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pavlov99/jsonapi | jsonapi/django_utils.py | clear_app_cache | def clear_app_cache(app_name):
""" Clear django cache for models.
:param str ap_name: name of application to clear model cache
"""
loading_cache = django.db.models.loading.cache
if django.VERSION[:2] < (1, 7):
loading_cache.app_models[app_name].clear()
else:
loading_cache.all_... | python | def clear_app_cache(app_name):
""" Clear django cache for models.
:param str ap_name: name of application to clear model cache
"""
loading_cache = django.db.models.loading.cache
if django.VERSION[:2] < (1, 7):
loading_cache.app_models[app_name].clear()
else:
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liamw9534/bt-manager | bt_manager/codecs.py | SBCCodec._init_sbc_config | def _init_sbc_config(self, config):
"""
Translator from namedtuple config representation to
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:param namedtuple config: See :py:class:`.SBCCodecConfig`
:returns:
"""
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self.conf... | python | def _init_sbc_config(self, config):
"""
Translator from namedtuple config representation to
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:param namedtuple config: See :py:class:`.SBCCodecConfig`
:returns:
"""
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liamw9534/bt-manager | bt_manager/codecs.py | SBCCodec.decode | def decode(self, fd, mtu, max_len=2560):
"""
Read the media transport descriptor, depay
the RTP payload and decode the SBC frames into
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec._transport_ready_handler | def _transport_ready_handler(self, fd, cb_condition):
"""
Wrapper for calling user callback routine to notify
when transport data is ready to read
"""
if(self.user_cb):
self.user_cb(self.user_arg)
return True | python | def _transport_ready_handler(self, fd, cb_condition):
"""
Wrapper for calling user callback routine to notify
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self.user_cb(self.user_arg)
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec.read_transport | def read_transport(self):
"""
Read data from media transport.
The returned data payload is SBC decoded and has
all RTP encapsulation removed.
:return data: Payload data that has been decoded,
with RTP encapsulation removed.
:rtype: array{byte}
"""
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"""
Read data from media transport.
The returned data payload is SBC decoded and has
all RTP encapsulation removed.
:return data: Payload data that has been decoded,
with RTP encapsulation removed.
:rtype: array{byte}
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec.write_transport | def write_transport(self, data):
"""
Write data to media transport. The data is
encoded using the SBC codec and RTP encapsulated
before being written to the transport file
descriptor.
:param array{byte} data: Payload data to encode,
encapsulate and send.
... | python | def write_transport(self, data):
"""
Write data to media transport. The data is
encoded using the SBC codec and RTP encapsulated
before being written to the transport file
descriptor.
:param array{byte} data: Payload data to encode,
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec.close_transport | def close_transport(self):
"""
Forcibly close previously acquired media transport.
.. note:: The user should first make sure any transport
event handlers are unregistered first.
"""
if (self.path):
self._release_media_transport(self.path,
... | python | def close_transport(self):
"""
Forcibly close previously acquired media transport.
.. note:: The user should first make sure any transport
event handlers are unregistered first.
"""
if (self.path):
self._release_media_transport(self.path,
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec._acquire_media_transport | def _acquire_media_transport(self, path, access_type):
"""
Should be called by subclass when it is ready
to acquire the media transport file descriptor
"""
transport = BTMediaTransport(path=path)
(fd, read_mtu, write_mtu) = transport.acquire(access_type)
self.fd =... | python | def _acquire_media_transport(self, path, access_type):
"""
Should be called by subclass when it is ready
to acquire the media transport file descriptor
"""
transport = BTMediaTransport(path=path)
(fd, read_mtu, write_mtu) = transport.acquire(access_type)
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec._release_media_transport | def _release_media_transport(self, path, access_type):
"""
Should be called by subclass when it is finished
with the media transport file descriptor
"""
try:
self._uninstall_transport_ready()
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t... | python | def _release_media_transport(self, path, access_type):
"""
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"""
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec._make_config | def _make_config(config):
"""Helper to turn SBC codec configuration params into a
a2dp_sbc_t structure usable by bluez"""
# The SBC config encoding is taken from a2dp_codecs.h, in particular,
# the a2dp_sbc_t type is converted into a 4-byte array:
# uint8_t channel_mode:4
... | python | def _make_config(config):
"""Helper to turn SBC codec configuration params into a
a2dp_sbc_t structure usable by bluez"""
# The SBC config encoding is taken from a2dp_codecs.h, in particular,
# the a2dp_sbc_t type is converted into a 4-byte array:
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liamw9534/bt-manager | bt_manager/audio.py | SBCAudioCodec._parse_config | def _parse_config(config):
"""Helper to turn a2dp_sbc_t structure into a
more usable set of SBC codec configuration params"""
frequency = config[0] >> 4
channel_mode = config[0] & 0xF
allocation_method = config[1] & 0x03
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python-wink/python-wink | src/pywink/devices/lock.py | WinkLock.add_new_key | def add_new_key(self, code, name):
"""Add a new user key code."""
device_json = {"code": code, "name": name}
return self.api_interface.create_lock_key(self, device_json) | python | def add_new_key(self, code, name):
"""Add a new user key code."""
device_json = {"code": code, "name": name}
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liamw9534/bt-manager | bt_manager/adapter.py | BTAdapter.create_paired_device | def create_paired_device(self, dev_id, agent_path,
capability, cb_notify_device, cb_notify_error):
"""
Creates a new object path for a remote device. This
method will connect to the remote device and retrieve
all SDP records and then initiate the pairing.
... | python | def create_paired_device(self, dev_id, agent_path,
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"""
Creates a new object path for a remote device. This
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xZise/flake8-string-format | flake8_string_format.py | TextVisitor._visit_body | def _visit_body(self, node):
"""
Traverse the body of the node manually.
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"""
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self.is_base_string(nod... | python | def _visit_body(self, node):
"""
Traverse the body of the node manually.
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claymation/python-builtwith | builtwith.py | BuiltWith.lookup | def lookup(self, domain, get_last_full_query=True):
"""
Lookup BuiltWith results for the given domain. If API version 2 is used and the get_last_full_query flag
enabled, it also queries for the date of the last full BuiltWith scan.
"""
last_full_builtwith_scan_date = None
... | python | def lookup(self, domain, get_last_full_query=True):
"""
Lookup BuiltWith results for the given domain. If API version 2 is used and the get_last_full_query flag
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last_full_builtwith_scan_date = None
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pavlov99/jsonapi | jsonapi/api.py | API.register | def register(self, resource=None, **kwargs):
""" Register resource for currnet API.
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:return: resource
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""" Register resource for currnet API.
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pavlov99/jsonapi | jsonapi/api.py | API.urls | def urls(self):
""" Get all of the api endpoints.
NOTE: only for django as of now.
NOTE: urlpatterns are deprecated since Django1.8
:return list: urls
"""
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""" Get all of the api endpoints.
NOTE: only for django as of now.
NOTE: urlpatterns are deprecated since Django1.8
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pavlov99/jsonapi | jsonapi/api.py | API.update_urls | def update_urls(self, request, resource_name=None, ids=None):
""" Update url configuration.
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pavlov99/jsonapi | jsonapi/api.py | API.map_view | def map_view(self, request):
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resource_info = {
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pavlov99/jsonapi | jsonapi/api.py | API.documentation | def documentation(self, request):
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pavlov99/jsonapi | jsonapi/api.py | API.handler_view | def handler_view(self, request, resource_name, ids=None):
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RobSpectre/Caesar-Cipher | caesarcipher/caesarcipher.py | CaesarCipher.decoded | def decoded(self):
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String decoded with cipher.
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pavlov99/jsonapi | jsonapi/request_parser.py | RequestParser.parse | def parse(cls, querydict):
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querydict : django.http.request.QueryDict
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python-wink/python-wink | src/pywink/devices/binary_switch.py | WinkBinarySwitch.binary_state_name | def binary_state_name(self):
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markovmodel/msmtools | msmtools/flux/dense/tpt.py | flux_production | def flux_production(F):
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker._init_journal | def _init_journal(self, permissive=True):
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker._new_from_rft | def _new_from_rft(self, base_template, rft_file):
"""Append a new file from .rft entry to the journal.
This instructs Revit to create a new model based on
the provided .rft template.
Args:
base_template (str): new file journal template from rmj.templates
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"""Append a new file from .rft entry to the journal.
This instructs Revit to create a new model based on
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.new_model | def new_model(self, template_name='<None>'):
"""Append a new model from .rft entry to the journal.
This instructs Revit to create a new model based on the
provided .rft template.
Args:
template_name (str): optional full path to .rft template
... | python | def new_model(self, template_name='<None>'):
"""Append a new model from .rft entry to the journal.
This instructs Revit to create a new model based on the
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template_name (str): optional full path to .rft template
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.new_template | def new_template(self, template_name='<None>'):
"""Append a new template from .rft entry to the journal.
This instructs Revit to create a new template model based on the
provided .rft template.
Args:
template_name (str): optional full path to .rft template
... | python | def new_template(self, template_name='<None>'):
"""Append a new template from .rft entry to the journal.
This instructs Revit to create a new template model based on the
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Args:
template_name (str): optional full path to .rft template
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.open_workshared_model | def open_workshared_model(self, model_path, central=False,
detached=False, keep_worksets=True, audit=False,
show_workset_config=1):
"""Append a open workshared model entry to the journal.
This instructs Revit to open a workshared model.
... | python | def open_workshared_model(self, model_path, central=False,
detached=False, keep_worksets=True, audit=False,
show_workset_config=1):
"""Append a open workshared model entry to the journal.
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.open_model | def open_model(self, model_path, audit=False):
"""Append a open non-workshared model entry to the journal.
This instructs Revit to open a non-workshared model.
Args:
model_path (str): full path to non-workshared model
audit (bool): if True audits the model when opening
... | python | def open_model(self, model_path, audit=False):
"""Append a open non-workshared model entry to the journal.
This instructs Revit to open a non-workshared model.
Args:
model_path (str): full path to non-workshared model
audit (bool): if True audits the model when opening
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.execute_command | def execute_command(self, tab_name, panel_name,
command_module, command_class, command_data=None):
"""Append an execute external command entry to the journal.
This instructs Revit to execute the provided command from the
provided module, tab, and panel.
Args:
... | python | def execute_command(self, tab_name, panel_name,
command_module, command_class, command_data=None):
"""Append an execute external command entry to the journal.
This instructs Revit to execute the provided command from the
provided module, tab, and panel.
Args:
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eirannejad/Revit-Journal-Maker | rjm/__init__.py | JournalMaker.execute_dynamo_definition | def execute_dynamo_definition(self, definition_path,
show_ui=False, shutdown=True,
automation=False, path_exec=True):
"""Execute a dynamo definition.
Args:
definition_path (str): full path to dynamo definition file
... | python | def execute_dynamo_definition(self, definition_path,
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"""Execute a dynamo definition.
Args:
definition_path (str): full path to dynamo definition file
... | [
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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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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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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
"""
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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,
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"""Append a sync model entry to the journal.
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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
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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
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"""Check whether the provided string exists in Journal file.
Only checks the last 5 lines of the journal file. This method is
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Args:
search_str (str): string to search for
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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
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C : (M, M) scipy.sparse matrix
Count matrix
alpha : float (optional)
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b_ij = alpha if Z_ij+Z_ji > 0
b_ij = 0 else
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C : (M, M) scipy.sparse matrix
Count matrix
alpha : float (optional)
Value of prior counts
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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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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
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Truth value : bool
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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
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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
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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
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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
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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
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T : scipy.sparse matrix
Transition matrix
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markovmodel/msmtools | msmtools/analysis/sparse/assessment.py | is_ergodic | def is_ergodic(T, tol):
"""
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Parameters
----------
T : scipy.sparse matrix
Transition matrix
tol : float
tolerance
Returns
-------
Truth value : bool
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False, otherwise
"""
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"""
checks if T is 'ergodic'
Parameters
----------
T : scipy.sparse matrix
Transition matrix
tol : float
tolerance
Returns
-------
Truth value : bool
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yaybu/callsign | callsign/scripts/daemon.py | spawn | def spawn(opts, conf):
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""" Acts like twistd """
if opts.config is not None:
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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
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e : tuple of int
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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
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r"""Check if the given graph contains a path connecting A and B.
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graph : scipy.sparse matrix
Adjacency matrix of the graph
A : array_like
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B : array_like
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----------
nodes : array_like
Nodes from breadth_first_oder_seatch
A : array_like
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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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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
-------
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r"""Compute the dominant reaction-pathway.
Parameters
----------
F : (M, M) scipy.sparse matrix
The flux network (matrix of netflux values)
A : array_like
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B : array_like
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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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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
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B : array_like
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Load script files into the context.\
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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
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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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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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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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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
transition matrices.
Parameters
----------
T : (M, M) ndarray
Transition matrix
norm: {'standard', 'reversible'}
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r"""Decomposition into left and right eigenvectors for reversible
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Parameters
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T : (M, M) ndarray
Transition matrix
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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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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
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"""
dirname, basename = os.path.split(filename)
name, ext = os.path.splitext(basename)
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"""Determine if the given filename indicates a dense or a sparse matrix
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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
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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
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P : (M, M) scipy.sparse matrix
Transition matrix
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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
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r"""Compute the eigenvalues of a sparse transition matrix.
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----------
T : (M, M) scipy.sparse matrix
Transition matrix
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Number of eigenvalues to compute.
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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
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r"""Compute the eigenvalues of a reversible, sparse transition matrix.
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----------
T : (M, M) scipy.sparse matrix
Transition matrix
k : int
Number of eigenvalues to compute.
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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
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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
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if (isinstance(dtrajs[0], (int))):
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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(
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"""
Samples n counts at the given lagtime from the given trajectory
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# check if length is sufficient
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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
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r"""Compute connected components for a directed graph with weights
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square matrix specifying edge weights.
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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.
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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.
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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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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
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Parameters
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F : (M, M) ndarray
Matrix of flux values between pairs of states.
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List of integer sta... | [
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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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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.
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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):
"""
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eigenvectors : ndarray
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markovmodel/msmtools | msmtools/analysis/dense/pcca.py | _fill_matrix | def _fill_matrix(rot_crop_matrix, eigvectors):
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# 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
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markovmodel/msmtools | msmtools/analysis/dense/pcca.py | coarsegrain | def coarsegrain(P, n):
"""
Coarse-grains transition matrix P to n sets using PCCA
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Coarse-grains transition matrix P to n sets using PCCA
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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
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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
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K : (M, M) ndarray or scipy.sparse matrix
Matrix to check
tol : float (optional)
Floating point tolerance to check with
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is_rate_matrix : bool
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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
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r"""Check connectivity of the given matrix.
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T : (M, M) ndarray or scipy.sparse matrix
Matrix to check
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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
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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
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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
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rather ... | [
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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
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... | [
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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
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r"""Dynamical fingerprint for equilibrium correlation experiment.
Parameters
----------
T : (M, M) ndarray or scipy.sparse matrix
Transition matrix
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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.
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----------
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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)
The stationary distribution of T. I... | [
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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
"""
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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
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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
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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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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
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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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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
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r"""Sensitivity matrix of the mean first-passage time from specified state.
Parameters
----------
T : (M, M) ndarray
Transition matrix
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Target state or set for mfpt computation
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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
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r"""Sensitivity matrix of a specified committor entry.
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T : (M, M) ndarray
Transition matrix
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] | 54dc76dd2113a0e8f3d15d5316abab41402941be | https://github.com/markovmodel/msmtools/blob/54dc76dd2113a0e8f3d15d5316abab41402941be/msmtools/analysis/api.py#L1787-L1822 | train | 64,897 |
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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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 will only
return cov(p_ij, p_... | [
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] | 54dc76dd2113a0e8f3d15d5316abab41402941be | https://github.com/markovmodel/msmtools/blob/54dc76dd2113a0e8f3d15d5316abab41402941be/msmtools/estimation/dense/covariance.py#L31-L73 | train | 64,898 |
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()
norm = alpha0 ** ... | [
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Parameters
----------
alpha : (M, ) ndarray
Parameters of Dirichlet distribution
Returns
-------
cov : (M, M) ndarray
Covariance matrix | [
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] | 54dc76dd2113a0e8f3d15d5316abab41402941be | https://github.com/markovmodel/msmtools/blob/54dc76dd2113a0e8f3d15d5316abab41402941be/msmtools/estimation/dense/covariance.py#L76-L103 | train | 64,899 |
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