V1.0.2: Updated optimization and clinical criteria json config files …

…to make them more mature and adapted code according to it
PortPy-Project · Nov 27, 2023 · 744146f · 744146f
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Showing 9 changed files with 176 additions and 165 deletions.
diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
   <img src="./images/PortPy_logo.png" width="40%" height="40%">
 </p>
 
-![Version](https://img.shields.io/static/v1?label=latest&message=v1.0.1&color=darkgreen)
+![Version](https://img.shields.io/static/v1?label=latest&message=v1.0.2&color=darkgreen)
 [![Total Downloads](https://static.pepy.tech/personalized-badge/portpy?period=total&units=international_system&left_color=grey&right_color=blue&left_text=total%20downloads)](https://pepy.tech/project/portpy?&left_text=totalusers)
 
 # What is PortPy?

diff --git a/portpy/__init__.py b/portpy/__init__.py
@@ -1,3 +1,3 @@
-__version__ = "1.0.1"
+__version__ = "1.0.2"
 
 from portpy import photon
diff --git a/portpy/config_files/clinical_criteria/Default/Lung_2Gy_30Fx.json b/portpy/config_files/clinical_criteria/Default/Lung_2Gy_30Fx.json
@@ -5,7 +5,7 @@
   "num_of_fractions": 30,
   "criteria": [
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "GTV"
       },
@@ -15,7 +15,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "PTV"
       },
@@ -25,7 +25,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "ESOPHAGUS"
       },
@@ -34,7 +34,7 @@
       }
     },
     {
-      "name": "mean_dose",
+      "type": "mean_dose",
       "parameters": {
         "structure_name": "ESOPHAGUS"
       },
@@ -44,7 +44,7 @@
       }
     },
     {
-      "name": "dose_volume_V",
+      "type": "dose_volume_V",
       "parameters": {
         "structure_name": "ESOPHAGUS",
         "dose_gy": 60
@@ -54,7 +54,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "HEART"
       },
@@ -63,7 +63,7 @@
       }
     },
     {
-      "name": "mean_dose",
+      "type": "mean_dose",
       "parameters": {
         "structure_name": "HEART"
       },
@@ -73,7 +73,7 @@
       }
     },
     {
-      "name": "dose_volume_V",
+      "type": "dose_volume_V",
       "parameters": {
         "structure_name": "HEART",
         "dose_gy": 30
@@ -83,7 +83,7 @@
       }
     },
     {
-      "name": "dose_volume_V",
+      "type": "dose_volume_V",
       "parameters": {
         "structure_name": "HEART",
         "dose_gy": 30
@@ -93,7 +93,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "LUNG_L"
       },
@@ -102,7 +102,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "LUNG_R"
       },
@@ -111,7 +111,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "CORD"
       },
@@ -121,7 +121,7 @@
       }
     },
     {
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "SKIN"
       },
@@ -130,7 +130,7 @@
       }
     },
 	{
-      "name": "max_dose",
+      "type": "max_dose",
       "parameters": {
         "structure_name": "LUNGS_NOT_GTV",
 		"structure_def": "(LUNG_L | LUNG_R) - GTV"
@@ -140,7 +140,7 @@
       }
     },
 	{
-      "name": "mean_dose",
+      "type": "mean_dose",
       "parameters": {
         "structure_name": "LUNGS_NOT_GTV",
 		"structure_def": "(LUNG_L | LUNG_R) - GTV"
@@ -151,7 +151,7 @@
       }
     },
 	{
-      "name": "dose_volume_V",
+      "type": "dose_volume_V",
       "parameters": {
         "structure_name": "LUNGS_NOT_GTV",
 		"structure_def": "(LUNG_L | LUNG_R) - GTV",

diff --git a/portpy/config_files/optimization_params/optimization_params_Lung_2Gy_30Fx.json b/portpy/config_files/optimization_params/optimization_params_Lung_2Gy_30Fx.json
@@ -2,11 +2,17 @@
   "prescription_gy": 60,
   "objective_functions": [	
 	{
-		"type": "quadratic-overdose",     
+		"type": "quadratic-overdose",
 		"structure_name": "PTV",
 		"weight": 10000,
 		"dose_gy": "prescription_gy"
     },
+    {
+		"type": "linear-overdose",
+		"structure_name": "CORD",
+		"weight": 100,
+		"dose_gy": 50
+    },
     {
 		"type": "quadratic-underdose",
 		"structure_name": "PTV",
@@ -81,30 +87,55 @@
 	],
 	"constraints":[
 	{
-		"type": "max",
+	  "type": "max_dose",
+	  "parameters": {
 		"structure_name": "RIND_0",
-        "upper_limit": "1.1*prescription_gy"
-    },
+		"structure_def": "(PTV+5) - PTV"
+	  },
+	  "constraints": {
+		"limit_dose_gy": "1.1*prescription_gy"
+	  }
+	},
 	{
-		"type": "max",
+	  "type": "max_dose",
+	  "parameters": {
 		"structure_name": "RIND_1",
-        "upper_limit": "1.05*prescription_gy"
-    },
+		"structure_def": "(PTV+10) - (PTV + 5)"
+	  },
+	  "constraints": {
+		"limit_dose_gy": "1.05*prescription_gy"
+	  }
+	},
 	{
-		"type": "max",
+	  "type": "max_dose",
+	  "parameters": {
 		"structure_name": "RIND_2",
-        "upper_limit": "0.9*prescription_gy"
-    },
+		"structure_def": "(PTV+30) - (PTV + 10)"
+	  },
+	  "constraints": {
+		"limit_dose_gy": "0.9*prescription_gy"
+	  }
+	},
 	{
-		"type": "max",
+	  "type": "max_dose",
+	  "parameters": {
 		"structure_name": "RIND_3",
-        "upper_limit": "0.85*prescription_gy"
-    },
+		"structure_def": "(PTV+50) - (PTV + 30)"
+	  },
+	  "constraints": {
+		"limit_dose_gy": "0.85*prescription_gy"
+	  }
+	},
 	{
-		"type": "max",
+	  "type": "max_dose",
+	  "parameters": {
 		"structure_name": "RIND_4",
-        "upper_limit": "0.75*prescription_gy"
-    }
+	   "structure_def": "(PTV + inf) - (PTV + 50)"
+	  },
+	  "constraints": {
+		"limit_dose_gy": "0.75*prescription_gy"
+	  }
+	}
 ]
 
 }
diff --git a/portpy/photon/clinical_criteria.py b/portpy/photon/clinical_criteria.py
@@ -17,7 +17,7 @@ class ClinicalCriteria:
                 "num_of_fractions": 30,
                 "criteria": [
                     {
-                      "name": "max_dose",
+                      "type": "max_dose",
                       "parameters": {
                         "struct": "GTV"
                       }}
@@ -54,75 +54,85 @@ def get_num_of_fractions(self) -> float:
         """
         return self.clinical_criteria_dict['num_of_fractions']
 
-    def add_criterion(self, criterion: str, parameters: dict, constraints: dict) -> None:
+    def add_criterion(self, type: str, parameters: dict, constraints: dict) -> None:
         """
         Add criterion to the clinical criteria dictionary
 
-        :param criterion: criterion name. e.g. max_dose
+        :param type: criterion name. e.g. max_dose
         :param parameters: parameters dictionary e.g. parameters = {'struct':'PTV'}
         :param constraints: constraints dictionary e.g. constraints = {'limit_dose_gy':66, 'goal_dose_gy':60}
         :return: add the criteria to clinical criteria dictionary
 
         """
 
-        self.clinical_criteria_dict['criteria'].append({'name': criterion})
+        self.clinical_criteria_dict['criteria'].append({'type': type})
         self.clinical_criteria_dict['criteria'][-1]['parameters'] = parameters
         self.clinical_criteria_dict['criteria'][-1]['constraints'] = constraints
 
-    def modify_criterion(self, criterion: str, parameters: dict, constraints: dict) -> None:
-        """
-
-        Modify the criterion in clinical criteria dictionary
-
-        :param criterion: criterion name. e.g. max_dose
-        :param parameters: parameters dictionary e.g. parameters = {'struct':'PTV'}
-        :param constraints: constraints dictionary e.g. constraints = {'limit_dose_gy':66, 'goal_dose_gy':60}
-        :return: add the criteria to clinical criteria dictionary
-
-        """
-        criteria = self.clinical_criteria_dict['criteria']
-        ind = [criteria[i]['name'] for i in range(len(criteria)) if criteria[i]['name'] == criterion and
-               criteria[i]['parameters'] == parameters]
-        if len(ind) > 0:
-
-            criteria[ind[0]]['constraints'] = constraints
-        else:
-            raise Exception('No criteria  for name {}  and parameters {}'.format(criterion, parameters))
-
     @staticmethod
-    def create_criterion(criterion: str, parameters: dict, constraints: dict):
+    def create_criterion(type: str, parameters: dict, constraints: dict):
         """
         Create criterion and return list
 
-        :param criterion: criterion name. e.g. max_dose
+        :param type: criterion name. e.g. max_dose
         :param parameters: parameters dictionary e.g. parameters = {'struct':'PTV'}
         :param constraints: constraints dictionary e.g. constraints = {'limit_dose_gy':66, 'goal_dose_gy':60}
         :return: add the criteria to clinical criteria dictionary
 
         """
 
-        criterion = [{'name': criterion, 'parameters': parameters, 'constraints': constraints}]
+        criterion = [{'type': type, 'parameters': parameters, 'constraints': constraints}]
         return criterion
 
-    def get_criteria(self, name: str = None) -> List[dict]:
+    def get_criteria(self, type: str = None) -> List[dict]:
         """
         Returns all the clinical criteria
         :return:
         """
         all_criteria = []
-        if name is None:
+        if type is None:
             all_criteria = self.clinical_criteria_dict['criteria']
-        elif name == 'max_dose':
+        elif type == 'max_dose':
             criteria = self.clinical_criteria_dict['criteria']
-            ind = [i for i in range(len(criteria)) if criteria[i]['name'] == name]
+            ind = [i for i in range(len(criteria)) if criteria[i]['type'] == type]
             if len(ind) > 0:
                 all_criteria = [criteria[i] for i in ind]
-        elif name == 'mean_dose':
+        elif type == 'mean_dose':
             criteria = self.clinical_criteria_dict['criteria']
-            ind = [i for i in range(len(criteria)) if criteria[i]['name'] == name]
+            ind = [i for i in range(len(criteria)) if criteria[i]['type'] == type]
             if len(ind) > 0:
                 all_criteria = [criteria[i] for i in ind]
 
         if isinstance(all_criteria, dict):
             all_criteria = [all_criteria]
         return all_criteria
+
+    def check_criterion_exists(self, criterion, return_ind:bool = False):
+        criterion_exist = False
+        criterion_ind = None
+        for ind, crit in enumerate(self.clinical_criteria_dict['criteria']):
+            if (crit['type'] == criterion['type']) and crit['parameters'] == criterion['parameters']:
+                for constraint in crit['constraints']:
+                    if constraint == criterion['constraints']:
+                        criterion_exist = True
+                        criterion_ind = ind
+        if return_ind:
+            return criterion_exist,criterion_ind
+        else:
+            return criterion_exist
+
+    def modify_criterion(self, criterion):
+        """
+        Modify the criterion the clinical criteria
+
+
+        """
+        criterion_found = False
+        for ind, crit in enumerate(self.clinical_criteria_dict['criteria']):
+            if (crit['type'] == criterion['type']) and crit['parameters'] == criterion['parameters']:
+                for constraint in crit['constraints']:
+                    if constraint == criterion['constraints']:
+                        self.clinical_criteria_dict['criteria'][ind]['constraints'][constraint] = criterion['constraints']
+                        criterion_found = True
+        if not criterion_found:
+            raise Warning('No criteria  for {}'.format(criterion))