home.md

@@ -79,75 +79,32 @@ This script
         # see which functions is first
         first_routine = functions_called_once_per_step[0]
   ```
-   
-
-    time_steps = time[(function_ids == first_routine) & (cpu_ids == nemo_proc[0])].shape[0]
-    print("Actual n_proc", nemo_proc_number, "with ", time_steps, "time steps")
-
-    # Find the index of the fastest step
-    time_step = np.ones([nemo_proc_number, time_steps])
-
-    ts_min_index = np.zeros(nemo_proc_number, dtype='int')
-    ts_max_index = np.zeros(nemo_proc_number, dtype='int')
-
-    ts_time = np.zeros([nemo_proc_number, time_steps])
-    start = clock_time.time()
-    for proc in nemo_proc:
+5. For all cpu finds the shortes timestep, then look on average which one has been the fastest
+  ```   python
+     for proc in nemo_proc:
         index = proc - nemo_proc[0]
         ts_time[index] = time[(cpu_ids == proc) & (function_ids == first_routine)]
 
-        # compute the duration of each ts
+        # Compute the duration of each ts
         ts_duration = np.diff(ts_time[index])
 
-        # find the index of the minimum ts for each proc
+        # Index of the minimum ts for each proc
         ts_min_index[index] = np.argmin(ts_duration)
 
-        # find the index of the maximum ts for each proc
-        ts_max_index[index] = np.argmax(ts_duration[1:-1]) + 1
-
     # Evaluate the most common index for best ts
     counts = np.bincount(ts_min_index)
     best_ts_index = np.argmax(counts)
 
-    counts = np.bincount(ts_max_index)
-    worst_ts_index = np.argmax(counts)
-
-    print("for finding the index of the slowest / fastest step: ", worst_ts_index, "/", best_ts_index)
-    end = clock_time.time()
-    print(end - start)
-
-    # Find the start and the end of the best step
-    best_ts_start = min(ts_time[:, best_ts_index])
-    best_ts_end = max(ts_time[:, best_ts_index + 1])
-
-    # Find the start and the end of the best step
-    worst_ts_start = min(ts_time[:, worst_ts_index])
-    worst_ts_end = max(ts_time[:, worst_ts_index + 1])
-
-    print("Worst / best time step's duration: ", worst_ts_end - worst_ts_start, best_ts_end - best_ts_start)
-
-    # Open the xml template
-    tree = ET.parse(template_path)
-
-    cutter = tree.find('cutter')
-    for tasks in tree.iter('tasks'):
-        tasks.text = str(nemo_proc_min) + "-" + str(nemo_proc_max)
-    for minimum_time in tree.iter('minimum_time'):
-        minimum_time.text = str(best_ts_start - 1000)
-    for maximum_time in tree.iter('maximum_time'):
-        maximum_time.text = str(best_ts_end + 1000)
-
-    # Create paramedir cutter file
-    tree.write('best_time_cutter.xml')
-    for minimum_time in tree.iter('minimum_time'):
-        minimum_time.text = str(worst_ts_start - 1000)
-    for maximum_time in tree.iter('maximum_time'):
-        maximum_time.text = str(worst_ts_end + 1000)
-
-    # Create paramedir cutter file
-    tree.write('worst_time_cutter.xml')
-
-    return unique_function_ids.tolist()
-
+    ```
+6. Once the index of the best time-step has been found find time begin/end times to pass to Extrae cutter
+   ```python
+      best_ts_start = min(ts_time[:, best_ts_index])
+      best_ts_end = max(ts_time[:, best_ts_index + 1])
+   ```
 
-[... to be continued]
+### Cutter template
+In the template folder an extrae xml template is stored: the value obtained with the traceCutter are inserted there and the trace is cut:
+```bash 
+  echo "start paramedir cutter"
+  time paramedir -c best_time_cutter.xml ${trace_file} -o ${trace_base_name}.best_cut.prv
+```