Updated with portpy version 1.0.4

Author: gourav3017

Tutorial.ipynb

@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74d02efa",
+   "id": "67c3f386",
    "metadata": {},
    "source": [
     "### 4) Saving and loading plan"
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c26a769",
+   "id": "f8d766aa",
    "metadata": {},
    "source": [
     "### 5) Visualize and compare the plans"
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fcf23fd",
+   "id": "781141ae",
    "metadata": {},
    "source": [
     "### 6) Evaluate the plans"

ex_wavelet.py

@@ -20,11 +20,11 @@ def ex_wavelet():
     #  Note: you first need to download the patient database from the link provided in the GitHub page.
 
     # specify the patient data location.
-    data_dir = r'../data'
+    data_dir = r'\\pisiz3echo\ECHO\Research\Data_newformat\PortPy\data'
 
     # Use PortPy DataExplorer class to explore PortPy data and pick one of the patient
     data = pp.DataExplorer(data_dir=data_dir)
-    patient_id = 'Lung_Patient_2'
+    patient_id = 'Lung_Patient_3'
     data.patient_id = patient_id
 
     # Load ct and structure set for the above patient using CT and Structures class
@@ -61,7 +61,7 @@ def ex_wavelet():
             opt_params['objective_functions'][i]['weight'] = 0
 
     # create a plan using ct, structures, beams and influence matrix. Clinical criteria is optional
-    my_plan = pp.Plan(ct, structs, beams, inf_matrix, clinical_criteria)
+    my_plan = pp.Plan(ct=ct, structs=structs, beams=beams, inf_matrix=inf_matrix, clinical_criteria=clinical_criteria)
 
     # create cvxpy problem using the clinical criteria and optimization parameters
     opt = pp.Optimization(my_plan, opt_params=opt_params)