Minor Adjustments

studywolf_control/arms/two_link/arm_python_todorov.py

@@ -15,7 +15,7 @@
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 '''
 
-from arm2base import Arm2Base
+from .arm2base import Arm2Base
 import numpy as np
 
 class Arm(Arm2Base):

studywolf_control/arms/two_link/arm_todorov.py

@@ -15,7 +15,7 @@
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 '''
 
-from arm2base import Arm2Base
+from .arm2base import Arm2Base
 import numpy as np
 
 class Arm(Arm2Base):
@@ -48,20 +48,20 @@ def apply_torque(self, u, dt=None):
         a1 = i[0] + i[1] + m[1]*l[0]**2
         a2 = m[1]*l[0]*s[1]
         a3 = i[1]
-        I = np.array([[a1 + 2*a2*np.cos(q[1]), a3 + a2*np.cos(q[1])],
-                      [a3 + a2*np.cos(q[1]), a3]])
+        I = np.array([[a1 + 2*a2*np.cos(self.q[1]), a3 + a2*np.cos(self.q[1])],
+                      [a3 + a2*np.cos(self.q[1]), a3]])
 
         # centripital and Coriolis effects
-        C = np.array([[-dq[1] * (2 * dq[0] + dq[1])],
-                      [dq[0]]]) * a2 * np.sin(q[1])
+        C = np.array([-self.dq[1] * (2 * self.dq[0] + self.dq[1]),
+                      self.dq[0]]) * a2 * np.sin(self.q[1])
 
         # joint friction
         B = np.array([[.05, .025],
                       [.025, .05]])
 
         # calculate forward dynamics
-        ddq = np.linalg.pinv(I) * (u - C - np.dot(B, dq))
-
+        ddq = np.dot(np.linalg.pinv(I) , (u - C - np.dot(B, self.dq)))
+        
         # transfer to next time step 
         self.q += dt * self.dq
         self.dq += dt * ddq

studywolf_control/controllers/ahf.py

@@ -40,7 +40,7 @@ def __init__(self, **kwargs):
         # this code goes into the weights folder, finds the most 
         # recent trial, and loads up the weights
         files = sorted(glob.glob('controllers/weights/rnn*'))
-        print 'loading weights from %s'%files[-1]
+        print('loading weights from %s'%files[-1])
         W = np.load(files[-1])['arr_0']
         num_states = 4
         self.rnn = RNNet(shape=[num_states * 2, 32, 32, num_states, num_states], 

studywolf_control/controllers/recorder.py

@@ -32,7 +32,7 @@ def record(self, t, x):
             self.signal.append(x.copy())
 
         if self.write_out == True:
-            print 'saving'
+            print('saving')
             np.savez_compressed(
                     'data/%s/%s/%s%.3i'%(self.task, \
                             self.controller, self.name, self.count),

studywolf_control/tasks/follow_mouse.py

@@ -43,7 +43,7 @@ def Task(arm, controller_class,
     # generate control shell -----------------
     additions = []
     if force is not None:
-        print 'applying joint velocity based forcefield...'
+        print('applying joint velocity based forcefield...')
         additions.append(forcefield.Addition(scale=force))
         task = 'arm%i/forcefield'%arm.DOF
 

studywolf_control/tasks/postural.py

@@ -72,7 +72,7 @@ def Task(arm, controller_type, x_bias=0., y_bias=2., dist=.4,
     # generate control shell -----------------
     additions = []
     force_index = np.random.randint(8) if force_index is None else force_index
-    print 'applying force %i...'%force_index
+    print('applying force %i...'%force_index)
     additions.append(Addition(index=force_index))
     task = 'arm%i/postural%i'%(arm.DOF, force_index)
 

studywolf_control/tasks/random_movements.py

@@ -43,7 +43,7 @@ def Task(arm, controller_class,
     # generate control shell -----------------
     additions = []
     if force is not None:
-        print 'applying joint velocity based forcefield...'
+        print('applying joint velocity based forcefield...')
         additions.append(forcefield.Addition(scale=force))
         task = 'arm%i/forcefield'%arm.DOF