use PythonCall instead of PyCall (#4)

* use PythonCall instead of PyCall

* remove build.jl

* add concurrency blob

* drop stale comment

* README update

* Update README.md

---------

Co-authored-by: mich11 <michelle.fogerson@beacon.bio>

.github/workflows/CI.yml

@@ -1,4 +1,7 @@
 name: CI
+concurrency:
+  group: ${{ github.head_ref }}.ci
+  cancel-in-progress: true
 on:
   push:
       paths-ignore:
@@ -18,7 +21,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1' # currently 1.6, but will hopefully change in the near future
+          - '1'
           - '1.6'
           - 'nightly'
         os:

.gitignore

@@ -22,3 +22,5 @@ docs/site/
 # committed for packages, but should be committed for applications that require a static
 # environment.
 Manifest.toml
+
+.CondaPkg/

CondaPkg.toml

@@ -0,0 +1,10 @@
+channels = ["anaconda", "conda-forge"]
+[deps]
+# Conda package names and versions
+python = ">=3.5,<4"
+
+# CondaPkg uses mamba by default
+fooof = ">=1.0, <2"
+
+[pip.deps]
+# Pip package names and versions

Project.toml

@@ -1,11 +1,21 @@
 name = "PyFOOOF"
 uuid = "bb993ee8-2a51-46a1-a315-55ff424041b4"
 authors = ["Beacon Biosignals, Inc."]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
-PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [compat]
-PyCall = "1.90"
+Aqua = "0.5, 0.6"
+PythonCall = "0.9"
+Reexport = "1"
 julia = "1.6"
+
+[extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Aqua", "Test"]

src/PyFOOOF.jl

@@ -1,19 +1,25 @@
 module PyFOOOF
 
-using PyCall
-
 #####
-##### init
+##### Dependencies
 #####
 
-const fooof = PyNULL()
+using Reexport
+
+@reexport using PythonCall
+
+######
+###### Actual functionality
+######
+
+const fooof = PythonCall.pynew()
 
 function __init__()
     # all of this is __init__() so that it plays nice with precompilation
-    # see https://github.com/JuliaPy/PyCall.jl/#using-pycall-from-julia-modules
-    copy!(fooof, pyimport("fooof"))
+    # see https://github.com/cjdoris/PythonCall.jl/blob/5ea63f13c291ed97a8bacad06400acb053829dd4/src/Py.jl#L85-L96
+    PythonCall.pycopy!(fooof, pyimport("fooof"))
     # don't eval into the module while precompiling; this breaks precompilation
-    # of downstream modules
+    # of downstream modules (see #4)
     if ccall(:jl_generating_output, Cint, ()) == 0
         # delegate everything else to fooof
         for pn in propertynames(fooof)

test/runtests.jl

@@ -1,7 +1,11 @@
-using PyCall
+using Aqua
 using PyFOOOF
 using Test
 
+@testset "Aqua" begin
+    Aqua.test_all(PyFOOOF; ambiguities=false)
+end
+
 # test that this works
 fooof = PyFOOOF.fooof
 
@@ -24,19 +28,23 @@ set_random_seed =  pyimport("fooof.sim.utils").set_random_seed
 
 f_range = [1, 50]
 ap_params = [20, 2]
-# XXX PyCall's aggressive conversion to NumPy arrays creates problems here
-# so we force it be treated as raw Python code (py-prefix)
-# and NOT be converted to a native Julia array (o-suffix)
-gauss_params = py"[[10, 1.0, 2.5], [20, 0.8, 2], [32, 0.6, 1]]"
+# FOOOF requires a list of lists for this, so we need to guarantee
+# that Vector{<:Number} is not coverted to NumPy arrays
+# NB: broadcasting so that we don't have a list of Vector{<:Number}, which
+# would become a a list of NumPy arrays
+gauss_params = pylist.([[10, 1.0, 2.5], [20, 0.8, 2], [32, 0.6, 1]])
 nlv = 0.1
 set_random_seed(21)
 
 freqs, spectrum = gen_power_spectrum(f_range, ap_params, gauss_params, nlv)
 
-fm = FOOOF(; peak_width_limits=py"[1, 8]"o, max_n_peaks=6, min_peak_height=0.4)
+fm = FOOOF(; peak_width_limits=[1, 8], max_n_peaks=6, min_peak_height=0.4)
 fm.fit(freqs, spectrum)
 
-ground_truth = float.(py"$(gauss_params)")
-actual_estimates = fm.gaussian_params_
+# not worth adding a compat dependency just to get stack() for one test,
+# ew this is messy without stack()
+ground_truth = mapreduce(x -> pyconvert(Vector{Float64}, x)',
+                         vcat, gauss_params; init=Matrix{Float64}(undef, 0, 3))
+actual_estimates = pyconvert(Matrix, fm.gaussian_params_)
 expected_estimates = [9.82  0.91  2.43; 20.03  0.82  1.94; 31.85  0.57  1.11]
 @test actual_estimates ≈ expected_estimates atol=0.01