first import

snp_landscapes.py

@@ -0,0 +1,68 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sho import *
+
+def yonly_cover_sum(sol, domain_width, sensor_range, fixed_x = (10,30)):
+    """Compute the coverage quality of the given vector."""
+    domain = np.zeros((domain_width,domain_width))
+    sensors = [ (fixed_x[0], sol[0]), (fixed_x[1], sol[1]) ]
+    return np.sum(pb.coverage(domain, sensors, sensor_range))
+
+
+if __name__ == "__main__":
+
+    d = 2
+    w = 40
+    n = 2
+    r = 0.3 * w
+
+    # Common termination and checkpointing.
+    history = []
+    iters = make.iter(
+                iters.several,
+                agains = [
+                    make.iter(iters.max,
+                        nb_it = 100),
+                    make.iter(iters.log,
+                        fmt="\r{it} {val}"),
+                    make.iter(iters.history,
+                        history = history)
+                ]
+            )
+
+    x0,x1 = 0.25*w, 0.75*w
+
+    val,sol = algo.greedy(
+            make.func(yonly_cover_sum,
+                domain_width = w,
+                sensor_range = r,
+                fixed_x = (x0,x1) ),
+            make.init(num.rand,
+                dim = 2, # Two sensors moving along y axis.
+                scale = w),
+            make.neig(num.neighb_square,
+                scale = 0.1 * w),
+            iters
+        )
+    sensors = [ (int(x0), int(round(sol[0]))), (int(x1), int(round(sol[1]))) ]
+
+    print("\n{} : {}".format(val,sensors))
+
+    shape=(w,w)
+    fig = plt.figure()
+    ax1 = fig.add_subplot(121, projection='3d')
+    ax2 = fig.add_subplot(122)
+
+    f = make.func(yonly_cover_sum,
+                    domain_width = w,
+                    sensor_range = r)
+    plot.surface(ax1, shape, f)
+    plot.path(ax1, shape, history)
+
+    domain = np.zeros(shape)
+    domain = pb.coverage(domain, sensors, r)
+    domain = plot.highlight_sensors(domain, sensors)
+    ax2.imshow(domain)
+
+    plt.show()