def maximum_noise_plot():
	''' Generates a plot showing the effectiveness of the Loopy BP 
	algorithm for varying mu values, used in the write-up.
	'''
	disk_fp = image_processing.generate_disk(100, 40)
	original_image = Image(disk_fp)

	FLIP_PROBS = [.005, .01, .05, .1, .2, .3, .4, .5]
	noises = []
	for prob in FLIP_PROBS:
		noisy_fp = image_processing.noisify(disk_fp, prob)
		# plt.imshow(np.genfromtxt(noisy_fp), cmap='Greys')
		# plt.show()
		noisy_image = Image(noisy_fp)
		denoised_fp = loopy_bp(noisy_image, THETA)
		denoised_image = Image(denoised_fp)
		fraction_noise = original_image.percent_different(denoised_image)
		print fraction_noise
		noises.append(fraction_noise)

	plt.plot(FLIP_PROBS, noises)
	plt.plot(FLIP_PROBS, FLIP_PROBS)
	plt.xlabel("Mu value (flip probability)")
	plt.ylabel("Fraction noise")
	plt.show()
def find_optimal_theta():
	''' Produces the plot finding the optimal Theta, used in the write-up.
	I found that the optimal thetas were the highest, and I decided to use
	0.9 for the bulk of my analysis.
	'''
	THETAS = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]
	FLIP_PROBS = [0.005, 0.05, 0.25, 0.5]
	noises = []

	disk_fp = image_processing.generate_disk(100, 40)
	original_image = Image(disk_fp)

	noisy_fps = []
	for prob in FLIP_PROBS:
		noisy_fps.append(image_processing.noisify(disk_fp, prob))

	for noisy_image_fp in noisy_fps:
		noise = []
		for theta in THETAS:
			print noisy_image_fp, theta
			noisy_image = Image(noisy_image_fp)
			loopy_bp(noisy_image, theta)
			denoised_image = Image(noisy_image.file_path + "_denoised")
			percent_noise = original_image.percent_different(denoised_image)
			noise.append(percent_noise)
			os.remove(noisy_image.file_path + "_denoised")
		noises.append(noise)

	for noise in noises:
		plt.plot(THETAS, noise)
		plt.xlabel("Theta")
		plt.ylabel("Fraction noise")
	plt.show()
def print_images():
	''' Prints all the images shown in the write-up.'''
	# Circle with radius 40
	disk_fp = image_processing.generate_disk(100, 40)
	show_image(disk_fp)

	# Circle with mu = 0.01
	noisy_fp = image_processing.noisify(disk_fp, 0.01)
	show_image(noisy_fp)

	# Circle cleaned up, mu = 0.01
	noisy_image = Image(noisy_fp)
	denoised_fp = loopy_bp(noisy_image, THETA)
	show_image(denoised_fp)

	# Circle with mu = 0.1
	noisy_fp = image_processing.noisify(disk_fp, 0.1)
	show_image(noisy_fp)

	# Circle cleaned up, mu = 0.1
	noisy_image = Image(noisy_fp)
	denoised_fp = loopy_bp(noisy_image, THETA)
	show_image(denoised_fp)

	# Square, 60 x 60
	square_fp = image_processing.generate_square(100, 60, 50)
	show_image(square_fp)

	# Square with mu = 0.01
	noisy_fp = image_processing.noisify(square_fp, 0.01)
	show_image(noisy_fp)

	# Square cleaned up, mu = 0.01
	noisy_image = Image(noisy_fp)
	denoised_fp = loopy_bp(noisy_image, THETA)
	show_image(denoised_fp)

	# Equals Sign
	equals_fp = image_processing.generate_equalsign()
	show_image(equals_fp)

	# Equals with mu = 0.01
	noisy_fp = image_processing.noisify(equals_fp, 0.01)
	show_image(noisy_fp)

	# Square cleaned up, mu = 0.01
	noisy_image = Image(noisy_fp)
	denoised_fp = loopy_bp(noisy_image, THETA)
	show_image(denoised_fp)