without custom MACOS repair command

quantumgizmos · Nov 23, 2023 · 908fdfd · 908fdfd
1 parent 0b9f198
commit 908fdfd
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Showing 2 changed files with 25 additions and 57 deletions.
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
@@ -36,7 +36,7 @@ jobs:
       CIBW_BUILD: "${{ matrix.os_dist.dist }}"
       MACOSX_DEPLOYMENT_TARGET: "10.15"
       CIBW_ARCHS_MACOS: "arm64 x86_64"
-      CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-wheel -w {dest_dir} -v {wheel}
+      # CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-wheel -w {dest_dir} -v {wheel}
     strategy:
       fail-fast: false
       matrix:

diff --git a/python_test/test_scratch.py b/python_test/test_scratch.py
@@ -3,6 +3,8 @@
 import ldpc.codes
 import ldpc.code_util
 
+import scipy.sparse
+
 import numpy as np
 import ldpc.codes
 from ldpc import BpDecoder
@@ -11,79 +13,45 @@
 from ldpc.monte_carlo_simulation import MonteCarloBscSimulation
 import ldpc.mod2
 
-if __name__ == "__main__":
-
-    H = ldpc.codes.rep_code(5)
-    error_rate = 0.001
-    dec = BpOsdDecoder(H, error_rate=error_rate, max_iter = 0, bp_method="product_sum", input_vector_type = "syndrome", osd_method="osd_e", osd_order = 1)
-    assert dec.osd_order == 1
+from qec.codes import ToricCode
 
-    span = ldpc.mod2.row_span(ldpc.codes.rep_code(5))
-    print(span.toarray())
 
-    H = ldpc.codes.hamming_code(3)
 
-    d = ldpc.mod2.compute_exact_code_distance(H)
-
-    print(d)
-
-    import numpy as np
-    import ldpc.codes
-    from ldpc import BeliefFindDecoder
+if __name__ == "__main__":
 
-    H = ldpc.codes.ring_code(3)
 
-    ## The 
-    bf = BeliefFindDecoder(
-                H,
-                error_rate = 0.1,
-                bp_method = 'product_sum',
-                max_iter = 1,
-                schedule = 'serial',
-                uf_method = True, # If uf_method is set to False, union-find clusters are solved using a peeling decoder
-                bits_per_step = 1 ## this is the number of bits by which clusters are expanded in each growth step 
-            )
-
-    error = np.random.randint(size=H.shape[0], low=0, high=2).astype(np.uint8)
-    syndrome = H@error % 2
-
 
-    print(f"Syndrome: {syndrome.__repr__()}")
+    H1 = ldpc.codes.hamming_code(3)
+    H2 = ldpc.codes.hamming_code(3)
 
-    decoding = bf.decode(syndrome)
-    print(f"Decoding: {decoding}")
-    decoding_syndrome = H@decoding % 2
-    print(f"Decoding syndrome: {decoding_syndrome}")
+    H3 = scipy.sparse.hstack([H1, scipy.sparse.csr_matrix((H1.shape[0], H2.shape[1]))])
+    H4 = scipy.sparse.hstack([scipy.sparse.csr_matrix((H2.shape[0], H1.shape[1])), H2])
 
+    H = scipy.sparse.vstack([H3, H4]).astype(np.uint8)
 
+    H5 = scipy.sparse.csr_matrix((H.shape[0], H.shape[1]))
 
 
+    vec= np.zeros(H.shape[1]).astype(np.uint8)
+    vec[[6,7]] = 1
+    vec = scipy.sparse.csr_matrix(vec)
 
-    # syndrome 
+    H = scipy.sparse.vstack([H, vec])
 
+    print(H.toarray())
 
 
-    # H = np.array([[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
-    #    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
-    #    [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1],
-    #    [1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],
-    #    [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],
-    #    [0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
-    #    [1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0],
-    #    [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],
-    #    [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0]])
-
+    c1 = H[:,:7]
+    c2 = H[:,7:]
 
-    # n,k,d = ldpc.code_util.compute_code_parameters(H,timeout_seconds=0.1)
-    # print(f"n = {n}, k = {k}, d = {d}")
+    # print(c1.toarray())
+    # print(c2.toarray())
 
-    # # exit(22)
+    # print(vec.toarray())
 
 
-    # for _ in range(10000):
-    #     H = ldpc.codes.random_binary_code(8,15,4, variance=1)
-    #     n,k,d = ldpc.code_util.compute_code_parameters(H,timeout_seconds=0.001)
+    plu1 = ldpc.mod2.PluDecomposition(c1)
+    plu2 = ldpc.mod2.PluDecomposition(c2)
 
-    #     if d > 4:
-    #         print(f"n = {n}, k = {k}, d = {d}")
-    #         print(H.toarray().__repr__())
+    print(plu1.U.toarray())
+    print(plu2.L.toarray())