Merge remote-tracking branch 'origin/master'

Author: ima18h

README.md

@@ -17,6 +17,7 @@ Jeg velger å starte med oppgave 2 også løse 1 med den generelle.
 2d) vet ikke hvordan strukturere data på bra måte og jeg jobber med immutable tuples
 mye bruk av np.asarray() på lister fordi jeg er usikker med å velge dtype manuelt
 2f) jeg må velge farger for kantene som kan være mange. jeg bare velger tilfeldige farger. 
+3) jeg bruker np.dot som kanskje ikke er rask nok. jeg laget bare 5000 punkter
 
 
 ## Authors

chaos_game.py

@@ -77,7 +77,7 @@ def _starting_point(self):
             spoint[1] += w[i] * self._corners[i][1]
         return spoint
 
-    def iterate(self, steps: int = 10, discard: int = 5):
+    def iterate(self, steps: int = 10000, discard: int = 100):
         """Discards the first discard points.
         The third element in each tuple is the random chosen corner index
         be aware that, currently, index is a float"""
@@ -131,7 +131,7 @@ def gradient_color(self):
             colors = iter([plt.cm.tab20(i) for i in range(20)])
             cc = []
             i = 0
-            # TODO: this can only create a limited number of colors then repeat
+            # TODO: this can only create a limited number of colors then repeat set pattern
             while len(cc) < self._corners.shape[0]:
                 cc.append(next(colors))
                 i += 1

fern.py

@@ -0,0 +1,41 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pathlib
+
+
+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._ef = np.asarray([[e], [f]])
+
+    def __call__(self, x, y):
+        # np.dot maybe introducing overhead?
+        return np.dot(self._abcd, [[x], [y]]) + self._ef
+
+
+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]
+
+
+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])
+
+plt.savefig(pathlib.Path(__file__).parent.resolve().__str__() + '\\figures\\' + "barnsley_fern",
+            dpi=400, s=0.1)
+

figures/barnsley_fern.png

@@ -0,0 +1,6 @@
+import chaos_game as cg
+
+
+triangle = cg.ChaosGame()
+triangle.iterate(20000, 100)
+triangle.show(True)