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Script for converting PL files back to MD #34

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Script for converting PL files back to MD #34

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221 changes: 221 additions & 0 deletions pl_to_md/.ipynb_checkpoints/PL to Markdown testing-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert from PrairieLearn format to a Markdown format"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import yaml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python Code section"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"from inspect import getmembers, isfunction\n",
"import importlib\n",
"import examples.Two_blocks_connected_by_a_string.server as server_module"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"path = 'examples/Two_blocks_connected_by_a_string/server.py'\n"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"functions_list = getmembers(server_module, isfunction)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('generate',\n",
" <function examples.Two_blocks_connected_by_a_string.server.generate(data)>),\n",
" ('grade',\n",
" <function examples.Two_blocks_connected_by_a_string.server.grade(data)>),\n",
" ('nested_dict',\n",
" <function examples.Two_blocks_connected_by_a_string.server.<lambda>()>),\n",
" ('parse',\n",
" <function examples.Two_blocks_connected_by_a_string.server.parse(data)>),\n",
" ('prepare',\n",
" <function examples.Two_blocks_connected_by_a_string.server.prepare(data)>)]"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"functions_list"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'generate'"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"functions_list[0][0]"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"code = []\n",
"with open(path,'r') as f:\n",
" code = f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['import random as rd\\n',\n",
" 'import problem_bank_helpers as pbh\\n',\n",
" 'from collections import defaultdict\\n',\n",
" 'nested_dict = lambda: defaultdict(nested_dict)\\n',\n",
" '\\n',\n",
" 'def generate(data):\\n',\n",
" ' # Start problem code\\n',\n",
" ' \\n',\n",
" ' data2 = nested_dict()\\n',\n",
" ' \\n',\n",
" ' # store phrases etc\\n',\n",
" ' data2[\"params\"][\"vars\"][\"title\"] = \\'Two Blocks Connected by a String\\'\\n',\n",
" ' data2[\"params\"][\"vars\"][\"units\"] = \"N\"\\n',\n",
" ' \\n',\n",
" ' # Define variables in case the image is randomized in the future.\\n',\n",
" ' m1 = 1\\n',\n",
" ' m2 = 1.2\\n',\n",
" ' g = 9.81\\n',\n",
" ' \\n',\n",
" ' # store the variables in the dictionary \"params\". \\n',\n",
" ' data2[\"params\"][\"m1\"] = m1\\n',\n",
" ' \\n',\n",
" ' # define possible answers\\n',\n",
" ' # round in traditional way using pbh.roundp() and then convert to int\\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans1\"][\"value\"] = int(pbh.roundp(m1*g, decimals = 0)) \\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans1\"][\"correct\"] = False\\n',\n",
" ' \\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans2\"][\"value\"] = int(pbh.roundp(m2*g, decimals = 0))\\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans2\"][\"correct\"] = True\\n',\n",
" ' \\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans3\"][\"value\"] = int(pbh.roundp(2*m2*g, decimals = 0))\\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans3\"][\"correct\"] = False\\n',\n",
" ' \\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans4\"][\"value\"] = int(pbh.roundp(2*m1*g, decimals = 0))\\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans4\"][\"correct\"] = False\\n',\n",
" ' \\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans5\"][\"value\"] = int(pbh.roundp(m2*g, decimals = 0)) + 1\\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans5\"][\"correct\"] = False\\n',\n",
" ' \\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans6\"][\"value\"] = int(pbh.roundp(m2*g, decimals = 0)) - 1\\n',\n",
" ' data2[\"params\"][\"part1\"][\"ans6\"][\"correct\"] = False\\n',\n",
" ' \\n',\n",
" ' # Update the data object with a new dict\\n',\n",
" ' data.update(data2)\\n',\n",
" ' \\n',\n",
" 'def prepare(data):\\n',\n",
" ' pass\\n',\n",
" ' \\n',\n",
" 'def parse(data):\\n',\n",
" ' pass\\n',\n",
" ' \\n',\n",
" 'def grade(data):\\n',\n",
" ' pass\\n',\n",
" ' \\n']"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"code "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for line in code:\n",
" if 'data.update(data2)' in line:\n",
" "
]
}
],
"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": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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