/dports/math/py-matplotlib/matplotlib-3.4.3/examples/statistics/ |
H A D | violinplot.py | 39 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)
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/dports/science/py-scipy/scipy-1.7.1/scipy/stats/ |
H A D | kde.py | 190 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
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/dports/math/py-matplotlib2/matplotlib-2.2.4/examples/statistics/ |
H A D | violinplot.py | 40 bw_method='silverman') 44 showextrema=True, showmedians=True, bw_method=0.5) 53 bw_method='silverman') 58 bw_method=0.5)
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/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/mpl_examples/statistics/ |
H A D | violinplot.py | 40 bw_method='silverman') 44 showextrema=True, showmedians=True, bw_method=0.5) 53 bw_method='silverman') 58 bw_method=0.5)
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/dports/comms/nanovna-saver/nanovna-saver-0.3.8/NanoVNASaver/Hardware/ |
H A D | VNA.py | 59 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:
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/dports/science/py-scipy/scipy-1.7.1/doc/source/tutorial/stats/plots/ |
H A D | kde_plot4.py | 21 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))
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H A D | kde_plot2.py | 8 kde2 = stats.gaussian_kde(x1, bw_method='silverman') 19 kde3 = stats.gaussian_kde(x1, bw_method=my_kde_bandwidth)
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H A D | kde_plot3.py | 11 kde2 = stats.gaussian_kde(x1, bw_method='silverman') 30 kde4 = stats.gaussian_kde(x2, bw_method='silverman')
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/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/ |
H A D | test_kdeoth.py | 163 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)
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/dports/math/py-pandas/pandas-1.2.5/pandas/plotting/_matplotlib/ |
H A D | hist.py | 120 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
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/dports/math/py-matplotlib/matplotlib-3.4.3/lib/matplotlib/ |
H A D | mlab.py | 887 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
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H A D | pyplot.py | 3247 quantiles=None, points=100, bw_method=None, *, data=None): argument 3252 bw_method=bw_method,
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/dports/math/py-seaborn/seaborn-0.11.0/seaborn/ |
H A D | _statistics.py | 40 bw_method=None, argument 72 self.bw_method = bw_method 130 fit_kws = {"bw_method": self.bw_method}
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H A D | distributions.py | 1610 bw_method="scott", bw_adjust=1, log_scale=None, argument 1660 bw_method = bw 1714 bw_method=bw_method,
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/dports/math/py-pandas/pandas-1.2.5/pandas/plotting/ |
H A D | _core.py | 1298 def kde(self, bw_method=None, ind=None, **kwargs): argument 1404 return self(kind="kde", bw_method=bw_method, ind=ind, **kwargs)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/nonparametric/ |
H A D | kde.py | 153 self.bw_method = bw 155 self.bw_method = "user-given"
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/dports/math/py-seaborn/seaborn-0.11.0/seaborn/tests/ |
H A D | test_statistics.py | 58 kde = KDE(cut=cut, bw_method=bw_scale, gridsize=1000) 99 kde1 = KDE(bw_method=.2) 100 kde2 = KDE(bw_method=2)
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H A D | test_distributions.py | 341 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)
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/dports/math/py-matplotlib/matplotlib-3.4.3/lib/matplotlib/tests/ |
H A D | test_mlab.py | 902 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")
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/dports/math/py-pandas/pandas-1.2.5/pandas/tests/plotting/ |
H A D | test_series.py | 618 _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)
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/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/matplotlib/ |
H A D | mlab.py | 3685 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
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/graphics/ |
H A D | boxplots.py | 178 kde = gaussian_kde(pos_data, bw_method=bw_factor)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/tests/ |
H A D | test_neighbors_tree.py | 228 gkde = gaussian_kde(x_in, bw_method=h / np.std(x_in))
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/dports/math/py-matplotlib2/matplotlib-2.2.4/lib/matplotlib/tests/ |
H A D | test_mlab.py | 2275 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")
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/dports/science/py-scipy/scipy-1.7.1/doc/source/tutorial/ |
H A D | stats.rst | 939 >>> 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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