DOC: Replace cumulative density with cumulative distribution (#9593)
DOC: stats: replace "cumulative density" with "cumulative distribution"
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Albert Thomas
Evgeni Burovski
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24f1c26394
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@ -199,7 +199,7 @@ Raw Statistical Functions
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bdtri -- Inverse function to `bdtr` with respect to `p`.
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bdtrik -- Inverse function to `bdtr` with respect to `k`.
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bdtrin -- Inverse function to `bdtr` with respect to `n`.
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btdtr -- Cumulative density function of the beta distribution.
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btdtr -- Cumulative distribution function of the beta distribution.
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btdtri -- The `p`-th quantile of the beta distribution.
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btdtria -- Inverse of `btdtr` with respect to `a`.
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btdtrib -- btdtria(a, p, x)
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@ -207,7 +207,7 @@ Raw Statistical Functions
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fdtrc -- F survival function.
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fdtri -- The `p`-th quantile of the F-distribution.
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fdtridfd -- Inverse to `fdtr` vs dfd
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gdtr -- Gamma distribution cumulative density function.
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gdtr -- Gamma distribution cumulative distribution function.
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gdtrc -- Gamma distribution survival function.
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gdtria -- Inverse of `gdtr` vs a.
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gdtrib -- Inverse of `gdtr` vs b.
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@ -232,7 +232,7 @@ Raw Statistical Functions
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pdtrc -- Poisson survival function
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pdtri -- Inverse to `pdtr` vs m
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pdtrik -- Inverse to `pdtr` vs k
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stdtr -- Student t distribution cumulative density function
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stdtr -- Student t distribution cumulative distribution function
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stdtridf -- Inverse of `stdtr` vs df
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stdtrit -- Inverse of `stdtr` vs `t`
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chdtr -- Chi square cumulative distribution function
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@ -599,7 +599,7 @@ add_newdoc("scipy.special", "btdtria",
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See Also
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--------
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btdtr : Cumulative density function of the beta distribution.
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btdtr : Cumulative distribution function of the beta distribution.
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btdtri : Inverse with respect to `x`.
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btdtrib : Inverse with respect to `b`.
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@ -652,7 +652,7 @@ add_newdoc("scipy.special", "btdtrib",
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See Also
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--------
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btdtr : Cumulative density function of the beta distribution.
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btdtr : Cumulative distribution function of the beta distribution.
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btdtri : Inverse with respect to `x`.
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btdtria : Inverse with respect to `a`.
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@ -925,7 +925,7 @@ add_newdoc("scipy.special", "btdtr",
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r"""
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btdtr(a, b, x)
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Cumulative density function of the beta distribution.
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Cumulative distribution function of the beta distribution.
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Returns the integral from zero to `x` of the beta probability density
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function,
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@ -947,8 +947,8 @@ add_newdoc("scipy.special", "btdtr",
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Returns
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-------
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I : ndarray
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Cumulative density function of the beta distribution with parameters
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`a` and `b` at `x`.
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Cumulative distribution function of the beta distribution with
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parameters `a` and `b` at `x`.
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See Also
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--------
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@ -2431,7 +2431,7 @@ add_newdoc("scipy.special", "fdtr",
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F cumulative distribution function.
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Returns the value of the cumulative density function of the
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Returns the value of the cumulative distribution function of the
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F-distribution, also known as Snedecor's F-distribution or the
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Fisher-Snedecor distribution.
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@ -2794,7 +2794,7 @@ add_newdoc("scipy.special", "gdtr",
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r"""
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gdtr(a, b, x)
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Gamma distribution cumulative density function.
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Gamma distribution cumulative distribution function.
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Returns the integral from zero to `x` of the gamma probability density
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function,
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@ -4489,7 +4489,7 @@ add_newdoc("scipy.special", "kolmogorov",
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Show the probability of a gap at least as big as 0, 0.5 and 1.0.
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>>> from scipy.special import kolmogorov
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>>> from scipy.stats import kstwobign
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>>> from scipy.stats import kstwobign
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>>> kolmogorov([0, 0.5, 1.0])
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array([ 1. , 0.96394524, 0.26999967])
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@ -6654,7 +6654,7 @@ add_newdoc("scipy.special", "stdtr",
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"""
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stdtr(df, t)
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Student t distribution cumulative density function
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Student t distribution cumulative distribution function
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Returns the integral from minus infinity to t of the Student t
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distribution with df > 0 degrees of freedom::
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@ -4002,8 +4002,10 @@ class levy_stable_gen(rv_continuous):
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data_out[data_mask] = np.array([levy_stable._cdf_single_value_zolotarev(_x, _alpha, _beta)
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for _x, _alpha, _beta in data_subset]).reshape(len(data_subset), 1)
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else:
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warnings.warn('Cumulative density calculations experimental for FFT method.' +
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' Use zolatarev method instead.', RuntimeWarning)
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warnings.warn('FFT method is considered experimental for ' +
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'cumulative distribution function ' +
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'evaluations. Use Zolotarev’s method instead).',
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RuntimeWarning)
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_alpha, _beta = pair
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_x = data_subset[:,(0,)]
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@ -1598,7 +1598,9 @@ class TestLevyStable(object):
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stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
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subdata = data[filter_func(data)] if filter_func is not None else data
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with suppress_warnings() as sup:
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sup.record(RuntimeWarning, "Cumulative density calculations experimental for FFT method.*")
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sup.record(RuntimeWarning, 'FFT method is considered ' +
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'experimental for cumulative distribution ' +
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'function evaluations.*')
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p = stats.levy_stable.cdf(subdata['x'], subdata['alpha'], subdata['beta'], scale=1, loc=0)
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subdata2 = rec_append_fields(subdata, 'calc', p)
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failures = subdata2[(np.abs(p-subdata['p']) >= 1.5*10.**(-decimal_places)) | np.isnan(p)]
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