SD glitch fixed.

demos/demo_excaliwir.py

@@ -167,8 +167,9 @@ class problem(object):
             power = numerator / denominator
 
             ## check convergence
-            dtau = np.log(power / self.S.get_power())
+            dtau = log(power / self.S.get_power(), base=self.S.get_power())
             iterating = (np.max(np.abs(dtau)) > 2E-2)
+            print max(np.abs(dtau))
 
             ## update signal covariance
             self.S.set_power(power, bare=False) ## auto-updates D

nifty_core.py

@@ -514,7 +514,7 @@ class _about(object): ## nifty support class for global settings
 
         """
         ## version
-        self._version = "0.7.6"
+        self._version = "0.7.7"
 
         ## switches and notifications
         self._errors = notification(default=True,ccode=notification._code)

nifty_tools.py

@@ -1035,11 +1035,14 @@ class steepest_descent(object):
             raise TypeError(about._errors.cstring("ERROR: invalid input."))
         self.x = x0
 
-        clevel = int(clevel)
+        clevel = max(1,int(clevel))
         limii = int(limii)
 
         E,g = self.eggs(self.x) ## energy and gradient
         norm = g.norm() ## gradient norm
+        if(norm==0):
+            self.note.cprint("\niteration : 00000000   alpha = 0.0E+00   delta = 0.0E+00\n... done.")
+            return self.x,clevel+2
 
         convergence = 0
         ii = 1
@@ -1060,6 +1063,10 @@ class steepest_descent(object):
             if(ii==limii):
                 self.note.cprint("\n... quit.")
                 break
+            elif(delta==0):
+                convergence = clevel+2
+                self.note.cprint("   convergence level : %u\n... done."%convergence)
+                break
             elif(delta<tol):
                 convergence += 1
                 self.note.cflush("   convergence level : %u"%convergence)