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237,900 | astropy/regions | ah_bootstrap.py | _Bootstrapper._check_submodule | def _check_submodule(self):
"""
Check if the given path is a git submodule.
See the docstrings for ``_check_submodule_using_git`` and
``_check_submodule_no_git`` for further details.
"""
if (self.path is None or
(os.path.exists(self.path) and not os.path... | python | def _check_submodule(self):
"""
Check if the given path is a git submodule.
See the docstrings for ``_check_submodule_using_git`` and
``_check_submodule_no_git`` for further details.
"""
if (self.path is None or
(os.path.exists(self.path) and not os.path... | [
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237,901 | EconForge/dolo | dolo/numeric/tensor.py | sdot | def sdot( U, V ):
'''
Computes the tensorproduct reducing last dimensoin of U with first dimension of V.
For matrices, it is equal to regular matrix product.
'''
nu = U.ndim
#nv = V.ndim
return np.tensordot( U, V, axes=(nu-1,0) ) | python | def sdot( U, V ):
'''
Computes the tensorproduct reducing last dimensoin of U with first dimension of V.
For matrices, it is equal to regular matrix product.
'''
nu = U.ndim
#nv = V.ndim
return np.tensordot( U, V, axes=(nu-1,0) ) | [
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237,902 | EconForge/dolo | dolo/numeric/interpolation/smolyak.py | SmolyakBasic.set_values | def set_values(self,x):
""" Updates self.theta parameter. No returns values"""
x = numpy.atleast_2d(x)
x = x.real # ahem
C_inv = self.__C_inv__
theta = numpy.dot( x, C_inv )
self.theta = theta
return theta | python | def set_values(self,x):
""" Updates self.theta parameter. No returns values"""
x = numpy.atleast_2d(x)
x = x.real # ahem
C_inv = self.__C_inv__
theta = numpy.dot( x, C_inv )
self.theta = theta
return theta | [
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237,903 | EconForge/dolo | dolo/numeric/discretization/discretization.py | tauchen | def tauchen(N, mu, rho, sigma, m=2):
"""
Approximate an AR1 process by a finite markov chain using Tauchen's method.
:param N: scalar, number of nodes for Z
:param mu: scalar, unconditional mean of process
:param rho: scalar
:param sigma: scalar, std. dev. of epsilons
:param m: max +- std. ... | python | def tauchen(N, mu, rho, sigma, m=2):
"""
Approximate an AR1 process by a finite markov chain using Tauchen's method.
:param N: scalar, number of nodes for Z
:param mu: scalar, unconditional mean of process
:param rho: scalar
:param sigma: scalar, std. dev. of epsilons
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237,904 | EconForge/dolo | dolo/numeric/discretization/discretization.py | rouwenhorst | def rouwenhorst(rho, sigma, N):
"""
Approximate an AR1 process by a finite markov chain using Rouwenhorst's method.
:param rho: autocorrelation of the AR1 process
:param sigma: conditional standard deviation of the AR1 process
:param N: number of states
:return [nodes, P]: equally spaced nodes ... | python | def rouwenhorst(rho, sigma, N):
"""
Approximate an AR1 process by a finite markov chain using Rouwenhorst's method.
:param rho: autocorrelation of the AR1 process
:param sigma: conditional standard deviation of the AR1 process
:param N: number of states
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237,905 | EconForge/dolo | dolo/numeric/discretization/discretization.py | tensor_markov | def tensor_markov( *args ):
"""Computes the product of two independent markov chains.
:param m1: a tuple containing the nodes and the transition matrix of the first chain
:param m2: a tuple containing the nodes and the transition matrix of the second chain
:return: a tuple containing the nodes and the ... | python | def tensor_markov( *args ):
"""Computes the product of two independent markov chains.
:param m1: a tuple containing the nodes and the transition matrix of the first chain
:param m2: a tuple containing the nodes and the transition matrix of the second chain
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237,906 | EconForge/dolo | trash/dolo/misc/modfile.py | dynare_import | def dynare_import(filename,full_output=False, debug=False):
'''Imports model defined in specified file'''
import os
basename = os.path.basename(filename)
fname = re.compile('(.*)\.(.*)').match(basename).group(1)
f = open(filename)
txt = f.read()
model = parse_dynare_text(txt,full_output=full... | python | def dynare_import(filename,full_output=False, debug=False):
'''Imports model defined in specified file'''
import os
basename = os.path.basename(filename)
fname = re.compile('(.*)\.(.*)').match(basename).group(1)
f = open(filename)
txt = f.read()
model = parse_dynare_text(txt,full_output=full... | [
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237,907 | EconForge/dolo | dolo/algos/perfect_foresight.py | _shocks_to_epsilons | def _shocks_to_epsilons(model, shocks, T):
"""
Helper function to support input argument `shocks` being one of many
different data types. Will always return a `T, n_e` matrix.
"""
n_e = len(model.calibration['exogenous'])
# if we have a DataFrame, convert it to a dict and rely on the method bel... | python | def _shocks_to_epsilons(model, shocks, T):
"""
Helper function to support input argument `shocks` being one of many
different data types. Will always return a `T, n_e` matrix.
"""
n_e = len(model.calibration['exogenous'])
# if we have a DataFrame, convert it to a dict and rely on the method bel... | [
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237,908 | EconForge/dolo | trash/dolo/misc/symbolic_interactive.py | clear_all | def clear_all():
"""
Clears all parameters, variables, and shocks defined previously
"""
frame = inspect.currentframe().f_back
try:
if frame.f_globals.get('variables_order'):
# we should avoid to declare symbols twice !
del frame.f_globals['variables_order']
... | python | def clear_all():
"""
Clears all parameters, variables, and shocks defined previously
"""
frame = inspect.currentframe().f_back
try:
if frame.f_globals.get('variables_order'):
# we should avoid to declare symbols twice !
del frame.f_globals['variables_order']
... | [
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237,909 | EconForge/dolo | trash/dolo/algos/dtcscc/nonlinearsystem.py | nonlinear_system | def nonlinear_system(model, initial_dr=None, maxit=10, tol=1e-8, grid={}, distribution={}, verbose=True):
'''
Finds a global solution for ``model`` by solving one large system of equations
using a simple newton algorithm.
Parameters
----------
model: NumericModel
"dtcscc" model to be ... | python | def nonlinear_system(model, initial_dr=None, maxit=10, tol=1e-8, grid={}, distribution={}, verbose=True):
'''
Finds a global solution for ``model`` by solving one large system of equations
using a simple newton algorithm.
Parameters
----------
model: NumericModel
"dtcscc" model to be ... | [
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237,910 | EconForge/dolo | dolo/numeric/discretization/quadrature.py | gauss_hermite_nodes | def gauss_hermite_nodes(orders, sigma, mu=None):
'''
Computes the weights and nodes for Gauss Hermite quadrature.
Parameters
----------
orders : int, list, array
The order of integration used in the quadrature routine
sigma : array-like
If one dimensional, the variance of the no... | python | def gauss_hermite_nodes(orders, sigma, mu=None):
'''
Computes the weights and nodes for Gauss Hermite quadrature.
Parameters
----------
orders : int, list, array
The order of integration used in the quadrature routine
sigma : array-like
If one dimensional, the variance of the no... | [
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237,911 | EconForge/dolo | dolo/numeric/optimize/newton.py | newton | def newton(f, x, verbose=False, tol=1e-6, maxit=5, jactype='serial'):
"""Solve nonlinear system using safeguarded Newton iterations
Parameters
----------
Return
------
"""
if verbose:
print = lambda txt: old_print(txt)
else:
print = lambda txt: None
it = 0
e... | python | def newton(f, x, verbose=False, tol=1e-6, maxit=5, jactype='serial'):
"""Solve nonlinear system using safeguarded Newton iterations
Parameters
----------
Return
------
"""
if verbose:
print = lambda txt: old_print(txt)
else:
print = lambda txt: None
it = 0
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237,912 | EconForge/dolo | dolo/numeric/extern/qz.py | qzordered | def qzordered(A,B,crit=1.0):
"Eigenvalues bigger than crit are sorted in the top-left."
TOL = 1e-10
def select(alpha, beta):
return alpha**2>crit*beta**2
[S,T,alpha,beta,U,V] = ordqz(A,B,output='real',sort=select)
eigval = abs(numpy.diag(S)/numpy.diag(T))
return [S,T,U,V,eigval] | python | def qzordered(A,B,crit=1.0):
"Eigenvalues bigger than crit are sorted in the top-left."
TOL = 1e-10
def select(alpha, beta):
return alpha**2>crit*beta**2
[S,T,alpha,beta,U,V] = ordqz(A,B,output='real',sort=select)
eigval = abs(numpy.diag(S)/numpy.diag(T))
return [S,T,U,V,eigval] | [
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237,913 | EconForge/dolo | dolo/numeric/extern/qz.py | ordqz | def ordqz(A, B, sort='lhp', output='real', overwrite_a=False,
overwrite_b=False, check_finite=True):
"""
QZ decomposition for a pair of matrices with reordering.
.. versionadded:: 0.17.0
Parameters
----------
A : (N, N) array_like
2d array to decompose
B : (N, N) array_li... | python | def ordqz(A, B, sort='lhp', output='real', overwrite_a=False,
overwrite_b=False, check_finite=True):
"""
QZ decomposition for a pair of matrices with reordering.
.. versionadded:: 0.17.0
Parameters
----------
A : (N, N) array_like
2d array to decompose
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237,914 | EconForge/dolo | trash/dolo/algos/dtcscc/time_iteration_2.py | parameterized_expectations_direct | def parameterized_expectations_direct(model, verbose=False, initial_dr=None,
pert_order=1, grid={}, distribution={},
maxit=100, tol=1e-8):
'''
Finds a global solution for ``model`` using parameterized expectations
function. Requires... | python | def parameterized_expectations_direct(model, verbose=False, initial_dr=None,
pert_order=1, grid={}, distribution={},
maxit=100, tol=1e-8):
'''
Finds a global solution for ``model`` using parameterized expectations
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237,915 | EconForge/dolo | dolo/compiler/misc.py | numdiff | def numdiff(fun, args):
"""Vectorized numerical differentiation"""
# vectorized version
epsilon = 1e-8
args = list(args)
v0 = fun(*args)
N = v0.shape[0]
l_v = len(v0)
dvs = []
for i, a in enumerate(args):
l_a = (a).shape[1]
dv = numpy.zeros((N, l_v, l_a))
na... | python | def numdiff(fun, args):
"""Vectorized numerical differentiation"""
# vectorized version
epsilon = 1e-8
args = list(args)
v0 = fun(*args)
N = v0.shape[0]
l_v = len(v0)
dvs = []
for i, a in enumerate(args):
l_a = (a).shape[1]
dv = numpy.zeros((N, l_v, l_a))
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237,916 | EconForge/dolo | dolo/numeric/filters.py | bandpass_filter | def bandpass_filter(data, k, w1, w2):
"""
This function will apply a bandpass filter to data. It will be kth
order and will select the band between w1 and w2.
Parameters
----------
data: array, dtype=float
The data you wish to filter
k: number, int
The order ... | python | def bandpass_filter(data, k, w1, w2):
"""
This function will apply a bandpass filter to data. It will be kth
order and will select the band between w1 and w2.
Parameters
----------
data: array, dtype=float
The data you wish to filter
k: number, int
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k: number, int
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237,917 | EconForge/dolo | dolo/misc/dprint.py | dprint | def dprint(s):
'''Prints `s` with additional debugging informations'''
import inspect
frameinfo = inspect.stack()[1]
callerframe = frameinfo.frame
d = callerframe.f_locals
if (isinstance(s,str)):
val = eval(s, d)
else:
val = s
cc = frameinfo.code_context[0]
... | python | def dprint(s):
'''Prints `s` with additional debugging informations'''
import inspect
frameinfo = inspect.stack()[1]
callerframe = frameinfo.frame
d = callerframe.f_locals
if (isinstance(s,str)):
val = eval(s, d)
else:
val = s
cc = frameinfo.code_context[0]
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237,918 | EconForge/dolo | dolo/compiler/function_compiler_sympy.py | non_decreasing_series | def non_decreasing_series(n, size):
'''Lists all combinations of 0,...,n-1 in increasing order'''
if size == 1:
return [[a] for a in range(n)]
else:
lc = non_decreasing_series(n, size-1)
ll = []
for l in lc:
last = l[-1]
for i in range(last, n):
... | python | def non_decreasing_series(n, size):
'''Lists all combinations of 0,...,n-1 in increasing order'''
if size == 1:
return [[a] for a in range(n)]
else:
lc = non_decreasing_series(n, size-1)
ll = []
for l in lc:
last = l[-1]
for i in range(last, n):
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237,919 | EconForge/dolo | dolo/compiler/function_compiler_sympy.py | higher_order_diff | def higher_order_diff(eqs, syms, order=2):
'''Takes higher order derivatives of a list of equations w.r.t a list of paramters'''
import numpy
eqs = list([sympy.sympify(eq) for eq in eqs])
syms = list([sympy.sympify(s) for s in syms])
neq = len(eqs)
p = len(syms)
D = [numpy.array(eqs)]
... | python | def higher_order_diff(eqs, syms, order=2):
'''Takes higher order derivatives of a list of equations w.r.t a list of paramters'''
import numpy
eqs = list([sympy.sympify(eq) for eq in eqs])
syms = list([sympy.sympify(s) for s in syms])
neq = len(eqs)
p = len(syms)
D = [numpy.array(eqs)]
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237,920 | pokerregion/poker | poker/website/pocketfives.py | get_ranked_players | def get_ranked_players():
"""Get the list of the first 100 ranked players."""
rankings_page = requests.get(RANKINGS_URL)
root = etree.HTML(rankings_page.text)
player_rows = root.xpath('//div[@id="ranked"]//tr')
for row in player_rows[1:]:
player_row = row.xpath('td[@class!="country"]//text... | python | def get_ranked_players():
"""Get the list of the first 100 ranked players."""
rankings_page = requests.get(RANKINGS_URL)
root = etree.HTML(rankings_page.text)
player_rows = root.xpath('//div[@id="ranked"]//tr')
for row in player_rows[1:]:
player_row = row.xpath('td[@class!="country"]//text... | [
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237,921 | pokerregion/poker | poker/card.py | Rank.difference | def difference(cls, first, second):
"""Tells the numerical difference between two ranks."""
# so we always get a Rank instance even if string were passed in
first, second = cls(first), cls(second)
rank_list = list(cls)
return abs(rank_list.index(first) - rank_list.index(second)) | python | def difference(cls, first, second):
"""Tells the numerical difference between two ranks."""
# so we always get a Rank instance even if string were passed in
first, second = cls(first), cls(second)
rank_list = list(cls)
return abs(rank_list.index(first) - rank_list.index(second)) | [
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237,922 | pokerregion/poker | poker/card.py | _CardMeta.make_random | def make_random(cls):
"""Returns a random Card instance."""
self = object.__new__(cls)
self.rank = Rank.make_random()
self.suit = Suit.make_random()
return self | python | def make_random(cls):
"""Returns a random Card instance."""
self = object.__new__(cls)
self.rank = Rank.make_random()
self.suit = Suit.make_random()
return self | [
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237,923 | pokerregion/poker | poker/commands.py | twoplustwo_player | def twoplustwo_player(username):
"""Get profile information about a Two plus Two Forum member given the username."""
from .website.twoplustwo import ForumMember, AmbiguousUserNameError, UserNotFoundError
try:
member = ForumMember(username)
except UserNotFoundError:
raise click.ClickExc... | python | def twoplustwo_player(username):
"""Get profile information about a Two plus Two Forum member given the username."""
from .website.twoplustwo import ForumMember, AmbiguousUserNameError, UserNotFoundError
try:
member = ForumMember(username)
except UserNotFoundError:
raise click.ClickExc... | [
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237,924 | pokerregion/poker | poker/commands.py | p5list | def p5list(num):
"""List pocketfives ranked players, max 100 if no NUM, or NUM if specified."""
from .website.pocketfives import get_ranked_players
format_str = '{:>4.4} {!s:<15.13}{!s:<18.15}{!s:<9.6}{!s:<10.7}'\
'{!s:<14.11}{!s:<12.9}{!s:<12.9}{!s:<12.9}{!s:<4.4}'
click.echo(form... | python | def p5list(num):
"""List pocketfives ranked players, max 100 if no NUM, or NUM if specified."""
from .website.pocketfives import get_ranked_players
format_str = '{:>4.4} {!s:<15.13}{!s:<18.15}{!s:<9.6}{!s:<10.7}'\
'{!s:<14.11}{!s:<12.9}{!s:<12.9}{!s:<12.9}{!s:<4.4}'
click.echo(form... | [
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237,925 | pokerregion/poker | poker/commands.py | psstatus | def psstatus():
"""Shows PokerStars status such as number of players, tournaments."""
from .website.pokerstars import get_status
_print_header('PokerStars status')
status = get_status()
_print_values(
('Info updated', status.updated),
('Tables', status.tables),
('Players', ... | python | def psstatus():
"""Shows PokerStars status such as number of players, tournaments."""
from .website.pokerstars import get_status
_print_header('PokerStars status')
status = get_status()
_print_values(
('Info updated', status.updated),
('Tables', status.tables),
('Players', ... | [
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237,926 | pokerregion/poker | poker/room/pokerstars.py | Notes.notes | def notes(self):
"""Tuple of notes.."""
return tuple(self._get_note_data(note) for note in self.root.iter('note')) | python | def notes(self):
"""Tuple of notes.."""
return tuple(self._get_note_data(note) for note in self.root.iter('note')) | [
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237,927 | pokerregion/poker | poker/room/pokerstars.py | Notes.labels | def labels(self):
"""Tuple of labels."""
return tuple(_Label(label.get('id'), label.get('color'), label.text) for label
in self.root.iter('label')) | python | def labels(self):
"""Tuple of labels."""
return tuple(_Label(label.get('id'), label.get('color'), label.text) for label
in self.root.iter('label')) | [
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237,928 | pokerregion/poker | poker/room/pokerstars.py | Notes.add_note | def add_note(self, player, text, label=None, update=None):
"""Add a note to the xml. If update param is None, it will be the current time."""
if label is not None and (label not in self.label_names):
raise LabelNotFoundError('Invalid label: {}'.format(label))
if update is None:
... | python | def add_note(self, player, text, label=None, update=None):
"""Add a note to the xml. If update param is None, it will be the current time."""
if label is not None and (label not in self.label_names):
raise LabelNotFoundError('Invalid label: {}'.format(label))
if update is None:
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237,929 | pokerregion/poker | poker/room/pokerstars.py | Notes.append_note | def append_note(self, player, text):
"""Append text to an already existing note."""
note = self._find_note(player)
note.text += text | python | def append_note(self, player, text):
"""Append text to an already existing note."""
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237,930 | pokerregion/poker | poker/room/pokerstars.py | Notes.prepend_note | def prepend_note(self, player, text):
"""Prepend text to an already existing note."""
note = self._find_note(player)
note.text = text + note.text | python | def prepend_note(self, player, text):
"""Prepend text to an already existing note."""
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237,931 | pokerregion/poker | poker/room/pokerstars.py | Notes.get_label | def get_label(self, name):
"""Find the label by name."""
label_tag = self._find_label(name)
return _Label(label_tag.get('id'), label_tag.get('color'), label_tag.text) | python | def get_label(self, name):
"""Find the label by name."""
label_tag = self._find_label(name)
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237,932 | pokerregion/poker | poker/room/pokerstars.py | Notes.add_label | def add_label(self, name, color):
"""Add a new label. It's id will automatically be calculated."""
color_upper = color.upper()
if not self._color_re.match(color_upper):
raise ValueError('Invalid color: {}'.format(color))
labels_tag = self.root[0]
last_id = int(labels... | python | def add_label(self, name, color):
"""Add a new label. It's id will automatically be calculated."""
color_upper = color.upper()
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raise ValueError('Invalid color: {}'.format(color))
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237,933 | pokerregion/poker | poker/room/pokerstars.py | Notes.del_label | def del_label(self, name):
"""Delete a label by name."""
labels_tag = self.root[0]
labels_tag.remove(self._find_label(name)) | python | def del_label(self, name):
"""Delete a label by name."""
labels_tag = self.root[0]
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237,934 | pokerregion/poker | poker/room/pokerstars.py | Notes.save | def save(self, filename):
"""Save the note XML to a file."""
with open(filename, 'w') as fp:
fp.write(str(self)) | python | def save(self, filename):
"""Save the note XML to a file."""
with open(filename, 'w') as fp:
fp.write(str(self)) | [
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237,935 | pokerregion/poker | poker/handhistory.py | _BaseHandHistory.board | def board(self):
"""Calculates board from flop, turn and river."""
board = []
if self.flop:
board.extend(self.flop.cards)
if self.turn:
board.append(self.turn)
if self.river:
board.append(self.river)
return tuple... | python | def board(self):
"""Calculates board from flop, turn and river."""
board = []
if self.flop:
board.extend(self.flop.cards)
if self.turn:
board.append(self.turn)
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board.append(self.river)
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237,936 | pokerregion/poker | poker/handhistory.py | _BaseHandHistory._parse_date | def _parse_date(self, date_string):
"""Parse the date_string and return a datetime object as UTC."""
date = datetime.strptime(date_string, self._DATE_FORMAT)
self.date = self._TZ.localize(date).astimezone(pytz.UTC) | python | def _parse_date(self, date_string):
"""Parse the date_string and return a datetime object as UTC."""
date = datetime.strptime(date_string, self._DATE_FORMAT)
self.date = self._TZ.localize(date).astimezone(pytz.UTC) | [
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237,937 | pokerregion/poker | poker/handhistory.py | _SplittableHandHistoryMixin._split_raw | def _split_raw(self):
"""Split hand history by sections."""
self._splitted = self._split_re.split(self.raw)
# search split locations (basically empty strings)
self._sections = [ind for ind, elem in enumerate(self._splitted) if not elem] | python | def _split_raw(self):
"""Split hand history by sections."""
self._splitted = self._split_re.split(self.raw)
# search split locations (basically empty strings)
self._sections = [ind for ind, elem in enumerate(self._splitted) if not elem] | [
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237,938 | pokerregion/poker | poker/website/twoplustwo.py | ForumMember._get_timezone | def _get_timezone(self, root):
"""Find timezone informatation on bottom of the page."""
tz_str = root.xpath('//div[@class="smallfont" and @align="center"]')[0].text
hours = int(self._tz_re.search(tz_str).group(1))
return tzoffset(tz_str, hours * 60) | python | def _get_timezone(self, root):
"""Find timezone informatation on bottom of the page."""
tz_str = root.xpath('//div[@class="smallfont" and @align="center"]')[0].text
hours = int(self._tz_re.search(tz_str).group(1))
return tzoffset(tz_str, hours * 60) | [
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237,939 | pokerregion/poker | poker/website/pokerstars.py | get_current_tournaments | def get_current_tournaments():
"""Get the next 200 tournaments from pokerstars."""
schedule_page = requests.get(TOURNAMENTS_XML_URL)
root = etree.XML(schedule_page.content)
for tour in root.iter('{*}tournament'):
yield _Tournament(
start_date=tour.findtext('{*}start_date'),
... | python | def get_current_tournaments():
"""Get the next 200 tournaments from pokerstars."""
schedule_page = requests.get(TOURNAMENTS_XML_URL)
root = etree.XML(schedule_page.content)
for tour in root.iter('{*}tournament'):
yield _Tournament(
start_date=tour.findtext('{*}start_date'),
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237,940 | RKrahl/pytest-dependency | setup.py | _filter_file | def _filter_file(src, dest, subst):
"""Copy src to dest doing substitutions on the fly.
"""
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def repl(m):
return subst[m.group(1)]
with open(src, "rt") as sf, open(dest, "wt") as df:
while True:
l = sf.readline()
... | python | def _filter_file(src, dest, subst):
"""Copy src to dest doing substitutions on the fly.
"""
substre = re.compile(r'\$(%s)' % '|'.join(subst.keys()))
def repl(m):
return subst[m.group(1)]
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237,941 | profusion/sgqlc | sgqlc/endpoint/base.py | BaseEndpoint._fixup_graphql_error | def _fixup_graphql_error(self, data):
'''Given a possible GraphQL error payload, make sure it's in shape.
This will ensure the given ``data`` is in the shape:
.. code-block:: json
{"errors": [{"message": "some string"}]}
If ``errors`` is not an array, it will be made into ... | python | def _fixup_graphql_error(self, data):
'''Given a possible GraphQL error payload, make sure it's in shape.
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.. code-block:: json
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237,942 | profusion/sgqlc | sgqlc/endpoint/base.py | BaseEndpoint.snippet | def snippet(code, locations, sep=' | ', colmark=('-', '^'), context=5):
'''Given a code and list of locations, convert to snippet lines.
return will include line number, a separator (``sep``), then
line contents.
At most ``context`` lines are shown before each location line.
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'''Given a code and list of locations, convert to snippet lines.
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237,943 | profusion/sgqlc | sgqlc/types/__init__.py | _create_non_null_wrapper | def _create_non_null_wrapper(name, t):
'creates type wrapper for non-null of given type'
def __new__(cls, json_data, selection_list=None):
if json_data is None:
raise ValueError(name + ' received null value')
return t(json_data, selection_list)
def __to_graphql_input__(value, in... | python | def _create_non_null_wrapper(name, t):
'creates type wrapper for non-null of given type'
def __new__(cls, json_data, selection_list=None):
if json_data is None:
raise ValueError(name + ' received null value')
return t(json_data, selection_list)
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237,944 | profusion/sgqlc | sgqlc/types/__init__.py | _create_list_of_wrapper | def _create_list_of_wrapper(name, t):
'creates type wrapper for list of given type'
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if json_data is None:
return None
return [t(v, selection_list) for v in json_data]
def __to_graphql_input__(value, indent=0, indent_string=' '):... | python | def _create_list_of_wrapper(name, t):
'creates type wrapper for list of given type'
def __new__(cls, json_data, selection_list=None):
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237,945 | profusion/sgqlc | sgqlc/endpoint/http.py | add_query_to_url | def add_query_to_url(url, extra_query):
'''Adds an extra query to URL, returning the new URL.
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'''Adds an extra query to URL, returning the new URL.
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237,946 | profusion/sgqlc | sgqlc/types/relay.py | connection_args | def connection_args(*lst, **mapping):
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By default, provides:
- ``after: String``
- ``before: String``
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- ``last: Int``
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'''Returns the default parameters for connection.
Extra parameters may be given as argument, both as iterable,
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237,947 | nchopin/particles | book/pmcmc/pmmh_lingauss_varying_scale.py | msjd | def msjd(theta):
"""Mean squared jumping distance.
"""
s = 0.
for p in theta.dtype.names:
s += np.sum(np.diff(theta[p], axis=0) ** 2)
return s | python | def msjd(theta):
"""Mean squared jumping distance.
"""
s = 0.
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s += np.sum(np.diff(theta[p], axis=0) ** 2)
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237,948 | nchopin/particles | particles/smc_samplers.py | StaticModel.loglik | def loglik(self, theta, t=None):
""" log-likelihood at given parameter values.
Parameters
----------
theta: dict-like
theta['par'] is a ndarray containing the N values for parameter par
t: int
time (if set to None, the full log-likelihood is returned)
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""" log-likelihood at given parameter values.
Parameters
----------
theta: dict-like
theta['par'] is a ndarray containing the N values for parameter par
t: int
time (if set to None, the full log-likelihood is returned)
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237,949 | nchopin/particles | particles/smc_samplers.py | StaticModel.logpost | def logpost(self, theta, t=None):
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Parameters
----------
theta: dict-like
theta['par'] is a ndarray containing the N values for parameter par
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"""Posterior log-density at given parameter values.
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theta['par'] is a ndarray containing the N values for parameter par
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237,950 | nchopin/particles | particles/smc_samplers.py | FancyList.copyto | def copyto(self, src, where=None):
"""
Same syntax and functionality as numpy.copyto
"""
for n, _ in enumerate(self.l):
if where[n]:
self.l[n] = src.l[n] | python | def copyto(self, src, where=None):
"""
Same syntax and functionality as numpy.copyto
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237,951 | nchopin/particles | particles/smc_samplers.py | ThetaParticles.copy | def copy(self):
"""Returns a copy of the object."""
attrs = {k: self.__dict__[k].copy() for k in self.containers}
attrs.update({k: cp.deepcopy(self.__dict__[k]) for k in self.shared})
return self.__class__(**attrs) | python | def copy(self):
"""Returns a copy of the object."""
attrs = {k: self.__dict__[k].copy() for k in self.containers}
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237,952 | nchopin/particles | particles/smc_samplers.py | ThetaParticles.copyto | def copyto(self, src, where=None):
"""Emulates function `copyto` in NumPy.
Parameters
----------
where: (N,) bool ndarray
True if particle n in src must be copied.
src: (N,) `ThetaParticles` object
source
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"""Emulates function `copyto` in NumPy.
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237,953 | nchopin/particles | particles/smc_samplers.py | ThetaParticles.copyto_at | def copyto_at(self, n, src, m):
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Parameters
----------
n: int
index where to copy
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m: int
index of the element to be copied
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----
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"""Copy to at a given location.
Parameters
----------
n: int
index where to copy
src: `ThetaParticles` object
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m: int
index of the element to be copied
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237,954 | nchopin/particles | particles/smc_samplers.py | MetroParticles.Metropolis | def Metropolis(self, compute_target, mh_options):
"""Performs a certain number of Metropolis steps.
Parameters
----------
compute_target: function
computes the target density for the proposed values
mh_options: dict
+ 'type_prop': {'random_walk', 'inde... | python | def Metropolis(self, compute_target, mh_options):
"""Performs a certain number of Metropolis steps.
Parameters
----------
compute_target: function
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237,955 | nchopin/particles | particles/hmm.py | BaumWelch.backward | def backward(self):
"""Backward recursion.
Upon completion, the following list of length T is available:
* smth: marginal smoothing probabilities
Note
----
Performs the forward step in case it has not been performed before.
"""
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... | python | def backward(self):
"""Backward recursion.
Upon completion, the following list of length T is available:
* smth: marginal smoothing probabilities
Note
----
Performs the forward step in case it has not been performed before.
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237,956 | nchopin/particles | particles/kalman.py | predict_step | def predict_step(F, covX, filt):
"""Predictive step of Kalman filter.
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----------
F: (dx, dx) numpy array
Mean of X_t | X_{t-1} is F * X_{t-1}
covX: (dx, dx) numpy array
covariance of X_t | X_{t-1}
filt: MeanAndCov object
filtering distribution at time t-1
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"""Predictive step of Kalman filter.
Parameters
----------
F: (dx, dx) numpy array
Mean of X_t | X_{t-1} is F * X_{t-1}
covX: (dx, dx) numpy array
covariance of X_t | X_{t-1}
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237,957 | nchopin/particles | particles/kalman.py | filter_step | def filter_step(G, covY, pred, yt):
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----------
G: (dy, dx) numpy array
mean of Y_t | X_t is G * X_t
covX: (dx, dx) numpy array
covariance of Y_t | X_t
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predictive distribution at time t
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... | python | def filter_step(G, covY, pred, yt):
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----------
G: (dy, dx) numpy array
mean of Y_t | X_t is G * X_t
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237,958 | nchopin/particles | particles/kalman.py | MVLinearGauss.check_shapes | def check_shapes(self):
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"""
assert self.covX.shape == (self.dx, self.dx), error_msg
assert self.covY.shape == (self.dy, self.dy), error_msg
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assert self.G.shape == (self.dy, ... | python | def check_shapes(self):
"""
Check all dimensions are correct.
"""
assert self.covX.shape == (self.dx, self.dx), error_msg
assert self.covY.shape == (self.dy, self.dy), error_msg
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237,959 | nchopin/particles | particles/qmc.py | sobol | def sobol(N, dim, scrambled=1):
""" Sobol sequence.
Parameters
----------
N : int
length of sequence
dim: int
dimension
scrambled: int
which scrambling method to use:
+ 0: no scrambling
+ 1: Owen's scrambling
+ 2: Faure-Tezuka
... | python | def sobol(N, dim, scrambled=1):
""" Sobol sequence.
Parameters
----------
N : int
length of sequence
dim: int
dimension
scrambled: int
which scrambling method to use:
+ 0: no scrambling
+ 1: Owen's scrambling
+ 2: Faure-Tezuka
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237,960 | nchopin/particles | particles/smoothing.py | smoothing_worker | def smoothing_worker(method=None, N=100, seed=None, fk=None, fk_info=None,
add_func=None, log_gamma=None):
"""Generic worker for off-line smoothing algorithms.
This worker may be used in conjunction with utils.multiplexer in order to
run in parallel (and eventually compare) off-line s... | python | def smoothing_worker(method=None, N=100, seed=None, fk=None, fk_info=None,
add_func=None, log_gamma=None):
"""Generic worker for off-line smoothing algorithms.
This worker may be used in conjunction with utils.multiplexer in order to
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237,961 | nchopin/particles | particles/smoothing.py | ParticleHistory.save | def save(self, X=None, w=None, A=None):
"""Save one "page" of history at a given time.
.. note::
This method is used internally by `SMC` to store the state of the
particle system at each time t. In most cases, users should not
have to call this method directly.
... | python | def save(self, X=None, w=None, A=None):
"""Save one "page" of history at a given time.
.. note::
This method is used internally by `SMC` to store the state of the
particle system at each time t. In most cases, users should not
have to call this method directly.
... | [
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237,962 | nchopin/particles | particles/smoothing.py | ParticleHistory.extract_one_trajectory | def extract_one_trajectory(self):
"""Extract a single trajectory from the particle history.
The final state is chosen randomly, then the corresponding trajectory
is constructed backwards, until time t=0.
"""
traj = []
for t in reversed(range(self.T)):
if t ... | python | def extract_one_trajectory(self):
"""Extract a single trajectory from the particle history.
The final state is chosen randomly, then the corresponding trajectory
is constructed backwards, until time t=0.
"""
traj = []
for t in reversed(range(self.T)):
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237,963 | nchopin/particles | particles/smoothing.py | ParticleHistory.compute_trajectories | def compute_trajectories(self):
"""Compute the N trajectories that constitute the current genealogy.
Compute and add attribute ``B`` to ``self`` where ``B`` is an array
such that ``B[t,n]`` is the index of ancestor at time t of particle X_T^n,
where T is the current length of history.
... | python | def compute_trajectories(self):
"""Compute the N trajectories that constitute the current genealogy.
Compute and add attribute ``B`` to ``self`` where ``B`` is an array
such that ``B[t,n]`` is the index of ancestor at time t of particle X_T^n,
where T is the current length of history.
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237,964 | nchopin/particles | particles/smoothing.py | ParticleHistory.twofilter_smoothing | def twofilter_smoothing(self, t, info, phi, loggamma, linear_cost=False,
return_ess=False, modif_forward=None,
modif_info=None):
"""Two-filter smoothing.
Parameters
----------
t: time, in range 0 <= t < T-1
info: SMC object... | python | def twofilter_smoothing(self, t, info, phi, loggamma, linear_cost=False,
return_ess=False, modif_forward=None,
modif_info=None):
"""Two-filter smoothing.
Parameters
----------
t: time, in range 0 <= t < T-1
info: SMC object... | [
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237,965 | nchopin/particles | particles/core.py | multiSMC | def multiSMC(nruns=10, nprocs=0, out_func=None, **args):
"""Run SMC algorithms in parallel, for different combinations of parameters.
`multiSMC` relies on the `multiplexer` utility, and obeys the same logic.
A basic usage is::
results = multiSMC(fk=my_fk_model, N=100, nruns=20, nprocs=0)
T... | python | def multiSMC(nruns=10, nprocs=0, out_func=None, **args):
"""Run SMC algorithms in parallel, for different combinations of parameters.
`multiSMC` relies on the `multiplexer` utility, and obeys the same logic.
A basic usage is::
results = multiSMC(fk=my_fk_model, N=100, nruns=20, nprocs=0)
T... | [
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237,966 | nchopin/particles | particles/core.py | SMC.reset_weights | def reset_weights(self):
"""Reset weights after a resampling step.
"""
if self.fk.isAPF:
lw = (rs.log_mean_exp(self.logetat, W=self.W)
- self.logetat[self.A])
self.wgts = rs.Weights(lw=lw)
else:
self.wgts = rs.Weights() | python | def reset_weights(self):
"""Reset weights after a resampling step.
"""
if self.fk.isAPF:
lw = (rs.log_mean_exp(self.logetat, W=self.W)
- self.logetat[self.A])
self.wgts = rs.Weights(lw=lw)
else:
self.wgts = rs.Weights() | [
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237,967 | nchopin/particles | particles/resampling.py | log_sum_exp | def log_sum_exp(v):
"""Log of the sum of the exp of the arguments.
Parameters
----------
v: ndarray
Returns
-------
l: float
l = log(sum(exp(v)))
Note
----
use the log_sum_exp trick to avoid overflow: i.e. we remove the max of v
before exponentiating, then we add... | python | def log_sum_exp(v):
"""Log of the sum of the exp of the arguments.
Parameters
----------
v: ndarray
Returns
-------
l: float
l = log(sum(exp(v)))
Note
----
use the log_sum_exp trick to avoid overflow: i.e. we remove the max of v
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See also
... | [
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237,968 | nchopin/particles | particles/resampling.py | log_sum_exp_ab | def log_sum_exp_ab(a, b):
"""log_sum_exp for two scalars.
Parameters
----------
a, b: float
Returns
-------
c: float
c = log(e^a + e^b)
"""
if a > b:
return a + np.log(1. + np.exp(b - a))
else:
return b + np.log(1. + np.exp(a - b)) | python | def log_sum_exp_ab(a, b):
"""log_sum_exp for two scalars.
Parameters
----------
a, b: float
Returns
-------
c: float
c = log(e^a + e^b)
"""
if a > b:
return a + np.log(1. + np.exp(b - a))
else:
return b + np.log(1. + np.exp(a - b)) | [
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237,969 | nchopin/particles | particles/resampling.py | wmean_and_var | def wmean_and_var(W, x):
"""Component-wise weighted mean and variance.
Parameters
----------
W: (N,) ndarray
normalised weights (must be >=0 and sum to one).
x: ndarray (such that shape[0]==N)
data
Returns
-------
dictionary
{'mean':weighted_means, 'var':weig... | python | def wmean_and_var(W, x):
"""Component-wise weighted mean and variance.
Parameters
----------
W: (N,) ndarray
normalised weights (must be >=0 and sum to one).
x: ndarray (such that shape[0]==N)
data
Returns
-------
dictionary
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237,970 | nchopin/particles | particles/resampling.py | wmean_and_var_str_array | def wmean_and_var_str_array(W, x):
"""Weighted mean and variance of each component of a structured array.
Parameters
----------
W: (N,) ndarray
normalised weights (must be >=0 and sum to one).
x: (N,) structured array
data
Returns
-------
dictionary
{'mean':... | python | def wmean_and_var_str_array(W, x):
"""Weighted mean and variance of each component of a structured array.
Parameters
----------
W: (N,) ndarray
normalised weights (must be >=0 and sum to one).
x: (N,) structured array
data
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dictionary
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237,971 | nchopin/particles | particles/resampling.py | wquantiles | def wquantiles(W, x, alphas=(0.25, 0.50, 0.75)):
"""Quantiles for weighted data.
Parameters
----------
W: (N,) ndarray
normalised weights (weights are >=0 and sum to one)
x: (N,) or (N,d) ndarray
data
alphas: list-like of size k (default: (0.25, 0.50, 0.75))
probabilitie... | python | def wquantiles(W, x, alphas=(0.25, 0.50, 0.75)):
"""Quantiles for weighted data.
Parameters
----------
W: (N,) ndarray
normalised weights (weights are >=0 and sum to one)
x: (N,) or (N,d) ndarray
data
alphas: list-like of size k (default: (0.25, 0.50, 0.75))
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237,972 | nchopin/particles | particles/resampling.py | wquantiles_str_array | def wquantiles_str_array(W, x, alphas=(0.25, 0.50, 0,75)):
"""quantiles for weighted data stored in a structured array.
Parameters
----------
W: (N,) ndarray
normalised weights (weights are >=0 and sum to one)
x: (N,) structured array
data
alphas: list-like of size k (default: ... | python | def wquantiles_str_array(W, x, alphas=(0.25, 0.50, 0,75)):
"""quantiles for weighted data stored in a structured array.
Parameters
----------
W: (N,) ndarray
normalised weights (weights are >=0 and sum to one)
x: (N,) structured array
data
alphas: list-like of size k (default: ... | [
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W: (N,) ndarray
normalised weights (weights are >=0 and sum to one)
x: (N,) structured array
data
alphas: list-like of size k (default: (0.25, 0.50, 0.75))
probabilities (between 0. and 1.)
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237,973 | nchopin/particles | particles/resampling.py | resampling_scheme | def resampling_scheme(func):
"""Decorator for resampling schemes."""
@functools.wraps(func)
def modif_func(W, M=None):
M = W.shape[0] if M is None else M
return func(W, M)
rs_funcs[func.__name__] = modif_func
modif_func.__doc__ = rs_doc % func.__name__.capitalize()
return modif... | python | def resampling_scheme(func):
"""Decorator for resampling schemes."""
@functools.wraps(func)
def modif_func(W, M=None):
M = W.shape[0] if M is None else M
return func(W, M)
rs_funcs[func.__name__] = modif_func
modif_func.__doc__ = rs_doc % func.__name__.capitalize()
return modif... | [
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237,974 | nchopin/particles | particles/resampling.py | inverse_cdf | def inverse_cdf(su, W):
"""Inverse CDF algorithm for a finite distribution.
Parameters
----------
su: (M,) ndarray
M sorted uniform variates (i.e. M ordered points in [0,1]).
W: (N,) ndarray
a vector of N normalized weights (>=0 and sum to one)
Re... | python | def inverse_cdf(su, W):
"""Inverse CDF algorithm for a finite distribution.
Parameters
----------
su: (M,) ndarray
M sorted uniform variates (i.e. M ordered points in [0,1]).
W: (N,) ndarray
a vector of N normalized weights (>=0 and sum to one)
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237,975 | nchopin/particles | particles/hilbert.py | hilbert_array | def hilbert_array(xint):
"""Compute Hilbert indices.
Parameters
----------
xint: (N, d) int numpy.ndarray
Returns
-------
h: (N,) int numpy.ndarray
Hilbert indices
"""
N, d = xint.shape
h = np.zeros(N, int64)
for n in range(N):
h[n] = Hilbert_to_int(xint[n... | python | def hilbert_array(xint):
"""Compute Hilbert indices.
Parameters
----------
xint: (N, d) int numpy.ndarray
Returns
-------
h: (N,) int numpy.ndarray
Hilbert indices
"""
N, d = xint.shape
h = np.zeros(N, int64)
for n in range(N):
h[n] = Hilbert_to_int(xint[n... | [
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237,976 | nchopin/particles | particles/mcmc.py | MCMC.mean_sq_jump_dist | def mean_sq_jump_dist(self, discard_frac=0.1):
"""Mean squared jumping distance estimated from chain.
Parameters
----------
discard_frac: float
fraction of iterations to discard at the beginning (as a burn-in)
Returns
-------
float
"""
... | python | def mean_sq_jump_dist(self, discard_frac=0.1):
"""Mean squared jumping distance estimated from chain.
Parameters
----------
discard_frac: float
fraction of iterations to discard at the beginning (as a burn-in)
Returns
-------
float
"""
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237,977 | nchopin/particles | particles/mcmc.py | VanishCovTracker.update | def update(self, v):
"""Adds point v"""
self.t += 1
g = self.gamma()
self.mu = (1. - g) * self.mu + g * v
mv = v - self.mu
self.Sigma = ((1. - g) * self.Sigma
+ g * np.dot(mv[:, np.newaxis], mv[np.newaxis, :]))
try:
self.L = chole... | python | def update(self, v):
"""Adds point v"""
self.t += 1
g = self.gamma()
self.mu = (1. - g) * self.mu + g * v
mv = v - self.mu
self.Sigma = ((1. - g) * self.Sigma
+ g * np.dot(mv[:, np.newaxis], mv[np.newaxis, :]))
try:
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237,978 | nchopin/particles | particles/utils.py | cartesian_lists | def cartesian_lists(d):
"""
turns a dict of lists into a list of dicts that represents
the cartesian product of the initial lists
Example
-------
cartesian_lists({'a':[0, 2], 'b':[3, 4, 5]}
returns
[ {'a':0, 'b':3}, {'a':0, 'b':4}, ... {'a':2, 'b':5} ]
"""
return [{k: v for k, ... | python | def cartesian_lists(d):
"""
turns a dict of lists into a list of dicts that represents
the cartesian product of the initial lists
Example
-------
cartesian_lists({'a':[0, 2], 'b':[3, 4, 5]}
returns
[ {'a':0, 'b':3}, {'a':0, 'b':4}, ... {'a':2, 'b':5} ]
"""
return [{k: v for k, ... | [
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237,979 | nchopin/particles | particles/utils.py | cartesian_args | def cartesian_args(args, listargs, dictargs):
""" Compute a list of inputs and outputs for a function
with kw arguments.
args: dict
fixed arguments, e.g. {'x': 3}, then x=3 for all inputs
listargs: dict
arguments specified as a list; then the inputs
should be the Cartesian product... | python | def cartesian_args(args, listargs, dictargs):
""" Compute a list of inputs and outputs for a function
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args: dict
fixed arguments, e.g. {'x': 3}, then x=3 for all inputs
listargs: dict
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237,980 | nchopin/particles | particles/utils.py | worker | def worker(qin, qout, f):
"""Worker for muliprocessing.
A worker repeatedly picks a dict of arguments in the queue and computes
f for this set of arguments, until the input queue is empty.
"""
while not qin.empty():
i, args = qin.get()
qout.put((i, f(**args))) | python | def worker(qin, qout, f):
"""Worker for muliprocessing.
A worker repeatedly picks a dict of arguments in the queue and computes
f for this set of arguments, until the input queue is empty.
"""
while not qin.empty():
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237,981 | nchopin/particles | particles/utils.py | distinct_seeds | def distinct_seeds(k):
""" returns k distinct seeds for random number generation
"""
seeds = []
for _ in range(k):
while True:
s = random.randint(2**32 - 1)
if s not in seeds:
break
seeds.append(s)
return seeds | python | def distinct_seeds(k):
""" returns k distinct seeds for random number generation
"""
seeds = []
for _ in range(k):
while True:
s = random.randint(2**32 - 1)
if s not in seeds:
break
seeds.append(s)
return seeds | [
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237,982 | nchopin/particles | particles/utils.py | multiplexer | def multiplexer(f=None, nruns=1, nprocs=1, seeding=None, **args):
"""Evaluate a function for different parameters, optionally in parallel.
Parameters
----------
f: function
function f to evaluate, must take only kw arguments as inputs
nruns: int
number of evaluations of f for each... | python | def multiplexer(f=None, nruns=1, nprocs=1, seeding=None, **args):
"""Evaluate a function for different parameters, optionally in parallel.
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----------
f: function
function f to evaluate, must take only kw arguments as inputs
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237,983 | nchopin/particles | particles/state_space_models.py | StateSpaceModel.simulate | def simulate(self, T):
"""Simulate state and observation processes.
Parameters
----------
T: int
processes are simulated from time 0 to time T-1
Returns
-------
x, y: lists
lists of length T
"""
x = []
for t in ra... | python | def simulate(self, T):
"""Simulate state and observation processes.
Parameters
----------
T: int
processes are simulated from time 0 to time T-1
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-------
x, y: lists
lists of length T
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237,984 | nchopin/particles | book/mle/malikpitt_interpolation.py | interpoled_resampling | def interpoled_resampling(W, x):
"""Resampling based on an interpolated CDF, as described in Malik and Pitt.
Parameters
----------
W: (N,) array
weights
x: (N,) array
particles
Returns
-------
xrs: (N,) array
the resampled particles
"""
N = W.shape[... | python | def interpoled_resampling(W, x):
"""Resampling based on an interpolated CDF, as described in Malik and Pitt.
Parameters
----------
W: (N,) array
weights
x: (N,) array
particles
Returns
-------
xrs: (N,) array
the resampled particles
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237,985 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | sort_items | def sort_items(self,items,args=False):
"""
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"""
if self.settings['sort'].lower() == 'src': return
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def permission(i):
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"""
Sort the `self`'s contents, as contained in the list `items` as
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237,986 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | FortranBase.contents_size | def contents_size(self):
'''
Returns the number of different categories to be shown in the
contents side-bar in the HTML documentation.
'''
count = 0
if hasattr(self,'variables'): count += 1
if hasattr(self,'types'): count += 1
if hasattr(self,'modules'): ... | python | def contents_size(self):
'''
Returns the number of different categories to be shown in the
contents side-bar in the HTML documentation.
'''
count = 0
if hasattr(self,'variables'): count += 1
if hasattr(self,'types'): count += 1
if hasattr(self,'modules'): ... | [
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237,987 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | FortranBase.sort | def sort(self):
'''
Sorts components of the object.
'''
if hasattr(self,'variables'):
sort_items(self,self.variables)
if hasattr(self,'modules'):
sort_items(self,self.modules)
if hasattr(self,'submodules'):
sort_items(self,self.submodul... | python | def sort(self):
'''
Sorts components of the object.
'''
if hasattr(self,'variables'):
sort_items(self,self.variables)
if hasattr(self,'modules'):
sort_items(self,self.modules)
if hasattr(self,'submodules'):
sort_items(self,self.submodul... | [
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237,988 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | FortranBase.make_links | def make_links(self, project):
"""
Process intra-site links to documentation of other parts of the program.
"""
self.doc = ford.utils.sub_links(self.doc,project)
if 'summary' in self.meta:
self.meta['summary'] = ford.utils.sub_links(self.meta['summary'],project)
... | python | def make_links(self, project):
"""
Process intra-site links to documentation of other parts of the program.
"""
self.doc = ford.utils.sub_links(self.doc,project)
if 'summary' in self.meta:
self.meta['summary'] = ford.utils.sub_links(self.meta['summary'],project)
... | [
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237,989 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | FortranBase.iterator | def iterator(self, *argv):
""" Iterator returning any list of elements via attribute lookup in `self`
This iterator retains the order of the arguments """
for arg in argv:
if hasattr(self, arg):
for item in getattr(self, arg):
yield item | python | def iterator(self, *argv):
""" Iterator returning any list of elements via attribute lookup in `self`
This iterator retains the order of the arguments """
for arg in argv:
if hasattr(self, arg):
for item in getattr(self, arg):
yield item | [
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237,990 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | FortranModule.get_used_entities | def get_used_entities(self,use_specs):
"""
Returns the entities which are imported by a use statement. These
are contained in dicts.
"""
if len(use_specs.strip()) == 0:
return (self.pub_procs, self.pub_absints, self.pub_types, self.pub_vars)
only = bool(self.O... | python | def get_used_entities(self,use_specs):
"""
Returns the entities which are imported by a use statement. These
are contained in dicts.
"""
if len(use_specs.strip()) == 0:
return (self.pub_procs, self.pub_absints, self.pub_types, self.pub_vars)
only = bool(self.O... | [
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237,991 | Fortran-FOSS-Programmers/ford | ford/sourceform.py | NameSelector.get_name | def get_name(self,item):
"""
Return the name for this item registered with this NameSelector.
If no name has previously been registered, then generate a new
one.
"""
if not isinstance(item,ford.sourceform.FortranBase):
raise Exception('{} is not of a type deri... | python | def get_name(self,item):
"""
Return the name for this item registered with this NameSelector.
If no name has previously been registered, then generate a new
one.
"""
if not isinstance(item,ford.sourceform.FortranBase):
raise Exception('{} is not of a type deri... | [
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237,992 | Fortran-FOSS-Programmers/ford | ford/__init__.py | main | def main(proj_data,proj_docs,md):
"""
Main driver of FORD.
"""
if proj_data['relative']: proj_data['project_url'] = '.'
# Parse the files in your project
project = ford.fortran_project.Project(proj_data)
if len(project.files) < 1:
print("Error: No source files with appropriate extens... | python | def main(proj_data,proj_docs,md):
"""
Main driver of FORD.
"""
if proj_data['relative']: proj_data['project_url'] = '.'
# Parse the files in your project
project = ford.fortran_project.Project(proj_data)
if len(project.files) < 1:
print("Error: No source files with appropriate extens... | [
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237,993 | Fortran-FOSS-Programmers/ford | ford/fixed2free2.py | convertToFree | def convertToFree(stream, length_limit=True):
"""Convert stream from fixed source form to free source form."""
linestack = []
for line in stream:
convline = FortranLine(line, length_limit)
if convline.is_regular:
if convline.isContinuation and linestack:
... | python | def convertToFree(stream, length_limit=True):
"""Convert stream from fixed source form to free source form."""
linestack = []
for line in stream:
convline = FortranLine(line, length_limit)
if convline.is_regular:
if convline.isContinuation and linestack:
... | [
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] | d46a44eae20d99205292c31785f936fbed47070f | https://github.com/Fortran-FOSS-Programmers/ford/blob/d46a44eae20d99205292c31785f936fbed47070f/ford/fixed2free2.py#L110-L127 |
237,994 | Fortran-FOSS-Programmers/ford | ford/fixed2free2.py | FortranLine.continueLine | def continueLine(self):
"""Insert line continuation symbol at end of line."""
if not (self.isLong and self.is_regular):
self.line_conv = self.line_conv.rstrip() + " &\n"
else:
temp = self.line_conv[:72].rstrip() + " &"
self.line_conv = temp.ljust(72) + self.e... | python | def continueLine(self):
"""Insert line continuation symbol at end of line."""
if not (self.isLong and self.is_regular):
self.line_conv = self.line_conv.rstrip() + " &\n"
else:
temp = self.line_conv[:72].rstrip() + " &"
self.line_conv = temp.ljust(72) + self.e... | [
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237,995 | Fortran-FOSS-Programmers/ford | ford/fortran_project.py | id_mods | def id_mods(obj,modlist,intrinsic_mods={},submodlist=[]):
"""
Match USE statements up with the right modules
"""
for i in range(len(obj.uses)):
for candidate in modlist:
if obj.uses[i][0].lower() == candidate.name.lower():
obj.uses[i] = [candidate, obj.uses[i][1]]
... | python | def id_mods(obj,modlist,intrinsic_mods={},submodlist=[]):
"""
Match USE statements up with the right modules
"""
for i in range(len(obj.uses)):
for candidate in modlist:
if obj.uses[i][0].lower() == candidate.name.lower():
obj.uses[i] = [candidate, obj.uses[i][1]]
... | [
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237,996 | Fortran-FOSS-Programmers/ford | ford/fortran_project.py | Project.allfiles | def allfiles(self):
""" Instead of duplicating files, it is much more efficient to create the itterator on the fly """
for f in self.files:
yield f
for f in self.extra_files:
yield f | python | def allfiles(self):
""" Instead of duplicating files, it is much more efficient to create the itterator on the fly """
for f in self.files:
yield f
for f in self.extra_files:
yield f | [
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237,997 | Fortran-FOSS-Programmers/ford | ford/fortran_project.py | Project.make_links | def make_links(self,base_url='..'):
"""
Substitute intrasite links to documentation for other parts of
the program.
"""
ford.sourceform.set_base_url(base_url)
for src in self.allfiles:
src.make_links(self) | python | def make_links(self,base_url='..'):
"""
Substitute intrasite links to documentation for other parts of
the program.
"""
ford.sourceform.set_base_url(base_url)
for src in self.allfiles:
src.make_links(self) | [
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237,998 | Fortran-FOSS-Programmers/ford | ford/utils.py | sub_notes | def sub_notes(docs):
"""
Substitutes the special controls for notes, warnings, todos, and bugs with
the corresponding div.
"""
def substitute(match):
ret = "</p><div class=\"alert alert-{}\" role=\"alert\"><h4>{}</h4>" \
"<p>{}</p></div>".format(NOTE_TYPE[match.group(1).lower()... | python | def sub_notes(docs):
"""
Substitutes the special controls for notes, warnings, todos, and bugs with
the corresponding div.
"""
def substitute(match):
ret = "</p><div class=\"alert alert-{}\" role=\"alert\"><h4>{}</h4>" \
"<p>{}</p></div>".format(NOTE_TYPE[match.group(1).lower()... | [
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237,999 | Fortran-FOSS-Programmers/ford | ford/utils.py | paren_split | def paren_split(sep,string):
"""
Splits the string into pieces divided by sep, when sep is outside of parentheses.
"""
if len(sep) != 1: raise Exception("Separation string must be one character long")
retlist = []
level = 0
blevel = 0
left = 0
for i in range(len(string)):
if ... | python | def paren_split(sep,string):
"""
Splits the string into pieces divided by sep, when sep is outside of parentheses.
"""
if len(sep) != 1: raise Exception("Separation string must be one character long")
retlist = []
level = 0
blevel = 0
left = 0
for i in range(len(string)):
if ... | [
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