133 lines
3.4 KiB
Plaintext
133 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Latex Anki Card - Bulk Division"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"def Form1(dividend,divisor):\n",
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" tmp=\"\"\"Solve [latex]\\mydiv {{{div}}}{{{divid}}} [/latex]\"\"\".format(div=divisor,divid=dividend)\n",
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" return tmp\n",
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"\n",
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"def Form2(dividend,divisor):\n",
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" tmp=\"\"\"Solve [$] {{{divid}}} \\div {{{div}}} [/$]\"\"\".format(div=divisor,divid=dividend)\n",
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" return tmp\n",
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"\n",
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"def Form3(dividend,divisor):\n",
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" tmp=\"\"\"Solve [$] \\\\frac {{{divid}}}{{{div}}} [/$]\"\"\".format(div=divisor,divid=dividend)\n",
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" return tmp\n",
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"\n",
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"def Back(dividend,divisor):\n",
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" ans=dividend/divisor\n",
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" rounded1=round(ans,1)\n",
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" rounded2=round(ans,2)\n",
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" rounded3=round(ans,3)\n",
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" temp=\"\"\" Answer(3 D.P):\\n\n",
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" {ans3} \\n\\n\n",
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" Working:\\n\n",
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" [$]\\longdivision[stage=5] {{{divid}}}{{{div}}}[/$]\n",
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"\n",
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" Check Answer:\\n\n",
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" [$]\\opmul{{{ans3}}}{{{div}}} \\quad \\opmul{{{ans2}}}{{{div}}} \n",
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" [/$]\\n \"\"\".format(ans2=rounded2, ans3=rounded3, divid=dividend, div=divisor)\n",
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" return temp\n",
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"\n",
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"dividends=[1023, 300, 300,300, 3520, 3932.5, 4732, 532.5,532.5,532.5,423, 4750]\n",
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"divisor=[9, 2000, 2475, 400, 10, 45,9,10,15,60,3,8]\n",
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"\n",
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"lsf1=[]\n",
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"lsf2=[]\n",
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"lsf3=[]\n",
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"lsb=[]\n",
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"lst=[]\n",
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"for divid, div in zip(dividends,divisor):\n",
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" lsf1.append (Form1(divid,div))\n",
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" lsf2.append (Form2(divid,div))\n",
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" lsf3.append (Form3(divid,div))\n",
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" lsb.append (Back(divid,div))\n",
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" lst.append ('Division')\n",
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"\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"location=Path('/home/dl92/SynologyDrive/Kids/Miri/FlashCards')\n",
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"fname=location / 'Division.csv'\n",
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"pd.DataFrame({'Form1':lsf1,'Form2':lsf2,'Form3':lsf3,'Back':lsb,'Tag':lst}).to_csv(fname,header=False, index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.6.9 64-bit",
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"language": "python",
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"name": "python36964bit5aad4b8392a24ab49b91cfc5ab558ab3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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