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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Receipt"
],
"metadata": {
"id": "wEKMGOFvSV_V"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iGlVFh9Yf1ar"
},
"outputs": [],
"source": [
"import random\n",
"import math\n",
"\n",
"vocabulary = [\n",
" \"Apple\", \"Banana\", \"Coffee\", \"Dumpling\", \"Eggs\", \"Fries\", \"Garlic\", \"Ham\",\n",
" \"Ice cream\", \"Juice\", \"Ketchup\", \"Lemon\", \"Milk\", \"Noodles\", \"Orange\",\n",
" \"Pasta\", \"Quinoa\", \"Rice\", \"Salad\", \"Tea\", \"Udon\", \"Vinegar\", \"Water\", \"Yogurt\",\n",
" \"Bread\", \"Cheese\", \"Donuts\", \"Espresso\", \"Fish\", \"Grapes\", \"Honey\",\n",
" \"Jam\", \"Kiwi\", \"Lobster\", \"Mango\", \"Nuts\", \"Oatmeal\", \"Pizza\", \"Ramen\",\n",
" \"Soda\", \"Tuna\", \"Vanilla\", \"Wine\", \"Zucchini\", \"Steak\", \"Burger\", \"Chicken\",\n",
" \"Pork\", \"Beef\", \"Lamb\", \"Tofu\", \"Avocado\", \"Tomato\", \"Potato\", \"Carrot\",\n",
" \"Broccoli\", \"Cauliflower\", \"Spinach\", \"Lettuce\", \"Cucumber\", \"Onion\",\n",
" \"Bottled water\", \"Sparkling water\", \"Green tea\", \"Black tea\", \"Beer\", \"Wine\",\n",
" \"Whiskey\", \"Vodka\", \"Rum\", \"Gin\", \"Tequila\", \"Cocktail\", \"Smoothie\", \"Milkshake\",\n",
" \"Shampoo\", \"Conditioner\", \"Soap\", \"Toothpaste\", \"Toothbrush\", \"Floss\", \"Mouthwash\",\n",
" \"Detergent\", \"Fabric softener\", \"Bleach\", \"Disinfectant\", \"Sponge\", \"Brush\",\n",
" \"Toilet paper\", \"Paper towel\", \"Tissues\", \"Napkins\", \"Trash bags\", \"Vacuum cleaner\",\n",
" \"Mop\", \"Broom\", \"Dustpan\", \"Duster\", \"Wipes\", \"Air freshener\", \"Candle\",\n",
" \"Light bulb\", \"Batteries\", \"Extension cord\", \"Plug adapter\", \"Hanger\",\n",
" \"Laundry basket\", \"Iron\", \"Ironing board\", \"Scissors\", \"Tape\", \"Glue\",\n",
" \"Nail clipper\", \"Razor\", \"Shaving cream\", \"Deodorant\", \"Perfume\", \"Cologne\",\n",
" \"Lotion\", \"Sunscreen\", \"Insect repellent\", \"Band-aids\", \"Cotton swabs\",\n",
" \"Notebook\", \"Journal\", \"Planner\", \"Calendar\", \"Pen\", \"Pencil\", \"Marker\",\n",
" \"Highlighter\", \"Eraser\", \"Ruler\", \"Stapler\", \"Staples\", \"Paper clips\",\n",
" \"Binder\", \"Folder\", \"Envelope\", \"Sticky notes\", \"Index cards\", \"Tape dispenser\",\n",
" \"Calculator\", \"Laptop\", \"Tablet\", \"E-reader\", \"Charger\", \"USB drive\",\n",
" \"Memory card\", \"External hard drive\", \"Mouse\", \"Keyboard\", \"Monitor\",\n",
" \"Headphones\", \"Speakers\", \"Webcam\", \"Microphone\", \"Printer\", \"Scanner\",\n",
" \"Ink cartridge\", \"Toner\", \"Paper\", \"Cardstock\", \"Laminating sheets\",\n",
" \"T-shirt\", \"Shirt\", \"Blouse\", \"Sweater\", \"Jacket\", \"Coat\", \"Jeans\",\n",
" \"Pants\", \"Shorts\", \"Skirt\", \"Dress\", \"Suit\", \"Tie\", \"Socks\", \"Underwear\",\n",
" \"Bra\", \"Pajamas\", \"Bathrobe\", \"Slippers\", \"Shoes\", \"Boots\", \"Sandals\",\n",
" \"Sneakers\", \"Hat\", \"Cap\", \"Beanie\", \"Scarf\", \"Gloves\", \"Mittens\",\n",
" \"Sunglasses\", \"Glasses\", \"Watch\", \"Wallet\", \"Purse\", \"Backpack\", \"Tote bag\",\n",
" \"Luggage\", \"Umbrella\", \"Belt\", \"Jewelry\", \"Necklace\", \"Bracelet\", \"Ring\",\n",
" \"Starbucks\", \"McDonald's\", \"Burger King\", \"KFC\", \"Subway\", \"Pizza Hut\",\n",
" \"Domino's\", \"Walmart\", \"Target\", \"Costco\", \"Kroger\", \"Safeway\", \"Trader Joe's\",\n",
" \"Whole Foods\", \"CVS\", \"Walgreens\", \"Home Depot\", \"Lowe's\", \"Best Buy\",\n",
" \"Apple Store\", \"Microsoft Store\", \"Amazon\", \"eBay\", \"Etsy\", \"Netflix\",\n",
" \"Spotify\", \"Uber\", \"Lyft\", \"Airbnb\", \"Nike\", \"Adidas\", \"Puma\", \"Reebok\",\n",
" \"H&M\", \"Zara\", \"Gap\", \"Old Navy\", \"IKEA\", \"Wayfair\", \"7-Eleven\", \"FedEx\",\n",
" \"UPS\", \"USPS\", \"Internet\", \"Phone service\", \"Cable TV\", \"Streaming\", \"Electricity\", \"Gas\",\n",
" \"Water\", \"Sewage\", \"Trash collection\", \"Rent\", \"Mortgage\", \"Insurance\",\n",
" \"Car payment\", \"Gas\", \"Parking\", \"Toll\", \"Bus fare\", \"Train ticket\",\n",
" \"Plane ticket\", \"Hotel\", \"Gym membership\", \"Haircut\", \"Manicure\", \"Pedicure\",\n",
" \"Massage\", \"Therapy\", \"Doctor visit\", \"Dentist\", \"Veterinarian\", \"Tuition\",\n",
" \"Tutoring\", \"Course fee\", \"Subscription\", \"Donation\", \"Tip\", \"Tax\",\n",
" \"Cleaning service\", \"Lawn care\", \"Snow removal\", \"Plumber\", \"Electrician\",\n",
" \"Repair service\", \"Installation fee\", \"Delivery fee\", \"Shipping\"\n",
"]\n",
"\n",
"def generate_random_price(op_type=None):\n",
" if op_type is None:\n",
" op_type = random.choice([\"simple\", \"divide\", \"minus\", \"multiply\"])\n",
"\n",
" if op_type == \"simple\":\n",
" # Simple price: 1.5-50 with 0-2 decimal places\n",
" price = round(random.uniform(1.5, 50), random.randint(0, 2))\n",
" return price, f\"{price:.2f}\" if price % 1 != 0 else f\"{int(price)}\"\n",
"\n",
" elif op_type == \"divide\":\n",
" # Price divided by (2-4)\n",
" original = round(random.uniform(8, 100), random.randint(0, 2))\n",
" divisor = random.randint(2, 4)\n",
" result = original / divisor\n",
" return result, f\"{original:.2f}/{divisor}={result:.2f}\"\n",
"\n",
" elif op_type == \"minus\":\n",
" # Price with a discount\n",
" original = round(random.uniform(10, 80), random.randint(0, 2))\n",
" discount = round(random.uniform(1, original/3), random.randint(0, 2))\n",
" result = original - discount\n",
" return result, f\"{original:.2f}-{discount:.2f}={result:.2f}\"\n",
"\n",
" elif op_type == \"multiply\":\n",
" # Price with tax or service charge\n",
" base = round(random.uniform(8, 50), random.randint(0, 2))\n",
" multiplier = round(random.uniform(1.05, 1.25), 2)\n",
" result = base * multiplier\n",
" return result, f\"{base:.2f}*{multiplier:.2f}={result:.2f}\"\n",
"\n",
"def format_item_line(item, price_text):\n",
" #shuffle\n",
" if random.random() < 0.5:\n",
" return f\"{price_text}{item}\"\n",
" else:\n",
" return f\"{item}{price_text}\"\n",
"\n",
"\n",
"def generate_random_bill(num_items=1000, include_division=True):\n",
" bill_items = []\n",
" total = 0\n",
"\n",
" # Create a copy of vocabulary and shuffle it to avoid duplicates\n",
" available_items = random.sample(vocabulary, min(len(vocabulary), num_items*2))\n",
"\n",
" # Generate items\n",
" for _ in range(num_items):\n",
" item = available_items.pop() if available_items else random.choice(vocabulary)\n",
"\n",
" # Decide operation type with weights\n",
" op_weights = {\"simple\": 0.55, \"divide\": 0.25, \"minus\": 0.1, \"multiply\": 0.1}\n",
" op_type = random.choices(\n",
" list(op_weights.keys()),\n",
" weights=list(op_weights.values()),\n",
" k=1\n",
" )[0]\n",
"\n",
" # Special case: if include_division is True, make sure we have at least one division\n",
" if include_division and _ == num_items - 1 and not any(item[1].find('/') != -1 for item in bill_items):\n",
" op_type = \"divide\"\n",
"\n",
" value, price_text = generate_random_price(op_type)\n",
" bill_items.append((item, price_text, value))\n",
" total += value\n",
"\n",
" # Format the bill\n",
" bill_text = \"\"\n",
" for item, price_text, _ in bill_items:\n",
" formatted_line = format_item_line(item, price_text)\n",
" bill_text += f\"{formatted_line}\\n\"\n",
"\n",
" # Add total\n",
" bill_text += f\"\\nTotal Number = {total:.2f}\"\n",
" bill_text += f\"\\nTotal Items = {len(bill_items)}\"\n",
"\n",
" return bill_text\n",
"\n",
"def main():\n",
" # Generate a bill with a random number of items\n",
" num_items = random.randint(1000, 1001)\n",
" bill = generate_random_bill(num_items)\n",
"\n",
" with open(\"random_bill.txt\", \"w\", encoding=\"utf-8\") as f:\n",
" f.write(bill)\n",
" print(\"\\nBill has been saved to 'random_bill.txt'\")\n"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import datetime\n",
"\n",
"def save_bill_to_file(bill_text, num_items, count, directory=\"/content/bills/\", filename=None):\n",
" # Create directory if it doesn't exist\n",
" os.makedirs(directory, exist_ok=True)\n",
"\n",
" if num_items is None and count is None:\n",
" raise ValueError(\"Either num_items or count must be provided\")\n",
"\n",
" filename = filename or f\"bill_{num_items}_{count}.txt\"\n",
"\n",
" if os.path.isfile(filename):\n",
" return filename\n",
"\n",
" # Full path\n",
" filepath = os.path.join(directory, filename)\n",
"\n",
" # Save the bill\n",
" with open(filepath, \"w\", encoding=\"utf-8\") as f:\n",
" f.write(bill_text)\n",
"\n",
" return filepath\n",
"\n",
"# Generate multiple bills\n",
"def generate_multiple_bills(count=100, num_items=1000, directory=\"/content/bills/\"):\n",
" bills_info = []\n",
"\n",
" for i in range(count):\n",
" # Random number of items\n",
" min_items = num_items - 50\n",
" max_items = num_items + 50\n",
" items = random.randint(min_items, max_items)\n",
"\n",
" # Generate bill\n",
" bill_text = generate_random_bill(items)\n",
"\n",
" # Create filename\n",
" filename = f\"bill_{num_items}_{i+1}.txt\"\n",
"\n",
" # Save bill\n",
" filepath = save_bill_to_file(bill_text, num_items, i+1, directory+str(num_items)+'/', filename)\n",
"\n",
" # Store info\n",
" bills_info.append({\n",
" \"index\": i+1,\n",
" \"filepath\": filepath,\n",
" })\n",
"\n",
" return bills_info\n",
"\n",
"generate_multiple_bills(count=100, num_items=1000, directory=\"/content/bills/\")"
],
"metadata": {
"id": "KkIxfRxFpwtv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Vital Log"
],
"metadata": {
"id": "uusf4fQBSKL2"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"import os\n",
"from datetime import datetime, timedelta\n",
"\n",
"\n",
"def generate_random_start_time():\n",
" start_date = datetime(2020, 1, 1)\n",
" end_date = datetime(2025, 12, 31)\n",
" delta = end_date - start_date\n",
" random_days = random.randint(0, delta.days)\n",
" random_seconds = random.randint(0, 86400 - 1)\n",
" return start_date + timedelta(days=random_days, seconds=random_seconds)\n",
"\n",
"def generate_heart_rate_data(num_points):\n",
" states = []\n",
" heart_rates = []\n",
"\n",
" for i in range(num_points):\n",
" state = random.choices(['rest', 'exercise', 'recovery'], weights=[0.8, 0.1, 0.1])[0]\n",
" if state == 'rest':\n",
" hr = np.random.normal(70, 3)\n",
" elif state == 'exercise':\n",
" hr = np.random.normal(140, 8)\n",
" else: # recovery\n",
" hr = np.random.normal(90, 5)\n",
"\n",
" states.append(state)\n",
" heart_rates.append(int(hr))\n",
"\n",
" return heart_rates, states\n",
"\n",
"\n",
"def generate_vital_log(count=50, num_points = 100, directory=None):\n",
" interval_minutes = 10\n",
" start_time = generate_random_start_time()\n",
" timestamps = [start_time + timedelta(minutes=i * interval_minutes) for i in range(num_points)]\n",
"\n",
" heart_rates, states = generate_heart_rate_data(num_points)\n",
"\n",
" vital_log = pd.DataFrame({\n",
" 'timestamp': timestamps,\n",
" 'heart_rate': heart_rates,\n",
" 'state': states\n",
" })\n",
"\n",
" vital_log_serialized = vital_log.copy()\n",
" vital_log_serialized['timestamp'] = vital_log_serialized['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')\n",
"\n",
" json_data = vital_log_serialized.to_json(orient='records', indent=2)\n",
"\n",
" os.makedirs(directory, exist_ok=True)\n",
" csv_path = f\"heartrate_{num_points}_{count+1}.csv\"\n",
" csv_path = os.path.join(directory, csv_path)\n",
" vital_log.to_csv(csv_path, index=False)\n",
"\n",
" json_path = f\"heartrate_{num_points}_{count+1}.json\"\n",
" json_path = os.path.join(directory, json_path)\n",
" with open(json_path, 'w') as f:\n",
" f.write(json_data)\n",
"\n",
"num_points = 1000\n",
"count = 50\n",
"directory=\"/content/drive/MyDrive/sheets_vital_log/\"\n",
"for i in range(count):\n",
" generate_vital_log(count=i, num_points=num_points, directory=directory+str(num_points)+'/')"
],
"metadata": {
"id": "XZeMvlAvpCSi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import json\n",
"import random\n",
"\n",
"counts = 50\n",
"\n",
"heart_rate_variations = [\n",
" lambda hr: f\"The true heart rate is {hr}.\",\n",
" lambda hr: f\"The heart rate = {hr}.\",\n",
" lambda hr: f\"HR: {hr} (beats per minute).\",\n",
" lambda hr: f\"Current heart rate: {hr} BPM.\",\n",
" lambda hr: f\"Heart rate measured at {hr}.\",\n",
" lambda hr: f\"{hr} — that's the heart rate!\",\n",
" lambda hr: f\"Your heart is beating at {hr} BPM.\",\n",
" lambda hr: f\"Pulse rate detected: {hr}.\",\n",
" lambda hr: f\"Heart rate reading: {hr} beats per minute.\",\n",
" lambda hr: f\"{hr} BPM — steady and normal.\",\n",
" lambda hr: f\"The monitor shows a heart rate of {hr}.\",\n",
" lambda hr: f\"Heart rate (measured): {hr}.\",\n",
" lambda hr: f\"BPM = {hr} (heart rate).\",\n",
" lambda hr: f\"Your current pulse is {hr} beats per minute.\",\n",
" lambda hr: f\"Heart rate recorded as {hr} BPM.\",\n",
" lambda hr: f\"{hr} — the magic number for your heart rate!\",\n",
" lambda hr: f\"HR measurement result: {hr}.\",\n",
" lambda hr: f\"Beats per minute: {hr}.\",\n",
" lambda hr: f\"The heart is ticking at {hr} BPM.\",\n",
" lambda hr: f\"Heart rate analysis: {hr} beats per minute.\"\n",
"]\n",
"\n",
"variations = [\n",
" lambda key,value: f\"{key}:{value}\",\n",
" lambda key,value: f\"{key}={value}\",\n",
" lambda key,value: f\"{key} is {value}\",\n",
"]\n",
"\n",
"fake_heart_rate_variations = [\n",
" lambda hr: f\"The fake heart rate is {hr}.\",\n",
" lambda hr: f\"Fake HR: {hr} bpm.\",\n",
"]\n",
"\n",
"#\n",
"for num_items in [100, 200, 500, 1000]:\n",
" for j in range(counts):\n",
" prompt_path = f'/content/drive/MyDrive/sheets_vital_log/{num_items}/heartrate_{num_items}_{j+1}.json'\n",
"\n",
" with open(prompt_path, 'r') as infile:\n",
" data = json.load(infile)\n",
"\n",
" lines = []\n",
" for line in data:\n",
" random_variation = random.choice(heart_rate_variations)\n",
" hr_text = random_variation(line[\"heart_rate\"])\n",
"\n",
" fake_hr = random.randint(50, 200) # Adjust range as needed\n",
" fake_hr_text = random.choice(fake_heart_rate_variations)(fake_hr)\n",
"\n",
" # line = f\"timestamp:{line['timestamp']} {hr_text} state:{line['state']}\"\n",
" # line = \" \".join(f\"{key}:{value}\" for key, value in line.items())\n",
" time_variation = random.choice(variations)\n",
" time_text = time_variation(\"timestamp\", line[\"timestamp\"])\n",
" state_variation = random.choice(variations)\n",
" state_text = state_variation(\"state\", line[\"state\"])\n",
"\n",
" if random.random() < 0.5:\n",
" line = f\"{time_text} {fake_hr_text} {hr_text} {state_text}\"\n",
" else:\n",
" line = f\"{time_text} {hr_text} {fake_hr_text} {state_text}\"\n",
"\n",
" lines.append(line)\n",
"\n",
" output_file = f'/content/drive/MyDrive/sheets_vital_log/{num_items}/heartrate_{num_items}_{j+1}.txt'\n",
"\n",
" with open(output_file, 'w') as outfile:\n",
" outfile.write(\"\\n\".join(lines))"
],
"metadata": {
"id": "RPPoDBfJ5JeM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Transcript"
],
"metadata": {
"id": "TxAubn6gSf9H"
}
},
{
"cell_type": "code",
"source": [
"!pip install faker\n",
"import pandas as pd\n",
"import random\n",
"from faker import Faker\n",
"import os\n",
"\n",
"def generate_transcript(count=50, num_students = 100, directory=\"/content/drive/MyDrive/sheets_transcript/\"):\n",
" fake = Faker()\n",
" names = [fake.name() for _ in range(num_students)]\n",
"\n",
" subjects = ['Math', 'Chemistry', 'Biology', 'Physics', 'Geography']\n",
"\n",
" data = {'Name': names}\n",
" for subject in subjects:\n",
" data[subject] = [random.randint(0, 100) for _ in range(num_students)]\n",
"\n",
" df = pd.DataFrame(data)\n",
" json_data = df.to_json(orient='records')\n",
"\n",
" os.makedirs(directory, exist_ok=True)\n",
" csv_path = f\"transcript_{num_students}_{count+1}.csv\"\n",
" csv_path = os.path.join(directory, csv_path)\n",
" df.to_csv(csv_path, index=False)\n",
"\n",
" json_path = f\"transcript_{num_students}_{count+1}.json\"\n",
" json_path = os.path.join(directory, json_path)\n",
" with open(json_path, 'w') as f:\n",
" f.write(json_data)\n",
"\n",
"count = 50\n",
"num_students = 1000\n",
"directory=\"/content/drive/MyDrive/sheets_transcript/\"\n",
"for i in range(count):\n",
" generate_transcript(count=i, num_students=num_students, directory=directory+str(num_students)+'/')"
],
"metadata": {
"id": "Ol17CCnIh9FP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import json\n",
"\n",
"counts = 50\n",
"variations = [\n",
" lambda key,value: f\"{key}:{value}\",\n",
" lambda key,value: f\"{key}={value}\",\n",
" lambda key,value: f\"{key} is {value}\",\n",
"]\n",
"#\n",
"for num_items in [100, 200, 500, 1000]:\n",
" for j in range(counts):\n",
" prompt_path = f'/content/drive/MyDrive/sheets_transcript/{num_items}/transcript_{num_items}_{j+1}.json'\n",
"\n",
" with open(prompt_path, 'r') as infile:\n",
" data = json.load(infile)\n",
"\n",
" lines = []\n",
"\n",
" for line_data in data:\n",
" line = \"\"\n",
" for key, value in line_data.items():\n",
" random_variation = random.choice(variations)\n",
" line=line+random_variation(key=key,value=value)+\" \"\n",
" lines.append(line.strip())\n",
"\n",
" output_file = f'/content/drive/MyDrive/sheets_transcript/{num_items}/transcript_{num_items}_{j+1}.txt'\n",
"\n",
" with open(output_file, 'w') as outfile:\n",
" outfile.write(\"\\n\".join(lines))"
],
"metadata": {
"id": "waA7fHuV5gM4"
},
"execution_count": null,
"outputs": []
}
]
} |