diff --git a/lib/boxcount.py b/lib/boxcount.py
index 89557ad..1450e30 100644
--- a/lib/boxcount.py
+++ b/lib/boxcount.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pandas
 import pandas as pd
 from PIL import Image
 import matplotlib.pyplot as plt
@@ -26,12 +27,33 @@ def blockshaped(data, square_size):
                                                                                                 square_size,
                                                                                                 square_size))
 
+def bc(df):
+    max_actual_radius = max(df.x.abs().max(), df.y.abs().max())
+
+    fb = []
+    dims = []
+
+    for i in range(1, max_actual_radius * 2):
+        box_width = (max_actual_radius * 2) / i
+        total = i ** 2
+
+        filled = pd.DataFrame(data={
+            'xi': np.floor_divide(df.x, box_width),
+            'yi': np.floor_divide(df.y, box_width),
+        }).drop_duplicates().shape[0]
+
+        fb.append(filled)
+        dims.append(box_width)
+
+    out = pd.DataFrame({'N': fb, 'w': dims})
+
+    return out[out.N != out.N.min()]
+
 
 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
@@ -39,16 +61,12 @@ 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