From b54b1fee74dfd639fbc0e1c6c6a4d6e000e9de34 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
 <laura.kuehle@uni-duesseldorf.de>
Date: Wed, 24 Nov 2021 15:22:45 +0100
Subject: [PATCH] Improved verbose output.

---
 ANN_Data_Generator.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 3ecc008..63f37fc 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -5,7 +5,7 @@
 TODO: Improve '_generate_cell_data'
 TODO: Extract normalization (Combine smooth and troubled before normalizing) -> Done
 TODO: Adapt code to generate both normalized and non-normalized data -> Done
-TODO: Improve verbose output
+TODO: Improve verbose output -> Done
 TODO: Change order of methods -> Done
 
 """
@@ -15,6 +15,7 @@ import os
 
 import Initial_Condition
 import DG_Approximation
+import timeit
 
 
 class TrainingDataGenerator(object):
@@ -46,11 +47,14 @@ class TrainingDataGenerator(object):
             os.makedirs(self._data_dir)
 
     def build_training_data(self, num_samples):
-        print('Calculating training data...')
+        tic = timeit.default_timer()
+        print('Calculating training data...\n')
         data_dict = self._calculate_data_set(num_samples)
         print('Finished calculating training data!')
 
         self._save_data(data_dict)
+        toc = timeit.default_timer()
+        print('Total runtime:', toc-tic)
         return data_dict
 
     def _calculate_data_set(self, num_samples):
@@ -78,6 +82,11 @@ class TrainingDataGenerator(object):
                 'normalized_input': norm_input_matrix}
 
     def _generate_cell_data(self, num_samples, initial_conditions, is_smooth):
+        troubled_indicator = 'without' if is_smooth else 'with'
+        print('Calculating data ' + troubled_indicator + ' troubled cells...')
+        print('Samples to complete:', num_samples)
+        tic = timeit.default_timer()
+
         num_function_samples = num_samples//len(initial_conditions)
         function_id = 0
         input_data = np.zeros((num_samples, 5))
@@ -121,6 +130,10 @@ class TrainingDataGenerator(object):
             if count % 100 == 0:
                 print(str(count) + ' samples completed.')
 
+        toc = timeit.default_timer()
+        print('Finished calculating data ' + troubled_indicator + ' troubled cells!')
+        print('Calculation time:', toc-tic, '\n')
+
         # Shuffle input data
         order = np.random.permutation(num_samples)
         input_data = input_data[order]
@@ -162,6 +175,7 @@ class TrainingDataGenerator(object):
         return normalized_input_data
 
     def _save_data(self, data):
+        print('Saving training data.')
         for key in data.keys():
             name = self._data_dir + '/' + key + '_data.npy'
             np.save(name, data[key])
-- 
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