Merge pull request #36 from tkf/fix

Fix DAE and DDE tests

.travis.yml

@@ -23,4 +23,12 @@ before_install:
 
   - pip install --quiet tox-travis
 script:
+
+  # Create an environment first so that we know what to use for $PYTHON:
+  - tox --notest
+
+  # PyCall requires to know the right Python executable to use:
+  - export PYTHON="$(echo $PWD/.tox/py*/bin/python)"
+  - echo "$PYTHON"
+
   - tox -- --cov diffeqpy

README.md

@@ -144,10 +144,11 @@ sol = de.solve(prob)
 
 Additionally, you can directly define the functions in Julia. This will allow
 for more specialization and could be helpful to increase the efficiency over
-the Numba version for repeat or long calls. This is done via `de.eval`:
+the Numba version for repeat or long calls. This is done via `julia.Main.eval`:
 
 ```py
-jul_f = de.eval("(u,p,t)->-u") # Define the anonymous function in Julia
+from julia import Main
+jul_f = Main.eval("(u,p,t)->-u") # Define the anonymous function in Julia
 prob = de.ODEProblem(jul_f, u0, tspan)
 sol = de.solve(prob)
 ```
@@ -214,7 +215,7 @@ sol = de.solve(prob)
 or using a Julia function:
 
 ```py
-jul_f = de.eval("""
+jul_f = Main.eval("""
 function f(du,u,p,t)
   x, y, z = u
   sigma, rho, beta = p
@@ -366,7 +367,7 @@ def f(du,u,p,t):
   resid3 = u[0] + u[1] + u[2] - 1.0
   return [resid1,resid2,resid3]
 
-u0 = [1.0, 0, 0]
+u0 = [1.0, 0.0, 0.0]
 du0 = [-0.04, 0.04, 0.0]
 tspan = (0.0,100000.0)
 differential_vars = [True,True,False]
@@ -404,14 +405,14 @@ the solver accuracy by accurately stepping at the points of discontinuity.
 Together this is:
 
 ```py
-f = de.eval("""
+f = Main.eval("""
 function f(du, u, h, p, t)
   du[1] = 1.1/(1 + sqrt(10)*(h(p, t-20)[1])^(5/4)) - 10*u[1]/(1 + 40*u[2])
   du[2] = 100*u[1]/(1 + 40*u[2]) - 2.43*u[2]
 end""")
 u0 = [1.05767027/3, 1.030713491/3]
 
-h = de.eval("""
+h = Main.eval("""
 function h(p,t)
   [1.05767027/3, 1.030713491/3]
 end

appveyor.yml

@@ -7,18 +7,16 @@ environment:
   matrix:
 
     # For Python versions available on Appveyor, see
-    # http://www.appveyor.com/docs/installed-software#python
-    # The list here is complete (excluding Python 2.6, which
-    # isn't covered by this document) at the time of writing.
+    # https://www.appveyor.com/docs/windows-images-software/#python
 
-    - PYTHON: "C:\\Python27-x64"
+    - PYTHONDIR: "C:\\Python27-x64"
       JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/1.0/julia-1.0.1-win64.exe"
-    - PYTHON: "C:\\Python36-x64"
+    - PYTHONDIR: "C:\\Python36-x64"
       JULIA_URL: "https://julialang-s3.julialang.org/bin/winnt/x64/1.0/julia-1.0.1-win64.exe"
 
 matrix:
   allow_failures:
-    - PYTHON: "C:\\Python27-x64"
+    - PYTHONDIR: "C:\\Python27-x64"
 
 notifications:
   - provider: Email
@@ -38,14 +36,15 @@ install:
   - set PATH=C:\projects\julia\bin;%PATH%
 
 # Install Python packages
-  - "%PYTHON%\\python.exe -m pip install --quiet tox"
+  - "%PYTHONDIR%\\python.exe -m pip install --quiet tox"
 
 build_script:
   # Empty so that it doesn't think it needs Visual Studio?
   - echo 'test'
 
 test_script:
-  - "%PYTHON%\\scripts\\tox -- --cov diffeqpy"
+  - "SET PYTHON=%PYTHONDIR%\\python.exe"
+  - "%PYTHONDIR%\\scripts\\tox -- --cov diffeqpy"
 
 artifacts:
   # bdist_wheel puts your built wheel in the dist directory

diffeqpy/install.jl

@@ -1,5 +1,6 @@
 using Pkg
 Pkg.add("DifferentialEquations")
+Pkg.add("DiffEqBase")
 Pkg.add("PyCall")
 using DifferentialEquations
 using PyCall

diffeqpy/tests/test_dae.py

@@ -8,7 +8,7 @@ def f(du,u,p,t):
         resid3 = u[0] + u[1] + u[2] - 1.0
         return [resid1,resid2,resid3]
 
-    u0 = [1.0, 0, 0]
+    u0 = [1.0, 0.0, 0.0]
     du0 = [-0.04, 0.04, 0.0]
     tspan = (0.0,100000.0)
     differential_vars = [True,True,False]

diffeqpy/tests/test_dde.py

@@ -1,16 +1,23 @@
+from julia import Main
+
 from .. import de
 
 
 def test():
-    def f(du,u,p,t):
-        resid1 = - 0.04*u[0]               + 1e4*u[1]*u[2] - du[0]
-        resid2 = + 0.04*u[0] - 3e7*u[1]**2 - 1e4*u[1]*u[2] - du[1]
-        resid3 = u[0] + u[1] + u[2] - 1.0
-        return [resid1,resid2,resid3]
+    f = Main.eval("""
+    function f(du, u, h, p, t)
+      du[1] = 1.1/(1 + sqrt(10)*(h(p, t-20)[1])^(5/4)) - 10*u[1]/(1 + 40*u[2])
+      du[2] = 100*u[1]/(1 + 40*u[2]) - 2.43*u[2]
+    end""")
+    u0 = [1.05767027/3, 1.030713491/3]
+
+    h = Main.eval("""
+    function h(p,t)
+      [1.05767027/3, 1.030713491/3]
+    end
+    """)
 
-    u0 = [1.0, 0, 0]
-    du0 = [-0.04, 0.04, 0.0]
-    tspan = (0.0,100000.0)
-    differential_vars = [True,True,False]
-    prob = de.DAEProblem(f,du0,u0,tspan,differential_vars=differential_vars)
-    sol = de.solve(prob)
+    tspan = (0.0, 100.0)
+    constant_lags = [20.0]
+    prob = de.DDEProblem(f,u0,h,tspan,constant_lags=constant_lags)
+    sol = de.solve(prob,saveat=0.1)

tox.ini

@@ -21,3 +21,8 @@ passenv =
     # Allow a workaround for "error initializing LibGit2 module":
     # https://github.com/JuliaLang/julia/issues/18693
     SSL_CERT_FILE
+
+    # PyCall uses $PYTHON to configure Python executable.  It is
+    # useful to specify this in CI so that `diffeqpy.install()` can
+    # directly configure PyCall.
+    PYTHON