feat: Optimize

it is so slow that 5000 is about max loops/points. this optimization makes it calculate 50000 points with ease, and much more if needed.

Author: ima18h

fern.py

@@ -5,37 +5,42 @@
 
 class AffineTransform:
     def __init__(self, a=0., b=0., c=0., d=0., e=0., f=0.):
-        self._abcd = self._ef = np.asarray([[a, b], [c, d]])
+        self._abcd = np.asarray([[a, b], [c, d]])
         self._ef = np.asarray([[e], [f]])
 
     def __call__(self, x, y):
         # np.dot maybe introducing overhead?
+        # probably not, it's much faster now after optimizing, by parametrizing the scatter plot
         return np.dot(self._abcd, [[x], [y]]) + self._ef
 
 
-f1 = AffineTransform(0,0,0,0.16,0,0)
+f1 = AffineTransform(0, 0, 0, 0.16, 0, 0)
 f2 = AffineTransform(0.85, 0.04, -0.04, 0.85, 0, 1.60)
 f3 = AffineTransform(0.20, -0.26, 0.23, 0.22, 0, 1.6)
 f4 = AffineTransform(-0.15, 0.28, 0.26, 0.24, 0, 0.44)
 
 functions = [f1, f2, f3, f4]
 p_cumulative = [0.01, 0.86, 0.92, 1.00]
+
+
 def select(funcs, probcum):
     r = np.random.random()
     for j, p in enumerate(probcum):
         if r < p:
             return funcs[j]
 
 
+#--------plotting and saving pic
 plt.axis("equal")
 plt.axis('off')
-x = [[0], [0]]
-plt.scatter(0, 0)
-# TODO: optimize
-for i in range(5000):
-    x = select(functions, p_cumulative)(x[0][0], x[1][0])
-    plt.scatter(x[0], x[1])
+n = 50000
+fern = np.zeros((n + 1, 2), dtype=float)
+
+for i in range(n):
+    fern[i + 1] = (select(functions, p_cumulative)(fern[i][0], fern[i][1])).reshape(2)
 
+plt.scatter(fern[:, 0], fern[:, 1], s=0.2, c="g", marker=",")
 plt.savefig(pathlib.Path(__file__).parent.resolve().__str__() + '\\figures\\' + "barnsley_fern",
-            dpi=400, s=0.1)
+            dpi=400)
 
+plt.show()