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/dports/math/py-matplotlib/matplotlib-3.4.3/examples/statistics/
H A Dviolinplot.py39 bw_method='silverman')
43 showextrema=True, showmedians=True, bw_method=0.5)
47 showextrema=True, showmedians=True, bw_method=0.5,
53 quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
62 bw_method='silverman')
67 bw_method=0.5)
73 bw_method=0.5)
78 quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
/dports/science/py-scipy/scipy-1.7.1/scipy/stats/
H A Dkde.py190 def __init__(self, dataset, bw_method=None, weights=None): argument
206 self.set_bandwidth(bw_method=bw_method)
494 def set_bandwidth(self, bw_method=None): argument
537 if bw_method is None:
539 elif bw_method == 'scott':
541 elif bw_method == 'silverman':
543 elif np.isscalar(bw_method) and not isinstance(bw_method, str):
545 self.covariance_factor = lambda: bw_method
546 elif callable(bw_method):
547 self._bw_method = bw_method
/dports/math/py-matplotlib2/matplotlib-2.2.4/examples/statistics/
H A Dviolinplot.py40 bw_method='silverman')
44 showextrema=True, showmedians=True, bw_method=0.5)
53 bw_method='silverman')
58 bw_method=0.5)
/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/mpl_examples/statistics/
H A Dviolinplot.py40 bw_method='silverman')
44 showextrema=True, showmedians=True, bw_method=0.5)
53 bw_method='silverman')
58 bw_method=0.5)
/dports/comms/nanovna-saver/nanovna-saver-0.3.8/NanoVNASaver/Hardware/
H A DVNA.py59 self.bw_method = "ttrftech"
119 self.bw_method = "dislord"
125 if self.bw_method == "dislord":
136 if self.bw_method == "dislord":
139 if self.bw_method == "ttrftech" and result:
/dports/science/py-scipy/scipy-1.7.1/doc/source/tutorial/stats/plots/
H A Dkde_plot4.py21 kde2 = stats.gaussian_kde(x2, bw_method='silverman')
22 kde3 = stats.gaussian_kde(x2, bw_method=partial(my_kde_bandwidth, fac=0.2))
23 kde4 = stats.gaussian_kde(x2, bw_method=partial(my_kde_bandwidth, fac=0.5))
H A Dkde_plot2.py8 kde2 = stats.gaussian_kde(x1, bw_method='silverman')
19 kde3 = stats.gaussian_kde(x1, bw_method=my_kde_bandwidth)
H A Dkde_plot3.py11 kde2 = stats.gaussian_kde(x1, bw_method='silverman')
30 kde4 = stats.gaussian_kde(x2, bw_method='silverman')
/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/
H A Dtest_kdeoth.py163 gkde2 = stats.gaussian_kde(xn, bw_method=scotts_factor)
165 gkde3 = stats.gaussian_kde(xn, bw_method=gkde.factor)
174 assert_raises(ValueError, stats.gaussian_kde, xn, bw_method='wrongstring')
189 gkde2 = stats.gaussian_kde(xn, bw_method=scotts_factor)
191 gkde3 = stats.gaussian_kde(xn, bw_method=gkde.factor)
200 assert_raises(ValueError, stats.gaussian_kde, xn, bw_method='wrongstring')
283 kde.set_bandwidth(bw_method=0.5)
284 kde.set_bandwidth(bw_method='scott')
305 kde2 = stats.gaussian_kde(x1, bw_method='silverman')
342 k = stats.kde.gaussian_kde(dataset, bw_method=bw, weights=weights)
/dports/math/py-pandas/pandas-1.2.5/pandas/plotting/_matplotlib/
H A Dhist.py120 def __init__(self, data, bw_method=None, ind=None, **kwargs): argument
122 self.bw_method = bw_method
154 bw_method=None, argument
163 gkde = gaussian_kde(y, bw_method=bw_method)
170 kwds["bw_method"] = self.bw_method
/dports/math/py-matplotlib/matplotlib-3.4.3/lib/matplotlib/
H A Dmlab.py887 def __init__(self, dataset, bw_method=None): argument
894 if bw_method is None:
896 elif cbook._str_equal(bw_method, 'scott'):
898 elif cbook._str_equal(bw_method, 'silverman'):
900 elif isinstance(bw_method, Number):
902 self.covariance_factor = lambda: bw_method
903 elif callable(bw_method):
904 self._bw_method = bw_method
H A Dpyplot.py3247 quantiles=None, points=100, bw_method=None, *, data=None): argument
3252 bw_method=bw_method,
/dports/math/py-seaborn/seaborn-0.11.0/seaborn/
H A D_statistics.py40 bw_method=None, argument
72 self.bw_method = bw_method
130 fit_kws = {"bw_method": self.bw_method}
H A Ddistributions.py1610 bw_method="scott", bw_adjust=1, log_scale=None, argument
1660 bw_method = bw
1714 bw_method=bw_method,
/dports/math/py-pandas/pandas-1.2.5/pandas/plotting/
H A D_core.py1298 def kde(self, bw_method=None, ind=None, **kwargs): argument
1404 return self(kind="kde", bw_method=bw_method, ind=ind, **kwargs)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/nonparametric/
H A Dkde.py153 self.bw_method = bw
155 self.bw_method = "user-given"
/dports/math/py-seaborn/seaborn-0.11.0/seaborn/tests/
H A Dtest_statistics.py58 kde = KDE(cut=cut, bw_method=bw_scale, gridsize=1000)
99 kde1 = KDE(bw_method=.2)
100 kde2 = KDE(bw_method=2)
H A Dtest_distributions.py341 kdeplot(data=long_df, x="x", bw_method="silverman")
617 kdeplot(data=long_df, x="x", bw_method=0.2, legend=False)
618 kdeplot(data=long_df, x="x", bw_method=1.0, legend=False)
619 kdeplot(data=long_df, x="x", bw_method=3.0, legend=False)
/dports/math/py-matplotlib/matplotlib-3.4.3/lib/matplotlib/tests/
H A Dtest_mlab.py902 gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
952 mlab.GaussianKDE([], bw_method=5)
960 kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
971 kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
980 kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
990 mlab.GaussianKDE(data, bw_method="invalid")
/dports/math/py-pandas/pandas-1.2.5/pandas/tests/plotting/
H A Dtest_series.py618 _check_plot_works(self.ts.plot.kde, bw_method="scott", ind=20)
619 _check_plot_works(self.ts.plot.kde, bw_method=None, ind=20)
620 _check_plot_works(self.ts.plot.kde, bw_method=None, ind=np.int_(20))
621 _check_plot_works(self.ts.plot.kde, bw_method=0.5, ind=sample_points)
622 _check_plot_works(self.ts.plot.density, bw_method=0.5, ind=sample_points)
624 ax = self.ts.plot.kde(logy=True, bw_method=0.5, ind=sample_points, ax=ax)
/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/matplotlib/
H A Dmlab.py3685 def __init__(self, dataset, bw_method=None): argument
3691 isString = isinstance(bw_method, six.string_types)
3693 if bw_method is None:
3695 elif (isString and bw_method == 'scott'):
3697 elif (isString and bw_method == 'silverman'):
3699 elif (np.isscalar(bw_method) and not isString):
3701 self.covariance_factor = lambda: bw_method
3702 elif callable(bw_method):
3703 self._bw_method = bw_method
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/graphics/
H A Dboxplots.py178 kde = gaussian_kde(pos_data, bw_method=bw_factor)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/tests/
H A Dtest_neighbors_tree.py228 gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/matplotlib/tests/
H A Dtest_mlab.py2275 gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
2330 mlab.GaussianKDE([], bw_method=5)
2339 kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
2351 kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
2361 kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
2371 mlab.GaussianKDE(data, bw_method="invalid")
/dports/science/py-scipy/scipy-1.7.1/doc/source/tutorial/
H A Dstats.rst939 >>> kde2 = stats.gaussian_kde(x1, bw_method='silverman')
964 >>> kde3 = stats.gaussian_kde(x1, bw_method=my_kde_bandwidth)
1002 >>> kde2 = stats.gaussian_kde(x2, bw_method='silverman')
1003 >>> kde3 = stats.gaussian_kde(x2, bw_method=partial(my_kde_bandwidth, fac=0.2))
1004 >>> kde4 = stats.gaussian_kde(x2, bw_method=partial(my_kde_bandwidth, fac=0.5))

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