id stringlengths 30 32 | content stringlengths 139 2.8k |
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codereview_new_python_data_13258 | def make_smoothing_spline(x, y, w=None, lam=None):
prediction, New York: Springer, 2017, pp. 241-249.
:doi:`10.1007/978-0-387-84858-7`
.. [4] E. Zemlyanoy, "Generalized cross-validation smoothing splines",
- BSc thesis, 2022. Might be available
- `here <https://www.hse.ru/ba/am/stu... |
codereview_new_python_data_13259 | def make_smoothing_spline(x, y, w=None, lam=None):
prediction, New York: Springer, 2017, pp. 241-249.
:doi:`10.1007/978-0-387-84858-7`
.. [4] E. Zemlyanoy, "Generalized cross-validation smoothing splines",
- BSc thesis, 2022. Might be available
- `here <https://www.hse.ru/ba/am/stu... |
codereview_new_python_data_13260 | def h(x):
maxiter = 5
if new_cb_interface:
- def callback_interface(*, intermediate_result): # type: ignore[misc] # noqa
assert intermediate_result.fun == f(intermediate_result.x)
callback()
else:
- def callback_interface(xk, *args)... |
codereview_new_python_data_13261 | def _add(self, minres):
self.minres.x = np.copy(minres.x)
def update(self, minres):
- cond1 = minres.fun < self.minres.fun and minres.success
- cond2 = minres.success and not self.minres.success
- if cond1 or cond2:
self._add(minres)
return True
e... |
codereview_new_python_data_13262 | def _add(self, minres):
def update(self, minres):
if minres.success and (minres.fun < self.minres.fun
- or not self.minres.success)
self._add(minres)
return True
else:
Oops missing the colon
```suggestion
if minres.success and ... |
codereview_new_python_data_13263 | def _sf(self, x):
return _norm_sf(np.log(x))
def _isf(self, p):
- return np.exp(-sc.ndtri(p))
def _stats(self):
p = np.e
Since we're using `_norm_ppf` in `_ppf`:
```suggestion
return np.exp(_norm_isf(p))
```
def _sf(self, x):
return _norm_sf(np.log(x))
... |
codereview_new_python_data_13264 |
'nautical_mile', 'neutron_mass', 'nu2lambda',
'ounce', 'oz', 'parsec', 'pebi', 'peta',
'pi', 'pico', 'point', 'pound', 'pound_force',
- 'proton_mass', 'psi', 'pt', 'quecto', 'quetta', 'ronna', 'ronto', 'short_ton',
- 'sigma', 'slinch', 'slug', 'speed_of_light',
'speed_of_sound', 'stone', 'sur... |
codereview_new_python_data_13265 | def test_electrocardiogram(self):
registry["ecg.dat"])
-def test_clear_cache():
- # Use Dummy path
- dummy_basepath = "dummy_cache_dir"
- os.makedirs(dummy_basepath, exist_ok=True)
# Create three dummy dataset files
dummy_registry = {}
for i in range(3):
... |
codereview_new_python_data_13266 | def test_beta_ppf_with_subnormal_a_b(self, method, a, b):
# the value), because our goal here is to verify that the call does
# not trigger a segmentation fault.
try:
- stats.beta.ppf(p, a, b)
except OverflowError:
# The OverflowError exception occurs with Bo... |
codereview_new_python_data_13267 | def test_pmf_cdf(self):
ref = stats.binom.cdf(r, n, p)
assert_allclose(res, ref, atol=1e-16)
- def test_pmf_cdf(self):
# Check that gh-15101 is resolved (no divide warnings when p~1, n~oo)
res = stats.binom.pmf(3, 2000, 0.999)
assert_allclose(res, 0, atol=1e-16)
```su... |
codereview_new_python_data_13268 | def spearmanr(a, b=None, axis=0, nan_policy='propagate',
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
Section 14.7
- .. [2] Kendall, M. G. and Stuart, A. (1973).
The Advanced Theory of Statistics, Volume 2: Inference and Relationship.
- Grif... |
codereview_new_python_data_13269 | def spearmanr(a, b=None, axis=0, nan_policy='propagate',
Probability and Statistics Tables and Formulae. Chapman & Hall: New
York. 2000.
Section 14.7
- .. [2] Kendall, M. G. and Stuart, A. (1973).
The Advanced Theory of Statistics, Volume 2: Inference and Relationship.
- Grif... |
codereview_new_python_data_13270 |
import numpy as np
-from scipy.fftpack import fft, ifft
from scipy.special import gammaincinv, ndtr, ndtri
from scipy.stats._qmc import n_primes, primes_from_2_to
```suggestion
from scipy.fft import fft, ifft
```
This should fix the FFT issue. I tested it locally and it works.
import numpy as np
+f... |
codereview_new_python_data_13271 | def false_discovery_control(ps, *, axis=0, method='bh'):
To control the FWER at 5%, we reject only the hypotheses corresponding
with adjusted p-values less than 0.05. In this case, only the hypotheses
corresponding with the first three p-values can be rejected. According to
- [1], these three hypothe... |
codereview_new_python_data_13272 | def false_discovery_control(ps, *, axis=0, method='bh'):
the more conservative Benjaminini-Yekutieli procedure.
The adjusted p-values produced by this function are comparable to those
- produced by the R function ``p.adjust``.
References
----------
@tupui how about this?
```suggestion
... |
codereview_new_python_data_13273 | def test_as_euler_degenerate_symmetric_axes():
def test_as_euler_compare_algorithms():
rnd = np.random.RandomState(0)
- n = 10
angles = np.empty((n, 3))
angles[:, 0] = rnd.uniform(low=-np.pi, high=np.pi, size=(n,))
angles[:, 2] = rnd.uniform(low=-np.pi, high=np.pi, size=(n,))
It can be easil... |
codereview_new_python_data_13276 | def test_bfgs_infinite(self):
assert not np.isfinite(func(x))
def test_bfgs_xrtol(self):
- # test for #17345 to test xrtol parameter
x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
res = optimize.minimize(optimize.rosen,
x0, method='bfgs', options={'xrtol':... |
codereview_new_python_data_13277 | def accept_reject(self, energy_new, energy_old):
#
# RuntimeWarning: invalid value encountered in multiply
#
- # Ignore this warning so so when the algorithm is on a flat plane, it always
# accepts the step, to try to move off the plane.
prod ... |
codereview_new_python_data_13278 | def find_objects(input, max_label=0):
A list of tuples, with each tuple containing N slices (with N the
dimension of the input array). Slices correspond to the minimal
parallelepiped that contains the object. If a number is missing,
- None is returned instead of a slice. The label `l`... |
codereview_new_python_data_13279 | def test_nonscalar_values_2(self, method):
assert_allclose(v, v2, atol=1e-14, err_msg=method)
def test_nonscalar_values_linear_2D(self):
- # Verify that non-scalar valued work in the 2D fast path
method = 'linear'
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5),
(0.... |
codereview_new_python_data_13280 | def add_newdoc(name, doc):
Plot the function for different parameter sets.
- >>> (fig, ax) = plt.subplots(figsize=(8, 8))
>>> x = np.linspace(-10, 10, 500)
>>> parameters_list = [(1.5, 0., "solid"), (1.3, 0.5, "dashed"),
... (0., 1.8, "dotted"), (1., 1., "dashdot")]
Nitpi... |
codereview_new_python_data_13281 | def test_frozen_distribution(self):
assert_equal(rvs1, rvs2)
assert_equal(rvs1, rvs3)
- def test_uniform_circle(self):
# test that for uniform 2D samples on the circle the resulting
# angles follow a uniform distribution
circular_dist = random_direction(2)
```suggesti... |
codereview_new_python_data_13282 | def test_to_corr(self):
class TestRandomDirection:
@pytest.mark.parametrize("dim", [1, 3])
- @pytest.mark.parametrize("size", [None, 5, (5, 4)])
def test_samples(self, dim, size):
# test that samples have correct shape and norm 1
- random_direction_dist = random_direction(dim)
... |
codereview_new_python_data_13283 | def _test_factory(case, dec):
_test_factory(case, min_decimal[ind])
-
def test_solve_discrete_are():
cases = [
```suggestion
```
Oops. Please commit this and merge with `[skip ci]` in the commit message if everything else looks ok.
def _test_factory(case, dec):
_test_factory(case, mi... |
codereview_new_python_data_13284 | def leastsq(func, x0, args=(), Dfun=None, full_output=0,
r = triu(transpose(retval[1]['fjac'])[:n, :])
R = dot(r, perm)
try:
- # Tis was `cov_x = inv(dot(transpose(R), R))`, but sometimes
# the result was not symmetric positive definite. See gh-455... |
codereview_new_python_data_13285 | def f(x, a, b, c, d, e):
y = np.linspace(2, 7, n) + rng.random(n)
p, cov = optimize.curve_fit(f, x, y, maxfev=100000)
assert np.all(np.diag(cov) > 0)
- assert np.all(linalg.eigh(cov)[0] > 0)
assert_allclose(cov, cov.T)
Well, there are some failing tests because approxima... |
codereview_new_python_data_13286 | def f(x, a, b, c, d, e):
p, cov = optimize.curve_fit(f, x, y, maxfev=100000)
assert np.all(np.diag(cov) > 0)
eigs = linalg.eigh(cov)[0] # separate line for debugging
- assert np.all(eigs > -1e-6)
assert_allclose(cov, cov.T)
```suggestion
# some platforms see a ... |
codereview_new_python_data_13287 | def extra(self):
return 42
s = S(xk=[1, 2, 3], pk=[0.2, 0.7, 0.1])
- assert_allclose(s.pmf([2, 3, 1]), [0.7, 0.1, 0.2], atol=42)
assert s.extra() == 42
The `atol` here looks a little loose...
def extra(self):
return 42
s = S(xk=[1, 2, 3], pk=[0.2, 0.7, 0.1])
+ as... |
codereview_new_python_data_13288 | class rv_count(rv_discrete):
This string is used as part of the first line of the docstring returned
when a subclass has no docstring of its own. Note: `longname` exists
for backwards compatibility, do not use for new subclasses.
- seed : {None, int, `numpy.random.Generator`, `numpy.rando... |
codereview_new_python_data_13289 | def __init__(self, points, values, method="linear", bounds_error=True,
self._validate_grid_dimensions(points, method)
self.method = method
self.bounds_error = bounds_error
- self.grid, self.descending_dimensions = self._check_points(points)
self.values = self._check_value... |
codereview_new_python_data_13290 | def __init__(self, points, values, method="linear", bounds_error=True,
self._validate_grid_dimensions(points, method)
self.method = method
self.bounds_error = bounds_error
- self.grid, self.descending_dimensions = self._check_points(points)
self.values = self._check_value... |
codereview_new_python_data_13291 | def __init__(self, points, values, method="linear", bounds_error=True,
self._validate_grid_dimensions(points, method)
self.method = method
self.bounds_error = bounds_error
- self.grid, self.descending_dimensions = self._check_points(points)
self.values = self._check_value... |
codereview_new_python_data_13292 | def __call__(self, values, method=None):
# check dimensionality
self._check_dimensionality(self.grid, values)
# flip, if needed
- self.values = np.flip(values, axis=self.descending_dimensions)
return super().__call__(self.xi, method=method)
Need to be changed here too.
... |
codereview_new_python_data_13293 | def __call__(self, values, method=None):
# check dimensionality
self._check_dimensionality(self.grid, values)
# flip, if needed
- self.values = np.flip(values, axis=self.descending_dimensions)
return super().__call__(self.xi, method=method)
```suggestion
self.va... |
codereview_new_python_data_13294 | def pmf(self, k, *args, **kwds):
k = asarray((k-loc))
cond0 = self._argcheck(*args)
cond1 = (k >= _a) & (k <= _b)
- cond = cond0 & cond1
if not isinstance(self, rv_sample):
cond1 = cond1 & self._nonzero(k, *args)
output = zeros(shape(cond), 'd')
... |
codereview_new_python_data_13295 | def check_pmf_cdf(distfn, arg, distname):
npt.assert_allclose(cdfs - cdfs[0], pmfs_cum - pmfs_cum[0],
atol=atol, rtol=rtol)
- # also check that pmf at non-integral args is zero
k = np.asarray(index)
- k_shifted = index[:-1] + np.diff(index)/2
npt.assert_equal(distfn.pmf(... |
codereview_new_python_data_13296 | def value_indices(arr, *, nullval=None):
Note for IDL users: this provides functionality equivalent to IDL's
REVERSE_INDICES option.
- .. versionadded:: 1.9.2
Examples
--------
Obviously subject to mailing list & review but this won't go in until 1.10.0 at the earliest.
```sugg... |
codereview_new_python_data_13297 | def value_indices(arr, *, ignore_value=None):
more flexible alternative to functions like ``scipy.ndimage.mean()``
and ``scipy.ndimage.variance()``.
Note for IDL users: this provides functionality equivalent to IDL's
REVERSE_INDICES option (as per the IDL documentation for the
`HISTOGRAM <ht... |
codereview_new_python_data_13298 | class LatinHypercube(QMCEngine):
distinct rows occur the same number of times. The elements of :math:`A`
are in the set :math:`\{0, 1, ..., p-1\}`, also called symbols.
The constraint that :math:`p` must be a prime number is to allow modular
- arithmetic. The strength add some symmetry in sub-project... |
codereview_new_python_data_13299 | def test_mse_accuracy_2(self):
a = (n*x[0] - x[-1])/(n - 1)
b = (n*x[-1] - x[0])/(n - 1)
ref = a, b-a # (3.6081133632151503, 5.509328130317254)
- assert_allclose(res.params, ref, atol=1e-5)
class TestFitResult:
```suggestion
assert_allclose(res.params, ref, atol=1e-4)
... |
codereview_new_python_data_13300 | def init_options(self, options):
"""
# Ensure that 'jac' and 'hess' are passed directly to `minimize` as
# keywords, not as part of its 'options' dictionary
- self.minimizer_kwargs['jac'] = options.pop('jac', None)
- self.minimizer_kwargs['hess'] = options.pop('hess', None)
... |
codereview_new_python_data_13301 | def init_options(self, options):
"""
# Ensure that 'jac' and 'hess' are passed directly to `minimize` as
# keywords, not as part of its 'options' dictionary
- self.minimizer_kwargs['jac'] = options.pop('jac', None)
- self.minimizer_kwargs['hess'] = options.pop('hess', None)
... |
codereview_new_python_data_13302 | def init_options(self, options):
# Ensure that 'jac', 'hess', and 'hessp' are passed directly to
# `minimize` as keywords, not as part of its 'options' dictionary.
for opt in ['jac', 'hess', 'hessp']:
- if opt in options:
self.minimizer_kwargs[opt] = (
... |
codereview_new_python_data_13303 | def directional_stats(samples, *, axis=0, normalize=True):
It is analogous to the sample mean, but it is for use when the length of
the data is irrelevant (e.g. unit vectors).
- The directional variance serves as a measure of "directional spread" for
vector data. The length of the mean vector can b... |
codereview_new_python_data_13304 | def test_directional_stats_correctness(self):
reference_mean = np.array([0.2984, -0.1346, -0.9449])
assert_allclose(mean_rounded, reference_mean)
- expected_var = 0.025335389565304012
- directional_var = 1 - dirstats.mean_resultant_length
- assert_allclose(expected_var, directi... |
codereview_new_python_data_13305 | def test_directional_stats_correctness(self):
@pytest.mark.parametrize('angles, ref', [
([-np.pi/2, np.pi/2], 1.),
- ([0, 2*np.pi], 0.),
- ([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2], stats.circvar)
])
- def test_mean_resultant_length(self, angles, ref):
if callable(ref):
... |
codereview_new_python_data_13306 | def binned_statistic_dd(sample, values, statistic='mean',
dedges = [np.diff(edges[i]) for i in builtins.range(Ndim)]
binnumbers = binned_statistic_result.binnumber
- # Use a float64 accumulator to avoid integer overflow
result_type = np.result_type(values, np.float64)
result = np.empty... |
codereview_new_python_data_13307 |
import numpy as np
from numpy.testing import assert_allclose
-from pytest import mark
from pytest import raises as assert_raises
from scipy.stats import (binned_statistic, binned_statistic_2d,
binned_statistic_dd)
```suggestion
import pytest
```
For consistency with the rest of stats.... |
codereview_new_python_data_13308 | def max_len_seq(nbits, state=None, length=None, taps=None):
>>> import numpy as np
>>> import matplotlib.pyplot as plt
- >>> from numpy.fft import fft, ifft, fftshift, fftfreq
>>> seq = max_len_seq(6)[0]*2-1 # +1 and -1
>>> spec = fft(seq)
>>> N = len(seq)
Minor nit: maybe we want to ke... |
codereview_new_python_data_13309 | def fft(x, n=None, axis=-1, norm=None, overwrite_x=False, workers=None, *,
In this example, real input has an FFT which is Hermitian, i.e., symmetric
in the real part and anti-symmetric in the imaginary part:
- >>> import numpy as np
>>> from scipy.fft import fft, fftfreq, fftshift
>>> import ... |
codereview_new_python_data_13310 | def max_len_seq(nbits, state=None, length=None, taps=None):
>>> import numpy as np
>>> import matplotlib.pyplot as plt
- >>> from np.fft import fft, ifft, fftshift, fftfreq
>>> seq = max_len_seq(6)[0]*2-1 # +1 and -1
>>> spec = fft(seq)
>>> N = len(seq)
This doesn't work--a variable nam... |
codereview_new_python_data_13311 | def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False,
parameters). Use ``np.inf`` with an appropriate sign to disable
bounds on all or some parameters.
- .. versionadded:: 0.17
method : {'lm', 'trf', 'dogbox'}, optional
Method to use for op... |
codereview_new_python_data_13312 | def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False,
parameters). Use ``np.inf`` with an appropriate sign to disable
bounds on all or some parameters.
- .. versionadded:: 0.17
method : {'lm', 'trf', 'dogbox'}, optional
Method to use for op... |
codereview_new_python_data_13313 | class Covariance:
object representing a covariance matrix using any of several
decompositions and perform calculations using a common interface.
- Note that the `Covariance` class cannot be instantiated directly.
- Instead, use one of the factory methods (e.g. `Covariance.from_diagonal`).
Exam... |
codereview_new_python_data_13314 | class Covariance:
object representing a covariance matrix using any of several
decompositions and perform calculations using a common interface.
- Note that the `Covariance` class cannot be instantiated directly.
- Instead, use one of the factory methods (e.g. `Covariance.from_diagonal`).
Exam... |
codereview_new_python_data_13315 | class Covariance:
representing a covariance matrix:
>>> from scipy import stats
>>> d = [1, 2, 3]
>>> A = np.diag(d) # a diagonal covariance matrix
>>> x = [4, -2, 5] # a point of interest
```suggestion
>>> from scipy import stats
>>> import numpy as np
```
class Covariance:
... |
codereview_new_python_data_13316 | def test_ncf_ppf_issue_17026():
par = (0.1, 2, 5, 0, 1)
with pytest.warns(RuntimeWarning):
q = stats.ncf.ppf(x, *par)
class TestHistogram:
```suggestion
q = stats.ncf.ppf(x, *par)
q0 = [stats.ncf.ppf(xi, *par) for xi in x]
assert_allclose(q, q0)
```
There was a PEP8 is... |
codereview_new_python_data_13317 | def y1p_zeros(nt, complex=False):
>>> ax.legend(ncol=2, bbox_to_anchor=(1., 0.75))
>>> plt.tight_layout()
>>> plt.show()
- """
if not isscalar(nt) or (floor(nt) != nt) or (nt <= 0):
raise ValueError("Arguments must be scalar positive integer.")
kf = 2
```suggestion
>>> pl... |
codereview_new_python_data_13318 | def jn_zeros(n, nt):
See Also
--------
jv: Real-order Bessel functions of the first kind
- jn: Integer-order Bessel functions of the first kind
jnp_zeros: Zeros of :math:`Jn'`
References
This seems to be the culprit.
```
/home/circleci/repo/build-install/lib/python3.8/site-packages/s... |
codereview_new_python_data_13319 | def test_nomodify_gh9900_regression():
# Use the right-half truncated normal
# Check that the cdf and _cdf return the same result.
npt.assert_almost_equal(tn.cdf(1, 0, np.inf), 0.6826894921370859)
- npt.assert_almost_equal(tn._cdf(1, 0, np.inf), 0.6826894921370859)
# Now use the left-half trun... |
codereview_new_python_data_13320 | def ks_2samp(data1, data2, alternative='two-sided', method='auto'):
minS = np.clip(-cddiffs[argminS], 0, 1)
maxS = cddiffs[argmaxS]
- if alternative == 'less':
d = minS
d_location = loc_minS
d_sign = '-'
- elif alternative == 'greater':
d = maxS
d_location... |
codereview_new_python_data_13321 | def ks_2samp(data1, data2, alternative='two-sided', method='auto'):
minS = np.clip(-cddiffs[argminS], 0, 1)
maxS = cddiffs[argmaxS]
- if alternative == 'less':
d = minS
d_location = loc_minS
d_sign = '-'
- elif alternative == 'greater':
d = maxS
d_location... |
codereview_new_python_data_13322 | def kstest(rvs, cdf, args=(), N=20, alternative='two-sided', method='auto'):
Value of `x` corresponding with the KS statistic; i.e. the
observation at which the difference between CDFs is measured.
statistic_sign: int
- 1 if the KS statistic is the maximal positive differe... |
codereview_new_python_data_13323 | def testLargeBoth(self):
def testNamedAttributes(self):
# test for namedtuple attribute results
attributes = ('statistic', 'pvalue')
- additional_attributes = ('statistic_location', 'statistic_sign')
res = stats.ks_2samp([1, 2], [3])
- check_named_results(res, attributes,
... |
codereview_new_python_data_13324 | def quad(func, a, b, args=(), full_output=0, epsabs=1.49e-8, epsrel=1.49e-8,
message is appended to the output tuple.
complex_func : bool, optional
Indicate if the function's (func) return type is real
- (`complex_func=false`: default) or complex (`complex_func=True`).
In both ca... |
codereview_new_python_data_13325 | def quad(func, a, b, args=(), full_output=0, epsabs=1.49e-8, epsrel=1.49e-8,
message is appended to the output tuple.
complex_func : bool, optional
Indicate if the function's (func) return type is real
- (`complex_func=false`: default) or complex (`complex_func=True`).
In both ca... |
codereview_new_python_data_13326 | def tfunc(x):
assert_quad(quad(tfunc, 0, np.pi/2, complex_func=True),
1+1j, error_tolerance=1e-6)
class TestNQuad:
def test_fixed_limits(self):
There needs to be a test with `full_output=True`.
def tfunc(x):
assert_quad(quad(tfunc, 0, np.pi/2, complex_func=True),
... |
codereview_new_python_data_13327 | def quad(func, a, b, args=(), full_output=0, epsabs=1.49e-8, epsrel=1.49e-8,
.. math::
\\int_a^b f(x) dx = \\int_a^b g(x) dx + i\\int_a^b h(x) dx
- This assumes that the integrals of :math:`g` and :math:`h` exist
- over the inteval :math:`[a,b]` [2]_.
References
```suggestion
assum... |
codereview_new_python_data_13328 | def tfunc(x):
1+1j, error_tolerance=1e-6)
full_res = quad(tfunc, 0, np.pi/2, complex_func=True, full_output=True)
- assert_quad(full_res[:-1],
- 1+1j, error_tolerance=1e-6)
assert set(("real output", "imag output")) == set(full_res[2].keys())
```s... |
codereview_new_python_data_13329 | def test_lsmr_output_shape():
assert_equal(x.shape, (1,))
def test_eigs(matrices):
A_dense, A_sparse, v0 = matrices
@mreineck I don't think you meant to delete this test, did you?
def test_lsmr_output_shape():
assert_equal(x.shape, (1,))
+def test_lsqr(matrices):
+ A_dense, A_sparse, b ... |
codereview_new_python_data_13330 | def op(a, b):
# 6. negative argument
# T_{alpha}(-X) = -T_{1-alpha}(X)
- assert (
- stats.expectile(-x, alpha=alpha) ==
pytest.approx(-stats.expectile(x, alpha=1-alpha))
)
I'm not a big fan of this equality op though, because it's one-sided - rounding only... |
codereview_new_python_data_13331 | def expectile(a, alpha=0.5, *, weights=None):
----------
a : array_like
Input array or object that can be converted to an array.
- alpha : float, default=0.5
The level of the expectile; `alpha=0.5` gives the mean.
weights : array_like, optional
The sample or case `weights` ... |
codereview_new_python_data_13332 | def expectile(a, alpha=0.5, *, weights=None):
----------
a : array_like
Input array or object that can be converted to an array.
- alpha : float, default=0.5
The level of the expectile; `alpha=0.5` gives the mean.
weights : array_like, optional
The sample or case `weights` ... |
codereview_new_python_data_13333 | def expectile(a, alpha=0.5, *, weights=None):
----------
a : array_like
Input array or object that can be converted to an array.
- alpha : float, default=0.5
The level of the expectile; `alpha=0.5` gives the mean.
weights : array_like, optional
The sample or case `weights` ... |
codereview_new_python_data_13334 | def expectile(a, alpha=0.5, *, weights=None):
Furthermore, the larger :math:`\alpha`, the larger the value of the
expectile.
- As a final remark, the expectile at level :math:`alpha` can also be
written as a minimization problem. One often used choice is
.. math::
```suggestion
As a fi... |
codereview_new_python_data_13335 | def weightedtau(x, y, rank=True, weigher=None, additive=True):
statistic : float
The weighted :math:`\tau` correlation index.
pvalue : float
- Presently ``np.nan``, as the null hypothesis is unknown (even in the
- additive hyperbolic case).
See Also
-------... |
codereview_new_python_data_13336 | def kendalltau(x, y, initial_lexsort=None, nan_policy='propagate',
Returns
-------
- res: SignificanceResult
An object containing attributes:
statistic : float
```suggestion
res : SignificanceResult
```
def kendalltau(x, y, initial_lexsort=None, nan_policy='propagate',
... |
codereview_new_python_data_13337 | def kendalltau(x, y, use_ties=True, use_missing=False, method='auto',
Returns
-------
- res: SignificanceResult
An object containing attributes:
statistic : float
```suggestion
res : SignificanceResult
```
def kendalltau(x, y, use_ties=True, use_missing=False, method='auto',... |
codereview_new_python_data_13338 | def spearmanr(x, y=None, use_ties=True, axis=None, nan_policy='propagate',
``a`` and ``b`` combined.
pvalue : float
The p-value for a hypothesis test whose null hypothesis
- is that two sets of data are uncorrelated. See `alternative` above
- for alternative hyp... |
codereview_new_python_data_13339 | def spearmanr(x, y=None, use_ties=True, axis=None, nan_policy='propagate',
Returns
-------
- res: SignificanceResult
An object containing attributes:
statistic : float or ndarray (2-D square)
```suggestion
res : SignificanceResult
```
def spearmanr(x, y=None, use_ties=True, ... |
codereview_new_python_data_13340 | def spearmanr(a, b=None, axis=0, nan_policy='propagate',
Returns
-------
- res: SignificanceResult
An object containing attributes:
statistic : float or ndarray (2-D square)
```suggestion
res : SignificanceResult
```
def spearmanr(a, b=None, axis=0, nan_policy='propagate',
... |
codereview_new_python_data_13341 | def anderson_ksamp(samples, midrank=True, n_resamples=0, random_state=None):
--------
>>> import numpy as np
>>> from scipy import stats
- >>> rng = np.random.default_rng(1638083107694713882823079058616272161)
>>> res = stats.anderson_ksamp([rng.normal(size=50),
... rng.normal(loc=0.5, size... |
codereview_new_python_data_13342 | def statistic(*samples):
if A2 < critical.min() and not n_resamples:
p = sig.max()
message = (f"p-value capped: true value larger than {p}. Consider "
- "setting `n_resamples` to a possible integer (e.g. 9999).")
warnings.warn(message, stacklevel=2)
elif A2 > crit... |
codereview_new_python_data_13343 | def solve_ivp(fun, t_span, y0, method='RK45', t_eval=None, dense_output=False,
options is determined by `vectorized` argument (see below). The
vectorized implementation allows a faster approximation of the Jacobian
by finite differences (required for stiff solvers).
- t_span : 2-member it... |
codereview_new_python_data_13344 | def goodness_of_fit(dist, data, *, known_params=None, fit_params=None,
First, any unknown parameters of the distribution family specified by
`dist` are fit to the provided `data` using maximum likelihood estimation.
(One exception is the normal distribution with unknown location and scale:
- we use ... |
codereview_new_python_data_13345 | def test_NaN_handling(self):
def _check_nan_policy(f, xdata_with_nan, xdata_without_nan,
ydata_with_nan, ydata_without_nan, method):
# propagate test
- error_msg = "`propagate` is not supported for nan_policy " \
- "in this function."
with ass... |
codereview_new_python_data_13346 | def test_NaN_handling(self):
def _check_nan_policy(f, xdata_with_nan, xdata_without_nan,
ydata_with_nan, ydata_without_nan, method):
# propagate test
- error_msg = "`propagate` is not supported for nan_policy " \
- "in this function."
with ass... |
codereview_new_python_data_13347 | def test_NaN_handling(self):
def _check_nan_policy(f, xdata_with_nan, xdata_without_nan,
ydata_with_nan, ydata_without_nan, method):
# propagate test
- error_msg = "`propagate` is not supported for nan_policy " \
- "in this function."
with ass... |
codereview_new_python_data_13348 | def _check_nan_policy(f, xdata_with_nan, xdata_without_nan,
# omit test
result_with_nan, _ = curve_fit(**kwargs, nan_policy="omit")
- result_without_nan, _ = curve_fit(**kwargs, nan_policy="omit")
assert_allclose(result_with_nan, result_without_nan)
@pytest.mark.parametrize('... |
codereview_new_python_data_13349 | def _plotting_positions(self, n, a=.5):
def _cdf_plot(self, ax, fit_params):
data = np.sort(self._data)
ecdf = self._plotting_positions(len(self._data))
- ls = '--' if self.discrete else '-'
xlabel = 'k' if self.discrete else 'x'
ax.step(data, ecdf, ls, label='Empirical ... |
codereview_new_python_data_13350 | def mat_reader_factory(file_name, appendmat=True, **kwargs):
elif mjv == 1:
return MatFile5Reader(byte_stream, **kwargs), file_opened
elif mjv == 2:
- raise NotImplementedError('Please use HDF reader for matlab v7.3 files,'
- ' e.g. h5py')
else:
... |
codereview_new_python_data_13351 | class truncnorm_gen(rv_continuous):
Notes
-----
- This distribution is the normal distribution centred on ``loc`` (default
- 0), with standard deviation ``scale`` (default 1), and clipped at ``a``,
- ``b`` standard deviations to the left, right (respectively) from ``loc``.
- If ``myclip_a`` ... |
codereview_new_python_data_13352 | class truncnorm_gen(rv_continuous):
Notes
-----
- This distribution is the normal distribution centred on ``loc`` (default
0), with standard deviation ``scale`` (default 1), and clipped at ``a``,
``b`` standard deviations to the left, right (respectively) from ``loc``.
If ``myclip_a`` and... |
codereview_new_python_data_13353 | class truncnorm_gen(rv_continuous):
Notes
-----
- This distribution is the normal distribution centred on ``loc`` (default
0), with standard deviation ``scale`` (default 1), and clipped at ``a``,
``b`` standard deviations to the left, right (respectively) from ``loc``.
If ``myclip_a`` and... |
codereview_new_python_data_13354 |
from scipy import optimize
from scipy import special
from scipy._lib._bunch import _make_tuple_bunch
-from scipy._lib._util import (_rename_parameter, _contains_nan)
from . import _statlib
from . import _stats_py
from ._fit import FitResult
-from ._stats_py import (find_repeats, _normtest_finish,
- ... |
codereview_new_python_data_13355 |
from scipy import optimize
from scipy import special
from scipy._lib._bunch import _make_tuple_bunch
-from scipy._lib._util import (_rename_parameter, _contains_nan)
from . import _statlib
from . import _stats_py
from ._fit import FitResult
-from ._stats_py import (find_repeats, _normtest_finish,
- ... |
codereview_new_python_data_13356 |
from collections import namedtuple
from . import distributions
-from scipy._lib._util import (_rename_parameter, _contains_nan)
from scipy._lib._bunch import _make_tuple_bunch
import scipy.special as special
import scipy.stats._stats_py
```suggestion
from scipy._lib._util import _rename_parameter, _contains_n... |
codereview_new_python_data_13357 |
import numpy as np
from numpy.core.multiarray import normalize_axis_index
-from scipy._lib._util import (_nan_allsame, _contains_nan)
from ._stats_py import _chk_asarray
```suggestion
from scipy._lib._util import _nan_allsame, _contains_nan
```
import numpy as np
from numpy.core.multiarray import nor... |
codereview_new_python_data_13358 | def qmc_quad(func, ranges, *, n_points=1024, n_offsets=8, qrng=None, log=False,
>>> t.interval(0.99)
(0.00018389017561108015, 0.00018461661169997918)
- Indeed, the value reported by `scipy.stats.multivariate_normal.cdf` is
within this range.
>>> stats.multivariate_normal.cdf(ub, mean, cov, l... |
codereview_new_python_data_13359 | def __init__(
optimization: Optional[Literal["random-cd", "lloyd"]] = None
) -> None:
self._init = {'d': d, 'scramble': True, 'bits': bits,
- 'optimization': optimization,}
super().__init__(d=d, optimization=optimization, seed=seed)
if d > self.MAXDIM:
`... |
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