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nilp0inter/cpe | cpe/cpeset2_3.py | CPESet2_3.cpe_superset | def cpe_superset(cls, source, target):
"""
Compares two WFNs and returns True if the set-theoretic relation
between the names is (non-proper) SUPERSET.
:param CPE2_3_WFN source: first WFN CPE Name
:param CPE2_3_WFN target: seconds WFN CPE Name
:returns: True if the set r... | python | def cpe_superset(cls, source, target):
"""
Compares two WFNs and returns True if the set-theoretic relation
between the names is (non-proper) SUPERSET.
:param CPE2_3_WFN source: first WFN CPE Name
:param CPE2_3_WFN target: seconds WFN CPE Name
:returns: True if the set r... | [
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nilp0inter/cpe | cpe/cpeset2_3.py | CPESet2_3.append | def append(self, cpe):
"""
Adds a CPE element to the set if not already.
Only WFN CPE Names are valid, so this function converts the input CPE
object of version 2.3 to WFN style.
:param CPE cpe: CPE Name to store in set
:returns: None
:exception: ValueError - inv... | python | def append(self, cpe):
"""
Adds a CPE element to the set if not already.
Only WFN CPE Names are valid, so this function converts the input CPE
object of version 2.3 to WFN style.
:param CPE cpe: CPE Name to store in set
:returns: None
:exception: ValueError - inv... | [
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nilp0inter/cpe | cpe/cpeset2_3.py | CPESet2_3.name_match | def name_match(self, wfn):
"""
Accepts a set of CPE Names K and a candidate CPE Name X. It returns
'True' if X matches any member of K, and 'False' otherwise.
:param CPESet self: A set of m known CPE Names K = {K1, K2, …, Km}.
:param CPE cpe: A candidate CPE Name X.
:ret... | python | def name_match(self, wfn):
"""
Accepts a set of CPE Names K and a candidate CPE Name X. It returns
'True' if X matches any member of K, and 'False' otherwise.
:param CPESet self: A set of m known CPE Names K = {K1, K2, …, Km}.
:param CPE cpe: A candidate CPE Name X.
:ret... | [
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nilp0inter/cpe | cpe/cpe2_3_wfn.py | CPE2_3_WFN._parse | def _parse(self):
"""
Checks if the CPE Name is valid.
:returns: None
:exception: ValueError - bad-formed CPE Name
"""
# Check prefix and initial bracket of WFN
if self._str[0:5] != CPE2_3_WFN.CPE_PREFIX:
errmsg = "Bad-formed CPE Name: WFN prefix not... | python | def _parse(self):
"""
Checks if the CPE Name is valid.
:returns: None
:exception: ValueError - bad-formed CPE Name
"""
# Check prefix and initial bracket of WFN
if self._str[0:5] != CPE2_3_WFN.CPE_PREFIX:
errmsg = "Bad-formed CPE Name: WFN prefix not... | [
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nilp0inter/cpe | cpe/cpelang2_2.py | CPELanguage2_2.language_match | def language_match(self, cpeset, cpel_dom=None):
"""
Accepts a set of known CPE Names and an expression in the CPE language,
and delivers the answer True if the expression matches with the set.
Otherwise, it returns False.
:param CPELanguage self: An expression in the CPE Applic... | python | def language_match(self, cpeset, cpel_dom=None):
"""
Accepts a set of known CPE Names and an expression in the CPE language,
and delivers the answer True if the expression matches with the set.
Otherwise, it returns False.
:param CPELanguage self: An expression in the CPE Applic... | [
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Otherwise, it returns False.
:param CPELanguage self: An expression in the CPE Applicability
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nilp0inter/cpe | cpe/cpeset2_2.py | CPESet2_2.append | def append(self, cpe):
"""
Adds a CPE Name to the set if not already.
:param CPE cpe: CPE Name to store in set
:returns: None
:exception: ValueError - invalid version of CPE Name
TEST:
>>> from .cpeset2_2 import CPESet2_2
>>> from .cpe2_2 import CPE2_2... | python | def append(self, cpe):
"""
Adds a CPE Name to the set if not already.
:param CPE cpe: CPE Name to store in set
:returns: None
:exception: ValueError - invalid version of CPE Name
TEST:
>>> from .cpeset2_2 import CPESet2_2
>>> from .cpe2_2 import CPE2_2... | [
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:exception: ValueError - invalid version of CPE Name
TEST:
>>> from .cpeset2_2 import CPESet2_2
>>> from .cpe2_2 import CPE2_2
>>> uri1 = 'cpe:/h:hp'
>>>... | [
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nilp0inter/cpe | cpe/comp/cpecomp2_3_wfn.py | CPEComponent2_3_WFN.set_value | def set_value(self, comp_str, comp_att):
"""
Set the value of component.
:param string comp_str: value of component
:param string comp_att: attribute associated with comp_str
:returns: None
:exception: ValueError - incorrect value of component
"""
# Del ... | python | def set_value(self, comp_str, comp_att):
"""
Set the value of component.
:param string comp_str: value of component
:param string comp_att: attribute associated with comp_str
:returns: None
:exception: ValueError - incorrect value of component
"""
# Del ... | [
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nilp0inter/cpe | cpe/cpeset1_1.py | CPESet1_1.append | def append(self, cpe):
"""
Adds a CPE Name to the set if not already.
:param CPE cpe: CPE Name to store in set
:returns: None
:exception: ValueError - invalid version of CPE Name
TEST:
>>> from .cpeset1_1 import CPESet1_1
>>> from .cpe1_1 import CPE1_1
... | python | def append(self, cpe):
"""
Adds a CPE Name to the set if not already.
:param CPE cpe: CPE Name to store in set
:returns: None
:exception: ValueError - invalid version of CPE Name
TEST:
>>> from .cpeset1_1 import CPESet1_1
>>> from .cpe1_1 import CPE1_1
... | [
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:exception: ValueError - invalid version of CPE Name
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>>> from .cpeset1_1 import CPESet1_1
>>> from .cpe1_1 import CPE1_1
>>> uri1 = 'cpe://microsoft:windows... | [
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nilp0inter/cpe | cpe/cpeset1_1.py | CPESet1_1.name_match | def name_match(self, cpe):
"""
Accepts a set of known instances of CPE Names and a candidate CPE Name,
and returns 'True' if the candidate can be shown to be
an instance based on the content of the known instances.
Otherwise, it returns 'False'.
:param CPESet self: A set... | python | def name_match(self, cpe):
"""
Accepts a set of known instances of CPE Names and a candidate CPE Name,
and returns 'True' if the candidate can be shown to be
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nilp0inter/cpe | cpe/cpe2_3_fs.py | CPE2_3_FS._parse | def _parse(self):
"""
Checks if the CPE Name is valid.
:returns: None
:exception: ValueError - bad-formed CPE Name
"""
# CPE Name must not have whitespaces
if (self._str.find(" ") != -1):
msg = "Bad-formed CPE Name: it must not have whitespaces"
... | python | def _parse(self):
"""
Checks if the CPE Name is valid.
:returns: None
:exception: ValueError - bad-formed CPE Name
"""
# CPE Name must not have whitespaces
if (self._str.find(" ") != -1):
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nilp0inter/cpe | cpe/cpe2_3_fs.py | CPE2_3_FS.get_attribute_values | def get_attribute_values(self, att_name):
"""
Returns the values of attribute "att_name" of CPE Name.
By default a only element in each part.
:param string att_name: Attribute name to get
:returns: List of attribute values
:rtype: list
:exception: ValueError - in... | python | def get_attribute_values(self, att_name):
"""
Returns the values of attribute "att_name" of CPE Name.
By default a only element in each part.
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nilp0inter/cpe | cpe/cpe2_2.py | CPE2_2._parse | def _parse(self):
"""
Checks if CPE Name is valid.
:returns: None
:exception: ValueError - bad-formed CPE Name
"""
# CPE Name must not have whitespaces
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... | python | def _parse(self):
"""
Checks if CPE Name is valid.
:returns: None
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nilp0inter/cpe | cpe/cpe2_2.py | CPE2_2.as_wfn | def as_wfn(self):
"""
Returns the CPE Name as WFN string of version 2.3.
Only shows the first seven components.
:return: CPE Name as WFN string
:rtype: string
:exception: TypeError - incompatible version
"""
wfn = []
wfn.append(CPE2_3_WFN.CPE_PRE... | python | def as_wfn(self):
"""
Returns the CPE Name as WFN string of version 2.3.
Only shows the first seven components.
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:rtype: string
:exception: TypeError - incompatible version
"""
wfn = []
wfn.append(CPE2_3_WFN.CPE_PRE... | [
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nilp0inter/cpe | cpe/cpelang2_3.py | CPELanguage2_3._fact_ref_eval | def _fact_ref_eval(cls, cpeset, wfn):
"""
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or equal to) any of the names in cpeset, otherwise False.
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nilp0inter/cpe | cpe/cpelang2_3.py | CPELanguage2_3._check_fact_ref_eval | def _check_fact_ref_eval(cls, cpel_dom):
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nilp0inter/cpe | cpe/cpelang2_3.py | CPELanguage2_3._unbind | def _unbind(cls, boundname):
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nilp0inter/cpe | cpe/cpelang2_3.py | CPELanguage2_3.language_match | def language_match(self, cpeset, cpel_dom=None):
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nilp0inter/cpe | cpe/cpeset.py | CPESet.name_match | def name_match(self, cpe):
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nilp0inter/cpe | cpe/comp/cpecomp2_2.py | CPEComponent2_2._decode | def _decode(self):
"""
Convert the encoded value of component to standard value (WFN value).
"""
result = []
idx = 0
s = self._encoded_value
while (idx < len(s)):
# Get the idx'th character of s
c = s[idx]
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"""
Convert the encoded value of component to standard value (WFN value).
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pysal/giddy | giddy/components.py | is_component | def is_component(w, ids):
"""Check if the set of ids form a single connected component
Parameters
----------
w : spatial weights boject
ids : list
identifiers of units that are tested to be a single connected
component
Returns
-------
True : if the list of ... | python | def is_component(w, ids):
"""Check if the set of ids form a single connected component
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w : spatial weights boject
ids : list
identifiers of units that are tested to be a single connected
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pysal/giddy | giddy/components.py | check_contiguity | def check_contiguity(w, neighbors, leaver):
"""Check if contiguity is maintained if leaver is removed from neighbors
Parameters
----------
w : spatial weights object
simple contiguity based weights
neighbors : list
nodes that are to be checked if th... | python | def check_contiguity(w, neighbors, leaver):
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w : spatial weights object
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pyinvoke/invocations | invocations/console.py | confirm | def confirm(question, assume_yes=True):
"""
Ask user a yes/no question and return their response as a boolean.
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"Do you wish to continue?", and will have a string similar to ``" [Y/n] "``
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"""
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kolypto/py-flask-jsontools | flask_jsontools/decorators.py | jsonapi | def jsonapi(f):
""" Declare the view as a JSON API method
This converts view return value into a :cls:JsonResponse.
The following return types are supported:
- tuple: a tuple of (response, status, headers)
- any other object is converted to JSON
"""
@wraps(f)
de... | python | def jsonapi(f):
""" Declare the view as a JSON API method
This converts view return value into a :cls:JsonResponse.
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- any other object is converted to JSON
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pyinvoke/invocations | invocations/packaging/vendorize.py | _unpack | def _unpack(c, tmp, package, version, git_url=None):
"""
Download + unpack given package into temp dir ``tmp``.
Return ``(real_version, source)`` where ``real_version`` is the "actual"
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"""
Download + unpack given package into temp dir ``tmp``.
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pyinvoke/invocations | invocations/packaging/vendorize.py | vendorize | def vendorize(
c,
distribution,
version,
vendor_dir,
package=None,
git_url=None,
license=None,
):
"""
Vendorize Python package ``distribution`` at version/SHA ``version``.
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c,
distribution,
version,
vendor_dir,
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git_url=None,
license=None,
):
"""
Vendorize Python package ``distribution`` at version/SHA ``version``.
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pyinvoke/invocations | invocations/travis.py | make_sudouser | def make_sudouser(c):
"""
Create a passworded sudo-capable user.
Used by other tasks to execute the test suite so sudo tests work.
"""
user = c.travis.sudo.user
password = c.travis.sudo.password
# --create-home because we need a place to put conf files, keys etc
# --groups travis becaus... | python | def make_sudouser(c):
"""
Create a passworded sudo-capable user.
Used by other tasks to execute the test suite so sudo tests work.
"""
user = c.travis.sudo.user
password = c.travis.sudo.password
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pyinvoke/invocations | invocations/travis.py | make_sshable | def make_sshable(c):
"""
Set up passwordless SSH keypair & authorized_hosts access to localhost.
"""
user = c.travis.sudo.user
home = "~{0}".format(user)
# Run sudo() as the new sudo user; means less chown'ing, etc.
c.config.sudo.user = user
ssh_dir = "{0}/.ssh".format(home)
# TODO: ... | python | def make_sshable(c):
"""
Set up passwordless SSH keypair & authorized_hosts access to localhost.
"""
user = c.travis.sudo.user
home = "~{0}".format(user)
# Run sudo() as the new sudo user; means less chown'ing, etc.
c.config.sudo.user = user
ssh_dir = "{0}/.ssh".format(home)
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pyinvoke/invocations | invocations/travis.py | sudo_run | def sudo_run(c, command):
"""
Run some command under Travis-oriented sudo subshell/virtualenv.
:param str command:
Command string to run, e.g. ``inv coverage``, ``inv integration``, etc.
(Does not necessarily need to be an Invoke task, but...)
"""
# NOTE: explicit shell wrapper beca... | python | def sudo_run(c, command):
"""
Run some command under Travis-oriented sudo subshell/virtualenv.
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Install and execute ``black`` under appropriate circumstances, with diffs.
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pysal/giddy | giddy/rank.py | Tau._calc | def _calc(self, x, y):
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pysal/giddy | giddy/ergodic.py | steady_state | def steady_state(P):
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P : array
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Calculates the steady state probability vector for a regular Markov
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pysal/giddy | giddy/ergodic.py | fmpt | def fmpt(P):
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P : array
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pysal/giddy | giddy/ergodic.py | var_fmpt | def var_fmpt(P):
"""
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P : array
(k, k), an ergodic Markov transition probability matrix.
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"""
Variances of first mean passage times for an ergodic transition
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pyinvoke/invocations | invocations/packaging/release.py | _converge | def _converge(c):
"""
Examine world state, returning data on what needs updating for release.
:param c: Invoke ``Context`` object or subclass.
:returns:
Two dicts (technically, dict subclasses, which allow attribute access),
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``actions... | python | def _converge(c):
"""
Examine world state, returning data on what needs updating for release.
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pyinvoke/invocations | invocations/packaging/release.py | status | def status(c):
"""
Print current release (version, changelog, tag, etc) status.
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Print current release (version, changelog, tag, etc) status.
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pyinvoke/invocations | invocations/packaging/release.py | prepare | def prepare(c):
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Edit changelog & version, git commit, and git tag, to set up for release.
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pyinvoke/invocations | invocations/packaging/release.py | _release_line | def _release_line(c):
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Examine current repo state to determine what type of release to prep.
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pyinvoke/invocations | invocations/packaging/release.py | _versions_from_changelog | def _versions_from_changelog(changelog):
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pyinvoke/invocations | invocations/packaging/release.py | _release_and_issues | def _release_and_issues(changelog, branch, release_type):
"""
Return most recent branch-appropriate release, if any, and its contents.
:param dict changelog:
Changelog contents, as returned by ``releases.util.parse_changelog``.
:param str branch:
Branch name.
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"""
Return most recent branch-appropriate release, if any, and its contents.
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Changelog contents, as returned by ``releases.util.parse_changelog``.
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Branch name.
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"""
Return sorted list of release-style tags as semver objects.
"""
tags_ = []
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pyinvoke/invocations | invocations/packaging/release.py | _find_package | def _find_package(c):
"""
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Try to find 'the' One True Package for this project.
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pyinvoke/invocations | invocations/packaging/release.py | build | def build(c, sdist=True, wheel=False, directory=None, python=None, clean=True):
"""
Build sdist and/or wheel archives, optionally in a temp base directory.
All parameters save ``directory`` honor config settings of the same name,
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"""
Build sdist and/or wheel archives, optionally in a temp base directory.
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pyinvoke/invocations | invocations/packaging/release.py | publish | def publish(
c,
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wheel=False,
index=None,
sign=False,
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directory=None,
dual_wheels=False,
alt_python=None,
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Publish code to PyPI or index of choice.
All parameters save ``dry_run`` and ``directory`` honor config settin... | python | def publish(
c,
sdist=True,
wheel=False,
index=None,
sign=False,
dry_run=False,
directory=None,
dual_wheels=False,
alt_python=None,
check_desc=False,
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Publish code to PyPI or index of choice.
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pyinvoke/invocations | invocations/packaging/release.py | upload | def upload(c, directory, index=None, sign=False, dry_run=False):
"""
Upload (potentially also signing) all artifacts in ``directory``.
:param str index:
Custom upload index/repository name.
By default, uses whatever the invoked ``pip`` is configured to use.
Modify your ``pypirc`` f... | python | def upload(c, directory, index=None, sign=False, dry_run=False):
"""
Upload (potentially also signing) all artifacts in ``directory``.
:param str index:
Custom upload index/repository name.
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pyinvoke/invocations | invocations/util.py | tmpdir | def tmpdir(skip_cleanup=False, explicit=None):
"""
Context-manage a temporary directory.
Can be given ``skip_cleanup`` to skip cleanup, and ``explicit`` to choose a
specific location.
(If both are given, this is basically not doing anything, but it allows
code that normally requires a secure t... | python | def tmpdir(skip_cleanup=False, explicit=None):
"""
Context-manage a temporary directory.
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(If both are given, this is basically not doing anything, but it allows
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pysal/giddy | giddy/directional.py | Rose.permute | def permute(self, permutations=99, alternative='two.sided'):
"""
Generate ransom spatial permutations for inference on LISA vectors.
Parameters
----------
permutations : int, optional
Number of random permutations of observations.
alternative : string, option... | python | def permute(self, permutations=99, alternative='two.sided'):
"""
Generate ransom spatial permutations for inference on LISA vectors.
Parameters
----------
permutations : int, optional
Number of random permutations of observations.
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pysal/giddy | giddy/directional.py | Rose.plot | def plot(self, attribute=None, ax=None, **kwargs):
"""
Plot the rose diagram.
Parameters
----------
attribute : (n,) ndarray, optional
Variable to specify colors of the colorbars.
ax : Matplotlib Axes instance, optional
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Plot the rose diagram.
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attribute : (n,) ndarray, optional
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pysal/giddy | giddy/directional.py | Rose.plot_origin | def plot_origin(self): # TODO add attribute option to color vectors
"""
Plot vectors of positional transition of LISA values starting
from the same origin.
"""
import matplotlib.cm as cm
import matplotlib.pyplot as plt
ax = plt.subplot(111)
xlim = [self._... | python | def plot_origin(self): # TODO add attribute option to color vectors
"""
Plot vectors of positional transition of LISA values starting
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"""
import matplotlib.cm as cm
import matplotlib.pyplot as plt
ax = plt.subplot(111)
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pysal/giddy | giddy/directional.py | Rose.plot_vectors | def plot_vectors(self, arrows=True):
"""
Plot vectors of positional transition of LISA values
within quadrant in scatterplot in a polar plot.
Parameters
----------
ax : Matplotlib Axes instance, optional
If given, the figure will be created inside this axis.
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"""
Plot vectors of positional transition of LISA values
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Parameters
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ax : Matplotlib Axes instance, optional
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pyinvoke/invocations | invocations/docs.py | _clean | def _clean(c):
"""
Nuke docs build target directory so next build is clean.
"""
if isdir(c.sphinx.target):
rmtree(c.sphinx.target) | python | def _clean(c):
"""
Nuke docs build target directory so next build is clean.
"""
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pyinvoke/invocations | invocations/docs.py | _browse | def _browse(c):
"""
Open build target's index.html in a browser (using 'open').
"""
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c.run("open {0}".format(index)) | python | def _browse(c):
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Open build target's index.html in a browser (using 'open').
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pyinvoke/invocations | invocations/docs.py | build | def build(
c,
clean=False,
browse=False,
nitpick=False,
opts=None,
source=None,
target=None,
):
"""
Build the project's Sphinx docs.
"""
if clean:
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if opts is None:
opts = ""
if nitpick:
opts += " -n -W -T"
cmd = "sphinx-build{0} {... | python | def build(
c,
clean=False,
browse=False,
nitpick=False,
opts=None,
source=None,
target=None,
):
"""
Build the project's Sphinx docs.
"""
if clean:
_clean(c)
if opts is None:
opts = ""
if nitpick:
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pyinvoke/invocations | invocations/docs.py | tree | def tree(c):
"""
Display documentation contents with the 'tree' program.
"""
ignore = ".git|*.pyc|*.swp|dist|*.egg-info|_static|_build|_templates"
c.run('tree -Ca -I "{0}" {1}'.format(ignore, c.sphinx.source)) | python | def tree(c):
"""
Display documentation contents with the 'tree' program.
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pyinvoke/invocations | invocations/docs.py | sites | def sites(c):
"""
Build both doc sites w/ maxed nitpicking.
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# TODO: This is super lolzy but we haven't actually tackled nontrivial
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Build both doc sites w/ maxed nitpicking.
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pyinvoke/invocations | invocations/docs.py | watch_docs | def watch_docs(c):
"""
Watch both doc trees & rebuild them if files change.
This includes e.g. rebuilding the API docs if the source code changes;
rebuilding the WWW docs if the README changes; etc.
Reuses the configuration values ``packaging.package`` or ``tests.package``
(the former winning ... | python | def watch_docs(c):
"""
Watch both doc trees & rebuild them if files change.
This includes e.g. rebuilding the API docs if the source code changes;
rebuilding the WWW docs if the README changes; etc.
Reuses the configuration values ``packaging.package`` or ``tests.package``
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pysal/giddy | giddy/util.py | shuffle_matrix | def shuffle_matrix(X, ids):
"""
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Parameters
----------
X : array
(k, k), array to be permutated.
ids : array
range (k, ).
Returns
-------
X : array
(k, k) with rows and columns randomly shuffled.
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"""
Random permutation of rows and columns of a matrix
Parameters
----------
X : array
(k, k), array to be permutated.
ids : array
range (k, ).
Returns
-------
X : array
(k, k) with rows and columns randomly shuffled.
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pysal/giddy | giddy/util.py | get_lower | def get_lower(matrix):
"""
Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector.
Parameters
----------
matrix : array
(n, n) numpy array, a distance matrix.
Returns
-------
lowvec : array
numpy array, the lower half of the distance matri... | python | def get_lower(matrix):
"""
Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector.
Parameters
----------
matrix : array
(n, n) numpy array, a distance matrix.
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pyinvoke/invocations | invocations/checks.py | blacken | def blacken(
c, line_length=79, folders=None, check=False, diff=False, find_opts=None
):
r"""
Run black on the current source tree (all ``.py`` files).
.. warning::
``black`` only runs on Python 3.6 or above. (However, it can be
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:param in... | python | def blacken(
c, line_length=79, folders=None, check=False, diff=False, find_opts=None
):
r"""
Run black on the current source tree (all ``.py`` files).
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kolypto/py-flask-jsontools | flask_jsontools/response.py | normalize_response_value | def normalize_response_value(rv):
""" Normalize the response value into a 3-tuple (rv, status, headers)
:type rv: tuple|*
:returns: tuple(rv, status, headers)
:rtype: tuple(Response|JsonResponse|*, int|None, dict|None)
"""
status = headers = None
if isinstance(rv, tuple):
... | python | def normalize_response_value(rv):
""" Normalize the response value into a 3-tuple (rv, status, headers)
:type rv: tuple|*
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:rtype: tuple(Response|JsonResponse|*, int|None, dict|None)
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""" Make JsonResponse
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pysal/giddy | giddy/mobility.py | markov_mobility | def markov_mobility(p, measure="P", ini=None):
"""
Markov-based mobility index.
Parameters
----------
p : array
(k, k), Markov transition probability matrix.
measure : string
If measure= "P",
:math:`M_{P} = \\frac{m-\sum_{i=1}^m P_{ii}}{m-1}`;
... | python | def markov_mobility(p, measure="P", ini=None):
"""
Markov-based mobility index.
Parameters
----------
p : array
(k, k), Markov transition probability matrix.
measure : string
If measure= "P",
:math:`M_{P} = \\frac{m-\sum_{i=1}^m P_{ii}}{m-1}`;
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pysal/giddy | giddy/markov.py | chi2 | def chi2(T1, T2):
"""
chi-squared test of difference between two transition matrices.
Parameters
----------
T1 : array
(k, k), matrix of transitions (counts).
T2 : array
(k, k), matrix of transitions (counts) to use to form the
probabilities under the n... | python | def chi2(T1, T2):
"""
chi-squared test of difference between two transition matrices.
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----------
T1 : array
(k, k), matrix of transitions (counts).
T2 : array
(k, k), matrix of transitions (counts) to use to form the
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pysal/giddy | giddy/markov.py | kullback | def kullback(F):
"""
Kullback information based test of Markov Homogeneity.
Parameters
----------
F : array
(s, r, r), values are transitions (not probabilities) for
s strata, r initial states, r terminal states.
Returns
-------
Results : dictionary
(key -... | python | def kullback(F):
"""
Kullback information based test of Markov Homogeneity.
Parameters
----------
F : array
(s, r, r), values are transitions (not probabilities) for
s strata, r initial states, r terminal states.
Returns
-------
Results : dictionary
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pysal/giddy | giddy/markov.py | prais | def prais(pmat):
"""
Prais conditional mobility measure.
Parameters
----------
pmat : matrix
(k, k), Markov probability transition matrix.
Returns
-------
pr : matrix
(1, k), conditional mobility measures for each of the k classes.
Notes
-----
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"""
Prais conditional mobility measure.
Parameters
----------
pmat : matrix
(k, k), Markov probability transition matrix.
Returns
-------
pr : matrix
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pysal/giddy | giddy/markov.py | homogeneity | def homogeneity(transition_matrices, regime_names=[], class_names=[],
title="Markov Homogeneity Test"):
"""
Test for homogeneity of Markov transition probabilities across regimes.
Parameters
----------
transition_matrices : list
of transition matrices for r... | python | def homogeneity(transition_matrices, regime_names=[], class_names=[],
title="Markov Homogeneity Test"):
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Test for homogeneity of Markov transition probabilities across regimes.
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pysal/giddy | giddy/markov.py | sojourn_time | def sojourn_time(p):
"""
Calculate sojourn time based on a given transition probability matrix.
Parameters
----------
p : array
(k, k), a Markov transition probability matrix.
Returns
-------
: array
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"""
Calculate sojourn time based on a given transition probability matrix.
Parameters
----------
p : array
(k, k), a Markov transition probability matrix.
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pysal/giddy | giddy/markov.py | Spatial_Markov._calc | def _calc(self, y, w):
'''Helper to estimate spatial lag conditioned Markov transition
probability matrices based on maximum likelihood techniques.
'''
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self.lclass_ids = weights.lag_categorical(w, self.class_ids,
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'''Helper to estimate spatial lag conditioned Markov transition
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'''
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pysal/giddy | giddy/markov.py | Spatial_Markov.summary | def summary(self, file_name=None):
"""
A summary method to call the Markov homogeneity test to test for
temporally lagged spatial dependence.
To learn more about the properties of the tests, refer to
:cite:`Rey2016a` and :cite:`Kang2018`.
"""
class_names = ["C%d... | python | def summary(self, file_name=None):
"""
A summary method to call the Markov homogeneity test to test for
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pysal/giddy | giddy/markov.py | Spatial_Markov._maybe_classify | def _maybe_classify(self, y, k, cutoffs):
'''Helper method for classifying continuous data.
'''
rows, cols = y.shape
if cutoffs is None:
if self.fixed:
mcyb = mc.Quantiles(y.flatten(), k=k)
yb = mcyb.yb.reshape(y.shape)
cutoff... | python | def _maybe_classify(self, y, k, cutoffs):
'''Helper method for classifying continuous data.
'''
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pysal/giddy | giddy/markov.py | LISA_Markov.spillover | def spillover(self, quadrant=1, neighbors_on=False):
"""
Detect spillover locations for diffusion in LISA Markov.
Parameters
----------
quadrant : int
which quadrant in the scatterplot should form the core
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"""
Detect spillover locations for diffusion in LISA Markov.
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which quadrant in the scatterplot should form the core
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kolypto/py-flask-jsontools | flask_jsontools/formatting.py | get_entity_propnames | def get_entity_propnames(entity):
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:type entity: sqlalchemy.ext.declarative.api.DeclarativeMeta
:returns: Set of entity property names
:rtype: set
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ins = entity if isinstance(entity, InstanceState) else inspect(entity)
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""" Get entity property names
:param entity: Entity
:type entity: sqlalchemy.ext.declarative.api.DeclarativeMeta
:returns: Set of entity property names
:rtype: set
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pyinvoke/invocations | invocations/packaging/semantic_version_monkey.py | next_minor | def next_minor(self):
"""
Return a Version whose minor number is one greater than self's.
.. note::
The new Version will always have a zeroed-out bugfix/tertiary version
number, because the "next minor release" of e.g. 1.2.1 is 1.3.0, not
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"""
clone = self.clone()
... | python | def next_minor(self):
"""
Return a Version whose minor number is one greater than self's.
.. note::
The new Version will always have a zeroed-out bugfix/tertiary version
number, because the "next minor release" of e.g. 1.2.1 is 1.3.0, not
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roskakori/pygount | pygount/command.py | _check_encoding | def _check_encoding(name, encoding_to_check, alternative_encoding, source=None):
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Check that ``encoding`` is a valid Python encoding
:param name: name under which the encoding is known to the user, e.g. 'default encoding'
:param encoding_to_check: name of the encoding to check, e.g. 'utf-8'
:para... | python | def _check_encoding(name, encoding_to_check, alternative_encoding, source=None):
"""
Check that ``encoding`` is a valid Python encoding
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roskakori/pygount | pygount/common.py | lines | def lines(text):
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roskakori/pygount | pygount/analysis.py | matching_number_line_and_regex | def matching_number_line_and_regex(source_lines, generated_regexes, max_line_count=15):
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The first line and its number (starting with 0) in the source code that
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roskakori/pygount | pygount/analysis.py | _pythonized_comments | def _pythonized_comments(tokens):
"""
Similar to tokens but converts strings after a colon (:) to comments.
"""
is_after_colon = True
for token_type, token_text in tokens:
if is_after_colon and (token_type in pygments.token.String):
token_type = pygments.token.Comment
eli... | python | def _pythonized_comments(tokens):
"""
Similar to tokens but converts strings after a colon (:) to comments.
"""
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token_type = pygments.token.Comment
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roskakori/pygount | pygount/analysis.py | encoding_for | def encoding_for(source_path, encoding='automatic', fallback_encoding=None):
"""
The encoding used by the text file stored in ``source_path``.
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* If ``encoding`` is ``'automatic``, attempt the following:
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2. Look for XML prolo... | python | def encoding_for(source_path, encoding='automatic', fallback_encoding=None):
"""
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roskakori/pygount | pygount/analysis.py | has_lexer | def has_lexer(source_path):
"""
Initial quick check if there is a lexer for ``source_path``. This removes
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which fully reads the source file.
"""
result = bool(pygments.lexers.find_lexer_class_for_filename(source_path))
... | python | def has_lexer(source_path):
"""
Initial quick check if there is a lexer for ``source_path``. This removes
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which fully reads the source file.
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result = bool(pygments.lexers.find_lexer_class_for_filename(source_path))
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roskakori/pygount | pygount/analysis.py | source_analysis | def source_analysis(
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duplicate_pool=None):
"""
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source_path, group, encoding='automatic', fallback_encoding='cp1252',
generated_regexes=pygount.common.regexes_from(DEFAULT_GENERATED_PATTERNS_TEXT),
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radjkarl/imgProcessor | DUMP/interpolationMethods.py | polynomial | def polynomial(img, mask, inplace=False, replace_all=False,
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radjkarl/imgProcessor | imgProcessor/uncertainty/simulateUncertDependencyOnExpTime.py | errorDist | def errorDist(scale, measExpTime, n_events_in_expTime,
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TODO
'''
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s1 = measExpTime * scale * 10
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'''
TODO
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radjkarl/imgProcessor | imgProcessor/uncertainty/simulateUncertDependencyOnExpTime.py | exampleSignals | def exampleSignals(std=1, dur1=1, dur2=3, dur3=0.2,
n1=0.5, n2=0.5, n3=2):
'''
std ... standard deviation of every signal
dur1...dur3 --> event duration per second
n1...n3 --> number of events per second
'''
np.random.seed(123)
t = np.linspace(0, 10, 100)
... | python | def exampleSignals(std=1, dur1=1, dur2=3, dur3=0.2,
n1=0.5, n2=0.5, n3=2):
'''
std ... standard deviation of every signal
dur1...dur3 --> event duration per second
n1...n3 --> number of events per second
'''
np.random.seed(123)
t = np.linspace(0, 10, 100)
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radjkarl/imgProcessor | imgProcessor/uncertainty/simulateUncertDependencyOnExpTime.py | _flux | def _flux(t, n, duration, std, offs=1):
'''
returns Gaussian shaped signal fluctuations [events]
t --> times to calculate event for
n --> numbers of events per sec
duration --> event duration per sec
std --> std of event if averaged over time
offs --> event offset
'''
... | python | def _flux(t, n, duration, std, offs=1):
'''
returns Gaussian shaped signal fluctuations [events]
t --> times to calculate event for
n --> numbers of events per sec
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std --> std of event if averaged over time
offs --> event offset
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radjkarl/imgProcessor | imgProcessor/uncertainty/simulateUncertDependencyOnExpTime.py | _capture | def _capture(f, t, t0, factor):
'''
capture signal and return its standard deviation
#TODO: more detail
'''
n_per_sec = len(t) / t[-1]
# len of one split:
n = int(t0 * factor * n_per_sec)
s = len(f) // n
m = s * n
f = f[:m]
ff = np.split(f, s)
m = np.mean(ff... | python | def _capture(f, t, t0, factor):
'''
capture signal and return its standard deviation
#TODO: more detail
'''
n_per_sec = len(t) / t[-1]
# len of one split:
n = int(t0 * factor * n_per_sec)
s = len(f) // n
m = s * n
f = f[:m]
ff = np.split(f, s)
m = np.mean(ff... | [
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radjkarl/imgProcessor | imgProcessor/utils/genericCameraMatrix.py | genericCameraMatrix | def genericCameraMatrix(shape, angularField=60):
'''
Return a generic camera matrix
[[fx, 0, cx],
[ 0, fy, cy],
[ 0, 0, 1]]
for a given image shape
'''
# http://nghiaho.com/?page_id=576
# assume that the optical centre is in the middle:
cy = int(shape[0] / 2)
cx ... | python | def genericCameraMatrix(shape, angularField=60):
'''
Return a generic camera matrix
[[fx, 0, cx],
[ 0, fy, cy],
[ 0, 0, 1]]
for a given image shape
'''
# http://nghiaho.com/?page_id=576
# assume that the optical centre is in the middle:
cy = int(shape[0] / 2)
cx ... | [
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radjkarl/imgProcessor | imgProcessor/filters/standardDeviation.py | standardDeviation2d | def standardDeviation2d(img, ksize=5, blurred=None):
'''
calculate the spatial resolved standard deviation
for a given 2d array
ksize -> kernel size
blurred(optional) -> with same ksize gaussian filtered image
setting this parameter reduces processing time
... | python | def standardDeviation2d(img, ksize=5, blurred=None):
'''
calculate the spatial resolved standard deviation
for a given 2d array
ksize -> kernel size
blurred(optional) -> with same ksize gaussian filtered image
setting this parameter reduces processing time
... | [
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radjkarl/imgProcessor | imgProcessor/filters/maskedFilter.py | maskedFilter | def maskedFilter(arr, mask, ksize=30, fill_mask=True,
fn='median'):
'''
fn['mean', 'median']
fill_mask=True:
replaced masked areas with filtered results
fill_mask=False:
masked areas are ignored
'''
if fill_mask:
mask1 = mask
out = ar... | python | def maskedFilter(arr, mask, ksize=30, fill_mask=True,
fn='median'):
'''
fn['mean', 'median']
fill_mask=True:
replaced masked areas with filtered results
fill_mask=False:
masked areas are ignored
'''
if fill_mask:
mask1 = mask
out = ar... | [
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radjkarl/imgProcessor | imgProcessor/camera/flatField/vignettingFromDifferentObjects.py | vignettingFromDifferentObjects | def vignettingFromDifferentObjects(imgs, bg):
'''
Extract vignetting from a set of images
containing different devices
The devices spatial inhomogeneities are averaged
This method is referred as 'Method C' in
---
K.Bedrich, M.Bokalic et al.:
ELECTROLUMINESCENCE IMAGING OF PV D... | python | def vignettingFromDifferentObjects(imgs, bg):
'''
Extract vignetting from a set of images
containing different devices
The devices spatial inhomogeneities are averaged
This method is referred as 'Method C' in
---
K.Bedrich, M.Bokalic et al.:
ELECTROLUMINESCENCE IMAGING OF PV D... | [
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ELECTROLUMINESCENCE IMAGING OF PV DEVICES:
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radjkarl/imgProcessor | imgProcessor/measure/SNR/SNR_IEC.py | SNR_IEC | def SNR_IEC(i1, i2, ibg=0, allow_color_images=False):
'''
Calculate the averaged signal-to-noise ratio SNR50
as defined by IEC NP 60904-13
needs 2 reference EL images and one background image
'''
# ensure images are type float64 (double precision):
i1 = np.asfarray(i1)
i2 = np.... | python | def SNR_IEC(i1, i2, ibg=0, allow_color_images=False):
'''
Calculate the averaged signal-to-noise ratio SNR50
as defined by IEC NP 60904-13
needs 2 reference EL images and one background image
'''
# ensure images are type float64 (double precision):
i1 = np.asfarray(i1)
i2 = np.... | [
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radjkarl/imgProcessor | imgProcessor/transform/StitchImages.py | StitchImages._rotate | def _rotate(img, angle):
'''
angle [DEG]
'''
s = img.shape
if angle == 0:
return img
else:
M = cv2.getRotationMatrix2D((s[1] // 2,
s[0] // 2), angle, 1)
return cv2.warpAffine(img, M, (s... | python | def _rotate(img, angle):
'''
angle [DEG]
'''
s = img.shape
if angle == 0:
return img
else:
M = cv2.getRotationMatrix2D((s[1] // 2,
s[0] // 2), angle, 1)
return cv2.warpAffine(img, M, (s... | [
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radjkarl/imgProcessor | imgProcessor/transform/StitchImages.py | StitchImages._findOverlap | def _findOverlap(self, img_rgb, overlap, overlapDeviation,
rotation, rotationDeviation):
'''
return offset(x,y) which fit best self._base_img
through template matching
'''
# get gray images
if len(img_rgb.shape) != len(img_rgb.shape):
... | python | def _findOverlap(self, img_rgb, overlap, overlapDeviation,
rotation, rotationDeviation):
'''
return offset(x,y) which fit best self._base_img
through template matching
'''
# get gray images
if len(img_rgb.shape) != len(img_rgb.shape):
... | [
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radjkarl/imgProcessor | imgProcessor/camera/NoiseLevelFunction.py | estimateFromImages | def estimateFromImages(imgs1, imgs2=None, mn_mx=None, nbins=100):
'''
estimate the noise level function as stDev over image intensity
from a set of 2 image groups
images at the same position have to show
the identical setup, so
imgs1[i] - imgs2[i] = noise
'''
if imgs2 is None:
... | python | def estimateFromImages(imgs1, imgs2=None, mn_mx=None, nbins=100):
'''
estimate the noise level function as stDev over image intensity
from a set of 2 image groups
images at the same position have to show
the identical setup, so
imgs1[i] - imgs2[i] = noise
'''
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radjkarl/imgProcessor | imgProcessor/camera/NoiseLevelFunction.py | _evaluate | def _evaluate(x, y, weights):
'''
get the parameters of the, needed by 'function'
through curve fitting
'''
i = _validI(x, y, weights)
xx = x[i]
y = y[i]
try:
fitParams = _fit(xx, y)
# bound noise fn to min defined y value:
minY = function(xx[0], *fit... | python | def _evaluate(x, y, weights):
'''
get the parameters of the, needed by 'function'
through curve fitting
'''
i = _validI(x, y, weights)
xx = x[i]
y = y[i]
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minY = function(xx[0], *fit... | [
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radjkarl/imgProcessor | imgProcessor/camera/NoiseLevelFunction.py | boundedFunction | def boundedFunction(x, minY, ax, ay):
'''
limit [function] to a minimum y value
'''
y = function(x, ax, ay)
return np.maximum(np.nan_to_num(y), minY) | python | def boundedFunction(x, minY, ax, ay):
'''
limit [function] to a minimum y value
'''
y = function(x, ax, ay)
return np.maximum(np.nan_to_num(y), minY) | [
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radjkarl/imgProcessor | imgProcessor/camera/NoiseLevelFunction.py | function | def function(x, ax, ay):
'''
general square root function
'''
with np.errstate(invalid='ignore'):
return ay * (x - ax)**0.5 | python | def function(x, ax, ay):
'''
general square root function
'''
with np.errstate(invalid='ignore'):
return ay * (x - ax)**0.5 | [
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