Added RMR

Author: gourav3017

compress_rtp/utils/get_sparse_only.py

@@ -3,7 +3,15 @@
 import scipy
 
 
-def get_sparse_only(matrix, threshold_perc, compression='naive'):
+def get_sparse_only(matrix: np.ndarray, threshold_perc: float = 1, compression: str = 'naive'):
+    """
+    Get sparse matrix using threshold and different methods
+    :param matrix: 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
     if compression == 'rmr':
         copy_matrix = matrix.copy()

examples/matrix_sparse_only.ipynb

@@ -124,7 +124,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# run optimization with naive thresold of 1% of max(A) and no low rank\n",
+    "# run optimization with naive thresold of 1% of max(A)\n",
     "# create cvxpy problem using the clinical criteria and optimization parameters\n",
     "A = deepcopy(inf_matrix.A)\n",
     "S_sparse = get_sparse_only(matrix=A, threshold_perc=1)\n",
@@ -278,6 +278,7 @@
     }
    ],
    "source": [
+    "# Run IMRT fluence map optimization\n",
     "opt = pp.Optimization(my_plan, inf_matrix=inf_matrix, opt_params=opt_params)\n",
     "opt.create_cvxpy_problem()\n",
     "sol_sparse_naive = opt.solve(solver='MOSEK', verbose=True)"
@@ -296,7 +297,7 @@
    "id": "005283cc",
    "metadata": {},
    "source": [
-    "Instead of optimizing the plan using sparse influence matrix $ S $ we optimize it using sparse matrix using RMR method discussed in (RMR)[]"
+    "Now instead of optimizing the plan using sparse influence matrix $ S $ we optimize it using sparse matrix obtained using RMR method discussed in (RMR)[]"
    ]
   },
   {