matthias muntwiler
17036e1daa
- find attributes of region scans in angle-scan panel - more detailed description of reduced import with regions
155 lines
128 KiB
HTML
155 lines
128 KiB
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<a href="pearl-fitfuncs_8ipf.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="preprocessor">#pragma rtGlobals=3// Use modern global access method and strict wave access.</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="preprocessor">#pragma IgorVersion = 6.2</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="preprocessor">#pragma ModuleName = PearlFitFuncs</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="preprocessor">#pragma version = 1.02</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#include "mm-physconst"</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">// Gaussian shapes</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00044"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#aad1418e71830c1ec71d7dd62b2ecf9ba"> 44</a></span> threadsafe variable <a class="code" href="pearl-fitfuncs_8ipf.html#aad1418e71830c1ec71d7dd62b2ecf9ba">MultiGaussLinBG</a>(wave w, variable x){</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  wave w</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  variable x</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  variable np = numpnts(w)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  variable ip</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  variable v = w[0] + x * w[1]</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">for</span> (ip = 2; ip < np; ip += 3)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  v += w[ip] * exp( -( (x - w[ip+1]) / w[ip+2] )^2 )</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  endfor</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">return</span> v</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> };</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a2c6547164c0b46efecf4d372ea04c263"> 79</a></span> threadsafe variable <a class="code" href="pearl-fitfuncs_8ipf.html#a2c6547164c0b46efecf4d372ea04c263">MultiGaussLinBG_AO</a>(wave pw, wave yw, wave xw){</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  wave pw</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  wave yw</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  wave xw</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  variable np = numpnts(pw)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  variable ip</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  yw = pw[0] + xw[p] * pw[1]</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">for</span> (ip = 2; ip < np; ip += 3)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  yw += pw[ip] * exp( -( (xw[p] - pw[ip+1]) / pw[ip+2] )^2 )</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  endfor</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> };</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment">// Voigt shapes</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00110"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a3a94468da285a31eed5e990cd90e5cdf"> 110</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a3a94468da285a31eed5e990cd90e5cdf">MultiVoigtLinBG</a>(wave w, variable x){</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  wave w</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  variable x</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  variable np = numpnts(w)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  variable ip</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  variable v = w[0] + x * w[1]</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">for</span> (ip = 2; ip < np; ip += 4)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  v += w[ip] * VoigtFunc((x - w[ip+1]) / w[ip+2], w[ip+3])</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  endfor</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">return</span> v</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> };</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment">// Doniach-Sunjic shapes</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea"> 140</a></span> threadsafe variable <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(variable x, variable amp, variable pos, variable sing, variable fwhm){</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  variable x</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  variable amp</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  variable pos</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  variable sing</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  variable fwhm</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  variable nom, denom</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  nom = cos(pi * sing / 2 + (1 - sing) * atan((x - pos) / fwhm * 2))</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  denom = ((x - pos)^2 + fwhm^2 / 4)^((1 - sing) / 2)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">return</span> amp * nom / denom * fwhm / 2</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> };</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00167"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a1520bd078ef77fd16ba20e95dbc6829d"> 167</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a1520bd078ef77fd16ba20e95dbc6829d">MultiDoniachSunjicLinBG</a>(wave w, variable x){</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  wave w</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  variable x</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  variable np = numpnts(w)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  variable ip</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  variable v = w[0] + x * w[1]</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">for</span> (ip = 2; ip < np; ip += 4)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  v += <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(x, w[ip], w[ip+1], w[ip+3], w[ip+2])</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  endfor</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">return</span> v</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> };</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#af62cb65b7444ff60e956a45bd5d0ec27"> 183</a></span> threadsafe variable <a class="code" href="pearl-fitfuncs_8ipf.html#af62cb65b7444ff60e956a45bd5d0ec27">ds1_bg</a>(wave w, variable x){</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="comment">// Doniach-Sunjic fit function</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="comment">// 0 <= sing < 1</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  wave w<span class="comment">// coefficients - see below</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  variable x<span class="comment">// independent variable</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">//CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">//CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog.</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">//CurveFitDialog/ Equation:</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="comment">//CurveFitDialog/ f(x) = DoniachSunjic(x, amp, pos, sing, fwhm) + bg</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="comment">//CurveFitDialog/ End of Equation</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="comment">//CurveFitDialog/ Independent Variables 1</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="comment">//CurveFitDialog/ x</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="comment">//CurveFitDialog/ Coefficients 5</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="comment">//CurveFitDialog/ w[0] = bg</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="comment">//CurveFitDialog/ w[1] = amp</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="comment">//CurveFitDialog/ w[2] = pos</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="comment">//CurveFitDialog/ w[3] = sing</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="comment">//CurveFitDialog/ w[4] = FWHM</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">return</span> <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(x, w[1], w[2], w[3], w[4]) + w[0]</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> };</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a1e729418252bf0d05ea6ec5cbd65b834"> 206</a></span> threadsafe variable <a class="code" href="pearl-fitfuncs_8ipf.html#a1e729418252bf0d05ea6ec5cbd65b834">ds2_bg</a>(wave w, variable x){</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  Wave w</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  Variable x</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">//CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="comment">//CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog.</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">//CurveFitDialog/ Equation:</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">//CurveFitDialog/ f(x) = w_0+( w_1*cos(pi*w_3/2+(1-w_3)*atan((x-w_2)/w_4)))/(((x-w_2)^2+w_4^2)^((1-w_3)/2)) +(w_5*cos(pi*w_7/2+(1-w_7)*atan((x-(w_6))/w_8)))/(((x-w_6)^2+w_8^2)^((1-w_7)/2))</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">//CurveFitDialog/ End of Equation</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">//CurveFitDialog/ Independent Variables 1</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="comment">//CurveFitDialog/ x</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="comment">//CurveFitDialog/ Coefficients 9</span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">//CurveFitDialog/ w[0] = bg</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">//CurveFitDialog/ w[1] = amp1</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="comment">//CurveFitDialog/ w[2] = pos1</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="comment">//CurveFitDialog/ w[3] = sing1</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="comment">//CurveFitDialog/ w[4] = wid1</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="comment">//CurveFitDialog/ w[5] = amp2</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">//CurveFitDialog/ w[6] = pos2</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">//CurveFitDialog/ w[7] = sing2</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="comment">//CurveFitDialog/ w[8] = wid2</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  variable ds1 = <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(x, w[1], w[2], w[3], w[4])</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  variable ds2 = <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(x, w[5], w[6], w[7], w[8])</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">return</span> w[0] + ds1 + ds2</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> };</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#ab32134566b2573672ac674565deebd36"> 234</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#ab32134566b2573672ac674565deebd36">ds4_bg</a>(wave w, variable x){</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  Wave w</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  Variable x</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">//CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="comment">//CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog.</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="comment">//CurveFitDialog/ Equation:</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="comment">//CurveFitDialog/ f(x) = w_0+( w_1*cos(pi*w_3/2+(1-w_3)*atan((x-w_2)/w_4)))/(((x-w_2)^2+w_4^2)^((1-w_3)/2)) +(w_5*cos(pi*w_7/2+(1-w_7)*atan((x-(w_6))/w_8)))/(((x-w_6)^2+w_8^2)^((1-w_7)/2)) +( w_9*cos(pi*w_11/2+(1-w_11)*atan((x-w_10)/w_12)))/(((x-w_10)^2+w_12^2)^((1-w_11)/2)) +( w_13*cos(pi*w_15/2+(1-w_15)*atan((x-w_14)/w_16)))/(((x-w_14)^2+w_16^2)^((1-w_15)/2)) </span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">//CurveFitDialog/ End of Equation</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">//CurveFitDialog/ Independent Variables 1</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="comment">//CurveFitDialog/ x</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="comment">//CurveFitDialog/ Coefficients 17</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="comment">//CurveFitDialog/ w[0] = w_0</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="comment">//CurveFitDialog/ w[1] = w_11</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">//CurveFitDialog/ w[2] = w_12</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="comment">//CurveFitDialog/ w[3] = w_13</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="comment">//CurveFitDialog/ w[4] = w_14</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">//CurveFitDialog/ w[5] = w_21</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="comment">//CurveFitDialog/ w[6] = w_22</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="comment">//CurveFitDialog/ w[7] = w_23</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">//CurveFitDialog/ w[8] = w_24</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="comment">//CurveFitDialog/ w[9] = w_31</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="comment">//CurveFitDialog/ w[10] = w_32</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">//CurveFitDialog/ w[11] = w_33</span></div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">//CurveFitDialog/ w[12] = w_34</span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="comment">//CurveFitDialog/ w[13] = w_41</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="comment">//CurveFitDialog/ w[14] = w_42</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">//CurveFitDialog/ w[15] = w_43</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="comment">//CurveFitDialog/ w[16] = w_44</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  Variable ds1, ds2, ds3, ds4</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  ds1=( w[1]*cos(pi*w[3]/2+(1-w[3])*atan((x-w[2])/w[4])))/(((x-w[2])^2+w[4]^2)^((1-w[3])/2))</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  ds2=( w[5]*cos(pi*w[7]/2+(1-w[7])*atan((x-w[6])/w[8])))/(((x-w[6])^2+w[8]^2)^((1-w[7])/2))</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  ds3=( w[9]*cos(pi*w[11]/2+(1-w[11])*atan((x-w[10])/w[12])))/(((x-w[10])^2+w[12]^2)^((1-w[11])/2))</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  ds4=( w[13]*cos(pi*w[15]/2+(1-w[15])*atan((x-w[14])/w[16])))/(((x-w[14])^2+w[16]^2)^((1-w[15])/2))</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> </div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">return</span> w[0]+ds1+ds2+ds3+ds4</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> };</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a5a2a03026b88f3dd99214ab1b26e6f80"> 275</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a5a2a03026b88f3dd99214ab1b26e6f80">ds6_bg</a>(wave w, variable x){</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  Wave w</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  Variable x</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="comment">//CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="comment">//CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog.</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="comment">//CurveFitDialog/ Equation:</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="comment">//CurveFitDialog/ </span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">//CurveFitDialog/ Variable g, ds1, ds2, ds3, ds4, ds5, ds6</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">//CurveFitDialog/ ds1=( w_11*cos(pi*w_13/2+(1-w_13)*atan((x-w_12)/w_14)))/(((x-w_12)^2+w_14^2)^((1-w_13)/2))</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="comment">//CurveFitDialog/ ds2=( w_21*cos(pi*w_23/2+(1-w_23)*atan((x-w_22)/w_24)))/(((x-w_22)^2+w_24^2)^((1-w_23)/2))</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">//CurveFitDialog/ ds3=( w_31*cos(pi*w_33/2+(1-w_33)*atan((x-w_32)/w_34)))/(((x-w_32)^2+w_34^2)^((1-w_33)/2))</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">//CurveFitDialog/ ds4=( w_41*cos(pi*w_43/2+(1-w_43)*atan((x-w_42)/w_44)))/(((x-w_42)^2+w_44^2)^((1-w_43)/2))</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="comment">//CurveFitDialog/ ds5=( w_51*cos(pi*w_53/2+(1-w_53)*atan((x-w_52)/w_54)))/(((x-w_52)^2+w_54^2)^((1-w_53)/2))</span></div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">//CurveFitDialog/ ds6=( w_61*cos(pi*w_63/2+(1-w_63)*atan((x-w_62)/w_64)))/(((x-w_62)^2+w_64^2)^((1-w_63)/2))</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="comment">//CurveFitDialog/ </span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">//CurveFitDialog/ f(x) =w_0+ds1+ds2+ds3+ds4+ds5+ds6</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">//CurveFitDialog/ </span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="comment">//CurveFitDialog/ End of Equation</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="comment">//CurveFitDialog/ Independent Variables 1</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="comment">//CurveFitDialog/ x</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="comment">//CurveFitDialog/ Coefficients 25</span></div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">//CurveFitDialog/ w[0] = w_0</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="comment">//CurveFitDialog/ w[1] = w_11</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="comment">//CurveFitDialog/ w[2] = w_12</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="comment">//CurveFitDialog/ w[3] = w_13</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="comment">//CurveFitDialog/ w[4] = w_14</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">//CurveFitDialog/ w[5] = w_21</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">//CurveFitDialog/ w[6] = w_22</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="comment">//CurveFitDialog/ w[7] = w_23</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="comment">//CurveFitDialog/ w[8] = w_24</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="comment">//CurveFitDialog/ w[9] = w_31</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="comment">//CurveFitDialog/ w[10] = w_32</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="comment">//CurveFitDialog/ w[11] = w_33</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="comment">//CurveFitDialog/ w[12] = w_34</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="comment">//CurveFitDialog/ w[13] = w_41</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="comment">//CurveFitDialog/ w[14] = w_42</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="comment">//CurveFitDialog/ w[15] = w_43</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="comment">//CurveFitDialog/ w[16] = w_44</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="comment">//CurveFitDialog/ w[17] = w_51</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">//CurveFitDialog/ w[18] = w_52</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="comment">//CurveFitDialog/ w[19] = w_53</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="comment">//CurveFitDialog/ w[20] = w_54</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="comment">//CurveFitDialog/ w[21] = w_61</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="comment">//CurveFitDialog/ w[22] = w_62</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="comment">//CurveFitDialog/ w[23] = w_63</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">//CurveFitDialog/ w[24] = w_64</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  Variable ds1, ds2, ds3, ds4, ds5, ds6</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  ds1=( w[1]*cos(pi*w[3]/2+(1-w[3])*atan((x-w[2])/w[4])))/(((x-w[2])^2+w[4]^2)^((1-w[3])/2))</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  ds2=( w[5]*cos(pi*w[7]/2+(1-w[7])*atan((x-w[6])/w[8])))/(((x-w[6])^2+w[8]^2)^((1-w[7])/2))</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  ds3=( w[9]*cos(pi*w[11]/2+(1-w[11])*atan((x-w[10])/w[12])))/(((x-w[10])^2+w[12]^2)^((1-w[11])/2))</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  ds4=( w[13]*cos(pi*w[15]/2+(1-w[15])*atan((x-w[14])/w[16])))/(((x-w[14])^2+w[16]^2)^((1-w[15])/2))</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  ds5=( w[17]*cos(pi*w[19]/2+(1-w[19])*atan((x-w[18])/w[20])))/(((x-w[18])^2+w[20]^2)^((1-w[19])/2))</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  ds6=( w[21]*cos(pi*w[23]/2+(1-w[23])*atan((x-w[22])/w[24])))/(((x-w[22])^2+w[24]^2)^((1-w[23])/2))</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">return</span> w[0]+ds1+ds2+ds3+ds4+ds5+ds6</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> };</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610"> 336</a></span> <span class="keyword">struct </span><a class="code" href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a>{;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="comment">// data structure for DoniachSunjicBroadS structural function fit</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="comment">// waves populated by the FuncFit operation </span></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>;</div><div class="line"><a name="l00341"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb"> 341</a></span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>;</div><div class="line"><a name="l00342"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982"> 342</a></span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a>;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="comment">// convolution parameters to be set upon creation of the structure</span></div><div class="line"><a name="l00345"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a906e214875392bc470dbd4bb4bdda2db"> 345</a></span>  variable <a class="code" href="struct_doniach_sunjic_struct.html#a906e214875392bc470dbd4bb4bdda2db">precision</a>;</div><div class="line"><a name="l00346"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6"> 346</a></span>  variable <a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a>;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="comment">// auxiliary fields used internally by DoniachSunjicBroadS</span></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="comment">// do not touch these</span></div><div class="line"><a name="l00350"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52"> 350</a></span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a>;</div><div class="line"><a name="l00351"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2"> 351</a></span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a>;</div><div class="line"><a name="l00352"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66"> 352</a></span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a>;</div><div class="line"><a name="l00353"></a><span class="lineno"><a class="line" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955"> 353</a></span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a>;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> };</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00357"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a9d110819fa3cd2173f3103724e394fdf"> 357</a></span> threadsafe variable <a class="code" href="pearl-fitfuncs_8ipf.html#a9d110819fa3cd2173f3103724e394fdf">DoniachSunjicBroadS</a>(<a class="code" href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a>* s){</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="comment">// Two-peak (bulk + surface) Doniach-Sunjic line shape with Gaussian broadening (convolution).</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="comment">// Hold parameter 5 at 0 to fit just one peak.</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="comment">// Structural fit function for efficient fitting in procedures.</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="comment">// Calculating the convolution requires auxiliary waves and additional, non-fitting parameters.</span></div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="comment">// To eliminate the time-consuming overhead of creating and killing the auxiliary waves,</span></div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="comment">// these waves are held in the fitting structure.</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  </div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="comment">// Caution: The function on its own is thread-safe.</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="comment">// However, since FuncFit uses the same structure in all threads, the fitting cannot run in parallel.</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="comment">// Set /NTHR=1.</span></div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="comment">// See also Fit_DoniachSunjicBroad (example), DoniachSunjicBroad (conventional fit function)</span></div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  Struct <a class="code" href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a> &s</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> </div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="comment">// pw[0] = bulk amplitude</span></div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="comment">// pw[1] = bulk position</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="comment">// pw[2] = Lorentzian FWHM</span></div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="comment">// pw[3] = Donjach-Sunjic singularity index (0..1)</span></div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="comment">// pw[4] = surface shift</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="comment">// pw[5] = surface/bulk ratio</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="comment">// pw[6] = Gaussian FWHM</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="comment">// pw[7] = constant background</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="comment">// pw[8] = linear background</span></div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  variable <a class="code" href="struct_doniach_sunjic_struct.html#a906e214875392bc470dbd4bb4bdda2db">precision</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a906e214875392bc470dbd4bb4bdda2db">precision</a></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  variable <a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">if</span> (WaveExists(s.<a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a>))</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a></div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a></div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  wave <a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a> = s.<a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  make /n=0 /free <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a>, <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a>, convolution</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  redimension /d <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a>, <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a>, convolution</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  wave fs.xdw = xdw</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  wave fs.model = model</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  wave fs.broadening = broadening</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  wave fs.convolution = convolution</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  endif</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="comment">// calculate wave spacing based on minimum spacing of desired x points</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  differentiate /p xw /d=xdw</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  xdw = abs(xdw)</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  variable xd = wavemin(xdw) / oversampling</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="comment">// calculate broadening wave size based on width and precision variable</span></div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  variable x0b = pw[6] * precision</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  variable nb = 2 * floor(x0b / xd) + 1</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="comment">// calculate model size based on desired range for yw</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  variable x0m = max(abs(wavemax(xw) - pw[1]), abs(wavemin(xw) - pw[1])) + x0b</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  variable nm = 2 * floor(x0m / xd) + 1</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  nb = min(nb, nm * 10)<span class="comment">// limit wave size to avoid runtime errors for unphysically large parameter</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="comment">// create and calculate initial waves, normalize exponential</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  redimension /n=(nb) broadening</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  redimension /n=(nm) model</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  setscale/i x -x0b, x0b, <span class="stringliteral">""</span>, broadening</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  setscale/i x -x0m, x0m, <span class="stringliteral">""</span>, model</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  broadening = exp( - (x / pw[6])^2 * 4 * ln(2))</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  variable nrm = area(broadening)</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  broadening /= nrm</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  model = <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(x, 1, 0, pw[3], pw[2])<span class="comment">// bulk</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  model += <a class="code" href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a>(x, pw[5], pw[4], pw[3], pw[2])<span class="comment">// surface</span></div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="comment">// calculate the convolution</span></div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  Convolve /a <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a>, model</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  variable scale = pw[0] / wavemax(model)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  model *= scale</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <span class="comment">// prepare output</span></div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  nm = numpnts(model)</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  x0m = xd * (nm - 1) / 2</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  setscale/i x -x0m, x0m, <span class="stringliteral">""</span>, model</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  yw = <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a>(xw[p] - pw[1]) + pw[7] + pw[8] * xw[p]</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  yw = numtype(yw) ? 0 : yw</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> };</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00446"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#ae2d138beb7cb39e8042487893095b461"> 446</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#ae2d138beb7cb39e8042487893095b461">DoniachSunjicBroad</a>(wave <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a>){</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="comment">// Two-peak (bulk + surface) Doniach-Sunjic line shape with Gaussian broadening (convolution).</span></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// Hold parameter 5 at 0 to fit just one peak.</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="comment">// Conventional fit function for use with the curve-fitting dialog.</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="comment">// Compared to DoniachSunjicBroadS this function incurs extra overhead</span></div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="comment">// because auxiliary waves are created and killed between function calls.</span></div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="comment">// See also DoniachSunjicBroadS (optimized structural fit function)</span></div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  Wave pw</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  Wave yw</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  Wave xw</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="comment">// pw[0] = bulk amplitude</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="comment">// pw[1] = bulk position</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="comment">// pw[2] = Lorentzian FWHM</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// pw[3] = Donjach-Sunjic singularity index (0..1)</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">// pw[4] = surface shift</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="comment">// pw[5] = surface/bulk ratio</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="comment">// pw[6] = Gaussian FWHM</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">// pw[7] = constant background</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="comment">// pw[8] = linear background</span></div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="comment">// set up data structure</span></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keyword">struct </span><a class="code" href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a> fs</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  fs.precision = 5</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  fs.<a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a> = 4</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a> = pw</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a> = xw</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a> = yw</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="comment">// create temporary calculation waves in a global folder</span></div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  dfref df = root:packages:pearl_fitfuncs:doniach_sunjic</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">if</span> (DataFolderRefStatus(df) == 0)</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  newdatafolder root:packages:pearl_fitfuncs:doniach_sunjic</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  dfref df = root:packages:pearl_fitfuncs:doniach_sunjic</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  endif</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  wave /z /sdfr=df fs.<a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a> = <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  wave /z /sdfr=df fs.<a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a> = <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  wave /z /sdfr=df fs.<a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a> = <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  wave /z /sdfr=df fs.<a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a> = <a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">if</span> (WaveExists(fs.<a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a>) == 0)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  dfref savedf = GetDataFolderDFR()</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  setdatafolder df</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  make /n=0 /d <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a>, <a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a> = xdw</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">model</a> = model</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">broadening</a> = broadening</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  wave fs.<a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a> = <a class="code" href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">convolution</a></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  setdatafolder savedf</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  endif</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="comment">// calculate</span></div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="pearl-fitfuncs_8ipf.html#a9d110819fa3cd2173f3103724e394fdf">DoniachSunjicBroadS</a>(fs)</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  yw = fs.<a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a></div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> };</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00507"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#aff8e8b103c32c8e723b57ce7ad5ef0f5"> 507</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#aff8e8b103c32c8e723b57ce7ad5ef0f5">Calc_DoniachSunjicBroad</a>(wave <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>){</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="comment">// Calculate the DoniachSunjicBroadS line shape</span></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  Wave pw<span class="comment">// coefficient wave</span></div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  Wave yw<span class="comment">// output wave, correct x-scaling required on input</span></div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keyword">struct </span><a class="code" href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a> fs</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  fs.precision = 5</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  fs.<a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a> = 4</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a></div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a></div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  fs.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a> = x</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  </div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="pearl-fitfuncs_8ipf.html#a9d110819fa3cd2173f3103724e394fdf">DoniachSunjicBroadS</a>(fs)</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  yw = fs.<a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a></div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> };</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00528"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a819902ab9f541b75a0fd33a7b52465d0"> 528</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a819902ab9f541b75a0fd33a7b52465d0">Fit_DoniachSunjicBroad</a>(wave <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a>, wave ww){</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <span class="comment">// Fit the DoniachSunjicBroadS line shape.</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="comment">// The function applies constraints which assume that the energy scale is in eV.</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="comment">// Returns chi^2.</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  wave pw<span class="comment">// coefficient wave- pre-load it with initial guess</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  wave yw</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  wave /z xw</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  wave /z ww<span class="comment">// weights (standard deviation)</span></div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keyword">struct </span><a class="code" href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a> fs</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  fs.precision = 5</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  fs.<a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a> = 4</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a></div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">if</span> (WaveExists(xw))</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  fs.<a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a> = x</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  endif</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, fs.<a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  </div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  variable v_chisq = nan</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  variable V_FitMaxIters = 100</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  make /n=1 /t /free constraints = {<span class="stringliteral">"K0 >= 0"</span>, <span class="stringliteral">"K2 > 0"</span>, <span class="stringliteral">"K2 < 10"</span>, <span class="stringliteral">"K3 >= 0"</span>, <span class="stringliteral">"K3 < 1"</span>, <span class="stringliteral">"K4 >= -10"</span>, <span class="stringliteral">"K4 <= 10"</span>, <span class="stringliteral">"K5 >= 0"</span>, <span class="stringliteral">"K5 <= 1"</span>, <span class="stringliteral">"K6 >= 0"</span>, <span class="stringliteral">"K6 < 10"</span>}</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="comment">// note: only single thread allowed</span></div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  FuncFit /NTHR=1 <a class="code" href="pearl-fitfuncs_8ipf.html#a9d110819fa3cd2173f3103724e394fdf">DoniachSunjicBroadS</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, yw /X=xw /D /STRC=fs /C=constraints /NWOK /I=1 /W=ww</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  </div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keywordflow">return</span> v_chisq</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> };</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> <span class="comment">// peak-specific fit functions</span></div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> <span class="comment">//------------------------------------------------------------------------------</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a13a5ee22049d9a3379cd6e55654e70a3"> 564</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a13a5ee22049d9a3379cd6e55654e70a3">Au4f</a>(wave w, variable x){</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="comment">// fit function for a nitrogen 1s-pi* absorption spectrum</span></div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="comment">// modelled as multiple Voigt shapes on a constant background</span></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="comment">// similar to the Igor VoigtFit function</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="comment">// but with a constant gaussian width (instrumental broadening) for all peaks</span></div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  <span class="comment">// gaussian and lorentzian widths are specified as FWHM</span></div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  wave w<span class="comment">// parameters</span></div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// w[0] constant background</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="comment">// w[1] linear background</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="comment">// w[2] global gaussian FWHM</span></div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// w[3 + 0 + (n-1) * 3] peak n area</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="comment">// w[3 + 1 + (n-1) * 3] peak n position</span></div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <span class="comment">// w[3 + 2 + (n-1) * 3] peak n lorentzian FWHM</span></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="comment">// length of wave defines number of peaks</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="comment">// for compatibility with older code the linear background term can be omitted.</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <span class="comment">// if the number of parameters divides by 3, the linear background term is added,</span></div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="comment">// otherwise only the constant background.</span></div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  variable x</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  variable np = 15</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  variable ip, ip0</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  </div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  variable bg = w[0]</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  variable v = bg</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">if</span> (mod(np, 3) == 0)</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  v += w[1] * x</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  ip0 = 3</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  ip0 = 2</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  endif</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> </div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  variable vc1, vc2, vc3, vc4</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  vc2 = 2 * sqrt(ln(2)) / w[ip0-1]</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keywordflow">for</span> (ip = ip0; ip < np; ip += 3)</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  vc1 = w[ip] / sqrt(pi) * vc2</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  vc3 = w[ip+1]</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  vc4 = vc2 * w[ip+2] / 2</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  endfor</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keywordflow">return</span> v</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  </div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span> };</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> </div><div class="line"><a name="l00609"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479"> 609</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a>(wave w, variable x){</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="comment">// Au 4f 5/2 and 7/2 2-component Voigt fit with a common gaussian width</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="comment">// gaussian and lorentzian widths are specified as FWHM</span></div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  wave w<span class="comment">// parameters</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="comment">// w[0] constant background</span></div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="comment">// w[1] linear background</span></div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="comment">// w[2] global gaussian FWHM</span></div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="comment">// w[3] 5/2 bulk area</span></div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// w[4] 5/2 bulk position</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="comment">// w[5] 5/2 lorentzian FWHM</span></div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="comment">// w[6] 7/2 bulk area</span></div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <span class="comment">// w[7] 7/2 bulk position</span></div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="comment">// w[8] 7/2 lorentzian FWHM</span></div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="comment">// w[9] surface/bulk area ratio</span></div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="comment">// w[10] surface core level shift</span></div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  variable x</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> </div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  variable bg = w[0] + w[1] * x</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  variable v = bg</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> </div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  variable vc1<span class="comment">// amplitude</span></div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  variable vc2<span class="comment">// width</span></div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  variable vc3<span class="comment">// position</span></div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  variable vc4<span class="comment">// shape</span></div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  vc2 = 2 * sqrt(ln(2)) / w[2]</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="comment">// 5/2 bulk</span></div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  vc1 = w[3] / sqrt(pi) * vc2</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  vc3 = w[4]</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  vc4 = vc2 * w[5] / 2</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="comment">// 5/2 surface</span></div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  vc1 = w[3] / sqrt(pi) * vc2 * w[9]</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  vc3 = w[4] + w[10]</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  vc4 = vc2 * w[5] / 2</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="comment">// 7/2 bulk</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  vc1 = w[6] / sqrt(pi) * vc2</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  vc3 = w[7]</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  vc4 = vc2 * w[8] / 2</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="comment">// 7/2 surface</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  vc1 = w[6] / sqrt(pi) * vc2 * w[9]</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  vc3 = w[7] + w[10]</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  vc4 = vc2 * w[8] / 2</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span> </div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">return</span> v</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  </div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> };</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span> </div><div class="line"><a name="l00663"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a84a0278284332631682ce032018d1716"> 663</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a84a0278284332631682ce032018d1716">ShowComponents_Au4f_2p2</a>(wave coef_wave, wave fit_wave){</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  wave coef_wave</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  wave fit_wave</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> </div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  duplicate /free coef_wave, coef1, coef2</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  coef1[9] = 0</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  coef2[3] *= coef_wave[9]</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  coef2[4] += coef_wave[10]</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  coef2[6] *= coef_wave[9]</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  coef2[7] += coef_wave[10]</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  coef2[9] = 0</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordtype">string</span> s_fit_wave = NameOfWave(fit_wave)</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keywordtype">string</span> s_fit_p1 = s_fit_wave + <span class="stringliteral">"_p1"</span></div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keywordtype">string</span> s_fit_p2 = s_fit_wave + <span class="stringliteral">"_p2"</span></div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  duplicate /o fit_wave, $(s_fit_p1) /wave=fit_p1</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  duplicate /o fit_wave, $(s_fit_p2) /wave=fit_p2</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  fit_p1 = <a class="code" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a>(coef1, x)</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  fit_p2 = <a class="code" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a>(coef2, x)</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> </div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keywordtype">string</span> traces = TraceNameList(<span class="stringliteral">""</span>, <span class="stringliteral">";"</span>, 1)</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <span class="keywordflow">if</span> ((WhichListItem(s_fit_wave, traces, <span class="stringliteral">";"</span>) >= 0) && (WhichListItem(s_fit_p1, traces, <span class="stringliteral">";"</span>) < 0))</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  appendtograph fit_p1, fit_p2</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  ModifyGraph lstyle($s_fit_p1)=2</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  ModifyGraph lstyle($s_fit_p2)=2</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  ModifyGraph rgb($s_fit_p1)=(0,0,65280)</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  ModifyGraph rgb($s_fit_p2)=(0,0,65280)</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  endif</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> };</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> </div><div class="line"><a name="l00694"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a709f7c4585b1d850ea8aae1885ac18cb"> 694</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a709f7c4585b1d850ea8aae1885ac18cb">Au4f_2p3</a>(wave w, variable x){</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="comment">// Au 4f 5/2 and 7/2 3-component Voigt fit with a common gaussian width</span></div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="comment">// gaussian and lorentzian widths are specified as FWHM</span></div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  wave w<span class="comment">// parameters</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="comment">// w[0] constant background</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="comment">// w[1] linear background</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="comment">// w[2] global gaussian FWHM</span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="comment">// w[3] 5/2 bulk area</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="comment">// w[4] 5/2 bulk position</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="comment">// w[5] 5/2 lorentzian FWHM</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <span class="comment">// w[6] 7/2 bulk area</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="comment">// w[7] 7/2 bulk position</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="comment">// w[8] 7/2 lorentzian FWHM</span></div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="comment">// w[9] surface/bulk area ratio</span></div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="comment">// w[10] surface core level shift</span></div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="comment">// w[11] 2nd layer/bulk area ratio</span></div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <span class="comment">// w[12] 2nd layer core level shift</span></div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  variable x</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span> </div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  variable bg = w[0] + w[1] * x</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  variable v = bg</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  variable vc1<span class="comment">// amplitude</span></div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  variable vc2<span class="comment">// width</span></div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  variable vc3<span class="comment">// position</span></div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  variable vc4<span class="comment">// shape</span></div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  vc2 = 2 * sqrt(ln(2)) / w[2]</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> </div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="comment">// 5/2 bulk</span></div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  vc1 = w[3] / sqrt(pi) * vc2</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  vc3 = w[4]</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  vc4 = vc2 * w[5] / 2</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> </div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <span class="comment">// 5/2 surface</span></div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  vc1 = w[3] / sqrt(pi) * vc2 * w[9]</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  vc3 = w[4] + w[10]</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  vc4 = vc2 * w[5] / 2</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> </div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="comment">// 5/2 2nd layer</span></div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  vc1 = w[3] / sqrt(pi) * vc2 * w[11]</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  vc3 = w[4] + w[12]</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  vc4 = vc2 * w[5] / 2</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span> </div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="comment">// 7/2 bulk</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  vc1 = w[6] / sqrt(pi) * vc2</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  vc3 = w[7]</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  vc4 = vc2 * w[8] / 2</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> </div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="comment">// 7/2 surface</span></div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  vc1 = w[6] / sqrt(pi) * vc2 * w[9]</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  vc3 = w[7] + w[10]</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  vc4 = vc2 * w[8] / 2</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> </div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="comment">// 7/2 2nd layer</span></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  vc1 = w[6] / sqrt(pi) * vc2 * w[11]</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  vc3 = w[7] + w[12]</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  vc4 = vc2 * w[8] / 2</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  v += vc1 * VoigtFunc(vc2 * (x - vc3), vc4)</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="keywordflow">return</span> v</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  </div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> };</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span> </div><div class="line"><a name="l00762"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a02368cc4adfbd746cd2f1e7d73884a61"> 762</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a02368cc4adfbd746cd2f1e7d73884a61">ShowComponents_Au4f_2p3</a>(wave coef_wave, wave fit_wave){</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  wave coef_wave</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  wave fit_wave</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> </div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  duplicate /free coef_wave, coef1, coef2, coef3</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  coef1[9] = 0</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  coef1[11] = 0</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  </div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  coef2[3] *= coef_wave[9]</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  coef2[4] += coef_wave[10]</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  coef2[6] *= coef_wave[9]</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  coef2[7] += coef_wave[10]</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  coef2[9] = 0</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  coef2[11] = 0</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  </div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  coef3[3] *= coef_wave[11]</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  coef3[4] += coef_wave[12]</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  coef3[6] *= coef_wave[11]</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  coef3[7] += coef_wave[12]</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  coef3[9] = 0</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  coef3[11] = 0</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  </div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keywordtype">string</span> s_fit_wave = NameOfWave(fit_wave)</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="keywordtype">string</span> s_fit_p1 = s_fit_wave + <span class="stringliteral">"_p1"</span></div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keywordtype">string</span> s_fit_p2 = s_fit_wave + <span class="stringliteral">"_p2"</span></div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="keywordtype">string</span> s_fit_p3 = s_fit_wave + <span class="stringliteral">"_p3"</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  duplicate /o fit_wave, $(s_fit_p1) /wave=fit_p1</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  duplicate /o fit_wave, $(s_fit_p2) /wave=fit_p2</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  duplicate /o fit_wave, $(s_fit_p3) /wave=fit_p3</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  </div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  fit_p1 = <a class="code" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a>(coef1, x)</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  fit_p2 = <a class="code" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a>(coef2, x)</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  fit_p3 = <a class="code" href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a>(coef3, x)</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> </div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="keywordtype">string</span> traces = TraceNameList(<span class="stringliteral">""</span>, <span class="stringliteral">";"</span>, 1)</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="keywordflow">if</span> ((WhichListItem(s_fit_wave, traces, <span class="stringliteral">";"</span>) >= 0) && (WhichListItem(s_fit_p1, traces, <span class="stringliteral">";"</span>) < 0))</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  appendtograph fit_p1, fit_p2, fit_p3</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  ModifyGraph lstyle($s_fit_p1)=2</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  ModifyGraph lstyle($s_fit_p2)=2</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  ModifyGraph lstyle($s_fit_p3)=2</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  ModifyGraph rgb($s_fit_p1)=(0,0,65280)</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  ModifyGraph rgb($s_fit_p2)=(0,0,65280)</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  ModifyGraph rgb($s_fit_p3)=(0,0,65280)</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  endif</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> };</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> </div><div class="line"><a name="l00817"></a><span class="lineno"><a class="line" href="pearl-fitfuncs_8ipf.html#a4d20215153c0e0cee3870dfceded8bc9"> 817</a></span> variable <a class="code" href="pearl-fitfuncs_8ipf.html#a4d20215153c0e0cee3870dfceded8bc9">FermiGaussConv</a>(wave <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, wave <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a>){</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  WAVE <a class="code" href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">pw</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">yw</a>, xw</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  </div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="comment">// half width of temporary gaussian wave is pw[5] multiplied by this factor (may be fractional)</span></div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  variable precision_g = 5</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  variable <a class="code" href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">oversampling</a> = 4</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  </div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <span class="comment">// calculate wave spacing based on minimum spacing of desired x points</span></div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  duplicate /free <a class="code" href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">xw</a>, <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a></div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  differentiate /p xw /d=<a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a></div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <a class="code" href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">xdw</a> = abs(xdw)</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  variable xd = wavemin(xdw) / oversampling</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  </div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <span class="comment">// calculate gausswave size based on pw[5] and precision variable</span></div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  variable x0g = abs(pw[5]) * precision_g</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  variable ng = 2 * floor(x0g / xd) + 1</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="comment">// calculate fermiwave size based on desired range for yw</span></div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  variable emax = wavemax(xw)</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  variable emin = wavemin(xw)</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  variable x0f = max(abs(emax - pw[3]), abs(emin - pw[3])) + x0g</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  variable ne = 2 * floor(x0f / xd) + 1</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  </div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="comment">// create and calculate initial waves, normalize exponential</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  make /d /n=(ng) /free gausswave</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  make /d /n=(ne) /free fermiwave</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  setscale/i x -x0g, x0g, <span class="stringliteral">""</span>, gausswave</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  setscale/i x -x0f, x0f, <span class="stringliteral">""</span>, fermiwave</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  </div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  gausswave = exp( - (x / pw[5] )^2 )</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  fermiwave = 1 / (exp( x / (kBoltzmann * pw[4])) + 1.0 )</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> </div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <span class="comment">// calculate the convolution</span></div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  duplicate /free fermiwave, resultwave</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  Convolve /a gausswave, resultwave</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  variable rmax = wavemax(resultwave)</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  resultwave /= rmax</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  </div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="comment">// prepare output</span></div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  ng = numpnts(resultwave)</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  x0g = xd * (ng - 1) / 2</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  setscale/i x -x0g, x0g, <span class="stringliteral">""</span>, resultwave</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  yw = pw[2] * resultwave(xw[p] - pw[3]) + pw[0] + pw[1] * xw[p]</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> };</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span> </div><div class="ttc" id="pearl-fitfuncs_8ipf_html_aad1418e71830c1ec71d7dd62b2ecf9ba"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#aad1418e71830c1ec71d7dd62b2ecf9ba">MultiGaussLinBG</a></div><div class="ttdeci">threadsafe variable MultiGaussLinBG(wave w, variable x)</div><div class="ttdoc">multiple gaussian peaks on a linear background fit function. </div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00044">pearl-fitfuncs.ipf:44</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_ae2d138beb7cb39e8042487893095b461"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#ae2d138beb7cb39e8042487893095b461">DoniachSunjicBroad</a></div><div class="ttdeci">variable DoniachSunjicBroad(wave pw, wave yw, wave xw)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00446">pearl-fitfuncs.ipf:446</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a2c6547164c0b46efecf4d372ea04c263"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a2c6547164c0b46efecf4d372ea04c263">MultiGaussLinBG_AO</a></div><div class="ttdeci">threadsafe variable MultiGaussLinBG_AO(wave pw, wave yw, wave xw)</div><div class="ttdoc">multiple gaussian peaks on a linear background fit function (all at once). </div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00079">pearl-fitfuncs.ipf:79</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a7f05f7827435fea3c986a8d538496955"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a7f05f7827435fea3c986a8d538496955">DoniachSunjicStruct::convolution</a></div><div class="ttdeci">wave convolution</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00353">pearl-fitfuncs.ipf:353</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_ab32134566b2573672ac674565deebd36"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#ab32134566b2573672ac674565deebd36">ds4_bg</a></div><div class="ttdeci">variable ds4_bg(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00234">pearl-fitfuncs.ipf:234</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html"><div class="ttname"><a href="struct_doniach_sunjic_struct.html">DoniachSunjicStruct</a></div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00336">pearl-fitfuncs.ipf:336</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a4d20215153c0e0cee3870dfceded8bc9"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a4d20215153c0e0cee3870dfceded8bc9">FermiGaussConv</a></div><div class="ttdeci">variable FermiGaussConv(wave pw, wave yw, wave xw)</div><div class="ttdoc">convolution of Fermi-Dirac distribution and a Gaussian. </div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00817">pearl-fitfuncs.ipf:817</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a9d110819fa3cd2173f3103724e394fdf"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a9d110819fa3cd2173f3103724e394fdf">DoniachSunjicBroadS</a></div><div class="ttdeci">threadsafe variable DoniachSunjicBroadS(DoniachSunjicStruct *s)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00357">pearl-fitfuncs.ipf:357</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a45c3a3fa68850032e545907ca65ab982"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a45c3a3fa68850032e545907ca65ab982">DoniachSunjicStruct::xw</a></div><div class="ttdeci">wave xw</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00342">pearl-fitfuncs.ipf:342</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_aaa48428994f8720a12e7237ef43e86ea"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#aaa48428994f8720a12e7237ef43e86ea">DoniachSunjic</a></div><div class="ttdeci">threadsafe variable DoniachSunjic(variable x, variable amp, variable pos, variable sing, variable fwhm)</div><div class="ttdoc">Doniach-Sunjic line shape. </div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00140">pearl-fitfuncs.ipf:140</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a13a5ee22049d9a3379cd6e55654e70a3"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a13a5ee22049d9a3379cd6e55654e70a3">Au4f</a></div><div class="ttdeci">variable Au4f(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00564">pearl-fitfuncs.ipf:564</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a709f7c4585b1d850ea8aae1885ac18cb"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a709f7c4585b1d850ea8aae1885ac18cb">Au4f_2p3</a></div><div class="ttdeci">variable Au4f_2p3(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00694">pearl-fitfuncs.ipf:694</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a24cd6a0c96ef8c720e371bb31ac0a479"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a24cd6a0c96ef8c720e371bb31ac0a479">Au4f_2p2</a></div><div class="ttdeci">variable Au4f_2p2(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00609">pearl-fitfuncs.ipf:609</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a84a0278284332631682ce032018d1716"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a84a0278284332631682ce032018d1716">ShowComponents_Au4f_2p2</a></div><div class="ttdeci">variable ShowComponents_Au4f_2p2(wave coef_wave, wave fit_wave)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00663">pearl-fitfuncs.ipf:663</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_aff8e8b103c32c8e723b57ce7ad5ef0f5"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#aff8e8b103c32c8e723b57ce7ad5ef0f5">Calc_DoniachSunjicBroad</a></div><div class="ttdeci">variable Calc_DoniachSunjicBroad(wave pw, wave yw)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00507">pearl-fitfuncs.ipf:507</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a02368cc4adfbd746cd2f1e7d73884a61"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a02368cc4adfbd746cd2f1e7d73884a61">ShowComponents_Au4f_2p3</a></div><div class="ttdeci">variable ShowComponents_Au4f_2p3(wave coef_wave, wave fit_wave)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00762">pearl-fitfuncs.ipf:762</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a819902ab9f541b75a0fd33a7b52465d0"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a819902ab9f541b75a0fd33a7b52465d0">Fit_DoniachSunjicBroad</a></div><div class="ttdeci">variable Fit_DoniachSunjicBroad(wave pw, wave yw, wave xw, wave ww)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00528">pearl-fitfuncs.ipf:528</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a6cef648ad0cf4be1dd9fbe33ff5df1eb"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a6cef648ad0cf4be1dd9fbe33ff5df1eb">DoniachSunjicStruct::yw</a></div><div class="ttdeci">wave yw</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00341">pearl-fitfuncs.ipf:341</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_ab5a630be50286c3cf04e40d5880506e6"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#ab5a630be50286c3cf04e40d5880506e6">DoniachSunjicStruct::oversampling</a></div><div class="ttdeci">variable oversampling</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00346">pearl-fitfuncs.ipf:346</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a906e214875392bc470dbd4bb4bdda2db"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a906e214875392bc470dbd4bb4bdda2db">DoniachSunjicStruct::precision</a></div><div class="ttdeci">variable precision</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00345">pearl-fitfuncs.ipf:345</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a1e729418252bf0d05ea6ec5cbd65b834"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a1e729418252bf0d05ea6ec5cbd65b834">ds2_bg</a></div><div class="ttdeci">threadsafe variable ds2_bg(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00206">pearl-fitfuncs.ipf:206</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a92bbb374f66840510e7cb8b316057610"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a92bbb374f66840510e7cb8b316057610">DoniachSunjicStruct::pw</a></div><div class="ttdeci">wave pw</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00336">pearl-fitfuncs.ipf:336</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a750e7260bf5d4c936dadde714fb2db52"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a750e7260bf5d4c936dadde714fb2db52">DoniachSunjicStruct::xdw</a></div><div class="ttdeci">wave xdw</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00350">pearl-fitfuncs.ipf:350</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a5a2a03026b88f3dd99214ab1b26e6f80"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a5a2a03026b88f3dd99214ab1b26e6f80">ds6_bg</a></div><div class="ttdeci">variable ds6_bg(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00275">pearl-fitfuncs.ipf:275</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_ac9b18c8b44b43c2ee438f37f8d002a66"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#ac9b18c8b44b43c2ee438f37f8d002a66">DoniachSunjicStruct::broadening</a></div><div class="ttdeci">wave broadening</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00352">pearl-fitfuncs.ipf:352</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a1520bd078ef77fd16ba20e95dbc6829d"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a1520bd078ef77fd16ba20e95dbc6829d">MultiDoniachSunjicLinBG</a></div><div class="ttdeci">variable MultiDoniachSunjicLinBG(wave w, variable x)</div><div class="ttdoc">multiple doniach-sunjic peaks on a linear background fit function. </div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00167">pearl-fitfuncs.ipf:167</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_af62cb65b7444ff60e956a45bd5d0ec27"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#af62cb65b7444ff60e956a45bd5d0ec27">ds1_bg</a></div><div class="ttdeci">threadsafe variable ds1_bg(wave w, variable x)</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00183">pearl-fitfuncs.ipf:183</a></div></div>
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<div class="ttc" id="struct_doniach_sunjic_struct_html_a02c13fdcf15e9adfee13464701bb7de2"><div class="ttname"><a href="struct_doniach_sunjic_struct.html#a02c13fdcf15e9adfee13464701bb7de2">DoniachSunjicStruct::model</a></div><div class="ttdeci">wave model</div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00351">pearl-fitfuncs.ipf:351</a></div></div>
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<div class="ttc" id="pearl-fitfuncs_8ipf_html_a3a94468da285a31eed5e990cd90e5cdf"><div class="ttname"><a href="pearl-fitfuncs_8ipf.html#a3a94468da285a31eed5e990cd90e5cdf">MultiVoigtLinBG</a></div><div class="ttdeci">variable MultiVoigtLinBG(wave w, variable x)</div><div class="ttdoc">multiple voigt peaks on a linear background fit function. </div><div class="ttdef"><b>Definition:</b> <a href="pearl-fitfuncs_8ipf_source.html#l00110">pearl-fitfuncs.ipf:110</a></div></div>
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