id stringlengths 30 32 | content stringlengths 139 2.8k |
|---|---|
codereview_new_python_data_13360 | def _entropy(self, kappa):
The default limits of integration are endpoints of the interval
of width ``2*pi`` centered at `loc` (e.g. ``[-pi, pi]`` when
``loc=0``).\n\n""")
- def expect(self, func=None, args=(), loc=0, scale=1, lb=-np.pi, ub=np.pi,
conditional=False, **kwds... |
codereview_new_python_data_13361 | def expect(self, func=None, args=(), loc=0, scale=1, lb=None, ub=None,
>>> from scipy.stats import vonmises
>>> res = vonmises(loc=2, kappa=1).expect(lambda x: np.exp(1j*x),
... complex_func=True)
(-0.18576377217422957+0.40590124735052263j)
... |
codereview_new_python_data_13362 |
PearsonRResult
FitResult
OddsRatioResult
- Ttest_Result
"""
__all__ = ['BinomTestResult', 'RelativeRiskResult', 'TukeyHSDResult',
'PearsonRResult', 'FitResult', 'OddsRatioResult',
- 'Ttest_Result']
from ._binomtest import BinomTestResult
from ._odds_ratio import OddsRati... |
codereview_new_python_data_13363 |
PearsonRResult
FitResult
OddsRatioResult
- Ttest_Result
"""
__all__ = ['BinomTestResult', 'RelativeRiskResult', 'TukeyHSDResult',
'PearsonRResult', 'FitResult', 'OddsRatioResult',
- 'Ttest_Result']
from ._binomtest import BinomTestResult
from ._odds_ratio import OddsRati... |
codereview_new_python_data_13364 |
PearsonRResult
FitResult
OddsRatioResult
- Ttest_Result
"""
__all__ = ['BinomTestResult', 'RelativeRiskResult', 'TukeyHSDResult',
'PearsonRResult', 'FitResult', 'OddsRatioResult',
- 'Ttest_Result']
from ._binomtest import BinomTestResult
from ._odds_ratio import OddsRati... |
codereview_new_python_data_13365 | class rv_histogram(rv_continuous):
The second containing the (n+1) bin boundaries
In particular the return value np.histogram is accepted
- .. versionadded:: 1.10.0
-
Notes
-----
There are no additional shape parameters except for the loc and scale.
```suggestion
```
This was i... |
codereview_new_python_data_13366 | def shgo(func, bounds, args=(), constraints=None, n=None, iters=1,
... {'type': 'eq', 'fun': h1})
>>> bounds = [(0, 1.0),]*4
>>> res = shgo(f, bounds, iters=3, constraints=cons)
message: Optimization terminated successfully.
success: True
fun: 29.894378159142136
```suggest... |
codereview_new_python_data_13367 | def least_squares(
is applied), a sparse matrix (csr_matrix preferred for performance) or
a `scipy.sparse.linalg.LinearOperator`.
bounds : 2-tuple of array_like or `Bounds`, optional
- There are two ways to specify bounds for methods 'trf' and 'dogbox':
1. Instance of `Boun... |
codereview_new_python_data_13368 | def test_bounds_shape(self):
method=self.method)
assert_allclose(res.x, [0.0, 0.5], atol=1e-5)
- def test_bound_instances(self):
res = least_squares(fun_trivial, 0.5, bounds=Bounds())
assert_allclose(res.x, 0.0, atol=1e-4)
```suggestion
def test_bounds_insta... |
codereview_new_python_data_13369 | class InterpolatedUnivariateSpline(UnivariateSpline):
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
- Degree of the smoothing spline. Must be 1 <= `k` <= 5. Default is
- `k` = 3, a cubic spline.
... |
codereview_new_python_data_13370 | class InterpolatedUnivariateSpline(UnivariateSpline):
2-sequence specifying the boundary of the approximation interval. If
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
- Degree of the smoothing spline. Must be 1 <= `k` <= 5. Default is
- `k` = 3, a cubic spline.
... |
codereview_new_python_data_13371 | class InterpolatedUnivariateSpline(UnivariateSpline):
None (default), ``bbox=[x[0], x[-1]]``.
k : int, optional
Degree of the smoothing spline. Must be ``1 <= k <= 5``. Default is
- ``k = 3`, a cubic spline.
ext : int or str, optional
Controls the extrapolation mode for ele... |
codereview_new_python_data_13372 | def ppf(self, q, *args, **kwds):
cond = cond0 & cond1
output = np.full(shape(cond), fill_value=self.badvalue, dtype='d')
# output type 'd' to handle nin and inf
- place(output, (q == 0), _a - 1 + loc)
place(output, cond2, _b + loc)
if np.any(cond):
gooda... |
codereview_new_python_data_13373 | def design_matrix(cls, x, t, k, extrapolate=False):
else:
int_dtype = np.int64
# Preallocate indptr and indices
- indices = np.zeros(n * (k + 1), dtype=int_dtype)
indptr = np.arange(0, (n + 1) * (k + 1), k + 1, dtype=int_dtype)
data, indices, indptr = _bspl._ma... |
codereview_new_python_data_13374 | def ttest_1samp(a, popmean, axis=0, nan_policy='propagate',
df = n - 1
mean = np.mean(a, axis)
- d = mean - popmean[..., 0] # popmean is an array because of decorator
v = _var(a, axis, ddof=1)
denom = np.sqrt(v / n)
Hmm the behavior was buggy before (see https://github.com/scipy/scipy/pull... |
codereview_new_python_data_13375 | def circstd(samples, high=2*pi, low=0, axis=None, nan_policy='propagate', *,
... 0.104, -0.136, -0.867, 0.012, 0.105])
>>> circstd_1 = circstd(samples_1)
>>> circstd_2 = circstd(samples_2)
- >>> fig, (left, right) = plt.subplots(ncols=2)
Plot the samples.
```suggesti... |
codereview_new_python_data_13376 | class of similar problems can be solved together.
Examples
--------
>>> import numpy as np
- >>> from scipy import optimize
>>> import matplotlib.pyplot as plt
>>> def f(x):
... return (x**3 - 1) # only one real root at x = 1
This plt import should be next to the np import
cl... |
codereview_new_python_data_13377 | def circmean(samples, high=2*pi, low=0, axis=None, nan_policy='propagate'):
Plot and compare the results of *circmean* and the regular *mean*.
- >>> plt.plot(np.cos(np.linspace(0, 2*np.pi, 500),
- ... np.sin(np.linspace(0, 2*np.pi, 500),
... c='k')
>>> plt.scatter(np.cos(angl... |
codereview_new_python_data_13378 | def test_infeasible_prob_16609():
_msg_iter = "Iteration limit reached. (HiGHS Status 14:"
-@pytest.mark.skipif(np.intp(0).itemsize < 8, reason="Unhandled 32-bit GCC FP bug")
@pytest.mark.slow
@pytest.mark.parametrize(["options", "msg"], [({"time_limit": 1}, _msg_time),
... |
codereview_new_python_data_13379 | def test_infeasible_prob_16609():
@pytest.mark.skipif(np.intp(0).itemsize < 8,
reason="Unhandled 32-bit GCC FP bug")
@pytest.mark.slow
-@pytest.mark.parametrize(["options", "msg"], [({"time_limit": 1}, _msg_time),
- ({"node_limit": 10}, _msg_iter)])
... |
codereview_new_python_data_13380 | def test_infeasible_prob_16609():
@pytest.mark.skipif(np.intp(0).itemsize < 8,
reason="Unhandled 32-bit GCC FP bug")
@pytest.mark.slow
@pytest.mark.parametrize(["options", "msg"], [({"time_limit": 10}, _msg_time),
({"node_limit": 1}, _msg_iter)])
... |
codereview_new_python_data_13381 | def design_matrix(cls, x, t, k, extrapolate=False):
# of knots.
if len(t) < 2 * k + 2:
raise ValueError(f"Length t is not enough for k={k}.")
- if not np.isfinite(t).all():
- raise ValueError("Knots should not have nans or infs.")
if extrapolate == 'periodic'... |
codereview_new_python_data_13382 | def skew_d(d): # skewness in terms of delta
# MoM won't provide a good guess. Get out early.
s = stats.skew(data)
s_max = skew_d(1)
- if np.abs(s) >= s_max and method == "mle":
return super().fit(data, *args, **kwds)
# If method is method of moments, we don't ... |
codereview_new_python_data_13383 | def skew_d(d): # skewness in terms of delta
# MoM won't provide a good guess. Get out early.
s = stats.skew(data)
s_max = skew_d(1)
- if np.abs(s) >= s_max and method == "mle":
return super().fit(data, *args, **kwds)
# If method is method of moments, we don't ... |
codereview_new_python_data_13384 | def skew_d(d): # skewness in terms of delta
# MoM won't provide a good guess. Get out early.
s = stats.skew(data)
s_max = skew_d(1)
- if np.abs(s) >= s_max and method == "mle":
return super().fit(data, *args, **kwds)
# If method is method of moments, we don't ... |
codereview_new_python_data_13385 | def test_incompatible_x_y(self, k):
def test_broken_x(self, k):
x = [0, 1, 1, 2, 3, 4] # duplicates
y = [0, 1, 2, 3, 4, 5]
- with assert_raises(ValueError, match="Expect x to not have duplicates"):
make_interp_spline(x, y, k=k)
x = [0, 2, 1, 3, 4, 5] # un... |
codereview_new_python_data_13386 | def dblquad(func, a, b, gfun, hfun, args=(), epsabs=1.49e-8, epsrel=1.49e-8):
:math:`\\iint^{\\infty}_{-\\infty} e^{-(x^{2} + y^{2})} \\,dy\\,dx`:
>>> import numpy as np
- >>> from scipy import integrate
>>> f = lambda x, y: np.exp(-(x ** 2 + y ** 2))
>>> integrate.dblquad(f, -np.inf, np.inf, ... |
codereview_new_python_data_13387 | def dblquad(func, a, b, gfun, hfun, args=(), epsabs=1.49e-8, epsrel=1.49e-8):
:math:`\\iint^{\\infty}_{-\\infty} e^{-(x^{2} + y^{2})} \\,dy\\,dx`:
>>> import numpy as np
- >>> from scipy import integrate
>>> f = lambda x, y: np.exp(-(x ** 2 + y ** 2))
>>> integrate.dblquad(f, -np.inf, np.inf, ... |
codereview_new_python_data_13388 | def statistic(i, axis=-1, data=data_iv, unpaired_statistic=statistic):
message = ("Either `bootstrap_result.bootstrap_distribution.size` or "
"`n_resamples` must be positive.")
- if n_resamples_int == 0 and (not bootstrap_result or
- not bootstrap_result.bootstrap_distribution.size... |
codereview_new_python_data_13389 | def test_concatenate():
def test_concatenate_wrong_type():
with pytest.raises(TypeError, match='Rotation objects only'):
Rotation.concatenate([Rotation.identity(), 1, None])
-
-
def test_len_and_bool():
rotation_single = Rotation([0, 0, 0, 1])
rotation_multi = Rotation([[0, 0,... |
codereview_new_python_data_13390 | def _compute_covariance(self):
self._data_covariance[::-1, ::-1]).T[::-1, ::-1]
self.covariance = self._data_covariance * self.factor**2
- self.cho_cov = self._data_cho_cov * self.factor
self.log_det = 2*np.log(np.diag(self.cho_cov
* ... |
codereview_new_python_data_13391 | def test_gaussian_kde_subclassing():
assert_array_almost_equal_nulp(ys, y2, nulp=10)
# subclass 3 was removed because we have no obligation to maintain support
- # for manual invocation of private methods
# subclass 4
kde4 = _kde_subclass4(x1)
```suggestion
# for user invocation of pri... |
codereview_new_python_data_13392 | def _compute_covariance(self):
@property
def inv_cov(self):
self.factor = self.covariance_factor()
self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,
bias=False, aweights=self.weights))
```suggestion
def inv_cov(self):
... |
codereview_new_python_data_13393 | def test_svd_LM_ones_matrix(self, shape, dtype):
# Check some generic properties of svd.
if (self.solver == 'arpack' and dtype is complex):
- pytest.skip("ARPACK has additional restriction for complex dtype")
_check_svds(A, k, U, s, VH, check_usvh_A=True, check_svd=False)
... |
codereview_new_python_data_13394 | def test_sg_filter_valid_window_length_3d():
savgol_filter(x, window_length=29, polyorder=3, mode='interp')
- with pytest.raises(ValueError, match='window_length must be less than'):
# window_length is more than x.shape[-1].
savgol_filter(x, window_length=31, polyorder=3, mode='interp')
... |
codereview_new_python_data_13395 | def anderson_ksamp(samples, midrank=True):
distribution can be rejected at the 5% level because the returned
test value is greater than the critical value for 5% (1.961) but
not at the 2.5% level. The interpolation gives an approximate
- p-value of 5.0%.
>>> res = stats.anderson_ksamp([rng.nor... |
codereview_new_python_data_13396 | def test_pvalue_literature(self):
assert_allclose(pvalue, 1/1001)
@pytest.mark.slow
- @pytest.mark.parametrize("is_twosamp", [True, False])
- def test_alias(self, is_twosamp):
- x = np.arange(100)
- y = np.arange(100)
- res = stats.multiscale_graphcorr(x, y, is_twosamp=is_two... |
codereview_new_python_data_13397 | def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False,
The model function, f(x, ...). It must take the independent
variable as the first argument and the parameters to fit as
separate remaining arguments.
- xdata : array_like or object
The independent variable ... |
codereview_new_python_data_14677 | def testConvertTimeValueFromSparkToH2OAndBack(spark, hc, timeZone, sparkType):
df = spark.createDataFrame(data, ['strings']).select(col('strings').cast(sparkType).alias('time'))
hf = hc.asH2OFrame(df)
- hfResultString = hf.__unicode__()
- hfParsedItems = hfResultString.split('\n')[1:5]
hfParsedI... |
codereview_new_python_data_14678 | def testConvertTimeValueFromSparkToH2OAndBack(spark, hc, timeZone, sparkType):
df = spark.createDataFrame(data, ['strings']).select(col('strings').cast(sparkType).alias('time'))
hf = hc.asH2OFrame(df)
- hfResultString = hf.__unicode__()
- hfParsedItems = hfResultString.split('\n')[1:5]
hfParsedI... |
codereview_new_python_data_14680 | def __add_url_to_classloader(gateway, url):
jvm = gateway.jvm
loader = jvm.Thread.currentThread().getContextClassLoader()
logger = Initializer.__get_logger(jvm)
while loader:
try:
- urlClassLoaderClass = jvm.py4j.reflection.ReflectionUtil.classForName("ja... |
codereview_new_python_data_14681 | def __add_url_to_classloader(gateway, url):
jvm = gateway.jvm
loader = jvm.Thread.currentThread().getContextClassLoader()
logger = Initializer.__get_logger(jvm)
while loader:
try:
- urlClassLoaderClass = jvm.py4j.reflection.ReflectionUtil.classForName("ja... |
codereview_new_python_data_14682 | def __add_url_to_classloader(gateway, url):
jvm = gateway.jvm
loader = jvm.Thread.currentThread().getContextClassLoader()
logger = Initializer.__get_logger(jvm)
while loader:
try:
- urlClassLoaderClass = jvm.py4j.reflection.ReflectionUtil.classForName("ja... |
codereview_new_python_data_14685 | def test_h2o_mojo_pipeline_contributions(spark):
mojo = H2OMOJOPipelineModel.createFromMojo(mojo_path, settings)
df = spark.read.csv(data_path, header=True, inferSchema=True)
- predictions_and_contributions = mojo.transform(df).select("prediction.*")
feature_columns = 1
prediction_columns = ... |
codereview_new_python_data_14743 | def test_is_start_event(self):
self.assertEqual(17, len(starts))
def test_is_end_event(self):
- starts = [e for e in LOG if log_utils.is_end_event(e)]
- self.assertEqual(17, len(starts))
def test_filter_and_sort_log_entries(self):
filtered = log_utils.filter_and_sort_log_e... |
codereview_new_python_data_14744 |
def build_windows_from_events(backpressure_events, window_width_in_hours=1):
"""
- Generate histogram-friendly time windows with counts of backpressuring durations within each window.
:param backpressure_events: a list of BackpressureEvents to be broken up into time windows
:param window_width_i... |
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