Merge pull request #119 from marscher/cleanup

Cleanup test structure, setup now numpy based (like sklearn)

Author: marscher

.travis.yml

@@ -5,7 +5,6 @@ env:
   global:
     - PATH=$HOME/miniconda/bin:$PATH
   matrix:
-    - python=2.7  CONDA_PY=27  CONDA_NPY=18
     - python=3.6  CONDA_PY=36  CONDA_NPY=111
     - python=3.7  CONDA_PY=37  CONDA_NPY=115
 
@@ -14,7 +13,7 @@ before_install:
 - conda install conda-build=3.15 conda-verify -qy
 
 script:
-- conda build tools/conda-recipe --numpy=$CONDA_NPY -c conda-forge/label/gcc7
+- conda build tools/conda-recipe --numpy=$CONDA_NPY -c conda-forge
 
 after_script:
 - bash <(curl -s https://codecov.io/bash) -f $HOME/coverage.xml -e CONDA_PY

MANIFEST.in

@@ -13,3 +13,6 @@ recursive-include msmtools *.pyx *.c *.h
 
 include README*
 include LICENSE*
+
+include pyproject.toml
+

appveyor.yml

@@ -8,9 +8,6 @@ environment:
     PYTHON: "C:\\Miniconda3-x64"
 
   matrix:
-    - CONDA_PY: "27"
-      CONDA_NUMPY: "19"
-
     - CONDA_PY: "36"
       CONDA_NUMPY: "114"
 
@@ -21,8 +18,6 @@ install:
   - "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%;"
   - 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%"
-
+  - conda build -c conda-forge -q tools/conda-recipe --numpy=%CONDA_NUMPY%"
 
 build: false
-

msmtools/__init__.py

@@ -36,7 +36,6 @@
 
 
 """
-from __future__ import absolute_import
 
 from . import analysis
 from . import estimation

msmtools/analysis/__init__.py

@@ -108,6 +108,5 @@
    expectation_sensitivity
 
 """
-from __future__ import absolute_import
 
-from .api import *
+from .api import *

msmtools/analysis/api.py

@@ -23,7 +23,6 @@
 =======================
 
 """
-from __future__ import absolute_import
 
 
 import warnings
@@ -32,10 +31,10 @@
 from scipy.sparse import issparse as _issparse
 from scipy.sparse import csr_matrix as _csr_matrix
 
-from msmtools.util.annotators import shortcut
+from ..util.annotators import shortcut
 
 # type-checking
-from msmtools.util import types as _types
+from ..util import types as _types
 
 from . import dense
 from . import sparse
@@ -63,7 +62,6 @@
            'mfpt',
            'committor',
            'hitting_probability',
-           'pcca',
            'pcca_sets',
            'pcca_assignments',
            'pcca_distributions',
@@ -1450,42 +1448,6 @@ def _pcca_object(T, m):
     return dense.pcca.PCCA(T, m)
 
 
-def pcca(T, m):
-    r"""Compute meta-stable sets using PCCA++ _[1] and return the membership of all states to these sets.
-
-    Parameters
-    ----------
-    T : (n, n) ndarray or scipy.sparse matrix
-        Transition matrix
-    m : int
-        Number of metastable sets
-
-    Returns
-    -------
-    clusters : (n, m) ndarray
-        Membership vectors. clusters[i, j] contains the membership of state i to metastable state j
-
-    Notes
-    -----
-    Perron cluster center analysis assigns each microstate a vector of
-    membership probabilities. This assignement is performed using the
-    right eigenvectors of the transition matrix. Membership
-    probabilities are computed via numerical optimization of the
-    entries of a membership matrix.
-
-    References
-    ----------
-    .. [1] Roeblitz, S and M Weber. 2013. Fuzzy spectral clustering by
-        PCCA+: application to Markov state models and data
-        classification. Advances in Data Analysis and Classification 7
-        (2): 147-179
-
-    """
-    warnings.warn('pcca method is deprecated because of its unspecific naming and will be removed soon. '+
-                  'Use pcca_memberships in the future.', DeprecationWarning)
-    return pcca_memberships(T, m)
-
-
 def pcca_memberships(T, m):
     r"""Compute meta-stable sets using PCCA++ _[1] and return the membership of all states to these sets.
 
@@ -1846,4 +1808,4 @@ def expectation_sensitivity(T, a):
         _showSparseConversionWarning()
         return dense.sensitivity.expectation_sensitivity(T.toarray(), a)
     else:
-        return dense.sensitivity.expectation_sensitivity(T, a)
+        return dense.sensitivity.expectation_sensitivity(T, a)

msmtools/analysis/dense/__init__.py

@@ -16,7 +16,6 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-from __future__ import absolute_import
 
 from . import assessment
 from . import committor
@@ -27,4 +26,4 @@
 from . import hitting_probability
 from . import mean_first_passage_time
 from . import sensitivity
-from . import pcca
+from . import pcca

msmtools/analysis/dense/assessment.py

@@ -16,7 +16,6 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-from __future__ import absolute_import
 import numpy as np
 
 # import decomposition
@@ -104,4 +103,4 @@ def is_reversible(T, mu=None, tol=1e-10):
         X = mu[:, np.newaxis] * T
         return np.allclose(X, np.transpose(X),  atol=tol)
     else:
-        raise ValueError("given matrix is not a valid transition matrix.")
+        raise ValueError("given matrix is not a valid transition matrix.")

msmtools/analysis/dense/committor.py

@@ -22,8 +22,6 @@
 .. moduleauthor:: B.Trendelkamp-Schroer <benjamin DOT trendelkamp-schroer AT fu-berlin DOT de>
 
 """
-from __future__ import absolute_import, division
-from six.moves import range
 
 import numpy as np
 from scipy.linalg import solve
@@ -160,4 +158,4 @@ def backward_committor(T, A, B, mu=None):
 
     u = solve(W, r)
 
-    return u
+    return u

msmtools/analysis/dense/correlations.py

@@ -21,12 +21,10 @@
 
 .. moduleauthor:: marscher, noe
 '''
-from __future__ import absolute_import
 
 import numpy as np
 
 from .decomposition import rdl_decomposition
-from six.moves import range
 
 
 def time_correlation_by_diagonalization(P, pi, obs1, obs2=None, time=1, rdl=None):
@@ -297,4 +295,4 @@ def time_relaxations_direct(P, p0, obs, times=[1]):
         start_values = None
         for i in range(n_t):
             f[i], start_values = time_relaxation_direct_by_mtx_vec_prod(P, p0, obs, times[i], start_values, True)
-    return f
+    return f