Added test LPM pattern for testing path curving. Updated git ignore to ignore .pyc

.gitignore

@@ -11,3 +11,4 @@ BW_LFandInac_Zelev.mat
 e_x_y_data.pkl
 .pytest_cache
 z__test__*.pyc
+*.pyc

mrmh_model/montecarlo.py

@@ -228,17 +228,36 @@ class MonteCarlo(params.Default):
             self.p[dist_mat <= self.params['ring_mobi'][3]] += 0.20 # containment ring
             # print(self.p)
             self.p = ndimage.gaussian_filter(self.p, sigma=8) # to make betta heatmap
+        #Makes heatmap favoring a corner
         elif self.params['lp_model'] == 'trust':    #test for trust
             self.p = np.zeros((self._x_shape, self._y_shape), dtype=float)
             for r in range(0, self._x_shape):
                 for c in range(0, self._y_shape):
                     self.p[r,c] = np.float(r+c) / np.float(self._x_shape+self._y_shape)
+        #Makes heatmap with horizontal stripes, paths should bend to them
+        elif self.params['lp_model'] == 'stripes':
+            self.p = np.zeros((self._x_shape, self._y_shape), dtype=float)
+            line_width = 5
+            line_focus = np.int(self._x_shape / (self.params['num_robots']))
+            for r in range(0, self._x_shape):
+                for c in range(0, self._y_shape):
+                    #ik there's a better way to do this...
+                    if(r > line_focus-line_width and r <= line_focus+line_width):
+                        self.p[r,c] = 1 - np.abs(line_focus-r)/line_width
+                    elif(r > 2*line_focus-line_width and r <= 2*line_focus+line_width):
+                        self.p[r,c] = 1 - np.abs(2*line_focus-r)/line_width
+                    elif(r > 3*line_focus-line_width and r <= 3*line_focus+line_width):
+                        self.p[r,c] = 1 - np.abs(3*line_focus-r)/line_width
+
+        #No heatmap loaded
         else:
             self.p = np.zeros((self._x_shape, self._y_shape), dtype=float)
 
+
         #Show LP Heatmap
         show = False
         if(show):
+            print('Environment shape = %d x %d' % (self._x_shape,self._y_shape))
             plt.imshow(self.p)
             plt.title('Selected heatmap')
             plt.show()