id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
251,700 | astropy/regions | regions/io/ds9/read.py | DS9RegionParser.parse | def parse(self):
"""
Convert line to shape object
"""
log.debug(self)
self.parse_composite()
self.split_line()
self.convert_coordinates()
self.convert_meta()
self.make_shape()
log.debug(self) | python | def parse(self):
log.debug(self)
self.parse_composite()
self.split_line()
self.convert_coordinates()
self.convert_meta()
self.make_shape()
log.debug(self) | [
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251,701 | astropy/regions | regions/io/ds9/read.py | DS9RegionParser.split_line | def split_line(self):
"""
Split line into coordinates and meta string
"""
# coordinate of the # symbol or end of the line (-1) if not found
hash_or_end = self.line.find("#")
temp = self.line[self.region_end:hash_or_end].strip(" |")
self.coord_str = regex_paren.sub... | python | def split_line(self):
# coordinate of the # symbol or end of the line (-1) if not found
hash_or_end = self.line.find("#")
temp = self.line[self.region_end:hash_or_end].strip(" |")
self.coord_str = regex_paren.sub("", temp)
# don't want any meta_str if there is no metadata found
... | [
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251,702 | astropy/regions | regions/io/ds9/read.py | DS9RegionParser.convert_coordinates | def convert_coordinates(self):
"""
Convert coordinate string to objects
"""
coord_list = []
# strip out "null" elements, i.e. ''. It might be possible to eliminate
# these some other way, i.e. with regex directly, but I don't know how.
# We need to copy in order ... | python | def convert_coordinates(self):
coord_list = []
# strip out "null" elements, i.e. ''. It might be possible to eliminate
# these some other way, i.e. with regex directly, but I don't know how.
# We need to copy in order not to burn up the iterators
elements = [x for x in regex_spl... | [
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251,703 | astropy/regions | regions/io/ds9/read.py | DS9RegionParser.convert_meta | def convert_meta(self):
"""
Convert meta string to dict
"""
meta_ = DS9Parser.parse_meta(self.meta_str)
self.meta = copy.deepcopy(self.global_meta)
self.meta.update(meta_)
# the 'include' is not part of the metadata string;
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meta_ = DS9Parser.parse_meta(self.meta_str)
self.meta = copy.deepcopy(self.global_meta)
self.meta.update(meta_)
# the 'include' is not part of the metadata string;
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251,704 | astropy/regions | regions/core/pixcoord.py | PixCoord._validate | def _validate(val, name, expected='any'):
"""Validate that a given object is an appropriate `PixCoord`.
This is used for input validation throughout the regions package,
especially in the `__init__` method of pixel region classes.
Parameters
----------
val : `PixCoord`
... | python | def _validate(val, name, expected='any'):
if not isinstance(val, PixCoord):
raise TypeError('{} must be a PixCoord'.format(name))
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pass
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251,705 | astropy/regions | regions/core/pixcoord.py | PixCoord.to_sky | def to_sky(self, wcs, origin=_DEFAULT_WCS_ORIGIN, mode=_DEFAULT_WCS_MODE):
"""Convert this `PixCoord` to `~astropy.coordinates.SkyCoord`.
Calls :meth:`astropy.coordinates.SkyCoord.from_pixel`.
See parameter description there.
"""
return SkyCoord.from_pixel(
xp=self.x... | python | def to_sky(self, wcs, origin=_DEFAULT_WCS_ORIGIN, mode=_DEFAULT_WCS_MODE):
return SkyCoord.from_pixel(
xp=self.x, yp=self.y, wcs=wcs,
origin=origin, mode=mode,
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251,706 | astropy/regions | regions/core/pixcoord.py | PixCoord.from_sky | def from_sky(cls, skycoord, wcs, origin=_DEFAULT_WCS_ORIGIN, mode=_DEFAULT_WCS_MODE):
"""Create `PixCoord` from `~astropy.coordinates.SkyCoord`.
Calls :meth:`astropy.coordinates.SkyCoord.to_pixel`.
See parameter description there.
"""
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x, y = skycoord.to_pixel(wcs=wcs, origin=origin, mode=mode)
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251,707 | astropy/regions | regions/core/pixcoord.py | PixCoord.separation | def separation(self, other):
r"""Separation to another pixel coordinate.
This is the two-dimensional cartesian separation :math:`d` with
.. math::
d = \sqrt{(x_1 - x_2) ^ 2 + (y_1 - y_2) ^ 2}
Parameters
----------
other : `PixCoord`
Other pixel ... | python | def separation(self, other):
r"""Separation to another pixel coordinate.
This is the two-dimensional cartesian separation :math:`d` with
.. math::
d = \sqrt{(x_1 - x_2) ^ 2 + (y_1 - y_2) ^ 2}
Parameters
----------
other : `PixCoord`
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251,708 | astropy/regions | regions/_utils/wcs_helpers.py | skycoord_to_pixel_scale_angle | def skycoord_to_pixel_scale_angle(skycoord, wcs, small_offset=1 * u.arcsec):
"""
Convert a set of SkyCoord coordinates into pixel coordinates, pixel
scales, and position angles.
Parameters
----------
skycoord : `~astropy.coordinates.SkyCoord`
Sky coordinates
wcs : `~astropy.wcs.WCS`... | python | def skycoord_to_pixel_scale_angle(skycoord, wcs, small_offset=1 * u.arcsec):
# Convert to pixel coordinates
x, y = skycoord_to_pixel(skycoord, wcs, mode=skycoord_to_pixel_mode)
pixcoord = PixCoord(x=x, y=y)
# We take a point directly 'above' (in latitude) the position requested
# and convert it to ... | [
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251,709 | astropy/regions | regions/_utils/wcs_helpers.py | assert_angle | def assert_angle(name, q):
"""
Check that ``q`` is an angular `~astropy.units.Quantity`.
"""
if isinstance(q, u.Quantity):
if q.unit.physical_type == 'angle':
pass
else:
raise ValueError("{0} should have angular units".format(name))
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raise TypeErr... | python | def assert_angle(name, q):
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raise ValueError("{0} should have angular units".format(name))
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251,710 | astropy/regions | ah_bootstrap.py | _silence | def _silence():
"""A context manager that silences sys.stdout and sys.stderr."""
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = _DummyFile()
sys.stderr = _DummyFile()
exception_occurred = False
try:
yield
except:
exception_occurred = True
# Go ahead... | python | def _silence():
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = _DummyFile()
sys.stderr = _DummyFile()
exception_occurred = False
try:
yield
except:
exception_occurred = True
# Go ahead and clean up so that exception handling can work normally
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251,711 | astropy/regions | ah_bootstrap.py | use_astropy_helpers | def use_astropy_helpers(**kwargs):
"""
Ensure that the `astropy_helpers` module is available and is importable.
This supports automatic submodule initialization if astropy_helpers is
included in a project as a git submodule, or will download it from PyPI if
necessary.
Parameters
----------
... | python | def use_astropy_helpers(**kwargs):
global BOOTSTRAPPER
config = BOOTSTRAPPER.config
config.update(**kwargs)
# Create a new bootstrapper with the updated configuration and run it
BOOTSTRAPPER = _Bootstrapper(**config)
BOOTSTRAPPER.run() | [
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251,712 | astropy/regions | ah_bootstrap.py | _Bootstrapper.config | def config(self):
"""
A `dict` containing the options this `_Bootstrapper` was configured
with.
"""
return dict((optname, getattr(self, optname))
for optname, _ in CFG_OPTIONS if hasattr(self, optname)) | python | def config(self):
return dict((optname, getattr(self, optname))
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251,713 | astropy/regions | ah_bootstrap.py | _Bootstrapper.get_local_directory_dist | def get_local_directory_dist(self):
"""
Handle importing a vendored package from a subdirectory of the source
distribution.
"""
if not os.path.isdir(self.path):
return
log.info('Attempting to import astropy_helpers from {0} {1!r}'.format(
's... | python | def get_local_directory_dist(self):
if not os.path.isdir(self.path):
return
log.info('Attempting to import astropy_helpers from {0} {1!r}'.format(
'submodule' if self.is_submodule else 'directory',
self.path))
dist = self._directory_import()
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251,714 | astropy/regions | ah_bootstrap.py | _Bootstrapper.get_local_file_dist | def get_local_file_dist(self):
"""
Handle importing from a source archive; this also uses setup_requires
but points easy_install directly to the source archive.
"""
if not os.path.isfile(self.path):
return
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251,715 | astropy/regions | ah_bootstrap.py | _Bootstrapper._directory_import | def _directory_import(self):
"""
Import astropy_helpers from the given path, which will be added to
sys.path.
Must return True if the import succeeded, and False otherwise.
"""
# Return True on success, False on failure but download is allowed, and
# otherwise r... | python | def _directory_import(self):
# Return True on success, False on failure but download is allowed, and
# otherwise raise SystemExit
path = os.path.abspath(self.path)
# Use an empty WorkingSet rather than the man
# pkg_resources.working_set, since on older versions of setuptools th... | [
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251,716 | 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):
if (self.path is None or
(os.path.exists(self.path) and not os.path.isdir(self.path))):
return False
if self.use_git:
return self._check_submodule_using_git()
else:
return self._check_submodule_no_git() | [
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251,717 | 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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251,718 | 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):
x = numpy.atleast_2d(x)
x = x.real # ahem
C_inv = self.__C_inv__
theta = numpy.dot( x, C_inv )
self.theta = theta
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251,719 | 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):
Z = np.zeros((N,1))
Zprob = np.zeros((N,N))
a = (1-rho)*mu
Z[-1] = m * math.sqrt(sigma**2 / (1 - (rho**2)))
Z[0] = -1 * Z[-1]
zstep = (Z[-1] - Z[0]) / (N - 1)
for i in range(1,N):
Z[i] = Z[0] + zstep * (i)
Z = Z + a / (1-rho)
... | [
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251,720 | 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):
from numpy import sqrt, linspace, array,zeros
sigma = float(sigma)
if N == 1:
nodes = array([0.0])
transitions = array([[1.0]])
return [nodes, transitions]
p = (rho+1)/2
q = p
nu = sqrt( (N-1)/(1-rho**2) )*sigma
nodes = linspace( -nu, nu,... | [
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251,721 | 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 ):
if len(args) > 2:
m1 = args[0]
m2 = args[1]
tail = args[2:]
prod = tensor_markov(m1,m2)
return tensor_markov( prod, tail )
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m1,m2 = args
n1, t1 = m1
n2, t2 = m2
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251,722 | 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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251,723 | 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):
n_e = len(model.calibration['exogenous'])
# if we have a DataFrame, convert it to a dict and rely on the method below
if isinstance(shocks, pd.DataFrame):
shocks = {k: shocks[k].tolist() for k in shocks.columns}
# handle case where shocks might be a d... | [
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251,724 | 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():
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']
if frame.f_globals.get('parameters_order'):
# we should avoid to declare ... | [
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251,725 | 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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251,726 | 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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251,727 | 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'):
if verbose:
print = lambda txt: old_print(txt)
else:
print = lambda txt: None
it = 0
error = 10
converged = False
maxbacksteps = 30
x0 = x
if jactype == 'sparse':
from scipy.sparse.linalg i... | [
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251,728 | 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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251,729 | 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):
import warnings
import numpy as np
from numpy import asarray_chkfinite
from scipy.linalg.misc import LinAlgError, _datacopied
from scipy.linalg.lapack import get_lapack_funcs
from sc... | [
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251,730 | 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
function. Requires... | [
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251,731 | 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 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))
nargs = list(args) #.copy()
for j in rang... | [
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251,732 | 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):
data = np.asarray(data)
low_w = np.pi * 2 / w2
high_w = np.pi * 2 / w1
bweights = np.zeros(2 * k + 1)
bweights[k] = (high_w - low_w) / np.pi
j = np.arange(1, int(k) + 1)
weights = 1 / (np.pi * j) * (sin(high_w * j) - sin(low_w * j))
bweights[k + j] =... | [
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Parameters
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data: array, dtype=float
The data you wish to filter
k: number, int
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251,733 | 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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251,734 | 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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251,735 | 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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251,736 | 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():
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()')
yield _Player(
name=player_row[1... | [
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251,737 | 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):
# 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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251,738 | 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):
self = object.__new__(cls)
self.rank = Rank.make_random()
self.suit = Suit.make_random()
return self | [
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251,739 | 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):
from .website.twoplustwo import ForumMember, AmbiguousUserNameError, UserNotFoundError
try:
member = ForumMember(username)
except UserNotFoundError:
raise click.ClickException('User "%s" not found!' % username)
except AmbiguousUserNameError as e:
... | [
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251,740 | 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):
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(format_str.format(
'Rank' , 'Player name', 'Country', 'Triple', 'Monthly', 'Bigg... | [
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251,741 | 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():
from .website.pokerstars import get_status
_print_header('PokerStars status')
status = get_status()
_print_values(
('Info updated', status.updated),
('Tables', status.tables),
('Players', status.players),
('Active tournaments', status.active_tournaments)... | [
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251,742 | 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):
return tuple(self._get_note_data(note) for note in self.root.iter('note')) | [
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251,743 | 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):
return tuple(_Label(label.get('id'), label.get('color'), label.text) for label
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251,744 | 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):
if label is not None and (label not in self.label_names):
raise LabelNotFoundError('Invalid label: {}'.format(label))
if update is None:
update = datetime.utcnow()
# converted to timestamp, rounded to ones
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251,745 | 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):
note = self._find_note(player)
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251,746 | 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):
note = self._find_note(player)
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251,747 | 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):
label_tag = self._find_label(name)
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251,748 | 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):
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_tag[-1].get('id'))
new_id = str(last_id + 1)
new_label ... | [
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251,749 | 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):
labels_tag = self.root[0]
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251,750 | 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):
with open(filename, 'w') as fp:
fp.write(str(self)) | [
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251,751 | 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):
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(board) if board else None | [
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251,752 | 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):
date = datetime.strptime(date_string, self._DATE_FORMAT)
self.date = self._TZ.localize(date).astimezone(pytz.UTC) | [
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251,753 | 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):
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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251,754 | 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):
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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251,755 | 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():
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'),
name=tour.findtext('{*}name'),
game=tour... | [
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251,756 | RKrahl/pytest-dependency | setup.py | _filter_file | 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)]
with open(src, "rt") as sf, open(dest, "wt") as df:
while True:
l = sf.readline()
... | python | def _filter_file(src, dest, subst):
substre = re.compile(r'\$(%s)' % '|'.join(subst.keys()))
def repl(m):
return subst[m.group(1)]
with open(src, "rt") as sf, open(dest, "wt") as df:
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if not l:
break
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251,757 | 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
{"errors": [{"message": "some string"}]}
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251,758 | 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.
A... | python | 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
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251,759 | 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):
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251,760 | profusion/sgqlc | sgqlc/types/__init__.py | _create_list_of_wrapper | def _create_list_of_wrapper(name, t):
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251,761 | 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.
Extra query may be a dict or a list as returned by
:func:`urllib.parse.parse_qsl()` and :func:`urllib.parse.parse_qs()`.
'''
split = urllib.parse.urlsplit(url)
merged_query = urllib.parse.parse_qsl(sp... | python | def add_query_to_url(url, extra_query):
'''Adds an extra query to URL, returning the new URL.
Extra query may be a dict or a list as returned by
:func:`urllib.parse.parse_qsl()` and :func:`urllib.parse.parse_qs()`.
'''
split = urllib.parse.urlsplit(url)
merged_query = urllib.parse.parse_qsl(sp... | [
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251,762 | profusion/sgqlc | sgqlc/types/relay.py | connection_args | def connection_args(*lst, **mapping):
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Extra parameters may be given as argument, both as iterable,
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By default, provides:
- ``after: String``
- ``before: String``
- ``first: Int``
- ``last: Int``
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'''Returns the default parameters for connection.
Extra parameters may be given as argument, both as iterable,
positional tuples or mapping.
By default, provides:
- ``after: String``
- ``before: String``
- ``first: Int``
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251,763 | 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):
s = 0.
for p in theta.dtype.names:
s += np.sum(np.diff(theta[p], axis=0) ** 2)
return s | [
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251,764 | 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)
... | python | def loglik(self, theta, t=None):
if t is None:
t = self.T - 1
l = np.zeros(shape=theta.shape[0])
for s in range(t + 1):
l += self.logpyt(theta, s)
return l | [
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251,765 | 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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return self.prior.logpdf(theta) + self.loglik(theta, t) | [
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251,766 | 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):
for n, _ in enumerate(self.l):
if where[n]:
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251,767 | 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})
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attrs = {k: self.__dict__[k].copy() for k in self.containers}
attrs.update({k: cp.deepcopy(self.__dict__[k]) for k in self.shared})
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251,768 | nchopin/particles | particles/smc_samplers.py | ThetaParticles.copyto | def copyto(self, src, where=None):
"""Emulates function `copyto` in NumPy.
Parameters
----------
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True if particle n in src must be copied.
src: (N,) `ThetaParticles` object
source
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251,769 | nchopin/particles | particles/smc_samplers.py | ThetaParticles.copyto_at | def copyto_at(self, n, src, m):
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----------
n: int
index where to copy
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source
m: int
index of the element to be copied
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for k in self.containers:
self.__dict__[k][n] = src.__dict__[k][m] | [
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251,770 | 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):
opts = mh_options.copy()
nsteps = opts.pop('nsteps', 0)
delta_dist = opts.pop('delta_dist', 0.1)
proposal = self.choose_proposal(**opts)
xout = self.copy()
xp = self.__class__(theta=np.empty_like(self.theta))
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251,771 | 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.
"""
if not self.filt:
... | python | def backward(self):
if not self.filt:
self.forward()
self.smth = [self.filt[-1]]
log_trans = np.log(self.hmm.trans_mat)
ctg = np.zeros(self.hmm.dim) # cost to go (log-lik of y_{t+1:T} given x_t=k)
for filt, next_ft in reversed(list(zip(self.filt[:-1],
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251,772 | nchopin/particles | particles/kalman.py | predict_step | def predict_step(F, covX, filt):
"""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}
filt: MeanAndCov object
filtering distribution at time t-1
... | python | def predict_step(F, covX, filt):
pred_mean = np.matmul(filt.mean, F.T)
pred_cov = dotdot(F, filt.cov, F.T) + covX
return MeanAndCov(mean=pred_mean, cov=pred_cov) | [
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251,773 | nchopin/particles | particles/kalman.py | filter_step | def filter_step(G, covY, pred, yt):
"""Filtering step of Kalman filter.
Parameters
----------
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
pred: MeanAndCov object
predictive distribution at time t
Returns
... | python | def filter_step(G, covY, pred, yt):
# data prediction
data_pred_mean = np.matmul(pred.mean, G.T)
data_pred_cov = dotdot(G, pred.cov, G.T) + covY
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logpyt = dists.Normal(loc=data_pred_mean,
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251,774 | nchopin/particles | particles/kalman.py | MVLinearGauss.check_shapes | 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
assert self.F.shape == (self.dx, self.dx), error_msg
assert self.G.shape == (self.dy, ... | python | def check_shapes(self):
assert self.covX.shape == (self.dx, self.dx), error_msg
assert self.covY.shape == (self.dy, self.dy), error_msg
assert self.F.shape == (self.dx, self.dx), error_msg
assert self.G.shape == (self.dy, self.dx), error_msg
assert self.mu0.shape == (self.dx,), e... | [
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251,775 | 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):
while(True):
seed = np.random.randint(2**32)
out = lowdiscrepancy.sobol(N, dim, scrambled, seed, 1, 0)
if (scrambled == 0) or ((out < 1.).all() and (out > 0.).all()):
# no need to test if scrambled==0
return out | [
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251,776 | 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):
T = fk.T
if fk_info is None:
fk_info = fk.__class__(ssm=fk.ssm, data=fk.data[::-1])
if seed:
random.seed(seed)
est = np.zeros(T - 1)
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251,777 | 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):
self.X.append(X)
self.wgt.append(w)
self.A.append(A) | [
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251,778 | 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):
traj = []
for t in reversed(range(self.T)):
if t == self.T - 1:
n = rs.multinomial_once(self.wgt[-1].W)
else:
n = self.A[t + 1][n]
traj.append(self.X[t][n])
return traj[::-1] | [
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251,779 | 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):
self.B = np.empty((self.T, self.N), 'int')
self.B[-1, :] = self.A[-1]
for t in reversed(range(self.T - 1)):
self.B[t, :] = self.A[t + 1][self.B[t + 1]] | [
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251,780 | 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):
ti = self.T - 2 - t # t+1 in reverse
if t < 0 or t >= self.T - 1:
raise ValueError(
'two-f... | [
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loggamma: function
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251,781 | 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):
def f(**args):
pf = SMC(**args)
pf.run()
return out_func(pf)
if out_func is None:
out_func = lambda x: x
return utils.multiplexer(f=f, nruns=nruns, nprocs=nprocs, seeding=True,
**args) | [
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251,782 | 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):
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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251,783 | 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):
m = v.max()
return m + np.log(np.sum(np.exp(v - m))) | [
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Note
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See also
... | [
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251,784 | 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):
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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251,785 | 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):
m = np.average(x, weights=W, axis=0)
m2 = np.average(x**2, weights=W, axis=0)
v = m2 - m**2
return {'mean': m, 'var': v} | [
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251,786 | 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):
m = np.empty(shape=x.shape[1:], dtype=x.dtype)
v = np.empty_like(m)
for p in x.dtype.names:
m[p], v[p] = wmean_and_var(W, x[p]).values()
return {'mean': m, 'var': v} | [
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251,787 | 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)):
if len(x.shape) == 1:
return _wquantiles(W, x, alphas=alphas)
elif len(x.shape) == 2:
return np.array([_wquantiles(W, x[:, i], alphas=alphas)
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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))
probabilities (between 0. and 1.)
Returns
-------
a (k... | [
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] | 3faa97a1073db45c5889eef3e015dd76ef350b52 | https://github.com/nchopin/particles/blob/3faa97a1073db45c5889eef3e015dd76ef350b52/particles/resampling.py#L359-L379 |
251,788 | 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)):
return {p: wquantiles(W, x[p], alphas) for p in x.dtype.names} | [
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normalised weights (weights are >=0 and sum to one)
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data
alphas: list-like of size k (default: (0.25, 0.50, 0.75))
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251,789 | 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):
@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_func | [
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251,790 | 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):
j = 0
s = W[0]
M = su.shape[0]
A = np.empty(M, 'int')
for n in range(M):
while su[n] > s:
j += 1
s += W[j]
A[n] = j
return A | [
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W: (N,) ndarray
a vector of N normalized weights (>=0 and sum to one)
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251,791 | 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):
N, d = xint.shape
h = np.zeros(N, int64)
for n in range(N):
h[n] = Hilbert_to_int(xint[n, :])
return h | [
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xint: (N, d) int numpy.ndarray
Returns
-------
h: (N,) int numpy.ndarray
Hilbert indices | [
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251,792 | 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):
discard = int(self.niter * discard_frac)
return msjd(self.chain.theta[discard:]) | [
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Parameters
----------
discard_frac: float
fraction of iterations to discard at the beginning (as a burn-in)
Returns
-------
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251,793 | 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):
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 = cholesky(self.Sigma, lower=True)... | [
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251,794 | 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):
return [{k: v for k, v in zip(d.keys(), args)}
for args in itertools.product(*d.values())] | [
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Example
-------
cartesian_lists({'a':[0, 2], 'b':[3, 4, 5]}
returns
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251,795 | 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):
ils = {k: [v, ] for k, v in args.items()}
ils.update(listargs)
ils.update({k: v.values() for k, v in dictargs.items()})
ols = listargs.copy()
ols.update({k: v.keys() for k, v in dictargs.items()})
return cartesian_lists(ils), cartesian_lists(ols) | [
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251,796 | 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):
while not qin.empty():
i, args = qin.get()
qout.put((i, f(**args))) | [
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251,797 | 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):
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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251,798 | 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):
if not callable(f):
raise ValueError('multiplexer: function f missing, or not callable')
if seeding is None:
seeding = (nruns > 1)
# extra arguments (meant to be arguments for f)
fixedargs, listargs, dictargs = {}, {}, {}
... | [
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251,799 | 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):
x = []
for t in range(T):
law_x = self.PX0() if t == 0 else self.PX(t, x[-1])
x.append(law_x.rvs(size=1))
y = self.simulate_given_x(x)
return x, y | [
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T: int
processes are simulated from time 0 to time T-1
Returns
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x, y: lists
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