Update notebooks

Author: gourav3017

README.md

@@ -84,7 +84,7 @@ If you are using PortPy for your radiotherapy research, you can apply RMR sparsi
 from compress_rtp.utils.get_sparse_only import get_sparse_only
 
 # Apply RMR sparsification to the matrix A
-S = get_sparse_only(matrix=A, threshold_perc=10, compression='rmr')
+S = get_sparse_only(A=A, threshold_perc=10, compression='rmr')
 
 # Replace the original matrix A with the sparsified matrix S
 inf_matrix.A = S

compress_rtp/utils/get_low_dim_basis.py

@@ -1,7 +1,6 @@
 
-from typing import TYPE_CHECKING
-if TYPE_CHECKING:
-    from portpy.photon.influence_matrix import InfluenceMatrix
+
+from portpy.photon.influence_matrix import InfluenceMatrix
 import numpy as np
 import scipy
 try:

compress_rtp/utils/get_sparse_only.py

@@ -3,23 +3,23 @@
 import scipy
 
 
-def get_sparse_only(matrix: np.ndarray, threshold_perc: float = 1, compression: str = 'naive'):
+def get_sparse_only(A: np.ndarray, threshold_perc: float = 1, compression: str = 'naive'):
     """
     Get sparse matrix using threshold and different methods
-    :param matrix: matrix to be sparsified
+    :param A: matrix to be sparsified
     :param threshold_perc: threshold for matrix sparsification
     :param compression: Method of Sparsification
 
     :return: Sparse influence matrix
     """
-    threshold = np.max(matrix) * threshold_perc*0.01
+    threshold = np.max(A) * threshold_perc * 0.01
     if compression == 'rmr':
-        copy_matrix = matrix.copy()
+        copy_matrix = A.copy()
         print('Generating sparse matrix using RMR...')
         np.apply_along_axis(row_operation, 1, copy_matrix, threshold)
         S = scipy.sparse.csr_matrix(copy_matrix)
     else:
-        S = np.where(matrix >= threshold, matrix, 0)
+        S = np.where(A >= threshold, A, 0)
         S = scipy.sparse.csr_matrix(S)
     return S
 

compress_rtp/utils/get_sparse_plus_low_rank.py

@@ -7,14 +7,14 @@
     pass
 
 
-def get_sparse_plus_low_rank(A: np.ndarray, thresold_perc: float = 1, rank: int = 5):
+def get_sparse_plus_low_rank(A: np.ndarray, threshold_perc: float = 1, rank: int = 5):
     """
         :param A: dose influence matrix
-        :param thresold_perc: thresold percentage. Default to 1% of max(A)
+        :param threshold_perc: thresold percentage. Default to 1% of max(A)
         :type rank: rank of L = A-S.
         :returns: S, H, W using randomized svd
     """
-    tol = np.max(A) * thresold_perc * 0.01
+    tol = np.max(A) * threshold_perc * 0.01
     S = np.where(A > tol, A, 0)
     if rank == 0:
         S = scipy.sparse.csr_matrix(S)

examples/fluence_wavelets.ipynb

@@ -158,6 +158,28 @@ def ex_wavelet():
                               sol=[sol_no_quad_no_wav, sol_no_quad_with_wav, sol_quad_no_wav, sol_quad_with_wav],
                               sol_names=['no_quad_no_wav', 'no_quad_with_wav', 'quad_no_wav', 'quad_with_wav'])
 
+    '''
+    7) Visualize the dose dicrepancy between optimized and final deliverable plan using leaf sequencing
+    '''
+    # perform leaf sequencing for quadratic objective without and with wavelets using PortPy
+    leaf_seq_quad_no_wav = pp.leaf_sequencing_siochi(my_plan, sol_quad_no_wav)
+    leaf_seq_quad_wav = pp.leaf_sequencing_siochi(my_plan, sol_quad_with_wav)
+
+    # Visualize the dvh after performing leaf sequencing using the optimal intensities
+    # plot DVH for the structures in the given list. Default dose_1d is in Gy and volume is in relative scale(%).
+    struct_names = ['PTV', 'ESOPHAGUS', 'HEART', 'CORD', 'LUNG_L', 'LUNG_R']
+    num_fractions = my_plan.get_num_of_fractions()
+    fig, ax = plt.subplots(1, 2, figsize=(20, 7))
+    ax0 = pp.Visualization.plot_dvh(my_plan, sol=sol_no_quad_no_wav, struct_names=struct_names, style='solid', ax=ax[0])
+    ax0 = pp.Visualization.plot_dvh(my_plan, dose_1d=inf_matrix.A @ leaf_seq_quad_no_wav['optimal_intensity'] * num_fractions, struct_names=struct_names, style='dashed', ax=ax0)
+    fig.suptitle('DVH comparison')
+    ax0.set_title('Without wavelets \n solid: Before leaf sequencing, Dash: After leaf sequencing')
+
+    # fig, ax = plt.subplots(figsize=(12, 8))
+    ax1 = pp.Visualization.plot_dvh(my_plan, sol=sol_quad_with_wav, struct_names=struct_names, style='solid', ax=ax[1])
+    ax1 = pp.Visualization.plot_dvh(my_plan, dose_1d=inf_matrix.A @ leaf_seq_quad_wav['optimal_intensity'] * num_fractions, struct_names=struct_names, style='dashed', ax=ax1)
+    ax1.set_title('With wavelets \n solid: Before leaf sequencing, Dash: After leaf sequencing')
+    plt.show()
 
 if __name__ == "__main__":
     ex_wavelet()

examples/fluence_wavelets.py

@@ -71,7 +71,7 @@ def sparse_plus_low_rank():
     # run optimization with naive thresold of 1% of max(A) and no low rank
     # create cvxpy problem using the clinical criteria and optimization parameters
     A = deepcopy(inf_matrix.A)
-    S = get_sparse_only(matrix=A, threshold_perc=1)
+    S = get_sparse_only(A=A, threshold_perc=1)
     # Users can also use below method to get sparse matrix using threshold. Rank=0 is equivalent to above method
     # S = get_sparse_plus_low_rank(A=A, thresold_perc=1, rank=0)
     inf_matrix.A = S
@@ -81,7 +81,7 @@ def sparse_plus_low_rank():
 
     # run optimization with thresold of 1% and rank 5
     # create cvxpy problem using the clinical criteria and optimization parameters
-    S, H, W = get_sparse_plus_low_rank(A=A, thresold_perc=1, rank=5)
+    S, H, W = get_sparse_plus_low_rank(A=A, threshold_perc=1, rank=5)
     opt = CompressRTPOptimization(my_plan, opt_params=opt_params)
     opt.create_cvxpy_problem_compressed(S=S, H=H, W=W)
 

examples/matrix_spare_plus_low_rank.py

@@ -69,15 +69,15 @@ def matrix_sparse_only_rmr():
     # run optimization with naive thresold of 1% of max(A) and no low rank
     # create cvxpy problem using the clinical criteria and optimization parameters
     A = deepcopy(inf_matrix.A)
-    S_sparse = get_sparse_only(matrix=A, threshold_perc=1)
+    S_sparse = get_sparse_only(A=A, threshold_perc=1)
     inf_matrix.A = S_sparse
     opt = pp.Optimization(my_plan, inf_matrix=inf_matrix, opt_params=opt_params)
     opt.create_cvxpy_problem()
     sol_sparse_naive = opt.solve(solver='MOSEK', verbose=True)
 
     # run optimization with thresold of 1% and sparsifying matrix using RMR method
     # create cvxpy problem using the clinical criteria and optimization parameters
-    S_rmr = get_sparse_only(matrix=A, threshold_perc=10, compression='rmr')
+    S_rmr = get_sparse_only(A=A, threshold_perc=10, compression='rmr')
     inf_matrix.A = S_rmr
     opt = pp.Optimization(my_plan, inf_matrix=inf_matrix, opt_params=opt_params)
     opt.create_cvxpy_problem()