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Lab02+Lab03 exercises #40

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Lab02+Lab03 exercises #40

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759a9e3
Lab02.py
6haruhi Feb 15, 2024
548ffe4
Lab03 exercises
6haruhi Feb 15, 2024
9889963
Merge branch 'qszhao:main' into PTUAlab
6haruhi Feb 19, 2024
b54833e
Lab03-2.ipynb
6haruhi Feb 26, 2024
9f465b0
Merge branch 'PTUAlab' of https://github.com/6haruhi/PTUA2024 into PT…
6haruhi Feb 29, 2024
e377eb4
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6haruhi Feb 29, 2024
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3 changes: 3 additions & 0 deletions Lab02.py
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# Print all odd numbers between 0 and 100
for num in range(1, 101, 2):
print(num)
120 changes: 120 additions & 0 deletions Lab03-2.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"id": "59e7cbcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enter the convergence criterion (e.g., 0.01 for 1%): 0.0001\n",
"Enter the sentinel value (maximum number of draws): 50000000\n",
"Convergence achieved with 727 draws. Estimated Pi: 3.1416781292984868\n"
]
}
],
"source": [
"import random\n",
"import math\n",
"\n",
"# User inputs for convergence criterion and sentinel value\n",
"convergence_criterion = float(input(\"Enter the convergence criterion (e.g., 0.01 for 1%): \"))\n",
"sentinel_value = int(input(\"Enter the sentinel value (maximum number of draws): \"))\n",
"\n",
"pi = math.pi # The true value of Pi for comparison\n",
"n = 0 # Number of points inside the unit circle\n",
"d = 0 # Total number of points generated\n",
"ratios = [] # To store the estimates of Pi after each iteration for plotting, if needed\n",
"\n",
"# Simulation loop\n",
"while True:\n",
" x, y = random.random(), random.random() # Generate random point\n",
" if x**2 + y**2 <= 1:\n",
" n += 1 # Increment count of points inside the unit circle\n",
" d += 1 # Increment total count of points\n",
" estimated_pi = 4 * n / d # Estimate of Pi\n",
" ratios.append(estimated_pi) # For plotting purposes\n",
" \n",
" # Check if convergence criterion is met\n",
" if abs(estimated_pi - pi) / pi <= convergence_criterion:\n",
" print(f\"Convergence achieved with {d} draws. Estimated Pi: {estimated_pi}\")\n",
" break\n",
" # Check if sentinel value is reached\n",
" if d >= sentinel_value:\n",
" print(f\"Sentinel value reached with {d} draws. Estimated Pi: {estimated_pi}\")\n",
" print(f\"Percentage difference from true Pi: {abs(estimated_pi - pi) / pi * 100}%\")\n",
" break\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2b4546f9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convergence Criterion: 0.01, Average Draws: 294.50, Standard Deviation: 425.72\n",
"Convergence Criterion: 0.001, Average Draws: 12031.20, Standard Deviation: 35729.05\n",
"Convergence Criterion: 0.0001, Average Draws: 3085.20, Standard Deviation: 4588.12\n",
"Convergence Criterion: 1e-05, Average Draws: 52795.00, Standard Deviation: 150229.89\n"
]
}
],
"source": [
"import random\n",
"import math\n",
"import numpy as np\n",
"\n",
"# Setting different convergence criteria\n",
"convergence_criteria = [0.01, 0.001, 0.0001, 0.00001]\n",
"\n",
"def estimate_pi(convergence_criterion):\n",
" n = 0 \n",
" d = 0 \n",
" while True:\n",
" x, y = random.random(), random.random()\n",
" if x**2 + y**2 <= 1:\n",
" n += 1\n",
" d += 1\n",
" pi_estimate = 4 * n / d\n",
" if d > 1 and abs(pi_estimate - math.pi) / math.pi < convergence_criterion:\n",
" break\n",
" return d \n",
"\n",
"# main program\n",
"for criterion in convergence_criteria:\n",
" draws = [estimate_pi(criterion) for _ in range(10)] \n",
" avg_draws = np.mean(draws) \n",
" std_draws = np.std(draws) \n",
" print(f\"Convergence Criterion: {criterion}, Average Draws: {avg_draws:.2f}, Standard Deviation: {std_draws:.2f}\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
180 changes: 180 additions & 0 deletions Lab03_chapter2.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e5ead4f2",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "cannot assign to literal (3090476294.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"C:\\Users\\haruhi\\AppData\\Local\\Temp\\ipykernel_12348\\3090476294.py\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m 42 = n\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m cannot assign to literal\n"
]
}
],
"source": [
"42 = n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1a0220b3",
"metadata": {},
"outputs": [],
"source": [
"x = y = 1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ad8b642b",
"metadata": {},
"outputs": [],
"source": [
"first = 'throat';"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cd7062c8",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (3550435703.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"C:\\Users\\haruhi\\AppData\\Local\\Temp\\ipykernel_12348\\3550435703.py\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m a = 'hello world'.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"a = 'hello world'."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "43ef3a4a",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'xy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12348\\3424147140.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mxy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'xy' is not defined"
]
}
],
"source": [
"xy"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9d572670",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"523.5987755982989\n"
]
}
],
"source": [
"import math\n",
"r = 5\n",
"V = (4/3) * math.pi * r**3\n",
"print(V)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ea78a2d1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"945.4499999999999\n"
]
}
],
"source": [
"Discounted_Price = (24.95 * 0.6) * 60\n",
"Shipping_Costs = 3 + 0.75 * 59\n",
"print(Discounted_Price + Shipping_Costs)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b5ca6708",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"07:30 AM\n"
]
}
],
"source": [
"from datetime import datetime, timedelta\n",
"\n",
"# Define the starting time\n",
"start_time = datetime.strptime(\"6:52\", \"%H:%M\")\n",
"\n",
"# Calculate time taken for each part of the run\n",
"easy_pace_time = timedelta(minutes=8, seconds=15)\n",
"tempo_pace_time = timedelta(minutes=7, seconds=12)\n",
"\n",
"# Calculate total time for the entire run\n",
"total_time = 2 * easy_pace_time + 3 * tempo_pace_time\n",
"\n",
"# Calculate the time for breakfast\n",
"end_time = start_time + total_time\n",
"\n",
"# Output the result\n",
"print(end_time.strftime(\"%I:%M %p\"))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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