📚 Python programming workbook for machine learning with NumPy and SciPy (2024)

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Topic: Python SciPy – From Easy to Top: Part 1 of 6: Introduction and Basics

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1. What is SciPy?

• SciPy is an open-source Python library used for scientific and technical computing.

• Built on top of NumPy, it provides many user-friendly and efficient numerical routines such as routines for numerical integration, optimization, interpolation, eigenvalue problems, algebraic equations, and others.

---

2. Installing SciPy

If you don’t have SciPy installed yet, use:

pip install scipy

---

3. Importing SciPy Modules

SciPy is organized into sub-packages for different tasks. Example:

import scipy.integrate
import scipy.optimize
import scipy.linalg

---

4. Key SciPy Sub-packages

• scipy.integrate — Numerical integration and ODE solvers.
• scipy.optimize — Optimization and root finding.
• scipy.linalg — Linear algebra routines (more advanced than NumPy’s).
• scipy.signal — Signal processing.
• scipy.fft — Fast Fourier Transforms.
• scipy.stats — Statistical functions.

---

5. Basic Example: Numerical Integration

Calculate the integral of sin(x) from 0 to pi:

import numpy as np
from scipy import integrate

result, error = integrate.quad(np.sin, 0, np.pi)
print("Integral of sin(x) from 0 to pi:", result)

---

6. Basic Example: Root Finding

Find the root of the function f(x) = x^2 - 4:

from scipy import optimize

def f(x):
    return x**2 - 4

root = optimize.root_scalar(f, bracket=[0, 3])
print("Root:", root.root)

---

7. SciPy vs NumPy

• NumPy focuses on basic array operations and linear algebra.

• SciPy extends functionality with advanced scientific algorithms.

---

8. Summary

• SciPy is essential for scientific computing in Python.

• It contains many specialized sub-packages.

• Understanding SciPy’s structure helps solve complex numerical problems easily.

---

Exercise

• Calculate the integral of e^(-x^2) from -infinity to +infinity using scipy.integrate.quad.

• Find the root of cos(x) - x = 0 using scipy.optimize.root_scalar.

---

#Python #SciPy #ScientificComputing #NumericalIntegration #Optimization

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Topic: Python SciPy – From Easy to Top: Part 2 of 6: Numerical Integration and Differentiation

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1. Numerical Integration Overview

• Numerical integration approximates the area under curves when an exact solution is difficult or impossible.

• SciPy provides several methods like quad, dblquad, and trapz.

---

2. Using `scipy.integrate.quad`

This function computes the definite integral of a function of one variable.

Example: Integrate cos(x) from 0 to pi divided by 2

import numpy as np
from scipy import integrate

result, error = integrate.quad(np.cos, 0, np.pi/2)
print("Integral of cos(x) from 0 to pi/2:", result)

---

3. Double Integration with `dblquad`

Integrate a function of two variables over a rectangular region.

Example: Integrate f(x, y) = x times y over x from 0 to 1, y from 0 to 2

def f(x, y):
    return x * y

result, error = integrate.dblquad(f, 0, 1, lambda x: 0, lambda x: 2)
print("Double integral result:", result)

---

4. Using the Trapezoidal Rule: `trapz`

Useful for integrating discrete data points.

Example:

import numpy as np
from scipy import integrate

x = np.linspace(0, np.pi, 100)
y = np.sin(x)

area = integrate.trapz(y, x)
print("Approximate integral using trapz:", area)

---

5. Numerical Differentiation with `derivative`

SciPy’s derivative function approximates the derivative of a function at a point.

Example: Derivative of sin(x) at x equals pi divided by 4

from scipy.misc import derivative
import numpy as np

def f(x):
    return np.sin(x)

dx = derivative(f, np.pi/4, dx=1e-6)
print("Derivative of sin(x) at pi/4:", dx)

---

6. Limitations of `derivative`

• derivative uses finite difference methods, which can be noisy for non-smooth functions.

• Suitable for simple derivative calculations but not for complex cases.

---

7. Summary

• quad is powerful for one-dimensional definite integrals.

• dblquad handles two-variable integration.

• trapz approximates integration from sampled data.

• derivative provides numerical differentiation.

---

Exercise

• Compute the integral of e to the power of negative x squared from 0 to 1 using quad.

• Calculate the derivative of cos(x) at 0.

• Use trapz to approximate the integral of x squared over \[0, 5] using 50 points.

---

#Python #SciPy #NumericalIntegration #Differentiation #ScientificComputing

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Topic: Python SciPy – From Easy to Top: Part 3 of 6: Optimization Basics

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1. What is Optimization?

• Optimization is the process of finding the minimum or maximum of a function.

• SciPy provides tools to solve these problems efficiently.

---

2. Using `scipy.optimize.minimize`

This function minimizes a scalar function of one or more variables.

Example: Minimize the function f(x) = (x - 3)^2

from scipy import optimize

def f(x):
    return (x - 3)**2

result = optimize.minimize(f, x0=0)
print("Minimum value:", result.fun)
print("At x =", result.x)

---

**3. Minimizing Multivariable Functions**

Example: Minimize f(x, y) = (x - 2)^2 + (y + 3)^2

def f(vars):
    x, y = vars
    return (x - 2)**2 + (y + 3)**2

result = optimize.minimize(f, x0=[0, 0])
print("Minimum value:", result.fun)
print("At x, y =", result.x)

---

**4. Using Bounds and Constraints**

You can restrict the variables within bounds or constraints.

Example: Minimize f(x) = (x - 3)^2 with x between 0 and 5

result = optimize.minimize(f, x0=0, bounds=[(0, 5)])
print("Minimum with bounds:", result.fun)
print("At x =", result.x)

---

5. Root Finding with `optimize.root_scalar`

Find a root of a scalar function.

Example: Find root of f(x) = x^3 - 1 between 0 and 2

def f(x):
    return x**3 - 1

root = optimize.root_scalar(f, bracket=[0, 2])
print("Root:", root.root)

---

6. Summary

• SciPy’s optimization tools help find minima, maxima, and roots.

• Supports single and multivariable problems with constraints.

---

Exercise

• Minimize the function f(x) = x^4 - 3x^3 + 2 over the range \[-2, 3].

• Find the root of f(x) = cos(x) - x near x=1.

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#Python #SciPy #Optimization #RootFinding #ScientificComputing

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Topic: Python SciPy – From Easy to Top: Part 4 of 6: Linear Algebra with SciPy

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1. Introduction to Linear Algebra in SciPy

• Linear algebra is fundamental in scientific computing, machine learning, and data science.

• SciPy provides advanced linear algebra routines built on top of LAPACK and BLAS libraries.

• The main sub-package is scipy.linalg which extends NumPy’s linear algebra capabilities.

---

2. Basic Matrix Operations

You can create matrices using NumPy arrays:

import numpy as np

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

---

3. Matrix Addition and Multiplication

# Addition
C = A + B
print("Matrix Addition:\n", C)

# Element-wise Multiplication
D = A * B
print("Element-wise Multiplication:\n", D)

# Matrix Multiplication
E = np.dot(A, B)
print("Matrix Multiplication:\n", E)

---

4. Using `scipy.linalg` for Advanced Operations

Import SciPy linear algebra module:

from scipy import linalg

---

5. Matrix Inverse

Calculate the inverse of a matrix (if invertible):

inv_A = linalg.inv(A)
print("Inverse of A:\n", inv_A)

---

6. Determinant

Calculate the determinant:

det_A = linalg.det(A)
print("Determinant of A:", det_A)

---

7. Eigenvalues and Eigenvectors

Find eigenvalues and eigenvectors:

eigvals, eigvecs = linalg.eig(A)
print("Eigenvalues:\n", eigvals)
print("Eigenvectors:\n", eigvecs)

---

8. Solving Linear Systems

Solve Ax = b where b is a vector:

b = np.array([5, 11])
x = linalg.solve(A, b)
print("Solution x:\n", x)

---

9. Singular Value Decomposition (SVD)

Decompose matrix A into U, Σ, and V^T:

U, s, VT = linalg.svd(A)
print("U matrix:\n", U)
print("Singular values:", s)
print("V^T matrix:\n", VT)

---

10. LU Decomposition

Decompose matrix A into lower and upper triangular matrices:

P, L, U = linalg.lu(A)
print("P matrix:\n", P)
print("L matrix:\n", L)
print("U matrix:\n", U)

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11. QR Decomposition

Factorize A into Q and R matrices:

Q, R = linalg.qr(A)
print("Q matrix:\n", Q)
print("R matrix:\n", R)

---

12. Norms of Vectors and Matrices

Calculate different norms:

# Vector norm
v = np.array([1, -2, 3])
norm_v = linalg.norm(v)
print("Vector norm:", norm_v)

# Matrix norm (Frobenius norm)
norm_A = linalg.norm(A, 'fro')
print("Matrix Frobenius norm:", norm_A)

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13. Checking if a Matrix is Positive Definite

Try Cholesky decomposition:

try:
    L = linalg.cholesky(A)
    print("Matrix is positive definite")
except linalg.LinAlgError:
    print("Matrix is not positive definite")

---

14. Summary

• SciPy’s linalg module provides extensive linear algebra tools beyond NumPy.

• Operations include inverse, determinant, eigenvalues, decompositions, and solving linear systems.

• These tools are essential for many scientific and engineering problems.

---

Exercise

• Compute the eigenvalues and eigenvectors of the matrix \[\[4, 2], \[1, 3]].

• Solve the system of equations represented by:

  2x + 3y = 8

  5x + 4y = 13

• Perform SVD on the matrix \[\[1, 0], \[0, -1]] and explain the singular values.

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#Python #SciPy #LinearAlgebra #SVD #Decomposition #ScientificComputing

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Topic: Python SciPy – From Easy to Top: Part 5 of 6: Working with SciPy Statistics

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1. Introduction to `scipy.stats`

• The scipy.stats module contains a large number of probability distributions and statistical functions.
• You can perform tasks like descriptive statistics, hypothesis testing, sampling, and fitting distributions.

---

2. Descriptive Statistics

Use these functions to summarize and describe data characteristics:

from scipy import stats
import numpy as np

data = [2, 4, 4, 4, 5, 5, 7, 9]

mean = np.mean(data)
median = np.median(data)
mode = stats.mode(data, keepdims=True)
std_dev = np.std(data)

print("Mean:", mean)
print("Median:", median)
print("Mode:", mode.mode[0])
print("Standard Deviation:", std_dev)

---

3. Probability Distributions

SciPy has built-in continuous and discrete distributions such as normal, binomial, Poisson, etc.

Normal Distribution Example

from scipy.stats import norm

# PDF at x = 0
print("PDF at 0:", norm.pdf(0, loc=0, scale=1))

# CDF at x = 1
print("CDF at 1:", norm.cdf(1, loc=0, scale=1))

# Generate 5 random numbers
samples = norm.rvs(loc=0, scale=1, size=5)
print("Random Samples:", samples)

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4. Hypothesis Testing

One-sample t-test – test if the mean of a sample is equal to a known value:

sample = [5.1, 5.3, 5.5, 5.7, 5.9]
t_stat, p_val = stats.ttest_1samp(sample, popmean=5.0)

print("T-statistic:", t_stat)
print("P-value:", p_val)

Interpretation: If the p-value is less than 0.05, reject the null hypothesis.

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5. Two-sample t-test

Test if two samples come from populations with equal means:

group1 = [20, 22, 19, 24, 25]
group2 = [28, 27, 26, 30, 31]

t_stat, p_val = stats.ttest_ind(group1, group2)

print("T-statistic:", t_stat)
print("P-value:", p_val)

---

6. Chi-Square Test for Independence

Use to test independence between two categorical variables:

# Example contingency table
data = [[10, 20], [20, 40]]
chi2, p, dof, expected = stats.chi2_contingency(data)

print("Chi-square statistic:", chi2)
print("P-value:", p)

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7. Correlation and Covariance

Measure linear relationship between variables:

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

corr, _ = stats.pearsonr(x, y)
print("Pearson Correlation Coefficient:", corr)

Covariance:

cov_matrix = np.cov(x, y)
print("Covariance Matrix:\n", cov_matrix)

---

8. Fitting Distributions to Data

You can fit a distribution to real-world data:

data = np.random.normal(loc=50, scale=10, size=1000)
params = norm.fit(data)  # returns mean and std dev

print("Fitted mean:", params[0])
print("Fitted std dev:", params[1])

---

9. Sampling from Distributions

Generate random numbers from different distributions:

# Binomial distribution
samples = stats.binom.rvs(n=10, p=0.5, size=10)
print("Binomial Samples:", samples)

# Poisson distribution
samples = stats.poisson.rvs(mu=3, size=10)
print("Poisson Samples:", samples)

---

10. Summary

• scipy.stats is a powerful tool for statistical analysis.
• You can compute summaries, perform tests, model distributions, and generate random samples.

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Exercise

• Generate 1000 samples from a normal distribution and compute mean, median, std, and mode.
• Test if a sample has a mean significantly different from 5.
• Fit a normal distribution to your own dataset and plot the histogram with the fitted PDF curve.

---

#Python #SciPy #Statistics #HypothesisTesting #DataAnalysis

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Topic: Python SciPy – From Easy to Top: Part 6 of 6: Signal Processing, Interpolation, and Fourier Transforms

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1. Introduction

SciPy contains powerful tools for signal processing, interpolation, and Fourier transforms. These are essential in fields like image and audio processing, scientific simulations, and data smoothing.

Main submodules covered in this part:

• scipy.signal – Signal processing
• scipy.fft – Fast Fourier Transform
• scipy.interpolate – Data interpolation and curve fitting

---

### 2. Signal Processing with `scipy.signal`

Filtering a Signal:

Let’s create a noisy sine wave and apply a low-pass filter.

import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

# Create a sample signal with noise
t = np.linspace(0, 1.0, 200)
x = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(200)

# Apply a Butterworth low-pass filter
b, a = signal.butter(3, 0.2)
filtered = signal.filtfilt(b, a, x)

# Plot original and filtered signals
plt.plot(t, x, label="Noisy Signal")
plt.plot(t, filtered, label="Filtered Signal")
plt.legend()
plt.title("Low-pass Filtering with Butterworth")
plt.show()

---

Find Peaks in a Signal:

peaks, _ = signal.find_peaks(x, height=0)
print("Peak Indices:", peaks)

---

### 3. Fourier Transform with `scipy.fft`

The Fourier Transform breaks a signal into its frequency components.

from scipy.fft import fft, fftfreq

# Number of sample points
N = 600
# Sample spacing
T = 1.0 / 800.0
x = np.linspace(0.0, N*T, N, endpoint=False)
y = np.sin(50.0 * 2.0 * np.pi * x) + 0.5 * np.sin(80.0 * 2.0 * np.pi * x)

yf = fft(y)
xf = fftfreq(N, T)[:N//2]

plt.plot(xf, 2.0/N * np.abs(yf[0:N//2]))
plt.grid()
plt.title("Fourier Transform of Signal")
plt.show()

---

### 4. Interpolation with `scipy.interpolate`

Interpolation estimates unknown values between known data points.

from scipy import interpolate

x = np.linspace(0, 10, 10)
y = np.sin(x)

# Create interpolating function
f = interpolate.interp1d(x, y, kind='cubic')

# Interpolate new values
xnew = np.linspace(0, 10, 100)
ynew = f(xnew)

plt.plot(x, y, 'o', label="Data Points")
plt.plot(xnew, ynew, '-', label="Cubic Interpolation")
plt.legend()
plt.title("Interpolation Example")
plt.show()

---

### 5. 2D Interpolation Example

from scipy.interpolate import griddata

# Known points
points = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
values = np.array([0, 1, 1, 0])

# Interpolation grid
grid_x, grid_y = np.mgrid[0:1:100j, 0:1:100j]
grid_z = griddata(points, values, (grid_x, grid_y), method='cubic')

plt.imshow(grid_z.T, extent=(0,1,0,1), origin='lower')
plt.title("2D Cubic Interpolation")
plt.colorbar()
plt.show()

---

### 6. Summary

• scipy.signal is used for filtering, finding peaks, convolution, etc.
• scipy.fft helps analyze signal frequencies.
• scipy.interpolate estimates unknown values smoothly between data points.

These tools are critical for real-time data analysis, image/audio processing, and engineering applications.

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Exercise

• Generate a noisy signal and apply both low-pass and high-pass filters.
• Plot the Fourier transform of a composed signal of multiple frequencies.
• Perform cubic interpolation on a dataset with missing values and plot both.

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#Python #SciPy #SignalProcessing #FFT #Interpolation #ScientificComputing

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