modify README.md

This commit is contained in:
Obie Hinojosa 2024-11-10 18:01:35 -06:00 committed by Obie Hinojosa
parent 78ad9071a0
commit 344c68fa0a
14 changed files with 60 additions and 0 deletions

2
.gitignore vendored
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@ -160,3 +160,5 @@ cython_debug/
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
.png
.gif

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qpsk.py Normal file
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import time
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
symbol_count = 1000
x_integer = np.random.randint(0, 4, symbol_count) # generates random integers between 0 and 4 to fill out our symbol values. (generates a random number for each symbol in 'symbol_count')
x_degrees = x_integer*360/4.0 + 45 # four possible angles (degrees), spaced at 45 (45, 135, 225, 315)
x_radians = x_degrees*np.pi/180.0 # sin() and cos() both need radians, this just converts x_degrees to radians for that one use case
x_symbols = np.cos(x_radians) + 1j*np.sin(x_radians)
noise_power = 0.05
timer = 0
while timer < 10:
noise = (np.random.randn(symbol_count) + 1j*np.random.randn(symbol_count))/np.sqrt(2)
noisy_symbols = x_symbols + noise * np.sqrt(noise_power)
plt.plot(np.real(noisy_symbols), np.imag(noisy_symbols), ',b')
plt.grid(True)
plt.savefig(f"{timer}.png")
timer = timer + 1
def create_gif(image_paths, output_path, duration=500, loop=0):
"""
Creates a GIF from a list of images.
Args:
image_paths (list): A list of file paths to the images.
output_path (str): The path to save the output GIF.
duration (int, optional): The duration of each frame in milliseconds. Defaults to 500.
loop (int, optional): Number of times the GIF should loop. 0 means infinite loop. Defaults to 0.
"""
images = [Image.open(image_path) for image_path in image_paths]
images[0].save(output_path, save_all=True, append_images=images[1:], duration=duration, loop=loop)
image_paths = ["0.png", "1.png", "2.png", "3.png", "4.png", "5.png", "6.png", "7.png", "8.png", "9.png" ]
output_path = "output.gif"
create_gif(image_paths, output_path)

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whatthefreak.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
# Generate tone plus noise
t = np.arange(1024*1000)/sample_rate # time vector
f = 50e3 # freq of tone
x = np.sin(2*np.pi*f*t)
# simulate the signal above, or use your own signal
fft_size = 1024
num_rows = len(x) // fft_size # // is an integer division which rounds down
spectrogram = np.zeros((num_rows, fft_size))
for i in range(num_rows):
spectrogram[i,:] = 10*np.log10(np.abs(np.fft.fftshift(np.fft.fft(x[i*fft_size:(i+1)*fft_size])))**2)
# different thingy
av_pwr = np.mean(np.abs(x)**2)