import numpy as np

# Array Creation - The foundation of NumPy
arr = np.array([1, 2, 3])
zeros = np.zeros((2, 3))        # 2x3 matrix of zeros
ones = np.ones((2, 2), dtype=int)  # Integer matrix
arange = np.arange(0, 10, 2)    # [0 2 4 6 8]
linspace = np.linspace(0, 1, 5) # [0.  0.25 0.5  0.75 1.  ]
print(linspace)

# Array Attributes - Master your data's structure
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.shape)  # Output: (2, 3)
print(matrix.ndim)   # Output: 2
print(matrix.dtype)  # Output: int64
print(matrix.size)   # Output: 6

# Indexing & Slicing - Precision data access
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(data[1, 2])      # Output: 6 (row 1, col 2)
print(data[0:2, 1:3])  # Output: [[2 3], [5 6]]
print(data[:, -1])     # Output: [3 6 9] (last column)

# Reshaping Arrays - Transform dimensions effortlessly
flat = np.arange(6)
reshaped = flat.reshape(2, 3)
raveled = reshaped.ravel()
print(reshaped)
# Output: [[0 1 2], [3 4 5]]
print(raveled)  # Output: [0 1 2 3 4 5]

# Stacking Arrays - Combine datasets vertically/horizontally
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(np.vstack((a, b)))  # Vertical stack
# Output: [[1 2 3], [4 5 6]]
print(np.hstack((a, b)))  # Horizontal stack
# Output: [1 2 3 4 5 6]

# Mathematical Operations - Vectorized calculations
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
print(x + y)       # Output: [5 7 9]
print(x * 2)       # Output: [2 4 6]
print(np.dot(x, y)) # Output: 32 (1*4 + 2*5 + 3*6)

# Broadcasting Magic - Operate on mismatched shapes
matrix = np.array([[1, 2, 3], [4, 5, 6]])
scalar = 10
print(matrix + scalar)
# Output: [[11 12 13], [14 15 16]]

# Aggregation Functions - Statistical power in one line
values = np.array([1, 5, 3, 9, 7])
print(np.sum(values))   # Output: 25
print(np.mean(values))  # Output: 5.0
print(np.max(values))   # Output: 9
print(np.std(values))   # Output: 2.8284271247461903

# Boolean Masking - Filter data like a pro
temperatures = np.array([18, 25, 12, 30, 22])
hot_days = temperatures > 24
print(temperatures[hot_days])  # Output: [25 30]

# Random Number Generation - Simulate real-world data
print(np.random.rand(2, 2))      # Uniform distribution
print(np.random.randn(3))        # Normal distribution
print(np.random.randint(0, 10, (2, 3)))  # Random integers

# Linear Algebra Essentials - Solve equations like a physicist
A = np.array([[3, 1], [1, 2]])
b = np.array([9, 8])
x = np.linalg.solve(A, b)
print(x)  # Output: [2. 3.] (Solution to 3x+y=9 and x+2y=8)

# Matrix inverse and determinant
print(np.linalg.inv(A))   # Output: [[ 0.4 -0.2], [-0.2  0.6]]
print(np.linalg.det(A))   # Output: 5.0

# File Operations - Save/load your computational work
data = np.array([[1, 2], [3, 4]])
np.save('array.npy', data)
loaded = np.load('array.npy')
print(np.array_equal(data, loaded))  # Output: True

# Interview Power Move: Vectorization vs Loops
# 10x faster than native Python loops!
def square_sum(n):
    arr = np.arange(n)
    return np.sum(arr ** 2)

print(square_sum(5))  # Output: 30 (0²+1²+2²+3²+4²)

# Pro Tip: Memory-efficient data processing
# Process 1GB array without loading entire dataset
large_array = np.memmap('large_data.bin', dtype='float32', mode='r', shape=(1000000, 100))
print(large_array[0:5, 0:3])  # Process small slice

# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import numpy as np

# 1. Load and preprocess the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Reshape images for CNN: (batch_size, height, width, channels)
# MNIST images are 28x28 grayscale, so channels = 1
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# 2. Define the CNN architecture
model = models.Sequential()

# First Convolutional Block
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))

# Second Convolutional Block
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Flatten the 3D output to 1D for the Dense layers
model.add(layers.Flatten())

# Dense (fully connected) layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # Output layer for 10 classes (digits 0-9)

# 3. Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Print a summary of the model layers
model.summary()

# 4. Train the model (uncomment to run training)
# print("\nTraining the model...")
# model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)

# 5. Evaluate the model (uncomment to run evaluation)
# print("\nEvaluating the model...")
# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
# print(f"Test accuracy: {test_acc:.4f}")