- {
- “cells”: [
- {
“cell_type”: “markdown”, “metadata”: {
“collapsed”: true, “pycharm”: {
“name”: “#%% mdn”
}
}, “source”: [
“# Code for beginner to learn how to use SIF4Scin”, “n”, “In this notebook, we will show you the basic usage to apply SIF to prepare data for conducting scientific experiments.n”, “n”, “We use the demo item (an exercise from LUNA) shown in the following Figure.n”, “.n”, “The SIF expression of this item can be written as follows:”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “outputs”: [
- {
- “data”: {
“text/plain”: “‘如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\\SIFChoice$$\\FigureID{1}$’”
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“item = {n”, ” "stem": r"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$",n”, ” "options": ["$p_1=p_2$", "$p_1=p_3$", "$p_2=p_3$", "$p_1=p_2+p_3$"]n”, “}n”, “item["stem"]”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 2, “outputs”: [
- {
- “data”: {
“text/plain”: “<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=230x136 at 0x23347BF9D60>”, “image/png”: “iVBORw0KGgoAAAANSUhEUgAAAOYAAACICAYAAADzlJeRAAAe3klEQVR4nO2df1RT9/3/nyE/SWJyBdSUOgs72mKP1XhK24j2CGon4FagbtTMtpZt6rDzR7rjKt+2C+rnHGxdBbr2xB9zgc4j1NMJbpXg2hlsp2DnjrSHVaqcgquWbFqNLUgQkvf3DwgiSSA/7uUm8f04530O3Ny87zu5ed7X+8fr/XoJCCEEFAolrIjhuwEUCsUTKkwKJQyhwqRQwhAR3w2IBJqamuBwOHy+rtFokJKSMo4tokQ7VJgAGhoa0NzcDLvdjn//+99oa2uDw+FAa2srAIBhmFHf73K58O233wIAYmNj8cADDwAAZs+ejblz50Kr1UKn00Emk3H6OSjRg+Bum5Vtbm5Gc3MzrFYrPvnkE7S2toJhGPT09KC3t5f160mlUsTGxqKrqwsMw2D69OlYunQpdDodMjMzWb8eJTqIemHa7XbU19fjT3/6E/72t79BoVDA6XSiq6uL13YxDIOuri48/vjjyM/PR2ZmJpKSknhtEyV8iEph2mw21NbW4tChQzh16hSkUulQVzMckcvliImJgUwmQ3Z2NgwGA7RaLd/NovBIVAmzuroar732Gs6fPw8AuHnzJs8tCg61Wg2FQoFXX30VK1asGHOMS4k+Il6YNpsNf/jDH7Bz504ACGvLGChyuRxOpxOLFy/G+vXr6Zj0LiJihdnU1IQ33ngDdXV1cLlcoy5nRAMMw0AkEmHnzp14/vnn+W4OhWMiTpjNzc0oLCxEa2sr7HY7380Zd5RKJWQyGRVolBMxwrTZbFi/fj0++OAD3Lhxg+/m8A4VaHQT9sK02+347W9/i8rKyqgaP7KFUqmESqXCn//8Z+h0Or6bQ2GJsPaV3bdvH5KTk7Fv3z4qSh90dXXh66+/RlZWFjZs2HBXdu+jEhKGdHZ2Ep1OR9RqNQFAi59FJpORSZMmEYvFwuLdqCNlZRdYrI/iD2FnMfft24dZs2ahqamJjiUDxOFw4MqVK9Dr9XjqqadYsJ5tKJ+fjUNsNI4SGHw/Gdxcv36d5OXlUSvJUhGLxWTSpEmksbEx6HtyoSyNACBr6li80RS/CAuL2dDQgPvvvx/vv/8+tZIs0dfXhytXruBHP/oRSkpKAq+grRw78QrK0thvG2VseJ+VLS0txbZt2+ikBYeoVCrMmzcP1dXVfrr3WbB2/nlsPnk/dgr+Dw9eOImN07lrX0NDA4Db+17/+c9/4uuvv/Z67uzZs5GcnAwASEpKuqNEFXya65/97GeEYRjeu313QxGLxSQ5OZmcO3duzPtSt2YNqSOEkAtlJA2Df7NAT08PsVqtxGg0Ep1OR2JjYwkAwjAMYRiGiESigD6TUqkkDMMQpVJJABCtVkuys7OJ0WgMqQsfDvAizOvXr5NHH3106MbQMn4lLi6OHDlyxOe9cY8rb5fQhHnu3Dny8ssvk+TkZCISicbtQcwwDImNjSUZGRnEZDKR9vb2ED7F+DPuwmxvbycpKSlEIpHw/iO9W8uUKVPIO++843lzLpSRNcOXRurWEKSVkUAXSxobG0lhYSFJSEggKpUqYEvIdpHL5USpVJLExERiMpnI9evXQ/kJjwvjKsyzZ8+SxMRE3n+YtIBMnDiRvP3227dvzoUykjZChBfK0vwWZk9PDzGbzSQxMTGshydyuZwoFAqSnZ3N8novu4ybMM+ePUs0Gg3vN4aW2yUuLm5InAPH0siAwbxAytKGn+u7O9vZ2Uk2bdpEJkyYMDTWi5TCMAxJSEggZrN5nFTgP+MizPb2dmopw7TExcWRjRs3BnxPOzs7ycqVK4lcLue9qxpqUSqVYSdQzoXZ3t5OkpKSeP/yafFdGIbxW5w9PT3EaDSS+Ph4IhaLeW87m8Ut0JqaGk414Q+cCpOKMnIKwzBk165do97PqqoqotFoiEKh4L29XBa1Wk10Oh2vM7mcCfP69eskJSWF9y+ZFv/LlClTvC6luDcVqFQq3ts4noVhGGI0GklPTw9XMvEJJ54/DocDjzzyCFpaWtiuOuqRy+WQSCQecW6Tk5OhVqvvODcnJwcA8NFHH+G///0vrl27ht7eXvT19QUdnnPKlCloaGgYiixfX1+PZ555Bt98802QnyiyUSgUiI+Px5EjR8Y1ciEnwnzqqafw/vvvo6+vj+2qowaGYYYiuLtFl5OTM5RuQavVshodr6GhAXa7Hc3Nzaivr4fNZsNXX30FAIiJiUF/f//QuSqVCrt27cJf/vIXfPTRR9RdEkB8fDxKSkqwevXqcbke68Kkvq+eiEQiKJVKdHV1ITU1FUuXLkV6enpY5Dxxi7WjowN//etfceLECXz33XdwuVx3iJUyEFZ00aJFOHjwIPfpLtjsFzc2NpKEhATexwZ8F7frmUgkIjqdjpSUlESU7+bhw4eJRqMharWayOVyIpVKSUxMDO/fazgUsVhMZsyY4ZfPcSiwJszOzk4ybdo03r84vopMJiNKpZJotdqIE+JwTCYTSU9PJ+3t7aSqqoqsWLGCnDt3jphMJpKZmUkkEknULZMEU6ZMmULOnj3L2X1gRZg9PT0kNTWV9y+Lj6JWq0liYiLZvn17xDlKD6enp4esWLGC/PKXvyQ9PT3k448/Jh9//DHZtGkTKS0tveM8s9k8tDtEKBTyfg/4KhqNhrMHMCvCNBqNUb+2NVKMKpWKPPfcc5w+NceLc+fOEa1WS6qqqoYE6S49PT0kMzOTWK1Wj/e1t7eT7du3k8TERCKXy3m/L3yUhISEUXfrBEvIwmxvbw9rp2U2i1KpJNnZ2WHhGcIWVVVVRKfTkXPnznmI0l06OzuJVqslnZ2dPus5e/Ysee6554hSqSRSqZT3ezWexdf6byiEPCs7b948NDU1hVJFWCMSiSCXy5Gbm4utW7dGzU55h8OBoqIi2Gw2mEymMdecRSIRioqKYLFYRp2RdDgceOutt1BSUoLu7m5Oco6GI1OmTMHevXvx5JNPslJfSMLct28fNm/eHJVxemQyGWQyGX7yk59g27Zt0Gg0fDeJNWw2G/R6PXJycpCamur3+86cOYOLFy+itLR0zHMdDgcqKirw6quvoru7Gz09PaE0OSKIj4/Hhx9+yIojQtDCtNlsmDt3Lmw2W8iNCCcUCgViYmKwbt06bNmyJepS4NXX18NgMMBsNge1Trl//34sXLgwoLQMFRUV2Lx5M3p6etDd3R3wNSOJpKQkWK3WkHtWQUfJW7duXVS5aYnFYjAMg+LiYvzvf//Djh07ok6UxcXFKC8vR2NjY9DOAyaTCZWVlQENX55//nlcuXIFBw4cQHJyMmJjY4O6diTQ0dGBH/zgByFnnwvKYjY3NyM9PT1qurAMw2DZsmV46623ok6MwIB3T15eHhYuXIglS5aEXN/UqVORl5cHq9Ua1Pf1u9/9Djt27IDdbofT6Qy5PeGGRCKBVqvF6dOng64jKGEuWrQIVqs16IuGCxMmTMC9994Ls9kctQl5mpqaUFBQAJPJBJFIxFq9/f392Lp1a9C/A5vNhnXr1uH48eNR84AfjkqlwpYtW1BUVBTU+wMWZjRYS7FYDIVCgW3btmH9+vV8N4czysrKcOTIEVRVVaGtrY31+v/xj3/gxo0bwQWUHqShoQErV67E9evXo26CKJTJoIDHmC+++GJEi5JhGOTn56O9vT1qRWm326HX63Hx4kVYLBZORAkACxYsQGtrK2pra4OuIz09HZcvX8ZvfvMbxMfHs9e4MOCbb75BTk5OUOPNgCxmJFtLsViMyZMn47333ovabisAtLa2oqCgABs3bsTUqVM5v96sWbOQlZUFs9kc8k4Zm82GvLw8fPbZZ7h58yZLLeQXsViM/Px8HDhwIKD3BWQxI9VaMgyDRYsWoaWlJapFWVFRAb1eD7PZPC6iBICWlhaYzWbo9fqQZyI1Gg0aGxuxefPmqJmE6+vrw9GjR1FfXx/Q+/y2mE1NTcjKyoq4fZZxcXF45ZVXYDAY+G4KZzgcDhgMBjgcDphMJpw5c2bc23D16lVUVlaipqaGlfqamprw5JNP4sqVK6zUxzfJycn48ssv/T7fb4tpMpkiSpRisRjTpk3DyZMno1qUHR0dyMjIwJw5c/Dzn/+cF1ECQEJCAlJSUrBjxw5W6tPpdDh//jx0Oh3kcjkrdfLJ1atX8fvf/97v8/0SpsPhwKFDkZO+dOLEifjhD3+IL774gvcIAVxSW1uLvLw8mEwmzJo1i+/mYNmyZTh27NhQ9q5QYRgmarq23333HYqLi/3u7vslzOrqalbXwLgkISEB5eXlOHz4MPfhH3ikqKgIlZWVsFqtQQfe4oKamhoYDAZ0dHSwVmdxcTEsFgsSExNZq5MPent7/e5R+DXGnDlzJlpbW0NuGNdoNBpUVVUhPT2d76ZwhtsBfenSpViwYAHfzfGKvztRAqWjowNZWVkR8Vv0hUqlwhdffDHmpogxLWZra6vPJKLhRGJiIiwWS1SL0j0BZzQaw1aUwIBX0KpVq1BYWMhqvUlJSWhsbERqamrE9OBG4nQ6/Vo6GVOYe/bsCfs1pXvvvRcnT54c17if482OHTtgMBhgsVgi4kc5ffp0yGQy7N69m9V6GYbBxx9/jLS0tIh0hu/u7kZJScmYY80xhXngwIGwDWMokUiQkpKClpaWqNnAPBK3A/qNGzewc+dOzrx4uKC0tBTvvvsu6xvpZTIZTpw4gSeeeMIjCHYk4HQ6x1zXHHWM2dHRgYceeiisJhfcyOVyzJkzB3V1dRE/Y+eL5uZmFBQUwGg0IiEhge/mBMXUqVOh1+tRU1PDyWbzTZs2oaKiIuIcX8Za1xzVYtbX18PlcrHeqFCRSCSYM2cOjh8/HrWi3L17NwoLC1FTUxOxogSAS5cuoaSkBAUFBSF7BnmjrKwMRqMREydOZL1uLrl27Rqam5t9vj6qMA8dOhSW48v7778fx48fj8rlEIfDgYKCAnz66aewWq24dOkS300KGZFIhKVLlwa9BWosDAYDnnvuuYjq1nZ3d6OystLn6z67sg6HAxMnTuTkKRcKKSkpaGxsjEpL6XZAX7t2LaZPn853c1jn7bffRk5ODlasWMFJ/Xq9HrW1tWH3m/VFQkKCT5dDnxazqakp7CzStGnTgt41H+7U1tYObWiORlECgNlsxmuvvcbZOmRVVRUeffTRiJi1BgaWlXxNjPkU5ocffhhWkz6JiYk4ceJEVEWrc2MwGFBZWQmLxRJW3znbnDlzBjU1NdDr9Zz5XR87dgyPPPIIxGIxJ/WzSVdXF959912vr/nsyoZT+BCNRgOLxRJ165Tu/YdPP/10QGEkIx22d6KMxG63Y968eRHhIeSrO+vTYv7rX//itEH+kpCQgJqamqgTZUNDAzIyMlBaWnpXiRIYuKdz5sxBcXExJ/UzDAOr1Ypp06ZxUj+bOBwOr37FXoVpt9vDIoK2SqXCiy++GHWbm4uLi7F169aQwkhGOkuWLMHp06cD3kDsLxqNBseOHUNcXBwn9bOJt904XoXZ3NzMu7uTWCyGVqvlbIqdD+x2O7KysgAA27dvHzMtQbRTVVWFoqIiVneiDMe9P1SlUnFSPxt0dXXhk08+8TjuU5h8RyybPHkyjhw5wmsb2KSpqQkZGRnYuHEjK7Fdo4GWlhZUVVUhLy+PsyWO1atXY/HixWE9U+ut1+BVmGfPnuW1K5uQkID33nsvapZFdu/ejaKiItTU1ECpVPLdnLDi6tWreOmll1BQUMDZNQ4ePBjWezm/+uorjweTV2F+9tln49Igb0TTuNLhcECv1+PTTz+FxWKJCi8eLpg6dSo0Gg3Kyso4qV8mk4X1eFOpVHq453kVJl8/ILFYjMWLF0fFuLK1tRXz5s1DTk4OVq5cyVssnkihpKSE1bAkIwnn8abL5fIYZ/ucleWD+Ph4HDx4kJdrs0l1dTX0ej2qqqrGLYxkpHPmzBmYzWYYDAbOMsi5x5tCoZCT+oOlt7fX4zPfIUzi6se3Xd9BoIoF1ApgHD3yGIaB2WwOOzfAQHA4HCgsLMSRI0fQ2NiIq1ev8t2kiKKtrQ0mk4mVGLW++OMf/xh2Xdre3l6cP3/+jmN3CFMQI8K1K/+DTCwGbnYD4+QLLBaL8dhjjyEzM3N8LsgB7ng0c+bMwQsvvEC7rkHS39+PnJwczoYzDMPgjTfeCLsu7UgvJY+ubMfFi4iJCTptZlCoVCpUVFSM6zXZpL6+Hnl5eSgpKQmLMJKRTmpqKux2O2e/iWeffRbf//73Oak7WDo7O+/432Nxx27/dvAvMaCWDPzpugV81wfIZIBUCPQOWlOxGJAPntM7zMIOPw4ncGOY6R3x2gSXGEaj0dM5nThxy+kCAQBBDCRCIQQjj0MAsUiEGBD09/fDCcHA+MHZDycAxIggFbhunx8jgjRGENAXNhbFxcU4ffo0rFbrXe8wwCYmkwlZWVlISUnhZIbebDZj8eLFuHbtGut1B8PIYY+HabTbrw9GLegDbt4C4BwQJQA4nED/rUEBigfGoDe6gRu3AKkMEA8elwsHusI3uoF+IaBwe/p7vjbpnslesm4R9DsJYoRiSEUiCOGCk4w8LoY4hqDPRQAIIBLGQAACp9OJGJEYUmEMBK5+9LoA8dD/TrAVj8FutyMjIwMA8PLLL1NRsox7MqiwsJCTyUitVouVK1eG7S6U0fusfX1AL24LSyYEnH23/46RDEwSqSUAhIBw8LjLCQyehlu3AJHw9nuGvaaWSlFd9a6nWIgTzhghRAJgQHTigb+JE06BAMJBoxcjGCk2AYRC0bAPJYAwZtDSDuIKOE2vJ01NTZg3bx6MRiP14uGQS5cuobS0FHl5eZzU//rrr2PChAmc1B0qvoXpFpbDOSAsMQCh884Jof5bgxZzsLhfixllOnrwNZFIhPzlyzFn9hzv5xF3d3XkceL9+DhRVlYGg8EAq9Ua1m5e0QKXYUlkMhn2798flt5YQ8J0ufrQ6yIDyxXEOeyUQaspV9y2lsCgYCXDllQGu7YOJwDh7eNCIdDvvP2ewddUKhXeLC8FiRF6eToIICDu7isAkAFLJxBCCIL+IbNHfLyffdxhJC9evBhxYSQjHTYS5PoiNzc3LCfshn7TAggAVz/iJ09GTP+ICRKHE8AIa4k+oNcJSBWD3Vnh4OuDY1P3cbET6O67/Z6bt6BQxWHz/9sCgUQGydBkDEF/fx/6BwUoEQrgdPaht78Pvf1OYKhbGwO4+geOuwSD7yfod7pAQOB09sM15v+B0dzcjKysLKxatQrLly8P8N0UNuAyLInJZAo7q+kRwaChoQF5eXkjBtxiQNbH2rpmXFwcLl++HBHOBBUVFdizZw/MZjN1GOCZhIQE6PV6NDY2sv7b4Ttix8hIBv71AmVCwDn2af6gUChQVFQU9qJ0e/GcOHECVquVijIMuHr1KoxGI/R6Pet179q1i1erOdJ100OYdwhGMdgdheP2ZFCISKVS/OpXv2KnMo4Il2SwFE/YTpDrRqvVYtKkSazWGQgjDZWHMDUaze1wF90jZltDJBKsZbglg6V4wnaCXDe7du3iLXv1mBYzKSmJsxCK4W4twzUZLMUTLhLk5ubmYsqUKazVFwgjrbXXMSYXDr7hbC1tNhvmzZsHtVqNX//619SLJwJoaWmByWRiPScKH1ZTLpdj9uzZdxzzKsz4+HjWLy4UCvHMM8+wXm+oNDQ0ICsrC6WlpWGdDJbiCRcJcnNzczF58mTW6vMHiUTiEZ7VqzC5MOcPP/xw2EVR37FjB7Zu3QqLxXLXhpGMdLhIkPvSSy+Nq1dXT08PUlJS7jjmVZhs77pXq9XYsGEDq3WGwvBksNu3b6dePBEO2wlyc3NzIZFIxj6RJaRSqUfgOa/CfOCBB1h9YgiFwrDZBN3c3IyMjAysWrUKy5Yt47s5FBZgOyyJRqPBY489xkLL/OPBBx/0OOZVmAsWLGBtsVUkEmH58uVhMekTLclgKZ6wnSB3w4YNkEqlLLRsdEQiERYvXuxx3KswdTodawGfFQoF1q1bx0pdwRKNyWApnrC5EyUzM3NcjIlSqfS6ddCrMBmGYW1m9p577uE1IZA7jOTChQtpGMm7gNTUVNhsNlRXV4dUj0wmQ05ODkut8o3D4fAaocGnrywbYpLL5di4cWPI9QRLdXU1CgoKUFVVFbXJYCmesLUTpbCwkHOrqdVqvV7DpzCfeOKJkPvYTqeTs7Teo+FwOGAwGHDkyBFYLBbqgH6XwVaCXJ1Ox2mEA6lUiqefftrraz6FqdPpQs74NXPmzHHPP2Kz2ZCVlYX77rsPL7zwAvXiuUu5dOkSjEZjyDlR1qxZw1KLPJFKpT5XK0btyjqdwe/1EolE474c4U4GW1JSctclg6V4wkaC3CVLlnAWsCs+Pt7DscCNT2HKZDI8/vjjQV/U12wTV9BksBRvhJogV6fTcZJSYazVilE3Sj/77LNBO7Q7nc5xydhFk8FSxiKUBLkymQxz585lv1EAfvGLX/h8bVRhZmZmBt2dTU1N5XxGyx1GkiaDpYxGqAly8/PzIRCwGyh84cKFo86/jCpMhmHw0EMPBXxRqVSKJ598MuD3BUJZWRmKiopgtVrDLpASJfwIJUFueno6q+NMhmG8BDm/kzFj/qxatSrg/WmjzTaFit1uh16vx8WLF2GxWKgDOsVvgk2Qy7aDzMSJE8fUx5jCzM3NDfjCEonE52xTKLS2tiIrKws5OTlYvnw59eKhBEywCXLZGmeq1Wrs2rVrzPPGFGYwnvaPPvpoQOf7gzsZrNlspslgKUET7E6UH//4x6xcPy4uzi9j51f4yg0bNkCtVvt1YZFIFNIyy0hoMlgK2wSTIHfJkiUhp6f011oCXgI+++Lee+/F119/7dfFKyoqguoCj6SjowMFBQV4+umnacQ6CuucOXMGFy9eRGlp6ZjnOhwOyOVy+CkXryQnJ+PLL7/061y/HwEGgwEKhWLM8wghrIwvaTJYCtcEkiBXJpOFtPynVqtx+PBhv8/3W5ijLYYO5+bNmyELs6ioCOXl5bBardSLh8IpJpMJlZWVfoUlCTZmlUgkQn5+fkCzu34Lk2EY/PSnPx0z5Mj3vvc9vy8+EpvNhoyMDKjVapoMljIuBJIgN9iZWZVKhTfffDOg9wQ0ml23bt2Y3dmZM2cG1AA3TU1NyMrKgtFopGEkKeOKvwlytVptwB5AarUa+/fvD7gbHJAwtVot8vPzfVrNYGdk3clgLRYLTQZL4QV/wpI88sgjAdUpFouxaNGioCZCA57/3bZtm09PIIVCEdD4kiaDpYQTYyXITUlJCchi3nPPPTh48GBQbQlYmBqNBqtWrfJqmgOZkaXJYCnhyGhhSZKSkvxeLomLi8OxY8eCnskNasX09ddf92o1XS6XXw2pqKhAYWEhzGYzDSNJCStaWlpgNpt9Oh/4EwharVZjx44dIa1OBCVMmUyGoqIij4kgh8Mx6pSyO4wkTQZLCWdGS5A7Vhws97hy9erVIbXBb8+fkTgcDsyePRsXLly447iv6lpbW1FQUIC1a9fSiHWUiODo0aNQq9XYsmXL0LH4+Hhcu3bN6/kSiQQPP/wwjh8/HvJe5KCd/2QyGd555507Nnv6inZQW1uLgoICmEwmKkpKxOAtQe5oET1mz57NiiiBEIQJDMRDefbZZ4ca4q3/bTAYUFlZCYvFQpPBUiKOkQlyfW2YTklJwQcffMBa1I7Q3OUxMBHk9vYZvh3LnQz2vvvuo8lgKRHLyAS53nZZpaSkoLGxkdVQrSELUyaT4dChQ3eY+OHJYGkYSUqkMzxB7vAJT3dAALZFCbAgTGDAIygtLQ0zZsygyWApUYk7Qa5UKoVQKERsbCy0Wi0nogQCEKZlrQACwYgyvxxuX50lS5bAarWitbUVf//736kXDyXqKC0txX/+8x/I5XLk5OTg9OnTnGUaCGi5pK18PmZ8/grIniwAFqwVZGPvmjqQPVk4cOAAtmzZgsuXL7PeSK1Wy8oXkJ6ePvR3RUVFUHFGKRSRSIS+vj5OrxGAMNtQPn8GPn+FYM9AfGVY1gqQjbpBoVIo0U9HRwc6OjrueMgDACxrIcjeO/TvmjqCPViLtdiDYOTh/xiz7SgOnVqD3MGLtJXPR/beNJRtpqKk3D0kJSWNEGUbyucLIMgG6ggBGSy5tQIIslvw4IzgruO3MNuOHsIp7EX24PhyFSpByElspP4ClLuWgV7kJpThAtmD4SYqa08d1mAW7g9SH34Ksw1HD53CmrrBJ8KFMmDTDMwvpxM8lLuXtvJV2HQqDWWVG+Gpvxl4sCwXwfYn/RtjtpVj/ozP8cqwp4JlrQDZe9egbsSTgkK5Oxi0lrO4mWPxy2K2HT2EU2uGq78N51sApD2IILvQFEqEcwGfnwLSgh1EjoEfcTwGu7Gv3JalZe0MbDoFrKnzZsIplLuAtvPg0sl0TIspEAyIcG/2bceC7L1pKLtAgpoGplCigun3YxaAU59f8Pqypfy2800wBL0fk0K56xlcu0wru4CTQ8sTnuv9wUCFSaGEQls55s/YhFNDB9JQdiH0ZUQqTAolDGFldwmFQmEXKkwKJQz5/6GFNae7ZI7lAAAAAElFTkSuQmCCn”
}, “execution_count”: 2, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“from PIL import Imagen”, “img = Image.open("../../asset/_static/item_figure.png")n”, “figures = {"1": img}n”, “img”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“## Preparation”
], “metadata”: {
“collapsed”: false
}
}, {
“cell_type”: “code”, “execution_count”: 3, “outputs”: [], “source”: [
“from EduNLP.SIF import sif4sci”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“## Verificationn”, “n”, “## Auto Correction”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“## Segment and Tokenizationn”, “n”, “After we verify an item obeys SIF, we can further process it, i.e., segment and tokenization.”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“### Segment”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “code”, “source”: [
“sif4sci(item["stem"], figures=figures, tokenization=False, symbol="tfgm")n”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}, “execution_count”: 4, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[FORMULA]’, ‘[TEXT]’, ‘[MARK]’, ‘[FIGURE]’]”
}, “execution_count”: 4, “metadata”: {}, “output_type”: “execute_result”
}
]
}, {
“cell_type”: “code”, “execution_count”: 5, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形’, ‘ABC’, ‘的斜边’, ‘BC’, ‘, 直角边’, ‘AB’, ‘, ‘, ‘AC’, ‘.’, ‘\\bigtriangleup ABC’, ‘的三边所围成的区域记为’, ‘I’, ‘,黑色部分记为’, ‘II’, ‘, 其余部分记为’, ‘III’, ‘.在整个图形中随机取一点，此点取自’, ‘I,II,III’, ‘的概率分别记为’, ‘p_1,p_2,p_3’, ‘,则’, ‘\\SIFChoice’, \FigureID{1}]”
}, “execution_count”: 5, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“segments = sif4sci(item["stem"], figures=figures, tokenization=False)n”, “segments”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 6, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形’,n ‘的斜边’,n ‘, 直角边’,n ‘, ‘,n ‘.’,n ‘的三边所围成的区域记为’,n ‘,黑色部分记为’,n ‘, 其余部分记为’,n ‘.在整个图形中随机取一点，此点取自’,n ‘的概率分别记为’,n ‘,则’]”
}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“segments.text_segments”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 7, “outputs”: [
- {
- “data”: {
“text/plain”: “[\FigureID{1}]”
}, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“segments.figure_segments”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 8, “outputs”: [
- {
- “data”: {
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”
}, “execution_count”: 8, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“segments.figure_segments[0].figure”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 9, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘ABC’,n ‘BC’,n ‘AB’,n ‘AC’,n ‘\\bigtriangleup ABC’,n ‘I’,n ‘II’,n ‘III’,n ‘I,II,III’,n ‘p_1,p_2,p_3’]”
}, “execution_count”: 9, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“segments.formula_segments”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 10, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘\\SIFChoice’]”
}, “execution_count”: 10, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“segments.ques_mark_segments”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“### Tokenization”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “code”, “execution_count”: 20, “outputs”: [], “source”: [
“tokens = sif4sci(item["stem"], figures=figures, tokenization=True)”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“#### Text”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “code”, “execution_count”: 12, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘如图’,n ‘古希腊’,n ‘数学家’,n ‘希波’,n ‘克拉底’,n ‘研究’,n ‘几何图形’,n ‘此图’,n ‘三个’,n ‘半圆’,n ‘三个’,n ‘半圆’,n ‘直径’,n ‘直角三角形’,n ‘斜边’,n ‘直角’,n ‘三边’,n ‘围成’,n ‘区域’,n ‘记’,n ‘黑色’,n ‘记’,n ‘其余部分’,n ‘记’,n ‘图形’,n ‘中’,n ‘随机’,n ‘取’,n ‘一点’,n ‘此点’,n ‘取自’,n ‘概率’,n ‘记’]”
}, “execution_count”: 12, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“tokens.text_tokens”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“#### Formula”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “code”, “execution_count”: 13, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘ABC’,n ‘BC’,n ‘AB’,n ‘AC’,n ‘\\bigtriangleup’,n ‘ABC’,n ‘I’,n ‘II’,n ‘III’,n ‘I’,n ‘,’,n ‘II’,n ‘,’,n ‘III’,n ‘p’,n ‘_’,n ‘1’,n ‘,’,n ‘p’,n ‘_’,n ‘2’,n ‘,’,n ‘p’,n ‘_’,n ‘3’]”
}, “execution_count”: 13, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“tokens.formula_tokens”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 14, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘A’,n ‘B’,n ‘C’,n ‘B’,n ‘C’,n ‘A’,n ‘B’,n ‘A’,n ‘C’,n ‘\\bigtriangleup’,n ‘A’,n ‘B’,n ‘C’,n ‘I’,n ‘I’,n ‘I’,n ‘I’,n ‘I’,n ‘I’,n ‘I’,n ‘,’,n ‘I’,n ‘I’,n ‘,’,n ‘I’,n ‘I’,n ‘I’,n ‘p’,n ‘1’,n ‘_’,n ‘,’,n ‘p’,n ‘2’,n ‘_’,n ‘,’,n ‘p’,n ‘3’,n ‘_’]”
}, “execution_count”: 14, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“sif4sci(n”, ” item["stem"],n”, ” figures=figures,n”, ” tokenization=True,n”, ” tokenization_params={n”, ” "formula_params": {n”, ” "method": "ast",n”, ” "return_type": "list"n”, ” }n”, ” }n”, “).formula_tokens”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 15, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘\\bigtriangleup’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘,’,n ‘mathord’,n ‘mathord’,n ‘,’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘mathord’,n ‘textord’,n ‘_’,n ‘,’,n ‘mathord’,n ‘textord’,n ‘_’,n ‘,’,n ‘mathord’,n ‘textord’,n ‘_’]”
}, “execution_count”: 15, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“sif4sci(n”, ” item["stem"],n”, ” figures=figures,n”, ” tokenization=True,n”, ” tokenization_params={n”, ” "formula_params":{n”, ” "method": "ast",n”, ” "return_type": "list",n”, ” "ord2token": Truen”, ” }n”, ” }n”, “).formula_tokens”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 16, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘mathord_0’,n ‘mathord_1’,n ‘mathord_2’,n ‘mathord_0’,n ‘mathord_1’,n ‘mathord_0’,n ‘mathord_1’,n ‘mathord_0’,n ‘mathord_1’,n ‘\\bigtriangleup’,n ‘mathord_0’,n ‘mathord_1’,n ‘mathord_2’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘,’,n ‘mathord_0’,n ‘mathord_0’,n ‘,’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘mathord_0’,n ‘textord’,n ‘_’,n ‘,’,n ‘mathord_0’,n ‘textord’,n ‘_’,n ‘,’,n ‘mathord_0’,n ‘textord’,n ‘_’]”
}, “execution_count”: 16, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“sif4sci(n”, ” item["stem"],n”, ” figures=figures,n”, ” tokenization=True,n”, ” tokenization_params={n”, ” "formula_params":{n”, ” "method": "ast",n”, ” "return_type": "list",n”, ” "ord2token": True,n”, ” "var_numbering": Truen”, ” }n”, ” }n”, “).formula_tokens”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 17, “outputs”: [
- {
- “data”: {
“text/plain”: “[<Formula: ABC>,n <Formula: BC>,n <Formula: AB>,n <Formula: AC>,n <Formula: \bigtriangleup ABC>,n <Formula: I>,n <Formula: II>,n <Formula: III>,n <Formula: I,II,III>,n <Formula: p_1,p_2,p_3>]”
}, “execution_count”: 17, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“sif4sci(n”, ” item["stem"],n”, ” figures=figures,n”, ” tokenization=True,n”, ” tokenization_params={n”, ” "formula_params":{n”, ” "method": "ast",n”, ” "return_type": "formula",n”, ” "ord2token": True,n”, ” "var_numbering": Truen”, ” }n”, ” }n”, “).formula_tokens”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “code”, “execution_count”: 18, “outputs”: [
- {
- “data”: {
“text/plain”: “[<networkx.classes.digraph.DiGraph at 0x23356118d00>,n <networkx.classes.digraph.DiGraph at 0x23356118c70>,n <networkx.classes.digraph.DiGraph at 0x23356118d30>,n <networkx.classes.digraph.DiGraph at 0x23356118e80>,n <networkx.classes.digraph.DiGraph at 0x233561189a0>,n <networkx.classes.digraph.DiGraph at 0x233561187c0>,n <networkx.classes.digraph.DiGraph at 0x233561185e0>,n <networkx.classes.digraph.DiGraph at 0x233561186a0>,n <networkx.classes.digraph.DiGraph at 0x23347bc7b50>,n <networkx.classes.digraph.DiGraph at 0x23347bc7a30>]”
}, “execution_count”: 18, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“sif4sci(n”, ” item["stem"],n”, ” figures=figures,n”, ” tokenization=True,n”, ” tokenization_params={n”, ” "formula_params":{n”, ” "method": "ast",n”, ” "return_type": "ast",n”, ” "ord2token": True,n”, ” "var_numbering": Truen”, ” }n”, ” }n”, “).formula_tokensn”, “n”, “#### Figure”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}, {
“cell_type”: “markdown”, “source”: [
“## Downstream tasksn”, “n”, “### Word to vector”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%% mdn”
}
}
}, {
“cell_type”: “code”, “execution_count”: 19, “outputs”: [
- {
- “data”: {
“text/plain”: “[‘如图’, ‘古希腊’, ‘数学家’, ‘希波’, ‘克拉底’, ‘研究’, ‘几何图形’, ‘此图’, ‘三个’, ‘半圆’, ‘三个’, ‘半圆’, ‘直径’, ‘直角三角形’, ‘[FORMULA]’, ‘斜边’, ‘[FORMULA]’, ‘直角’, ‘[FORMULA]’, ‘[FORMULA]’, ‘[FORMULA]’, ‘三边’, ‘围成’, ‘区域’, ‘记’, ‘[FORMULA]’, ‘黑色’, ‘记’, ‘[FORMULA]’, ‘其余部分’, ‘记’, ‘[FORMULA]’, ‘图形’, ‘中’, ‘随机’, ‘取’, ‘一点’, ‘此点’, ‘取自’, ‘[FORMULA]’, ‘概率’, ‘记’, ‘[FORMULA]’, ‘[MARK]’, ‘[FIGURE]’]”
}, “execution_count”: 19, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“sif4sci(item["stem"], figures=figures, tokenization=True, symbol="fgm")”
], “metadata”: {
“collapsed”: false, “pycharm”: {
“name”: “#%%n”
}
}
}
], “metadata”: {
- “kernelspec”: {
“display_name”: “Python 3”, “language”: “python”, “name”: “python3”
}, “language_info”: {
- “codemirror_mode”: {
“name”: “ipython”, “version”: 2
}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython2”, “version”: “2.7.6”
}
}, “nbformat”: 4, “nbformat_minor”: 0
}