Fix warnings in structures and visualization.py. Fix bug in clinical_criteria.py dvh constraints. Remove typing from dependency

Author: gourav3017

.gitignore

@@ -54,3 +54,4 @@ docs/_build/
 /ai_data/
 /checkpoints/
 /results/
+/metadata/

README.md

@@ -10,7 +10,7 @@
 
 </h2>
 
-![Version](https://img.shields.io/static/v1?label=latest&message=v1.0.5&color=darkgreen)
+![Version](https://img.shields.io/static/v1?label=latest&message=v1.0.6&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)
 [![Monthly Downloads](https://static.pepy.tech/badge/portpy/month)](https://pepy.tech/project/portpy)
 

portpy/__init__.py

@@ -1,3 +1,3 @@
-__version__ = "1.0.5"
+__version__ = "1.0.6"
 
 from portpy import photon

portpy/photon/clinical_criteria.py

@@ -188,14 +188,14 @@ def convert_dvh_to_dose_gy_vol_perc(my_plan, old_criteria):
         criteria = deepcopy(old_criteria)
         struct_name = criteria['parameters']['structure_name']
         if criteria['type'] == 'dose_volume_D':
-            constraint_keys = criteria['constraints'].keys()
+            constraint_keys = list(criteria['constraints'].keys())
             for key in constraint_keys:
                 if 'perc' in key:
                     value = criteria['constraints'][key]
                     new_key = key.replace('perc', 'gy')
                     criteria['constraints'][new_key] = criteria['constraints'].pop(key)
                     criteria['constraints'][new_key] = value / 100 * my_plan.get_prescription()
-            param_keys = criteria['parameters'].keys()
+            param_keys = list(criteria['parameters'].keys())
             for key in param_keys:
                 if 'volume_cc' in key:
                     value = criteria['parameters'][key]
@@ -204,15 +204,15 @@ def convert_dvh_to_dose_gy_vol_perc(my_plan, old_criteria):
                     criteria['parameters'][new_key] = value / my_plan.structures.get_volume_cc(
                         struct_name.upper()) * 100
         if criteria['type'] == 'dose_volume_V':
-            constraint_keys = criteria['constraints'].keys()
+            constraint_keys = list(criteria['constraints'].keys())
             for key in constraint_keys:
                 if 'volume_cc' in key:
                     value = criteria['constraints'][key]
                     new_key = key.replace('cc', 'perc')
                     criteria['constraints'][new_key] = criteria['constraints'].pop(key)
                     criteria['constraints'][new_key] = value / my_plan.structures.get_volume_cc(
                         struct_name.upper()) * 100
-            param_keys = criteria['parameters'].keys()
+            param_keys = list(criteria['parameters'].keys())
             for key in param_keys:
                 if 'dose_perc' in key:
                     value = criteria['parameters'][key]

portpy/photon/structures.py

@@ -333,7 +333,10 @@ def create_structure(self, new_struct_name: str, mask_3d: np.ndarray) -> None:
                 frac_of_mask_in_calc_box = mask_3d & dose_mask
                 counts = np.count_nonzero(frac_of_mask_in_calc_box)
                 volume_cc_in_calc_box = counts * np.prod(self._ct_voxel_resolution_xyz_mm) / 1000
-                self.structures_dict['fraction_of_vol_in_calc_box'].append(volume_cc_in_calc_box/volume_cc)
+                if volume_cc == 0:
+                    self.structures_dict['fraction_of_vol_in_calc_box'].append(0)
+                else:
+                    self.structures_dict['fraction_of_vol_in_calc_box'].append(volume_cc_in_calc_box/volume_cc) # avoid divide by zero error
             else:
                 self.structures_dict[key].append(None)
 

portpy/photon/visualization.py

@@ -123,15 +123,15 @@ def plot_dvh(my_plan: Plan, sol: dict = None, dose_1d: np.ndarray = None, struct
             elif dose_scale == 'Relative(%)':
                 x = x / pres * 100
                 max_dose = np.maximum(max_dose, x[-1])
-                ax.set_xlabel('Dose ($\%$)', fontsize=fontsize)
+                ax.set_xlabel(r'Dose ($\%$)', fontsize=fontsize)
 
             if volume_scale == 'Absolute(cc)':
                 y = y * my_plan.structures.get_volume_cc(all_orgs[i]) / 100
                 max_vol = np.maximum(max_vol, y[1] * 100)
                 ax.set_ylabel('Volume (cc)', fontsize=fontsize)
             elif volume_scale == 'Relative(%)':
                 max_vol = np.maximum(max_vol, y[0] * 100)
-                ax.set_ylabel('Fractional Volume ($\%$)', fontsize=fontsize)
+                ax.set_ylabel(r'Fractional Volume ($\%$)', fontsize=fontsize)
             ax.plot(x, 100 * y, linestyle=style, linewidth=width, color=colors[count], label=struct_names[i])
             count = count + 1
             # legend.append(struct_names[i])
@@ -275,6 +275,7 @@ def plot_robust_dvh(my_plan: Plan, sol: dict = None, dose_1d_list: list = None,
                 print('Skipping Structure {} as it is not within calculation box.'.format(all_orgs[i]))
                 continue
             dose_sort_list = []
+            y = []
             for dose_1d in dose_1d_list:
                 x, y = Evaluation.get_dvh(sol, struct=all_orgs[i], dose_1d=dose_1d)
                 dose_sort_list.append(x)
@@ -289,15 +290,15 @@ def plot_robust_dvh(my_plan: Plan, sol: dict = None, dose_1d_list: list = None,
             elif dose_scale == 'Relative(%)':
                 max_dose = np.maximum(max_dose, d_max_mat[-1])
                 max_dose = max_dose / pres * 100
-                ax.set_xlabel('Dose ($\%$)', fontsize=fontsize)
+                ax.set_xlabel(r'Dose ($\%$)', fontsize=fontsize)
 
             if volume_scale == 'Absolute(cc)':
                 y = y * my_plan.structures.get_volume_cc(all_orgs[i]) / 100
                 max_vol = np.maximum(max_vol, y[1] * 100)
                 ax.set_ylabel('Volume (cc)', fontsize=fontsize)
             elif volume_scale == 'Relative(%)':
                 max_vol = np.maximum(max_vol, y[0] * 100)
-                ax.set_ylabel('Fractional Volume ($\%$)', fontsize=fontsize)
+                ax.set_ylabel(r'Fractional Volume ($\%$)', fontsize=fontsize)
             # ax.plot(x, 100 * y, linestyle=style, linewidth=width, color=colors[count])
 
             # ax.plot(d_min_mat, 100 * y, linestyle='dotted', linewidth=width*0.5, color=colors[count])

requirements.txt

@@ -64,7 +64,6 @@ def _get_portpy_photon_version():
         "Shapely>=1.8.4",
         "SimpleITK>=2.0.2",
         "tabulate>=0.9.0",
-        "typing>=3.7.4.3",
         "jinja2>=3.0.1",
         "typing-extensions>=3.10.0.0",
         "scikit-image>=0.17.2",
@@ -79,7 +78,7 @@ def _get_portpy_photon_version():
     extras_require={
         'mosek': ["Mosek>=9.3.14"],
         'pydicom': ["pydicom>=2.2.0"],
-        'full': ["Mosek>=9.3.14", "pydicom>=2.2.0"],
+        'full': ["Mosek>=9.3.14", "pydicom>=2.2.0", "websocket-client>=1.8.0", "visdom>=0.2.4", "dominate>=2.6.0", "torch>=2.2.0", "torchfile==0.1.0", "torchvision==0.9.1"],
         'ai': ["websocket-client>=1.8.0", "visdom>=0.2.4", "dominate>=2.6.0", "torch>=2.2.0", "torchfile==0.1.0", "torchvision==0.9.1"]
     }