Added Barnsley fern in Coconut (+ .editorconfig for Coconut)

.editorconfig

@@ -172,3 +172,6 @@ indent_size = 4
 indent_style = space
 indent_size = 2
 
+[*.coco]
+indent_style = space
+indent_size = 4

contents/barnsley/barnsley.md

@@ -85,7 +85,7 @@ To account for this, each function is also given a probability of being chosen:
 | Function | Probability |
 | -------- | ----------- |
 | $$f_1(P) = \begin{bmatrix} 0 &0 \\ 0 &0.16 \end{bmatrix}P + \begin{bmatrix} 0 \\ 0 \end{bmatrix}$$ | 0.01 |
-| $$f_2(P) = \begin{bmatrix} 0.85 &0.04 \\ -0.04 &0.85 \end{bmatrix}P + \begin{bmatrix} 0 \\ 1.6 \end{bmatrix}$$ | 0.85 | 
+| $$f_2(P) = \begin{bmatrix} 0.85 &0.04 \\ -0.04 &0.85 \end{bmatrix}P + \begin{bmatrix} 0 \\ 1.6 \end{bmatrix}$$ | 0.85 |
 | $$f_3(P) = \begin{bmatrix} 0.2 &-0.26 \\ 0.23 &022 \end{bmatrix}P + \begin{bmatrix} 0 \\ 1.6 \end{bmatrix}$$ | 0.07 |
 | $$f_4(P) = \begin{bmatrix} -0.15 &0.28 \\ 0.26 &0.24 \end{bmatrix}P + \begin{bmatrix} 0 \\ 0.44 \end{bmatrix}$$ | 0.07 |
 
@@ -131,6 +131,9 @@ The biggest differences between the two code implementations is that the Barnsle
 [import, lang:"c"](code/c/barnsley.c)
 {% sample lang="java" %}
 [import, lang:"java"](code/java/Barnsley.java)
+{% sample lang="coco" %}
+[import, lang:"coconut"](code/julia/barnsley.coco)
+
 {% endmethod %}
 
 ### Bibliography

contents/barnsley/code/coconut/barnsley.coco

@@ -0,0 +1,44 @@
+from random import choices
+import numpy as np
+
+data Point(x=0, y=0):
+    def __rmatmul__(self, mat: np.array):
+        point_array = np.array([self.x, self.y, 1])
+        x, y, *_ = tuple(*(mat @ point_array))
+        return Point(x, y)
+
+
+def chaos_game(initial_location is Point, hutchinson_op, probabilities):
+    point = initial_location
+    while True:
+        yield (point := choices(hutchinson_op, probabilities) @ point)
+
+barnsley_hutchinson = [
+    np.array([
+        [0., 0., 0.],
+        [0., 0.16, 0.],
+        [0., 0., 1.],
+    ]),
+    np.array([
+        [0.85, 0.04, 0.],
+        [-0.04, 0.85, 1.6],
+        [0., 0., 1.],
+    ]),
+    np.array([
+        [0.2, -0.26, 0.],
+        [0.23, 0.22, 1.6],
+        [0., 0., 1.],
+    ]),
+    np.array([
+        [-0.15, 0.28, 0.],
+        [0.26, 0.24, 0.44],
+        [0., 0., 1.],
+    ]),
+]
+
+barnsley_probabilities = [0.01, 0.85, 0.07, 0.07]
+
+if __name__ == '__main__':
+    output_gen = chaos_game(Point(0, 0), barnsley_hutchinson, barnsley_probabilities)
+    output_points = np.array([*output_gen$[:10000]])
+    np.savetxt("out.dat", output_points)