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Documentation updated
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@ -3,7 +3,7 @@
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta name="generator" content="pdoc 13.0.1"/>
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<meta name="generator" content="pdoc 13.1.0"/>
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<title>pyerrors.fits API documentation</title>
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<style>/*! * Bootstrap Reboot v5.0.0 (https://getbootstrap.com/) * Copyright 2011-2021 The Bootstrap Authors * Copyright 2011-2021 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) */*,::after,::before{box-sizing:border-box}@media (prefers-reduced-motion:no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:transparent}hr{margin:1rem 0;color:inherit;background-color:currentColor;border:0;opacity:.25}hr:not([size]){height:1px}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media (min-width:1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + .9vw)}@media (min-width:1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + .6vw)}@media (min-width:1200px){h3{font-size:1.75rem}}h4{font-size:calc(1.275rem + .3vw)}@media (min-width:1200px){h4{font-size:1.5rem}}h5{font-size:1.25rem}h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[data-bs-original-title],abbr[title]{-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;-webkit-text-decoration-skip-ink:none;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}b,strong{font-weight:bolder}small{font-size:.875em}mark{padding:.2em;background-color:#fcf8e3}sub,sup{position:relative;font-size:.75em;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#0d6efd;text-decoration:underline}a:hover{color:#0a58ca}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}code,kbd,pre,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em;direction:ltr;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:.875em}pre code{font-size:inherit;color:inherit;word-break:normal}code{font-size:.875em;color:#d63384;word-wrap:break-word}a>code{color:inherit}kbd{padding:.2rem .4rem;font-size:.875em;color:#fff;background-color:#212529;border-radius:.2rem}kbd kbd{padding:0;font-size:1em;font-weight:700}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:#6c757d;text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}tbody,td,tfoot,th,thead,tr{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]::-webkit-calendar-picker-indicator{display:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled),button:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + .3vw);line-height:inherit}@media (min-width:1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-text,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none!important}</style>
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@ -320,7 +320,7 @@
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</span><span id="L-212"><a href="#L-212"><span class="linenos">212</span></a> <span class="c1"># number of fit parameters</span>
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</span><span id="L-213"><a href="#L-213"><span class="linenos">213</span></a> <span class="n">n_parms_ls</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="L-214"><a href="#L-214"><span class="linenos">214</span></a> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">key_ls</span><span class="p">:</span>
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</span><span id="L-215"><a href="#L-215"><span class="linenos">215</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">funcd</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
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</span><span id="L-215"><a href="#L-215"><span class="linenos">215</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="n">funcd</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
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</span><span id="L-216"><a href="#L-216"><span class="linenos">216</span></a> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'func (key='</span> <span class="o">+</span> <span class="n">key</span> <span class="o">+</span> <span class="s1">') is not a function.'</span><span class="p">)</span>
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</span><span id="L-217"><a href="#L-217"><span class="linenos">217</span></a> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xd</span><span class="p">[</span><span class="n">key</span><span class="p">])</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">yd</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
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</span><span id="L-218"><a href="#L-218"><span class="linenos">218</span></a> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'x and y input (key='</span> <span class="o">+</span> <span class="n">key</span> <span class="o">+</span> <span class="s1">') do not have the same length'</span><span class="p">)</span>
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@ -505,7 +505,7 @@
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</span><span id="L-397"><a href="#L-397"><span class="linenos">397</span></a> <span class="k">try</span><span class="p">:</span>
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</span><span id="L-398"><a href="#L-398"><span class="linenos">398</span></a> <span class="n">hess</span> <span class="o">=</span> <span class="n">hessian</span><span class="p">(</span><span class="n">chisqfunc</span><span class="p">)(</span><span class="n">fitp</span><span class="p">)</span>
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</span><span id="L-399"><a href="#L-399"><span class="linenos">399</span></a> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
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</span><span id="L-400"><a href="#L-400"><span class="linenos">400</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="bp">None</span>
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</span><span id="L-400"><a href="#L-400"><span class="linenos">400</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
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</span><span id="L-401"><a href="#L-401"><span class="linenos">401</span></a>
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</span><span id="L-402"><a href="#L-402"><span class="linenos">402</span></a> <span class="n">len_y</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_f</span><span class="p">)</span>
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</span><span id="L-403"><a href="#L-403"><span class="linenos">403</span></a>
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@ -610,7 +610,7 @@
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</span><span id="L-502"><a href="#L-502"><span class="linenos">502</span></a> <span class="n">jacobian</span> <span class="o">=</span> <span class="n">auto_jacobian</span>
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</span><span id="L-503"><a href="#L-503"><span class="linenos">503</span></a> <span class="n">hessian</span> <span class="o">=</span> <span class="n">auto_hessian</span>
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</span><span id="L-504"><a href="#L-504"><span class="linenos">504</span></a>
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</span><span id="L-505"><a href="#L-505"><span class="linenos">505</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
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</span><span id="L-505"><a href="#L-505"><span class="linenos">505</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
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</span><span id="L-506"><a href="#L-506"><span class="linenos">506</span></a> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'func has to be a function.'</span><span class="p">)</span>
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</span><span id="L-507"><a href="#L-507"><span class="linenos">507</span></a>
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</span><span id="L-508"><a href="#L-508"><span class="linenos">508</span></a> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">42</span><span class="p">):</span>
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@ -706,7 +706,7 @@
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</span><span id="L-598"><a href="#L-598"><span class="linenos">598</span></a> <span class="k">try</span><span class="p">:</span>
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</span><span id="L-599"><a href="#L-599"><span class="linenos">599</span></a> <span class="n">hess</span> <span class="o">=</span> <span class="n">hessian</span><span class="p">(</span><span class="n">odr_chisquare</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">fitp</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">xplus</span><span class="o">.</span><span class="n">ravel</span><span class="p">())))</span>
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</span><span id="L-600"><a href="#L-600"><span class="linenos">600</span></a> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
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</span><span id="L-601"><a href="#L-601"><span class="linenos">601</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="bp">None</span>
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</span><span id="L-601"><a href="#L-601"><span class="linenos">601</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
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</span><span id="L-602"><a href="#L-602"><span class="linenos">602</span></a>
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</span><span id="L-603"><a href="#L-603"><span class="linenos">603</span></a> <span class="k">def</span> <span class="nf">odr_chisquare_compact_x</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
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</span><span id="L-604"><a href="#L-604"><span class="linenos">604</span></a> <span class="n">model</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">d</span><span class="p">[:</span><span class="n">n_parms</span><span class="p">],</span> <span class="n">d</span><span class="p">[</span><span class="n">n_parms</span><span class="p">:</span><span class="n">n_parms</span> <span class="o">+</span> <span class="n">m</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_shape</span><span class="p">))</span>
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@ -1225,7 +1225,7 @@ Hotelling t-squared p-value for correlated fits.</li>
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</span><span id="least_squares-213"><a href="#least_squares-213"><span class="linenos">213</span></a> <span class="c1"># number of fit parameters</span>
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</span><span id="least_squares-214"><a href="#least_squares-214"><span class="linenos">214</span></a> <span class="n">n_parms_ls</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="least_squares-215"><a href="#least_squares-215"><span class="linenos">215</span></a> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">key_ls</span><span class="p">:</span>
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</span><span id="least_squares-216"><a href="#least_squares-216"><span class="linenos">216</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">funcd</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
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</span><span id="least_squares-216"><a href="#least_squares-216"><span class="linenos">216</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="n">funcd</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
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</span><span id="least_squares-217"><a href="#least_squares-217"><span class="linenos">217</span></a> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'func (key='</span> <span class="o">+</span> <span class="n">key</span> <span class="o">+</span> <span class="s1">') is not a function.'</span><span class="p">)</span>
|
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</span><span id="least_squares-218"><a href="#least_squares-218"><span class="linenos">218</span></a> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xd</span><span class="p">[</span><span class="n">key</span><span class="p">])</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">yd</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
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</span><span id="least_squares-219"><a href="#least_squares-219"><span class="linenos">219</span></a> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'x and y input (key='</span> <span class="o">+</span> <span class="n">key</span> <span class="o">+</span> <span class="s1">') do not have the same length'</span><span class="p">)</span>
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@ -1410,7 +1410,7 @@ Hotelling t-squared p-value for correlated fits.</li>
|
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</span><span id="least_squares-398"><a href="#least_squares-398"><span class="linenos">398</span></a> <span class="k">try</span><span class="p">:</span>
|
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</span><span id="least_squares-399"><a href="#least_squares-399"><span class="linenos">399</span></a> <span class="n">hess</span> <span class="o">=</span> <span class="n">hessian</span><span class="p">(</span><span class="n">chisqfunc</span><span class="p">)(</span><span class="n">fitp</span><span class="p">)</span>
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||||
</span><span id="least_squares-400"><a href="#least_squares-400"><span class="linenos">400</span></a> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
|
||||
</span><span id="least_squares-401"><a href="#least_squares-401"><span class="linenos">401</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="bp">None</span>
|
||||
</span><span id="least_squares-401"><a href="#least_squares-401"><span class="linenos">401</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
|
||||
</span><span id="least_squares-402"><a href="#least_squares-402"><span class="linenos">402</span></a>
|
||||
</span><span id="least_squares-403"><a href="#least_squares-403"><span class="linenos">403</span></a> <span class="n">len_y</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_f</span><span class="p">)</span>
|
||||
</span><span id="least_squares-404"><a href="#least_squares-404"><span class="linenos">404</span></a>
|
||||
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@ -1628,7 +1628,7 @@ Parameters and information on the fitted result.</li>
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|||
</span><span id="total_least_squares-503"><a href="#total_least_squares-503"><span class="linenos">503</span></a> <span class="n">jacobian</span> <span class="o">=</span> <span class="n">auto_jacobian</span>
|
||||
</span><span id="total_least_squares-504"><a href="#total_least_squares-504"><span class="linenos">504</span></a> <span class="n">hessian</span> <span class="o">=</span> <span class="n">auto_hessian</span>
|
||||
</span><span id="total_least_squares-505"><a href="#total_least_squares-505"><span class="linenos">505</span></a>
|
||||
</span><span id="total_least_squares-506"><a href="#total_least_squares-506"><span class="linenos">506</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
|
||||
</span><span id="total_least_squares-506"><a href="#total_least_squares-506"><span class="linenos">506</span></a> <span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
|
||||
</span><span id="total_least_squares-507"><a href="#total_least_squares-507"><span class="linenos">507</span></a> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'func has to be a function.'</span><span class="p">)</span>
|
||||
</span><span id="total_least_squares-508"><a href="#total_least_squares-508"><span class="linenos">508</span></a>
|
||||
</span><span id="total_least_squares-509"><a href="#total_least_squares-509"><span class="linenos">509</span></a> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">42</span><span class="p">):</span>
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@ -1724,7 +1724,7 @@ Parameters and information on the fitted result.</li>
|
|||
</span><span id="total_least_squares-599"><a href="#total_least_squares-599"><span class="linenos">599</span></a> <span class="k">try</span><span class="p">:</span>
|
||||
</span><span id="total_least_squares-600"><a href="#total_least_squares-600"><span class="linenos">600</span></a> <span class="n">hess</span> <span class="o">=</span> <span class="n">hessian</span><span class="p">(</span><span class="n">odr_chisquare</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">fitp</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">xplus</span><span class="o">.</span><span class="n">ravel</span><span class="p">())))</span>
|
||||
</span><span id="total_least_squares-601"><a href="#total_least_squares-601"><span class="linenos">601</span></a> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
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</span><span id="total_least_squares-602"><a href="#total_least_squares-602"><span class="linenos">602</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="bp">None</span>
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||||
</span><span id="total_least_squares-602"><a href="#total_least_squares-602"><span class="linenos">602</span></a> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details."</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
|
||||
</span><span id="total_least_squares-603"><a href="#total_least_squares-603"><span class="linenos">603</span></a>
|
||||
</span><span id="total_least_squares-604"><a href="#total_least_squares-604"><span class="linenos">604</span></a> <span class="k">def</span> <span class="nf">odr_chisquare_compact_x</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
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</span><span id="total_least_squares-605"><a href="#total_least_squares-605"><span class="linenos">605</span></a> <span class="n">model</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">d</span><span class="p">[:</span><span class="n">n_parms</span><span class="p">],</span> <span class="n">d</span><span class="p">[</span><span class="n">n_parms</span><span class="p">:</span><span class="n">n_parms</span> <span class="o">+</span> <span class="n">m</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_shape</span><span class="p">))</span>
|
||||
|
|
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Add table
Add a link
Reference in a new issue