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50  <h1>Source code for sas.invariant.invariant</h1><div class="highlight"><pre>
51<span class="c">#####################################################################</span>
52<span class="c">#This software was developed by the University of Tennessee as part of the</span>
53<span class="c">#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)</span>
54<span class="c">#project funded by the US National Science Foundation. </span>
55<span class="c">#See the license text in license.txt</span>
56<span class="c">#copyright 2010, University of Tennessee</span>
57<span class="c">######################################################################</span>
58
59<span class="sd">&quot;&quot;&quot;</span>
60<span class="sd">This module implements invariant and its related computations.</span>
61
62<span class="sd">:author: Gervaise B. Alina/UTK</span>
63<span class="sd">:author: Mathieu Doucet/UTK</span>
64<span class="sd">:author: Jae Cho/UTK</span>
65
66<span class="sd">&quot;&quot;&quot;</span>
67<span class="kn">import</span> <span class="nn">math</span> 
68<span class="kn">import</span> <span class="nn">numpy</span>
69
70<span class="kn">from</span> <span class="nn">sas.dataloader.data_info</span> <span class="kn">import</span> <span class="n">Data1D</span> <span class="k">as</span> <span class="n">LoaderData1D</span>
71
72<span class="c"># The minimum q-value to be used when extrapolating</span>
73<span class="n">Q_MINIMUM</span>  <span class="o">=</span> <span class="mf">1e-5</span>
74
75<span class="c"># The maximum q-value to be used when extrapolating</span>
76<span class="n">Q_MAXIMUM</span>  <span class="o">=</span> <span class="mi">10</span>
77
78<span class="c"># Number of steps in the extrapolation</span>
79<span class="n">INTEGRATION_NSTEPS</span> <span class="o">=</span> <span class="mi">1000</span>
80
81<div class="viewcode-block" id="Transform"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform">[docs]</a><span class="k">class</span> <span class="nc">Transform</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
82    <span class="sd">&quot;&quot;&quot;</span>
83<span class="sd">    Define interface that need to compute a function or an inverse</span>
84<span class="sd">    function given some x, y </span>
85<span class="sd">    &quot;&quot;&quot;</span>
86   
87<div class="viewcode-block" id="Transform.linearize_data"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform.linearize_data">[docs]</a>    <span class="k">def</span> <span class="nf">linearize_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
88        <span class="sd">&quot;&quot;&quot;</span>
89<span class="sd">        Linearize data so that a linear fit can be performed. </span>
90<span class="sd">        Filter out the data that can&#39;t be transformed.</span>
91<span class="sd">        </span>
92<span class="sd">        :param data: LoadData1D instance</span>
93<span class="sd">        </span>
94<span class="sd">        &quot;&quot;&quot;</span>
95        <span class="c"># Check that the vector lengths are equal</span>
96        <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">))</span>
97        <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dy</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
98            <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span><span class="p">))</span>
99            <span class="n">dy</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dy</span>
100        <span class="k">else</span><span class="p">:</span>
101            <span class="n">dy</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">))</span>
102           
103        <span class="c"># Transform the data</span>
104        <span class="n">data_points</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">dy</span><span class="p">)</span>
105
106        <span class="n">output_points</span> <span class="o">=</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">linearize_q_value</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
107                          <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
108                          <span class="n">p</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">data_points</span> <span class="k">if</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span> <span class="ow">and</span> \
109                          <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">p</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">]</span>
110       
111        <span class="n">x_out</span><span class="p">,</span> <span class="n">y_out</span><span class="p">,</span> <span class="n">dy_out</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">output_points</span><span class="p">)</span>
112       
113        <span class="c"># Create Data1D object</span>
114        <span class="n">x_out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x_out</span><span class="p">)</span>
115        <span class="n">y_out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_out</span><span class="p">)</span>
116        <span class="n">dy_out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">dy_out</span><span class="p">)</span>
117        <span class="n">linear_data</span> <span class="o">=</span> <span class="n">LoaderData1D</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x_out</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_out</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy_out</span><span class="p">)</span>
118       
119        <span class="k">return</span> <span class="n">linear_data</span>
120    </div>
121<div class="viewcode-block" id="Transform.get_allowed_bins"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform.get_allowed_bins">[docs]</a>    <span class="k">def</span> <span class="nf">get_allowed_bins</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
122        <span class="sd">&quot;&quot;&quot;</span>
123<span class="sd">        Goes through the data points and returns a list of boolean values</span>
124<span class="sd">        to indicate whether each points is allowed by the model or not.</span>
125<span class="sd">        </span>
126<span class="sd">        :param data: Data1D object</span>
127<span class="sd">        &quot;&quot;&quot;</span>
128        <span class="k">return</span> <span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">p</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">,</span>
129                                                           <span class="n">data</span><span class="o">.</span><span class="n">dy</span><span class="p">)]</span>
130        </div>
131<div class="viewcode-block" id="Transform.linearize_q_value"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform.linearize_q_value">[docs]</a>    <span class="k">def</span> <span class="nf">linearize_q_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
132        <span class="sd">&quot;&quot;&quot;</span>
133<span class="sd">        Transform the input q-value for linearization</span>
134<span class="sd">        &quot;&quot;&quot;</span>
135        <span class="k">return</span> <span class="bp">NotImplemented</span>
136</div>
137<div class="viewcode-block" id="Transform.extract_model_parameters"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform.extract_model_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">extract_model_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">constant</span><span class="p">,</span> <span class="n">slope</span><span class="p">,</span> <span class="n">dconstant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dslope</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
138        <span class="sd">&quot;&quot;&quot;</span>
139<span class="sd">        set private member</span>
140<span class="sd">        &quot;&quot;&quot;</span>
141        <span class="k">return</span> <span class="bp">NotImplemented</span>
142     </div>
143<div class="viewcode-block" id="Transform.evaluate_model"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform.evaluate_model">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
144        <span class="sd">&quot;&quot;&quot;</span>
145<span class="sd">        Returns an array f(x) values where f is the Transform function.</span>
146<span class="sd">        &quot;&quot;&quot;</span>
147        <span class="k">return</span> <span class="bp">NotImplemented</span>
148    </div>
149<div class="viewcode-block" id="Transform.evaluate_model_errors"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Transform.evaluate_model_errors">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate_model_errors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
150        <span class="sd">&quot;&quot;&quot;</span>
151<span class="sd">        Returns an array of I(q) errors</span>
152<span class="sd">        &quot;&quot;&quot;</span>
153        <span class="k">return</span> <span class="bp">NotImplemented</span>
154    </div></div>
155<div class="viewcode-block" id="Guinier"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Guinier">[docs]</a><span class="k">class</span> <span class="nc">Guinier</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
156    <span class="sd">&quot;&quot;&quot;</span>
157<span class="sd">    class of type Transform that performs operations related to guinier </span>
158<span class="sd">    function</span>
159<span class="sd">    &quot;&quot;&quot;</span>
160    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">60</span><span class="p">):</span>
161        <span class="n">Transform</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
162        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span>
163        <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">=</span> <span class="n">radius</span>
164        <span class="c">## Uncertainty of scale parameter</span>
165        <span class="bp">self</span><span class="o">.</span><span class="n">dscale</span>  <span class="o">=</span> <span class="mi">0</span>
166        <span class="c">## Unvertainty of radius parameter</span>
167        <span class="bp">self</span><span class="o">.</span><span class="n">dradius</span> <span class="o">=</span> <span class="mi">0</span>
168       
169<div class="viewcode-block" id="Guinier.linearize_q_value"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Guinier.linearize_q_value">[docs]</a>    <span class="k">def</span> <span class="nf">linearize_q_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
170        <span class="sd">&quot;&quot;&quot;</span>
171<span class="sd">        Transform the input q-value for linearization</span>
172<span class="sd">        </span>
173<span class="sd">        :param value: q-value</span>
174<span class="sd">        </span>
175<span class="sd">        :return: q*q</span>
176<span class="sd">        &quot;&quot;&quot;</span>
177        <span class="k">return</span> <span class="n">value</span> <span class="o">*</span> <span class="n">value</span>
178    </div>
179<div class="viewcode-block" id="Guinier.extract_model_parameters"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Guinier.extract_model_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">extract_model_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">constant</span><span class="p">,</span> <span class="n">slope</span><span class="p">,</span> <span class="n">dconstant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dslope</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
180        <span class="sd">&quot;&quot;&quot;</span>
181<span class="sd">           assign new value to the scale and the radius</span>
182<span class="sd">       &quot;&quot;&quot;</span>
183        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">constant</span><span class="p">)</span>
184        <span class="k">if</span> <span class="n">slope</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
185            <span class="n">slope</span> <span class="o">=</span> <span class="mf">0.0</span>
186        <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span> <span class="o">*</span> <span class="n">slope</span><span class="p">)</span>
187        <span class="c"># Errors</span>
188        <span class="bp">self</span><span class="o">.</span><span class="n">dscale</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">constant</span><span class="p">)</span><span class="o">*</span><span class="n">dconstant</span>
189        <span class="k">if</span> <span class="n">slope</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
190            <span class="n">n_zero</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0e-24</span>
191            <span class="bp">self</span><span class="o">.</span><span class="n">dradius</span> <span class="o">=</span> <span class="o">-</span><span class="mf">3.0</span><span class="o">/</span><span class="mf">2.0</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span> <span class="o">*</span> <span class="n">n_zero</span><span class="p">)</span><span class="o">*</span><span class="n">dslope</span>
192        <span class="k">else</span><span class="p">:</span>
193            <span class="bp">self</span><span class="o">.</span><span class="n">dradius</span> <span class="o">=</span> <span class="o">-</span><span class="mf">3.0</span><span class="o">/</span><span class="mf">2.0</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span> <span class="o">*</span> <span class="n">slope</span><span class="p">)</span><span class="o">*</span><span class="n">dslope</span>
194       
195        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">],</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">dradius</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dscale</span><span class="p">]</span>
196        </div>
197<div class="viewcode-block" id="Guinier.evaluate_model"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Guinier.evaluate_model">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
198        <span class="sd">&quot;&quot;&quot;</span>
199<span class="sd">        return F(x)= scale* e-((radius*x)**2/3)</span>
200<span class="sd">        &quot;&quot;&quot;</span>
201        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_guinier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
202             </div>
203<div class="viewcode-block" id="Guinier.evaluate_model_errors"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Guinier.evaluate_model_errors">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate_model_errors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
204        <span class="sd">&quot;&quot;&quot;</span>
205<span class="sd">        Returns the error on I(q) for the given array of q-values</span>
206<span class="sd">        </span>
207<span class="sd">        :param x: array of q-values</span>
208<span class="sd">        &quot;&quot;&quot;</span>
209        <span class="n">p1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">dscale</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">*</span> <span class="n">q</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">))</span> \
210                          <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
211        <span class="n">p2</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">*</span> <span class="n">q</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">))</span>\
212                     <span class="o">*</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">q</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">))</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dradius</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
213        <span class="n">diq2</span> <span class="o">=</span> <span class="n">p1</span><span class="o">*</span><span class="n">p1</span> <span class="o">+</span> <span class="n">p2</span><span class="o">*</span><span class="n">p2</span>       
214        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">err</span><span class="p">)</span> <span class="k">for</span> <span class="n">err</span> <span class="ow">in</span> <span class="n">diq2</span><span class="p">]</span> <span class="p">)</span>
215             </div>
216    <span class="k">def</span> <span class="nf">_guinier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
217        <span class="sd">&quot;&quot;&quot;</span>
218<span class="sd">        Retrieve the guinier function after apply an inverse guinier function</span>
219<span class="sd">        to x</span>
220<span class="sd">        Compute a F(x) = scale* e-((radius*x)**2/3).</span>
221<span class="sd">        </span>
222<span class="sd">        :param x: a vector of q values </span>
223<span class="sd">        :param scale: the scale value</span>
224<span class="sd">        :param radius: the guinier radius value</span>
225<span class="sd">        </span>
226<span class="sd">        :return: F(x)</span>
227<span class="sd">        &quot;&quot;&quot;</span>   
228        <span class="c"># transform the radius of coming from the inverse guinier function to a </span>
229        <span class="c"># a radius of a guinier function</span>
230        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
231            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Rg expected positive value, but got </span><span class="si">%s</span><span class="s">&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">radius</span>
232            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> 
233        <span class="n">value</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">*</span> <span class="n">i</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span> <span class="p">])</span> 
234        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">value</span>
235</div>
236<div class="viewcode-block" id="PowerLaw"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.PowerLaw">[docs]</a><span class="k">class</span> <span class="nc">PowerLaw</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
237    <span class="sd">&quot;&quot;&quot;</span>
238<span class="sd">    class of type transform that perform operation related to power_law </span>
239<span class="sd">    function</span>
240<span class="sd">    &quot;&quot;&quot;</span>
241    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">power</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
242        <span class="n">Transform</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
243        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span>
244        <span class="bp">self</span><span class="o">.</span><span class="n">power</span> <span class="o">=</span> <span class="n">power</span>
245   
246<div class="viewcode-block" id="PowerLaw.linearize_q_value"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.PowerLaw.linearize_q_value">[docs]</a>    <span class="k">def</span> <span class="nf">linearize_q_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
247        <span class="sd">&quot;&quot;&quot;</span>
248<span class="sd">        Transform the input q-value for linearization</span>
249<span class="sd">        </span>
250<span class="sd">        :param value: q-value</span>
251<span class="sd">        </span>
252<span class="sd">        :return: log(q)</span>
253<span class="sd">        &quot;&quot;&quot;</span>
254        <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
255    </div>
256<div class="viewcode-block" id="PowerLaw.extract_model_parameters"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.PowerLaw.extract_model_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">extract_model_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">constant</span><span class="p">,</span> <span class="n">slope</span><span class="p">,</span> <span class="n">dconstant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dslope</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
257        <span class="sd">&quot;&quot;&quot;</span>
258<span class="sd">        Assign new value to the scale and the power </span>
259<span class="sd">        &quot;&quot;&quot;</span>
260        <span class="bp">self</span><span class="o">.</span><span class="n">power</span> <span class="o">=</span> <span class="o">-</span><span class="n">slope</span>
261        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">constant</span><span class="p">)</span>
262       
263        <span class="c"># Errors</span>
264        <span class="bp">self</span><span class="o">.</span><span class="n">dscale</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">constant</span><span class="p">)</span><span class="o">*</span><span class="n">dconstant</span>
265        <span class="bp">self</span><span class="o">.</span><span class="n">dpower</span> <span class="o">=</span> <span class="o">-</span><span class="n">dslope</span>
266       
267        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">],</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">dpower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dscale</span><span class="p">]</span>
268        </div>
269<div class="viewcode-block" id="PowerLaw.evaluate_model"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.PowerLaw.evaluate_model">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
270        <span class="sd">&quot;&quot;&quot;</span>
271<span class="sd">        given a scale and a radius transform x, y using a power_law</span>
272<span class="sd">        function</span>
273<span class="sd">        &quot;&quot;&quot;</span>
274        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_power_law</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
275    </div>
276<div class="viewcode-block" id="PowerLaw.evaluate_model_errors"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.PowerLaw.evaluate_model_errors">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate_model_errors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
277        <span class="sd">&quot;&quot;&quot;</span>
278<span class="sd">        Returns the error on I(q) for the given array of q-values</span>
279<span class="sd">        :param x: array of q-values</span>
280<span class="sd">        &quot;&quot;&quot;</span>
281        <span class="n">p1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">dscale</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
282        <span class="n">p2</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>\
283                           <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dpower</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
284        <span class="n">diq2</span> <span class="o">=</span> <span class="n">p1</span><span class="o">*</span><span class="n">p1</span> <span class="o">+</span> <span class="n">p2</span><span class="o">*</span><span class="n">p2</span>       
285        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">err</span><span class="p">)</span> <span class="k">for</span> <span class="n">err</span> <span class="ow">in</span> <span class="n">diq2</span><span class="p">]</span> <span class="p">)</span>
286       </div>
287    <span class="k">def</span> <span class="nf">_power_law</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
288        <span class="sd">&quot;&quot;&quot;</span>
289<span class="sd">        F(x) = scale* (x)^(-power)</span>
290<span class="sd">            when power= 4. the model is porod </span>
291<span class="sd">            else power_law</span>
292<span class="sd">        The model has three parameters: ::</span>
293<span class="sd">            1. x: a vector of q values</span>
294<span class="sd">            2. power: power of the function</span>
295<span class="sd">            3. scale : scale factor value</span>
296<span class="sd">        </span>
297<span class="sd">        :param x: array</span>
298<span class="sd">        :return: F(x)</span>
299<span class="sd">        &quot;&quot;&quot;</span>
300        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
301            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Power_law function expected positive power,&quot;</span>
302            <span class="n">msg</span> <span class="o">+=</span> <span class="s">&quot; but got </span><span class="si">%s</span><span class="s">&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span>
303            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
304        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
305            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;scale expected positive value, but got </span><span class="si">%s</span><span class="s">&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span>
306            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> 
307       
308        <span class="n">value</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span> <span class="p">])</span> 
309        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">value</span>
310</div>
311<div class="viewcode-block" id="Extrapolator"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Extrapolator">[docs]</a><span class="k">class</span> <span class="nc">Extrapolator</span><span class="p">:</span>
312    <span class="sd">&quot;&quot;&quot;</span>
313<span class="sd">    Extrapolate I(q) distribution using a given model</span>
314<span class="sd">    &quot;&quot;&quot;</span>
315    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
316        <span class="sd">&quot;&quot;&quot;</span>
317<span class="sd">        Determine a and b given a linear equation y = ax + b</span>
318<span class="sd">        </span>
319<span class="sd">        If a model is given, it will be used to linearize the data before </span>
320<span class="sd">        the extrapolation is performed. If None,</span>
321<span class="sd">        a simple linear fit will be done.</span>
322<span class="sd">        </span>
323<span class="sd">        :param data: data containing x and y  such as  y = ax + b </span>
324<span class="sd">        :param model: optional Transform object </span>
325<span class="sd">        &quot;&quot;&quot;</span>
326        <span class="bp">self</span><span class="o">.</span><span class="n">data</span>  <span class="o">=</span> <span class="n">data</span>
327        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
328       
329        <span class="c"># Set qmin as the lowest non-zero value</span>
330        <span class="bp">self</span><span class="o">.</span><span class="n">qmin</span> <span class="o">=</span> <span class="n">Q_MINIMUM</span>
331        <span class="k">for</span> <span class="n">q_value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">:</span>
332            <span class="k">if</span> <span class="n">q_value</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> 
333                <span class="bp">self</span><span class="o">.</span><span class="n">qmin</span> <span class="o">=</span> <span class="n">q_value</span>
334                <span class="k">break</span>
335        <span class="bp">self</span><span class="o">.</span><span class="n">qmax</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
336             
337<div class="viewcode-block" id="Extrapolator.fit"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.Extrapolator.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">power</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">qmin</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">qmax</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
338        <span class="sd">&quot;&quot;&quot;</span>
339<span class="sd">        Fit data for y = ax + b  return a and b</span>
340<span class="sd">       </span>
341<span class="sd">        :param power: a fixed, otherwise None</span>
342<span class="sd">        :param qmin: Minimum Q-value</span>
343<span class="sd">        :param qmax: Maximum Q-value</span>
344<span class="sd">        &quot;&quot;&quot;</span>
345        <span class="k">if</span> <span class="n">qmin</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
346            <span class="n">qmin</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">qmin</span>
347        <span class="k">if</span> <span class="n">qmax</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
348            <span class="n">qmax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">qmax</span>
349           
350        <span class="c"># Identify the bin range for the fit</span>
351        <span class="n">idx</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">qmin</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span> <span class="o">&lt;=</span> <span class="n">qmax</span><span class="p">)</span>
352       
353        <span class="n">fx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">))</span>
354
355        <span class="c"># Uncertainty</span>
356        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span><span class="o">==</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="ow">and</span> \
357            <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span><span class="o">==</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="ow">and</span> \
358            <span class="n">numpy</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">):</span>
359            <span class="n">sigma</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span>
360        <span class="k">else</span><span class="p">:</span>
361            <span class="n">sigma</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">))</span>
362
363        <span class="c"># Compute theory data f(x)</span>
364        <span class="n">fx</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
365       
366        <span class="c"># Linearize the data</span>
367        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
368            <span class="n">linearized_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">linearize_data</span><span class="p">(</span>\
369                                            <span class="n">LoaderData1D</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
370                                                                <span class="n">fx</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
371                                                            <span class="n">dy</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span>
372        <span class="k">else</span><span class="p">:</span>
373            <span class="n">linearized_data</span> <span class="o">=</span> <span class="n">LoaderData1D</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
374                                           <span class="n">fx</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
375                                           <span class="n">dy</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
376       
377        <span class="c">##power is given only for function = power_law    </span>
378        <span class="k">if</span> <span class="n">power</span> <span class="o">!=</span> <span class="bp">None</span><span class="p">:</span>
379            <span class="n">sigma2</span> <span class="o">=</span> <span class="n">linearized_data</span><span class="o">.</span><span class="n">dy</span> <span class="o">*</span> <span class="n">linearized_data</span><span class="o">.</span><span class="n">dy</span>
380            <span class="n">a</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">power</span><span class="p">)</span>
381            <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">y</span><span class="o">/</span><span class="n">sigma2</span><span class="p">)</span> \
382                 <span class="o">-</span> <span class="n">a</span><span class="o">*</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">x</span><span class="o">/</span><span class="n">sigma2</span><span class="p">))</span><span class="o">/</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mf">1.0</span><span class="o">/</span><span class="n">sigma2</span><span class="p">)</span>
383           
384           
385            <span class="n">deltas</span> <span class="o">=</span> <span class="n">linearized_data</span><span class="o">.</span><span class="n">x</span><span class="o">*</span><span class="n">a</span> <span class="o">+</span> \
386                    <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">x</span><span class="p">))</span><span class="o">*</span><span class="n">b</span><span class="o">-</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">y</span>
387            <span class="n">residuals</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">deltas</span><span class="o">*</span><span class="n">deltas</span><span class="o">/</span><span class="n">sigma2</span><span class="p">)</span>
388
389            <span class="n">err</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">fabs</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mf">1.0</span><span class="o">/</span><span class="n">sigma2</span><span class="p">)</span>
390            <span class="k">return</span> <span class="p">[</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">err</span><span class="p">)]</span>
391        <span class="k">else</span><span class="p">:</span>
392            <span class="n">A</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span> <span class="n">linearized_data</span><span class="o">.</span><span class="n">x</span><span class="o">/</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">dy</span><span class="p">,</span>
393                               <span class="mf">1.0</span><span class="o">/</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">dy</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>       
394            <span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">residuals</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">A</span><span class="p">,</span>
395                                        <span class="n">linearized_data</span><span class="o">.</span><span class="n">y</span><span class="o">/</span><span class="n">linearized_data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span>
396           
397            <span class="c"># Get the covariance matrix, defined as inv_cov = a_transposed * a</span>
398            <span class="n">err</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
399            <span class="k">try</span><span class="p">:</span>
400                <span class="n">inv_cov</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">transpose</span><span class="p">(),</span> <span class="n">A</span><span class="p">)</span>
401                <span class="n">cov</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">inv_cov</span><span class="p">)</span>
402                <span class="n">err_matrix</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">fabs</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span> <span class="o">*</span> <span class="n">cov</span>
403                <span class="n">err</span> <span class="o">=</span> <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">err_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">err_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])]</span>
404            <span class="k">except</span><span class="p">:</span>
405                <span class="n">err</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">]</span>
406               
407            <span class="k">return</span> <span class="n">p</span><span class="p">,</span> <span class="n">err</span>
408       
409</div></div>
410<div class="viewcode-block" id="InvariantCalculator"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator">[docs]</a><span class="k">class</span> <span class="nc">InvariantCalculator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
411    <span class="sd">&quot;&quot;&quot;</span>
412<span class="sd">    Compute invariant if data is given.</span>
413<span class="sd">    Can provide volume fraction and surface area if the user provides</span>
414<span class="sd">    Porod constant  and contrast values.</span>
415<span class="sd">    </span>
416<span class="sd">    :precondition:  the user must send a data of type DataLoader.Data1D</span>
417<span class="sd">                    the user provide background and scale values.</span>
418<span class="sd">                    </span>
419<span class="sd">    :note: Some computations depends on each others. </span>
420<span class="sd">    &quot;&quot;&quot;</span>
421    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">background</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span> <span class="p">):</span>
422        <span class="sd">&quot;&quot;&quot;</span>
423<span class="sd">        Initialize variables.</span>
424<span class="sd">        </span>
425<span class="sd">        :param data: data must be of type DataLoader.Data1D</span>
426<span class="sd">        :param background: Background value. The data will be corrected </span>
427<span class="sd">            before processing</span>
428<span class="sd">        :param scale: Scaling factor for I(q). The data will be corrected</span>
429<span class="sd">            before processing</span>
430<span class="sd">        &quot;&quot;&quot;</span>
431        <span class="c"># Background and scale should be private data member if the only way to</span>
432        <span class="c"># change them are by instantiating a new object.</span>
433        <span class="bp">self</span><span class="o">.</span><span class="n">_background</span> <span class="o">=</span> <span class="n">background</span>
434        <span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">=</span> <span class="n">scale</span>
435        <span class="c"># slit height for smeared data</span>
436        <span class="bp">self</span><span class="o">.</span><span class="n">_smeared</span> <span class="o">=</span> <span class="bp">None</span>
437        <span class="c"># The data should be private</span>
438        <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
439        <span class="c"># get the dxl if the data is smeared: This is done only once on init.</span>
440        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">dxl</span> <span class="o">!=</span> <span class="bp">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">dxl</span><span class="o">.</span><span class="n">all</span><span class="p">()</span> <span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
441            <span class="c"># assumes constant dxl</span>
442            <span class="bp">self</span><span class="o">.</span><span class="n">_smeared</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">dxl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
443     
444        <span class="c"># Since there are multiple variants of Q*, you should force the</span>
445        <span class="c"># user to use the get method and keep Q* a private data member</span>
446        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span> <span class="o">=</span> <span class="bp">None</span>
447       
448        <span class="c"># You should keep the error on Q* so you can reuse it without</span>
449        <span class="c"># recomputing the whole thing.</span>
450        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span> <span class="o">=</span> <span class="mi">0</span>
451       
452        <span class="c"># Extrapolation parameters</span>
453        <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_npts</span> <span class="o">=</span> <span class="mi">4</span>
454        <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_function</span> <span class="o">=</span> <span class="n">Guinier</span><span class="p">()</span>
455        <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power</span> <span class="o">=</span> <span class="bp">None</span>
456        <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power_fitted</span> <span class="o">=</span> <span class="bp">None</span>
457   
458        <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_npts</span> <span class="o">=</span> <span class="mi">4</span>
459        <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_function</span> <span class="o">=</span> <span class="n">PowerLaw</span><span class="p">()</span>
460        <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power</span> <span class="o">=</span> <span class="bp">None</span>
461        <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power_fitted</span> <span class="o">=</span> <span class="bp">None</span>
462       
463        <span class="c"># Extrapolation range</span>
464        <span class="bp">self</span><span class="o">.</span><span class="n">_low_q_limit</span> <span class="o">=</span> <span class="n">Q_MINIMUM</span>
465       
466    <span class="k">def</span> <span class="nf">_get_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
467        <span class="sd">&quot;&quot;&quot;</span>
468<span class="sd">        :note: this function must be call before computing any type</span>
469<span class="sd">         of invariant</span>
470<span class="sd">         </span>
471<span class="sd">        :return: new data = self._scale *data - self._background</span>
472<span class="sd">        &quot;&quot;&quot;</span>
473        <span class="k">if</span> <span class="ow">not</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">__class__</span><span class="p">,</span> <span class="n">LoaderData1D</span><span class="p">):</span>
474            <span class="c">#Process only data that inherited from DataLoader.Data_info.Data1D</span>
475            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span><span class="s">&quot;Data must be of type DataLoader.Data1D&quot;</span>
476        <span class="c">#from copy import deepcopy</span>
477        <span class="n">new_data</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">*</span> <span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_background</span>   
478       
479        <span class="c"># Check that the vector lengths are equal</span>
480        <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">y</span><span class="p">))</span>
481       
482        <span class="c"># Verify that the errors are set correctly</span>
483        <span class="k">if</span> <span class="n">new_data</span><span class="o">.</span><span class="n">dy</span> <span class="ow">is</span> <span class="bp">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span> <span class="ow">or</span> \
484            <span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span><span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="nb">max</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span><span class="o">==</span><span class="mi">0</span><span class="p">):</span>
485            <span class="n">new_data</span><span class="o">.</span><span class="n">dy</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">x</span><span class="p">))</span> 
486        <span class="k">return</span>  <span class="n">new_data</span>
487     
488    <span class="k">def</span> <span class="nf">_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">qmin</span><span class="o">=</span><span class="n">Q_MINIMUM</span><span class="p">,</span> <span class="n">qmax</span><span class="o">=</span><span class="n">Q_MAXIMUM</span><span class="p">,</span> <span class="n">power</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
489        <span class="sd">&quot;&quot;&quot;</span>
490<span class="sd">        fit data with function using </span>
491<span class="sd">        data = self._get_data()</span>
492<span class="sd">        fx = Functor(data , function)</span>
493<span class="sd">        y = data.y</span>
494<span class="sd">        slope, constant = linalg.lstsq(y,fx)</span>
495<span class="sd">        </span>
496<span class="sd">        :param qmin: data first q value to consider during the fit</span>
497<span class="sd">        :param qmax: data last q value to consider during the fit</span>
498<span class="sd">        :param power : power value to consider for power-law </span>
499<span class="sd">        :param function: the function to use during the fit</span>
500<span class="sd">        </span>
501<span class="sd">        :return a: the scale of the function</span>
502<span class="sd">        :return b: the other parameter of the function for guinier will be radius</span>
503<span class="sd">                for power_law will be the power value</span>
504<span class="sd">        &quot;&quot;&quot;</span>
505        <span class="n">extrapolator</span> <span class="o">=</span> <span class="n">Extrapolator</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
506        <span class="n">p</span><span class="p">,</span> <span class="n">dp</span> <span class="o">=</span> <span class="n">extrapolator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">power</span><span class="o">=</span><span class="n">power</span><span class="p">,</span> <span class="n">qmin</span><span class="o">=</span><span class="n">qmin</span><span class="p">,</span> <span class="n">qmax</span><span class="o">=</span><span class="n">qmax</span><span class="p">)</span> 
507       
508        <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">extract_model_parameters</span><span class="p">(</span><span class="n">constant</span><span class="o">=</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">slope</span><span class="o">=</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
509                                              <span class="n">dconstant</span><span class="o">=</span><span class="n">dp</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dslope</span><span class="o">=</span><span class="n">dp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
510   
511    <span class="k">def</span> <span class="nf">_get_qstar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
512        <span class="sd">&quot;&quot;&quot;</span>
513<span class="sd">        Compute invariant for pinhole data.</span>
514<span class="sd">        This invariant is given by: ::</span>
515<span class="sd">    </span>
516<span class="sd">            q_star = x0**2 *y0 *dx0 +x1**2 *y1 *dx1 </span>
517<span class="sd">                        + ..+ xn**2 *yn *dxn    for non smeared data</span>
518<span class="sd">                        </span>
519<span class="sd">            q_star = dxl0 *x0 *y0 *dx0 +dxl1 *x1 *y1 *dx1 </span>
520<span class="sd">                        + ..+ dlxn *xn *yn *dxn    for smeared data</span>
521<span class="sd">                        </span>
522<span class="sd">            where n &gt;= len(data.x)-1</span>
523<span class="sd">            dxl = slit height dQl</span>
524<span class="sd">            dxi = 1/2*(xi+1 - xi) + (xi - xi-1)</span>
525<span class="sd">            dx0 = (x1 - x0)/2</span>
526<span class="sd">            dxn = (xn - xn-1)/2</span>
527<span class="sd">            </span>
528<span class="sd">        :param data: the data to use to compute invariant.</span>
529<span class="sd">        </span>
530<span class="sd">        :return q_star: invariant value for pinhole data. q_star &gt; 0</span>
531<span class="sd">        &quot;&quot;&quot;</span>
532        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span><span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">):</span>
533            <span class="n">msg</span> <span class="o">=</span>  <span class="s">&quot;Length x and y must be equal&quot;</span>
534            <span class="n">msg</span> <span class="o">+=</span> <span class="s">&quot; and greater than 1; got x=</span><span class="si">%s</span><span class="s">, y=</span><span class="si">%s</span><span class="s">&quot;</span><span class="o">%</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">),</span>
535                                                          <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">))</span>
536            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="n">msg</span>
537        <span class="k">else</span><span class="p">:</span>
538            <span class="c"># Take care of smeared data</span>
539            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_smeared</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
540                <span class="n">gx</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span>
541            <span class="c"># assumes that len(x) == len(dxl).</span>
542            <span class="k">else</span><span class="p">:</span>               
543                <span class="n">gx</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dxl</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span> 
544               
545            <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span><span class="o">-</span> <span class="mi">1</span>
546            <span class="c">#compute the first delta q</span>
547            <span class="n">dx0</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
548            <span class="c">#compute the last delta q</span>
549            <span class="n">dxn</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
550            <span class="nb">sum</span> <span class="o">=</span> <span class="mi">0</span>
551            <span class="nb">sum</span> <span class="o">+=</span> <span class="n">gx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">dx0</span>
552            <span class="nb">sum</span> <span class="o">+=</span> <span class="n">gx</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">dxn</span>
553           
554            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
555                <span class="k">return</span> <span class="nb">sum</span>
556            <span class="k">else</span><span class="p">:</span>
557                <span class="c">#iterate between for element different</span>
558                <span class="c">#from the first and the last</span>
559                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
560                    <span class="n">dxi</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
561                    <span class="nb">sum</span> <span class="o">+=</span> <span class="n">gx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">dxi</span>
562                <span class="k">return</span> <span class="nb">sum</span>
563           
564    <span class="k">def</span> <span class="nf">_get_qstar_uncertainty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
565        <span class="sd">&quot;&quot;&quot;</span>
566<span class="sd">        Compute invariant uncertainty with with pinhole data.</span>
567<span class="sd">        This uncertainty is given as follow: ::</span>
568<span class="sd">        </span>
569<span class="sd">           dq_star = math.sqrt[(x0**2*(dy0)*dx0)**2 +</span>
570<span class="sd">                (x1**2 *(dy1)*dx1)**2 + ..+ (xn**2 *(dyn)*dxn)**2 ]</span>
571<span class="sd">        where n &gt;= len(data.x)-1</span>
572<span class="sd">        dxi = 1/2*(xi+1 - xi) + (xi - xi-1)</span>
573<span class="sd">        dx0 = (x1 - x0)/2</span>
574<span class="sd">        dxn = (xn - xn-1)/2</span>
575<span class="sd">        dyn: error on dy</span>
576<span class="sd">       </span>
577<span class="sd">        :param data:</span>
578<span class="sd">        :note: if data doesn&#39;t contain dy assume dy= math.sqrt(data.y)</span>
579<span class="sd">        &quot;&quot;&quot;</span>         
580        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">or</span> \
581            <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> <span class="ow">or</span> \
582            <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span> <span class="ow">and</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dy</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">))):</span>
583            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Length of data.x and data.y must be equal&quot;</span>
584            <span class="n">msg</span> <span class="o">+=</span> <span class="s">&quot; and greater than 1; got x=</span><span class="si">%s</span><span class="s">, y=</span><span class="si">%s</span><span class="s">&quot;</span><span class="o">%</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">),</span>
585                                                         <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">))</span>
586            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="n">msg</span>
587        <span class="k">else</span><span class="p">:</span>
588            <span class="c">#Create error for data without dy error</span>
589            <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dy</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
590                <span class="n">dy</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> 
591            <span class="k">else</span><span class="p">:</span>
592                <span class="n">dy</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dy</span>
593            <span class="c"># Take care of smeared data</span>
594            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_smeared</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
595                <span class="n">gx</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span>
596            <span class="c"># assumes that len(x) == len(dxl).</span>
597            <span class="k">else</span><span class="p">:</span>
598                <span class="n">gx</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dxl</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span>
599 
600            <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
601            <span class="c">#compute the first delta</span>
602            <span class="n">dx0</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
603            <span class="c">#compute the last delta</span>
604            <span class="n">dxn</span><span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
605            <span class="nb">sum</span> <span class="o">=</span> <span class="mi">0</span>
606            <span class="nb">sum</span> <span class="o">+=</span> <span class="p">(</span><span class="n">gx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">dy</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">dx0</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
607            <span class="nb">sum</span> <span class="o">+=</span> <span class="p">(</span><span class="n">gx</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">dy</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">dxn</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
608            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
609                <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">)</span>
610            <span class="k">else</span><span class="p">:</span>
611                <span class="c">#iterate between for element different</span>
612                <span class="c">#from the first and the last</span>
613                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
614                    <span class="n">dxi</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
615                    <span class="nb">sum</span> <span class="o">+=</span> <span class="p">(</span><span class="n">gx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">dy</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">dxi</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
616                <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">)</span>
617       
618    <span class="k">def</span> <span class="nf">_get_extrapolated_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">npts</span><span class="o">=</span><span class="n">INTEGRATION_NSTEPS</span><span class="p">,</span>
619                              <span class="n">q_start</span><span class="o">=</span><span class="n">Q_MINIMUM</span><span class="p">,</span> <span class="n">q_end</span><span class="o">=</span><span class="n">Q_MAXIMUM</span><span class="p">):</span>
620        <span class="sd">&quot;&quot;&quot;</span>
621<span class="sd">        :return: extrapolate data create from data</span>
622<span class="sd">        &quot;&quot;&quot;</span>
623        <span class="c">#create new Data1D to compute the invariant</span>
624        <span class="n">q</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">q_start</span><span class="p">,</span>
625                           <span class="n">stop</span><span class="o">=</span><span class="n">q_end</span><span class="p">,</span>
626                           <span class="n">num</span><span class="o">=</span><span class="n">npts</span><span class="p">,</span>
627                           <span class="n">endpoint</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
628        <span class="n">iq</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate_model</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
629        <span class="n">diq</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate_model_errors</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
630         
631        <span class="n">result_data</span> <span class="o">=</span> <span class="n">LoaderData1D</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">iq</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">diq</span><span class="p">)</span>
632        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_smeared</span> <span class="o">!=</span> <span class="bp">None</span><span class="p">:</span>
633            <span class="n">result_data</span><span class="o">.</span><span class="n">dxl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_smeared</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">q</span><span class="p">))</span>
634        <span class="k">return</span> <span class="n">result_data</span>
635   
636<div class="viewcode-block" id="InvariantCalculator.get_data"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_data">[docs]</a>    <span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
637        <span class="sd">&quot;&quot;&quot;</span>
638<span class="sd">        :return: self._data</span>
639<span class="sd">        &quot;&quot;&quot;</span>
640        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span>
641    </div>
642<div class="viewcode-block" id="InvariantCalculator.get_extrapolation_power"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_extrapolation_power">[docs]</a>    <span class="k">def</span> <span class="nf">get_extrapolation_power</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="s">&#39;high&#39;</span><span class="p">):</span>
643        <span class="sd">&quot;&quot;&quot;</span>
644<span class="sd">        :return: the fitted power for power law function for a given</span>
645<span class="sd">            extrapolation range</span>
646<span class="sd">        &quot;&quot;&quot;</span>
647        <span class="k">if</span> <span class="nb">range</span> <span class="o">==</span> <span class="s">&#39;low&#39;</span><span class="p">:</span>
648            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power_fitted</span>
649        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power_fitted</span>
650    </div>
651<div class="viewcode-block" id="InvariantCalculator.get_qstar_low"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_qstar_low">[docs]</a>    <span class="k">def</span> <span class="nf">get_qstar_low</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
652        <span class="sd">&quot;&quot;&quot;</span>
653<span class="sd">        Compute the invariant for extrapolated data at low q range.</span>
654<span class="sd">        </span>
655<span class="sd">        Implementation:</span>
656<span class="sd">            data = self._get_extra_data_low()</span>
657<span class="sd">            return self._get_qstar()</span>
658<span class="sd">            </span>
659<span class="sd">        :return q_star: the invariant for data extrapolated at low q.</span>
660<span class="sd">        &quot;&quot;&quot;</span>
661        <span class="c"># Data boundaries for fitting</span>
662        <span class="n">qmin</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
663        <span class="n">qmax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_npts</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
664       
665        <span class="c"># Extrapolate the low-Q data</span>
666        <span class="n">p</span><span class="p">,</span> <span class="n">dp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_function</span><span class="p">,</span>
667                              <span class="n">qmin</span><span class="o">=</span><span class="n">qmin</span><span class="p">,</span>
668                          <span class="n">qmax</span><span class="o">=</span><span class="n">qmax</span><span class="p">,</span>
669                          <span class="n">power</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power</span><span class="p">)</span>
670        <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power_fitted</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
671       
672        <span class="c"># Distribution starting point</span>
673        <span class="bp">self</span><span class="o">.</span><span class="n">_low_q_limit</span> <span class="o">=</span> <span class="n">Q_MINIMUM</span>
674        <span class="k">if</span> <span class="n">Q_MINIMUM</span> <span class="o">&gt;=</span> <span class="n">qmin</span><span class="p">:</span>
675            <span class="bp">self</span><span class="o">.</span><span class="n">_low_q_limit</span> <span class="o">=</span> <span class="n">qmin</span><span class="o">/</span><span class="mi">10</span>
676       
677        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_extrapolated_data</span><span class="p">(</span>\
678                                    <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_function</span><span class="p">,</span>
679                                            <span class="n">npts</span><span class="o">=</span><span class="n">INTEGRATION_NSTEPS</span><span class="p">,</span>
680                                        <span class="n">q_start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_q_limit</span><span class="p">,</span> <span class="n">q_end</span><span class="o">=</span><span class="n">qmin</span><span class="p">)</span>
681       
682        <span class="c"># Systematic error</span>
683        <span class="c"># If we have smearing, the shape of the I(q) distribution at low Q will</span>
684        <span class="c"># may not be a Guinier or simple power law. The following is </span>
685        <span class="c"># a conservative estimation for the systematic error.</span>
686        <span class="n">err</span> <span class="o">=</span> <span class="n">qmin</span><span class="o">*</span><span class="n">qmin</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">fabs</span><span class="p">((</span><span class="n">qmin</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_q_limit</span><span class="p">)</span><span class="o">*</span>\
687                                  <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">INTEGRATION_NSTEPS</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
688        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_qstar</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_qstar_uncertainty</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">+</span><span class="n">err</span>
689        </div>
690<div class="viewcode-block" id="InvariantCalculator.get_qstar_high"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_qstar_high">[docs]</a>    <span class="k">def</span> <span class="nf">get_qstar_high</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
691        <span class="sd">&quot;&quot;&quot;</span>
692<span class="sd">        Compute the invariant for extrapolated data at high q range.</span>
693<span class="sd">        </span>
694<span class="sd">        Implementation:</span>
695<span class="sd">            data = self._get_extra_data_high()</span>
696<span class="sd">            return self._get_qstar()</span>
697<span class="sd">            </span>
698<span class="sd">        :return q_star: the invariant for data extrapolated at high q.</span>
699<span class="sd">        &quot;&quot;&quot;</span>
700        <span class="c"># Data boundaries for fitting</span>
701        <span class="n">x_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
702        <span class="n">qmin</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">x_len</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_npts</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>
703        <span class="n">qmax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">x_len</span><span class="p">]</span>
704       
705        <span class="c"># fit the data with a model to get the appropriate parameters</span>
706        <span class="n">p</span><span class="p">,</span> <span class="n">dp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_function</span><span class="p">,</span>
707                          <span class="n">qmin</span><span class="o">=</span><span class="n">qmin</span><span class="p">,</span>
708                          <span class="n">qmax</span><span class="o">=</span><span class="n">qmax</span><span class="p">,</span>
709                          <span class="n">power</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power</span><span class="p">)</span>
710        <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power_fitted</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
711       
712        <span class="c">#create new Data1D to compute the invariant</span>
713        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_extrapolated_data</span><span class="p">(</span>\
714                                    <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_function</span><span class="p">,</span>
715                                           <span class="n">npts</span><span class="o">=</span><span class="n">INTEGRATION_NSTEPS</span><span class="p">,</span>
716                                           <span class="n">q_start</span><span class="o">=</span><span class="n">qmax</span><span class="p">,</span> <span class="n">q_end</span><span class="o">=</span><span class="n">Q_MAXIMUM</span><span class="p">)</span>       
717       
718        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_qstar</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_qstar_uncertainty</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
719    </div>
720<div class="viewcode-block" id="InvariantCalculator.get_extra_data_low"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_extra_data_low">[docs]</a>    <span class="k">def</span> <span class="nf">get_extra_data_low</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">npts_in</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">q_start</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">npts</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
721        <span class="sd">&quot;&quot;&quot;</span>
722<span class="sd">        Returns the extrapolated data used for the loew-Q invariant calculation.</span>
723<span class="sd">        By default, the distribution will cover the data points used for the </span>
724<span class="sd">        extrapolation. The number of overlap points is a parameter (npts_in).</span>
725<span class="sd">        By default, the maximum q-value of the distribution will be  </span>
726<span class="sd">        the minimum q-value used when extrapolating for the purpose of the </span>
727<span class="sd">        invariant calculation. </span>
728<span class="sd">        </span>
729<span class="sd">        :param npts_in: number of data points for which</span>
730<span class="sd">            the extrapolated data overlap</span>
731<span class="sd">        :param q_start: is the minimum value to uses for extrapolated data</span>
732<span class="sd">        :param npts: the number of points in the extrapolated distribution</span>
733<span class="sd">           </span>
734<span class="sd">        &quot;&quot;&quot;</span>
735        <span class="c"># Get extrapolation range</span>
736        <span class="k">if</span> <span class="n">q_start</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
737            <span class="n">q_start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_low_q_limit</span>
738           
739        <span class="k">if</span> <span class="n">npts_in</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
740            <span class="n">npts_in</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_npts</span>
741        <span class="n">q_end</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">npts_in</span><span class="o">-</span><span class="mi">1</span><span class="p">)]</span>
742       
743        <span class="k">if</span> <span class="n">q_start</span> <span class="o">&gt;=</span> <span class="n">q_end</span><span class="p">:</span>
744            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
745
746        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_extrapolated_data</span><span class="p">(</span>\
747                                    <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_function</span><span class="p">,</span>
748                                           <span class="n">npts</span><span class="o">=</span><span class="n">npts</span><span class="p">,</span>
749                                           <span class="n">q_start</span><span class="o">=</span><span class="n">q_start</span><span class="p">,</span> <span class="n">q_end</span><span class="o">=</span><span class="n">q_end</span><span class="p">)</span>
750          </div>
751<div class="viewcode-block" id="InvariantCalculator.get_extra_data_high"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_extra_data_high">[docs]</a>    <span class="k">def</span> <span class="nf">get_extra_data_high</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">npts_in</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">q_end</span><span class="o">=</span><span class="n">Q_MAXIMUM</span><span class="p">,</span> <span class="n">npts</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
752        <span class="sd">&quot;&quot;&quot;</span>
753<span class="sd">        Returns the extrapolated data used for the high-Q invariant calculation.</span>
754<span class="sd">        By default, the distribution will cover the data points used for the </span>
755<span class="sd">        extrapolation. The number of overlap points is a parameter (npts_in).</span>
756<span class="sd">        By default, the maximum q-value of the distribution will be Q_MAXIMUM, </span>
757<span class="sd">        the maximum q-value used when extrapolating for the purpose of the </span>
758<span class="sd">        invariant calculation. </span>
759<span class="sd">        </span>
760<span class="sd">        :param npts_in: number of data points for which the</span>
761<span class="sd">            extrapolated data overlap</span>
762<span class="sd">        :param q_end: is the maximum value to uses for extrapolated data</span>
763<span class="sd">        :param npts: the number of points in the extrapolated distribution</span>
764<span class="sd">        &quot;&quot;&quot;</span>
765        <span class="c"># Get extrapolation range</span>
766        <span class="k">if</span> <span class="n">npts_in</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
767            <span class="n">npts_in</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_npts</span>
768        <span class="n">_npts</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
769        <span class="n">q_start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="nb">min</span><span class="p">(</span><span class="n">_npts</span><span class="p">,</span> <span class="n">_npts</span><span class="o">-</span><span class="n">npts_in</span><span class="p">)]</span>
770       
771        <span class="k">if</span> <span class="n">q_start</span> <span class="o">&gt;=</span> <span class="n">q_end</span><span class="p">:</span>
772            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
773       
774        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_extrapolated_data</span><span class="p">(</span>\
775                                <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_function</span><span class="p">,</span>
776                                           <span class="n">npts</span><span class="o">=</span><span class="n">npts</span><span class="p">,</span>
777                                           <span class="n">q_start</span><span class="o">=</span><span class="n">q_start</span><span class="p">,</span> <span class="n">q_end</span><span class="o">=</span><span class="n">q_end</span><span class="p">)</span>
778     </div>
779<div class="viewcode-block" id="InvariantCalculator.set_extrapolation"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.set_extrapolation">[docs]</a>    <span class="k">def</span> <span class="nf">set_extrapolation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">range</span><span class="p">,</span> <span class="n">npts</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">power</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
780        <span class="sd">&quot;&quot;&quot;</span>
781<span class="sd">        Set the extrapolation parameters for the high or low Q-range.</span>
782<span class="sd">        Note that this does not turn extrapolation on or off.</span>
783<span class="sd">        </span>
784<span class="sd">        :param range: a keyword set the type of extrapolation . type string</span>
785<span class="sd">        :param npts: the numbers of q points of data to consider</span>
786<span class="sd">            for extrapolation</span>
787<span class="sd">        :param function: a keyword to select the function to use</span>
788<span class="sd">            for extrapolation.</span>
789<span class="sd">            of type string.</span>
790<span class="sd">        :param power: an power to apply power_low function</span>
791<span class="sd">                </span>
792<span class="sd">        &quot;&quot;&quot;</span>
793        <span class="nb">range</span> <span class="o">=</span> <span class="nb">range</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
794        <span class="k">if</span> <span class="nb">range</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s">&#39;high&#39;</span><span class="p">,</span> <span class="s">&#39;low&#39;</span><span class="p">]:</span>
795            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="s">&quot;Extrapolation range should be &#39;high&#39; or &#39;low&#39;&quot;</span>
796        <span class="n">function</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
797        <span class="k">if</span> <span class="n">function</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s">&#39;power_law&#39;</span><span class="p">,</span> <span class="s">&#39;guinier&#39;</span><span class="p">]:</span>
798            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Extrapolation function should be &#39;guinier&#39; or &#39;power_law&#39;&quot;</span>
799            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="n">msg</span>
800       
801        <span class="k">if</span> <span class="nb">range</span> <span class="o">==</span> <span class="s">&#39;high&#39;</span><span class="p">:</span>
802            <span class="k">if</span> <span class="n">function</span> <span class="o">!=</span> <span class="s">&#39;power_law&#39;</span><span class="p">:</span>
803                <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Extrapolation only allows a power law at high Q&quot;</span>
804                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="n">msg</span>
805            <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_npts</span>  <span class="o">=</span> <span class="n">npts</span>
806            <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power</span> <span class="o">=</span> <span class="n">power</span>
807            <span class="bp">self</span><span class="o">.</span><span class="n">_high_extrapolation_power_fitted</span> <span class="o">=</span> <span class="n">power</span>
808        <span class="k">else</span><span class="p">:</span>
809            <span class="k">if</span> <span class="n">function</span> <span class="o">==</span> <span class="s">&#39;power_law&#39;</span><span class="p">:</span>
810                <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_function</span> <span class="o">=</span> <span class="n">PowerLaw</span><span class="p">()</span>
811            <span class="k">else</span><span class="p">:</span>
812                <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_function</span> <span class="o">=</span> <span class="n">Guinier</span><span class="p">()</span>
813            <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_npts</span>  <span class="o">=</span> <span class="n">npts</span>
814            <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power</span> <span class="o">=</span> <span class="n">power</span>
815            <span class="bp">self</span><span class="o">.</span><span class="n">_low_extrapolation_power_fitted</span> <span class="o">=</span> <span class="n">power</span>
816        </div>
817<div class="viewcode-block" id="InvariantCalculator.get_qstar"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_qstar">[docs]</a>    <span class="k">def</span> <span class="nf">get_qstar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extrapolation</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
818        <span class="sd">&quot;&quot;&quot;</span>
819<span class="sd">        Compute the invariant of the local copy of data.</span>
820<span class="sd">       </span>
821<span class="sd">        :param extrapolation: string to apply optional extrapolation </span>
822<span class="sd">           </span>
823<span class="sd">        :return q_star: invariant of the data within data&#39;s q range</span>
824<span class="sd">        </span>
825<span class="sd">        :warning: When using setting data to Data1D ,</span>
826<span class="sd">            the user is responsible of</span>
827<span class="sd">            checking that the scale and the background are</span>
828<span class="sd">            properly apply to the data</span>
829<span class="sd">        </span>
830<span class="sd">        &quot;&quot;&quot;</span>
831        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_qstar</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span>
832        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_qstar_uncertainty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">)</span>
833       
834        <span class="k">if</span> <span class="n">extrapolation</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
835            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span>
836       
837        <span class="c"># Compute invariant plus invariant of extrapolated data</span>
838        <span class="n">extrapolation</span> <span class="o">=</span> <span class="n">extrapolation</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>   
839        <span class="k">if</span> <span class="n">extrapolation</span> <span class="o">==</span> <span class="s">&quot;low&quot;</span><span class="p">:</span>
840            <span class="n">qs_low</span><span class="p">,</span> <span class="n">dqs_low</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_qstar_low</span><span class="p">()</span>
841            <span class="n">qs_hi</span><span class="p">,</span> <span class="n">dqs_hi</span>   <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
842           
843        <span class="k">elif</span> <span class="n">extrapolation</span> <span class="o">==</span> <span class="s">&quot;high&quot;</span><span class="p">:</span>
844            <span class="n">qs_low</span><span class="p">,</span> <span class="n">dqs_low</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
845            <span class="n">qs_hi</span><span class="p">,</span> <span class="n">dqs_hi</span>   <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_qstar_high</span><span class="p">()</span>
846           
847        <span class="k">elif</span> <span class="n">extrapolation</span> <span class="o">==</span> <span class="s">&quot;both&quot;</span><span class="p">:</span>
848            <span class="n">qs_low</span><span class="p">,</span> <span class="n">dqs_low</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_qstar_low</span><span class="p">()</span>
849            <span class="n">qs_hi</span><span class="p">,</span> <span class="n">dqs_hi</span>   <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_qstar_high</span><span class="p">()</span>
850           
851        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span>     <span class="o">+=</span> <span class="n">qs_low</span> <span class="o">+</span> <span class="n">qs_hi</span>
852        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span> \
853                                    <span class="o">+</span> <span class="n">dqs_low</span><span class="o">*</span><span class="n">dqs_low</span> <span class="o">+</span> <span class="n">dqs_hi</span><span class="o">*</span><span class="n">dqs_hi</span><span class="p">)</span>
854       
855        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span>
856       </div>
857<div class="viewcode-block" id="InvariantCalculator.get_surface"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_surface">[docs]</a>    <span class="k">def</span> <span class="nf">get_surface</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">contrast</span><span class="p">,</span> <span class="n">porod_const</span><span class="p">,</span> <span class="n">extrapolation</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
858        <span class="sd">&quot;&quot;&quot;</span>
859<span class="sd">        Compute the specific surface from the data.</span>
860<span class="sd">        </span>
861<span class="sd">        Implementation::</span>
862<span class="sd">        </span>
863<span class="sd">          V =  self.get_volume_fraction(contrast, extrapolation)</span>
864<span class="sd">    </span>
865<span class="sd">          Compute the surface given by:</span>
866<span class="sd">            surface = (2*pi *V(1- V)*porod_const)/ q_star</span>
867<span class="sd">           </span>
868<span class="sd">        :param contrast: contrast value to compute the volume</span>
869<span class="sd">        :param porod_const: Porod constant to compute the surface </span>
870<span class="sd">        :param extrapolation: string to apply optional extrapolation</span>
871<span class="sd">        </span>
872<span class="sd">        :return: specific surface </span>
873<span class="sd">        &quot;&quot;&quot;</span>
874        <span class="c"># Compute the volume</span>
875        <span class="n">volume</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_volume_fraction</span><span class="p">(</span><span class="n">contrast</span><span class="p">,</span> <span class="n">extrapolation</span><span class="p">)</span>
876        <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">volume</span> <span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">volume</span><span class="p">)</span> <span class="o">*</span> \
877            <span class="nb">float</span><span class="p">(</span><span class="n">porod_const</span><span class="p">)</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span>
878        </div>
879<div class="viewcode-block" id="InvariantCalculator.get_volume_fraction"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_volume_fraction">[docs]</a>    <span class="k">def</span> <span class="nf">get_volume_fraction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">contrast</span><span class="p">,</span> <span class="n">extrapolation</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
880        <span class="sd">&quot;&quot;&quot;</span>
881<span class="sd">        Compute volume fraction is deduced as follow: ::</span>
882<span class="sd">        </span>
883<span class="sd">            q_star = 2*(pi*contrast)**2* volume( 1- volume)</span>
884<span class="sd">            for k = 10^(-8)*q_star/(2*(pi*|contrast|)**2)</span>
885<span class="sd">            we get 2 values of volume:</span>
886<span class="sd">                 with   1 - 4 * k &gt;= 0</span>
887<span class="sd">                 volume1 = (1- sqrt(1- 4*k))/2</span>
888<span class="sd">                 volume2 = (1+ sqrt(1- 4*k))/2</span>
889<span class="sd">           </span>
890<span class="sd">            q_star: the invariant value included extrapolation is applied</span>
891<span class="sd">                         unit  1/A^(3)*1/cm</span>
892<span class="sd">                    q_star = self.get_qstar()</span>
893<span class="sd">                    </span>
894<span class="sd">            the result returned will be 0 &lt;= volume &lt;= 1</span>
895<span class="sd">        </span>
896<span class="sd">        :param contrast: contrast value provides by the user of type float.</span>
897<span class="sd">                 contrast unit is 1/A^(2)= 10^(16)cm^(2)</span>
898<span class="sd">        :param extrapolation: string to apply optional extrapolation</span>
899<span class="sd">        </span>
900<span class="sd">        :return: volume fraction</span>
901<span class="sd">        </span>
902<span class="sd">        :note: volume fraction must have no unit</span>
903<span class="sd">        &quot;&quot;&quot;</span>
904        <span class="k">if</span> <span class="n">contrast</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
905            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="s">&quot;The contrast parameter must be greater than zero&quot;</span> 
906       
907        <span class="c"># Make sure Q star is up to date</span>
908        <span class="bp">self</span><span class="o">.</span><span class="n">get_qstar</span><span class="p">(</span><span class="n">extrapolation</span><span class="p">)</span>
909       
910        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
911            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Invalid invariant: Invariant Q* must be greater than zero&quot;</span>
912            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">msg</span>
913       
914        <span class="c"># Compute intermediate constant</span>
915        <span class="n">k</span> <span class="o">=</span>  <span class="mf">1.e-8</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">fabs</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">contrast</span><span class="p">)))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
916        <span class="c"># Check discriminant value</span>
917        <span class="n">discrim</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">k</span>
918       
919        <span class="c"># Compute volume fraction</span>
920        <span class="k">if</span> <span class="n">discrim</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
921            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Could not compute the volume fraction: negative discriminant&quot;</span>
922            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">msg</span>
923        <span class="k">elif</span> <span class="n">discrim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
924            <span class="k">return</span> <span class="mi">1</span><span class="o">/</span><span class="mi">2</span>
925        <span class="k">else</span><span class="p">:</span>
926            <span class="n">volume1</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">discrim</span><span class="p">))</span>
927            <span class="n">volume2</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">discrim</span><span class="p">))</span>
928           
929            <span class="k">if</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">volume1</span> <span class="ow">and</span> <span class="n">volume1</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
930                <span class="k">return</span> <span class="n">volume1</span>
931            <span class="k">elif</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">volume2</span> <span class="ow">and</span> <span class="n">volume2</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span> 
932                <span class="k">return</span> <span class="n">volume2</span> 
933            <span class="n">msg</span> <span class="o">=</span> <span class="s">&quot;Could not compute the volume fraction: inconsistent results&quot;</span>
934            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">msg</span>
935    </div>
936<div class="viewcode-block" id="InvariantCalculator.get_qstar_with_error"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_qstar_with_error">[docs]</a>    <span class="k">def</span> <span class="nf">get_qstar_with_error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extrapolation</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
937        <span class="sd">&quot;&quot;&quot;</span>
938<span class="sd">        Compute the invariant uncertainty.</span>
939<span class="sd">        This uncertainty computation depends on whether or not the data is</span>
940<span class="sd">        smeared.</span>
941<span class="sd">        </span>
942<span class="sd">        :param extrapolation: string to apply optional extrapolation</span>
943<span class="sd">        </span>
944<span class="sd">        :return: invariant, the invariant uncertainty</span>
945<span class="sd">        &quot;&quot;&quot;</span>   
946        <span class="bp">self</span><span class="o">.</span><span class="n">get_qstar</span><span class="p">(</span><span class="n">extrapolation</span><span class="p">)</span>
947        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span>
948    </div>
949<div class="viewcode-block" id="InvariantCalculator.get_volume_fraction_with_error"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_volume_fraction_with_error">[docs]</a>    <span class="k">def</span> <span class="nf">get_volume_fraction_with_error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">contrast</span><span class="p">,</span> <span class="n">extrapolation</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
950        <span class="sd">&quot;&quot;&quot;</span>
951<span class="sd">        Compute uncertainty on volume value as well as the volume fraction</span>
952<span class="sd">        This uncertainty is given by the following equation: ::</span>
953<span class="sd">        </span>
954<span class="sd">            dV = 0.5 * (4*k* dq_star) /(2* math.sqrt(1-k* q_star))</span>
955<span class="sd">                                 </span>
956<span class="sd">            for k = 10^(-8)*q_star/(2*(pi*|contrast|)**2)</span>
957<span class="sd">            </span>
958<span class="sd">            q_star: the invariant value including extrapolated value if existing</span>
959<span class="sd">            dq_star: the invariant uncertainty</span>
960<span class="sd">            dV: the volume uncertainty</span>
961<span class="sd">        </span>
962<span class="sd">        The uncertainty will be set to -1 if it can&#39;t be computed.</span>
963<span class="sd">        </span>
964<span class="sd">        :param contrast: contrast value </span>
965<span class="sd">        :param extrapolation: string to apply optional extrapolation</span>
966<span class="sd">        </span>
967<span class="sd">        :return: V, dV = volume fraction, error on volume fraction</span>
968<span class="sd">        &quot;&quot;&quot;</span>
969        <span class="n">volume</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_volume_fraction</span><span class="p">(</span><span class="n">contrast</span><span class="p">,</span> <span class="n">extrapolation</span><span class="p">)</span>
970       
971        <span class="c"># Compute error</span>
972        <span class="n">k</span> <span class="o">=</span>  <span class="mf">1.e-8</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span> <span class="o">/</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">fabs</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">contrast</span><span class="p">)))</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
973        <span class="c"># Check value inside the sqrt function</span>
974        <span class="n">value</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">k</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span>
975        <span class="k">if</span> <span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
976            <span class="n">uncertainty</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
977        <span class="c"># Compute uncertainty</span>
978        <span class="n">uncertainty</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">fabs</span><span class="p">((</span><span class="mf">0.5</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">k</span> <span class="o">*</span> \
979                        <span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">k</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span><span class="p">)))</span>
980       
981        <span class="k">return</span> <span class="n">volume</span><span class="p">,</span> <span class="n">uncertainty</span>
982    </div>
983<div class="viewcode-block" id="InvariantCalculator.get_surface_with_error"><a class="viewcode-back" href="../../../dev/api/sas.invariant.html#sas.invariant.invariant.InvariantCalculator.get_surface_with_error">[docs]</a>    <span class="k">def</span> <span class="nf">get_surface_with_error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">contrast</span><span class="p">,</span> <span class="n">porod_const</span><span class="p">,</span> <span class="n">extrapolation</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
984        <span class="sd">&quot;&quot;&quot;</span>
985<span class="sd">        Compute uncertainty of the surface value as well as the surface value.</span>
986<span class="sd">        The uncertainty is given as follow: ::</span>
987<span class="sd">        </span>
988<span class="sd">            dS = porod_const *2*pi[( dV -2*V*dV)/q_star</span>
989<span class="sd">                 + dq_star(v-v**2)</span>
990<span class="sd">                 </span>
991<span class="sd">            q_star: the invariant value</span>
992<span class="sd">            dq_star: the invariant uncertainty</span>
993<span class="sd">            V: the volume fraction value</span>
994<span class="sd">            dV: the volume uncertainty</span>
995<span class="sd">        </span>
996<span class="sd">        :param contrast: contrast value</span>
997<span class="sd">        :param porod_const: porod constant value </span>
998<span class="sd">        :param extrapolation: string to apply optional extrapolation</span>
999<span class="sd">        </span>
1000<span class="sd">        :return S, dS: the surface, with its uncertainty</span>
1001<span class="sd">        &quot;&quot;&quot;</span>
1002        <span class="c"># We get the volume fraction, with error</span>
1003        <span class="c">#   get_volume_fraction_with_error calls get_volume_fraction</span>
1004        <span class="c">#   get_volume_fraction calls get_qstar</span>
1005        <span class="c">#   which computes Qstar and dQstar</span>
1006        <span class="n">v</span><span class="p">,</span> <span class="n">dv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_volume_fraction_with_error</span><span class="p">(</span><span class="n">contrast</span><span class="p">,</span> <span class="n">extrapolation</span><span class="p">)</span>
1007
1008        <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_surface</span><span class="p">(</span><span class="n">contrast</span><span class="o">=</span><span class="n">contrast</span><span class="p">,</span> <span class="n">porod_const</span><span class="o">=</span><span class="n">porod_const</span><span class="p">,</span> 
1009                             <span class="n">extrapolation</span><span class="o">=</span><span class="n">extrapolation</span><span class="p">)</span>
1010        <span class="n">ds</span> <span class="o">=</span> <span class="n">porod_const</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="p">((</span> <span class="n">dv</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">v</span> <span class="o">*</span> <span class="n">dv</span><span class="p">)</span><span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar</span>\
1011                 <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_qstar_err</span> <span class="o">*</span> <span class="p">(</span> <span class="n">v</span> <span class="o">-</span> <span class="n">v</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
1012
1013        <span class="k">return</span> <span class="n">s</span><span class="p">,</span> <span class="n">ds</span></div></div>
1014</pre></div>
1015
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