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

We won’t cover filtering in detail as it’s a very extensive topic. Instead, we’ll create a simple filter just by using the Fast Fourier Transform (FFT). The goal is to get comfortable with NumPy.

Here is the complete code:


filtered_freq = []
index = 0
for f in freq:
    # Filter between lower and upper limits
    # Choosing 950, as closest to 1000. In real world, won't get exact numbers like \these
    if index > 950 and index < 1050:
        # Has a real value. I'm choosing >1, as many values are like 0.000000001 etc
        if f > 1:
            filtered_freq.append(f)
        else:
            filtered_freq.append(0)

    else:
        filtered_freq.append(0)
    index += 1


Now, let’s go over it line by line:

In lines 1–3, we’ll create an empty list called `filtered_freq`. Remember, `freq` stores the absolute values of the FFT or the frequencies present.

Then, in line 6, `index` is the current array element in the array `freq`. As we said, the `fft` returns all frequencies in the signal. These are stored in the array based on the index, so `freq[1]` will have the frequency of 1Hz, `freq[2]` will have 2Hz, and so on.

Since we know the frequency is 1000Hz, we’ll search around that. We’ll never get the exact frequency virtually, since noise means some data will be lost. We’re using a lower limit of 950 and an upper limit of 1050. In lines 8-9, we’ll check if the frequency that we’re looping is within this range.

We mentioned this earlier. While all frequencies will be present, their absolute values will be minuscule, usually less than 1. So if we find a value greater than 1, we’ll save it to our `filtered_freq` array.

Now, look at the `else` loop in lines 46–51 in the code block below. If our frequency is not within the range we are looking for, or if the value is too low, we’ll append a zero. This is to remove all frequencies we don’t want. And then, we’ll increment the index.

Now we’ll plot the main signal.


Learn how to filter out the noise from a sine wave.

Cleaning 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

Cleaning a Noisy Sine Wave