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value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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47,900 | eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.import_family | def import_family(self, rfa_file):
"""Append a import family entry to the journal.
This instructs Revit to import a family into the opened model.
Args:
rfa_file (str): full path of the family file
"""
self._add_entry(templates.IMPORT_FAMILY
... | python | def import_family(self, rfa_file):
"""Append a import family entry to the journal.
This instructs Revit to import a family into the opened model.
Args:
rfa_file (str): full path of the family file
"""
self._add_entry(templates.IMPORT_FAMILY
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This instructs Revit to import a family into the opened model.
Args:
rfa_file (str): full path of the family file | [
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47,901 | eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.export_warnings | def export_warnings(self, export_file):
"""Append an export warnings entry to the journal.
This instructs Revit to export warnings from the opened model.
Currently Revit will stop journal execution if the model does not
have any warnings and the export warnings UI button is disabled.
... | python | def export_warnings(self, export_file):
"""Append an export warnings entry to the journal.
This instructs Revit to export warnings from the opened model.
Currently Revit will stop journal execution if the model does not
have any warnings and the export warnings UI button is disabled.
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47,902 | eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.purge_unused | def purge_unused(self, pass_count=3):
"""Append an purge model entry to the journal.
This instructs Revit to purge the open model.
Args:
pass_count (int): number of times to execute the purge.
default is 3
"""
for purge_count in range(0... | python | def purge_unused(self, pass_count=3):
"""Append an purge model entry to the journal.
This instructs Revit to purge the open model.
Args:
pass_count (int): number of times to execute the purge.
default is 3
"""
for purge_count in range(0... | [
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This instructs Revit to purge the open model.
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pass_count (int): number of times to execute the purge.
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47,903 | eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.sync_model | def sync_model(self, comment='', compact_central=False,
release_borrowed=True, release_workset=True,
save_local=False):
"""Append a sync model entry to the journal.
This instructs Revit to sync the currently open workshared model.
Args:
comment... | python | def sync_model(self, comment='', compact_central=False,
release_borrowed=True, release_workset=True,
save_local=False):
"""Append a sync model entry to the journal.
This instructs Revit to sync the currently open workshared model.
Args:
comment... | [
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47,904 | eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.write_journal | def write_journal(self, journal_file_path):
"""Write the constructed journal in to the provided file.
Args:
journal_file_path (str): full path to output journal file
"""
# TODO: assert the extension is txt and not other
with open(journal_file_path, "w") as jrn_file:
... | python | def write_journal(self, journal_file_path):
"""Write the constructed journal in to the provided file.
Args:
journal_file_path (str): full path to output journal file
"""
# TODO: assert the extension is txt and not other
with open(journal_file_path, "w") as jrn_file:
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47,905 | eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalReader.endswith | def endswith(self, search_str):
"""Check whether the provided string exists in Journal file.
Only checks the last 5 lines of the journal file. This method is
usually used when tracking a journal from an active Revit session.
Args:
search_str (str): string to search for
... | python | def endswith(self, search_str):
"""Check whether the provided string exists in Journal file.
Only checks the last 5 lines of the journal file. This method is
usually used when tracking a journal from an active Revit session.
Args:
search_str (str): string to search for
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Only checks the last 5 lines of the journal file. This method is
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47,906 | markovmodel/msmtools | msmtools/estimation/sparse/prior.py | prior_neighbor | def prior_neighbor(C, alpha=0.001):
r"""Neighbor prior of strength alpha for the given count matrix.
Prior is defined by
b_ij = alpha if Z_ij+Z_ji > 0
b_ij = 0 else
Parameters
----------
C : (M, M) scipy.sparse matrix
Count matrix
alpha : float (optional)
... | python | def prior_neighbor(C, alpha=0.001):
r"""Neighbor prior of strength alpha for the given count matrix.
Prior is defined by
b_ij = alpha if Z_ij+Z_ji > 0
b_ij = 0 else
Parameters
----------
C : (M, M) scipy.sparse matrix
Count matrix
alpha : float (optional)
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47,907 | markovmodel/msmtools | msmtools/estimation/sparse/prior.py | prior_const | def prior_const(C, alpha=0.001):
"""Constant prior of strength alpha.
Prior is defined via
b_ij=alpha for all i,j
Parameters
----------
C : (M, M) ndarray or scipy.sparse matrix
Count matrix
alpha : float (optional)
Value of prior counts
Returns
-------
B ... | python | def prior_const(C, alpha=0.001):
"""Constant prior of strength alpha.
Prior is defined via
b_ij=alpha for all i,j
Parameters
----------
C : (M, M) ndarray or scipy.sparse matrix
Count matrix
alpha : float (optional)
Value of prior counts
Returns
-------
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Count matrix
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Value of prior counts
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47,908 | markovmodel/msmtools | msmtools/analysis/dense/assessment.py | is_transition_matrix | def is_transition_matrix(T, tol=1e-10):
"""
Tests whether T is a transition matrix
Parameters
----------
T : ndarray shape=(n, n)
matrix to test
tol : float
tolerance to check with
Returns
-------
Truth value : bool
True, if all elements are in interval [0, ... | python | def is_transition_matrix(T, tol=1e-10):
"""
Tests whether T is a transition matrix
Parameters
----------
T : ndarray shape=(n, n)
matrix to test
tol : float
tolerance to check with
Returns
-------
Truth value : bool
True, if all elements are in interval [0, ... | [
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47,909 | markovmodel/msmtools | msmtools/estimation/sparse/count_matrix.py | count_matrix_coo2_mult | def count_matrix_coo2_mult(dtrajs, lag, sliding=True, sparse=True, nstates=None):
r"""Generate a count matrix from a given list discrete trajectories.
The generated count matrix is a sparse matrix in compressed
sparse row (CSR) or numpy ndarray format.
Parameters
----------
dtraj : list of nda... | python | def count_matrix_coo2_mult(dtrajs, lag, sliding=True, sparse=True, nstates=None):
r"""Generate a count matrix from a given list discrete trajectories.
The generated count matrix is a sparse matrix in compressed
sparse row (CSR) or numpy ndarray format.
Parameters
----------
dtraj : list of nda... | [
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47,910 | markovmodel/msmtools | msmtools/analysis/sparse/assessment.py | is_transition_matrix | def is_transition_matrix(T, tol):
"""
True if T is a transition matrix
Parameters
----------
T : scipy.sparse matrix
Matrix to check
tol : float
tolerance to check with
Returns
-------
Truth value: bool
True, if T is positive and normed
False, otherw... | python | def is_transition_matrix(T, tol):
"""
True if T is a transition matrix
Parameters
----------
T : scipy.sparse matrix
Matrix to check
tol : float
tolerance to check with
Returns
-------
Truth value: bool
True, if T is positive and normed
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47,911 | markovmodel/msmtools | msmtools/analysis/sparse/assessment.py | is_connected | def is_connected(T, directed=True):
r"""Check connectivity of the transition matrix.
Return true, if the input matrix is completely connected,
effectively checking if the number of connected components equals one.
Parameters
----------
T : scipy.sparse matrix
Transition matrix
dire... | python | def is_connected(T, directed=True):
r"""Check connectivity of the transition matrix.
Return true, if the input matrix is completely connected,
effectively checking if the number of connected components equals one.
Parameters
----------
T : scipy.sparse matrix
Transition matrix
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47,912 | markovmodel/msmtools | msmtools/analysis/sparse/assessment.py | is_ergodic | def is_ergodic(T, tol):
"""
checks if T is 'ergodic'
Parameters
----------
T : scipy.sparse matrix
Transition matrix
tol : float
tolerance
Returns
-------
Truth value : bool
True, if # strongly connected components = 1
False, otherwise
"""
if isdense... | python | def is_ergodic(T, tol):
"""
checks if T is 'ergodic'
Parameters
----------
T : scipy.sparse matrix
Transition matrix
tol : float
tolerance
Returns
-------
Truth value : bool
True, if # strongly connected components = 1
False, otherwise
"""
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47,913 | yaybu/callsign | callsign/scripts/daemon.py | spawn | def spawn(opts, conf):
""" Acts like twistd """
if opts.config is not None:
os.environ["CALLSIGN_CONFIG_FILE"] = opts.config
sys.argv[1:] = [
"-noy", sibpath(__file__, "callsign.tac"),
"--pidfile", conf['pidfile'],
"--logfile", conf['logfile'],
]
twistd.run() | python | def spawn(opts, conf):
""" Acts like twistd """
if opts.config is not None:
os.environ["CALLSIGN_CONFIG_FILE"] = opts.config
sys.argv[1:] = [
"-noy", sibpath(__file__, "callsign.tac"),
"--pidfile", conf['pidfile'],
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47,914 | markovmodel/msmtools | msmtools/flux/sparse/pathways.py | find_bottleneck | def find_bottleneck(F, A, B):
r"""Find dynamic bottleneck of flux network.
Parameters
----------
F : scipy.sparse matrix
The flux network
A : array_like
The set of starting states
B : array_like
The set of end states
Returns
-------
e : tuple of int
The ... | python | def find_bottleneck(F, A, B):
r"""Find dynamic bottleneck of flux network.
Parameters
----------
F : scipy.sparse matrix
The flux network
A : array_like
The set of starting states
B : array_like
The set of end states
Returns
-------
e : tuple of int
The ... | [
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47,915 | markovmodel/msmtools | msmtools/flux/sparse/pathways.py | has_connection | def has_connection(graph, A, B):
r"""Check if the given graph contains a path connecting A and B.
Parameters
----------
graph : scipy.sparse matrix
Adjacency matrix of the graph
A : array_like
The set of starting states
B : array_like
The set of end states
Returns
... | python | def has_connection(graph, A, B):
r"""Check if the given graph contains a path connecting A and B.
Parameters
----------
graph : scipy.sparse matrix
Adjacency matrix of the graph
A : array_like
The set of starting states
B : array_like
The set of end states
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47,916 | markovmodel/msmtools | msmtools/flux/sparse/pathways.py | has_path | def has_path(nodes, A, B):
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Parameters
----------
nodes : array_like
Nodes from breadth_first_oder_seatch
A : array_like
The set of educt states
B : array_like
The set of product states
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r"""Test if nodes from a breadth_first_order search lead from A to
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nodes : array_like
Nodes from breadth_first_oder_seatch
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47,917 | markovmodel/msmtools | msmtools/flux/sparse/pathways.py | pathway | def pathway(F, A, B):
r"""Compute the dominant reaction-pathway.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
A : array_like
The set of starting states
B : array_like
The set of end states
Returns
-------
w... | python | def pathway(F, A, B):
r"""Compute the dominant reaction-pathway.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
A : array_like
The set of starting states
B : array_like
The set of end states
Returns
-------
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47,918 | markovmodel/msmtools | msmtools/flux/sparse/pathways.py | remove_path | def remove_path(F, path):
r"""Remove capacity along a path from flux network.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
path : list
Reaction path
Returns
-------
F : (M, M) scipy.sparse matrix
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r"""Remove capacity along a path from flux network.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
path : list
Reaction path
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47,919 | markovmodel/msmtools | msmtools/flux/sparse/pathways.py | add_endstates | def add_endstates(F, A, B):
r"""Adds artifical end states replacing source and sink sets.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
A : array_like
The set of starting states
B : array_like
The set of end states
... | python | def add_endstates(F, A, B):
r"""Adds artifical end states replacing source and sink sets.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
A : array_like
The set of starting states
B : array_like
The set of end states
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47,920 | nitely/v8-cffi | v8cffi/context.py | _is_utf_8 | def _is_utf_8(txt):
"""
Check a string is utf-8 encoded
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:rtype: bool
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assert isinstance(txt, six.binary_type)
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"""
Check a string is utf-8 encoded
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47,921 | nitely/v8-cffi | v8cffi/context.py | Context.load_libs | def load_libs(self, scripts_paths):
"""
Load script files into the context.\
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"""
Load script files into the context.\
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47,922 | nitely/v8-cffi | v8cffi/context.py | Context.run_script | def run_script(self, script, identifier=_DEFAULT_SCRIPT_NAME):
"""
Run a JS script within the context.\
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:param script: utf-8 encoded or unicode string
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Run a JS script within the context.\
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47,923 | markovmodel/msmtools | msmtools/analysis/dense/decomposition.py | eigenvalues | def eigenvalues(T, k=None, reversible=False, mu=None):
r"""Compute eigenvalues of given transition matrix.
Parameters
----------
T : (d, d) ndarray
Transition matrix (stochastic matrix)
k : int or tuple of ints, optional
Compute the first k eigenvalues of T
reversible : bool, op... | python | def eigenvalues(T, k=None, reversible=False, mu=None):
r"""Compute eigenvalues of given transition matrix.
Parameters
----------
T : (d, d) ndarray
Transition matrix (stochastic matrix)
k : int or tuple of ints, optional
Compute the first k eigenvalues of T
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47,924 | markovmodel/msmtools | msmtools/analysis/dense/decomposition.py | eigenvalues_rev | def eigenvalues_rev(T, k=None, mu=None):
r"""Compute eigenvalues of reversible transition matrix.
Parameters
----------
T : (d, d) ndarray
Transition matrix (stochastic matrix)
k : int or tuple of ints, optional
Compute the first k eigenvalues of T
mu : (d,) ndarray, optional
... | python | def eigenvalues_rev(T, k=None, mu=None):
r"""Compute eigenvalues of reversible transition matrix.
Parameters
----------
T : (d, d) ndarray
Transition matrix (stochastic matrix)
k : int or tuple of ints, optional
Compute the first k eigenvalues of T
mu : (d,) ndarray, optional
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47,925 | markovmodel/msmtools | msmtools/analysis/dense/decomposition.py | rdl_decomposition_nrev | def rdl_decomposition_nrev(T, norm='standard'):
r"""Decomposition into left and right eigenvectors.
Parameters
----------
T : (M, M) ndarray
Transition matrix
norm: {'standard', 'reversible'}
standard: (L'R) = Id, L[:,0] is a probability distribution,
the stationary dist... | python | def rdl_decomposition_nrev(T, norm='standard'):
r"""Decomposition into left and right eigenvectors.
Parameters
----------
T : (M, M) ndarray
Transition matrix
norm: {'standard', 'reversible'}
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47,926 | markovmodel/msmtools | msmtools/analysis/dense/decomposition.py | rdl_decomposition_rev | def rdl_decomposition_rev(T, norm='reversible', mu=None):
r"""Decomposition into left and right eigenvectors for reversible
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Parameters
----------
T : (M, M) ndarray
Transition matrix
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r"""Decomposition into left and right eigenvectors for reversible
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T : (M, M) ndarray
Transition matrix
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47,927 | markovmodel/msmtools | msmtools/analysis/dense/decomposition.py | timescales_from_eigenvalues | def timescales_from_eigenvalues(evals, tau=1):
r"""Compute implied time scales from given eigenvalues
Parameters
----------
evals : eigenvalues
tau : lag time
Returns
-------
ts : ndarray
The implied time scales to the given eigenvalues, in the same order.
"""
"""Chec... | python | def timescales_from_eigenvalues(evals, tau=1):
r"""Compute implied time scales from given eigenvalues
Parameters
----------
evals : eigenvalues
tau : lag time
Returns
-------
ts : ndarray
The implied time scales to the given eigenvalues, in the same order.
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Parameters
----------
evals : eigenvalues
tau : lag time
Returns
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ts : ndarray
The implied time scales to the given eigenvalues, in the same order. | [
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47,928 | markovmodel/msmtools | msmtools/util/matrix/matrix.py | is_sparse_file | def is_sparse_file(filename):
"""Determine if the given filename indicates a dense or a sparse matrix
If pathname is xxx.coo.yyy return True otherwise False.
"""
dirname, basename = os.path.split(filename)
name, ext = os.path.splitext(basename)
matrix_name, matrix_ext = os.path.splitext(nam... | python | def is_sparse_file(filename):
"""Determine if the given filename indicates a dense or a sparse matrix
If pathname is xxx.coo.yyy return True otherwise False.
"""
dirname, basename = os.path.split(filename)
name, ext = os.path.splitext(basename)
matrix_name, matrix_ext = os.path.splitext(nam... | [
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47,929 | markovmodel/msmtools | msmtools/analysis/sparse/stationary_vector.py | stationary_distribution_from_backward_iteration | def stationary_distribution_from_backward_iteration(P, eps=1e-15):
r"""Fast computation of the stationary vector using backward
iteration.
Parameters
----------
P : (M, M) scipy.sparse matrix
Transition matrix
eps : float (optional)
Perturbation parameter for the true eigenvalue... | python | def stationary_distribution_from_backward_iteration(P, eps=1e-15):
r"""Fast computation of the stationary vector using backward
iteration.
Parameters
----------
P : (M, M) scipy.sparse matrix
Transition matrix
eps : float (optional)
Perturbation parameter for the true eigenvalue... | [
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47,930 | markovmodel/msmtools | msmtools/analysis/sparse/decomposition.py | eigenvalues | def eigenvalues(T, k=None, ncv=None, reversible=False, mu=None):
r"""Compute the eigenvalues of a sparse transition matrix.
Parameters
----------
T : (M, M) scipy.sparse matrix
Transition matrix
k : int, optional
Number of eigenvalues to compute.
ncv : int, optional
The ... | python | def eigenvalues(T, k=None, ncv=None, reversible=False, mu=None):
r"""Compute the eigenvalues of a sparse transition matrix.
Parameters
----------
T : (M, M) scipy.sparse matrix
Transition matrix
k : int, optional
Number of eigenvalues to compute.
ncv : int, optional
The ... | [
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47,931 | markovmodel/msmtools | msmtools/analysis/sparse/decomposition.py | eigenvalues_rev | def eigenvalues_rev(T, k, ncv=None, mu=None):
r"""Compute the eigenvalues of a reversible, sparse transition matrix.
Parameters
----------
T : (M, M) scipy.sparse matrix
Transition matrix
k : int
Number of eigenvalues to compute.
ncv : int, optional
The number of Lanczos... | python | def eigenvalues_rev(T, k, ncv=None, mu=None):
r"""Compute the eigenvalues of a reversible, sparse transition matrix.
Parameters
----------
T : (M, M) scipy.sparse matrix
Transition matrix
k : int
Number of eigenvalues to compute.
ncv : int, optional
The number of Lanczos... | [
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Number of eigenvalues to compute.
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47,932 | markovmodel/msmtools | msmtools/estimation/dense/bootstrapping.py | number_of_states | def number_of_states(dtrajs):
r"""
Determine the number of states from a set of discrete trajectories
Parameters
----------
dtrajs : list of int-arrays
discrete trajectories
"""
# determine number of states n
nmax = 0
for dtraj in dtrajs:
nmax = max(nmax, np.max(dtra... | python | def number_of_states(dtrajs):
r"""
Determine the number of states from a set of discrete trajectories
Parameters
----------
dtrajs : list of int-arrays
discrete trajectories
"""
# determine number of states n
nmax = 0
for dtraj in dtrajs:
nmax = max(nmax, np.max(dtra... | [
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Determine the number of states from a set of discrete trajectories
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dtrajs : list of int-arrays
discrete trajectories | [
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47,933 | markovmodel/msmtools | msmtools/estimation/dense/bootstrapping.py | determine_lengths | def determine_lengths(dtrajs):
r"""
Determines the lengths of all trajectories
Parameters
----------
dtrajs : list of int-arrays
discrete trajectories
"""
if (isinstance(dtrajs[0], (int))):
return len(dtrajs) * np.ones((1))
lengths = np.zeros((len(dtrajs)))
for i in ... | python | def determine_lengths(dtrajs):
r"""
Determines the lengths of all trajectories
Parameters
----------
dtrajs : list of int-arrays
discrete trajectories
"""
if (isinstance(dtrajs[0], (int))):
return len(dtrajs) * np.ones((1))
lengths = np.zeros((len(dtrajs)))
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47,934 | markovmodel/msmtools | msmtools/estimation/dense/bootstrapping.py | bootstrap_counts_singletraj | def bootstrap_counts_singletraj(dtraj, lagtime, n):
"""
Samples n counts at the given lagtime from the given trajectory
"""
# check if length is sufficient
L = len(dtraj)
if (lagtime > L):
raise ValueError(
'Cannot sample counts with lagtime ' + str(lagtime) + ' from a trajec... | python | def bootstrap_counts_singletraj(dtraj, lagtime, n):
"""
Samples n counts at the given lagtime from the given trajectory
"""
# check if length is sufficient
L = len(dtraj)
if (lagtime > L):
raise ValueError(
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47,935 | markovmodel/msmtools | msmtools/estimation/sparse/connectivity.py | connected_sets | def connected_sets(C, directed=True):
r"""Compute connected components for a directed graph with weights
represented by the given count matrix.
Parameters
----------
C : scipy.sparse matrix or numpy ndarray
square matrix specifying edge weights.
directed : bool, optional
Whether ... | python | def connected_sets(C, directed=True):
r"""Compute connected components for a directed graph with weights
represented by the given count matrix.
Parameters
----------
C : scipy.sparse matrix or numpy ndarray
square matrix specifying edge weights.
directed : bool, optional
Whether ... | [
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47,936 | markovmodel/msmtools | msmtools/estimation/sparse/connectivity.py | largest_connected_submatrix | def largest_connected_submatrix(C, directed=True, lcc=None):
r"""Compute the count matrix of the largest connected set.
The input count matrix is used as a weight matrix for the
construction of a directed graph. The largest connected set of the
constructed graph is computed. Vertices belonging to the l... | python | def largest_connected_submatrix(C, directed=True, lcc=None):
r"""Compute the count matrix of the largest connected set.
The input count matrix is used as a weight matrix for the
construction of a directed graph. The largest connected set of the
constructed graph is computed. Vertices belonging to the l... | [
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47,937 | markovmodel/msmtools | msmtools/estimation/sparse/connectivity.py | is_connected | def is_connected(C, directed=True):
r"""Return true, if the input count matrix is completely connected.
Effectively checking if the number of connected components equals one.
Parameters
----------
C : scipy.sparse matrix or numpy ndarray
Count matrix specifying edge weights.
directed : ... | python | def is_connected(C, directed=True):
r"""Return true, if the input count matrix is completely connected.
Effectively checking if the number of connected components equals one.
Parameters
----------
C : scipy.sparse matrix or numpy ndarray
Count matrix specifying edge weights.
directed : ... | [
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47,938 | markovmodel/msmtools | msmtools/flux/api.py | coarsegrain | def coarsegrain(F, sets):
r"""Coarse-grains the flux to the given sets.
Parameters
----------
F : (n, n) ndarray or scipy.sparse matrix
Matrix of flux values between pairs of states.
sets : list of array-like of ints
The sets of states onto which the flux is coarse-grained.
Not... | python | def coarsegrain(F, sets):
r"""Coarse-grains the flux to the given sets.
Parameters
----------
F : (n, n) ndarray or scipy.sparse matrix
Matrix of flux values between pairs of states.
sets : list of array-like of ints
The sets of states onto which the flux is coarse-grained.
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47,939 | markovmodel/msmtools | msmtools/flux/api.py | total_flux | def total_flux(F, A=None):
r"""Compute the total flux, or turnover flux, that is produced by
the flux sources and consumed by the flux sinks.
Parameters
----------
F : (M, M) ndarray
Matrix of flux values between pairs of states.
A : array_like (optional)
List of integer sta... | python | def total_flux(F, A=None):
r"""Compute the total flux, or turnover flux, that is produced by
the flux sources and consumed by the flux sinks.
Parameters
----------
F : (M, M) ndarray
Matrix of flux values between pairs of states.
A : array_like (optional)
List of integer sta... | [
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47,940 | markovmodel/msmtools | msmtools/flux/api.py | mfpt | def mfpt(totflux, pi, qminus):
r"""Mean first passage time for reaction A to B.
Parameters
----------
totflux : float
The total flux between reactant and product
pi : (M,) ndarray
Stationary distribution
qminus : (M,) ndarray
Backward comittor
Returns
-------
... | python | def mfpt(totflux, pi, qminus):
r"""Mean first passage time for reaction A to B.
Parameters
----------
totflux : float
The total flux between reactant and product
pi : (M,) ndarray
Stationary distribution
qminus : (M,) ndarray
Backward comittor
Returns
-------
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47,941 | markovmodel/msmtools | msmtools/analysis/dense/pcca.py | _pcca_connected_isa | def _pcca_connected_isa(evec, n_clusters):
"""
PCCA+ spectral clustering method using the inner simplex algorithm.
Clusters the first n_cluster eigenvectors of a transition matrix in order to cluster the states.
This function assumes that the state space is fully connected, i.e. the transition matrix w... | python | def _pcca_connected_isa(evec, n_clusters):
"""
PCCA+ spectral clustering method using the inner simplex algorithm.
Clusters the first n_cluster eigenvectors of a transition matrix in order to cluster the states.
This function assumes that the state space is fully connected, i.e. the transition matrix w... | [
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Clusters the first n_cluster eigenvectors of a transition matrix in order to cluster the states.
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47,942 | markovmodel/msmtools | msmtools/analysis/dense/pcca.py | _opt_soft | def _opt_soft(eigvectors, rot_matrix, n_clusters):
"""
Optimizes the PCCA+ rotation matrix such that the memberships are exclusively nonnegative.
Parameters
----------
eigenvectors : ndarray
A matrix with the sorted eigenvectors in the columns. The stationary eigenvector should
be f... | python | def _opt_soft(eigvectors, rot_matrix, n_clusters):
"""
Optimizes the PCCA+ rotation matrix such that the memberships are exclusively nonnegative.
Parameters
----------
eigenvectors : ndarray
A matrix with the sorted eigenvectors in the columns. The stationary eigenvector should
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47,943 | markovmodel/msmtools | msmtools/analysis/dense/pcca.py | _fill_matrix | def _fill_matrix(rot_crop_matrix, eigvectors):
"""
Helper function for opt_soft
"""
(x, y) = rot_crop_matrix.shape
row_sums = np.sum(rot_crop_matrix, axis=1)
row_sums = np.reshape(row_sums, (x, 1))
# add -row_sums as leftmost column to rot_crop_matrix
rot_crop_matrix = np.concatenate... | python | def _fill_matrix(rot_crop_matrix, eigvectors):
"""
Helper function for opt_soft
"""
(x, y) = rot_crop_matrix.shape
row_sums = np.sum(rot_crop_matrix, axis=1)
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# add -row_sums as leftmost column to rot_crop_matrix
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47,944 | markovmodel/msmtools | msmtools/analysis/dense/pcca.py | coarsegrain | def coarsegrain(P, n):
"""
Coarse-grains transition matrix P to n sets using PCCA
Coarse-grains transition matrix P such that the dominant eigenvalues are preserved, using:
..math:
\tilde{P} = M^T P M (M^T M)^{-1}
See [2]_ for the derivation of this form from the coarse-graining method fi... | python | def coarsegrain(P, n):
"""
Coarse-grains transition matrix P to n sets using PCCA
Coarse-grains transition matrix P such that the dominant eigenvalues are preserved, using:
..math:
\tilde{P} = M^T P M (M^T M)^{-1}
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Coarse-grains transition matrix P such that the dominant eigenvalues are preserved, using:
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See [2]_ for the derivation of this form from the coarse-graining method first derived in [1]_.
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47,945 | markovmodel/msmtools | msmtools/analysis/api.py | is_transition_matrix | def is_transition_matrix(T, tol=1e-12):
r"""Check if the given matrix is a transition matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Matrix to check
tol : float (optional)
Floating point tolerance to check with
Returns
-------
is_transition_matr... | python | def is_transition_matrix(T, tol=1e-12):
r"""Check if the given matrix is a transition matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Matrix to check
tol : float (optional)
Floating point tolerance to check with
Returns
-------
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47,946 | markovmodel/msmtools | msmtools/analysis/api.py | is_rate_matrix | def is_rate_matrix(K, tol=1e-12):
r"""Check if the given matrix is a rate matrix.
Parameters
----------
K : (M, M) ndarray or scipy.sparse matrix
Matrix to check
tol : float (optional)
Floating point tolerance to check with
Returns
-------
is_rate_matrix : bool
... | python | def is_rate_matrix(K, tol=1e-12):
r"""Check if the given matrix is a rate matrix.
Parameters
----------
K : (M, M) ndarray or scipy.sparse matrix
Matrix to check
tol : float (optional)
Floating point tolerance to check with
Returns
-------
is_rate_matrix : bool
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47,947 | markovmodel/msmtools | msmtools/analysis/api.py | is_connected | def is_connected(T, directed=True):
r"""Check connectivity of the given matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Matrix to check
directed : bool (optional)
If True respect direction of transitions, if False do not
distinguish between forward and ... | python | def is_connected(T, directed=True):
r"""Check connectivity of the given matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Matrix to check
directed : bool (optional)
If True respect direction of transitions, if False do not
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47,948 | markovmodel/msmtools | msmtools/analysis/api.py | is_reversible | def is_reversible(T, mu=None, tol=1e-12):
r"""Check reversibility of the given transition matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
mu : (M,) ndarray (optional)
Test reversibility with respect to this vector
tol : float (optional)... | python | def is_reversible(T, mu=None, tol=1e-12):
r"""Check reversibility of the given transition matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
mu : (M,) ndarray (optional)
Test reversibility with respect to this vector
tol : float (optional)... | [
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47,949 | markovmodel/msmtools | msmtools/analysis/api.py | timescales | def timescales(T, tau=1, k=None, ncv=None, reversible=False, mu=None):
r"""Compute implied time scales of given transition matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
tau : int (optional)
The time-lag (in elementary time steps of the mic... | python | def timescales(T, tau=1, k=None, ncv=None, reversible=False, mu=None):
r"""Compute implied time scales of given transition matrix.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
tau : int (optional)
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The time-lag (in elementary time steps of the microstate
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47,950 | markovmodel/msmtools | msmtools/analysis/api.py | committor | def committor(T, A, B, forward=True, mu=None):
r"""Compute the committor between sets of microstates.
The committor assigns to each microstate a probability that being
at this state, the set B will be hit next, rather than set A
(forward committor), or that the set A has been hit previously
rather ... | python | def committor(T, A, B, forward=True, mu=None):
r"""Compute the committor between sets of microstates.
The committor assigns to each microstate a probability that being
at this state, the set B will be hit next, rather than set A
(forward committor), or that the set A has been hit previously
rather ... | [
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47,951 | markovmodel/msmtools | msmtools/analysis/api.py | expected_counts | def expected_counts(T, p0, N):
r"""Compute expected transition counts for Markov chain with n steps.
Parameters
----------
T : (M, M) ndarray or sparse matrix
Transition matrix
p0 : (M,) ndarray
Initial (probability) vector
N : int
Number of steps to take
Returns
... | python | def expected_counts(T, p0, N):
r"""Compute expected transition counts for Markov chain with n steps.
Parameters
----------
T : (M, M) ndarray or sparse matrix
Transition matrix
p0 : (M,) ndarray
Initial (probability) vector
N : int
Number of steps to take
Returns
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Initial (probability) vector
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47,952 | markovmodel/msmtools | msmtools/analysis/api.py | fingerprint_correlation | def fingerprint_correlation(T, obs1, obs2=None, tau=1, k=None, ncv=None):
r"""Dynamical fingerprint for equilibrium correlation experiment.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
obs1 : (M,) ndarray
Observable, represented as vector on stat... | python | def fingerprint_correlation(T, obs1, obs2=None, tau=1, k=None, ncv=None):
r"""Dynamical fingerprint for equilibrium correlation experiment.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
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47,953 | markovmodel/msmtools | msmtools/analysis/api.py | fingerprint_relaxation | def fingerprint_relaxation(T, p0, obs, tau=1, k=None, ncv=None):
r"""Dynamical fingerprint for relaxation experiment.
The dynamical fingerprint is given by the implied time-scale
spectrum together with the corresponding amplitudes.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matri... | python | def fingerprint_relaxation(T, p0, obs, tau=1, k=None, ncv=None):
r"""Dynamical fingerprint for relaxation experiment.
The dynamical fingerprint is given by the implied time-scale
spectrum together with the corresponding amplitudes.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matri... | [
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47,954 | markovmodel/msmtools | msmtools/analysis/api.py | expectation | def expectation(T, a, mu=None):
r"""Equilibrium expectation value of a given observable.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
a : (M,) ndarray
Observable vector
mu : (M,) ndarray (optional)
The stationary distribution of T. I... | python | def expectation(T, a, mu=None):
r"""Equilibrium expectation value of a given observable.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
a : (M,) ndarray
Observable vector
mu : (M,) ndarray (optional)
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T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
a : (M,) ndarray
Observable vector
mu : (M,) ndarray (optional)
The stationary distribution of T. If given, the stationary
dist... | [
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47,955 | markovmodel/msmtools | msmtools/analysis/api.py | _pcca_object | def _pcca_object(T, m):
"""
Constructs the pcca object from dense or sparse
Parameters
----------
T : (n, n) ndarray or scipy.sparse matrix
Transition matrix
m : int
Number of metastable sets
Returns
-------
pcca : PCCA
PCCA object
"""
if _issparse(T... | python | def _pcca_object(T, m):
"""
Constructs the pcca object from dense or sparse
Parameters
----------
T : (n, n) ndarray or scipy.sparse matrix
Transition matrix
m : int
Number of metastable sets
Returns
-------
pcca : PCCA
PCCA object
"""
if _issparse(T... | [
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47,956 | markovmodel/msmtools | msmtools/analysis/api.py | eigenvalue_sensitivity | def eigenvalue_sensitivity(T, k):
r"""Sensitivity matrix of a specified eigenvalue.
Parameters
----------
T : (M, M) ndarray
Transition matrix
k : int
Compute sensitivity matrix for k-th eigenvalue
Returns
-------
S : (M, M) ndarray
Sensitivity matrix for k-th e... | python | def eigenvalue_sensitivity(T, k):
r"""Sensitivity matrix of a specified eigenvalue.
Parameters
----------
T : (M, M) ndarray
Transition matrix
k : int
Compute sensitivity matrix for k-th eigenvalue
Returns
-------
S : (M, M) ndarray
Sensitivity matrix for k-th e... | [
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47,957 | markovmodel/msmtools | msmtools/analysis/api.py | eigenvector_sensitivity | def eigenvector_sensitivity(T, k, j, right=True):
r"""Sensitivity matrix of a selected eigenvector element.
Parameters
----------
T : (M, M) ndarray
Transition matrix (stochastic matrix).
k : int
Eigenvector index
j : int
Element index
right : bool
If True co... | python | def eigenvector_sensitivity(T, k, j, right=True):
r"""Sensitivity matrix of a selected eigenvector element.
Parameters
----------
T : (M, M) ndarray
Transition matrix (stochastic matrix).
k : int
Eigenvector index
j : int
Element index
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47,958 | markovmodel/msmtools | msmtools/analysis/api.py | stationary_distribution_sensitivity | def stationary_distribution_sensitivity(T, j):
r"""Sensitivity matrix of a stationary distribution element.
Parameters
----------
T : (M, M) ndarray
Transition matrix (stochastic matrix).
j : int
Index of stationary distribution element
for which sensitivity matrix is compute... | python | def stationary_distribution_sensitivity(T, j):
r"""Sensitivity matrix of a stationary distribution element.
Parameters
----------
T : (M, M) ndarray
Transition matrix (stochastic matrix).
j : int
Index of stationary distribution element
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47,959 | markovmodel/msmtools | msmtools/analysis/api.py | mfpt_sensitivity | def mfpt_sensitivity(T, target, i):
r"""Sensitivity matrix of the mean first-passage time from specified state.
Parameters
----------
T : (M, M) ndarray
Transition matrix
target : int or list
Target state or set for mfpt computation
i : int
Compute the sensitivity for st... | python | def mfpt_sensitivity(T, target, i):
r"""Sensitivity matrix of the mean first-passage time from specified state.
Parameters
----------
T : (M, M) ndarray
Transition matrix
target : int or list
Target state or set for mfpt computation
i : int
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Target state or set for mfpt computation
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47,960 | markovmodel/msmtools | msmtools/analysis/api.py | committor_sensitivity | def committor_sensitivity(T, A, B, i, forward=True):
r"""Sensitivity matrix of a specified committor entry.
Parameters
----------
T : (M, M) ndarray
Transition matrix
A : array_like
List of integer state labels for set A
B : array_like
List of integer state labels for s... | python | def committor_sensitivity(T, A, B, i, forward=True):
r"""Sensitivity matrix of a specified committor entry.
Parameters
----------
T : (M, M) ndarray
Transition matrix
A : array_like
List of integer state labels for set A
B : array_like
List of integer state labels for s... | [
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47,961 | markovmodel/msmtools | msmtools/estimation/dense/covariance.py | tmatrix_cov | def tmatrix_cov(C, row=None):
r"""Covariance tensor for the non-reversible transition matrix ensemble
Normally the covariance tensor cov(p_ij, p_kl) would carry four indices
(i,j,k,l). In the non-reversible case rows are independent so that
cov(p_ij, p_kl)=0 for i not equal to k. Therefore the function... | python | def tmatrix_cov(C, row=None):
r"""Covariance tensor for the non-reversible transition matrix ensemble
Normally the covariance tensor cov(p_ij, p_kl) would carry four indices
(i,j,k,l). In the non-reversible case rows are independent so that
cov(p_ij, p_kl)=0 for i not equal to k. Therefore the function... | [
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47,962 | markovmodel/msmtools | msmtools/estimation/dense/covariance.py | dirichlet_covariance | def dirichlet_covariance(alpha):
r"""Covariance matrix for Dirichlet distribution.
Parameters
----------
alpha : (M, ) ndarray
Parameters of Dirichlet distribution
Returns
-------
cov : (M, M) ndarray
Covariance matrix
"""
alpha0 = alpha.sum()
norm = alpha0 ** ... | python | def dirichlet_covariance(alpha):
r"""Covariance matrix for Dirichlet distribution.
Parameters
----------
alpha : (M, ) ndarray
Parameters of Dirichlet distribution
Returns
-------
cov : (M, M) ndarray
Covariance matrix
"""
alpha0 = alpha.sum()
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47,963 | markovmodel/msmtools | msmtools/analysis/sparse/mean_first_passage_time.py | mfpt_between_sets | def mfpt_between_sets(T, target, origin, mu=None):
"""Compute mean-first-passage time between subsets of state space.
Parameters
----------
T : scipy.sparse matrix
Transition matrix.
target : int or list of int
Set of target states.
origin : int or list of int
Set of sta... | python | def mfpt_between_sets(T, target, origin, mu=None):
"""Compute mean-first-passage time between subsets of state space.
Parameters
----------
T : scipy.sparse matrix
Transition matrix.
target : int or list of int
Set of target states.
origin : int or list of int
Set of sta... | [
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47,964 | markovmodel/msmtools | msmtools/estimation/sparse/newton/linsolve.py | mydot | def mydot(A, B):
r"""Dot-product that can handle dense and sparse arrays
Parameters
----------
A : numpy ndarray or scipy sparse matrix
The first factor
B : numpy ndarray or scipy sparse matrix
The second factor
Returns
C : numpy ndarray or scipy sparse matrix
The d... | python | def mydot(A, B):
r"""Dot-product that can handle dense and sparse arrays
Parameters
----------
A : numpy ndarray or scipy sparse matrix
The first factor
B : numpy ndarray or scipy sparse matrix
The second factor
Returns
C : numpy ndarray or scipy sparse matrix
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47,965 | markovmodel/msmtools | msmtools/analysis/sparse/expectations.py | expected_counts | def expected_counts(p0, T, N):
r"""Compute expected transition counts for Markov chain after N steps.
Expected counts are computed according to ..math::
E[C_{ij}^{(n)}]=\sum_{k=0}^{N-1} (p_0^T T^{k})_{i} p_{ij}
Parameters
----------
p0 : (M,) ndarray
Starting (probability) vector of t... | python | def expected_counts(p0, T, N):
r"""Compute expected transition counts for Markov chain after N steps.
Expected counts are computed according to ..math::
E[C_{ij}^{(n)}]=\sum_{k=0}^{N-1} (p_0^T T^{k})_{i} p_{ij}
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----------
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Starting (probability) vector of t... | [
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47,966 | markovmodel/msmtools | msmtools/analysis/sparse/fingerprints.py | fingerprint | def fingerprint(P, obs1, obs2=None, p0=None, tau=1, k=None, ncv=None):
r"""Dynamical fingerprint for equilibrium or relaxation experiment
The dynamical fingerprint is given by the implied time-scale
spectrum together with the corresponding amplitudes.
Parameters
----------
P : (M, M) scipy.spa... | python | def fingerprint(P, obs1, obs2=None, p0=None, tau=1, k=None, ncv=None):
r"""Dynamical fingerprint for equilibrium or relaxation experiment
The dynamical fingerprint is given by the implied time-scale
spectrum together with the corresponding amplitudes.
Parameters
----------
P : (M, M) scipy.spa... | [
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Transition matrix
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47,967 | markovmodel/msmtools | msmtools/analysis/sparse/fingerprints.py | correlation_matvec | def correlation_matvec(P, obs1, obs2=None, times=[1]):
r"""Time-correlation for equilibrium experiment - via matrix vector products.
Parameters
----------
P : (M, M) ndarray
Transition matrix
obs1 : (M,) ndarray
Observable, represented as vector on state space
obs2 : (M,) ndarra... | python | def correlation_matvec(P, obs1, obs2=None, times=[1]):
r"""Time-correlation for equilibrium experiment - via matrix vector products.
Parameters
----------
P : (M, M) ndarray
Transition matrix
obs1 : (M,) ndarray
Observable, represented as vector on state space
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47,968 | markovmodel/msmtools | msmtools/analysis/sparse/fingerprints.py | propagate | def propagate(A, x, N):
r"""Use matrix A to propagate vector x.
Parameters
----------
A : (M, M) scipy.sparse matrix
Matrix of propagator
x : (M, ) ndarray or scipy.sparse matrix
Vector to propagate
N : int
Number of steps to propagate
Returns
-------
y : (M... | python | def propagate(A, x, N):
r"""Use matrix A to propagate vector x.
Parameters
----------
A : (M, M) scipy.sparse matrix
Matrix of propagator
x : (M, ) ndarray or scipy.sparse matrix
Vector to propagate
N : int
Number of steps to propagate
Returns
-------
y : (M... | [
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47,969 | markovmodel/msmtools | msmtools/generation/api.py | generate_traj | def generate_traj(P, N, start=None, stop=None, dt=1):
"""
Generates a realization of the Markov chain with transition matrix P.
Parameters
----------
P : (n, n) ndarray
transition matrix
N : int
trajectory length
start : int, optional, default = None
starting state. ... | python | def generate_traj(P, N, start=None, stop=None, dt=1):
"""
Generates a realization of the Markov chain with transition matrix P.
Parameters
----------
P : (n, n) ndarray
transition matrix
N : int
trajectory length
start : int, optional, default = None
starting state. ... | [
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47,970 | markovmodel/msmtools | msmtools/generation/api.py | generate_trajs | def generate_trajs(P, M, N, start=None, stop=None, dt=1):
"""
Generates multiple realizations of the Markov chain with transition matrix P.
Parameters
----------
P : (n, n) ndarray
transition matrix
M : int
number of trajectories
N : int
trajectory length
start :... | python | def generate_trajs(P, M, N, start=None, stop=None, dt=1):
"""
Generates multiple realizations of the Markov chain with transition matrix P.
Parameters
----------
P : (n, n) ndarray
transition matrix
M : int
number of trajectories
N : int
trajectory length
start :... | [
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47,971 | markovmodel/msmtools | msmtools/generation/api.py | transition_matrix_metropolis_1d | def transition_matrix_metropolis_1d(E, d=1.0):
r"""Transition matrix describing the Metropolis chain jumping
between neighbors in a discrete 1D energy landscape.
Parameters
----------
E : (M,) ndarray
Energies in units of kT
d : float (optional)
Diffusivity of the chain, d in (0... | python | def transition_matrix_metropolis_1d(E, d=1.0):
r"""Transition matrix describing the Metropolis chain jumping
between neighbors in a discrete 1D energy landscape.
Parameters
----------
E : (M,) ndarray
Energies in units of kT
d : float (optional)
Diffusivity of the chain, d in (0... | [
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Energies in units of kT
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47,972 | markovmodel/msmtools | msmtools/generation/api.py | MarkovChainSampler.trajectory | def trajectory(self, N, start=None, stop=None):
"""
Generates a trajectory realization of length N, starting from state s
Parameters
----------
N : int
trajectory length
start : int, optional, default = None
starting state. If not given, will samp... | python | def trajectory(self, N, start=None, stop=None):
"""
Generates a trajectory realization of length N, starting from state s
Parameters
----------
N : int
trajectory length
start : int, optional, default = None
starting state. If not given, will samp... | [
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starting state. If not given, will sample from the stationary distribution of P
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47,973 | markovmodel/msmtools | msmtools/generation/api.py | MarkovChainSampler.trajectories | def trajectories(self, M, N, start=None, stop=None):
"""
Generates M trajectories, each of length N, starting from state s
Parameters
----------
M : int
number of trajectories
N : int
trajectory length
start : int, optional, default = None... | python | def trajectories(self, M, N, start=None, stop=None):
"""
Generates M trajectories, each of length N, starting from state s
Parameters
----------
M : int
number of trajectories
N : int
trajectory length
start : int, optional, default = None... | [
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47,974 | markovmodel/msmtools | msmtools/estimation/sparse/effective_counts.py | _split_sequences_singletraj | def _split_sequences_singletraj(dtraj, nstates, lag):
""" splits the discrete trajectory into conditional sequences by starting state
Parameters
----------
dtraj : int-iterable
discrete trajectory
nstates : int
total number of discrete states
lag : int
lag time
"""
... | python | def _split_sequences_singletraj(dtraj, nstates, lag):
""" splits the discrete trajectory into conditional sequences by starting state
Parameters
----------
dtraj : int-iterable
discrete trajectory
nstates : int
total number of discrete states
lag : int
lag time
"""
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47,975 | markovmodel/msmtools | msmtools/estimation/sparse/effective_counts.py | _split_sequences_multitraj | def _split_sequences_multitraj(dtrajs, lag):
""" splits the discrete trajectories into conditional sequences by starting state
Parameters
----------
dtrajs : list of int-iterables
discrete trajectories
nstates : int
total number of discrete states
lag : int
lag time
... | python | def _split_sequences_multitraj(dtrajs, lag):
""" splits the discrete trajectories into conditional sequences by starting state
Parameters
----------
dtrajs : list of int-iterables
discrete trajectories
nstates : int
total number of discrete states
lag : int
lag time
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47,976 | markovmodel/msmtools | msmtools/estimation/sparse/effective_counts.py | _indicator_multitraj | def _indicator_multitraj(ss, i, j):
""" Returns conditional sequence for transition i -> j given all conditional sequences """
iseqs = ss[i]
res = []
for iseq in iseqs:
x = np.zeros(len(iseq))
I = np.where(iseq == j)
x[I] = 1.0
res.append(x)
return res | python | def _indicator_multitraj(ss, i, j):
""" Returns conditional sequence for transition i -> j given all conditional sequences """
iseqs = ss[i]
res = []
for iseq in iseqs:
x = np.zeros(len(iseq))
I = np.where(iseq == j)
x[I] = 1.0
res.append(x)
return res | [
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47,977 | markovmodel/msmtools | msmtools/estimation/sparse/effective_counts.py | statistical_inefficiencies | def statistical_inefficiencies(dtrajs, lag, C=None, truncate_acf=True, mact=2.0, n_jobs=1, callback=None):
r""" Computes statistical inefficiencies of sliding-window transition counts at given lag
Consider a discrete trajectory :math`{ x_t }` with :math:`x_t \in {1, ..., n}`. For each starting state :math:`i`,... | python | def statistical_inefficiencies(dtrajs, lag, C=None, truncate_acf=True, mact=2.0, n_jobs=1, callback=None):
r""" Computes statistical inefficiencies of sliding-window transition counts at given lag
Consider a discrete trajectory :math`{ x_t }` with :math:`x_t \in {1, ..., n}`. For each starting state :math:`i`,... | [
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47,978 | markovmodel/msmtools | msmtools/estimation/dense/transition_matrix.py | transition_matrix_non_reversible | def transition_matrix_non_reversible(C):
r"""
Estimates a non-reversible transition matrix from count matrix C
T_ij = c_ij / c_i where c_i = sum_j c_ij
Parameters
----------
C: ndarray, shape (n,n)
count matrix
Returns
-------
T: Estimated transition matrix
"""
# ... | python | def transition_matrix_non_reversible(C):
r"""
Estimates a non-reversible transition matrix from count matrix C
T_ij = c_ij / c_i where c_i = sum_j c_ij
Parameters
----------
C: ndarray, shape (n,n)
count matrix
Returns
-------
T: Estimated transition matrix
"""
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C: ndarray, shape (n,n)
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47,979 | markovmodel/msmtools | msmtools/analysis/dense/correlations.py | time_correlation_direct_by_mtx_vec_prod | def time_correlation_direct_by_mtx_vec_prod(P, mu, obs1, obs2=None, time=1, start_values=None, return_P_k_obs=False):
r"""Compute time-correlation of obs1, or time-cross-correlation with obs2.
The time-correlation at time=k is computed by the matrix-vector expression:
cor(k) = obs1' diag(pi) P^k obs2
... | python | def time_correlation_direct_by_mtx_vec_prod(P, mu, obs1, obs2=None, time=1, start_values=None, return_P_k_obs=False):
r"""Compute time-correlation of obs1, or time-cross-correlation with obs2.
The time-correlation at time=k is computed by the matrix-vector expression:
cor(k) = obs1' diag(pi) P^k obs2
... | [
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cor(k) = obs1' diag(pi) P^k obs2
Parameters
----------
P : ndarray, shape=(n, n) or scipy.sparse matrix
Transition matrix
obs1 : ndarr... | [
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47,980 | markovmodel/msmtools | msmtools/analysis/dense/correlations.py | time_correlations_direct | def time_correlations_direct(P, pi, obs1, obs2=None, times=[1]):
r"""Compute time-correlations of obs1, or time-cross-correlation with obs2.
The time-correlation at time=k is computed by the matrix-vector expression:
cor(k) = obs1' diag(pi) P^k obs2
Parameters
----------
P : ndarray, shape=(n... | python | def time_correlations_direct(P, pi, obs1, obs2=None, times=[1]):
r"""Compute time-correlations of obs1, or time-cross-correlation with obs2.
The time-correlation at time=k is computed by the matrix-vector expression:
cor(k) = obs1' diag(pi) P^k obs2
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----------
P : ndarray, shape=(n... | [
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47,981 | markovmodel/msmtools | msmtools/estimation/sparse/newton/linsolve_sparse.py | factor_aug | def factor_aug(z, DPhival, G, A):
r"""Set up augmented system and return.
Parameters
----------
z : (N+P+M+M,) ndarray
Current iterate, z = (x, nu, l, s)
DPhival : LinearOperator
Jacobian of the variational inequality mapping
G : (M, N) ndarray or sparse matrix
Inequalit... | python | def factor_aug(z, DPhival, G, A):
r"""Set up augmented system and return.
Parameters
----------
z : (N+P+M+M,) ndarray
Current iterate, z = (x, nu, l, s)
DPhival : LinearOperator
Jacobian of the variational inequality mapping
G : (M, N) ndarray or sparse matrix
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Jacobian of the variational inequality mapping
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47,982 | markovmodel/msmtools | msmtools/flux/reactive_flux.py | ReactiveFlux.I | def I(self):
r"""Returns the set of intermediate states
"""
return list(set(range(self.nstates)) - set(self._A) - set(self._B)) | python | def I(self):
r"""Returns the set of intermediate states
"""
return list(set(range(self.nstates)) - set(self._A) - set(self._B)) | [
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47,983 | markovmodel/msmtools | msmtools/flux/reactive_flux.py | ReactiveFlux._pathways_to_flux | def _pathways_to_flux(self, paths, pathfluxes, n=None):
r"""Sums up the flux from the pathways given
Parameters
-----------
paths : list of int-arrays
list of pathways
pathfluxes : double-array
array with path fluxes
n : int
number of st... | python | def _pathways_to_flux(self, paths, pathfluxes, n=None):
r"""Sums up the flux from the pathways given
Parameters
-----------
paths : list of int-arrays
list of pathways
pathfluxes : double-array
array with path fluxes
n : int
number of st... | [
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47,984 | markovmodel/msmtools | msmtools/flux/reactive_flux.py | ReactiveFlux.major_flux | def major_flux(self, fraction=0.9):
r"""Returns the main pathway part of the net flux comprising
at most the requested fraction of the full flux.
"""
(paths, pathfluxes) = self.pathways(fraction=fraction)
return self._pathways_to_flux(paths, pathfluxes, n=self.nstates) | python | def major_flux(self, fraction=0.9):
r"""Returns the main pathway part of the net flux comprising
at most the requested fraction of the full flux.
"""
(paths, pathfluxes) = self.pathways(fraction=fraction)
return self._pathways_to_flux(paths, pathfluxes, n=self.nstates) | [
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47,985 | markovmodel/msmtools | msmtools/flux/reactive_flux.py | ReactiveFlux._compute_coarse_sets | def _compute_coarse_sets(self, user_sets):
r"""Computes the sets to coarse-grain the tpt flux to.
Parameters
----------
(tpt_sets, A, B) with
tpt_sets : list of int-iterables
sets of states that shall be distinguished in the coarse-grained flux.
... | python | def _compute_coarse_sets(self, user_sets):
r"""Computes the sets to coarse-grain the tpt flux to.
Parameters
----------
(tpt_sets, A, B) with
tpt_sets : list of int-iterables
sets of states that shall be distinguished in the coarse-grained flux.
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sets of states that shall be distinguished in the coarse-grained flux.
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47,986 | markovmodel/msmtools | msmtools/flux/reactive_flux.py | ReactiveFlux.coarse_grain | def coarse_grain(self, user_sets):
r"""Coarse-grains the flux onto user-defined sets.
Parameters
----------
user_sets : list of int-iterables
sets of states that shall be distinguished in the coarse-grained flux.
Returns
-------
(sets, tpt) : (list o... | python | def coarse_grain(self, user_sets):
r"""Coarse-grains the flux onto user-defined sets.
Parameters
----------
user_sets : list of int-iterables
sets of states that shall be distinguished in the coarse-grained flux.
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-------
(sets, tpt) : (list o... | [
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47,987 | markovmodel/msmtools | msmtools/util/birth_death_chain.py | BirthDeathChain.committor_forward | def committor_forward(self, a, b):
r"""Forward committor for birth-and-death-chain.
The forward committor is the probability to hit
state b before hitting state a starting in state x,
u_x=P_x(T_b<T_a)
T_i is the first arrival time of the chain to state i,
T_i ... | python | def committor_forward(self, a, b):
r"""Forward committor for birth-and-death-chain.
The forward committor is the probability to hit
state b before hitting state a starting in state x,
u_x=P_x(T_b<T_a)
T_i is the first arrival time of the chain to state i,
T_i ... | [
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47,988 | markovmodel/msmtools | msmtools/estimation/sparse/transition_matrix.py | transition_matrix_non_reversible | def transition_matrix_non_reversible(C):
"""implementation of transition_matrix"""
if not scipy.sparse.issparse(C):
C = scipy.sparse.csr_matrix(C)
rowsum = C.tocsr().sum(axis=1)
# catch div by zero
if np.min(rowsum) == 0.0:
raise ValueError("matrix C contains rows with sum zero.")
... | python | def transition_matrix_non_reversible(C):
"""implementation of transition_matrix"""
if not scipy.sparse.issparse(C):
C = scipy.sparse.csr_matrix(C)
rowsum = C.tocsr().sum(axis=1)
# catch div by zero
if np.min(rowsum) == 0.0:
raise ValueError("matrix C contains rows with sum zero.")
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47,989 | markovmodel/msmtools | msmtools/estimation/sparse/transition_matrix.py | correct_transition_matrix | def correct_transition_matrix(T, reversible=None):
r"""Normalize transition matrix
Fixes a the row normalization of a transition matrix.
To be used with the reversible estimators to fix an almost coverged
transition matrix.
Parameters
----------
T : (M, M) ndarray
matrix to correct... | python | def correct_transition_matrix(T, reversible=None):
r"""Normalize transition matrix
Fixes a the row normalization of a transition matrix.
To be used with the reversible estimators to fix an almost coverged
transition matrix.
Parameters
----------
T : (M, M) ndarray
matrix to correct... | [
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47,990 | markovmodel/msmtools | msmtools/analysis/dense/fingerprints.py | expectation | def expectation(P, obs):
r"""Equilibrium expectation of given observable.
Parameters
----------
P : (M, M) ndarray
Transition matrix
obs : (M,) ndarray
Observable, represented as vector on state space
Returns
-------
x : float
Expectation value
"""
pi =... | python | def expectation(P, obs):
r"""Equilibrium expectation of given observable.
Parameters
----------
P : (M, M) ndarray
Transition matrix
obs : (M,) ndarray
Observable, represented as vector on state space
Returns
-------
x : float
Expectation value
"""
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x : float
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47,991 | markovmodel/msmtools | msmtools/analysis/dense/fingerprints.py | correlation_decomp | def correlation_decomp(P, obs1, obs2=None, times=[1], k=None):
r"""Time-correlation for equilibrium experiment - via decomposition.
Parameters
----------
P : (M, M) ndarray
Transition matrix
obs1 : (M,) ndarray
Observable, represented as vector on state space
obs2 : (M,) ndarray... | python | def correlation_decomp(P, obs1, obs2=None, times=[1], k=None):
r"""Time-correlation for equilibrium experiment - via decomposition.
Parameters
----------
P : (M, M) ndarray
Transition matrix
obs1 : (M,) ndarray
Observable, represented as vector on state space
obs2 : (M,) ndarray... | [
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47,992 | markovmodel/msmtools | msmtools/estimation/sparse/likelihood.py | log_likelihood | def log_likelihood(C, T):
"""
implementation of likelihood of C given T
"""
C = C.tocsr()
T = T.tocsr()
ind = scipy.nonzero(C)
relT = np.array(T[ind])[0, :]
relT = np.log(relT)
relC = np.array(C[ind])[0, :]
return relT.dot(relC) | python | def log_likelihood(C, T):
"""
implementation of likelihood of C given T
"""
C = C.tocsr()
T = T.tocsr()
ind = scipy.nonzero(C)
relT = np.array(T[ind])[0, :]
relT = np.log(relT)
relC = np.array(C[ind])[0, :]
return relT.dot(relC) | [
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47,993 | juanpabloaj/slacker-cli | slacker_cli/__init__.py | upload_file | def upload_file(token, channel_name, file_name):
""" upload file to a channel """
slack = Slacker(token)
slack.files.upload(file_name, channels=channel_name) | python | def upload_file(token, channel_name, file_name):
""" upload file to a channel """
slack = Slacker(token)
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47,994 | markovmodel/msmtools | msmtools/estimation/dense/ratematrix.py | _ReversibleRateMatrixEstimator.run | def run(self):
"""Run the minimization.
Returns
-------
K : (N,N) ndarray
the optimal rate matrix
"""
if self.verbose:
self.selftest()
self.count = 0
if self.verbose:
logging.info('initial value of the objective functio... | python | def run(self):
"""Run the minimization.
Returns
-------
K : (N,N) ndarray
the optimal rate matrix
"""
if self.verbose:
self.selftest()
self.count = 0
if self.verbose:
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47,995 | myint/unify | unify.py | unify_quotes | def unify_quotes(token_string, preferred_quote):
"""Return string with quotes changed to preferred_quote if possible."""
bad_quote = {'"': "'",
"'": '"'}[preferred_quote]
allowed_starts = {
'': bad_quote,
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'b': 'b' + bad_quote
}
if not ... | python | def unify_quotes(token_string, preferred_quote):
"""Return string with quotes changed to preferred_quote if possible."""
bad_quote = {'"': "'",
"'": '"'}[preferred_quote]
allowed_starts = {
'': bad_quote,
'f': 'f' + bad_quote,
'b': 'b' + bad_quote
}
if not ... | [
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47,996 | myint/unify | unify.py | detect_encoding | def detect_encoding(filename):
"""Return file encoding."""
try:
with open(filename, 'rb') as input_file:
from lib2to3.pgen2 import tokenize as lib2to3_tokenize
encoding = lib2to3_tokenize.detect_encoding(input_file.readline)[0]
# Check for correctness of encoding.
... | python | def detect_encoding(filename):
"""Return file encoding."""
try:
with open(filename, 'rb') as input_file:
from lib2to3.pgen2 import tokenize as lib2to3_tokenize
encoding = lib2to3_tokenize.detect_encoding(input_file.readline)[0]
# Check for correctness of encoding.
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47,997 | myint/unify | unify.py | _main | def _main(argv, standard_out, standard_error):
"""Run quotes unifying on files.
Returns `1` if any quoting changes are still needed, otherwise
`None`.
"""
import argparse
parser = argparse.ArgumentParser(description=__doc__, prog='unify')
parser.add_argument('-i', '--in-place', action='sto... | python | def _main(argv, standard_out, standard_error):
"""Run quotes unifying on files.
Returns `1` if any quoting changes are still needed, otherwise
`None`.
"""
import argparse
parser = argparse.ArgumentParser(description=__doc__, prog='unify')
parser.add_argument('-i', '--in-place', action='sto... | [
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47,998 | markovmodel/msmtools | msmtools/estimation/api.py | count_matrix | def count_matrix(dtraj, lag, sliding=True, sparse_return=True, nstates=None):
r"""Generate a count matrix from given microstate trajectory.
Parameters
----------
dtraj : array_like or list of array_like
Discretized trajectory or list of discretized trajectories
lag : int
Lagtime in ... | python | def count_matrix(dtraj, lag, sliding=True, sparse_return=True, nstates=None):
r"""Generate a count matrix from given microstate trajectory.
Parameters
----------
dtraj : array_like or list of array_like
Discretized trajectory or list of discretized trajectories
lag : int
Lagtime in ... | [
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Parameters
----------
dtraj : array_like or list of array_like
Discretized trajectory or list of discretized trajectories
lag : int
Lagtime in trajectory steps
sliding : bool, optional
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] | 54dc76dd2113a0e8f3d15d5316abab41402941be | https://github.com/markovmodel/msmtools/blob/54dc76dd2113a0e8f3d15d5316abab41402941be/msmtools/estimation/api.py#L128-L219 |
47,999 | markovmodel/msmtools | msmtools/estimation/api.py | bootstrap_counts | def bootstrap_counts(dtrajs, lagtime, corrlength=None):
r"""Generates a randomly resampled count matrix given the input coordinates.
Parameters
----------
dtrajs : array-like or array-like of array-like
single or multiple discrete trajectories. Every trajectory is assumed to be
a statis... | python | def bootstrap_counts(dtrajs, lagtime, corrlength=None):
r"""Generates a randomly resampled count matrix given the input coordinates.
Parameters
----------
dtrajs : array-like or array-like of array-like
single or multiple discrete trajectories. Every trajectory is assumed to be
a statis... | [
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Parameters
----------
dtrajs : array-like or array-like of array-like
single or multiple discrete trajectories. Every trajectory is assumed to be
a statistically independent realization. Note that this is often not... | [
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