import numpy as np

# Universal functions in action
data = np.array([1, 4, 9, 16, 25])
print(f"Data: {data}")

# Fast element-wise operations
print(f"Square roots: {np.sqrt(data)}")
print(f"Logarithms: {np.log(data)}")
print(f"Exponentials: {np.exp([1, 2, 3])}")

import numpy as np

# Large array demonstration
large_array = np.arange(1000000)

# NumPy ufunc (fast)
result = np.sqrt(large_array)

print(f"Processed {len(large_array)} elements instantly!")
print(f"First 5 results: {result[:5]}")

import numpy as np

a = np.array([10, 20, 30, 40])
b = np.array([1, 2, 3, 4])

# Arithmetic ufuncs
print(f"Addition: {np.add(a, b)}")
print(f"Subtraction: {np.subtract(a, b)}")
print(f"Multiplication: {np.multiply(a, b)}")
print(f"Division: {np.divide(a, b)}")

import numpy as np

numbers = np.array([1, 4, 9, 16, 25])

# Power and roots
print(f"Squares: {np.square(numbers)}")
print(f"Square roots: {np.sqrt(numbers)}")
print(f"Cube roots: {np.cbrt([8, 27, 64])}")
print(f"Power of 3: {np.power([2, 3, 4], 3)}")

import numpy as np

# Common angles
angles_deg = np.array([0, 30, 45, 60, 90])
angles_rad = np.deg2rad(angles_deg)

print(f"Angles (deg): {angles_deg}")
print(f"Sine: {np.sin(angles_rad).round(3)}")
print(f"Cosine: {np.cos(angles_rad).round(3)}")
print(f"Tangent: {np.tan(angles_rad).round(3)}")

import numpy as np

values = np.array([0, 0.5, 0.707, 0.866, 1])

# Inverse trigonometry (returns radians)
arcsin_rad = np.arcsin(values)
arcsin_deg = np.rad2deg(arcsin_rad)

print(f"Values: {values}")
print(f"Arcsine (deg): {arcsin_deg.round(1)}")

import numpy as np

x = np.array([0, 1, 2, 3, 4])

# Exponential functions
print(f"x: {x}")
print(f"e^x: {np.exp(x).round(3)}")
print(f"2^x: {np.exp2(x)}")
print(f"10^x: {np.power(10, x)}")

import numpy as np

values = np.array([1, 10, 100, 1000])

# Logarithmic functions
print(f"Values: {values}")
print(f"Natural log: {np.log(values)}")
print(f"Log base 10: {np.log10(values)}")
print(f"Log base 2: {np.log2([1, 2, 4, 8])}")

import numpy as np

decimals = np.array([3.2, 4.7, -2.1, -1.8, 5.5])

# Rounding operations
print(f"Original: {decimals}")
print(f"Round: {np.round(decimals)}")
print(f"Floor: {np.floor(decimals)}")
print(f"Ceiling: {np.ceil(decimals)}")

import numpy as np

a = np.array([1, 5, 3, 8, 2])
b = np.array([2, 4, 3, 6, 7])

# Element-wise comparisons
print(f"a: {a}")
print(f"b: {b}")
print(f"Maximum: {np.maximum(a, b)}")
print(f"Minimum: {np.minimum(a, b)}")

import numpy as np

mixed = np.array([-5, -2, 0, 3, 7])

# Absolute and sign functions
print(f"Original: {mixed}")
print(f"Absolute: {np.abs(mixed)}")
print(f"Sign: {np.sign(mixed)}")
print(f"Clipped [-2, 5]: {np.clip(mixed, -2, 5)}")

import numpy as np

# Compound interest
principals = np.array([1000, 5000, 10000])
rate = 0.06  # 6% annual
years = 10

# A = P(1 + r)^t
amounts = principals * (1 + rate) ** years

print(f"After {years} years at {rate*100}%:")
for p, a in zip(principals, amounts):
    print(f"${p:,} → ${a:,.0f}")

import numpy as np

# Generate sine wave
time = np.linspace(0, 2*np.pi, 20)
signal = 3 * np.sin(2 * time)

print(f"Signal range: [{np.min(signal):.2f}, {np.max(signal):.2f}]")
print(f"RMS value: {np.sqrt(np.mean(signal**2)):.2f}")