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Documentation updated
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fjosw
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9 changed files with 744 additions and 670 deletions
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@ -175,7 +175,7 @@
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<span class="k">def</span> <span class="nf">least_squares</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="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="sd">"""Performs a non-linear fit to y = func(x).</span>
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<span class="sd"> Arguments:</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> x : list</span>
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<span class="sd"> list of floats.</span>
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|
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@ -203,22 +203,23 @@
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<span class="sd"> enough.</span>
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<span class="sd"> silent : bool, optional</span>
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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"> Keyword arguments</span>
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<span class="sd"> -----------------</span>
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<span class="sd"> initial_guess -- can provide an initial guess for the input parameters. Relevant for</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"> method -- 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 -- If true, a plot which displays fit, data and residuals is generated (default False).</span>
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<span class="sd"> qqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).</span>
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<span class="sd"> expected_chisquare -- 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"> method : str</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"> """</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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@ -370,6 +371,8 @@
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<span class="k">def</span> <span class="nf">total_least_squares</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">silent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="sd">"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> x : list</span>
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<span class="sd"> list of Obs, or a tuple of lists of Obs</span>
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<span class="sd"> y : list</span>
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|
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@ -392,15 +395,14 @@
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<span class="sd"> silent : bool, optional</span>
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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"> Based on the orthogonal distance regression module of scipy</span>
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<span class="sd"> Keyword arguments</span>
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<span class="sd"> -----------------</span>
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<span class="sd"> initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear</span>
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<span class="sd"> fits with many parameters.</span>
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<span class="sd"> expected_chisquare -- 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"> initial_guess : list</span>
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<span class="sd"> can provide an initial guess for the input parameters. Relevant for non-linear</span>
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<span class="sd"> fits with many parameters.</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"> """</span>
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<span class="n">output</span> <span class="o">=</span> <span class="n">Fit_result</span><span class="p">()</span>
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@ -1044,7 +1046,7 @@ also accesible via indices.</li>
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<div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">least_squares</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="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="sd">"""Performs a non-linear fit to y = func(x).</span>
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<span class="sd"> Arguments:</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> x : list</span>
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<span class="sd"> list of floats.</span>
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|
@ -1072,22 +1074,23 @@ also accesible via indices.</li>
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<span class="sd"> enough.</span>
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<span class="sd"> silent : bool, optional</span>
|
||||
<span class="sd"> If true all output to the console is omitted (default False).</span>
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||||
|
||||
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<span class="sd"> Keyword arguments</span>
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<span class="sd"> -----------------</span>
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<span class="sd"> initial_guess -- can provide an initial guess for the input parameters. Relevant for</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"> method -- can be used to choose an alternative method for the minimization of chisquare.</span>
|
||||
<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>
|
||||
<span class="sd"> Reliable alternatives are migrad, Powell and Nelder-Mead.</span>
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||||
<span class="sd"> resplot -- If true, a plot which displays fit, data and residuals is generated (default False).</span>
|
||||
<span class="sd"> qqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).</span>
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||||
<span class="sd"> expected_chisquare -- If true prints the expected chisquare which is</span>
|
||||
<span class="sd"> corrected by effects caused by correlated input data.</span>
|
||||
<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"> method : str</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"> """</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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|
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@ -1099,51 +1102,53 @@ also accesible via indices.</li>
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<div class="docstring"><p>Performs a non-linear fit to y = func(x).</p>
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<h2 id="arguments">Arguments:</h2>
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<h6 id="parameters">Parameters</h6>
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<p>x : list
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list of floats.
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y : list
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list of Obs.
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func : object
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fit function, has to be of the form</p>
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<ul>
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<li><strong>x</strong> (list):
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list of floats.</li>
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<li><strong>y</strong> (list):
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list of Obs.</li>
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<li><p><strong>func</strong> (object):
|
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fit function, has to be of the form</p>
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<pre><code>def func(a, x):
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return a[0] + a[1] * x + a[2] * anp.sinh(x)
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<p>def func(a, x):
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return a[0] + a[1] * x + a[2] * anp.sinh(x)</p>
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For multiple x values func can be of the form
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<p>For multiple x values func can be of the form</p>
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|
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def func(a, x):
|
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(x1, x2) = x
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return a[0] * x1 ** 2 + a[1] * x2
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<p>def func(a, x):
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(x1, x2) = x
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return a[0] * x1 ** 2 + a[1] * x2</p>
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|
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It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
|
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will not work
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</code></pre>
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<p>priors : 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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0.548(23), 500(40) or 0.5(0.4)
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It is important for the subsequent error estimation that the e_tag for the gamma method is large
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enough.
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silent : bool, optional
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If true all output to the console is omitted (default False).</p>
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|
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<h6 id="keyword-arguments">Keyword arguments</h6>
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|
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<p>initial_guess -- can provide an initial guess for the input parameters. Relevant for
|
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non-linear fits with many parameters.
|
||||
method -- can be used to choose an alternative method for the minimization of chisquare.
|
||||
The possible methods are the ones which can be used for scipy.optimize.minimize and
|
||||
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
|
||||
Reliable alternatives are migrad, Powell and Nelder-Mead.
|
||||
resplot -- If true, a plot which displays fit, data and residuals is generated (default False).
|
||||
qqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).
|
||||
expected_chisquare -- If true prints the expected chisquare which is
|
||||
corrected by effects caused by correlated input data.
|
||||
This can take a while as the full correlation matrix
|
||||
has to be calculated (default False).</p>
|
||||
<p>It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
|
||||
will not work</p></li>
|
||||
<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
|
||||
Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
|
||||
0.548(23), 500(40) or 0.5(0.4)
|
||||
It is important for the subsequent error estimation that the e_tag for the gamma method is large
|
||||
enough.</li>
|
||||
<li><strong>silent</strong> (bool, optional):
|
||||
If true all output to the console is omitted (default False).</li>
|
||||
<li><strong>initial_guess</strong> (list):
|
||||
can provide an initial guess for the input parameters. Relevant for
|
||||
non-linear fits with many parameters.</li>
|
||||
<li><strong>method</strong> (str):
|
||||
can be used to choose an alternative method for the minimization of chisquare.
|
||||
The possible methods are the ones which can be used for scipy.optimize.minimize and
|
||||
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
|
||||
Reliable alternatives are migrad, Powell and Nelder-Mead.</li>
|
||||
<li><strong>resplot</strong> (bool):
|
||||
If true, a plot which displays fit, data and residuals is generated (default False).</li>
|
||||
<li><strong>qqplot</strong> (bool):
|
||||
If true, a quantile-quantile plot of the fit result is generated (default False).</li>
|
||||
<li><strong>expected_chisquare</strong> (bool):
|
||||
If true prints the expected chisquare which is
|
||||
corrected by effects caused by correlated input data.
|
||||
This can take a while as the full correlation matrix
|
||||
has to be calculated (default False).</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
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|
||||
|
|
@ -1201,6 +1206,8 @@ expected_chisquare -- If true prints the expected chisquare which is
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<div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">total_least_squares</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">silent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.</span>
|
||||
|
||||
<span class="sd"> Parameters</span>
|
||||
<span class="sd"> ----------</span>
|
||||
<span class="sd"> x : list</span>
|
||||
<span class="sd"> list of Obs, or a tuple of lists of Obs</span>
|
||||
<span class="sd"> y : list</span>
|
||||
|
|
@ -1223,15 +1230,14 @@ expected_chisquare -- If true prints the expected chisquare which is
|
|||
<span class="sd"> silent : bool, optional</span>
|
||||
<span class="sd"> If true all output to the console is omitted (default False).</span>
|
||||
<span class="sd"> Based on the orthogonal distance regression module of scipy</span>
|
||||
|
||||
<span class="sd"> Keyword arguments</span>
|
||||
<span class="sd"> -----------------</span>
|
||||
<span class="sd"> initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear</span>
|
||||
<span class="sd"> fits with many parameters.</span>
|
||||
<span class="sd"> expected_chisquare -- If true prints the expected chisquare which is</span>
|
||||
<span class="sd"> corrected by effects caused by correlated input data.</span>
|
||||
<span class="sd"> This can take a while as the full correlation matrix</span>
|
||||
<span class="sd"> has to be calculated (default False).</span>
|
||||
<span class="sd"> initial_guess : list</span>
|
||||
<span class="sd"> can provide an initial guess for the input parameters. Relevant for non-linear</span>
|
||||
<span class="sd"> fits with many parameters.</span>
|
||||
<span class="sd"> expected_chisquare : bool</span>
|
||||
<span class="sd"> If true prints the expected chisquare which is</span>
|
||||
<span class="sd"> corrected by effects caused by correlated input data.</span>
|
||||
<span class="sd"> This can take a while as the full correlation matrix</span>
|
||||
<span class="sd"> has to be calculated (default False).</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">Fit_result</span><span class="p">()</span>
|
||||
|
|
@ -1366,39 +1372,40 @@ expected_chisquare -- If true prints the expected chisquare which is
|
|||
|
||||
<div class="docstring"><p>Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.</p>
|
||||
|
||||
<p>x : list
|
||||
list of Obs, or a tuple of lists of Obs
|
||||
y : list
|
||||
list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
|
||||
func : object
|
||||
func has to be of the form</p>
|
||||
<h6 id="parameters">Parameters</h6>
|
||||
|
||||
<pre><code>def func(a, x):
|
||||
y = a[0] + a[1] * x + a[2] * anp.sinh(x)
|
||||
return y
|
||||
<ul>
|
||||
<li><strong>x</strong> (list):
|
||||
list of Obs, or a tuple of lists of Obs</li>
|
||||
<li><strong>y</strong> (list):
|
||||
list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.</li>
|
||||
<li><p><strong>func</strong> (object):
|
||||
func has to be of the form</p>
|
||||
|
||||
For multiple x values func can be of the form
|
||||
<p>def func(a, x):
|
||||
y = a[0] + a[1] * x + a[2] * anp.sinh(x)
|
||||
return y</p>
|
||||
|
||||
def func(a, x):
|
||||
(x1, x2) = x
|
||||
return a[0] * x1 ** 2 + a[1] * x2
|
||||
<p>For multiple x values func can be of the form</p>
|
||||
|
||||
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
|
||||
will not work.
|
||||
</code></pre>
|
||||
<p>def func(a, x):
|
||||
(x1, x2) = x
|
||||
return a[0] * x1 ** 2 + a[1] * x2</p>
|
||||
|
||||
<p>silent : bool, optional
|
||||
If true all output to the console is omitted (default False).
|
||||
Based on the orthogonal distance regression module of scipy</p>
|
||||
|
||||
<h6 id="keyword-arguments">Keyword arguments</h6>
|
||||
|
||||
<p>initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear
|
||||
fits with many parameters.
|
||||
expected_chisquare -- If true prints the expected chisquare which is
|
||||
corrected by effects caused by correlated input data.
|
||||
This can take a while as the full correlation matrix
|
||||
has to be calculated (default False).</p>
|
||||
<p>It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
|
||||
will not work.</p></li>
|
||||
<li><strong>silent</strong> (bool, optional):
|
||||
If true all output to the console is omitted (default False).</li>
|
||||
<li><strong>Based on the orthogonal distance regression module of scipy</strong></li>
|
||||
<li><strong>initial_guess</strong> (list):
|
||||
can provide an initial guess for the input parameters. Relevant for non-linear
|
||||
fits with many parameters.</li>
|
||||
<li><strong>expected_chisquare</strong> (bool):
|
||||
If true prints the expected chisquare which is
|
||||
corrected by effects caused by correlated input data.
|
||||
This can take a while as the full correlation matrix
|
||||
has to be calculated (default False).</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
|
||||
|
|
|
|||
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