/dports/math/py-numpy/numpy-1.20.3/numpy/lib/tests/ |
H A D | test_nanfunctions.py | 760 np.nanpercentile(ndat, 30) 768 res = np.nanpercentile(mat, 70, axis=axis, out=None, 797 res = np.nanpercentile(nan_mat, 42, axis=1, out=resout) 812 res = np.nanpercentile(_ndat, 28, axis=1) 816 res = np.nanpercentile(_ndat, (28, 98), axis=1) 831 assert_(np.isnan(np.nanpercentile(np.nan, 60))) 853 assert_equal(np.nanpercentile(0., 100), 0.) 855 r = np.nanpercentile(a, 50, axis=0) 903 np.nanpercentile(ar, q=50, axis=0)) 905 np.nanpercentile(ar, q=50, axis=1)) [all …]
|
/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/numpy/ |
H A D | fallback.py | 153 nanpercentile = onp.nanpercentile variable
|
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/numpy/ |
H A D | fallback.py | 153 nanpercentile = onp.nanpercentile variable
|
/dports/math/py-numpy/numpy-1.20.3/doc/source/reference/ |
H A D | routines.statistics.rst | 19 nanpercentile
|
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/distributions/copula/ |
H A D | copulas.py | 446 min_ = np.nanpercentile(data, 5) 447 max_ = np.nanpercentile(data, 95)
|
/dports/math/py-numpy/numpy-1.20.3/benchmarks/benchmarks/ |
H A D | bench_lib.py | 115 np.nanpercentile(self.arr, q=50)
|
/dports/math/py-optuna/optuna-2.10.0/optuna/pruners/ |
H A D | _percentile.py | 48 np.nanpercentile(
|
/dports/math/py-arviz/arviz-0.11.4/arviz/plots/backends/matplotlib/ |
H A D | violinplot.py | 79 per = np.nanpercentile(val, [25, 75, 50])
|
/dports/math/py-arviz/arviz-0.11.4/arviz/plots/backends/bokeh/ |
H A D | violinplot.py | 75 per = np.nanpercentile(val, [25, 75, 50])
|
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/ |
H A D | descriptivestats.py | 69 def nanpercentile(arr, axis=0): function 70 return np.nanpercentile(arr, PERCENTILES, axis=axis) 92 "percentiles": nanpercentile,
|
/dports/devel/py-fbprophet/fbprophet-0.5/fbprophet/ |
H A D | forecaster.py | 1304 data[component + '_lower'] = np.nanpercentile( 1307 data[component + '_upper'] = np.nanpercentile( 1385 series['{}_lower'.format(key)] = np.nanpercentile( 1387 series['{}_upper'.format(key)] = np.nanpercentile(
|
/dports/math/py-numpy/numpy-1.20.3/numpy/lib/ |
H A D | __init__.pyi | 177 nanpercentile: Any
|
H A D | nanfunctions.py | 1127 def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, function
|
/dports/math/py-jax/jax-0.2.9/jax/numpy/ |
H A D | __init__.py | 50 nanmedian, nanpercentile, nanquantile,
|
/dports/devel/py-xarray/xarray-0.20.1/xarray/plot/ |
H A D | utils.py | 696 vmax = np.nanpercentile(darray, 100 - ROBUST_PERCENTILE) 698 vmin = np.nanpercentile(darray, ROBUST_PERCENTILE)
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/preprocessing/ |
H A D | _data.py | 1522 quantiles.append(np.nanpercentile(column_data, self.quantile_range)) 2493 self.quantiles_.append(np.nanpercentile(col, references)) 2538 self.quantiles_.append(np.nanpercentile(column_data, references))
|
/dports/math/py-seaborn/seaborn-0.11.0/seaborn/ |
H A D | matrix.py | 202 vmin = np.nanpercentile(calc_data, 2) 207 vmax = np.nanpercentile(calc_data, 98)
|
/dports/science/py-nilearn/nilearn-0.8.1/nilearn/ |
H A D | signal.py | 399 var_thr = np.nanpercentile(var, 100. - percentile)
|
/dports/math/py-pandas/pandas-1.2.5/pandas/core/ |
H A D | nanops.py | 1618 def nanpercentile( function 1649 result = nanpercentile(
|
/dports/math/py-numpy/numpy-1.20.3/doc/source/release/ |
H A D | 1.9.0-notes.rst | 13 * Addition of `nanmedian` and `nanpercentile` rounds out the nanfunction set. 308 The ``np.nanmedian`` and ``np.nanpercentile`` functions behave like
|
H A D | 1.15.0-notes.rst | 41 * `numpy.nanquantile` function, an interface to ``nanpercentile`` without 260 Like ``np.percentile`` and ``np.nanpercentile``, but takes quantiles in [0, 1]
|
/dports/astro/py-astropy/astropy-5.0/astropy/units/tests/ |
H A D | test_quantity_non_ufuncs.py | 977 self.check(np.nanpercentile, 0.5) 978 o = np.nanpercentile(self.q, 0.5 * u.one) 979 expected = np.nanpercentile(self.q.value, 50) * u.m
|
/dports/math/py-pandas/pandas-1.2.5/pandas/core/internals/ |
H A D | blocks.py | 83 from pandas.core.nanops import nanpercentile 1601 result = nanpercentile(
|
/dports/devel/py-numba/numba-0.51.2/numba/tests/ |
H A D | test_array_reductions.py | 126 return np.nanpercentile(arr, q)
|
/dports/astro/py-astropy/astropy-5.0/astropy/units/quantity_helper/ |
H A D | function_helpers.py | 526 @function_helper(helps={np.percentile, np.nanpercentile})
|