Merge pull request #117 from marscher/ceff_no_multiprocess

do not require multiprocess, but use stdlib in efficient_count_matrix parallelization

Author: marscher

appveyor.yml

@@ -19,7 +19,6 @@ environment:
 
 install:
   - "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%;"
-  - conda config --add channels conda-forge
   - conda install conda-build -yq -c defaults
   # Build the compiled extension and run the project tests
   - "%CMD_IN_ENV% conda build -c conda-forge -q tools/conda-recipe --numpy=%CONDA_NUMPY%"

doc/source/CHANGELOG.rst

@@ -1,6 +1,20 @@
 Changelog
 =========
 
+1.2.4 (11-12-18)
+---------------
+
+**Fixes:**
+- Effective count matrix parallel evaluation fixes. #116, #117
+
+1.2.3 (7-27-18)
+---------------
+
+**New features**
+
+- Effective count matrix can be evaluated in parallel. #112
+
+
 1.2.2 (6-25-18)
 ---------------
 

msmtools/estimation/sparse/effective_counts.py

@@ -199,12 +199,7 @@ def statistical_inefficiencies(dtrajs, lag, C=None, truncate_acf=True, mact=2.0,
     # compute inefficiencies
     I, J = C.nonzero()
     if n_jobs > 1:
-        try:
-            from multiprocess.pool import Pool, MapResult
-        except ImportError:
-            raise RuntimeError('using multiple jobs requires the multiprocess library. '
-                               'Install it with conda or pip')
-
+        from multiprocessing.pool import Pool, MapResult
         from contextlib import closing
         import tempfile
 

msmtools/estimation/tests/test_effective_count_matrix.py

@@ -27,13 +27,6 @@
 
 """Unit tests for the transition_matrix module"""
 
-have_multiprocess_lib = True
-try:
-    import multiprocess
-    del multiprocess
-except ImportError:
-    have_multiprocess_lib = False
-
 
 class TestEffectiveCountMatrix(unittest.TestCase):
 
@@ -70,9 +63,7 @@ def test_multitraj(self):
         assert np.array_equal(C.nonzero(), Ceff.nonzero())
         assert np.all(Ceff.toarray() <= C.toarray())
 
-    @unittest.skipIf(not have_multiprocess_lib, 'multiprocess lib missing')
     def test_multitraj_njobs(self):
-        import _multiprocess
         dtrajs = [[1, 0, 1, 0, 1, 1, 0, 0, 0, 1], [2], [0, 1, 0, 1]]
         # lag 1
         C = count_matrix(dtrajs, 1)
@@ -94,8 +85,8 @@ def test_multitraj_njobs(self):
         assert np.array_equal(Ceff2.shape, C.shape)
         assert np.array_equal(C.nonzero(), Ceff2.nonzero())
         assert np.all(Ceff2.toarray() <= C.toarray())
-
-    @unittest.skipIf(os.getenv('CI', False), 'need physical processors >=2, dont have on CI')
+    
+    @unittest.skipIf(os.getenv('CI', False), 'need physical cores')
     def test_njobs_speedup(self):
         artificial_dtraj = [np.random.randint(0, 100, size=10000) for _ in range(10)]
         import time

tools/conda-recipe/meta.yaml

@@ -37,7 +37,6 @@ test:
   requires:
     - pytest
     - pytest-cov
-    - multiprocess
 
 about:
   home: http://github.com/markovmodel/msmtools