switch to PythonCall

CondaPkg.toml

@@ -0,0 +1,6 @@
+
+[pip.deps]
+pymatching = ""
+ldpc = ""
+numpy = ""
+scipy = ""

Project.toml

@@ -1,13 +1,11 @@
 name = "PyQDecoders"
 uuid = "17f5de1a-9b79-4409-a58d-4d45812840f7"
 authors = ["Stefan Krastanov <[email protected]>"]
-version = "0.1.1"
+version = "0.2.0"
 
 [deps]
-Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
-PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 
 [compat]
-Conda = "1.10"
-PyCall = "1.96"
+PythonCall = "0.9.16"
 julia = "1.9"

README.md

@@ -1,15 +1,5 @@
 A Julia meta-package for accessing the python libraries `pymatching` (for MWPM-like decoders) and `ldpc` (for BP-like decoders).
 
-# How to install
-
-If you plan to use a python environment you are managing yourself, just make sure that julia is started from that environment and run
-
-`] add https://github.com/QuantumSavory/PyQDecoders.jl#master`
-
-You might get error messages telling you that you need to install some python packages in that environment of yours.
-
-If you want julia to automatically take care of creating a hidden python environment that it manages itself, do `ENV["PYTHON"] = ""` before you install `PyQDecoders.jl`
-
 # How to use
 
 ## `pymatching`
@@ -20,7 +10,7 @@ The python pymatching module is immediately available:
 julia> using PyQDecoders
 
 julia> PyQDecoders.pm
-PyObject <module 'pymatching' from ...>
+Python: <module 'pymatching' from ...>
 ```
 
 Running the example from `pymatching`'s original Readme:
@@ -33,7 +23,8 @@ julia> H = sps.csc_matrix(
             0 1 1 0 0;
             0 0 1 1 0;
             0 0 0 1 1])
-PyObject <4x5 sparse matrix of type '<class 'numpy.int64'>'
+Python:
+<4x5 sparse matrix of type '<class 'numpy.int64'>'
         with 8 stored elements in Compressed Sparse Column format>
 
 julia> weights = [4, 3, 2, 3, 4]
@@ -45,28 +36,18 @@ julia> weights = [4, 3, 2, 3, 4]
  4
 
 julia> matching = pm.Matching(H, weights=weights)
-PyObject <pymatching.Matching object with 4 detectors, 1 boundary node, and 5 edges>
+Python: <pymatching.Matching object with 4 detectors, 1 boundary node, and 5 edges>
 
 julia> prediction = matching.decode([0, 1, 0, 1])
-5-element Vector{UInt8}:
- 0x00
- 0x00
- 0x01
- 0x01
- 0x00
+Python: array([0, 0, 1, 1, 0], dtype=uint8)
 
 julia> prediction, solution_weight = matching.decode([0, 1, 0, 1], return_weight=true);
 
 julia> prediction
-5-element Vector{UInt8}:
- 0x00
- 0x00
- 0x01
- 0x01
- 0x00
+Python: array([0, 0, 1, 1, 0], dtype=uint8)
 
 julia> solution_weight
-5.0
+Python: 5.0
 ```
 
 ## ldpc
@@ -77,7 +58,7 @@ The python ldpc module is immediately available:
 julia> using PyQDecoders
 
 julia> PyQDecoders.ldpc
-PyObject <module 'ldpc' from ...>
+Python: <module 'ldpc' from ...>
 ```
 
 Running the example from `ldpc`'s original Readme:
@@ -87,12 +68,12 @@ Running the example from `ldpc`'s original Readme:
 julia> using PyQDecoders: sps, np, ldpc
 
 julia> H = ldpc.codes.rep_code(3) # parity check matrix for the length-3 repetition code
-2×3 Matrix{Int64}:
- 1  1  0
- 0  1  1
+Python:
+array([[1, 1, 0],
+       [0, 1, 1]])
 
-julia> n = size(H, 2) # the codeword length
-3
+julia> n = H.shape[1] # the codeword length, same as `size(H, 2)`
+Python: 3
 
 julia> bpd = ldpc.bp_decoder(
            H, # the parity check matrix
@@ -101,22 +82,21 @@ julia> bpd = ldpc.bp_decoder(
            bp_method="product_sum", # BP method. The other option is `minimum_sum'
            channel_probs=[nothing] # channel probability probabilities. Will override error rate.
        )
-PyObject <ldpc.bp_decoder.bp_decoder object at 0x7fea8283ab80>
+Python: <ldpc.bp_decoder.bp_decoder object at 0x...>
 
 julia> error = [0,1,0]
 3-element Vector{Int64}:
  0
  1
  0
 
-julia> syndrome = H*error .% 2
+julia> using PythonCall: PyArray
+
+julia> syndrome = PyArray(H)*error .% 2
 2-element Vector{Int64}:
  1
  1
 
-julia> decoding = bpd.decode(syndrome)
-3-element Vector{Int64}:
- 0
- 1
- 0
+julia> decoding = bpd.decode(np.array(syndrome))
+Python: array([0, 1, 0])
 ```

src/PyQDecoders.jl

@@ -1,23 +1,20 @@
 module PyQDecoders
-import Conda
-using PyCall
+import PythonCall
 
-const sps = PyNULL()
-const np = PyNULL()
-const pm = PyNULL()
-const ldpc = PyNULL()
+const sp = PythonCall.pynew()
+const sps = PythonCall.pynew()
+const np = PythonCall.pynew()
+const pm = PythonCall.pynew()
+const ldpc = PythonCall.pynew()
+const ldpccodes = PythonCall.pynew()
 
 function __init__()
-    copy!(sps, pyimport_conda("scipy.sparse", "scipy"))
-    copy!(np, pyimport_conda("numpy", "numpy"))
-    if PyCall.conda
-        Conda.pip_interop(true)
-        Conda.pip("install", "pymatching")
-        Conda.pip("install", "ldpc")
-    end
-    copy!(pm, pyimport_conda("pymatching", "pymatching"))
-    copy!(ldpc, pyimport_conda("ldpc", "ldpc"))
-    pyimport("ldpc.codes")
+    PythonCall.pycopy!(sp, PythonCall.pyimport("scipy"))
+    PythonCall.pycopy!(sps, PythonCall.pyimport("scipy.sparse"))
+    PythonCall.pycopy!(np, PythonCall.pyimport("numpy"))
+    PythonCall.pycopy!(pm, PythonCall.pyimport("pymatching"))
+    PythonCall.pycopy!(ldpc, PythonCall.pyimport("ldpc"))
+    PythonCall.pycopy!(ldpccodes, PythonCall.pyimport("ldpc.codes"))
 end
 
 end # module

test/Project.toml

@@ -1,10 +1,10 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
-Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
-PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"

test/test_aqua.jl

@@ -1,5 +1,4 @@
 using Aqua
 using PyQDecoders
 Aqua.test_all(PyQDecoders,
-    ambiguities=(broken=true,),
 )

test/test_jet.jl

@@ -19,14 +19,14 @@ function (::MayThrowIsOk)(report_type::Type{<:InferenceErrorReport}, @nospeciali
 end
 
 # imported to be declared as modules filtered out from analysis result
-using Conda, PyCall
+using CondaPkg, PythonCall
 
 @testset "JET checks" begin
     rep = report_package("PyQDecoders";
         report_pass=MayThrowIsOk(),
         ignored_modules=(
-            AnyFrameModule(Conda),
-            AnyFrameModule(PyCall),
+            AnyFrameModule(CondaPkg),
+            AnyFrameModule(PythonCall),
         )
     )
     @show rep