Update headers, readme etc.

README.rst

@@ -91,6 +91,10 @@ effort to translate the original Sorting routine for real Schur forms `SRSchur`_
 into `Python code`_,
 M. Weber and `Alexander Sikorski`_ for pointing us to `SLEPc`_ for sorted partial Schur decompositions,
 and A. Sikorski for supplying us with an `code example`_ and guidance how to interface SLEPc in Python.
+The development of *pyGPCCA* started at the beginning of 2020 in a fork of `MSMTools`_,
+since at this time it was planned to integrate GPCCA into it.
+Due to this, some similarities in structure and code (indicated were evident) can be found.
+Futher the utility functions found in `pygpcca/utils/_utils.py`_ originate from MSMTools.
 
 .. _`Marcus Weber`: https://www.zib.de/members/weber
 .. _`CMD`: https://www.zib.de/numeric/cmd
@@ -102,6 +106,8 @@ and A. Sikorski for supplying us with an `code example`_ and guidance how to int
 .. _`Alexander Sikorski`: https://www.zib.de/members/sikorski
 .. _`SLEPc`: https://slepc.upv.es/
 .. _`code example`: https://github.com/zib-cmd/cmdtools/blob/1c6b6d8e1c35bb487fcf247c5c1c622b4b665b0a/src/cmdtools/analysis/pcca.py#L64
+.. _`MSMTools`: https://github.com/markovmodel/msmtools
+.. _`pygpcca/utils/_utils.py`: https://github.com/msmdev/pyGPCCA/blob/main/pygpcca/utils/_utils.py
 
 .. |br| raw:: html
 

docs/source/acknowledgments.rst

@@ -9,6 +9,10 @@ effort to translate the original Sorting routine for real Schur forms `SRSchur`_
 into `Python code`_,
 M. Weber and `Alexander Sikorski`_ for pointing us to `SLEPc`_ for sorted partial Schur decompositions,
 and A. Sikorski for supplying us with an `code example`_ and guidance how to interface SLEPc in Python.
+The development of *pyGPCCA* started at the beginning of 2020 in a fork of `MSMTools`_,
+since at this time it was planned to integrate GPCCA into it.
+Due to this, some similarities in structure and code (indicated were evident) can be found.
+Futher the utility functions found in `pygpcca/utils/_utils.py`_ originate from MSMTools.
 
 .. _`Marcus Weber`: https://www.zib.de/members/weber
 .. _`CMD`: https://www.zib.de/numeric/cmd
@@ -20,3 +24,5 @@ and A. Sikorski for supplying us with an `code example`_ and guidance how to int
 .. _`Alexander Sikorski`: https://www.zib.de/members/sikorski
 .. _`SLEPc`: https://slepc.upv.es/
 .. _`code example`: https://github.com/zib-cmd/cmdtools/blob/1c6b6d8e1c35bb487fcf247c5c1c622b4b665b0a/src/cmdtools/analysis/pcca.py#L64
+.. _`MSMTools`: https://github.com/markovmodel/msmtools
+.. _`pygpcca/utils/_utils.py`: https://github.com/msmdev/pyGPCCA/blob/main/pygpcca/utils/_utils.py

pygpcca/_gpcca.py

@@ -4,28 +4,35 @@
 # With contributions of Marius Lange and Michal Klein.
 # Based on the original MATLAB GPCCA code authored by Bernhard Reuter, Susanna Roeblitz and Marcus Weber,
 # Zuse Institute Berlin, Takustrasse 7, 14195 Berlin
-# --------------------------------------------------
+# ---------------------------------------------------------------------------------------------------------------------
+# The development of pyGPCCA started at the beginning of 2020 in a fork of MSMTools
+# (Copyright (c) 2015, 2014 Computational Molecular Biology Group, Freie Universitaet Berlin (GER);
+# provided under LGPL-3.0 License), since at this time it was planned to integrate GPCCA into it.
+# Due to this, some similarities in structure/AST and code (indicated were evident) between pcca.py
+# https://github.com/markovmodel/msmtools/blob/93126608c6fa9c3197f4fae2f6da93140762b047/msmtools/analysis/dense/pcca.py
+# and _gpcca.py can be found.
+# ---------------------------------------------------------------------------------------------------------------------
 # If you use this code or parts of it, cite the following reference:
-# ------------------------------------------------------------------
+# ---------------------------------------------------------------------------------------------------------------------
 # Reuter, B., Weber, M., Fackeldey, K., Röblitz, S., & Garcia, M. E. (2018).
 # Generalized Markov State Modeling Method for Nonequilibrium Biomolecular Dynamics:
 # Exemplified on Amyloid β Conformational Dynamics Driven by an Oscillating Electric Field.
 # Journal of Chemical Theory and Computation, 14(7), 3579–3594. https://doi.org/10.1021/acs.jctc.8b00079
-# ----------------------------------------------------------------
-# pyGPCCA 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.
+# ---------------------------------------------------------------------------------------------------------------------
+# pyGPCCA 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.
+# 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/>.
+# 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/>.
+# ---------------------------------------------------------------------------------------------------------------------
+
 __author__ = __maintainer__ = "Bernhard Reuter"
-__email__ = "bernhard.reuter AT uni-tuebingen DOT de"
+__email__ = "bernhard-reuter@gmx.de"
 __copyright__ = "Copyright 2020, Bernhard Reuter"
 __credits__ = [
     "Bernhard Reuter",
@@ -957,6 +964,9 @@ def optimize(
         if min(m_list) in [0, 1]:
             raise ValueError(f"There is no point in clustering into `{m}` clusters.")
 
+        # The following code enclosed by >>>... ...<<< originates (with some adjustments) from MSMTools
+        # Copyright (c) 2015, 2014 Computational Molecular Biology Group, Freie Universitaet Berlin (GER).
+        # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
         # test connectivity
         components = connected_sets(self._P)
         n_components = len(components)
@@ -970,6 +980,7 @@ def optimize(
             if np.sum(self._P[component, :][:, rest]) == 0:
                 closed_components.append(component)
         n_closed_components = len(closed_components)
+        # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
         # Calculate Schur matrix R and Schur vector matrix X, if not adequately given.
         self._do_schur_helper(max(m_list))
@@ -1138,6 +1149,12 @@ def macrostate_assignment(self) -> OArray:
         -------
         Integer vector of shape `(n,)` containing the macrostate
         each microstate is located in.
+
+        Credits
+        -------
+        The code and docstring of this property origins (with some adjustments) from MSMTools
+        Copyright (c) 2015, 2014 Computational Molecular Biology Group
+        Freie Universitaet Berlin (GER)
         """
         return None if self.memberships is None else np.argmax(self.memberships, axis=1)
 
@@ -1155,6 +1172,12 @@ def macrostate_sets(self) -> Optional[List[np.ndarray]]:
         A list of length equal to :attr:`n_m`.
 
         Each element is an array with microstate indexes contained in it.
+
+        Credits
+        -------
+        The code and docstring of this property origins (with some adjustments) from MSMTools
+        Copyright (c) 2015, 2014 Computational Molecular Biology Group
+        Freie Universitaet Berlin (GER)
         """
         return (
             None

pygpcca/_sort_real_schur.py

@@ -1,31 +1,30 @@
-# Python version (translated by Fabian Paul and revised by Bernhard Reuter)
-# of the following original work:
+# Python version (translated by Fabian Paul; revised by Bernhard Reuter, Michal Klein) of the following original work:
+# --------------------------------------------------------------------------------------------------------------------
 # Title:	Sorting Real Schur Forms
 # Author:	Jan Brandts
 # E-Mail:	brandts-AT-science.uva.nl
 # http://m2matlabdb.ma.tum.de/download.jsp?MC_ID=3&MP_ID=119
 # http://dx.doi.org/10.1002/nla.274
 # Institution:	University of Amsterdam
-# Description:	In Matlab 6, there exists a command to generate a real Schur
-# form, wheras another transforms a real Schur form into a complex one.
-# There do not exist commands to prescribe the order in which the eigenvalues
-# appear on the diagonal of the upper (quasi-) triangular factor T.
-# For the complex case, a routine is sketched in Golub and Van Loan (1996),
+# Description:	In Matlab 6, there exists a command to generate a real Schur form,
+# wheras another transforms a real Schur form into a complex one.
+# There do not exist commands to prescribe the order in which the eigenvalues appear on the diagonal of the upper
+# (quasi-) triangular factor T. For the complex case, a routine is sketched in Golub and Van Loan (1996),
 # that orders the diagonal of T according to their distance to a target value.
-# In the reference below, we give a Matlab routine to sort real Schur forms
-# in Matlab. It is based on a block-swapping procedure by Bai and Demmel (1993).
-# Sorting real Schur forms, both partially and completely, has important
-# applications in the computation of real invariant subspaces.
+# In the reference below, we give a Matlab routine to sort real Schur forms in Matlab.
+# It is based on a block-swapping procedure by Bai and Demmel (1993).
+# Sorting real Schur forms, both partially and completely,
+# has important applications in the computation of real invariant subspaces.
 # Reference:    J.H. Brandts. Matlab code for sorted real Schur forms.
 # Numerical Linear Algebra with Applications 9(3):249-261 (2002)
 # Keywords:	    Real Schur Form, sorting, Bai-Demmel algorithm, swapping
 # Based on the original Matlab File Version: 1.0
-# -------------------------------------------------------------------------
+# --------------------------------------------------------------------------------------------------------------------
 # All references to equations or pages made in the comments are referencing
 # Jan Brandts. Matlab Code for Sorted Real Schur Forms. Preprint No. 1180,
 # January, 2001, Universiteit Utrecht,
 # https://www.math.uu.nl/publications/preprints/1180.pdf
-# -------------------------------------------------------------------------
+# --------------------------------------------------------------------------------------------------------------------
 
 from typing import List, Tuple, Union
 

pygpcca/_sorted_schur.py

@@ -9,27 +9,25 @@
 # Further parts of sorted_krylov_schur were developed based on the function krylov_schur
 # https://github.com/zib-cmd/cmdtools/blob/1c6b6d8e1c35bb487fcf247c5c1c622b4b665b0a/src/cmdtools/analysis/pcca.py#L64,
 # written by Alexander Sikorski.
-# --------------------------------------------------
+# --------------------------------------------------------------------------------------------------------------------
 # If you use this code or parts of it, cite the following reference:
-# ------------------------------------------------------------------
+# --------------------------------------------------------------------------------------------------------------------
 # Reuter, B., Weber, M., Fackeldey, K., Röblitz, S., & Garcia, M. E. (2018).
 # Generalized Markov State Modeling Method for Nonequilibrium Biomolecular Dynamics:
 # Exemplified on Amyloid β Conformational Dynamics Driven by an Oscillating Electric Field.
 # Journal of Chemical Theory and Computation, 14(7), 3579–3594. https://doi.org/10.1021/acs.jctc.8b00079
-# ----------------------------------------------------------------
-# pyGPCCA 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.
+# --------------------------------------------------------------------------------------------------------------------
+# pyGPCCA 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.
+# 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/>.
-
+# 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 typing import Tuple, Union
 import sys