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4 changed files with 62 additions and 43 deletions
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@ -187,7 +187,7 @@
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<span class="sd"> ```</span>
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<span class="sd"> It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation</span>
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<span class="sd"> will not work</span>
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<span class="sd"> will not work.</span>
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<span class="sd"> priors : list, optional</span>
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<span class="sd"> priors has to be a list with an entry for every parameter in the fit. The entries can either be</span>
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<span class="sd"> Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like</span>
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@ -196,24 +196,25 @@
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<span class="sd"> If true all output to the console is omitted (default False).</span>
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<span class="sd"> initial_guess : list</span>
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<span class="sd"> can provide an initial guess for the input parameters. Relevant for</span>
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<span class="sd"> non-linear fits with many parameters.</span>
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<span class="sd"> non-linear fits with many parameters.</span>
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<span class="sd"> method : str, optional</span>
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<span class="sd"> can be used to choose an alternative method for the minimization of chisquare.</span>
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<span class="sd"> The possible methods are the ones which can be used for scipy.optimize.minimize and</span>
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<span class="sd"> migrad of iminuit. If no method is specified, Levenberg-Marquard is used.</span>
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<span class="sd"> Reliable alternatives are migrad, Powell and Nelder-Mead.</span>
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<span class="sd"> resplot : bool</span>
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<span class="sd"> If true, a plot which displays fit, data and residuals is generated (default False).</span>
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<span class="sd"> qqplot : bool</span>
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<span class="sd"> If true, a quantile-quantile plot of the fit result is generated (default False).</span>
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<span class="sd"> expected_chisquare : bool</span>
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<span class="sd"> If true prints the expected chisquare which is</span>
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<span class="sd"> corrected by effects caused by correlated input data.</span>
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<span class="sd"> This can take a while as the full correlation matrix</span>
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<span class="sd"> has to be calculated (default False).</span>
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<span class="sd"> correlated_fit : bool</span>
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<span class="sd"> If true, use the full correlation matrix in the definition of the chisquare</span>
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<span class="sd"> (only works for prior==None and when no method is given, at the moment).</span>
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<span class="sd"> If True, use the full inverse covariance matrix in the definition of the chisquare cost function.</span>
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<span class="sd"> For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.</span>
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<span class="sd"> In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).</span>
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<span class="sd"> This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).</span>
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<span class="sd"> At the moment this option only works for `prior==None` and when no `method` is given.</span>
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<span class="sd"> expected_chisquare : bool</span>
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<span class="sd"> If True estimates the expected chisquare which is</span>
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<span class="sd"> corrected by effects caused by correlated input data (default False).</span>
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<span class="sd"> resplot : bool</span>
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<span class="sd"> If True, a plot which displays fit, data and residuals is generated (default False).</span>
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<span class="sd"> qqplot : bool</span>
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<span class="sd"> If True, a quantile-quantile plot of the fit result is generated (default False).</span>
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<span class="sd"> '''</span>
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<span class="k">if</span> <span class="n">priors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">_prior_fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">priors</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="n">silent</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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@ -972,7 +973,7 @@ also accessible via indices.</li>
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<span class="sd"> ```</span>
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<span class="sd"> It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation</span>
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<span class="sd"> will not work</span>
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<span class="sd"> will not work.</span>
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<span class="sd"> priors : list, optional</span>
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<span class="sd"> priors has to be a list with an entry for every parameter in the fit. The entries can either be</span>
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<span class="sd"> Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like</span>
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@ -981,24 +982,25 @@ also accessible via indices.</li>
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<span class="sd"> If true all output to the console is omitted (default False).</span>
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<span class="sd"> initial_guess : list</span>
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<span class="sd"> can provide an initial guess for the input parameters. Relevant for</span>
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<span class="sd"> non-linear fits with many parameters.</span>
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<span class="sd"> non-linear fits with many parameters.</span>
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<span class="sd"> method : str, optional</span>
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<span class="sd"> can be used to choose an alternative method for the minimization of chisquare.</span>
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<span class="sd"> The possible methods are the ones which can be used for scipy.optimize.minimize and</span>
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<span class="sd"> migrad of iminuit. If no method is specified, Levenberg-Marquard is used.</span>
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<span class="sd"> Reliable alternatives are migrad, Powell and Nelder-Mead.</span>
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<span class="sd"> resplot : bool</span>
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<span class="sd"> If true, a plot which displays fit, data and residuals is generated (default False).</span>
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<span class="sd"> qqplot : bool</span>
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<span class="sd"> If true, a quantile-quantile plot of the fit result is generated (default False).</span>
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<span class="sd"> expected_chisquare : bool</span>
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<span class="sd"> If true prints the expected chisquare which is</span>
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<span class="sd"> corrected by effects caused by correlated input data.</span>
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<span class="sd"> This can take a while as the full correlation matrix</span>
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<span class="sd"> has to be calculated (default False).</span>
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<span class="sd"> correlated_fit : bool</span>
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<span class="sd"> If true, use the full correlation matrix in the definition of the chisquare</span>
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<span class="sd"> (only works for prior==None and when no method is given, at the moment).</span>
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<span class="sd"> If True, use the full inverse covariance matrix in the definition of the chisquare cost function.</span>
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<span class="sd"> For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.</span>
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<span class="sd"> In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).</span>
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<span class="sd"> This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).</span>
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<span class="sd"> At the moment this option only works for `prior==None` and when no `method` is given.</span>
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<span class="sd"> expected_chisquare : bool</span>
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<span class="sd"> If True estimates the expected chisquare which is</span>
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<span class="sd"> corrected by effects caused by correlated input data (default False).</span>
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<span class="sd"> resplot : bool</span>
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<span class="sd"> If True, a plot which displays fit, data and residuals is generated (default False).</span>
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<span class="sd"> qqplot : bool</span>
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<span class="sd"> If True, a quantile-quantile plot of the fit result is generated (default False).</span>
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<span class="sd"> '''</span>
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<span class="k">if</span> <span class="n">priors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">_prior_fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">priors</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="n">silent</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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@ -1034,7 +1036,7 @@ fit function, has to be of the form</p>
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</code></pre></div>
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<p>It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
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will not work</p></li>
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will not work.</p></li>
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<li><strong>priors</strong> (list, optional):
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priors has to be a list with an entry for every parameter in the fit. The entries can either be
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Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
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@ -1043,24 +1045,25 @@ Obs (e.g. results from a previous fit) or strings containing a value and an erro
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If true all output to the console is omitted (default False).</li>
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<li><strong>initial_guess</strong> (list):
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can provide an initial guess for the input parameters. Relevant for
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non-linear fits with many parameters.</li>
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non-linear fits with many parameters.</li>
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<li><strong>method</strong> (str, optional):
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can be used to choose an alternative method for the minimization of chisquare.
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The possible methods are the ones which can be used for scipy.optimize.minimize and
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migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
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Reliable alternatives are migrad, Powell and Nelder-Mead.</li>
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<li><strong>resplot</strong> (bool):
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If true, a plot which displays fit, data and residuals is generated (default False).</li>
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<li><strong>qqplot</strong> (bool):
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If true, a quantile-quantile plot of the fit result is generated (default False).</li>
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<li><strong>expected_chisquare</strong> (bool):
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If true prints the expected chisquare which is
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corrected by effects caused by correlated input data.
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This can take a while as the full correlation matrix
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has to be calculated (default False).</li>
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<li><strong>correlated_fit</strong> (bool):
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If true, use the full correlation matrix in the definition of the chisquare
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(only works for prior==None and when no method is given, at the moment).</li>
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If True, use the full inverse covariance matrix in the definition of the chisquare cost function.
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For details about how the covariance matrix is estimated see <code><a href="obs.html#covariance">pyerrors.obs.covariance</a></code>.
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In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).
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This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
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At the moment this option only works for <code>prior==None</code> and when no <code>method</code> is given.</li>
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<li><strong>expected_chisquare</strong> (bool):
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If True estimates the expected chisquare which is
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corrected by effects caused by correlated input data (default False).</li>
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<li><strong>resplot</strong> (bool):
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If True, a plot which displays fit, data and residuals is generated (default False).</li>
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<li><strong>qqplot</strong> (bool):
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If True, a quantile-quantile plot of the fit result is generated (default False).</li>
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</ul>
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</div>
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