BC work

lib/boxcount.py

@@ -0,0 +1,99 @@
+import numpy as np
+import pandas as pd
+from PIL import Image
+import matplotlib.pyplot as plt
+
+from lib import read_load, read_xy_alt
+
+
+def boxcount(data, size):
+    np.sign(blockshaped(data, 2).sum(axis=(1, 2))).sum()
+    blocked_arrays = blockshaped(data, size)
+    counter = 0
+    a = blockshaped(data, 2).sum(axis=(1, 2)).signum()
+    for i in range(len(blocked_arrays)):
+        for j in range(len(blocked_arrays[i])):
+            if (blocked_arrays[i][j].any()):
+                counter += 1
+                break
+    return counter
+
+
+def blockshaped(data, square_size):
+    h, w = data.shape
+    assert h % square_size == 0, f"Array is not evenly divisible {h} {square_size} {h % square_size}"
+    return (data.reshape(h // square_size, square_size, -1, square_size).swapaxes(1, 2).reshape(-1,
+                                                                                                square_size,
+                                                                                                square_size))
+
+
+df = read_xy_alt(
+    "/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/data-analysis/data/alpha/1.csv"
+)
+
+max_actual_radius = max(df.x.abs().max(), df.y.abs().max())
+max_radius = int(np.exp2(np.floor(np.log2(max_actual_radius)) + 1))
+data = np.zeros((max_radius * 2, max_radius * 2))
+data[df.x + max_radius, df.y + max_radius] = 1
+
+iterations = int(np.floor(np.log2(max_actual_radius)) + 1)
+sizes = 2 ** np.arange(1, iterations)
+
+fb = []
+dims = []
+
+for size in sizes:
+    filled_box = np.sign(blockshaped(data, size).sum(axis=(1, 2))).sum()
+    fb.append(filled_box)
+    dims.append(size / np.exp2(iterations))
+
+
+
+class ImageFractalDimension:
+    def __init__(self, image_name, SIZE):
+        self.SIZE = SIZE
+        image = Image.open(image_name)
+
+        assert image.size[0] == image.size[1] and image.size[
+            0] == self.SIZE, "Height and Width of the image must be equal."
+
+        image = np.asarray(image)
+        self.img_px_array = np.copy(image)
+
+        self.convertImg()
+        self.fractal_dim = self.calculate_fractal_dim()
+
+    def convertImg(self):
+        for i in range(len(self.img_px_array)):
+            for j in range(len(self.img_px_array[i])):
+                for k in range(len(self.img_px_array[i][j])):
+                    if (self.img_px_array[i][j][k] == 255):
+                        self.img_px_array[i][j][k] = 0
+                    else:
+                        self.img_px_array[i][j][k] = 1
+
+    def calculate_fractal_dim(self):
+        self.dimensions = []
+        self.filled_boxes = []
+
+        self.img_px_array = self.img_px_array[:, :, 0]
+
+        2 ** np.range(1, self.SIZE)
+
+        size = 1
+        while size != self.SIZE:
+            size *= 2
+            filled_box = self.boxcount(size)
+            self.filled_boxes.append(filled_box)
+            self.dimensions.append(size / self.SIZE)
+
+        return -np.polyfit(np.log(self.dimensions), np.log(self.filled_boxes), 1)[0]
+
+    def graph(self):
+        plt.plot(-np.log(self.dimensions), np.log(self.filled_boxes))
+        plt.title("Fractal Dimension : " + str(self.fractal_dim))
+        plt.show()
+        plt.clf()
+
+
+ImageFractalDimension("/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/data-analysis/figures/dla-eg.png", 1024)