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draw.py

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+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import BoundaryNorm
+from matplotlib.ticker import MaxNLocator
+import numpy as np
+
+
+# magins
+min_val = 0.2
+max_val = 0.6
+
+max_cycles = 500
+details = 500
+
+# make these smaller to increase the resolution
+delta = max_val - min_val
+# dx, dy = 0.005, 0.005
+dx, dy = delta/details, delta/details
+
+# generate 2 2d grids for the x & y bounds
+y, x = np.mgrid[slice(min_val, max_val + dy, dy),
+                slice(min_val, max_val + dx, dx)]
+y *= -1
+# x = np.arange(0, 5, 0.5)
+# print("Printing x values:")
+# print(x)
+# print(len(x))
+# print("Printing y values:")
+# print(y)
+# print(len(x))
+
+# Function
+# z = np.sin(y) * np.sin(x)
+z = x+y
+
+# for n in range(len(x)):
+#     for k in range(len(y)): 
+#         for a in range(max_cycles):
+#             temp = 
+#             z[[k],[n]] = a 
+#             # print(k*n)
+
+n = 0
+k = 0
+a = 0
+Zr= 0
+Zi= 0
+while n < len(x):
+    while k < len(y):
+        # print(k)
+        while a < max_cycles:
+            tempZr = Zr**2 - Zi**2
+            tempZi = 2 * Zr * Zi
+            temp = tempZr**2 + tempZi**2
+            if temp >= 4:
+                break
+            z[[k],[n]] = a 
+            a += 1 
+            Zr = tempZr + x[[k],[n]]
+            Zi = tempZi + y[[k],[n]] 
+        k += 1
+        a = 0
+        Zr= 0
+        Zi= 0
+    n += 1
+    k = 0
+
+# print("Printing z values:")
+# print(z)
+# z[[k],[n]] = 0
+# print(z)
+# print(len(z))
+
+# x and y are bounds, so z should be the value *inside* those bounds.
+# Therefore, remove the last value from the z array.
+z = z[:-1, :-1]
+levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())
+# print(len(z))
+# print(z)
+# print(z.min() , z.max())
+# print(levels)
+
+# pick the desired colormap, sensible levels, and define a normalization
+# instance which takes data values and translates those into levels.
+
+fig, ax = plt.subplots()
+
+cmap = plt.get_cmap('PiYG')
+norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
+# print(cmap)
+# print(norm)
+
+# im = ax.pcolor(x, y, z, cmap=cmap, norm=norm)
+
+# contours are *point* based plots, so convert our bound into point
+# centers
+
+im = ax.contourf(x[:-1, :-1] + dx/2.,
+                  y[:-1, :-1] + dy/2., z, cmap=cmap, levels=levels)
+
+fig.colorbar(im, ax=ax)
+
+#fig = plt.figure()  # an empty figure with no axes
+#fig.suptitle('Title of Figure')  # Add a title so we know which it is
+
+plt.xlabel('real')
+plt.ylabel('imaginary')
+
+plt.title("Manderbrot set")
+
+# plt.legend()
+
+plt.show()
+