cr-nimble refactoring related changes

examples/pursuit/cosamp_step_by_step.py

@@ -52,6 +52,11 @@
 import cr.sparse as crs
 import cr.sparse.dict as crdict
 import cr.sparse.data as crdata
+from cr.nimble.dsp import (
+    nonzero_indices,
+    nonzero_values,
+    largest_indices
+)
 
 # %%
 # Problem Setup
@@ -147,7 +152,7 @@
 
 # %%
 # Pick the indices of 3K atoms with largest matches with the residual
-I_sub =  crs.largest_indices(h, K3)
+I_sub =  largest_indices(h, K3)
 # Update the flags array
 flags = flags.at[I_sub].set(True)
 # Sort the ``I_sub`` array with the help of flags array
@@ -165,7 +170,7 @@
 # Compute the least squares solution of ``y`` over this subdictionary
 x_sub, r_sub_norms, rank_sub, s_sub = jnp.linalg.lstsq(Phi_sub, y)
 # Pick the indices of K largest entries in in ``x_sub`` 
-Ia = crs.largest_indices(x_sub, K)
+Ia = largest_indices(x_sub, K)
 print(f"{Ia=}")
 # %%
 # We need to map the indices in ``Ia`` to the actual indices of atoms in ``Phi``
@@ -215,7 +220,7 @@
 h = Phi.T @ r
 # %%
 # Pick the indices of 2K atoms with largest matches with the residual
-I_2k =  crs.largest_indices(h, K2 if iterations else K3)
+I_2k =  largest_indices(h, K2 if iterations else K3)
 # We can check if these include the atoms missed out in first iteration.
 print(jnp.intersect1d(omega, I_2k))
 # %%
@@ -237,7 +242,7 @@
 # Compute the least squares solution of ``y`` over this subdictionary
 x_sub, r_sub_norms, rank_sub, s_sub = jnp.linalg.lstsq(Phi_sub, y)
 # Pick the indices of K largest entries in in ``x_sub`` 
-Ia = crs.largest_indices(x_sub, K)
+Ia = largest_indices(x_sub, K)
 print(Ia)
 # %%
 # We need to map the indices in ``Ia`` to the actual indices of atoms in ``Phi``

examples/pursuit/cs1bit_biht.py

@@ -22,6 +22,9 @@
 import cr.sparse.data as crdata
 import cr.sparse.cs.cs1bit as cs1bit
 
+from cr.nimble.dsp import (
+    build_signal_from_indices_and_values
+)
 
 # %%
 # Setup
@@ -76,7 +79,7 @@
 sol = cs1bit.biht_jit(Phi, y, K, tau)
 # %%
 # reconstructed signal
-x_rec = crs.build_signal_from_indices_and_values(N, sol.I, sol.x_I)
+x_rec = build_signal_from_indices_and_values(N, sol.I, sol.x_I)
 
 # %%
 # Verification

examples/rec_l1/spikes_l1ls.py

@@ -27,6 +27,11 @@
 import cr.sparse.data as crdata
 import cr.sparse.lop as lop
 import cr.sparse.cvx.l1ls as l1ls
+from cr.nimble.dsp import (
+  hard_threshold_by,
+  support,
+  largest_indices_by
+)
 
 # %%
 # Setup
@@ -114,7 +119,7 @@
 # %%
 # Thresholding for large values 
 # '''''''''''''''''''''''''''''''''''''
-x = crs.hard_threshold_by(sol.x, 0.5)
+x = hard_threshold_by(sol.x, 0.5)
 plt.figure(figsize=(8,6), dpi= 100, facecolor='w', edgecolor='k')
 plt.subplot(211)
 plt.plot(xs)
@@ -124,8 +129,8 @@
 # %%
 # Verifying the support recovery 
 # '''''''''''''''''''''''''''''''''''''
-support_xs = crs.support(xs)
-support_x = crs.support(x)
+support_xs = support(xs)
+support_x = support(x)
 jnp.all(jnp.equal(support_xs, support_x))
 
 
@@ -134,7 +139,7 @@
 # ------------------------------------------------
 
 # Identify the sub-matrix of columns for the support of recovered solution's large entries
-support_x = crs.largest_indices_by(sol.x, 0.5)
+support_x = largest_indices_by(sol.x, 0.5)
 AI = A.columns(support_x)
 print(AI.shape)
 

examples/sparse_vector_normals.py

@@ -14,6 +14,10 @@
 import jax.numpy as jnp
 import cr.sparse as crs
 import cr.sparse.data as crdata
+from cr.nimble.dsp import (
+    nonzero_indices,
+    nonzero_values
+)
 
 # %%
 # Let's define the size of model and number of sparse entries
@@ -31,11 +35,11 @@
 
 # %% 
 # We can easily find the locations of non-zero entries
-print(crs.nonzero_indices(x))
+print(nonzero_indices(x))
 
 # %%
 # We can  extract corresponding non-zero values in a compact vector
-print(crs.nonzero_values(x))
+print(nonzero_values(x))
 
 # %% 
 # Let's plot the vector to see where the non-zero entries are