Gain insights into using Python for scientific and engineering applications. Delve into real-world scenarios, learning NumPy, Matplotlib, audio processing, and more through practical, hands-on projects.

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jupter.tar.gz

notebook

obesity_plot_gender

print_gender_pandas

create_sine_wave

notebook_exercise

range

print_gender_pandas_remove_NaN

print_gender_year

print_data_age

print_data_age_with_year

print_data_age_remove_NaN

obesity_plot_age_simple

obesity_plot_age_clear

print_data_age_under_16

print_data_age_25_34

obesity_plot_children_vs_adults

print_movie_top_rated

print_movies_oldie_teen

print_movies_by_gender

print_movies_for_women

print_movies_by_gender_loc

print_movies_diff_gender

movies_diff_gender_plot

print_u_data

print_movies_oldie_index

print_movies_oldies

print_movies_teens

display_image_nouse

grey_image

blur_image

edge_detection_low

edge_detection_high

plot_sin_wave

create_wave_file

display_again

print_frequencies

count_objects

detect_faces

detect_faces_little_mix

count_exact_lines

webcam_detection

single_thread

find_correlation

webcam_face_detection

motion_detection

motion_detection_live

In this course, you will learn to use Python for real-world scientific/engineering applications. For each topic, there will be a real case scenario where you will build a quick solution in Python to solve the problem.

More specifically, you will cover topics such as creating a word counter, NumPy and Matplotlib, audio processing, and a lot more. Throughout each chapter, you will get hands-on experience in building solutions for complex problems that engineers and scientists face every day.

The Practical Guide to Python for Scientists and Engineers

# Adding noise to a sound wave

In this lesson, we’ll generate a sine wave, add noise to it, and then filter the noise. Let’s start with the code.

Frequency is the number of times a wave repeats a second.
```
frequency = 1000
noisy_freq = 50
num_samples = 48000
```

The sampling rate of the analog to digital conversion is:
```
sampling_rate = 48000.0
```

The primary frequency is 1000Hz, and we’ll add 50Hz of noise to it.

```
#Create the sine wave and noise
sine_wave = [np.sin(2 * np.pi * frequency * x1 / sampling_rate) for x1 in range(num_\
samples)]
sine_noise = [np.sin(2 * np.pi * noisy_freq * x1/  sampling_rate) for x1 in range(nu\
m_samples)]
#Convert them to numpy arrays
sine_wave = np.array(sine_wave)
sine_noise = np.array(sine_noise)

```

We’ll generate two sine waves, one for the signal and one for the noise, and convert them to NumPy arrays.

Now we’ll add these two waves to create a noisy signal.
```
combined_signal = sine_wave + sine_noise
```

We’re adding the noise to the signal. As we mentioned earlier, this is only possible with NumPy. With standard Python, we’d have to use a `for` loop or use list comprehensions. With NumPy, we can add two arrays like they were regular numbers, and NumPy takes care of the low-level detail for us.

All of the above code has already been added in the code widget below, starting from line 17 onwards.

# Plotting the original and noisy sine wave


Learn how to add noise into an audio file and then plot the data.

Plotting a Noisy Sine Wave

Introduction

Create a Word Counter in Python

An Introduction to NumPy and Matplotlib

Python pandas

Audio Processing

Image and Video Processing

MultiThreading vs. Multiprocessing in Python

Machine Learning with an Amazon-like Recommendation Engine

Conclusion

Plotting a Noisy Sine Wave

Adding noise to a sound wave