Update msynth.py

msynth.py

@@ -311,7 +311,33 @@ class msynth(datasets.GeneratorBasedBuilder):
         else:
             return "Not available for this raw file"
 
-
+    def sort_file_paths(self, file_paths):
+        digit_numbers = []
+        for file_path in file_paths:
+            for word in _DENSITY:
+                if word in file_path:
+                    if self.config.name == "device-data":
+                        pattern = rf"{word}.(\d+\.)(\d+)"
+                    elif self.config.name == "segmentation-mask":
+                        pattern = rf"{word}.(\d+)"
+                    match = re.search(pattern, file_path)
+                    if self.config.name == "device-data":
+                        digit_numbers.append(int(match.group(2)))
+                    elif self.config.name == "segmentation-mask":
+                        digit_numbers.append(int(match.group(1)))
+                break
+                    
+        
+        # Sort the list of numbers while keeping track of the original indices
+        sorted_numbers_with_indices = sorted(enumerate(digit_numbers), key=lambda x: x[1])
+    
+        # Extract the sorted numbers and their original indices
+        sorted_indices, sorted_numbers = zip(*sorted_numbers_with_indices)
+       
+        # Sort the file paths
+        sorted_file_paths = [file_paths[i] for i in sorted_indices]
+        
+        return sorted_file_paths
 
     def _generate_examples(self, files, name):
         if self.config.name == "device_data":
@@ -350,37 +376,62 @@ class msynth(datasets.GeneratorBasedBuilder):
 
 
         if self.config.name == "segmentation_mask":
-            key = 0
+             key = 0
             data_dir = []
+            examples = []
             for folder in files:
                 tmp_dir = []
                 tmp_dir = self.get_all_file_paths(os.path.join(folder, DATA_DIR[name]))
                 data_dir = data_dir + tmp_dir
 
-            for path in data_dir:
-                res_dic = {}
-                for word in _DENSITY:
-                    if word in path:
-                        breast_density = word
-                        pattern = rf"(\d+\.\d+)_{word}"
-                        match = re.search(pattern, path)
-                        matched_text = match.group(1)
-                        break
+            data_dir = self.sort_file_paths(data_dir)
 
-                # Get image id to filter the respective row of the csv
-                image_id = os.path.basename(path)
-                # Use os.path.splitext() to split the filename into root and extension
-                root, extension = os.path.splitext(image_id)
-                # Get the extension without the dot
-                image_labels = extension.lstrip(".")
-                res_dic["Raw"] = path
-                res_dic["mhd"] = self.get_support_file_path(path, "mhd")
-                res_dic["loc"] = self.get_support_file_path(path, "loc")
-                res_dic["density"] = breast_density
-                res_dic["mass_radius"] = matched_text
+            new_data_dir = [];
+            count = 1;
+            loc = 0;
+            while loc < len(data_dir):
+                if count % 2 == 1:
+                    new_data_dir.append(data_dir[loc])
+                    loc += 1 
+                else:
+                    new_data_dir.append('None')
+                count += 1
 
-                yield key, res_dic
+            for path in new_data_dir:
+                res_dic = {}
+                if path == "None":
+                    res_dic["Raw"] = "None"
+                    res_dic["mhd"] = "None"
+                    res_dic["loc"] = "None"
+                    res_dic["density"] = "None"
+                    res_dic["mass_radius"] = "None"
+                    
+                else:
+                    for word in _DENSITY:
+                        if word in path:
+                            breast_density = word
+                            pattern = rf"(\d+\.\d+)_{word}"
+                            match = re.search(pattern, path)
+                            matched_text = match.group(1)
+                            break            
+                    # Get image id to filter the respective row of the csv
+                    image_id = os.path.basename(path)
+                    # Use os.path.splitext() to split the filename into root and extension
+                    root, extension = os.path.splitext(image_id)
+                    # Get the extension without the dot
+                    image_labels = extension.lstrip(".")
+                    res_dic["Raw"] = path
+                    res_dic["mhd"] = self.get_support_file_path(path, "mhd")
+                    res_dic["loc"] = self.get_support_file_path(path, "loc")
+                    res_dic["density"] = breast_density
+                    res_dic["mass_radius"] = matched_text
+
+                examples.append(res_dic)
+
+            for example in examples:
+                yield key, {**example}
                 key += 1
+            examples = []
             
         if self.config.name == "metadata":
             key = 0