diff --git a/fractal_dimension/fancymcmullen.py b/fractal_dimension/fancymcmullen.py
new file mode 100644
index 0000000..933999f
--- /dev/null
+++ b/fractal_dimension/fancymcmullen.py
@@ -0,0 +1,135 @@
+from node import Node
+import math
+import numpy as np
+import time
+from scipy.sparse import csr_matrix
+from functools import reduce
+
+def functions(n, z):
+    if n == 0:
+        return np.conj((2-math.sqrt(3))**2 / z)
+    elif n ==1:
+        return 2j + np.conj(3 / (z - 2j))
+    elif n == 2:
+        return -1 * math.sqrt(3) - 1j + np.conj(3 / (z + math.sqrt(3) + 1j))
+    elif n == 3:
+        return math.sqrt(3) - 1j + np.conj(3 / (z - math.sqrt(3) + 1j))
+
+def derivatives(n, z):
+    if n == 0:
+        return abs(z**2 / (2-math.sqrt(3))**2)
+    elif n == 1:
+        return abs((z-2j)**2 / 3)
+    elif n == 2:
+        return abs((z+math.sqrt(3)+1j)**2 / 3)
+    elif n == 3:
+        return abs((z-math.sqrt(3)+1j)**2 / 3)
+
+def samplePoint(word):
+    if word[-1] == 0:
+        p = 0 + 0j
+    elif word[-1] == 1:
+        p = 0 + 0.5j
+    elif word[-1] == 2:
+        p = -1 * math.sqrt(3) / 4 - 0.25j
+    elif word[-1] == 3:
+        p = math.sqrt(3) / 4 - 0.25j
+    for letter in word[-2::-1]:
+        p = functions(letter, p)
+    return p
+
+def sampleValue(word):
+    return derivatives(word[0], samplePoint(word))
+
+def generateTree(words, dc):
+    generators = [np.array([[-1,2,2,2],[0,1,0,0],[0,0,1,0],[0,0,0,1]]),
+        np.array([[1,0,0,0],[2,-1,2,2],[0,0,1,0],[0,0,0,1]]),
+        np.array([[1,0,0,0],[0,1,0,0],[2,2,-1,2],[0,0,0,1]]),
+        np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[2,2,2,-1]])]
+    root = Node([-1., 2. + math.sqrt(3), 2. + math.sqrt(3), 2. + math.sqrt(3)], [], words, False)
+    current_leaves = [root]
+    while True:
+        new_leaves = []
+        for leaf in current_leaves:
+            new_leaves += leaf.next_generation(words, dc, generators)
+        if current_leaves == new_leaves:
+            break
+        else:
+            current_leaves = new_leaves
+    for i,leaf in enumerate(current_leaves):
+        words[str(leaf.word)] = i
+    print(len(current_leaves), "partitions")
+    return current_leaves
+
+def constructMatrix(words, dc):
+    leave = generateTree(words, dc)
+    row = []
+    col = []
+    data = []
+    for i,leaf in enumerate(leave):
+        thing = words[str(leaf.word[1:])]
+        if isinstance(thing,int):
+            row.append(i)
+            col.append(thing)
+            data.append(sampleValue(leaf.word))
+        else:
+            for wor in thing.leaves():
+                row.append(i)
+                col.append(words[str(wor.word)])
+                data.append(sampleValue(leaf.word))
+    return csr_matrix((data,(row,col)),shape=(len(leave),len(leave)))
+
+def secant(x0,y0,x1,y1,z):
+    return x0 - (y0-z) * ((x1-x0)/(y1-y0))
+
+def matrixFunction(matrix,l,a):
+    matrix = matrix.power(a)
+    vec = np.ones(l)
+    previous_entry = vec[0]
+    previous_val = 0
+    current = matrix * vec
+    current_val = current[0] / previous_entry
+    count = 0
+    while count < 1000000000 and abs(current_val - previous_val) > 1e-15:
+        previous_val = current_val
+        previous_entry = current[0]
+        current = matrix * current
+        current_val = current[0] / previous_entry
+        count += 1
+    print("power method:", count)
+    return current_val
+
+def secantMethod(matrix,l,z,x1,x2,e,its):
+    k1 = x1
+    k2 = x2
+    y1 = matrixFunction(matrix,l,k1)
+    y2 = matrixFunction(matrix,l,k2)
+    #y1 = testFunction(k1)
+    #y2 = testFunction(k2)
+    count = 1
+    print(count,k1,y1)
+    while abs(y1-z)>e and count<its:
+        k3 = secant(k1,y1,k2,y2,z)
+        k1 = k2
+        y1 = y2
+        k2 = k3
+        y2 = matrixFunction(matrix,l,k2)
+        #y2 = testFunction(k2)
+        count += 1
+        print(count,k1,y1)
+
+def main():
+    start = time.time()
+
+    words = {}
+    matrix = constructMatrix(words,100)
+    #print(matrix.toarray())
+    
+    print("construction (s): %f\nconstruction (m): %f" % (time.time()-start, (time.time()-start)/60))
+    #print(csr_matrix.transpose(matrix))
+    print(csr_matrix.count_nonzero(matrix), (matrix.shape[0])*3)
+    secantMethod(matrix,matrix.shape[0],1,1.30,1.31,10**(-10),1000)
+
+    print("total (s): %f\ntotal (m): %f" % (time.time()-start, (time.time()-start)/60))
+
+main()
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diff --git a/fractal_dimension/node.py b/fractal_dimension/node.py
new file mode 100644
index 0000000..0e21a46
--- /dev/null
+++ b/fractal_dimension/node.py
@@ -0,0 +1,51 @@
+import numpy as np
+import math
+
+class Node:
+    def __init__(self, tuple, word, words, infertile):
+        self.tuple = tuple
+        self.children = []
+        self.word = word
+        self.infertile = infertile
+        words[str(word)] = self
+
+    def dc_not_too_big(self, g, generator, dual_curvature):
+        temp = np.matmul(generator, self.tuple)
+        temp = np.delete(temp,g)
+        #print(g, self.word, math.sqrt(temp[0]*temp[1] + temp[0]*temp[2] + temp[1]*temp[2]), math.sqrt(temp[0]*temp[1] + temp[0]*temp[2] + temp[1]*temp[2]) < dual_curvature)
+        return math.sqrt(temp[0]*temp[1] + temp[0]*temp[2] + temp[1]*temp[2]) < dual_curvature
+    
+    def next_generation(self, words, dual_curvature, generators):
+        if self.infertile:
+            return [self]
+        if self.children == []:
+            for g,generator in enumerate(generators):
+                if len(self.word) == 0 or g != self.word[-1]:
+                    self.children.append(Node(np.matmul(generator,self.tuple), self.word[:] + [g], words, not self.dc_not_too_big(g, generator, dual_curvature)))
+        if self.children == []:
+            return [self]
+        else:
+            return self.children
+    
+    def next(self):
+        if self.children == []:
+            return [self]
+        else:
+            return self.children
+    
+    def leaves(self):
+        current_leaves = [self]
+        while True:
+            new_leaves = []
+            for leaf in current_leaves:
+                new_leaves += leaf.next()
+            if current_leaves == new_leaves:
+                break
+            else:
+                current_leaves = new_leaves
+        return current_leaves
+    
+    def dual_curvature(self):
+        temp = self.tuple[:]
+        temp = np.delete(temp,self.word[-1])
+        return math.sqrt(temp[0]*temp[1] + temp[0]*temp[2] + temp[1]*temp[2])
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