fix typo with greater/less than

Chaos/CombinedStrangeAttractors.py

@@ -39,7 +39,7 @@ def converges_with_complexity(self):
         total_unique_ratio = total_unique_points / total_points
         return False, total_unique_points, total_points, total_unique_ratio
 
-    def get_convergence_points(self, x_min=-1, x_max=1, y_min=-1, y_max=1, resolution=100, max_iterations=10000, max_abs=1e6):
+    def get_convergence_points(self, x_min=-1, x_max=1, y_min=-1, y_max=1, resolution=100, max_iterations=10000, max_abs=100):
         print("getting convergence array")
         xs = np.linspace(x_min, x_max, resolution)
         ys = np.linspace(y_min, y_max, resolution)
@@ -56,7 +56,7 @@ def get_convergence_points(self, x_min=-1, x_max=1, y_min=-1, y_max=1, resolutio
         print("done getting convergence array")
         return convergence
 
-    def plot_convergence_points(self, x_min=-1, x_max=1, y_min=-1, y_max=1, resolution=100, max_iterations=10000, max_abs=1e6):
+    def plot_convergence_points(self, x_min=-1, x_max=1, y_min=-1, y_max=1, resolution=100, max_iterations=10000, max_abs=100):
         convergence_array = self.get_convergence_points(x_min, x_max, y_min, y_max, resolution, max_iterations, max_abs)
         plt.imshow(convergence_array)
         plt.colorbar()
@@ -77,7 +77,7 @@ def find_convergent_initial_condition(self, min_index, max_index):
             x0 = r * np.cos(theta)
             y0 = r * np.sin(theta)
             # x0, y0 = np.random.uniform(-1, 1, (2,))
-            trajectory = get_late_trajectory(self, x0, y0, min_index=min_index, max_index=max_index, max_abs=1e6)
+            trajectory = get_late_trajectory(self, x0, y0, min_index=min_index, max_index=max_index, max_abs=100)
             if trajectory_diverges(trajectory):
                 continue
             else:
@@ -199,21 +199,20 @@ def __repr__(self):
         return f"<Random choice mapping from the sub-mappings:\n\t{str_of_mapping_reprs}\n>"
 
 
-def get_late_trajectory(mapping, x0, y0, min_index, max_index, max_abs=1e9):
+def get_late_trajectory(mapping, x0, y0, min_index, max_index, max_abs=100):
     assert max_index > min_index
     res = []
     x,y = x0,y0
-    BIG = max_abs  # if it gets to this distance then assume it diverges
     for i in range(max_index):
         x,y = mapping(x,y)
         if i < min_index:
             # don't store these, let it settle into attractor state, if any
             continue
         else:
             res.append((x,y))
-        if not np.isfinite(x) or not np.isfinite(y) or abs(x) > BIG or abs(y) > BIG:
-            # assume it will diverge forever if it ever reaches inf/NaN
-            # just put a nonfinite pair at the end and return
+        if not np.isfinite(x) or not np.isfinite(y) or abs(x) > max_abs or abs(y) > max_abs:
+            # assume it will diverge forever if it ever reaches inf/NaN or max_abs
+            # just put a nonfinite pair at the end and return without checking later iterations
             res.append((np.inf, np.inf))
             return res
     return res
@@ -303,7 +302,7 @@ def run_mixing_experiment(mapping_names=None):
 
         divergence_count = 0
         plot_success = False
-        while not (plot_success or divergence_count < 5):
+        while not (plot_success or divergence_count > 5):
             try:
                 mapping.plot_attractor()
                 plot_success = True  # this will break while loop
@@ -410,7 +409,7 @@ class MappingRecord:
     # plt.show()
 
     run_mixing_experiment()
-    # run_mixing_experiment(["W", "X"])
+    # run_mixing_experiment(["AL", "AS"])
     # while True:
     #     run_perturbation_experiment(perturbation_nelda=random.choice([1, 1.5, 2, 2.5, 3]))
 
@@ -429,6 +428,6 @@ class MappingRecord:
     # - AS + Z (zigzag spiral)
     # - AS + AK (looks like letters in a curvy script)
     # - X + W (messy dusty danish)
-
+    # - AS + AL (pentagon made of line bundles)