Upload Sheets_generator.ipynb
Browse files- Sheets_generator.ipynb +497 -0
Sheets_generator.ipynb
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| 1 |
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{
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| 2 |
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"nbformat": 4,
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| 3 |
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"nbformat_minor": 0,
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| 4 |
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"metadata": {
|
| 5 |
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"colab": {
|
| 6 |
+
"provenance": []
|
| 7 |
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},
|
| 8 |
+
"kernelspec": {
|
| 9 |
+
"name": "python3",
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| 10 |
+
"display_name": "Python 3"
|
| 11 |
+
},
|
| 12 |
+
"language_info": {
|
| 13 |
+
"name": "python"
|
| 14 |
+
}
|
| 15 |
+
},
|
| 16 |
+
"cells": [
|
| 17 |
+
{
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| 18 |
+
"cell_type": "markdown",
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| 19 |
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"source": [
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| 20 |
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"# Receipt"
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| 21 |
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],
|
| 22 |
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"metadata": {
|
| 23 |
+
"id": "wEKMGOFvSV_V"
|
| 24 |
+
}
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"cell_type": "code",
|
| 28 |
+
"execution_count": null,
|
| 29 |
+
"metadata": {
|
| 30 |
+
"id": "iGlVFh9Yf1ar"
|
| 31 |
+
},
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| 32 |
+
"outputs": [],
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| 33 |
+
"source": [
|
| 34 |
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"import random\n",
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| 35 |
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"import math\n",
|
| 36 |
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"\n",
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| 37 |
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"vocabulary = [\n",
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| 38 |
+
" \"Apple\", \"Banana\", \"Coffee\", \"Dumpling\", \"Eggs\", \"Fries\", \"Garlic\", \"Ham\",\n",
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| 39 |
+
" \"Ice cream\", \"Juice\", \"Ketchup\", \"Lemon\", \"Milk\", \"Noodles\", \"Orange\",\n",
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| 40 |
+
" \"Pasta\", \"Quinoa\", \"Rice\", \"Salad\", \"Tea\", \"Udon\", \"Vinegar\", \"Water\", \"Yogurt\",\n",
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| 41 |
+
" \"Bread\", \"Cheese\", \"Donuts\", \"Espresso\", \"Fish\", \"Grapes\", \"Honey\",\n",
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| 42 |
+
" \"Jam\", \"Kiwi\", \"Lobster\", \"Mango\", \"Nuts\", \"Oatmeal\", \"Pizza\", \"Ramen\",\n",
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| 43 |
+
" \"Soda\", \"Tuna\", \"Vanilla\", \"Wine\", \"Zucchini\", \"Steak\", \"Burger\", \"Chicken\",\n",
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| 44 |
+
" \"Pork\", \"Beef\", \"Lamb\", \"Tofu\", \"Avocado\", \"Tomato\", \"Potato\", \"Carrot\",\n",
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| 45 |
+
" \"Broccoli\", \"Cauliflower\", \"Spinach\", \"Lettuce\", \"Cucumber\", \"Onion\",\n",
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| 46 |
+
" \"Bottled water\", \"Sparkling water\", \"Green tea\", \"Black tea\", \"Beer\", \"Wine\",\n",
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| 47 |
+
" \"Whiskey\", \"Vodka\", \"Rum\", \"Gin\", \"Tequila\", \"Cocktail\", \"Smoothie\", \"Milkshake\",\n",
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| 48 |
+
" \"Shampoo\", \"Conditioner\", \"Soap\", \"Toothpaste\", \"Toothbrush\", \"Floss\", \"Mouthwash\",\n",
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| 49 |
+
" \"Detergent\", \"Fabric softener\", \"Bleach\", \"Disinfectant\", \"Sponge\", \"Brush\",\n",
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| 50 |
+
" \"Toilet paper\", \"Paper towel\", \"Tissues\", \"Napkins\", \"Trash bags\", \"Vacuum cleaner\",\n",
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| 51 |
+
" \"Mop\", \"Broom\", \"Dustpan\", \"Duster\", \"Wipes\", \"Air freshener\", \"Candle\",\n",
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| 52 |
+
" \"Light bulb\", \"Batteries\", \"Extension cord\", \"Plug adapter\", \"Hanger\",\n",
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| 53 |
+
" \"Laundry basket\", \"Iron\", \"Ironing board\", \"Scissors\", \"Tape\", \"Glue\",\n",
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| 54 |
+
" \"Nail clipper\", \"Razor\", \"Shaving cream\", \"Deodorant\", \"Perfume\", \"Cologne\",\n",
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| 55 |
+
" \"Lotion\", \"Sunscreen\", \"Insect repellent\", \"Band-aids\", \"Cotton swabs\",\n",
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| 56 |
+
" \"Notebook\", \"Journal\", \"Planner\", \"Calendar\", \"Pen\", \"Pencil\", \"Marker\",\n",
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| 57 |
+
" \"Highlighter\", \"Eraser\", \"Ruler\", \"Stapler\", \"Staples\", \"Paper clips\",\n",
|
| 58 |
+
" \"Binder\", \"Folder\", \"Envelope\", \"Sticky notes\", \"Index cards\", \"Tape dispenser\",\n",
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| 59 |
+
" \"Calculator\", \"Laptop\", \"Tablet\", \"E-reader\", \"Charger\", \"USB drive\",\n",
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| 60 |
+
" \"Memory card\", \"External hard drive\", \"Mouse\", \"Keyboard\", \"Monitor\",\n",
|
| 61 |
+
" \"Headphones\", \"Speakers\", \"Webcam\", \"Microphone\", \"Printer\", \"Scanner\",\n",
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| 62 |
+
" \"Ink cartridge\", \"Toner\", \"Paper\", \"Cardstock\", \"Laminating sheets\",\n",
|
| 63 |
+
" \"T-shirt\", \"Shirt\", \"Blouse\", \"Sweater\", \"Jacket\", \"Coat\", \"Jeans\",\n",
|
| 64 |
+
" \"Pants\", \"Shorts\", \"Skirt\", \"Dress\", \"Suit\", \"Tie\", \"Socks\", \"Underwear\",\n",
|
| 65 |
+
" \"Bra\", \"Pajamas\", \"Bathrobe\", \"Slippers\", \"Shoes\", \"Boots\", \"Sandals\",\n",
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| 66 |
+
" \"Sneakers\", \"Hat\", \"Cap\", \"Beanie\", \"Scarf\", \"Gloves\", \"Mittens\",\n",
|
| 67 |
+
" \"Sunglasses\", \"Glasses\", \"Watch\", \"Wallet\", \"Purse\", \"Backpack\", \"Tote bag\",\n",
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| 68 |
+
" \"Luggage\", \"Umbrella\", \"Belt\", \"Jewelry\", \"Necklace\", \"Bracelet\", \"Ring\",\n",
|
| 69 |
+
" \"Starbucks\", \"McDonald's\", \"Burger King\", \"KFC\", \"Subway\", \"Pizza Hut\",\n",
|
| 70 |
+
" \"Domino's\", \"Walmart\", \"Target\", \"Costco\", \"Kroger\", \"Safeway\", \"Trader Joe's\",\n",
|
| 71 |
+
" \"Whole Foods\", \"CVS\", \"Walgreens\", \"Home Depot\", \"Lowe's\", \"Best Buy\",\n",
|
| 72 |
+
" \"Apple Store\", \"Microsoft Store\", \"Amazon\", \"eBay\", \"Etsy\", \"Netflix\",\n",
|
| 73 |
+
" \"Spotify\", \"Uber\", \"Lyft\", \"Airbnb\", \"Nike\", \"Adidas\", \"Puma\", \"Reebok\",\n",
|
| 74 |
+
" \"H&M\", \"Zara\", \"Gap\", \"Old Navy\", \"IKEA\", \"Wayfair\", \"7-Eleven\", \"FedEx\",\n",
|
| 75 |
+
" \"UPS\", \"USPS\", \"Internet\", \"Phone service\", \"Cable TV\", \"Streaming\", \"Electricity\", \"Gas\",\n",
|
| 76 |
+
" \"Water\", \"Sewage\", \"Trash collection\", \"Rent\", \"Mortgage\", \"Insurance\",\n",
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| 77 |
+
" \"Car payment\", \"Gas\", \"Parking\", \"Toll\", \"Bus fare\", \"Train ticket\",\n",
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| 78 |
+
" \"Plane ticket\", \"Hotel\", \"Gym membership\", \"Haircut\", \"Manicure\", \"Pedicure\",\n",
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| 79 |
+
" \"Massage\", \"Therapy\", \"Doctor visit\", \"Dentist\", \"Veterinarian\", \"Tuition\",\n",
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| 80 |
+
" \"Tutoring\", \"Course fee\", \"Subscription\", \"Donation\", \"Tip\", \"Tax\",\n",
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| 81 |
+
" \"Cleaning service\", \"Lawn care\", \"Snow removal\", \"Plumber\", \"Electrician\",\n",
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| 82 |
+
" \"Repair service\", \"Installation fee\", \"Delivery fee\", \"Shipping\"\n",
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| 83 |
+
"]\n",
|
| 84 |
+
"\n",
|
| 85 |
+
"def generate_random_price(op_type=None):\n",
|
| 86 |
+
" if op_type is None:\n",
|
| 87 |
+
" op_type = random.choice([\"simple\", \"divide\", \"minus\", \"multiply\"])\n",
|
| 88 |
+
"\n",
|
| 89 |
+
" if op_type == \"simple\":\n",
|
| 90 |
+
" # Simple price: 1.5-50 with 0-2 decimal places\n",
|
| 91 |
+
" price = round(random.uniform(1.5, 50), random.randint(0, 2))\n",
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| 92 |
+
" return price, f\"{price:.2f}\" if price % 1 != 0 else f\"{int(price)}\"\n",
|
| 93 |
+
"\n",
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| 94 |
+
" elif op_type == \"divide\":\n",
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| 95 |
+
" # Price divided by (2-4)\n",
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| 96 |
+
" original = round(random.uniform(8, 100), random.randint(0, 2))\n",
|
| 97 |
+
" divisor = random.randint(2, 4)\n",
|
| 98 |
+
" result = original / divisor\n",
|
| 99 |
+
" return result, f\"{original:.2f}/{divisor}={result:.2f}\"\n",
|
| 100 |
+
"\n",
|
| 101 |
+
" elif op_type == \"minus\":\n",
|
| 102 |
+
" # Price with a discount\n",
|
| 103 |
+
" original = round(random.uniform(10, 80), random.randint(0, 2))\n",
|
| 104 |
+
" discount = round(random.uniform(1, original/3), random.randint(0, 2))\n",
|
| 105 |
+
" result = original - discount\n",
|
| 106 |
+
" return result, f\"{original:.2f}-{discount:.2f}={result:.2f}\"\n",
|
| 107 |
+
"\n",
|
| 108 |
+
" elif op_type == \"multiply\":\n",
|
| 109 |
+
" # Price with tax or service charge\n",
|
| 110 |
+
" base = round(random.uniform(8, 50), random.randint(0, 2))\n",
|
| 111 |
+
" multiplier = round(random.uniform(1.05, 1.25), 2)\n",
|
| 112 |
+
" result = base * multiplier\n",
|
| 113 |
+
" return result, f\"{base:.2f}*{multiplier:.2f}={result:.2f}\"\n",
|
| 114 |
+
"\n",
|
| 115 |
+
"def format_item_line(item, price_text):\n",
|
| 116 |
+
" #shuffle\n",
|
| 117 |
+
" if random.random() < 0.5:\n",
|
| 118 |
+
" return f\"{price_text}{item}\"\n",
|
| 119 |
+
" else:\n",
|
| 120 |
+
" return f\"{item}{price_text}\"\n",
|
| 121 |
+
"\n",
|
| 122 |
+
"\n",
|
| 123 |
+
"def generate_random_bill(num_items=1000, include_division=True):\n",
|
| 124 |
+
" bill_items = []\n",
|
| 125 |
+
" total = 0\n",
|
| 126 |
+
"\n",
|
| 127 |
+
" # Create a copy of vocabulary and shuffle it to avoid duplicates\n",
|
| 128 |
+
" available_items = random.sample(vocabulary, min(len(vocabulary), num_items*2))\n",
|
| 129 |
+
"\n",
|
| 130 |
+
" # Generate items\n",
|
| 131 |
+
" for _ in range(num_items):\n",
|
| 132 |
+
" item = available_items.pop() if available_items else random.choice(vocabulary)\n",
|
| 133 |
+
"\n",
|
| 134 |
+
" # Decide operation type with weights\n",
|
| 135 |
+
" op_weights = {\"simple\": 0.55, \"divide\": 0.25, \"minus\": 0.1, \"multiply\": 0.1}\n",
|
| 136 |
+
" op_type = random.choices(\n",
|
| 137 |
+
" list(op_weights.keys()),\n",
|
| 138 |
+
" weights=list(op_weights.values()),\n",
|
| 139 |
+
" k=1\n",
|
| 140 |
+
" )[0]\n",
|
| 141 |
+
"\n",
|
| 142 |
+
" # Special case: if include_division is True, make sure we have at least one division\n",
|
| 143 |
+
" if include_division and _ == num_items - 1 and not any(item[1].find('/') != -1 for item in bill_items):\n",
|
| 144 |
+
" op_type = \"divide\"\n",
|
| 145 |
+
"\n",
|
| 146 |
+
" value, price_text = generate_random_price(op_type)\n",
|
| 147 |
+
" bill_items.append((item, price_text, value))\n",
|
| 148 |
+
" total += value\n",
|
| 149 |
+
"\n",
|
| 150 |
+
" # Format the bill\n",
|
| 151 |
+
" bill_text = \"\"\n",
|
| 152 |
+
" for item, price_text, _ in bill_items:\n",
|
| 153 |
+
" formatted_line = format_item_line(item, price_text)\n",
|
| 154 |
+
" bill_text += f\"{formatted_line}\\n\"\n",
|
| 155 |
+
"\n",
|
| 156 |
+
" # Add total\n",
|
| 157 |
+
" bill_text += f\"\\nTotal Number = {total:.2f}\"\n",
|
| 158 |
+
" bill_text += f\"\\nTotal Items = {len(bill_items)}\"\n",
|
| 159 |
+
"\n",
|
| 160 |
+
" return bill_text\n",
|
| 161 |
+
"\n",
|
| 162 |
+
"def main():\n",
|
| 163 |
+
" # Generate a bill with a random number of items\n",
|
| 164 |
+
" num_items = random.randint(1000, 1001)\n",
|
| 165 |
+
" bill = generate_random_bill(num_items)\n",
|
| 166 |
+
"\n",
|
| 167 |
+
" with open(\"random_bill.txt\", \"w\", encoding=\"utf-8\") as f:\n",
|
| 168 |
+
" f.write(bill)\n",
|
| 169 |
+
" print(\"\\nBill has been saved to 'random_bill.txt'\")\n"
|
| 170 |
+
]
|
| 171 |
+
},
|
| 172 |
+
{
|
| 173 |
+
"cell_type": "code",
|
| 174 |
+
"source": [
|
| 175 |
+
"import os\n",
|
| 176 |
+
"import datetime\n",
|
| 177 |
+
"\n",
|
| 178 |
+
"def save_bill_to_file(bill_text, num_items, count, directory=\"/content/bills/\", filename=None):\n",
|
| 179 |
+
" # Create directory if it doesn't exist\n",
|
| 180 |
+
" os.makedirs(directory, exist_ok=True)\n",
|
| 181 |
+
"\n",
|
| 182 |
+
" if num_items is None and count is None:\n",
|
| 183 |
+
" raise ValueError(\"Either num_items or count must be provided\")\n",
|
| 184 |
+
"\n",
|
| 185 |
+
" filename = filename or f\"bill_{num_items}_{count}.txt\"\n",
|
| 186 |
+
"\n",
|
| 187 |
+
" if os.path.isfile(filename):\n",
|
| 188 |
+
" return filename\n",
|
| 189 |
+
"\n",
|
| 190 |
+
" # Full path\n",
|
| 191 |
+
" filepath = os.path.join(directory, filename)\n",
|
| 192 |
+
"\n",
|
| 193 |
+
" # Save the bill\n",
|
| 194 |
+
" with open(filepath, \"w\", encoding=\"utf-8\") as f:\n",
|
| 195 |
+
" f.write(bill_text)\n",
|
| 196 |
+
"\n",
|
| 197 |
+
" return filepath\n",
|
| 198 |
+
"\n",
|
| 199 |
+
"# Generate multiple bills\n",
|
| 200 |
+
"def generate_multiple_bills(count=100, num_items=1000, directory=\"/content/bills/\"):\n",
|
| 201 |
+
" bills_info = []\n",
|
| 202 |
+
"\n",
|
| 203 |
+
" for i in range(count):\n",
|
| 204 |
+
" # Random number of items\n",
|
| 205 |
+
" min_items = num_items - 50\n",
|
| 206 |
+
" max_items = num_items + 50\n",
|
| 207 |
+
" items = random.randint(min_items, max_items)\n",
|
| 208 |
+
"\n",
|
| 209 |
+
" # Generate bill\n",
|
| 210 |
+
" bill_text = generate_random_bill(items)\n",
|
| 211 |
+
"\n",
|
| 212 |
+
" # Create filename\n",
|
| 213 |
+
" filename = f\"bill_{num_items}_{i+1}.txt\"\n",
|
| 214 |
+
"\n",
|
| 215 |
+
" # Save bill\n",
|
| 216 |
+
" filepath = save_bill_to_file(bill_text, num_items, i+1, directory+str(num_items)+'/', filename)\n",
|
| 217 |
+
"\n",
|
| 218 |
+
" # Store info\n",
|
| 219 |
+
" bills_info.append({\n",
|
| 220 |
+
" \"index\": i+1,\n",
|
| 221 |
+
" \"filepath\": filepath,\n",
|
| 222 |
+
" })\n",
|
| 223 |
+
"\n",
|
| 224 |
+
" return bills_info\n",
|
| 225 |
+
"\n",
|
| 226 |
+
"generate_multiple_bills(count=100, num_items=1000, directory=\"/content/bills/\")"
|
| 227 |
+
],
|
| 228 |
+
"metadata": {
|
| 229 |
+
"id": "KkIxfRxFpwtv"
|
| 230 |
+
},
|
| 231 |
+
"execution_count": null,
|
| 232 |
+
"outputs": []
|
| 233 |
+
},
|
| 234 |
+
{
|
| 235 |
+
"cell_type": "markdown",
|
| 236 |
+
"source": [
|
| 237 |
+
"# Vital Log"
|
| 238 |
+
],
|
| 239 |
+
"metadata": {
|
| 240 |
+
"id": "uusf4fQBSKL2"
|
| 241 |
+
}
|
| 242 |
+
},
|
| 243 |
+
{
|
| 244 |
+
"cell_type": "code",
|
| 245 |
+
"source": [
|
| 246 |
+
"import numpy as np\n",
|
| 247 |
+
"import pandas as pd\n",
|
| 248 |
+
"import matplotlib.pyplot as plt\n",
|
| 249 |
+
"import random\n",
|
| 250 |
+
"import os\n",
|
| 251 |
+
"from datetime import datetime, timedelta\n",
|
| 252 |
+
"\n",
|
| 253 |
+
"\n",
|
| 254 |
+
"def generate_random_start_time():\n",
|
| 255 |
+
" start_date = datetime(2020, 1, 1)\n",
|
| 256 |
+
" end_date = datetime(2025, 12, 31)\n",
|
| 257 |
+
" delta = end_date - start_date\n",
|
| 258 |
+
" random_days = random.randint(0, delta.days)\n",
|
| 259 |
+
" random_seconds = random.randint(0, 86400 - 1)\n",
|
| 260 |
+
" return start_date + timedelta(days=random_days, seconds=random_seconds)\n",
|
| 261 |
+
"\n",
|
| 262 |
+
"def generate_heart_rate_data(num_points):\n",
|
| 263 |
+
" states = []\n",
|
| 264 |
+
" heart_rates = []\n",
|
| 265 |
+
"\n",
|
| 266 |
+
" for i in range(num_points):\n",
|
| 267 |
+
" state = random.choices(['rest', 'exercise', 'recovery'], weights=[0.8, 0.1, 0.1])[0]\n",
|
| 268 |
+
" if state == 'rest':\n",
|
| 269 |
+
" hr = np.random.normal(70, 3)\n",
|
| 270 |
+
" elif state == 'exercise':\n",
|
| 271 |
+
" hr = np.random.normal(140, 8)\n",
|
| 272 |
+
" else: # recovery\n",
|
| 273 |
+
" hr = np.random.normal(90, 5)\n",
|
| 274 |
+
"\n",
|
| 275 |
+
" states.append(state)\n",
|
| 276 |
+
" heart_rates.append(int(hr))\n",
|
| 277 |
+
"\n",
|
| 278 |
+
" return heart_rates, states\n",
|
| 279 |
+
"\n",
|
| 280 |
+
"\n",
|
| 281 |
+
"def generate_vital_log(count=50, num_points = 100, directory=None):\n",
|
| 282 |
+
" interval_minutes = 10\n",
|
| 283 |
+
" start_time = generate_random_start_time()\n",
|
| 284 |
+
" timestamps = [start_time + timedelta(minutes=i * interval_minutes) for i in range(num_points)]\n",
|
| 285 |
+
"\n",
|
| 286 |
+
" heart_rates, states = generate_heart_rate_data(num_points)\n",
|
| 287 |
+
"\n",
|
| 288 |
+
" vital_log = pd.DataFrame({\n",
|
| 289 |
+
" 'timestamp': timestamps,\n",
|
| 290 |
+
" 'heart_rate': heart_rates,\n",
|
| 291 |
+
" 'state': states\n",
|
| 292 |
+
" })\n",
|
| 293 |
+
"\n",
|
| 294 |
+
" vital_log_serialized = vital_log.copy()\n",
|
| 295 |
+
" vital_log_serialized['timestamp'] = vital_log_serialized['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')\n",
|
| 296 |
+
"\n",
|
| 297 |
+
" json_data = vital_log_serialized.to_json(orient='records', indent=2)\n",
|
| 298 |
+
"\n",
|
| 299 |
+
" os.makedirs(directory, exist_ok=True)\n",
|
| 300 |
+
" csv_path = f\"heartrate_{num_points}_{count+1}.csv\"\n",
|
| 301 |
+
" csv_path = os.path.join(directory, csv_path)\n",
|
| 302 |
+
" vital_log.to_csv(csv_path, index=False)\n",
|
| 303 |
+
"\n",
|
| 304 |
+
" json_path = f\"heartrate_{num_points}_{count+1}.json\"\n",
|
| 305 |
+
" json_path = os.path.join(directory, json_path)\n",
|
| 306 |
+
" with open(json_path, 'w') as f:\n",
|
| 307 |
+
" f.write(json_data)\n",
|
| 308 |
+
"\n",
|
| 309 |
+
"num_points = 1000\n",
|
| 310 |
+
"count = 50\n",
|
| 311 |
+
"directory=\"/content/drive/MyDrive/sheets_vital_log/\"\n",
|
| 312 |
+
"for i in range(count):\n",
|
| 313 |
+
" generate_vital_log(count=i, num_points=num_points, directory=directory+str(num_points)+'/')"
|
| 314 |
+
],
|
| 315 |
+
"metadata": {
|
| 316 |
+
"id": "XZeMvlAvpCSi"
|
| 317 |
+
},
|
| 318 |
+
"execution_count": null,
|
| 319 |
+
"outputs": []
|
| 320 |
+
},
|
| 321 |
+
{
|
| 322 |
+
"cell_type": "code",
|
| 323 |
+
"source": [
|
| 324 |
+
"import json\n",
|
| 325 |
+
"import random\n",
|
| 326 |
+
"\n",
|
| 327 |
+
"counts = 50\n",
|
| 328 |
+
"\n",
|
| 329 |
+
"heart_rate_variations = [\n",
|
| 330 |
+
" lambda hr: f\"The true heart rate is {hr}.\",\n",
|
| 331 |
+
" lambda hr: f\"The heart rate = {hr}.\",\n",
|
| 332 |
+
" lambda hr: f\"HR: {hr} (beats per minute).\",\n",
|
| 333 |
+
" lambda hr: f\"Current heart rate: {hr} BPM.\",\n",
|
| 334 |
+
" lambda hr: f\"Heart rate measured at {hr}.\",\n",
|
| 335 |
+
" lambda hr: f\"{hr} — that's the heart rate!\",\n",
|
| 336 |
+
" lambda hr: f\"Your heart is beating at {hr} BPM.\",\n",
|
| 337 |
+
" lambda hr: f\"Pulse rate detected: {hr}.\",\n",
|
| 338 |
+
" lambda hr: f\"Heart rate reading: {hr} beats per minute.\",\n",
|
| 339 |
+
" lambda hr: f\"{hr} BPM — steady and normal.\",\n",
|
| 340 |
+
" lambda hr: f\"The monitor shows a heart rate of {hr}.\",\n",
|
| 341 |
+
" lambda hr: f\"Heart rate (measured): {hr}.\",\n",
|
| 342 |
+
" lambda hr: f\"BPM = {hr} (heart rate).\",\n",
|
| 343 |
+
" lambda hr: f\"Your current pulse is {hr} beats per minute.\",\n",
|
| 344 |
+
" lambda hr: f\"Heart rate recorded as {hr} BPM.\",\n",
|
| 345 |
+
" lambda hr: f\"{hr} — the magic number for your heart rate!\",\n",
|
| 346 |
+
" lambda hr: f\"HR measurement result: {hr}.\",\n",
|
| 347 |
+
" lambda hr: f\"Beats per minute: {hr}.\",\n",
|
| 348 |
+
" lambda hr: f\"The heart is ticking at {hr} BPM.\",\n",
|
| 349 |
+
" lambda hr: f\"Heart rate analysis: {hr} beats per minute.\"\n",
|
| 350 |
+
"]\n",
|
| 351 |
+
"\n",
|
| 352 |
+
"variations = [\n",
|
| 353 |
+
" lambda key,value: f\"{key}:{value}\",\n",
|
| 354 |
+
" lambda key,value: f\"{key}={value}\",\n",
|
| 355 |
+
" lambda key,value: f\"{key} is {value}\",\n",
|
| 356 |
+
"]\n",
|
| 357 |
+
"\n",
|
| 358 |
+
"fake_heart_rate_variations = [\n",
|
| 359 |
+
" lambda hr: f\"The fake heart rate is {hr}.\",\n",
|
| 360 |
+
" lambda hr: f\"Fake HR: {hr} bpm.\",\n",
|
| 361 |
+
"]\n",
|
| 362 |
+
"\n",
|
| 363 |
+
"#\n",
|
| 364 |
+
"for num_items in [100, 200, 500, 1000]:\n",
|
| 365 |
+
" for j in range(counts):\n",
|
| 366 |
+
" prompt_path = f'/content/drive/MyDrive/sheets_vital_log/{num_items}/heartrate_{num_items}_{j+1}.json'\n",
|
| 367 |
+
"\n",
|
| 368 |
+
" with open(prompt_path, 'r') as infile:\n",
|
| 369 |
+
" data = json.load(infile)\n",
|
| 370 |
+
"\n",
|
| 371 |
+
" lines = []\n",
|
| 372 |
+
" for line in data:\n",
|
| 373 |
+
" random_variation = random.choice(heart_rate_variations)\n",
|
| 374 |
+
" hr_text = random_variation(line[\"heart_rate\"])\n",
|
| 375 |
+
"\n",
|
| 376 |
+
" fake_hr = random.randint(50, 200) # Adjust range as needed\n",
|
| 377 |
+
" fake_hr_text = random.choice(fake_heart_rate_variations)(fake_hr)\n",
|
| 378 |
+
"\n",
|
| 379 |
+
" # line = f\"timestamp:{line['timestamp']} {hr_text} state:{line['state']}\"\n",
|
| 380 |
+
" # line = \" \".join(f\"{key}:{value}\" for key, value in line.items())\n",
|
| 381 |
+
" time_variation = random.choice(variations)\n",
|
| 382 |
+
" time_text = time_variation(\"timestamp\", line[\"timestamp\"])\n",
|
| 383 |
+
" state_variation = random.choice(variations)\n",
|
| 384 |
+
" state_text = state_variation(\"state\", line[\"state\"])\n",
|
| 385 |
+
"\n",
|
| 386 |
+
" if random.random() < 0.5:\n",
|
| 387 |
+
" line = f\"{time_text} {fake_hr_text} {hr_text} {state_text}\"\n",
|
| 388 |
+
" else:\n",
|
| 389 |
+
" line = f\"{time_text} {hr_text} {fake_hr_text} {state_text}\"\n",
|
| 390 |
+
"\n",
|
| 391 |
+
" lines.append(line)\n",
|
| 392 |
+
"\n",
|
| 393 |
+
" output_file = f'/content/drive/MyDrive/sheets_vital_log/{num_items}/heartrate_{num_items}_{j+1}.txt'\n",
|
| 394 |
+
"\n",
|
| 395 |
+
" with open(output_file, 'w') as outfile:\n",
|
| 396 |
+
" outfile.write(\"\\n\".join(lines))"
|
| 397 |
+
],
|
| 398 |
+
"metadata": {
|
| 399 |
+
"id": "RPPoDBfJ5JeM"
|
| 400 |
+
},
|
| 401 |
+
"execution_count": null,
|
| 402 |
+
"outputs": []
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"cell_type": "markdown",
|
| 406 |
+
"source": [
|
| 407 |
+
"# Transcript"
|
| 408 |
+
],
|
| 409 |
+
"metadata": {
|
| 410 |
+
"id": "TxAubn6gSf9H"
|
| 411 |
+
}
|
| 412 |
+
},
|
| 413 |
+
{
|
| 414 |
+
"cell_type": "code",
|
| 415 |
+
"source": [
|
| 416 |
+
"!pip install faker\n",
|
| 417 |
+
"import pandas as pd\n",
|
| 418 |
+
"import random\n",
|
| 419 |
+
"from faker import Faker\n",
|
| 420 |
+
"import os\n",
|
| 421 |
+
"\n",
|
| 422 |
+
"def generate_transcript(count=50, num_students = 100, directory=\"/content/drive/MyDrive/sheets_transcript/\"):\n",
|
| 423 |
+
" fake = Faker()\n",
|
| 424 |
+
" names = [fake.name() for _ in range(num_students)]\n",
|
| 425 |
+
"\n",
|
| 426 |
+
" subjects = ['Math', 'Chemistry', 'Biology', 'Physics', 'Geography']\n",
|
| 427 |
+
"\n",
|
| 428 |
+
" data = {'Name': names}\n",
|
| 429 |
+
" for subject in subjects:\n",
|
| 430 |
+
" data[subject] = [random.randint(0, 100) for _ in range(num_students)]\n",
|
| 431 |
+
"\n",
|
| 432 |
+
" df = pd.DataFrame(data)\n",
|
| 433 |
+
" json_data = df.to_json(orient='records')\n",
|
| 434 |
+
"\n",
|
| 435 |
+
" os.makedirs(directory, exist_ok=True)\n",
|
| 436 |
+
" csv_path = f\"transcript_{num_students}_{count+1}.csv\"\n",
|
| 437 |
+
" csv_path = os.path.join(directory, csv_path)\n",
|
| 438 |
+
" df.to_csv(csv_path, index=False)\n",
|
| 439 |
+
"\n",
|
| 440 |
+
" json_path = f\"transcript_{num_students}_{count+1}.json\"\n",
|
| 441 |
+
" json_path = os.path.join(directory, json_path)\n",
|
| 442 |
+
" with open(json_path, 'w') as f:\n",
|
| 443 |
+
" f.write(json_data)\n",
|
| 444 |
+
"\n",
|
| 445 |
+
"count = 50\n",
|
| 446 |
+
"num_students = 1000\n",
|
| 447 |
+
"directory=\"/content/drive/MyDrive/sheets_transcript/\"\n",
|
| 448 |
+
"for i in range(count):\n",
|
| 449 |
+
" generate_transcript(count=i, num_students=num_students, directory=directory+str(num_students)+'/')"
|
| 450 |
+
],
|
| 451 |
+
"metadata": {
|
| 452 |
+
"id": "Ol17CCnIh9FP"
|
| 453 |
+
},
|
| 454 |
+
"execution_count": null,
|
| 455 |
+
"outputs": []
|
| 456 |
+
},
|
| 457 |
+
{
|
| 458 |
+
"cell_type": "code",
|
| 459 |
+
"source": [
|
| 460 |
+
"import json\n",
|
| 461 |
+
"\n",
|
| 462 |
+
"counts = 50\n",
|
| 463 |
+
"variations = [\n",
|
| 464 |
+
" lambda key,value: f\"{key}:{value}\",\n",
|
| 465 |
+
" lambda key,value: f\"{key}={value}\",\n",
|
| 466 |
+
" lambda key,value: f\"{key} is {value}\",\n",
|
| 467 |
+
"]\n",
|
| 468 |
+
"#\n",
|
| 469 |
+
"for num_items in [100, 200, 500, 1000]:\n",
|
| 470 |
+
" for j in range(counts):\n",
|
| 471 |
+
" prompt_path = f'/content/drive/MyDrive/sheets_transcript/{num_items}/transcript_{num_items}_{j+1}.json'\n",
|
| 472 |
+
"\n",
|
| 473 |
+
" with open(prompt_path, 'r') as infile:\n",
|
| 474 |
+
" data = json.load(infile)\n",
|
| 475 |
+
"\n",
|
| 476 |
+
" lines = []\n",
|
| 477 |
+
"\n",
|
| 478 |
+
" for line_data in data:\n",
|
| 479 |
+
" line = \"\"\n",
|
| 480 |
+
" for key, value in line_data.items():\n",
|
| 481 |
+
" random_variation = random.choice(variations)\n",
|
| 482 |
+
" line=line+random_variation(key=key,value=value)+\" \"\n",
|
| 483 |
+
" lines.append(line.strip())\n",
|
| 484 |
+
"\n",
|
| 485 |
+
" output_file = f'/content/drive/MyDrive/sheets_transcript/{num_items}/transcript_{num_items}_{j+1}.txt'\n",
|
| 486 |
+
"\n",
|
| 487 |
+
" with open(output_file, 'w') as outfile:\n",
|
| 488 |
+
" outfile.write(\"\\n\".join(lines))"
|
| 489 |
+
],
|
| 490 |
+
"metadata": {
|
| 491 |
+
"id": "waA7fHuV5gM4"
|
| 492 |
+
},
|
| 493 |
+
"execution_count": null,
|
| 494 |
+
"outputs": []
|
| 495 |
+
}
|
| 496 |
+
]
|
| 497 |
+
}
|