--- a
+++ b/tester_GUDB.py
@@ -0,0 +1,108 @@
+import numpy as np
+import pandas as pd
+import _tester_utils
+import pathlib
+from ecgdetectors import Detectors
+
+current_dir = pathlib.Path(__file__).resolve()
+data_dir = str(pathlib.Path(current_dir).parents[1]/'dataset_716'/'experiment_data')
+code_dir = str(pathlib.Path(current_dir).parents[1]/'dataset_716'/'example_code')
+
+import sys
+sys.path.insert(0, code_dir)
+from ecg_gla_database import Ecg
+
+
+class GUDB_test:
+    """
+    This class benchmarks detectors against the GU database.
+    You need to download both the GU database from: http://researchdata.gla.ac.uk/716/
+    and needs to be placed below this directory: "../dataset_716".
+    """
+    
+    def single_classifier_test(self, detector, tolerance=0, config="chest_strap"):
+
+        max_delay_in_samples = 250 / 3
+
+        total_subjects = Ecg.total_subjects
+
+        results = np.zeros((total_subjects, (4*len(Ecg.experiments))+1), dtype=int)
+
+        for subject_number in range(0, total_subjects):
+            progress = int(subject_number/float(total_subjects)*100.0)
+            print("GUDB "+config+" progress: %i%%" % progress)
+
+            results[subject_number, 0] = subject_number
+            exp_counter = 1
+            for experiment in Ecg.experiments:
+                
+                ecg_class = Ecg(data_dir, subject_number, experiment)
+
+                anno_exists = False
+                if config=="chest_strap" and ecg_class.anno_cs_exists:
+                    unfiltered_ecg = ecg_class.cs_V2_V1                   
+                    anno = ecg_class.anno_cs
+                    anno_exists = True
+                elif config=="loose_cables" and ecg_class.anno_cables_exists:
+                    unfiltered_ecg = ecg_class.einthoven_II 
+                    anno = ecg_class.anno_cables
+                    anno_exists = True
+                elif config!="chest_strap" and config!="loose_cables":
+                    raise RuntimeError("Config argument must be chest_strap or loose_cables!")
+                    return results
+
+                if anno_exists:                  
+
+                    r_peaks = detector(unfiltered_ecg)
+
+                    delay = _tester_utils.calcMedianDelay(r_peaks, unfiltered_ecg, max_delay_in_samples)
+                    print("delay = ",delay)
+
+                    # there must be a delay in all cases so anything below is a bad sign
+                    if delay > 1:
+
+                        TP, FP, FN = _tester_utils.evaluate_detector(r_peaks, anno, delay, tol=tolerance)
+                        TN = len(unfiltered_ecg)-(TP+FP+FN)
+
+                        results[subject_number, exp_counter] = TP
+                        results[subject_number, exp_counter+1] = FP
+                        results[subject_number, exp_counter+2] = FN
+                        results[subject_number, exp_counter+3] = TN
+
+                exp_counter = exp_counter+4
+
+        return results
+
+
+    def classifer_test_all(self, tolerance=0, config="chest_strap"):
+
+        det_names = ['two_average', 'matched_filter', 'swt', 'engzee', 'christov', 'hamilton', 'pan_tompkins']
+        output_names = ['TP', 'FP', 'FN', 'TN']
+
+        total_results = np.zeros((Ecg.total_subjects, 4*len(Ecg.experiments)*len(det_names)), dtype=int)
+
+        counter = 0
+        for det_name in det_names:
+
+            print('\n'+config+" "+det_name+":")
+
+            result = self.single_classifier_test(_tester_utils.det_from_name(det_name, 250), tolerance=tolerance, config=config)
+            result = result[:, 1:]
+
+            total_results[:, counter:counter+(4*len(Ecg.experiments))] = result
+
+            counter = counter+(4*len(Ecg.experiments))        
+
+        index_labels = np.arange(Ecg.total_subjects)
+        col_labels = []
+
+        for det_name in det_names:
+            for experiment_name in Ecg.experiments:
+                for output_name in output_names:
+                    label = det_name+" "+experiment_name+" "+output_name
+                    col_labels.append(label)
+
+        total_results_pd = pd.DataFrame(total_results, index_labels, col_labels, dtype=int)            
+        total_results_pd.to_csv('results_GUDB_'+config+'.csv', sep=',')
+
+        return total_results_pd