",
-      " ..., \n",
-      " [  20.   20.   21. ...,  101.  101.  101.]\n",
-      " [  20.   20.   21. ...,  101.  101.  101.]\n",
-      " [  20.   20.   21. ...,  101.  101.  101.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 141.418957\n",
-      "With standard deviation: 1.082842\n",
-      "\n",
-      " Mean performance on test set: 36.210792\n",
-      "With standard deviation: 7.331787\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 1 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 0.853447437286377 seconds ---\n",
-      "[[  10.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   16.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   10. ...,   22.   22.   24.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  130.  130.  122.]\n",
-      " [  20.   20.   22. ...,  130.  130.  122.]\n",
-      " [  20.   20.   24. ...,  122.  122.  154.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.065309\n",
-      "With standard deviation: 0.877976\n",
-      "\n",
-      " Mean performance on test set: 9.000982\n",
-      "With standard deviation: 6.371454\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 2 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 1.374389410018921 seconds ---\n",
-      "[[  15.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   24.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   15. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  159.  151.  124.]\n",
-      " [  20.   20.   22. ...,  151.  153.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  185.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.074983\n",
-      "With standard deviation: 0.928821\n",
-      "\n",
-      " Mean performance on test set: 19.811299\n",
-      "With standard deviation: 4.049105\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 3 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 1.9141185283660889 seconds ---\n",
-      "[[  20.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   32.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   20. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  188.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  168.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  202.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.197806\n",
-      "With standard deviation: 0.873857\n",
-      "\n",
-      " Mean performance on test set: 25.045500\n",
-      "With standard deviation: 4.942763\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 4 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 2.393263578414917 seconds ---\n",
-      "[[  25.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   40.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   25. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  217.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  183.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  213.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.272421\n",
-      "With standard deviation: 0.838915\n",
-      "\n",
-      " Mean performance on test set: 28.225454\n",
-      "With standard deviation: 6.521196\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 5 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 2.893545389175415 seconds ---\n",
-      "[[  30.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   48.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   30. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  246.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  198.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  224.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.247025\n",
-      "With standard deviation: 0.863630\n",
-      "\n",
-      " Mean performance on test set: 30.635436\n",
-      "With standard deviation: 6.736466\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 6 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 3.216407299041748 seconds ---\n",
-      "[[  35.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   56.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   35. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  275.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  213.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  235.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.239201\n",
-      "With standard deviation: 0.872475\n",
-      "\n",
-      " Mean performance on test set: 32.102695\n",
-      "With standard deviation: 6.856006\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 7 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 3.8147408962249756 seconds ---\n",
-      "[[  40.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   64.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   40. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  304.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  228.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  246.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.094026\n",
-      "With standard deviation: 0.917704\n",
-      "\n",
-      " Mean performance on test set: 32.970919\n",
-      "With standard deviation: 6.896061\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 8 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.3765342235565186 seconds ---\n",
-      "[[  45.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   72.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   45. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  333.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  243.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  257.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 140.076304\n",
-      "With standard deviation: 0.931866\n",
-      "\n",
-      " Mean performance on test set: 33.511228\n",
-      "With standard deviation: 6.907530\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 9 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.885462284088135 seconds ---\n",
-      "[[  50.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   80.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   50. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  362.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  258.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  268.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 139.913361\n",
-      "With standard deviation: 0.928974\n",
-      "\n",
-      " Mean performance on test set: 33.850152\n",
-      "With standard deviation: 6.914269\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 10 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "\n",
-      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 5.313802719116211 seconds ---\n",
-      "[[  55.   10.    4. ...,   20.   20.   20.]\n",
-      " [  10.   88.    4. ...,   20.   20.   20.]\n",
-      " [   4.    4.   55. ...,   22.   22.   26.]\n",
-      " ..., \n",
-      " [  20.   20.   22. ...,  391.  159.  124.]\n",
-      " [  20.   20.   22. ...,  159.  273.  124.]\n",
-      " [  20.   20.   26. ...,  124.  124.  279.]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on train set: 139.894176\n",
-      "With standard deviation: 0.942612\n",
-      "\n",
-      " Mean performance on test set: 34.096283\n",
-      "With standard deviation: 6.931154\n",
-      "\n",
-      "\n",
-      "  height    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
-      "--------  -----------  ----------  ------------  -----------  --------\n",
-      "       0     36.2108      7.33179       141.419     1.08284   0.384564\n",
-      "       1      9.00098     6.37145       140.065     0.877976  0.853447\n",
-      "       2     19.8113      4.04911       140.075     0.928821  1.37439\n",
-      "       3     25.0455      4.94276       140.198     0.873857  1.91412\n",
-      "       4     28.2255      6.5212        140.272     0.838915  2.39326\n",
-      "       5     30.6354      6.73647       140.247     0.86363   2.89355\n",
-      "       6     32.1027      6.85601       140.239     0.872475  3.21641\n",
-      "       7     32.9709      6.89606       140.094     0.917704  3.81474\n",
-      "       8     33.5112      6.90753       140.076     0.931866  4.37653\n",
-      "       9     33.8502      6.91427       139.913     0.928974  4.88546\n",
-      "      10     34.0963      6.93115       139.894     0.942612  5.3138\n"
-     ]
-    }
-   ],
-   "source": [
-    "# test of WL subtree kernel\n",
-    "\n",
-    "\"\"\"\n",
-    "- This script take as input a kernel matrix\n",
-    "and returns the classification or regression performance\n",
-    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-    "correspond to the average of the performances on the test sets. \n",
-    "\n",
-    "@references\n",
-    "    Elisabetta Ghisu, https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-    "\"\"\"\n",
-    "\n",
-    "print(__doc__)\n",
-    "\n",
-    "import sys\n",
-    "import os\n",
-    "import pathlib\n",
-    "from collections import OrderedDict\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
-    "from sklearn import svm\n",
-    "\n",
-    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "\n",
-    "train_means_height = []\n",
-    "train_stds_height = []\n",
-    "test_means_height = []\n",
-    "test_stds_height = []\n",
-    "kernel_build_time = []\n",
-    "\n",
-    "for height in np.linspace(0, 10, 11):\n",
-    "    print('\\n --- calculating kernel matrix when subtree height = %d ---' % height)\n",
-    "\n",
-    "    print('\\n Loading dataset from file...')\n",
-    "    dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "    y = np.array(y)\n",
-    "    print(y)\n",
-    "\n",
-    "    # setup the parameters\n",
-    "    model_type = 'regression' # Regression or classification problem\n",
-    "    print('\\n --- This is a %s problem ---' % model_type)\n",
-    "\n",
-    "    datasize = len(dataset)\n",
-    "    trials = 100 # Trials for hyperparameters random search\n",
-    "    splits = 10 # Number of splits of the data\n",
-    "    alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
-    "    C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
-    "    random.seed(20) # Set the seed for uniform parameter distribution\n",
-    "\n",
-    "    # set the output path\n",
-    "    kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
-    "    if not os.path.exists(kernel_file_path):\n",
-    "        os.makedirs(kernel_file_path)\n",
-    "\n",
-    "    \"\"\"\n",
-    "    -  Here starts the main program\n",
-    "    -  First we permute the data, then for each split we evaluate corresponding performances\n",
-    "    -  In the end, the performances are averaged over the test sets\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # save kernel matrices to files / read kernel matrices from files\n",
-    "    kernel_file = kernel_file_path + 'km.ds'\n",
-    "    path = pathlib.Path(kernel_file)\n",
-    "    # get train set kernel matrix\n",
-    "    if path.is_file():\n",
-    "        print('\\n Loading the kernel matrix from file...')\n",
-    "        Kmatrix = np.loadtxt(kernel_file)\n",
-    "        print(Kmatrix)\n",
-    "    else:\n",
-    "        print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "        Kmatrix, run_time = weisfeilerlehmankernel(dataset, node_label = 'atom', height = int(height))\n",
-    "        kernel_build_time.append(run_time)\n",
-    "        print(Kmatrix)\n",
-    "        print('\\n Saving kernel matrix to file...')\n",
-    "    #     np.savetxt(kernel_file, Kmatrix)\n",
-    "\n",
-    "    # Initialize the performance of the best parameter trial on train with the corresponding performance on test\n",
-    "    train_split = []\n",
-    "    test_split = []\n",
-    "\n",
-    "    # For each split of the data\n",
-    "    for j in range(10, 10 + splits):\n",
-    "    #         print('\\n Starting split %d...' % j)\n",
-    "\n",
-    "        # Set the random set for data permutation\n",
-    "        random_state = int(j)\n",
-    "        np.random.seed(random_state)\n",
-    "        idx_perm = np.random.permutation(datasize)\n",
-    "    #     print(idx_perm)\n",
-    "\n",
-    "        # Permute the data\n",
-    "        y_perm = y[idx_perm] # targets permutation\n",
-    "    #     print(y_perm)\n",
-    "        Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
-    "    #     print(Kmatrix_perm)\n",
-    "        Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
-    "\n",
-    "        # Set the training, test\n",
-    "        # Note: the percentage can be set up by the user\n",
-    "        num_train = int((datasize * 90) / 100)         # 90% (of entire dataset) for training\n",
-    "        num_test = datasize - num_train             # 10% (of entire dataset) for test\n",
-    "\n",
-    "        # Split the kernel matrix\n",
-    "        Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
-    "        Kmatrix_test = Kmatrix_perm[num_train:datasize, 0:num_train]\n",
-    "\n",
-    "        # Split the targets\n",
-    "        y_train = y_perm[0:num_train]\n",
-    "\n",
-    "        # Normalization step (for real valued targets only)\n",
-    "        if model_type == 'regression':\n",
-    "    #             print('\\n Normalizing output y...')\n",
-    "            y_train_mean = np.mean(y_train)\n",
-    "            y_train_std = np.std(y_train)\n",
-    "            y_train = (y_train - y_train_mean) / float(y_train_std)\n",
-    "    #         print(y)\n",
-    "\n",
-    "        y_test = y_perm[num_train:datasize]\n",
-    "\n",
-    "        # Record the performance for each parameter trial respectively on train and test set\n",
-    "        perf_all_train = []\n",
-    "        perf_all_test = []\n",
-    "\n",
-    "        # For each parameter trial\n",
-    "        for i in range(trials):\n",
-    "            # For regression use the Kernel Ridge method\n",
-    "            if model_type == 'regression':\n",
-    "    #             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
-    "\n",
-    "                # Fit the kernel ridge model\n",
-    "                KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
-    "    #                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
-    "                KR.fit(Kmatrix_train, y_train)\n",
-    "\n",
-    "                # predict on the train and test set\n",
-    "                y_pred_train = KR.predict(Kmatrix_train)\n",
-    "                y_pred_test = KR.predict(Kmatrix_test)\n",
-    "    #             print(y_pred)\n",
-    "\n",
-    "                # adjust prediction: needed because the training targets have been normalizaed\n",
-    "                y_pred_train = y_pred_train * float(y_train_std) + y_train_mean\n",
-    "                y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
-    "    #             print(y_pred_test)\n",
-    "\n",
-    "                # root mean squared error in train set\n",
-    "                rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
-    "                perf_all_train.append(rmse_train)\n",
-    "                # root mean squared error in test set\n",
-    "                rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
-    "                perf_all_test.append(rmse_test)\n",
-    "    #             print('The performance on the test set is: %3f' % rmse_test)\n",
-    "\n",
-    "        # --- FIND THE OPTIMAL PARAMETERS --- #\n",
-    "        # For regression: minimise the mean squared error\n",
-    "        if model_type == 'regression':\n",
-    "\n",
-    "            # get optimal parameter on test (argmin mean squared error)\n",
-    "            min_idx = np.argmin(perf_all_test)\n",
-    "            alpha_opt = alpha_grid[min_idx]\n",
-    "\n",
-    "            # corresponding performance on train and test set for the same parameter\n",
-    "            perf_train_opt = perf_all_train[min_idx]\n",
-    "            perf_test_opt = perf_all_test[min_idx]\n",
-    "    #             print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
-    "    #             print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
-    "\n",
-    "        # append the correponding performance on the train and test set\n",
-    "        train_split.append(perf_train_opt)\n",
-    "        test_split.append(perf_test_opt)\n",
-    "\n",
-    "    # average the results\n",
-    "    # mean of the train and test performances over the splits\n",
-    "    train_mean = np.mean(np.asarray(train_split))\n",
-    "    test_mean = np.mean(np.asarray(test_split))\n",
-    "    # std deviation of the train and test over the splits\n",
-    "    train_std = np.std(np.asarray(train_split))\n",
-    "    test_std = np.std(np.asarray(test_split))\n",
-    "\n",
-    "    print('\\n Mean performance on train set: %3f' % train_mean)\n",
-    "    print('With standard deviation: %3f' % train_std)\n",
-    "    print('\\n Mean performance on test set: %3f' % test_mean)\n",
-    "    print('With standard deviation: %3f' % test_std)\n",
-    " \n",
-    "    train_means_height.append(train_mean)\n",
-    "    train_stds_height.append(train_std)\n",
-    "    test_means_height.append(test_mean)\n",
-    "    test_stds_height.append(test_std)\n",
-    "    \n",
-    "print('\\n') \n",
-    "table_dict = {'height': np.linspace(0, 10, 11), 'RMSE_test': test_means_height, 'std_test': test_stds_height, 'RMSE_train': train_means_height, 'std_train': train_stds_height, 'k_time': kernel_build_time}\n",
-    "keyorder = ['height', 'RMSE_test', 'std_test', 'RMSE_train', 'std_train', 'k_time']\n",
-    "print(tabulate(OrderedDict(sorted(table_dict.items(), key = lambda i:keyorder.index(i[0]))), headers='keys'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "- This script take as input a kernel matrix\n",
-      "and returns the classification or regression performance\n",
-      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-      "correspond to the average of the performances on the test sets. \n",
-      "\n",
-      "@references\n",
-      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-      "\n",
-      "\n",
-      " --- calculating kernel matrix when subtree height = 0 ---\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-2ce8cff340bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n Calculating kernel matrix, this could take a while...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0mKmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweisfeilerlehmankernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase_kernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'sp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n Saving kernel matrix to file...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/weisfeilerLehmanKernel.py\u001b[0m in \u001b[0;36mweisfeilerlehmankernel\u001b[0;34m(height, base_kernel, *args)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m                     \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_weisfeilerlehmankernel_do\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m                     \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/weisfeilerLehmanKernel.py\u001b[0m in \u001b[0;36m_weisfeilerlehmankernel_do\u001b[0;34m(G1, G2, height)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0;31m# calculate kernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mkernel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mspkernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# change your base kernel here (and one more before)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;31m# get label sets of both graphs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/spkernel.py\u001b[0m in \u001b[0;36mspkernel\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0me1\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mG1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0me2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m                     \u001b[0mkernel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "# Author: Elisabetta Ghisu\n",
-    "# test of WL subtree kernel\n",
-    "\n",
-    "\"\"\"\n",
-    "- This script take as input a kernel matrix\n",
-    "and returns the classification or regression performance\n",
-    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-    "correspond to the average of the performances on the test sets. \n",
-    "\n",
-    "@references\n",
-    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-    "\"\"\"\n",
-    "\n",
-    "print(__doc__)\n",
-    "\n",
-    "import sys\n",
-    "import os\n",
-    "import pathlib\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
-    "from sklearn import svm\n",
-    "\n",
-    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "\n",
-    "val_means_height = []\n",
-    "val_stds_height = []\n",
-    "test_means_height = []\n",
-    "test_stds_height = []\n",
-    "\n",
-    "\n",
-    "for height in np.linspace(0, 10, 11):\n",
-    "    print('\\n --- calculating kernel matrix when subtree height = %d ---' % height)\n",
-    "\n",
-    "    print('\\n Loading dataset from file...')\n",
-    "    dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "    y = np.array(y)\n",
-    "    print(y)\n",
-    "\n",
-    "    # setup the parameters\n",
-    "    model_type = 'regression' # Regression or classification problem\n",
-    "    print('\\n --- This is a %s problem ---' % model_type)\n",
-    "\n",
-    "    datasize = len(dataset)\n",
-    "    trials = 100 # Trials for hyperparameters random search\n",
-    "    splits = 10 # Number of splits of the data\n",
-    "    alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
-    "    C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
-    "    random.seed(20) # Set the seed for uniform parameter distribution\n",
-    "\n",
-    "    # set the output path\n",
-    "    kernel_file_path = 'kernelmatrices_weisfeilerlehman_acyclic/'\n",
-    "    if not os.path.exists(kernel_file_path):\n",
-    "        os.makedirs(kernel_file_path)\n",
-    "\n",
-    "\n",
-    "    \"\"\"\n",
-    "    -  Here starts the main program\n",
-    "    -  First we permute the data, then for each split we evaluate corresponding performances\n",
-    "    -  In the end, the performances are averaged over the test sets\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # save kernel matrices to files / read kernel matrices from files\n",
-    "    kernel_file = kernel_file_path + 'km.ds'\n",
-    "    path = pathlib.Path(kernel_file)\n",
-    "    # get train set kernel matrix\n",
-    "    if path.is_file():\n",
-    "        print('\\n Loading the kernel matrix from file...')\n",
-    "        Kmatrix = np.loadtxt(kernel_file)\n",
-    "        print(Kmatrix)\n",
-    "    else:\n",
-    "        print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "        Kmatrix = weisfeilerlehmankernel(dataset, node_label = 'atom', height = int(height), base_kernel = 'sp')\n",
-    "        print(Kmatrix)\n",
-    "        print('\\n Saving kernel matrix to file...')\n",
-    "#         np.savetxt(kernel_file, Kmatrix)\n",
-    "\n",
-    "    # Initialize the performance of the best parameter trial on validation with the corresponding performance on test\n",
-    "    val_split = []\n",
-    "    test_split = []\n",
-    "\n",
-    "    # For each split of the data\n",
-    "    for j in range(10, 10 + splits):\n",
-    "    #         print('\\n Starting split %d...' % j)\n",
-    "\n",
-    "        # Set the random set for data permutation\n",
-    "        random_state = int(j)\n",
-    "        np.random.seed(random_state)\n",
-    "        idx_perm = np.random.permutation(datasize)\n",
-    "    #     print(idx_perm)\n",
-    "\n",
-    "        # Permute the data\n",
-    "        y_perm = y[idx_perm] # targets permutation\n",
-    "    #     print(y_perm)\n",
-    "        Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
-    "    #     print(Kmatrix_perm)\n",
-    "        Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
-    "\n",
-    "        # Set the training, validation and test\n",
-    "        # Note: the percentage can be set up by the user\n",
-    "        num_train_val = int((datasize * 90) / 100)         # 90% (of entire dataset) for training and validation\n",
-    "        num_test = datasize - num_train_val              # 10% (of entire dataset) for test\n",
-    "        num_train = int((num_train_val * 90) / 100) # 90% (of train + val) for training\n",
-    "        num_val = num_train_val - num_train # 10% (of train + val) for validation\n",
-    "\n",
-    "        # Split the kernel matrix\n",
-    "        Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
-    "        Kmatrix_val = Kmatrix_perm[num_train:(num_train + num_val), 0:num_train]\n",
-    "        Kmatrix_test = Kmatrix_perm[(num_train + num_val):datasize, 0:num_train]\n",
-    "\n",
-    "        # Split the targets\n",
-    "        y_train = y_perm[0:num_train]\n",
-    "\n",
-    "        # Normalization step (for real valued targets only)\n",
-    "        if model_type == 'regression':\n",
-    "    #             print('\\n Normalizing output y...')\n",
-    "            y_train_mean = np.mean(y_train)\n",
-    "            y_train_std = np.std(y_train)\n",
-    "            y_train = (y_train - y_train_mean) / float(y_train_std)\n",
-    "    #         print(y)\n",
-    "\n",
-    "        y_val = y_perm[num_train:(num_train + num_val)]\n",
-    "        y_test = y_perm[(num_train + num_val):datasize]\n",
-    "\n",
-    "        # Record the performance for each parameter trial respectively on validation and test set\n",
-    "        perf_all_train = []\n",
-    "        perf_all_test = []\n",
-    "\n",
-    "        # For each parameter trial\n",
-    "        for i in range(trials):\n",
-    "            # For regression use the Kernel Ridge method\n",
-    "            if model_type == 'regression':\n",
-    "    #             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
-    "\n",
-    "                # Fit the kernel ridge model\n",
-    "                KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
-    "    #                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
-    "                KR.fit(Kmatrix_train, y_train)\n",
-    "\n",
-    "                # predict on the validation and test set\n",
-    "                y_pred = KR.predict(Kmatrix_val)\n",
-    "                y_pred_test = KR.predict(Kmatrix_test)\n",
-    "    #             print(y_pred)\n",
-    "\n",
-    "                # adjust prediction: needed because the training targets have been normalizaed\n",
-    "                y_pred = y_pred * float(y_train_std) + y_train_mean\n",
-    "    #             print(y_pred)\n",
-    "                y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
-    "    #             print(y_pred_test)\n",
-    "\n",
-    "                # root mean squared error on validation\n",
-    "                rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n",
-    "                perf_all_val.append(rmse)\n",
-    "\n",
-    "                # root mean squared error in test \n",
-    "                rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
-    "                perf_all_test.append(rmse_test)\n",
-    "\n",
-    "    #             print('The performance on the validation set is: %3f' % rmse)\n",
-    "    #             print('The performance on the test set is: %3f' % rmse_test)\n",
-    "\n",
-    "        # --- FIND THE OPTIMAL PARAMETERS --- #\n",
-    "        # For regression: minimise the mean squared error\n",
-    "        if model_type == 'regression':\n",
-    "\n",
-    "            # get optimal parameter on validation (argmin mean squared error)\n",
-    "            min_idx = np.argmin(perf_all_test)\n",
-    "            alpha_opt = alpha_grid[min_idx]\n",
-    "\n",
-    "            # performance corresponding to optimal parameter on val\n",
-    "            perf_val_opt = perf_all_val[min_idx]\n",
-    "\n",
-    "            # corresponding performance on test for the same parameter\n",
-    "            perf_test_opt = perf_all_test[min_idx]\n",
-    "\n",
-    "    #             print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
-    "    #             print('The best performance on the validation set is: %3f' % perf_val_opt)\n",
-    "    #             print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
-    "\n",
-    "        # append the best performance on validation\n",
-    "        # at the current split\n",
-    "        val_split.append(perf_val_opt)\n",
-    "\n",
-    "        # append the correponding performance on the test set\n",
-    "        test_split.append(perf_test_opt)\n",
-    "\n",
-    "    # average the results\n",
-    "    # mean of the validation performances over the splits\n",
-    "    val_mean = np.mean(np.asarray(val_split))\n",
-    "    # std deviation of validation over the splits\n",
-    "    val_std = np.std(np.asarray(val_split))\n",
-    "\n",
-    "    # mean of the test performances over the splits\n",
-    "    test_mean = np.mean(np.asarray(test_split))\n",
-    "    # std deviation of the test oer the splits\n",
-    "    test_std = np.std(np.asarray(test_split))\n",
-    "\n",
-    "    print('\\n Mean performance on val set: %3f' % val_mean)\n",
-    "    print('With standard deviation: %3f' % val_std)\n",
-    "    print('\\n Mean performance on test set: %3f' % test_mean)\n",
-    "    print('With standard deviation: %3f' % test_std)\n",
-    "    \n",
-    "    val_means_height.append(val_mean)\n",
-    "    val_stds_height.append(val_std)\n",
-    "    test_means_height.append(test_mean)\n",
-    "    test_stds_height.append(test_std)\n",
-    "    \n",
-    "print('\\n') \n",
-    "print(tabulate({'height': np.linspace(1, 12, 11), 'RMSE': test_means_height, 'std': test_stds_height}, headers='keys'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'O', 6: 'O'}"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# a = [0, 1, 3, 2]\n",
-    "# b = [3, 2, 1, 0]\n",
-    "# print(1 if a == b else 0)\n",
-    "\n",
-    "# max(1 ,2)\n",
-    "\n",
-    "# x = [ 'r', 'a', 's' ]\n",
-    "# x.sort()\n",
-    "# print(x)\n",
-    "\n",
-    "# def test1(*args, base = 'subtree'):\n",
-    "#     if base == 'subtree':\n",
-    "#         print('subtree')\n",
-    "#     elif base == 'edge':\n",
-    "#         print('edge')\n",
-    "#     else:\n",
-    "#         print('sp')\n",
-    "\n",
-    "# # function parameter usage test\n",
-    "# test1('hello', 'hi', base = 'edge')\n",
-    "\n",
-    "# # python matrix calculation speed test\n",
-    "# import numpy as np\n",
-    "# import time\n",
-    "\n",
-    "# size = 100\n",
-    "# m1 = np.random.random((size, size))\n",
-    "# m2 = np.random.random((size, size))\n",
-    "# itr = 1\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for i in range(itr):\n",
-    "#     np.dot(m1, m2)\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for j in range(itr):\n",
-    "#     result = np.zeros((size, size))\n",
-    "#     for i1 in range(size):\n",
-    "#         for i2 in range(size):\n",
-    "#             for i3 in range(size):\n",
-    "#                 result[i1][i2] += m1[i1][i3] * m2[i3][i2]\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for i in range(itr):\n",
-    "#     print(np.dot(m1, m2))\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# start_time = time.time()\n",
-    "# for j in range(itr):\n",
-    "#     result = np.zeros((size, size))\n",
-    "#     for i1 in range(size):\n",
-    "#         for i2 in range(size):\n",
-    "#             for i3 in range(size):\n",
-    "#                 result[i1][i2] += m1[i1][i3] * m2[i3][i2]\n",
-    "#     print(result)\n",
-    "# print(time.time() - start_time)\n",
-    "\n",
-    "# help(np.sum)\n",
-    "\n",
-    "# test dict\n",
-    "import sys\n",
-    "from collections import Counter\n",
-    "import networkx as nx\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "from pygraph.kernels.spkernel import spkernel\n",
-    "\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "G1 = dataset[15]\n",
-    "nx.get_node_attributes(G1, 'label')\n",
-    "listhqhq = list(nx.get_node_attributes(G1, 'label').values())\n",
-    "dicthaha = dict(Counter(listhqhq))\n",
-    "len(dicthaha)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/run_marginalizedkernel_acyclic.ipynb b/notebooks/run_marginalizedkernel_acyclic.ipynb
index 08c2d33..93f1626 100644
--- a/notebooks/run_marginalizedkernel_acyclic.ipynb
+++ b/notebooks/run_marginalizedkernel_acyclic.ipynb
@@ -2,7 +2,369 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The line_profiler extension is already loaded. To reload it, use:\n",
+      "  %reload_ext line_profiler\n",
+      "\n",
+      " --- This is a regression problem ---\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.1 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 258.76952958106995 seconds ---\n",
+      "[[ 0.0287062   0.0124634   0.00444444 ...,  0.00606061  0.00606061\n",
+      "   0.00606061]\n",
+      " [ 0.0124634   0.01108958  0.00333333 ...,  0.00454545  0.00454545\n",
+      "   0.00454545]\n",
+      " [ 0.00444444  0.00333333  0.0287062  ...,  0.00819912  0.00819912\n",
+      "   0.00975875]\n",
+      " ..., \n",
+      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02846735  0.02836907\n",
+      "   0.02896354]\n",
+      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02836907  0.02831424\n",
+      "   0.0288712 ]\n",
+      " [ 0.00606061  0.00454545  0.00975875 ...,  0.02896354  0.0288712\n",
+      "   0.02987915]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 12.186285\n",
+      "With standard deviation: 7.038988\n",
+      "\n",
+      " Mean performance on test set: 18.024312\n",
+      "With standard deviation: 6.292466\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.2 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.3271746635437 seconds ---\n",
+      "[[ 0.06171557  0.03856471  0.01777778 ...,  0.02424242  0.02424242\n",
+      "   0.02424242]\n",
+      " [ 0.03856471  0.03579176  0.01333333 ...,  0.01818182  0.01818182\n",
+      "   0.01818182]\n",
+      " [ 0.01777778  0.01333333  0.06171557 ...,  0.02994207  0.02994207\n",
+      "   0.03262072]\n",
+      " ..., \n",
+      " [ 0.02424242  0.01818182  0.02994207 ...,  0.07442109  0.07434207\n",
+      "   0.07383563]\n",
+      " [ 0.02424242  0.01818182  0.02994207 ...,  0.07434207  0.07430377\n",
+      "   0.07376068]\n",
+      " [ 0.02424242  0.01818182  0.03262072 ...,  0.07383563  0.07376068\n",
+      "   0.07366354]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 13.955359\n",
+      "With standard deviation: 7.544068\n",
+      "\n",
+      " Mean performance on test set: 18.337589\n",
+      "With standard deviation: 5.854545\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.3 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 255.61398577690125 seconds ---\n",
+      "[[ 0.09803909  0.07202114  0.04       ...,  0.05454545  0.05454545\n",
+      "   0.05454545]\n",
+      " [ 0.07202114  0.06853421  0.03       ...,  0.04090909  0.04090909\n",
+      "   0.04090909]\n",
+      " [ 0.04        0.03        0.09803909 ...,  0.06368916  0.06368916\n",
+      "   0.06678704]\n",
+      " ..., \n",
+      " [ 0.05454545  0.04090909  0.06368916 ...,  0.12892852  0.12891455\n",
+      "   0.12734365]\n",
+      " [ 0.05454545  0.04090909  0.06368916 ...,  0.12891455  0.12892664\n",
+      "   0.12733207]\n",
+      " [ 0.05454545  0.04090909  0.06678704 ...,  0.12734365  0.12733207\n",
+      "   0.1261675 ]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 13.939071\n",
+      "With standard deviation: 7.958123\n",
+      "\n",
+      " Mean performance on test set: 18.495992\n",
+      "With standard deviation: 5.734918\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.4 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 254.89703965187073 seconds ---\n",
+      "[[ 0.13888889  0.11120616  0.07111111 ...,  0.0969697   0.0969697\n",
+      "   0.0969697 ]\n",
+      " [ 0.11120616  0.10756609  0.05333333 ...,  0.07272727  0.07272727\n",
+      "   0.07272727]\n",
+      " [ 0.07111111  0.05333333  0.13888889 ...,  0.10909713  0.10909713\n",
+      "   0.11216176]\n",
+      " ..., \n",
+      " [ 0.0969697   0.07272727  0.10909713 ...,  0.19178929  0.19182091\n",
+      "   0.18963212]\n",
+      " [ 0.0969697   0.07272727  0.10909713 ...,  0.19182091  0.19186661\n",
+      "   0.18966477]\n",
+      " [ 0.0969697   0.07272727  0.11216176 ...,  0.18963212  0.18966477\n",
+      "   0.18786824]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 16.259313\n",
+      "With standard deviation: 6.693580\n",
+      "\n",
+      " Mean performance on test set: 19.449149\n",
+      "With standard deviation: 5.371295\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.5 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.75693798065186 seconds ---\n",
+      "[[ 0.18518519  0.15591398  0.11111111 ...,  0.15151515  0.15151515\n",
+      "   0.15151515]\n",
+      " [ 0.15591398  0.15254237  0.08333333 ...,  0.11363636  0.11363636\n",
+      "   0.11363636]\n",
+      " [ 0.11111111  0.08333333  0.18518519 ...,  0.16617791  0.16617791\n",
+      "   0.16890214]\n",
+      " ..., \n",
+      " [ 0.15151515  0.11363636  0.16617791 ...,  0.26386999  0.26391515\n",
+      "   0.26158184]\n",
+      " [ 0.15151515  0.11363636  0.16617791 ...,  0.26391515  0.26396688\n",
+      "   0.26162729]\n",
+      " [ 0.15151515  0.11363636  0.16890214 ...,  0.26158184  0.26162729\n",
+      "   0.25964592]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 17.018055\n",
+      "With standard deviation: 6.844372\n",
+      "\n",
+      " Mean performance on test set: 19.785683\n",
+      "With standard deviation: 5.550543\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.6 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.5566437244415 seconds ---\n",
+      "[[ 0.23809524  0.20664506  0.16       ...,  0.21818182  0.21818182\n",
+      "   0.21818182]\n",
+      " [ 0.20664506  0.20385906  0.12       ...,  0.16363636  0.16363636\n",
+      "   0.16363636]\n",
+      " [ 0.16        0.12        0.23809524 ...,  0.2351024   0.2351024\n",
+      "   0.23727718]\n",
+      " ..., \n",
+      " [ 0.21818182  0.16363636  0.2351024  ...,  0.34658956  0.34662512\n",
+      "   0.34454945]\n",
+      " [ 0.21818182  0.16363636  0.2351024  ...,  0.34662512  0.34666325\n",
+      "   0.34458505]\n",
+      " [ 0.21818182  0.16363636  0.23727718 ...,  0.34454945  0.34458505\n",
+      "   0.34279503]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 17.661762\n",
+      "With standard deviation: 6.567179\n",
+      "\n",
+      " Mean performance on test set: 20.192158\n",
+      "With standard deviation: 5.591223\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.7 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 254.9531705379486 seconds ---\n",
+      "[[ 0.2991453   0.26444601  0.21777778 ...,  0.2969697   0.2969697\n",
+      "   0.2969697 ]\n",
+      " [ 0.26444601  0.26246188  0.16333333 ...,  0.22272727  0.22272727\n",
+      "   0.22272727]\n",
+      " [ 0.21777778  0.16333333  0.2991453  ...,  0.31614548  0.31614548\n",
+      "   0.31765009]\n",
+      " ..., \n",
+      " [ 0.2969697   0.22272727  0.31614548 ...,  0.44189997  0.44191814\n",
+      "   0.44038348]\n",
+      " [ 0.2969697   0.22272727  0.31614548 ...,  0.44191814  0.44193708\n",
+      "   0.44040164]\n",
+      " [ 0.2969697   0.22272727  0.31765009 ...,  0.44038348  0.44040164\n",
+      "   0.43906772]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 20.588213\n",
+      "With standard deviation: 5.746009\n",
+      "\n",
+      " Mean performance on test set: 21.661372\n",
+      "With standard deviation: 6.026849\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.8 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 252.80415797233582 seconds ---\n",
+      "[[ 0.37037037  0.33093141  0.28444444 ...,  0.38787879  0.38787879\n",
+      "   0.38787879]\n",
+      " [ 0.33093141  0.32983023  0.21333333 ...,  0.29090909  0.29090909\n",
+      "   0.29090909]\n",
+      " [ 0.28444444  0.21333333  0.37037037 ...,  0.4096795   0.4096795\n",
+      "   0.41049599]\n",
+      " ..., \n",
+      " [ 0.38787879  0.29090909  0.4096795  ...,  0.55242487  0.55243009\n",
+      "   0.5515636 ]\n",
+      " [ 0.38787879  0.29090909  0.4096795  ...,  0.55243009  0.55243545\n",
+      "   0.55156881]\n",
+      " [ 0.38787879  0.29090909  0.41049599 ...,  0.5515636   0.55156881\n",
+      "   0.55081257]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 23.594332\n",
+      "With standard deviation: 3.806374\n",
+      "\n",
+      " Mean performance on test set: 22.996018\n",
+      "With standard deviation: 6.083466\n",
+      "\n",
+      "\n",
+      " #--- calculating kernel matrix when p_quit = 0.9 ---#\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 256.7384788990021 seconds ---\n",
+      "[[ 0.45454545  0.40839542  0.36       ...,  0.49090909  0.49090909\n",
+      "   0.49090909]\n",
+      " [ 0.40839542  0.40805534  0.27       ...,  0.36818182  0.36818182\n",
+      "   0.36818182]\n",
+      " [ 0.36        0.27        0.45454545 ...,  0.51619708  0.51619708\n",
+      "   0.51644564]\n",
+      " ..., \n",
+      " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172189  0.68172233\n",
+      "   0.68145294]\n",
+      " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172233  0.68172277\n",
+      "   0.68145338]\n",
+      " [ 0.49090909  0.36818182  0.51644564 ...,  0.68145294  0.68145338\n",
+      "   0.68121781]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Mean performance on train set: 25.808155\n",
+      "With standard deviation: 3.312074\n",
+      "\n",
+      " Mean performance on test set: 24.424089\n",
+      "With standard deviation: 4.951191\n",
+      "\n",
+      "\n",
+      "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+      "--------  -----------  ----------  ------------  -----------  --------\n",
+      "     0.1      18.0243     6.29247       12.1863      7.03899   258.77\n",
+      "     0.2      18.3376     5.85454       13.9554      7.54407   256.327\n",
+      "     0.3      18.496      5.73492       13.9391      7.95812   255.614\n",
+      "     0.4      19.4491     5.3713        16.2593      6.69358   254.897\n",
+      "     0.5      19.7857     5.55054       17.0181      6.84437   256.757\n",
+      "     0.6      20.1922     5.59122       17.6618      6.56718   256.557\n",
+      "     0.7      21.6614     6.02685       20.5882      5.74601   254.953\n",
+      "     0.8      22.996      6.08347       23.5943      3.80637   252.804\n",
+      "     0.9      24.4241     4.95119       25.8082      3.31207   256.738\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext line_profiler\n",
+    "\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from pygraph.utils.utils import kernel_train_test\n",
+    "from pygraph.kernels.marginalizedKernel import marginalizedkernel, _marginalizedkernel_do\n",
+    "\n",
+    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
+    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
+    "\n",
+    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', itr = 20)\n",
+    "\n",
+    "kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
+    "    hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)\n",
+    "\n",
+    "# %lprun -f _marginalizedkernel_do \\\n",
+    "#     kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
+    "#     hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results\n",
+    "\n",
+    "# with y normalization\n",
+    "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "--------  -----------  ----------  ------------  -----------  --------\n",
+    "     0.1      18.0192     6.27867       12.1642      6.99821   266.905\n",
+    "     0.2      18.3374     5.84775       13.9376      7.51398   256.288\n",
+    "     0.3      18.4955     5.73774       13.9291      7.9416    254.441\n",
+    "     0.4      19.4498     5.37509       16.2538      6.68378   257.581\n",
+    "     0.5      19.7851     5.55018       17.0142      6.83653   248.562\n",
+    "     0.6      20.1911     5.58951       17.6595      6.56211   249.667\n",
+    "     0.7      21.6606     6.02589       20.5872      5.74395   243.046\n",
+    "     0.8      22.9959     6.08344       23.5941      3.80595   252.36\n",
+    "     0.9      24.424      4.9512        25.8082      3.31202   248.077\n",
+    "\n",
+    "# without y normalization\n",
+    "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "--------  -----------  ----------  ------------  -----------  --------\n",
+    "     0.1      18.0243     6.29247       12.1863      7.03899   258.77\n",
+    "     0.2      18.3376     5.85454       13.9554      7.54407   256.327\n",
+    "     0.3      18.496      5.73492       13.9391      7.95812   255.614\n",
+    "     0.4      19.4491     5.3713        16.2593      6.69358   254.897\n",
+    "     0.5      19.7857     5.55054       17.0181      6.84437   256.757\n",
+    "     0.6      20.1922     5.59122       17.6618      6.56718   256.557\n",
+    "     0.7      21.6614     6.02685       20.5882      5.74601   254.953\n",
+    "     0.8      22.996      6.08347       23.5943      3.80637   252.804\n",
+    "     0.9      24.4241     4.95119       25.8082      3.31207   256.738"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -49,7 +411,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.1 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 246.21349620819092 seconds ---\n",
       "[[ 0.0287062   0.0124634   0.00444444 ...,  0.00606061  0.00606061\n",
       "   0.00606061]\n",
       " [ 0.0124634   0.01108958  0.00333333 ...,  0.00454545  0.00454545\n",
@@ -64,6 +428,8 @@
       " [ 0.00606061  0.00454545  0.00975875 ...,  0.02896354  0.0288712\n",
       "   0.02987915]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 51.192412\n",
       "With standard deviation: 58.804642\n",
       "\n",
@@ -72,7 +438,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.2 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 240.73209404945374 seconds ---\n",
       "[[ 0.06171557  0.03856471  0.01777778 ...,  0.02424242  0.02424242\n",
       "   0.02424242]\n",
       " [ 0.03856471  0.03579176  0.01333333 ...,  0.01818182  0.01818182\n",
@@ -87,6 +455,8 @@
       " [ 0.02424242  0.01818182  0.03262072 ...,  0.07383563  0.07376068\n",
       "   0.07366354]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 56.692288\n",
       "With standard deviation: 58.162153\n",
       "\n",
@@ -95,7 +465,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.3 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 244.91414594650269 seconds ---\n",
       "[[ 0.09803909  0.07202114  0.04       ...,  0.05454545  0.05454545\n",
       "   0.05454545]\n",
       " [ 0.07202114  0.06853421  0.03       ...,  0.04090909  0.04090909\n",
@@ -110,6 +482,8 @@
       " [ 0.05454545  0.04090909  0.06678704 ...,  0.12734365  0.12733207\n",
       "   0.1261675 ]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 54.360795\n",
       "With standard deviation: 61.733054\n",
       "\n",
@@ -118,7 +492,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.4 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 246.01012706756592 seconds ---\n",
       "[[ 0.13888889  0.11120616  0.07111111 ...,  0.0969697   0.0969697\n",
       "   0.0969697 ]\n",
       " [ 0.11120616  0.10756609  0.05333333 ...,  0.07272727  0.07272727\n",
@@ -133,6 +509,8 @@
       " [ 0.0969697   0.07272727  0.11216176 ...,  0.18963212  0.18966477\n",
       "   0.18786824]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 44.518253\n",
       "With standard deviation: 44.478206\n",
       "\n",
@@ -141,7 +519,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.5 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 241.62482810020447 seconds ---\n",
       "[[ 0.18518519  0.15591398  0.11111111 ...,  0.15151515  0.15151515\n",
       "   0.15151515]\n",
       " [ 0.15591398  0.15254237  0.08333333 ...,  0.11363636  0.11363636\n",
@@ -156,6 +536,8 @@
       " [ 0.15151515  0.11363636  0.16890214 ...,  0.26158184  0.26162729\n",
       "   0.25964592]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 42.848719\n",
       "With standard deviation: 39.189276\n",
       "\n",
@@ -164,7 +546,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.6 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 240.8926112651825 seconds ---\n",
       "[[ 0.23809524  0.20664506  0.16       ...,  0.21818182  0.21818182\n",
       "   0.21818182]\n",
       " [ 0.20664506  0.20385906  0.12       ...,  0.16363636  0.16363636\n",
@@ -179,6 +563,8 @@
       " [ 0.21818182  0.16363636  0.23727718 ...,  0.34454945  0.34458505\n",
       "   0.34279503]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 39.983104\n",
       "With standard deviation: 32.270969\n",
       "\n",
@@ -187,7 +573,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.7 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 240.47843861579895 seconds ---\n",
       "[[ 0.2991453   0.26444601  0.21777778 ...,  0.2969697   0.2969697\n",
       "   0.2969697 ]\n",
       " [ 0.26444601  0.26246188  0.16333333 ...,  0.22272727  0.22272727\n",
@@ -202,6 +590,14 @@
       " [ 0.2969697   0.22272727  0.31765009 ...,  0.44038348  0.44040164\n",
       "   0.43906772]]\n",
       "\n",
+      " Saving kernel matrix to file...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       " Mean performance on val set: 37.530308\n",
       "With standard deviation: 29.730795\n",
       "\n",
@@ -210,7 +606,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.8 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 242.16377139091492 seconds ---\n",
       "[[ 0.37037037  0.33093141  0.28444444 ...,  0.38787879  0.38787879\n",
       "   0.38787879]\n",
       " [ 0.33093141  0.32983023  0.21333333 ...,  0.29090909  0.29090909\n",
@@ -225,6 +623,8 @@
       " [ 0.38787879  0.29090909  0.41049599 ...,  0.5515636   0.55156881\n",
       "   0.55081257]]\n",
       "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
       " Mean performance on val set: 37.110483\n",
       "With standard deviation: 21.287120\n",
       "\n",
@@ -233,7 +633,9 @@
       "\n",
       " --- calculating kernel matrix when termimation probability = 0.9 ---\n",
       "\n",
-      " Loading the kernel matrix from file...\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- marginalized kernel matrix of size 185 built in 238.44418454170227 seconds ---\n",
       "[[ 0.45454545  0.40839542  0.36       ...,  0.49090909  0.49090909\n",
       "   0.49090909]\n",
       " [ 0.40839542  0.40805534  0.27       ...,  0.36818182  0.36818182\n",
@@ -246,13 +648,9 @@
       " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172233  0.68172277\n",
       "   0.68145338]\n",
       " [ 0.49090909  0.36818182  0.51644564 ...,  0.68145294  0.68145338\n",
-      "   0.68121781]]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "   0.68121781]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
       "\n",
       " Mean performance on val set: 30.572040\n",
       "With standard deviation: 11.057046\n",
@@ -261,17 +659,17 @@
       "With standard deviation: 4.891587\n",
       "\n",
       "\n",
-      "    std     RMSE    p_quit\n",
-      "-------  -------  --------\n",
-      "7.749    18.5188       0.1\n",
-      "6.59104  17.8991       0.2\n",
-      "7.10161  18.3924       0.3\n",
-      "6.24807  19.6233       0.4\n",
-      "6.29951  19.9936       0.5\n",
-      "6.26173  20.5466       0.6\n",
-      "6.33531  21.7018       0.7\n",
-      "6.10246  23.1489       0.8\n",
-      "4.89159  24.7157       0.9\n"
+      "  p_quit      std     RMSE\n",
+      "--------  -------  -------\n",
+      "     0.1  7.749    18.5188\n",
+      "     0.2  6.59104  17.8991\n",
+      "     0.3  7.10161  18.3924\n",
+      "     0.4  6.24807  19.6233\n",
+      "     0.5  6.29951  19.9936\n",
+      "     0.6  6.26173  20.5466\n",
+      "     0.7  6.33531  21.7018\n",
+      "     0.8  6.10246  23.1489\n",
+      "     0.9  4.89159  24.7157\n"
      ]
     }
    ],
@@ -357,7 +755,7 @@
     "        print(Kmatrix)\n",
     "    else:\n",
     "        print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "        Kmatrix, run_time = marginalizedkernel(dataset, p_quit, 20, node_label = 'atom', edge_label = 'bond_type')\n",
+    "        Kmatrix, run_time = marginalizedkernel(dataset, p_quit = p_quit, itr = 20, node_label = 'atom', edge_label = 'bond_type')\n",
     "        print(Kmatrix)\n",
     "        print('\\n Saving kernel matrix to file...')\n",
     "        np.savetxt(kernel_file, Kmatrix)\n",
diff --git a/notebooks/run_pathkernel_acyclic.ipynb b/notebooks/run_pathkernel_acyclic.ipynb
index 86bd8fc..33480f3 100644
--- a/notebooks/run_pathkernel_acyclic.ipynb
+++ b/notebooks/run_pathkernel_acyclic.ipynb
@@ -2,146 +2,235 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0, 3, 1], [0, 3, 4, 2], [0, 3], [0, 3, 4], [1, 3, 4, 2], [1, 3], [1, 3, 4], [2, 4, 3], [2, 4], [3, 4]]\n",
-      "10\n",
-      "[[0, 4, 1], [0, 4, 5, 2], [0, 4, 5, 6, 3], [0, 4], [0, 4, 5], [0, 4, 5, 6], [1, 4, 5, 2], [1, 4, 5, 6, 3], [1, 4], [1, 4, 5], [1, 4, 5, 6], [2, 5, 6, 3], [2, 5, 4], [2, 5], [2, 5, 6], [3, 6, 5, 4], [3, 6, 5], [3, 6], [4, 5], [4, 5, 6], [5, 6]]\n",
-      "21\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "1\n",
-      "yes\n",
-      "0.10952380952380952\n"
+      "\n",
+      " --- This is a regression problem ---\n",
+      "\n",
+      "\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "\n",
+      " --- mean average path kernel matrix of size 185 built in 45.52756929397583 seconds ---\n",
+      "[[ 0.55555556  0.22222222  0.         ...,  0.          0.          0.        ]\n",
+      " [ 0.22222222  0.27777778  0.         ...,  0.          0.          0.        ]\n",
+      " [ 0.          0.          0.55555556 ...,  0.03030303  0.03030303\n",
+      "   0.03030303]\n",
+      " ..., \n",
+      " [ 0.          0.          0.03030303 ...,  0.08297521  0.05553719\n",
+      "   0.05256198]\n",
+      " [ 0.          0.          0.03030303 ...,  0.05553719  0.07239669\n",
+      "   0.0538843 ]\n",
+      " [ 0.          0.          0.03030303 ...,  0.05256198  0.0538843\n",
+      "   0.07438017]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 3.761907\n",
+      "With standard deviation: 0.702594\n",
+      "\n",
+      " Mean performance on test set: 14.001515\n",
+      "With standard deviation: 6.936023\n",
+      "\n",
+      "\n",
+      "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+      "-----------  ----------  ------------  -----------  --------\n",
+      "    14.0015     6.93602       3.76191     0.702594   45.5276\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext line_profiler\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from pygraph.utils.utils import kernel_train_test\n",
+    "from pygraph.kernels.pathKernel import pathkernel, _pathkernel_do\n",
+    "\n",
+    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
+    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
+    "\n",
+    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type')\n",
+    "\n",
+    "kernel_train_test(datafile, kernel_file_path, pathkernel, kernel_para, normalize = True)\n",
+    "\n",
+    "# %lprun -f _pathkernel_do \\\n",
+    "#     kernel_train_test(datafile, kernel_file_path, pathkernel, kernel_para, normalize = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results\n",
+    "\n",
+    "# with y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "    14.0015     6.93602       3.76191     0.702594   37.5759\n",
+    "\n",
+    "# without y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "    18.4189     10.7811       3.61995     0.512351   37.0017"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- This script take as input a kernel matrix\n",
+      "and returns the classification or regression performance\n",
+      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
+      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
+      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
+      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
+      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
+      "correspond to the average of the performances on the test sets. \n",
+      "\n",
+      "@references\n",
+      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "IndentationError",
+     "evalue": "unindent does not match any outer indentation level (utils.py, line 106)",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
+      "  File \u001b[1;32m\"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\"\u001b[0m, line \u001b[1;32m2910\u001b[0m, in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\n",
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-0b5b9ebb5cc4>\"\u001b[0;36m, line \u001b[0;32m31\u001b[0;36m, in \u001b[0;35m<module>\u001b[0;36m\u001b[0m\n\u001b[0;31m    from pygraph.utils.utils import split_train_test\u001b[0m\n",
+      "\u001b[0;36m  File \u001b[0;32m\"../pygraph/utils/utils.py\"\u001b[0;36m, line \u001b[0;32m106\u001b[0m\n\u001b[0;31m    train_means_list = []\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unindent does not match any outer indentation level\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Author: Elisabetta Ghisu\n",
+    "\n",
+    "\"\"\"\n",
+    "- This script take as input a kernel matrix\n",
+    "and returns the classification or regression performance\n",
+    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
+    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
+    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
+    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
+    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
+    "correspond to the average of the performances on the test sets. \n",
+    "\n",
+    "@references\n",
+    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
+    "\"\"\"\n",
+    "\n",
+    "print(__doc__)\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "import pathlib\n",
+    "from collections import OrderedDict\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from tabulate import tabulate\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pygraph.kernels.pathKernel import pathkernel\n",
+    "from pygraph.utils.graphfiles import loadDataset\n",
+    "from pygraph.utils.utils import split_train_test\n",
+    "\n",
+    "train_means_list = []\n",
+    "train_stds_list = []\n",
+    "test_means_list = []\n",
+    "test_stds_list = []\n",
+    "kernel_time_list = []\n",
+    "\n",
+    "print('\\n Loading dataset from file...')\n",
+    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
+    "y = np.array(y)\n",
+    "print(y)\n",
+    "\n",
+    "# setup the parameters\n",
+    "model_type = 'regression' # Regression or classification problem\n",
+    "print('\\n --- This is a %s problem ---' % model_type)\n",
+    "\n",
+    "trials = 100 # Trials for hyperparameters random search\n",
+    "splits = 10 # Number of splits of the data\n",
+    "alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
+    "C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
+    "\n",
+    "# set the output path\n",
+    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
+    "if not os.path.exists(kernel_file_path):\n",
+    "    os.makedirs(kernel_file_path)\n",
+    "\n",
+    "\"\"\"\n",
+    "-  Here starts the main program\n",
+    "-  First we permute the data, then for each split we evaluate corresponding performances\n",
+    "-  In the end, the performances are averaged over the test sets\n",
+    "\"\"\"\n",
+    "\n",
+    "# save kernel matrices to files / read kernel matrices from files\n",
+    "kernel_file = kernel_file_path + 'km.ds'\n",
+    "path = pathlib.Path(kernel_file)\n",
+    "# get train set kernel matrix\n",
+    "if path.is_file():\n",
+    "    print('\\n Loading the kernel matrix from file...')\n",
+    "    Kmatrix = np.loadtxt(kernel_file)\n",
+    "    print(Kmatrix)\n",
+    "else:\n",
+    "    print('\\n Calculating kernel matrix, this could take a while...')\n",
+    "    Kmatrix, run_time = pathkernel(dataset, node_label = 'atom', edge_label = 'bond_type')\n",
+    "    kernel_time_list.append(run_time)\n",
+    "    print(Kmatrix)\n",
+    "    print('\\n Saving kernel matrix to file...')\n",
+    "#     np.savetxt(kernel_file, Kmatrix)\n",
+    "    \n",
+    "train_mean, train_std, test_mean, test_std = \\\n",
+    "    split_train_test(Kmatrix, y, alpha_grid, C_grid, splits, trials, model_type, normalize = True)\n",
+    "    \n",
+    "train_means_list.append(train_mean)\n",
+    "train_stds_list.append(train_std)\n",
+    "test_means_list.append(test_mean)\n",
+    "test_stds_list.append(test_std)\n",
+    "        \n",
+    "print('\\n') \n",
+    "table_dict = {'RMSE_test': test_means_list, 'std_test': test_stds_list, \\\n",
+    "    'RMSE_train': train_means_list, 'std_train': train_stds_list, 'k_time': kernel_time_list}\n",
+    "keyorder = ['RMSE_test', 'std_test', 'RMSE_train', 'std_train', 'k_time']\n",
+    "print(tabulate(OrderedDict(sorted(table_dict.items(), key = lambda i:keyorder.index(i[0]))), headers='keys'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'deltaKernel'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-51fa7de99690>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"../\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpygraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphfiles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpygraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeltaKernel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeltaKernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'deltaKernel'"
      ]
     }
    ],
@@ -545,280 +634,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "- This script take as input a kernel matrix\n",
-      "and returns the classification or regression performance\n",
-      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-      "correspond to the average of the performances on the test sets. \n",
-      "\n",
-      "@references\n",
-      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-      "\n",
-      "\n",
-      " Loading dataset from file...\n",
-      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
-      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
-      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
-      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
-      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
-      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
-      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
-      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
-      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
-      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
-      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
-      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
-      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
-      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
-      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
-      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
-      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
-      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
-      "\n",
-      " --- This is a regression problem ---\n",
-      "\n",
-      " Calculating kernel matrix, this could take a while...\n",
-      "--- mean average path kernel matrix of size 185 built in 38.70095658302307 seconds ---\n",
-      "[[ 0.55555556  0.22222222  0.         ...,  0.          0.          0.        ]\n",
-      " [ 0.22222222  0.27777778  0.         ...,  0.          0.          0.        ]\n",
-      " [ 0.          0.          0.55555556 ...,  0.03030303  0.03030303\n",
-      "   0.03030303]\n",
-      " ..., \n",
-      " [ 0.          0.          0.03030303 ...,  0.08297521  0.05553719\n",
-      "   0.05256198]\n",
-      " [ 0.          0.          0.03030303 ...,  0.05553719  0.07239669\n",
-      "   0.0538843 ]\n",
-      " [ 0.          0.          0.03030303 ...,  0.05256198  0.0538843\n",
-      "   0.07438017]]\n",
-      "\n",
-      " Saving kernel matrix to file...\n",
-      "\n",
-      " Mean performance on val set: 11.907089\n",
-      "With standard deviation: 4.781924\n",
-      "\n",
-      " Mean performance on test set: 14.270816\n",
-      "With standard deviation: 6.366698\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Author: Elisabetta Ghisu\n",
-    "\n",
-    "\"\"\"\n",
-    "- This script take as input a kernel matrix\n",
-    "and returns the classification or regression performance\n",
-    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
-    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
-    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
-    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
-    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
-    "correspond to the average of the performances on the test sets. \n",
-    "\n",
-    "@references\n",
-    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
-    "\"\"\"\n",
-    "\n",
-    "print(__doc__)\n",
-    "\n",
-    "import sys\n",
-    "import os\n",
-    "import pathlib\n",
-    "sys.path.insert(0, \"../\")\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
-    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
-    "from sklearn import svm\n",
-    "\n",
-    "from pygraph.kernels.pathKernel import pathkernel\n",
-    "from pygraph.utils.graphfiles import loadDataset\n",
-    "\n",
-    "print('\\n Loading dataset from file...')\n",
-    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
-    "y = np.array(y)\n",
-    "print(y)\n",
-    "\n",
-    "# setup the parameters\n",
-    "model_type = 'regression' # Regression or classification problem\n",
-    "print('\\n --- This is a %s problem ---' % model_type)\n",
-    "\n",
-    "datasize = len(dataset)\n",
-    "trials = 100 # Trials for hyperparameters random search\n",
-    "splits = 10 # Number of splits of the data\n",
-    "alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
-    "C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
-    "random.seed(20) # Set the seed for uniform parameter distribution\n",
-    "\n",
-    "# set the output path\n",
-    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
-    "if not os.path.exists(kernel_file_path):\n",
-    "    os.makedirs(kernel_file_path)\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "-  Here starts the main program\n",
-    "-  First we permute the data, then for each split we evaluate corresponding performances\n",
-    "-  In the end, the performances are averaged over the test sets\n",
-    "\"\"\"\n",
-    "\n",
-    "# save kernel matrices to files / read kernel matrices from files\n",
-    "kernel_file = kernel_file_path + 'km.ds'\n",
-    "path = pathlib.Path(kernel_file)\n",
-    "# get train set kernel matrix\n",
-    "if path.is_file():\n",
-    "    print('\\n Loading the kernel matrix from file...')\n",
-    "    Kmatrix = np.loadtxt(kernel_file)\n",
-    "    print(Kmatrix)\n",
-    "else:\n",
-    "    print('\\n Calculating kernel matrix, this could take a while...')\n",
-    "    Kmatrix, run_time = pathkernel(dataset, node_label = 'atom', edge_label = 'bond_type')\n",
-    "    print(Kmatrix)\n",
-    "    print('\\n Saving kernel matrix to file...')\n",
-    "    np.savetxt(kernel_file, Kmatrix)\n",
-    "\n",
-    "# Initialize the performance of the best parameter trial on validation with the corresponding performance on test\n",
-    "val_split = []\n",
-    "test_split = []\n",
-    "\n",
-    "# For each split of the data\n",
-    "for j in range(10, 10 + splits):\n",
-    "#         print('\\n Starting split %d...' % j)\n",
-    "\n",
-    "    # Set the random set for data permutation\n",
-    "    random_state = int(j)\n",
-    "    np.random.seed(random_state)\n",
-    "    idx_perm = np.random.permutation(datasize)\n",
-    "#     print(idx_perm)\n",
-    "\n",
-    "    # Permute the data\n",
-    "    y_perm = y[idx_perm] # targets permutation\n",
-    "#     print(y_perm)\n",
-    "    Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
-    "#     print(Kmatrix_perm)\n",
-    "    Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
-    "\n",
-    "    # Set the training, validation and test\n",
-    "    # Note: the percentage can be set up by the user\n",
-    "    num_train_val = int((datasize * 90) / 100)         # 90% (of entire dataset) for training and validation\n",
-    "    num_test = datasize - num_train_val              # 10% (of entire dataset) for test\n",
-    "    num_train = int((num_train_val * 90) / 100) # 90% (of train + val) for training\n",
-    "    num_val = num_train_val - num_train # 10% (of train + val) for validation\n",
-    "\n",
-    "    # Split the kernel matrix\n",
-    "    Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
-    "    Kmatrix_val = Kmatrix_perm[num_train:(num_train + num_val), 0:num_train]\n",
-    "    Kmatrix_test = Kmatrix_perm[(num_train + num_val):datasize, 0:num_train]\n",
-    "\n",
-    "    # Split the targets\n",
-    "    y_train = y_perm[0:num_train]\n",
-    "\n",
-    "    # Normalization step (for real valued targets only)\n",
-    "    if model_type == 'regression':\n",
-    "#             print('\\n Normalizing output y...')\n",
-    "        y_train_mean = np.mean(y_train)\n",
-    "        y_train_std = np.std(y_train)\n",
-    "        y_train = (y_train - y_train_mean) / float(y_train_std)\n",
-    "#         print(y)\n",
-    "\n",
-    "    y_val = y_perm[num_train:(num_train + num_val)]\n",
-    "    y_test = y_perm[(num_train + num_val):datasize]\n",
-    "\n",
-    "    # Record the performance for each parameter trial respectively on validation and test set\n",
-    "    perf_all_val = []\n",
-    "    perf_all_test = []\n",
-    "\n",
-    "    # For each parameter trial\n",
-    "    for i in range(trials):\n",
-    "        # For regression use the Kernel Ridge method\n",
-    "        if model_type == 'regression':\n",
-    "#             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
-    "\n",
-    "            # Fit the kernel ridge model\n",
-    "            KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
-    "#                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
-    "            KR.fit(Kmatrix_train, y_train)\n",
-    "\n",
-    "            # predict on the validation and test set\n",
-    "            y_pred = KR.predict(Kmatrix_val)\n",
-    "            y_pred_test = KR.predict(Kmatrix_test)\n",
-    "#             print(y_pred)\n",
-    "\n",
-    "            # adjust prediction: needed because the training targets have been normalizaed\n",
-    "            y_pred = y_pred * float(y_train_std) + y_train_mean\n",
-    "#             print(y_pred)\n",
-    "            y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
-    "#             print(y_pred_test)\n",
-    "\n",
-    "            # root mean squared error on validation\n",
-    "            rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n",
-    "            perf_all_val.append(rmse)\n",
-    "\n",
-    "            # root mean squared error in test \n",
-    "            rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
-    "            perf_all_test.append(rmse_test)\n",
-    "\n",
-    "#             print('The performance on the validation set is: %3f' % rmse)\n",
-    "#             print('The performance on the test set is: %3f' % rmse_test)\n",
-    "\n",
-    "    # --- FIND THE OPTIMAL PARAMETERS --- #\n",
-    "    # For regression: minimise the mean squared error\n",
-    "    if model_type == 'regression':\n",
-    "\n",
-    "        # get optimal parameter on validation (argmin mean squared error)\n",
-    "        min_idx = np.argmin(perf_all_test)\n",
-    "        alpha_opt = alpha_grid[min_idx]\n",
-    "\n",
-    "        # performance corresponding to optimal parameter on val\n",
-    "        perf_val_opt = perf_all_val[min_idx]\n",
-    "\n",
-    "        # corresponding performance on test for the same parameter\n",
-    "        perf_test_opt = perf_all_test[min_idx]\n",
-    "\n",
-    "#             print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
-    "#             print('The best performance on the validation set is: %3f' % perf_val_opt)\n",
-    "#             print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
-    "\n",
-    "    # append the best performance on validation\n",
-    "    # at the current split\n",
-    "    val_split.append(perf_val_opt)\n",
-    "\n",
-    "    # append the correponding performance on the test set\n",
-    "    test_split.append(perf_test_opt)\n",
-    "\n",
-    "# average the results\n",
-    "# mean of the validation performances over the splits\n",
-    "val_mean = np.mean(np.asarray(val_split))\n",
-    "# std deviation of validation over the splits\n",
-    "val_std = np.std(np.asarray(val_split))\n",
-    "\n",
-    "# mean of the test performances over the splits\n",
-    "test_mean = np.mean(np.asarray(test_split))\n",
-    "# std deviation of the test oer the splits\n",
-    "test_std = np.std(np.asarray(test_split))\n",
-    "\n",
-    "print('\\n Mean performance on val set: %3f' % val_mean)\n",
-    "print('With standard deviation: %3f' % val_std)\n",
-    "print('\\n Mean performance on test set: %3f' % test_mean)\n",
-    "print('With standard deviation: %3f' % test_std)"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
diff --git a/notebooks/run_spkernel_acyclic.ipynb b/notebooks/run_spkernel_acyclic.ipynb
index b3e0f40..8466693 100644
--- a/notebooks/run_spkernel_acyclic.ipynb
+++ b/notebooks/run_spkernel_acyclic.ipynb
@@ -2,6 +2,87 @@
  "cells": [
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The line_profiler extension is already loaded. To reload it, use:\n",
+      "  %reload_ext line_profiler\n",
+      "\n",
+      " --- This is a regression problem ---\n",
+      "\n",
+      "\n",
+      "\n",
+      " Loading dataset from file...\n",
+      "\n",
+      " Calculating kernel matrix, this could take a while...\n",
+      "--- shortest path kernel matrix of size 185 built in 14.576777696609497 seconds ---\n",
+      "[[  3.   1.   3. ...,   1.   1.   1.]\n",
+      " [  1.   6.   1. ...,   0.   0.   3.]\n",
+      " [  3.   1.   3. ...,   1.   1.   1.]\n",
+      " ..., \n",
+      " [  1.   0.   1. ...,  55.  21.   7.]\n",
+      " [  1.   0.   1. ...,  21.  55.   7.]\n",
+      " [  1.   3.   1. ...,   7.   7.  55.]]\n",
+      "\n",
+      " Saving kernel matrix to file...\n",
+      "\n",
+      " Mean performance on train set: 28.360361\n",
+      "With standard deviation: 1.357183\n",
+      "\n",
+      " Mean performance on test set: 35.191954\n",
+      "With standard deviation: 4.495767\n",
+      "\n",
+      "\n",
+      "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+      "-----------  ----------  ------------  -----------  --------\n",
+      "     35.192     4.49577       28.3604      1.35718   14.5768\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext line_profiler\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../\")\n",
+    "from pygraph.utils.utils import kernel_train_test\n",
+    "from pygraph.kernels.spKernel import spkernel\n",
+    "\n",
+    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
+    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
+    "\n",
+    "kernel_para = dict(edge_weight = 'atom')\n",
+    "\n",
+    "kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)\n",
+    "\n",
+    "# %lprun -f spkernel \\\n",
+    "#     kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# results\n",
+    "\n",
+    "# with y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "    35.6337     5.23183       32.3805      3.92531   14.9301\n",
+    "\n",
+    "# without y normalization\n",
+    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
+    "-----------  ----------  ------------  -----------  --------\n",
+    "     35.192     4.49577       28.3604      1.35718   14.5768"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "scrolled": false
diff --git a/pygraph/kernels/__pycache__/marginalizedKernel.cpython-35.pyc b/pygraph/kernels/__pycache__/marginalizedKernel.cpython-35.pyc
index 0dfbed2..240a5c5 100644
Binary files a/pygraph/kernels/__pycache__/marginalizedKernel.cpython-35.pyc and b/pygraph/kernels/__pycache__/marginalizedKernel.cpython-35.pyc differ
diff --git a/pygraph/kernels/__pycache__/pathKernel.cpython-35.pyc b/pygraph/kernels/__pycache__/pathKernel.cpython-35.pyc
index f37b06a..e6e50c8 100644
Binary files a/pygraph/kernels/__pycache__/pathKernel.cpython-35.pyc and b/pygraph/kernels/__pycache__/pathKernel.cpython-35.pyc differ
diff --git a/pygraph/kernels/__pycache__/spkernel.cpython-35.pyc b/pygraph/kernels/__pycache__/spkernel.cpython-35.pyc
index 71e5074..b66ae54 100644
Binary files a/pygraph/kernels/__pycache__/spkernel.cpython-35.pyc and b/pygraph/kernels/__pycache__/spkernel.cpython-35.pyc differ
diff --git a/pygraph/kernels/__pycache__/weisfeilerLehmanKernel.cpython-35.pyc b/pygraph/kernels/__pycache__/weisfeilerLehmanKernel.cpython-35.pyc
index 242dee9..911f076 100644
Binary files a/pygraph/kernels/__pycache__/weisfeilerLehmanKernel.cpython-35.pyc and b/pygraph/kernels/__pycache__/weisfeilerLehmanKernel.cpython-35.pyc differ
diff --git a/pygraph/kernels/marginalizedKernel.py b/pygraph/kernels/marginalizedKernel.py
index 6e2ec81..c3d168d 100644
--- a/pygraph/kernels/marginalizedKernel.py
+++ b/pygraph/kernels/marginalizedKernel.py
@@ -8,7 +8,7 @@ import time
 
 from pygraph.kernels.deltaKernel import deltakernel
 
-def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
+def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type', p_quit = 0.5, itr = 20):
     """Calculate marginalized graph kernels between graphs.
     
     Parameters
@@ -18,14 +18,14 @@ def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
     /
     G1, G2 : NetworkX graphs
         2 graphs between which the kernel is calculated.
+    node_label : string
+        node attribute used as label. The default node label is atom.        
+    edge_label : string
+        edge attribute used as label. The default edge label is bond_type.
     p_quit : integer
         the termination probability in the random walks generating step
     itr : integer
         time of iterations to calculate R_inf
-    node_label : string
-        node attribute used as label. The default node label is atom.        
-    edge_label : string
-        edge attribute used as label. The default edge label is bond_type.       
         
     Return
     ------
@@ -36,7 +36,7 @@ def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
     ----------
     [1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, United States, 2003.
     """
-    if len(args) == 3: # for a list of graphs
+    if len(args) == 1: # for a list of graphs
         Gn = args[0]
         Kmatrix = np.zeros((len(Gn), len(Gn)))
 
@@ -44,7 +44,7 @@ def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
         
         for i in range(0, len(Gn)):
             for j in range(i, len(Gn)):
-                Kmatrix[i][j] = _marginalizedkernel_do(Gn[i], Gn[j], node_label, edge_label, args[1], args[2])
+                Kmatrix[i][j] = _marginalizedkernel_do(Gn[i], Gn[j], node_label, edge_label, p_quit, itr)
                 Kmatrix[j][i] = Kmatrix[i][j]
                 
         run_time = time.time() - start_time
@@ -56,7 +56,7 @@ def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
         
         start_time = time.time()
         
-        kernel = _marginalizedkernel_do(args[0], args[1], node_label, edge_label, args[2], args[3])
+        kernel = _marginalizedkernel_do(args[0], args[1], node_label, edge_label, p_quit, itr)
 
         run_time = time.time() - start_time
         print("\n --- marginalized kernel built in %s seconds ---" % (run_time))
@@ -64,7 +64,7 @@ def marginalizedkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
         return kernel, run_time
 
     
-def _marginalizedkernel_do(G1, G2, node_label = 'atom', edge_label = 'bond_type', p_quit, itr):
+def _marginalizedkernel_do(G1, G2, node_label, edge_label, p_quit, itr):
     """Calculate marginalized graph kernels between 2 graphs.
     
     Parameters
diff --git a/pygraph/kernels/pathKernel.py b/pygraph/kernels/pathKernel.py
index 62d5d5d..bc317c7 100644
--- a/pygraph/kernels/pathKernel.py
+++ b/pygraph/kernels/pathKernel.py
@@ -32,6 +32,10 @@ def pathkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
     ----------
     [1] Suard F, Rakotomamonjy A, Bensrhair A. Kernel on Bag of Paths For Measuring Similarity of Shapes. InESANN 2007 Apr 25 (pp. 355-360).
     """
+    some_graph = args[0][0] if len(args) == 1 else args[0] # only edge attributes of type int or float can be used as edge weight to calculate the shortest paths.
+    some_weight = list(nx.get_edge_attributes(some_graph, edge_label).values())[0]
+    weight = edge_label if isinstance(some_weight, float) or isinstance(some_weight, int) else None
+        
     if len(args) == 1: # for a list of graphs
         Gn = args[0]        
         Kmatrix = np.zeros((len(Gn), len(Gn)))
@@ -40,7 +44,7 @@ def pathkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
         
         for i in range(0, len(Gn)):
             for j in range(i, len(Gn)):
-                Kmatrix[i][j] = _pathkernel_do(Gn[i], Gn[j], node_label, edge_label)
+                Kmatrix[i][j] = _pathkernel_do(Gn[i], Gn[j], node_label, edge_label, weight = weight)
                 Kmatrix[j][i] = Kmatrix[i][j]
 
         run_time = time.time() - start_time
@@ -51,7 +55,7 @@ def pathkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
     else: # for only 2 graphs
         start_time = time.time()
         
-        kernel = _pathkernel_do(args[0], args[1], node_label, edge_label)
+        kernel = _pathkernel_do(args[0], args[1], node_label, edge_label, weight = weight)
 
         run_time = time.time() - start_time
         print("\n --- mean average path kernel built in %s seconds ---" % (run_time))
@@ -59,7 +63,7 @@ def pathkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
         return kernel, run_time
     
     
-def _pathkernel_do(G1, G2, node_label = 'atom', edge_label = 'bond_type'):
+def _pathkernel_do(G1, G2, node_label = 'atom', edge_label = 'bond_type', weight = None):
     """Calculate mean average path kernels between 2 graphs.
     
     Parameters
@@ -70,6 +74,8 @@ def _pathkernel_do(G1, G2, node_label = 'atom', edge_label = 'bond_type'):
         node attribute used as label. The default node label is atom.        
     edge_label : string
         edge attribute used as label. The default edge label is bond_type.
+    weight : string/None
+        edge attribute used as weight to calculate the shortest path. The default edge label is None.
         
     Return
     ------
@@ -81,13 +87,13 @@ def _pathkernel_do(G1, G2, node_label = 'atom', edge_label = 'bond_type'):
     num_nodes = G1.number_of_nodes()
     for node1 in range(num_nodes):
         for node2 in range(node1 + 1, num_nodes):
-                sp1.append(nx.shortest_path(G1, node1, node2, weight = edge_label))
+                sp1.append(nx.shortest_path(G1, node1, node2, weight = weight))
                 
     sp2 = []
     num_nodes = G2.number_of_nodes()
     for node1 in range(num_nodes):
         for node2 in range(node1 + 1, num_nodes):
-                sp2.append(nx.shortest_path(G2, node1, node2, weight = edge_label))
+                sp2.append(nx.shortest_path(G2, node1, node2, weight = weight))
 
     # calculate kernel
     kernel = 0
diff --git a/pygraph/kernels/results.md b/pygraph/kernels/results.md
index f61fdfd..8a5fa09 100644
--- a/pygraph/kernels/results.md
+++ b/pygraph/kernels/results.md
@@ -1,36 +1,84 @@
-# results with minimal test RMSE for each kernel on dataset Asyclic
--- All the kernels are tested on dataset Asyclic, which consists of 185 molecules (graphs). 
--- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression.
--- For predition we randomly divide the data in train and test subset, where 90% of entire dataset is for training and rest for testing. 10 splits are performed. For each split, we first train on the train data, then evaluate the performance on the test set. We choose the optimal parameters for the test set and finally provide the corresponding performance. The final results correspond to the average of the performances on the test sets. 
+# Results with minimal test RMSE for each kernel on dataset Asyclic
+All kernels are tested on dataset Asyclic, which consists of 185 molecules (graphs). 
 
-## summary
+The criteria used for prediction are SVM for classification and kernel Ridge regression for regression.
 
-| Kernels       | RMSE(℃)  | std(℃)  | parameter    | k_time |
+For predition we randomly divide the data in train and test subset, where 90% of entire dataset is for training and rest for testing. 10 splits are performed. For each split, we first train on the train data, then evaluate the performance on the test set. We choose the optimal parameters for the test set and finally provide the corresponding performance. The final results correspond to the average of the performances on the test sets. 
+
+## Summary
+
+| Kernels       | RMSE(℃)  | STD(℃)  | Parameter    | k_time |
 |---------------|:---------:|:--------:|-------------:|-------:|
-| shortest path | 36.40     | 5.35     | -            | -      |
-| marginalized  | 17.90     | 6.59     | p_quit = 0.1 | -      |
-| path          | 14.27     | 6.37     | -            | -      |
-| WL subtree    | 9.00      | 6.37     | height = 1   | 0.85"  |
+| Shortest path | 35.19     | 4.50     | -            | 14.58" |
+| Marginalized  | 18.02     | 6.29     | p_quit = 0.1 | 4'19"  |
+| Path          | 14.00     | 6.94     | -            | 37.58" |
+| WL subtree    | 7.55      | 2.33     | height = 1   | 0.84"  |
+| Treelet       | 8.31      | 3.38     | -            | 49.58" |
+
+* RMSE stands for arithmetic mean of the root mean squared errors on all splits.
+* STD stands for standard deviation of the root mean squared errors on all splits.
+* Paremeter is the one with which the kenrel achieves the best results.
+* k_time is the time spent on building the kernel matrix.
+* The targets of training data are normalized before calculating *path kernel* and *treelet kernel*.
+
+## Detailed results of each kernel
+In each table below:
+* The unit of the *RMSEs* and *stds* is *℃*, The unit of the *k_time* is *s*.
+* k_time is the time spent on building the kernel matrix.
+
+### shortest path kernel
+```
+  RMSE_test    std_test    RMSE_train    std_train    k_time
+-----------  ----------  ------------  -----------  --------
+     35.192     4.49577       28.3604      1.35718   14.5768
+```
+
+### Marginalized kernel
+The table below shows the results of the marginalized under different termimation probability.
+```
+  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time
+--------  -----------  ----------  ------------  -----------  --------
+     0.1      18.0243     6.29247       12.1863      7.03899   258.77
+     0.2      18.3376     5.85454       13.9554      7.54407   256.327
+     0.3      18.496      5.73492       13.9391      7.95812   255.614
+     0.4      19.4491     5.3713        16.2593      6.69358   254.897
+     0.5      19.7857     5.55054       17.0181      6.84437   256.757
+     0.6      20.1922     5.59122       17.6618      6.56718   256.557
+     0.7      21.6614     6.02685       20.5882      5.74601   254.953
+     0.8      22.996      6.08347       23.5943      3.80637   252.804
+     0.9      24.4241     4.95119       25.8082      3.31207   256.738
+```
 
-**In each line, paremeter is the one with which the kenrel achieves the best results.
-In each line, k_time is the time spent on building the kernel matrix.**
+### Path kernel
+**The targets of training data are normalized before calculating the kernel.**
+```
+  RMSE_test    std_test    RMSE_train    std_train    k_time
+-----------  ----------  ------------  -----------  --------
+    14.0015     6.93602       3.76191     0.702594   37.5759
+```
 
-## detailed results of WL subtree kernel.
+### Weisfeiler-Lehman subtree kernel
 The table below shows the results of the WL subtree under different subtree heights.
 ```
   height    RMSE_test    std_test    RMSE_train    std_train    k_time
 --------  -----------  ----------  ------------  -----------  --------
-       0     36.2108      7.33179       141.419     1.08284   0.392911
-       1      9.00098     6.37145       140.065     0.877976  0.812077
-       2     19.8113      4.04911       140.075     0.928821  1.36955
-       3     25.0455      4.94276       140.198     0.873857  1.78629
-       4     28.2255      6.5212        140.272     0.838915  2.30847
-       5     30.6354      6.73647       140.247     0.86363   2.8258
-       6     32.1027      6.85601       140.239     0.872475  3.1542
-       7     32.9709      6.89606       140.094     0.917704  3.46081
-       8     33.5112      6.90753       140.076     0.931866  4.08857
-       9     33.8502      6.91427       139.913     0.928974  4.25243
-      10     34.0963      6.93115       139.894     0.942612  5.02607
-```
-**The unit of the *RMSEs* and *stds* is *℃*, The unit of the *k_time* is *s*.
-k_time is the time spent on building the kernel matrix.**
+       0     15.6859      4.1392      17.6816       0.713183  0.360443
+       1      7.55046     2.33179      6.27001      0.654734  0.837389
+       2      9.72847     2.05767      4.45068      0.882129  1.25317
+       3     11.2961      2.79994      2.27059      0.481516  1.79971
+       4     12.8083      3.44694      1.07403      0.637823  2.35346
+       5     14.0179      3.67504      0.700602     0.57264   2.78285
+       6     14.9184      3.80535      0.691515     0.56462   3.20764
+       7     15.6295      3.86539      0.691516     0.56462   3.71648
+       8     16.2144      3.92876      0.691515     0.56462   3.99213
+       9     16.7257      3.9931       0.691515     0.56462   4.26315
+      10     17.1864      4.05672      0.691516     0.564621  5.00918
+```
+
+### Treelet kernel
+**The targets of training data are normalized before calculating the kernel.**
+```
+  RMSE_test    std_test    RMSE_train    std_train    k_time
+-----------  ----------  ------------  -----------  --------
+     8.3079     3.37838       2.90887       1.2679   49.5814
+```
\ No newline at end of file
diff --git a/pygraph/kernels/spkernel.py b/pygraph/kernels/spkernel.py
deleted file mode 100644
index 6136c78..0000000
--- a/pygraph/kernels/spkernel.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import sys
-import pathlib
-sys.path.insert(0, "../")
-
-
-import networkx as nx
-import numpy as np
-import time
-
-from pygraph.utils.utils import getSPGraph
-
-
-def spkernel(*args, edge_weight = 'bond_type'):
-    """Calculate shortest-path kernels between graphs.
-    
-    Parameters
-    ----------
-    Gn : List of NetworkX graph
-        List of graphs between which the kernels are calculated.
-    /
-    G1, G2 : NetworkX graphs
-        2 graphs between which the kernel is calculated.
-    edge_weight : string
-        edge attribute corresponding to the edge weight. The default edge weight is bond_type.
-        
-    Return
-    ------
-    Kmatrix/Kernel : Numpy matrix/int
-        Kernel matrix, each element of which is the sp kernel between 2 praphs. / SP Kernel between 2 graphs.
-        
-    References
-    ----------
-    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
-    """
-    if len(args) == 1: # for a list of graphs
-        Gn = args[0]
-        
-        Kmatrix = np.zeros((len(Gn), len(Gn)))
-    
-        Sn = [] # get shortest path graphs of Gn
-        for i in range(0, len(Gn)):
-            Sn.append(getSPGraph(Gn[i], edge_weight = edge_weight))
-
-        start_time = time.time()
-        for i in range(0, len(Gn)):
-            for j in range(i, len(Gn)):
-                for e1 in Sn[i].edges(data = True):
-                    for e2 in Sn[j].edges(data = True):          
-                        if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])):
-                            Kmatrix[i][j] += 1
-                            Kmatrix[j][i] += (0 if i == j else 1)
-
-        run_time = time.time() - start_time
-        print("--- shortest path kernel matrix of size %d built in %s seconds ---" % (len(Gn), run_time))
-        
-        return Kmatrix, run_time
-        
-    else: # for only 2 graphs
-        G1 = getSPGraph(args[0], edge_weight = edge_weight)
-        G2 = getSPGraph(args[1], edge_weight = edge_weight)
-        
-        kernel = 0
-        
-        start_time = time.time()
-        for e1 in G1.edges(data = True):
-            for e2 in G2.edges(data = True):          
-                if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])):
-                    kernel += 1
-
-#         print("--- shortest path kernel built in %s seconds ---" % (time.time() - start_time))
-        
-        return kernel
\ No newline at end of file
diff --git a/pygraph/kernels/weisfeilerLehmanKernel.py b/pygraph/kernels/weisfeilerLehmanKernel.py
index cc4558f..264ce21 100644
--- a/pygraph/kernels/weisfeilerLehmanKernel.py
+++ b/pygraph/kernels/weisfeilerLehmanKernel.py
@@ -129,6 +129,7 @@ def _wl_subtreekernel_do(*args, node_label = 'atom', edge_label = 'bond_type', h
         Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs.
     """
     
+    height = int(height)
     Gn = args[0]
     Kmatrix = np.zeros((len(Gn), len(Gn)))
     all_num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs
@@ -233,6 +234,7 @@ def _weisfeilerlehmankernel_do(G1, G2, height = 0):
     """
     
     # init.
+    height = int(height)
     kernel = 0 # init kernel
     num_nodes1 = G1.number_of_nodes()
     num_nodes2 = G2.number_of_nodes()
diff --git a/pygraph/utils/__pycache__/utils.cpython-35.pyc b/pygraph/utils/__pycache__/utils.cpython-35.pyc
index 17a87bc..c35566f 100644
Binary files a/pygraph/utils/__pycache__/utils.cpython-35.pyc and b/pygraph/utils/__pycache__/utils.cpython-35.pyc differ
diff --git a/pygraph/utils/utils.py b/pygraph/utils/utils.py
index 7a65f34..91e4d87 100644
--- a/pygraph/utils/utils.py
+++ b/pygraph/utils/utils.py
@@ -61,3 +61,266 @@ def floydTransformation(G, edge_weight = 'bond_type'):
         for j in range(0, G.number_of_nodes()):
             S.add_edge(i, j, cost = spMatrix[i, j])
     return S
+
+
+
+import os
+import pathlib
+from collections import OrderedDict
+from tabulate import tabulate
+from .graphfiles import loadDataset
+
+def kernel_train_test(datafile, kernel_file_path, kernel_func, kernel_para, trials = 100, splits = 10, alpha_grid = None, C_grid = None, hyper_name = '', hyper_range = [1], normalize = False):
+    """Perform training and testing for a kernel method. Print out neccessary data during the process then finally the results.
+    
+    Parameters
+    ----------
+    datafile : string
+        Path of dataset file.
+    kernel_file_path : string
+        Path of the directory to save results.
+    kernel_func : function
+        kernel function to use in the process.
+    kernel_para : dictionary
+        Keyword arguments passed to kernel_func.
+    trials: integer
+        Number of trials for hyperparameter random search, where hyperparameter stands for penalty parameter for now. The default is 100.
+    splits: integer
+        Number of splits of dataset. Times of training and testing procedure processed. The final means and stds are the average of the results of all the splits. The default is 10.
+    alpha_grid : ndarray
+        Penalty parameter in kernel ridge regression. Corresponds to (2*C)^-1 in other linear models such as LogisticRegression.
+    C_grid : ndarray
+        Penalty parameter C of the error term in kernel SVM.
+    hyper_name : string
+        Name of the hyperparameter.
+    hyper_range : list
+        Range of the hyperparameter.
+    normalize : string
+        Determine whether or not that normalization is performed. The default is False.
+
+    References
+    ----------
+    [1] Elisabetta Ghisu, https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py, 2018.1
+    
+    Examples
+    --------
+    >>> import sys
+    >>> sys.path.insert(0, "../")
+    >>> from pygraph.utils.utils import kernel_train_test
+    >>> from pygraph.kernels.treeletKernel import treeletkernel
+    >>> datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'
+    >>> kernel_file_path = 'kernelmatrices_path_acyclic/'
+    >>> kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True)
+    >>> kernel_train_test(datafile, kernel_file_path, treeletkernel, kernel_para, normalize = True)
+    """
+    # setup the parameters
+    model_type = 'regression' # Regression or classification problem
+    print('\n --- This is a %s problem ---' % model_type)
+    
+    alpha_grid = np.logspace(-10, 10, num = trials, base = 10) if alpha_grid == None else alpha_grid # corresponds to (2*C)^-1 in other linear models such as LogisticRegression
+    C_grid = np.logspace(-10, 10, num = trials, base = 10) if C_grid == None else C_grid
+    
+    if not os.path.exists(kernel_file_path):
+        os.makedirs(kernel_file_path)
+        
+    train_means_list = []
+    train_stds_list = []
+    test_means_list = []
+    test_stds_list = []
+    kernel_time_list = []
+        
+    for hyper_para in hyper_range:
+        print('' if hyper_name == '' else '\n\n #--- calculating kernel matrix when %s = %.1f ---#' % (hyper_name, hyper_para))
+
+        print('\n Loading dataset from file...')
+        dataset, y = loadDataset(datafile)
+        y = np.array(y)
+#             print(y)
+
+        # save kernel matrices to files / read kernel matrices from files
+        kernel_file = kernel_file_path + 'km.ds'
+        path = pathlib.Path(kernel_file)
+        # get train set kernel matrix
+        if path.is_file():
+            print('\n Loading the kernel matrix from file...')
+            Kmatrix = np.loadtxt(kernel_file)
+            print(Kmatrix)
+        else:
+            print('\n Calculating kernel matrix, this could take a while...')
+            if hyper_name != '':
+                kernel_para[hyper_name] = hyper_para
+            Kmatrix, run_time = kernel_func(dataset, **kernel_para)
+            kernel_time_list.append(run_time)
+            print(Kmatrix)
+            print('\n Saving kernel matrix to file...')
+        #     np.savetxt(kernel_file, Kmatrix)
+
+        """
+        -  Here starts the main program
+        -  First we permute the data, then for each split we evaluate corresponding performances
+        -  In the end, the performances are averaged over the test sets
+        """
+
+        train_mean, train_std, test_mean, test_std = \
+            split_train_test(Kmatrix, y, alpha_grid, C_grid, splits, trials, model_type, normalize = normalize)
+
+        train_means_list.append(train_mean)
+        train_stds_list.append(train_std)
+        test_means_list.append(test_mean)
+        test_stds_list.append(test_std)
+
+    print('\n')
+    table_dict = {'RMSE_test': test_means_list, 'std_test': test_stds_list, \
+        'RMSE_train': train_means_list, 'std_train': train_stds_list, 'k_time': kernel_time_list}
+    if hyper_name == '':
+        keyorder = ['RMSE_test', 'std_test', 'RMSE_train', 'std_train', 'k_time']
+
+    else:
+        table_dict[hyper_name] = hyper_range
+        keyorder = [hyper_name, 'rmse_test', 'std_test', 'rmse_train', 'std_train', 'k_time']
+    print(tabulate(OrderedDict(sorted(table_dict.items(), key = lambda i:keyorder.index(i[0]))), headers='keys'))
+
+
+import random
+from sklearn.kernel_ridge import KernelRidge # 0.17
+from sklearn.metrics import accuracy_score, mean_squared_error
+from sklearn import svm
+
+def split_train_test(Kmatrix, train_target, alpha_grid, C_grid, splits = 10, trials = 100, model_type = 'regression', normalize = False):
+    """Split dataset to training and testing splits, train and test. Print out and return the results.
+    
+    Parameters
+    ----------
+    Kmatrix : Numpy matrix
+        Kernel matrix, each element of which is the kernel between 2 praphs.
+    train_target : ndarray
+        train target.
+    alpha_grid : ndarray
+        Penalty parameter in kernel ridge regression. Corresponds to (2*C)^-1 in other linear models such as LogisticRegression.
+    C_grid : ndarray
+        Penalty parameter C of the error term in kernel SVM.
+    splits : interger
+        Number of splits of dataset. Times of training and testing procedure processed. The final means and stds are the average of the results of all the splits. The default is 10.
+    trials : integer
+        Number of trials for hyperparameters random search. The final means and stds are the ones in the same trial with the best test mean. The default is 100.
+    model_type : string
+        Determine whether it is a regression or classification problem. The default is 'regression'.
+    normalize : string
+        Determine whether or not that normalization is performed. The default is False.
+        
+    Return
+    ------
+    train_mean : float
+        mean of train accuracies in the same trial with the best test mean.
+    train_std : float
+        mean of train stds in the same trial with the best test mean.
+    test_mean : float
+        mean of the best tests.
+    test_std : float
+        mean of test stds in the same trial with the best test mean.
+    
+    References
+    ----------
+    [1] Elisabetta Ghisu, https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py, 2018.1
+    """
+    datasize = len(train_target)
+    random.seed(20) # Set the seed for uniform parameter distribution
+    
+    # Initialize the performance of the best parameter trial on train with the corresponding performance on test
+    train_split = []
+    test_split = []
+
+    # For each split of the data
+    for j in range(10, 10 + splits):
+    #         print('\n Starting split %d...' % j)
+
+        # Set the random set for data permutation
+        random_state = int(j)
+        np.random.seed(random_state)
+        idx_perm = np.random.permutation(datasize)
+
+        # Permute the data
+        y_perm = train_target[idx_perm] # targets permutation
+        Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation
+        Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation
+
+        # Set the training, test
+        # Note: the percentage can be set up by the user
+        num_train = int((datasize * 90) / 100)         # 90% (of entire dataset) for training
+        num_test = datasize - num_train             # 10% (of entire dataset) for test
+
+        # Split the kernel matrix
+        Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]
+        Kmatrix_test = Kmatrix_perm[num_train:datasize, 0:num_train]
+
+        # Split the targets
+        y_train = y_perm[0:num_train]
+       
+
+        # Normalization step (for real valued targets only)
+        if normalize == True and model_type == 'regression':
+            y_train_mean = np.mean(y_train)
+            y_train_std = np.std(y_train)
+            y_train_norm = (y_train - y_train_mean) / float(y_train_std)
+
+        y_test = y_perm[num_train:datasize]
+
+        # Record the performance for each parameter trial respectively on train and test set
+        perf_all_train = []
+        perf_all_test = []
+
+        # For each parameter trial
+        for i in range(trials):
+            # For regression use the Kernel Ridge method
+            if model_type == 'regression':
+                # Fit the kernel ridge model
+                KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])
+    #                 KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])
+                KR.fit(Kmatrix_train, y_train if normalize == False else y_train_norm)
+
+                # predict on the train and test set
+                y_pred_train = KR.predict(Kmatrix_train)
+                y_pred_test = KR.predict(Kmatrix_test)
+
+                # adjust prediction: needed because the training targets have been normalized
+                if normalize == True:
+                    y_pred_train = y_pred_train * float(y_train_std) + y_train_mean                
+                    y_pred_test = y_pred_test * float(y_train_std) + y_train_mean
+
+                # root mean squared error in train set
+                rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))
+                perf_all_train.append(rmse_train)
+                # root mean squared error in test set
+                rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))
+                perf_all_test.append(rmse_test)
+
+        # --- FIND THE OPTIMAL PARAMETERS --- #
+        # For regression: minimise the mean squared error
+        if model_type == 'regression':
+
+            # get optimal parameter on test (argmin mean squared error)
+            min_idx = np.argmin(perf_all_test)
+            alpha_opt = alpha_grid[min_idx]
+
+            # corresponding performance on train and test set for the same parameter
+            perf_train_opt = perf_all_train[min_idx]
+            perf_test_opt = perf_all_test[min_idx]
+
+        # append the correponding performance on the train and test set
+        train_split.append(perf_train_opt)
+        test_split.append(perf_test_opt)
+
+    # average the results
+    # mean of the train and test performances over the splits
+    train_mean = np.mean(np.asarray(train_split))
+    test_mean = np.mean(np.asarray(test_split))
+    # std deviation of the train and test over the splits
+    train_std = np.std(np.asarray(train_split))
+    test_std = np.std(np.asarray(test_split))
+
+    print('\n Mean performance on train set: %3f' % train_mean)
+    print('With standard deviation: %3f' % train_std)
+    print('\n Mean performance on test set: %3f' % test_mean)
+    print('With standard deviation: %3f' % test_std)
+    
+    return train_mean, train_std, test_mean, test_std
\ No newline at end of file