From a4de07ea79a857de2cb91480e3015a6501aa99e7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Szymon=20Parafi=C5=84ski?= <szypar@st.amu.edu.pl>
Date: Tue, 17 May 2022 22:29:10 +0200
Subject: [PATCH] =?UTF-8?q?Usu=C5=84=20'svd=5Foperations.py'?=
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---
 svd_operations.py | 94 -----------------------------------------------
 1 file changed, 94 deletions(-)
 delete mode 100644 svd_operations.py

diff --git a/svd_operations.py b/svd_operations.py
deleted file mode 100644
index 27ebc74..0000000
--- a/svd_operations.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from skimage import data
-from skimage.color import rgb2gray
-from skimage import img_as_ubyte, img_as_float
-from ipywidgets import interact, interactive, interact_manual
-from numpy.linalg import svd
-import ipywidgets as widgets
-
-gray_images = {
-    "cat": rgb2gray(img_as_float(data.chelsea())),
-    "astro": rgb2gray(img_as_float(data.astronaut())),
-    "camera": data.camera(),
-    "coin": data.coins(),
-    "clock": data.clock(),
-    "blobs": data.binary_blobs(),
-    "coffee": rgb2gray(img_as_float(data.coffee()))
-}
-
-
-def mc_pi(ntrials):
-    """
-    calculate the value of pi using montecarlo method and visualize the process
-
-    """
-    x = np.random.random(ntrials)
-    y = np.random.random(ntrials)
-    # masking
-    inside_circle = x ** 2 + y ** 2 < 1
-    unit_circle_x = np.linspace(0, 1, 100)
-    unit_circle = [unit_circle_x, np.sqrt(1.0 - unit_circle_x ** 2)]
-    plt.plot(*unit_circle, color='black')
-    plt.scatter(x[inside_circle], y[inside_circle], marker='.', color='blue', s=1)
-    plt.scatter(x[~inside_circle], y[~inside_circle], marker='.', color='red', s=1)
-    plt.title("value of $\pi$=" + str(4.0 * np.sum(inside_circle) / float(ntrials)))
-
-
-def compress_svd(image, k):
-    """
-    Perform svd decomposition and truncated (using k singular values/vectors) reconstruction
-    returns
-    --------
-      reconstructed matrix reconst_matrix, array of singular values s
-    """
-    U, s, V = svd(image, full_matrices=False)
-    reconst_matrix = np.dot(U[:, :k], np.dot(np.diag(s[:k]), V[:k, :]))
-
-    return reconst_matrix, s
-
-
-def compress_show_gray_images(img_name, k):
-    """
-     compresses gray scale images and display the reconstructed image.
-     Also displays a plot of singular values
-    """
-    image = gray_images[img_name]
-    original_shape = image.shape
-    reconst_img, s = compress_svd(image, k)
-    fig, axes = plt.subplots(1, 2, figsize=(8, 5))
-    axes[0].plot(s)
-    compression_ratio = 100.0 * (k * (original_shape[0] + original_shape[1]) + k) / (
-            original_shape[0] * original_shape[1])
-    axes[1].set_title("compression ratio={:.2f}".format(compression_ratio) + "%")
-    axes[1].imshow(reconst_img, cmap='gray')
-    axes[1].axis('off')
-    fig.tight_layout()
-
-
-def compute_k_max(img_name):
-    """
-    utility function for calculating max value of the slider range
-  """
-    img = gray_images[img_name]
-    m, n = img.shape
-    return m * n / (m + n + 1)
-
-
-# set up the widgets
-list_widget = widgets.Dropdown(options=list(gray_images.keys()))
-int_slider_widget = widgets.IntSlider(min=1, max=compute_k_max('cat'))
-
-
-def update_k_max(*args):
-    img_name = list_widget.value
-    int_slider_widget.max = compute_k_max(img_name)
-
-
-list_widget.observe(update_k_max, 'value')
-
-interact(compress_show_gray_images, img_name=list_widget, k=int_slider_widget)
-
-mc_widget = interactive(mc_pi, ntrials=(1, 100000, 10))
-mc_widget
-