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Documentation updated
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fjosw
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3 changed files with 269 additions and 53 deletions
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@ -316,7 +316,7 @@
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<div class="attr function">
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<span class="def">def</span>
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<span class="name">errorbar</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="n">x</span>,</span><span class="param"> <span class="n">y</span>,</span><span class="param"> <span class="n">axes</span><span class="o">=<</span><span class="n">module</span> <span class="s1">'matplotlib.pyplot'</span> <span class="kn">from</span><span class="w"> </span><span class="s1">'/opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/matplotlib/pyplot.py'</span><span class="o">></span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">):</span></span>
|
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<span class="name">errorbar</span><span class="signature pdoc-code multiline">(<span class="param"> <span class="n">x</span>,</span><span class="param"> <span class="n">y</span>,</span><span class="param"> <span class="n">axes</span><span class="o">=<</span><span class="n">module</span> <span class="s1">'matplotlib.pyplot'</span> <span class="kn">from</span><span class="w"> </span><span class="s1">'/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/matplotlib/pyplot.py'</span><span class="o">></span>,</span><span class="param"> <span class="o">**</span><span class="n">kwargs</span></span><span class="return-annotation">):</span></span>
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<label class="view-source-button" for="errorbar-view-source"><span>View Source</span></label>
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|
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|||
|
|
@ -338,9 +338,7 @@ Eq. 5.12.1. <a href="https://dlmf.nist.gov/5.12">https://dlmf.nist.gov/5.12</a>&
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</span></pre></div>
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<div class="docstring"><p>betainc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
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<p>betainc(a, b, x, out=None)</p>
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<div class="docstring"><p>betainc(a, b, x, out=None)</p>
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<p>Regularized incomplete beta function.</p>
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|
|
@ -374,10 +372,14 @@ Optional output array for the function values</li>
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<h6 id="see-also">See Also</h6>
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<p><code><a href="#beta">beta</a></code>: beta function<br />
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<code>betaincinv</code>: inverse of the regularized incomplete beta function<br />
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<code>betaincc</code>: complement of the regularized incomplete beta function<br />
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<code>scipy.stats.beta</code>: beta distribution </p>
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<p><code>beta()`
|
||||
beta`, `function`
|
||||
</code>betaincinv()<code>
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inverse</code>, <code>of</code>, <code>the</code>, <code>regularized</code>, <code>incomplete</code>, <code><a href="#beta">beta</a></code>, <code>function</code><br />
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<code>betaincc()`
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complement`, `of`, `the`, `regularized`, `incomplete`, `beta`, `function`
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||||
</code>scipy.stats.beta()<code>
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||||
beta</code>, <code>distribution</code> </p>
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||||
|
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<h6 id="notes">Notes</h6>
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|
|
@ -390,9 +392,40 @@ without the gamma terms. One can use the function <code><a href="#beta">beta</a
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function by multiplying the result of <code>betainc(a, b, x)</code> by
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<code>beta(a, b)</code>.</p>
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<p><code>betainc(a, b, x)</code> is treated as a two parameter family of functions
|
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of a single variable <code>x</code>, rather than as a function of three variables.
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This impacts only the limiting cases <code>a = 0</code>, <code>b = 0</code>, <code>a = inf</code>,
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<code>b = inf</code>.</p>
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<p>In general</p>
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<p>$$\lim_{(a, b) \rightarrow (a_0, b_0)} \mathrm{betainc}(a, b, x)$$</p>
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|
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<p>is treated as a pointwise limit in <code>x</code>. Thus for example,
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<code>betainc(0, b, 0)</code> equals <code>0</code> for <code>b > 0</code>, although it would be
|
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indeterminate when considering the simultaneous limit <code>(a, x) -> (0+, 0+)</code>.</p>
|
||||
|
||||
<p>This function wraps the <code>ibeta</code> routine from the
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Boost Math C++ library <sup class="footnote-ref" id="fnref-2"><a href="#fn-2">2</a></sup>.</p>
|
||||
|
||||
<p><code><a href="#betainc">betainc</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ⛔<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
|
@ -1050,9 +1083,7 @@ as</p>
|
|||
</span></pre></div>
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<div class="docstring"><p>gammaln(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
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|
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<p>gammaln(x, out=None)</p>
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<div class="docstring"><p>gammaln(x, out=None)</p>
|
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<p>Logarithm of the absolute value of the gamma function.</p>
|
||||
|
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|
|
@ -1080,8 +1111,10 @@ Optional output array for the function results</li>
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|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#gammasgn">gammasgn</a></code>: sign of the gamma function<br />
|
||||
<code>loggamma</code>: principal branch of the logarithm of the gamma function </p>
|
||||
<p><code>gammasgn()`
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sign`, `of`, `the`, `gamma`, `function`
|
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</code>loggamma()<code>
|
||||
principal</code>, <code>branch</code>, <code>of</code>, <code>the</code>, <code>logarithm</code>, <code>of</code>, <code>the</code>, <code><a href="#gamma">gamma</a></code>, <code>function</code> </p>
|
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|
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<h6 id="notes">Notes</h6>
|
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|
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|
|
@ -1095,6 +1128,24 @@ gammasgn(x) * gamma(x)</code>.</p>
|
|||
|
||||
<p>For complex-valued log-gamma, use <code>loggamma</code> instead of <code><a href="#gammaln">gammaln</a></code>.</p>
|
||||
|
||||
<p><code><a href="#gammaln">gammaln</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
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<p>.. [dlmf] NIST Digital Library of Mathematical Functions
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|
|
@ -1167,9 +1218,7 @@ gammasgn(x) * gamma(x)</code>.</p>
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|||
</span></pre></div>
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<div class="docstring"><p>gammainc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>gammainc(a, x, out=None)</p>
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||||
<div class="docstring"><p>gammainc(a, x, out=None)</p>
|
||||
|
||||
<p>Regularized lower incomplete gamma function.</p>
|
||||
|
||||
|
|
@ -1198,9 +1247,12 @@ Optional output array for the function values</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#gammaincc">gammaincc</a></code>: regularized upper incomplete gamma function<br />
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||||
<code>gammaincinv</code>: inverse of the regularized lower incomplete gamma function<br />
|
||||
<code>gammainccinv</code>: inverse of the regularized upper incomplete gamma function </p>
|
||||
<p><code>gammaincc()`
|
||||
regularized`, `upper`, `incomplete`, `gamma`, `function`
|
||||
</code>gammaincinv()<code>
|
||||
inverse</code>, <code>of</code>, <code>the</code>, <code>regularized</code>, <code>lower</code>, <code>incomplete</code>, <code><a href="#gamma">gamma</a></code>, <code>function</code><br />
|
||||
`<code>gammainccinv()</code>
|
||||
inverse<code>,</code>of<code>,</code>the<code>,</code>regularized<code>,</code>upper<code>,</code>incomplete<code>,</code>gamma<code>,</code>function` </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -1210,6 +1262,24 @@ incomplete gamma function.</p>
|
|||
|
||||
<p>The implementation largely follows that of [boost]_.</p>
|
||||
|
||||
<p><code><a href="#gammainc">gammainc</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
<p>.. [dlmf] NIST Digital Library of Mathematical functions
|
||||
|
|
@ -1276,9 +1346,7 @@ monotonically increases to 1.</p>
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>gammaincc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>gammaincc(a, x, out=None)</p>
|
||||
<div class="docstring"><p>gammaincc(a, x, out=None)</p>
|
||||
|
||||
<p>Regularized upper incomplete gamma function.</p>
|
||||
|
||||
|
|
@ -1307,9 +1375,12 @@ Optional output array for the function values</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#gammainc">gammainc</a></code>: regularized lower incomplete gamma function<br />
|
||||
<code>gammaincinv</code>: inverse of the regularized lower incomplete gamma function<br />
|
||||
<code>gammainccinv</code>: inverse of the regularized upper incomplete gamma function </p>
|
||||
<p><code>gammainc()`
|
||||
regularized`, `lower`, `incomplete`, `gamma`, `function`
|
||||
</code>gammaincinv()<code>
|
||||
inverse</code>, <code>of</code>, <code>the</code>, <code>regularized</code>, <code>lower</code>, <code>incomplete</code>, <code><a href="#gamma">gamma</a></code>, <code>function</code><br />
|
||||
`<code>gammainccinv()</code>
|
||||
inverse<code>,</code>of<code>,</code>the<code>,</code>regularized<code>,</code>upper<code>,</code>incomplete<code>,</code>gamma<code>,</code>function` </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -1319,6 +1390,24 @@ incomplete gamma function.</p>
|
|||
|
||||
<p>The implementation largely follows that of [boost]_.</p>
|
||||
|
||||
<p><code><a href="#gammaincc">gammaincc</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
<p>.. [dlmf] NIST Digital Library of Mathematical functions
|
||||
|
|
@ -2521,9 +2610,7 @@ To calculate the orders 0 and 1 for an 1D array:</p>
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>i0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>i0(x, out=None)</p>
|
||||
<div class="docstring"><p>i0(x, out=None)</p>
|
||||
|
||||
<p>Modified Bessel function of order 0.</p>
|
||||
|
||||
|
|
@ -2551,8 +2638,10 @@ Value of the modified Bessel function of order 0 at <code>x</code>.</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#iv">iv</a></code>: Modified Bessel function of any order<br />
|
||||
<code>i0e</code>: Exponentially scaled modified Bessel function of order 0 </p>
|
||||
<p><code>iv()`
|
||||
Modified`, `Bessel`, `function`, `of`, `any`, `order`
|
||||
</code>i0e()<code>
|
||||
Exponentially</code>, <code>scaled</code>, <code>modified</code>, <code>Bessel</code>, <code>function</code>, <code>of</code>, <code>order</code>, <code>0</code> </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -2561,6 +2650,24 @@ Chebyshev polynomial expansions are employed in each interval.</p>
|
|||
|
||||
<p>This function is a wrapper for the Cephes <sup class="footnote-ref" id="fnref-1"><a href="#fn-1">1</a></sup> routine <code><a href="#i0">i0</a></code>.</p>
|
||||
|
||||
<p><code><a href="#i0">i0</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
|
@ -2637,9 +2744,7 @@ Chebyshev polynomial expansions are employed in each interval.</p>
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>i1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>i1(x, out=None)</p>
|
||||
<div class="docstring"><p>i1(x, out=None)</p>
|
||||
|
||||
<p>Modified Bessel function of order 1.</p>
|
||||
|
||||
|
|
@ -2668,8 +2773,10 @@ Value of the modified Bessel function of order 1 at <code>x</code>.</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#iv">iv</a></code>: Modified Bessel function of the first kind<br />
|
||||
<code>i1e</code>: Exponentially scaled modified Bessel function of order 1 </p>
|
||||
<p><code>iv()`
|
||||
Modified`, `Bessel`, `function`, `of`, `the`, `first`, `kind`
|
||||
</code>i1e()<code>
|
||||
Exponentially</code>, <code>scaled</code>, <code>modified</code>, <code>Bessel</code>, <code>function</code>, <code>of</code>, <code>order</code>, <code>1</code> </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -2678,6 +2785,24 @@ Chebyshev polynomial expansions are employed in each interval.</p>
|
|||
|
||||
<p>This function is a wrapper for the Cephes <sup class="footnote-ref" id="fnref-1"><a href="#fn-1">1</a></sup> routine <code><a href="#i1">i1</a></code>.</p>
|
||||
|
||||
<p><code><a href="#i1">i1</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
|
@ -3097,9 +3222,7 @@ of a Complex Argument and Nonnegative Order",
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>erf(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>erf(z, out=None)</p>
|
||||
<div class="docstring"><p>erf(z, out=None)</p>
|
||||
|
||||
<p>Returns the error function of complex argument.</p>
|
||||
|
||||
|
|
@ -3123,13 +3246,32 @@ The values of the error function at the given points <code>x</code>.</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code>erfc,</code>, <code>erfinv,</code>, <code>erfcinv,</code>, <code>wofz,</code>, <code>erfcx,</code>, <code>erfi</code> </p>
|
||||
<p><code>erfc()`,`,</code>erfinv()<code>,</code>, <code>erfcinv()`,`,</code>wofz()<code>,</code>, <code>erfcx()`,`,</code>erfi()<code>
|
||||
..</code> </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
<p>The cumulative of the unit normal distribution is given by
|
||||
<code>Phi(z) = 1/2[1 + erf(z/sqrt(2))]</code>.</p>
|
||||
|
||||
<p><code><a href="#erf">erf</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
|
@ -3184,9 +3326,7 @@ The values of the error function at the given points <code>x</code>.</li>
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>erfc(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>erfc(x, out=None)</p>
|
||||
<div class="docstring"><p>erfc(x, out=None)</p>
|
||||
|
||||
<p>Complementary error function, <code>1 - erf(x)</code>.</p>
|
||||
|
||||
|
|
@ -3207,7 +3347,28 @@ Optional output array for the function results</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code>erf,</code>, <code>erfi,</code>, <code>erfcx,</code>, <code>dawsn,</code>, <code>wofz</code> </p>
|
||||
<p><code>erf()`,`,</code>erfi()<code>,</code>, <code>erfcx()`,`,</code>dawsn()<code>,</code>, `<code>wofz()</code>
|
||||
..` </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
<p><code><a href="#erfc">erfc</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="references">References</h6>
|
||||
|
||||
|
|
@ -3497,9 +3658,7 @@ The inverse of erfc of y, element-wise</li>
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>logit(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>logit(x, out=None)</p>
|
||||
<div class="docstring"><p>logit(x, out=None)</p>
|
||||
|
||||
<p>Logit ufunc for ndarrays.</p>
|
||||
|
||||
|
|
@ -3525,7 +3684,8 @@ are logit of the corresponding entry of x.</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#expit">expit</a></code> </p>
|
||||
<p>`<code><a href="#expit">expit()</a></code>
|
||||
..` </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -3535,6 +3695,24 @@ see <a href="https://docs.scipy.org/doc/numpy/reference/ufuncs.html">ufuncs </a>
|
|||
|
||||
<p><em>New in version 0.10.0.</em></p>
|
||||
|
||||
<p><code><a href="#logit">logit</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
||||
<div class="pdoc-code codehilite">
|
||||
|
|
@ -3604,9 +3782,7 @@ see <a href="https://docs.scipy.org/doc/numpy/reference/ufuncs.html">ufuncs </a>
|
|||
</span></pre></div>
|
||||
|
||||
|
||||
<div class="docstring"><p>expit(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])</p>
|
||||
|
||||
<p>expit(x, out=None)</p>
|
||||
<div class="docstring"><p>expit(x, out=None)</p>
|
||||
|
||||
<p>Expit (a.k.a. logistic sigmoid) ufunc for ndarrays.</p>
|
||||
|
||||
|
|
@ -3632,7 +3808,8 @@ are <code><a href="#expit">expit</a></code> of the corresponding entry of x.</li
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code><a href="#logit">logit</a></code> </p>
|
||||
<p>`<code><a href="#logit">logit()</a></code>
|
||||
..` </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -3642,6 +3819,24 @@ see <a href="https://docs.scipy.org/doc/numpy/reference/ufuncs.html">ufuncs </a>
|
|||
|
||||
<p><em>New in version 0.10.0.</em></p>
|
||||
|
||||
<p><code><a href="#expit">expit</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
||||
<div class="pdoc-code codehilite">
|
||||
|
|
@ -3761,7 +3956,10 @@ If <code>return_sign</code> is False, only one result is returned.</li>
|
|||
|
||||
<h6 id="see-also">See Also</h6>
|
||||
|
||||
<p><code>numpy.logaddexp,</code>, <code>numpy.logaddexp2</code> </p>
|
||||
<p><code>numpy.logaddexp`
|
||||
..`
|
||||
</code>numpy.logaddexp2<code>
|
||||
..</code> </p>
|
||||
|
||||
<h6 id="notes">Notes</h6>
|
||||
|
||||
|
|
@ -3773,6 +3971,24 @@ function, but may be less stable.</p>
|
|||
infinite number of \( z \) such that \( exp(z) = x \). The convention
|
||||
is to return the \( z \) whose imaginary part lies in \( (-pi, pi] \).</p>
|
||||
|
||||
<p><code><a href="#logsumexp">logsumexp</a></code> has experimental support for Python Array API Standard compatible
|
||||
backends in addition to NumPy. Please consider testing these features
|
||||
by setting an environment variable <code>SCIPY_ARRAY_API=1</code> and providing
|
||||
CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following
|
||||
combinations of backend and device (or other capability) are supported.</p>
|
||||
|
||||
<p>==================== ==================== ====================
|
||||
Library CPU GPU
|
||||
==================== ==================== ====================
|
||||
NumPy ✅ n/a<br />
|
||||
CuPy n/a ✅<br />
|
||||
PyTorch ✅ ✅<br />
|
||||
JAX ✅ ✅<br />
|
||||
Dask ✅ n/a<br />
|
||||
==================== ==================== ====================</p>
|
||||
|
||||
<p>See :ref:<code>dev-arrayapi</code> for more information.</p>
|
||||
|
||||
<h6 id="examples">Examples</h6>
|
||||
|
||||
<div class="pdoc-code codehilite">
|
||||
|
|
|
|||
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