Various improvements on Beam

Fix bug on Beam.distance_between_bunches for the last bunch when the first bunches are empty.
Change Beam.distance_between_bunches to compute the array only at initalization with Beam.update_distance_between_bunches
Beam.mpi_share_distributions can now share all bunch dimensions.

tracking/particles.py

@@ -460,6 +460,8 @@ class Beam:
     mpi_gather()
         Gather beam, all bunches of the different processors are sent to 
         all processors. Rather slow
+    mpi_share_distributions()
+        Compute the bunch profile and share it between the different bunches.
     mpi_close()
         Call mpi_gather and switch off MPI parallelisation
     plot(var, option=None)
@@ -507,12 +509,22 @@ class Beam:
     
     @property
     def distance_between_bunches(self):
+        """Return an array which contains the distance to the next bunch in 
+        units of the RF period (ring.T1)"""
+        return self._distance_between_bunches
+    
+    def update_distance_between_bunches(self):
+        """Update the distance_between_bunches array"""
         filling_pattern = self.filling_pattern
         distance = np.zeros(filling_pattern.shape)
+        last_value = 0
+        
+        # All bunches
         for index, value in enumerate(filling_pattern):
             if value == False:
                 pass
             elif value == True:
+                last_value = index
                 count = 1
                 for value2 in filling_pattern[index+1:]:
                     if value2 == False:
@@ -520,7 +532,17 @@ class Beam:
                     elif value2 == True:
                         break
                 distance[index] = count
-        return distance
+        
+        # Last bunch case
+        count2 = 0
+        for index2, value2 in enumerate(filling_pattern):
+            if value2 == True:
+                break
+            if value2 == False:
+                count2 += 1
+        distance[last_value] += count2
+        
+        self._distance_between_bunches =  distance
         
     def init_beam(self, filling_pattern, current_per_bunch=1e-3, 
                   mp_per_bunch=1e3, track_alive=True):
@@ -574,6 +596,7 @@ class Beam:
                 
         self.bunch_list = bunch_list
         self.update_filling_pattern()
+        self.update_distance_between_bunches()
         
         for bunch in self.not_empty:
             bunch.init_gaussian()
@@ -676,28 +699,46 @@ class Beam:
         self.mpi_switch = False
         self.mpi = None
         
-    def mpi_share_distributions(self):
-        """Share distribution between bunches"""
+    def mpi_share_distributions(self, dimensions="tau", n_bins=75):
+        """
+        Compute the bunch profile and share it between the different bunches.
+
+        Parameters
+        ----------
+        dimension : str or list of str, optional
+            Dimensions in which the binning is done. The default is "tau".
+        n_bin : int or list of int, optional
+            Number of bins. The default is 75.
+
+        """
         
         if(self.mpi_switch == False):
             print("Error, mpi is not initialised.")
-        
+            
+        if isinstance(dimensions, str):
+            dimensions = [dimensions]
+            
+        if isinstance(n_bins, int):
+            n_bins = np.ones((len(dimensions),), dtype=int)*n_bins
+            
         bunch = self[self.mpi.bunch_num]
-        bins, sorted_index, profile, center = bunch.binning(n_bin=75)
-        
-        self.mpi.bins_all = np.empty((len(self), len(bins)), dtype=np.float64)
-        self.mpi.comm.Allgather([bins,  MPI.DOUBLE], [self.mpi.bins_all, MPI.DOUBLE])
-        
-        self.mpi.center_all = np.empty((len(self), len(center)), dtype=np.float64)
-        self.mpi.comm.Allgather([center,  MPI.DOUBLE], [self.mpi.center_all, MPI.DOUBLE])
-        
-        self.mpi.profile_all = np.empty((len(self), len(profile)), dtype=np.int64)
-        self.mpi.comm.Allgather([profile,  MPI.INT64_T], [self.mpi.profile_all, MPI.INT64_T])
         
         charge_per_mp_all = self.mpi.comm.allgather(bunch.charge_per_mp)
         self.mpi.charge_per_mp_all = charge_per_mp_all
-        
-        self.mpi.sorted_index = sorted_index
+            
+        for i in range(len(dimensions)):
+            
+            dim = dimensions[i]
+            n_bin = n_bins[i]
+            bins, sorted_index, profile, center = bunch.binning(dimension=dim, n_bin=n_bin)
+            
+            self.mpi.__setattr__(dim + "_center", np.empty((len(self), len(center)), dtype=np.float64))
+            self.mpi.comm.Allgather([center,  MPI.DOUBLE], [self.mpi.__getattribute__(dim + "_center"), MPI.DOUBLE])
+            
+            self.mpi.__setattr__(dim + "_profile", np.empty((len(self), len(profile)), dtype=np.int64))
+            self.mpi.comm.Allgather([center,  MPI.INT64_T], [self.mpi.__getattribute__(dim + "_profile"), MPI.INT64_T])
+            
+            self.mpi.__setattr__(dim + "_sorted_index", sorted_index)
         
     def plot(self, var, option=None):
         """