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

# Using the DFT to get frequencies

To get the frequency of a sine wave, we need to get the  **Discrete Fourier Transform (DFT)**. We don’t need to understand how to derive the DFT, we just need to know how to use it.

In its simplest terms, the DFT takes a signal and calculates which frequencies are present in it. In more technical terms, the DFT converts a time-domain signal to a frequency domain. 

What does that mean? Let’s look at our sine wave from the previous lesson. 

The wave is changing with time. If this was an audio file, we could imagine the player moving right as the file plays. In the frequency domain, we see the frequency of the signal. The image below shows what the frequency domain looks like:

The signal will change if we add or remove frequencies but the time will stay constant. For example, if we take a 1000 Hz audio tone and take its frequency, the frequency will remain the same no matter how long we look at it. But if we look at it in the time domain, we will see the signal moving.

The DFT was slow to run on computers back in the 70s, so the **Fast Fourier Transform (FFT)** was invented. The FFT is what is usually used nowadays. The DFT works by taking a signal and running the FFT on it, and getting the signal’s frequency back. If you have never used an FFT, don’t worry. That’s what this course is for.

Let’s look at the code to get the frequencies in lines 8–14.

We’re reading the wave file we generated in the last example. This code should be clear enough. The wave `readframes()` function reads all the audio frames from a wave file.

Remember how we had to pack the data to make it readable in binary format? Well, we’ll do the opposite now. The first parameter to the function is a format string, which is the same thing we use when we make a `print()`. We’re telling the unpacker to unpack `num_samples` 16-bit words. Remember, `h` means 16 bits.

Next we’ll convert the data to a NumPy array in line 17 of the code block below.

After that, in line 19 of the code block below, we’ll take the `fft` of the data. This will create an array with all the frequencies present in the signal. Now, here’s the problem. The `fft` returns an array of complex numbers that don’t tell us anything. If we print out the first 8 values of the `fft`, we’ll get:





Learn how to calculate the frequency of a sine wave.

Calculate the Frequency of a 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

Calculate the Frequency of a Sine Wave

Using the DFT to get frequencies