[msm] split up separate MSM estimators into individual files (#1390)

* [msm] split up separate MSM estimators into individual files

Rationale:
The maximum-likelihood module contained a base class, ml, oom and augmented
estimators adding up to >2k lines of code, which makes it hard to maintain.

Fixes #1260

devtools/conda-recipe/meta.yaml

@@ -68,8 +68,7 @@ test:
     - pytest
     - pytest-cov
     - coverage
-   # TODO: disabled on win64, until https://bugs.python.org/issue31701 is fixed.
-    - pytest-faulthandler # [not win]
+    - pytest-faulthandler 
     - pytest-xdist
     - scikit-learn
     # https://github.com/Frozenball/pytest-sugar/issues/124

pyemma/msm/__init__.py

@@ -57,7 +57,7 @@
    BayesianHMSM
 
 
-**Models** of the kinetics or stationary properties of the data. 
+**Models** of the kinetics or stationary properties of the data.
 If you are not an expert user, use the API functions above.
 
 .. autosummary::
@@ -84,14 +84,14 @@
 
 #####################################################
 # Estimators and models
+from .models import MSM, HMSM, SampledMSM, SampledHMSM, ReactiveFlux
+
 from .estimators import MaximumLikelihoodMSM, BayesianMSM
 from .estimators import MaximumLikelihoodHMSM, BayesianHMSM
-from .estimators import OOMReweightedMSM
-from .estimators import AugmentedMarkovModel 
+from .estimators import AugmentedMarkovModel, OOMReweightedMSM
 from .estimators import ImpliedTimescales
 from .estimators import ChapmanKolmogorovValidator
 
-from .models import MSM, HMSM, SampledMSM, SampledHMSM, ReactiveFlux
 
 # high-level api
 from .api import *

pyemma/msm/api.py

@@ -57,7 +57,6 @@
 # =============================================================================
 
 
-# TODO: show_progress is not documented
 @shortcut('its')
 def timescales_msm(dtrajs, lags=None, nits=None, reversible=True, connected=True, weights='empirical',
                    errors=None, nsamples=50, n_jobs=None, show_progress=True, mincount_connectivity='1/n',
@@ -119,6 +118,9 @@ def timescales_msm(dtrajs, lags=None, nits=None, reversible=True, connected=True
     n_jobs : int, optional
         how many subprocesses to start to estimate the models for each lag time.
 
+    show_progress : bool, default=True
+        whether to show progress of estimation.
+
     mincount_connectivity : float or '1/n'
         minimum number of counts to consider a connection between two states.
         Counts lower than that will count zero in the connectivity check and

pyemma/msm/estimators/_OOM_MSM.py

@@ -1,3 +1,21 @@
+
+# This file is part of PyEMMA.
+#
+# Copyright (c) 2014-2019 Computational Molecular Biology Group, Freie Universitaet Berlin (GER)
+#
+# PyEMMA is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# 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/>.
+
 import numpy as np
 import scipy.linalg as scl
 import scipy.sparse

pyemma/msm/estimators/__init__.py

@@ -19,8 +19,8 @@
 __author__ = 'noe'
 
 from .maximum_likelihood_msm import MaximumLikelihoodMSM
-from .maximum_likelihood_msm import OOMReweightedMSM
-from .maximum_likelihood_msm import AugmentedMarkovModel
+from .oom_reweighted_msm import OOMReweightedMSM
+from .augmented_msm import AugmentedMarkovModel
 from .bayesian_msm import BayesianMSM
 from .maximum_likelihood_hmsm import MaximumLikelihoodHMSM
 from .bayesian_hmsm import BayesianHMSM