initial file

test/unitcube/results/create_unitcube_chart.py

@@ -11,6 +11,7 @@ def p(data, width=14):
 
 
 filename = 'unitcube128.output'
+filename = 'unitcube128.output.NEW'
 
 f = pd.read_csv(filename, sep=';')
 
@@ -20,10 +21,16 @@ f['swap_mode'].replace(swap_di, inplace=True)
 
 q = f[['num_iterations','swap_mode','ave_halo_t_pi']]
 ni = 100
-q_isend_irecv = q[(q.swap_mode=='isend/irecv') & (q.num_iterations > ni)]
-q_persistent  = q[(q.swap_mode=='persistent')  & (q.num_iterations > ni)]
-q_alltoallv   = q[(q.swap_mode=='alltoallv')   & (q.num_iterations > ni)]
-q_ialltoallv  = q[(q.swap_mode=='ialltoallv')  & (q.num_iterations > ni)]
+q_isend_irecv = q[(q.swap_mode=='isend/irecv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+q_persistent  = q[(q.swap_mode=='persistent')  & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+q_alltoallv   = q[(q.swap_mode=='alltoallv')   & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+q_ialltoallv  = q[(q.swap_mode=='ialltoallv')  & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+
+
+w1 = q[(q.swap_mode=='isend/irecv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+w2 = q[(q.swap_mode=='persistent')  & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+w3 = q[(q.swap_mode=='alltoallv')   & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
+w4 = q[(q.swap_mode=='ialltoallv')  & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
 
 isend_irecv_marker    = 'gs-'
 persistent_col_marker = 'mo-'
@@ -55,14 +62,15 @@ plt.legend()
 plt.title(ave_halo_title)
 plt.xlabel(x_axis_label)
 plt.ylabel(y_axis_label)
+plt.axis(ymax=200)
 # plt.axis(ymin=y_axis_min, ymax=y_axis_max)
 
 # iterations below
 ni = 101
-q_isend_irecv = q[(q.swap_mode=='isend/irecv') & (q.num_iterations < ni)]
-q_persistent  = q[(q.swap_mode=='persistent')  & (q.num_iterations < ni)]
-q_alltoallv   = q[(q.swap_mode=='alltoallv')   & (q.num_iterations < ni)]
-q_ialltoallv  = q[(q.swap_mode=='ialltoallv')  & (q.num_iterations < ni)]
+q_isend_irecv = q[(q.swap_mode=='isend/irecv') & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
+q_persistent  = q[(q.swap_mode=='persistent')  & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
+q_alltoallv   = q[(q.swap_mode=='alltoallv')   & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
+q_ialltoallv  = q[(q.swap_mode=='ialltoallv')  & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
 plt.figure()
 plt.plot(q_isend_irecv.num_iterations, q_isend_irecv.ave_halo_t_pi, isend_irecv_marker, label=isend_irecv_label)
 plt.plot(q_persistent.num_iterations, q_persistent.ave_halo_t_pi, persistent_col_marker, label=persistent_col_label)
@@ -74,17 +82,18 @@ plt.xlabel(x_axis_label)
 plt.ylabel(y_axis_label)
 # plt.axis(ymin=y_axis_min)
 
+
 plt.show()
 
-w = f[f.np==16]
+# w = f[f.np==16]
 
 # q = f.loc[f.np==16, 'total_time':'ave_request_create_t']
 # w = f.loc[f.np==16, 'ave_alltoall_comm_t':'ave_request_create_t']
-w  = f.loc[f.np==16, 'num_iterations':'ave_halo_t_pi']
-w2 = f.loc[f.np==16, 'ave_neighbors':'min_rcv']
+e  = f.loc[f.np==16, 'num_iterations':'ave_halo_t_pi']
+e2 = f.loc[f.np==16, 'ave_neighbors':'min_rcv']
 
 # p(w)
-p(w,10)
-p(w2,6)
+p(e,10)
+p(e2,6)