bugfixes

Author: pancetta

.travis.yml

@@ -16,7 +16,7 @@ before_install:
   - conda update --yes conda
 
 install:
-  - conda install --yes python=${TRAVIS_PYTHON_VERSION} numpy future scipy matplotlib nose sphinx pep8 coverage mpi4py sympy dill
+  - conda install --yes python=${TRAVIS_PYTHON_VERSION} numpy future scipy matplotlib nose sphinx pep8 coverage mpi4py sympy dill numba
   - conda list
   - pip install --user coloredlogs
   - pip install --user travis-sphinx

docs/source/index.rst

@@ -11,6 +11,11 @@ The code is hosted on GitHub, see `https://github.com/Parallel-in-Time/pySDC <ht
 News
 ----
 
+- February 8, 2018: Ever got annoyed at `pySDC`'s incredibly slow setup phase when multiple time-steps are used? Version 2.3
+  changes this by copying the data structure of the first step to all other steps using the `dill Package <https://pypi.python.org/pypi/dill>`_.
+  Setup times could be reduced by 90% and more for certain problems. We also increase the speed for certain calculations,
+  in particular for the Penning trap example.
+
 - November 7, 2017: Version 2.2 contains matrix-based versions of PFASST within the project ``matrixPFASST``. This involved quite a few
   changes in more or less unexpected places, e.g. in the multigrid controller and the transfer base class. The impact
   of these changes on other projects should be negligible, though.

pySDC/playgrounds/parallel/__init__.py

@@ -42,7 +42,7 @@ def check_Q_transfer(collclass):
 
         for order in range(2,coll_coarse.num_nodes+1):
 
-            Pcoll = th.interpolation_matrix_1d(fine_grid, coarse_grid, k=order, pad=0)
+            Pcoll = th.interpolation_matrix_1d(fine_grid, coarse_grid, k=order, pad=0, equidist_nested=False)
             Rcoll = th.restriction_matrix_1d(fine_grid, coarse_grid, k=order, pad=0)
 
             for polyorder in range(1,order+2):
@@ -87,7 +87,7 @@ def check_Q_transfer_minimal(collclass):
         fine_grid = coll_fine.nodes
         coarse_grid = coll_coarse.nodes
 
-        Pcoll = th.interpolation_matrix_1d(fine_grid, coarse_grid, k=2, pad=0)
+        Pcoll = th.interpolation_matrix_1d(fine_grid, coarse_grid, k=2, pad=0, equidist_nested=False)
         Rcoll = th.restriction_matrix_1d(fine_grid, coarse_grid, k=2, pad=0)
 
         for polyorder in range(1,3):

pySDC/playgrounds/parallel/playground.py

@@ -6,4 +6,5 @@ matplotlib>=1.5.3
 sympy>=1.0
 pep8
 coloredlogs
-dill>=0.2.6
+dill>=0.2.6
+numba>=0.35