Index: sansinvariant/test/PolySpheres.txt
===================================================================
--- sansinvariant/test/PolySpheres.txt	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/PolySpheres.txt	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,129 @@
+0 0 0
+0.003	5.58459588740281	1.18158747955905
+0.0031098987853131	5.58031083426704	0.590567038651574
+0.00322382348496395	5.57570963566376	0.393549009924917
+0.00334192157998441	5.57076925142489	0.295030963719936
+0.00346434595406838	5.56546500626888	0.235912377934454
+0.00359125509148719	5.55977048060564	0.19649304625464
+0.00372281328225516	5.55365739513176	0.168329993494493
+0.00385919083481093	5.54709548904188	0.147201704317816
+0.00400056429648997	5.54005239171167	0.130762866039685
+0.0041471166820737	5.53249348774542	0.117606265646706
+0.00429903771071089	5.52438177532811	0.106836379255833
+0.00445652405151833	5.51567771787968	0.0978561667516099
+0.00461977957817848	5.50633908908146	0.0902522688095343
+0.00478901563286383	5.49632081142928	0.0837294051088642
+0.00496445129982955	5.48557478857245	0.078071013176555
+0.00514631368902864	5.47404973182027	0.0731146475833039
+0.00533483823011677	5.46169098134419	0.0687360616971425
+0.00553026897722733	5.44844032277542	0.0648385954423108
+0.00573285892491132	5.43423580009968	0.0613459142176948
+0.00594287033565102	5.41901152598558	0.0581969260678001
+0.00616057507937144	5.40269749095603	0.0553421510214438
+0.00638625498538912	5.38521937312758	0.0527410805447586
+0.00662020220725377	5.36649835060511	0.0503602257700007
+0.00686271960095519	5.34645091903489	0.0481716536085972
+0.00711412111698497	5.32498871729341	0.0461518741430656
+0.00737473220676059	5.30201836482721	0.0442809847258853
+0.00764489024393804	5.27744131476757	0.042542004235883
+0.00792494496115828	5.25115372762826	0.0409203499581539
+0.00821525890279309	5.22304637115972	0.0394034226678704
+0.00851620789427627	5.19300455278556	0.0379802746786099
+0.00882818152862785	5.16090809198815	0.0366413421311323
+0.00915158367080108	5.12663134104523	0.0353782274817065
+0.00948683298050514	5.09004326364579	0.0341835215553371
+0.00983436345418038	5.05100758213086	0.0330506570341895
+0.0101946249868277	5.00938300540588	0.0319737871126917
+0.0105680839544193	4.96502355094501	0.0309476844470793
+0.0109552238176451	4.91777897573675	0.0299676565842223
+0.0113565457477759	4.86749533247943	0.0290294748617302
+0.0117725692754536	4.81401566878959	0.028129314392701
+0.0122038329632491	4.75718088858923	0.0272637032305237
+0.0126508951028575	4.69683079612906	0.0264294791858833
+0.0131143344378333	4.6328053442061	0.0256237530646399
+0.0135947509128025	4.56494610894868	0.0248438773302176
+0.0140927664501195	4.49309801395011	0.0240874193815478
+0.0146090257549759	4.41711132638873	0.0233521387879979
+0.0151441971500024	4.33684394690402	0.0226359679441049
+0.0156989734404448	4.25216401320184	0.0219369957054209
+0.016274072811034	4.16295283440202	0.02125345364705
+0.0168702397557105	4.06910816875267	0.0205837046520924
+0.0174882460414082	3.97054785122191	0.0199262335909773
+0.018128891707144	3.86721376932252	0.0192796398966539
+0.0187930060997066	3.75907617499481	0.0186426318764236
+0.0194814489472863	3.64613830713778	0.0180140226300296
+0.020195111472435	3.52844128312914	0.017392727466335
+0.020934917545796	3.40606919815357	0.0167777627281307
+0.0217018248821003	3.27915434819955	0.016168245946699
+0.0224968262799737	3.14788246616219	0.0155633972549466
+0.0233209509071633	3.01249783077841	0.0149625419902677
+0.0241752656328445	2.87330807557354	0.014365114415782
+0.0250608764087348	2.73068849042531	0.0137706624811018
+0.025978929700802	2.58508557301448	0.0131788535311698
+0.0269306139734194	2.43701955313547	0.0125894808538549
+0.027917161227891	2.28708558202309	0.0120024709338308
+0.028939848597336	2.13595325464285	0.0114178912518828
+0.03	1.98436411914945	0.0108359584355028
+0.031098987853131	1.83312682898386	0.0102570465290936
+0.0322382348496395	1.68310961446225	0.00968169511143006
+0.0334192157998441	1.53522979763462	0.00911061694590931
+0.0346434595406838	1.39044015199819	0.00854470480801729
+0.0359125509148719	1.24971202201179	0.00798503709757077
+0.0372281328225516	1.11401526949531	0.00743288181473221
+0.0385919083481093	0.984295305704227	0.00688969846309937
+0.0400056429648997	0.861447696381298	0.00635713744463823
+0.041471166820737	0.746291084887552	0.00583703653193501
+0.0429903771071089	0.639539452226916	0.00533141403893279
+0.0445652405151833	0.541775002358059	0.00484245834291776
+0.0461977957817848	0.453423199715101	0.0043725133873374
+0.0478901563286383	0.374731660262486	0.00392405960261756
+0.0496445129982955	0.305754670463505	0.00349968908399676
+0.0514631368902864	0.246345042432469	0.00310207240728167
+0.0533483823011677	0.196154775102989	0.0027339113493175
+0.0553026897722733	0.154645558706487	0.00239786582424827
+0.0573285892491132	0.121109530476365	0.00209643340961455
+0.0594287033565102	0.0946998869997846	0.00183174683016576
+0.0616057507937144	0.0744700387975269	0.00160524700637364
+0.0638625498538912	0.0594190450218197	0.00141721058339134
+0.0662020220725377	0.0485402095658083	0.00126619786010495
+0.0686271960095519	0.0408690909004557	0.00114864234027263
+0.0711412111698497	0.0355269108644891	0.00105890919834154
+0.073747322067606	0.0317555481867159	0.000990004803996262
+0.0764489024393804	0.0289410192634603	0.000934728583041217
+0.0792494496115828	0.0266235482140674	0.000886778478341639
+0.0821525890279309	0.0244938820712354	0.00084142588278621
+0.0851620789427627	0.0223771978566425	0.00079569201840763
+0.0882818152862785	0.0202074997238088	0.000748174134333421
+0.0915158367080108	0.0179965321442625	0.000698703927563412
+0.0948683298050514	0.0158017121279252	0.000647963202033436
+0.0983436345418038	0.0136973016001803	0.000597119752789763
+0.101946249868277	0.0117520513949583	0.000547509489443302
+0.105680839544193	0.0100150628847075	0.000500376430417827
+0.109552238176451	0.00850996118857552	0.000456680449925514
+0.113565457477759	0.00723601933523946	0.00041698428883672
+0.117725692754536	0.00617391666825772	0.000381428518691
+0.122038329632491	0.00529351205721941	0.000349790989777702
+0.126508951028575	0.00456133414053745	0.000321607856191534
+0.131143344378333	0.00394624637582449	0.000296316774158286
+0.135947509128025	0.00342265609940122	0.000273380704822851
+0.140927664501195	0.00297143438544347	0.000252365114993151
+0.146090257549759	0.0025792165050889	0.000232963244452651
+0.151441971500024	0.00223690866172535	0.000214981676219225
+0.156989734404448	0.00193810132984136	0.000198305730955978
+0.16274072811034	0.00167781751700682	0.000182862369844488
+0.168702397557105	0.00145174220250147	0.000168591747351613
+0.174882460414082	0.00125587606251196	0.000155431323249698
+0.18128891707144	0.00108646218165673	0.000143310981646312
+0.187930060997066	0.000940032983950856	0.000132154988524658
+0.194814489472863	0.000813473243471077	0.000121886548453162
+0.20195111472435	0.000704050391277569	0.000112431982636513
+0.20934917545796	0.000609404536153752	0.000103723194596338
+0.217018248821003	0.000527512107322615	9.56984655450874e-05
+0.224968262799737	0.000456639295657688	8.83021493485053e-05
+0.233209509071633	0.000395296548373191	8.14838770434946e-05
+0.241752656328445	0.000342200112605765	7.51977709061413e-05
+0.250608764087348	0.000296240753246859	6.94017978268918e-05
+0.25978929700802	0.000256458705152568	6.40573124821912e-05
+0.269306139734194	0.000222023195373744	5.91287412349099e-05
+0.27917161227891	0.000192215032598116	5.45833212982567e-05
+0.28939848597336	0.000166411797938958	5.03908972162823e-05
Index: sansinvariant/test/latex_smeared.xml
===================================================================
--- sansinvariant/test/latex_smeared.xml	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/latex_smeared.xml	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,885 @@
+<?xml version="1.0"?>
+
+<?xml-stylesheet type="text/xsl" href="example.xsl" ?>
+
+<SASroot version="1.0"
+
+    xmlns="cansas1d/1.0"
+
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+
+    xsi:schemaLocation="cansas1d/1.0 http://svn.smallangles.net/svn/canSAS/1dwg/trunk/cansas1d.xsd"
+
+    >
+
+	<SASentry>
+
+		<Title>latex particles 0.5micron diameter in D2O Qdev</Title>
+
+		<Run>latex_sans</Run>
+
+		<SASdata>
+
+			<Idata><Q unit="1/A">0.003797</Q><I unit="1/cm">4006.05896074137</I><Idev unit="1/cm">160.350516888371</Idev><Qdev unit="1/A">0.00109</Qdev><Qmean unit="1/A">0.003945</Qmean><Shadowfactor>0.9956</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.004197</Q><I unit="1/cm">1594.1292143245</I><Idev unit="1/cm">144.193913580953</Idev><Qdev unit="1/A">0.001093</Qdev><Qmean unit="1/A">0.004328</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.004596</Q><I unit="1/cm">772.577974895497</I><Idev unit="1/cm">47.4452686423936</Idev><Qdev unit="1/A">0.001096</Qdev><Qmean unit="1/A">0.004716</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.004996</Q><I unit="1/cm">491.192789065555</I><Idev unit="1/cm">1.64212795726675</Idev><Qdev unit="1/A">0.0011</Qdev><Qmean unit="1/A">0.005106</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.005396</Q><I unit="1/cm">339.222462507531</I><Idev unit="1/cm">18.6638380002217</Idev><Qdev unit="1/A">0.001105</Qdev><Qmean unit="1/A">0.005498</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.005795</Q><I unit="1/cm">234.025331038665</I><Idev unit="1/cm">10.9659413510261</Idev><Qdev unit="1/A">0.001111</Qdev><Qmean unit="1/A">0.00589</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.006195</Q><I unit="1/cm">167.755826933366</I><Idev unit="1/cm">14.4727399222556</Idev><Qdev unit="1/A">0.001117</Qdev><Qmean unit="1/A">0.006284</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.006595</Q><I unit="1/cm">127.184502137658</I><Idev unit="1/cm">11.3388420587577</Idev><Qdev unit="1/A">0.001124</Qdev><Qmean unit="1/A">0.006678</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.006995</Q><I unit="1/cm">97.8743226629875</I><Idev unit="1/cm">7.72496924983092</Idev><Qdev unit="1/A">0.001131</Qdev><Qmean unit="1/A">0.007073</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.007394</Q><I unit="1/cm">75.7387226323965</I><Idev unit="1/cm">1.28726671212578</Idev><Qdev unit="1/A">0.00114</Qdev><Qmean unit="1/A">0.007468</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.007794</Q><I unit="1/cm">59.9674693155301</I><Idev unit="1/cm">5.66729694206856</Idev><Qdev unit="1/A">0.001148</Qdev><Qmean unit="1/A">0.007864</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.008194</Q><I unit="1/cm">48.5642877717183</I><Idev unit="1/cm">0.775068066134696</Idev><Qdev unit="1/A">0.001157</Qdev><Qmean unit="1/A">0.008261</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.008593</Q><I unit="1/cm">39.562368466145</I><Idev unit="1/cm">2.99469690504217</Idev><Qdev unit="1/A">0.001167</Qdev><Qmean unit="1/A">0.008657</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.008993</Q><I unit="1/cm">32.4131561966359</I><Idev unit="1/cm">0.112809624406009</Idev><Qdev unit="1/A">0.001177</Qdev><Qmean unit="1/A">0.009054</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.009393</Q><I unit="1/cm">26.9845122194651</I><Idev unit="1/cm">1.7559522605892</Idev><Qdev unit="1/A">0.001187</Qdev><Qmean unit="1/A">0.009451</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.009792</Q><I unit="1/cm">22.7193453130392</I><Idev unit="1/cm">1.70872993541491</Idev><Qdev unit="1/A">0.001198</Qdev><Qmean unit="1/A">0.009848</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01019</Q><I unit="1/cm">19.1396044557923</I><Idev unit="1/cm">0.870622123390108</Idev><Qdev unit="1/A">0.001209</Qdev><Qmean unit="1/A">0.01025</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01059</Q><I unit="1/cm">16.3106897029038</I><Idev unit="1/cm">1.2784566380161</Idev><Qdev unit="1/A">0.00122</Qdev><Qmean unit="1/A">0.01064</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01099</Q><I unit="1/cm">13.9942384293878</I><Idev unit="1/cm">1.09311729425028</Idev><Qdev unit="1/A">0.001232</Qdev><Qmean unit="1/A">0.01104</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01139</Q><I unit="1/cm">12.0788620780442</I><Idev unit="1/cm">0.321912704167634</Idev><Qdev unit="1/A">0.001244</Qdev><Qmean unit="1/A">0.01144</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01179</Q><I unit="1/cm">10.4538680640273</I><Idev unit="1/cm">0.826219886472332</Idev><Qdev unit="1/A">0.001257</Qdev><Qmean unit="1/A">0.01184</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01219</Q><I unit="1/cm">9.1009265046415</I><Idev unit="1/cm">0.719848600677436</Idev><Qdev unit="1/A">0.00127</Qdev><Qmean unit="1/A">0.01224</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01259</Q><I unit="1/cm">8.00642412230866</I><Idev unit="1/cm">0.684722867161744</Idev><Qdev unit="1/A">0.001283</Qdev><Qmean unit="1/A">0.01263</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01299</Q><I unit="1/cm">7.0456897705157</I><Idev unit="1/cm">0.166699720295245</Idev><Qdev unit="1/A">0.001296</Qdev><Qmean unit="1/A">0.01303</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01339</Q><I unit="1/cm">6.21724872865112</I><Idev unit="1/cm">0.153868634641546</Idev><Qdev unit="1/A">0.00131</Qdev><Qmean unit="1/A">0.01343</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01374</Q><I unit="1/cm">6.74668413666121</I><Idev unit="1/cm">0.184975656820248</Idev><Qdev unit="1/A">0.002327</Qdev><Qmean unit="1/A">0.01391</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01379</Q><I unit="1/cm">5.51301078640481</I><Idev unit="1/cm">0.118762537848476</Idev><Qdev unit="1/A">0.001324</Qdev><Qmean unit="1/A">0.01383</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01419</Q><I unit="1/cm">4.91258237718056</I><Idev unit="1/cm">0.176969813745123</Idev><Qdev unit="1/A">0.001338</Qdev><Qmean unit="1/A">0.01423</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01459</Q><I unit="1/cm">4.38999875927883</I><Idev unit="1/cm">0.311353476052822</Idev><Qdev unit="1/A">0.001353</Qdev><Qmean unit="1/A">0.01463</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01499</Q><I unit="1/cm">3.94346403459969</I><Idev unit="1/cm">0.387860159730271</Idev><Qdev unit="1/A">0.001367</Qdev><Qmean unit="1/A">0.01502</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01505</Q><I unit="1/cm">4.50082504231816</I><Idev unit="1/cm">0.0139779788973798</Idev><Qdev unit="1/A">0.002353</Qdev><Qmean unit="1/A">0.01521</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01539</Q><I unit="1/cm">3.54650814465845</I><Idev unit="1/cm">0.231354575544875</Idev><Qdev unit="1/A">0.001382</Qdev><Qmean unit="1/A">0.01542</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01579</Q><I unit="1/cm">3.20079465045656</I><Idev unit="1/cm">0.236881998414208</Idev><Qdev unit="1/A">0.001397</Qdev><Qmean unit="1/A">0.01582</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01619</Q><I unit="1/cm">2.89569892696003</I><Idev unit="1/cm">0.0558838715831364</Idev><Qdev unit="1/A">0.001413</Qdev><Qmean unit="1/A">0.01622</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.01636</Q><I unit="1/cm">3.14491132919071</I><Idev unit="1/cm">0.286903957843761</Idev><Qdev unit="1/A">0.002381</Qdev><Qmean unit="1/A">0.0165</Qmean><Shadowfactor>1</Shadowfactor></Idata>
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+			<Idata><Q unit="1/A">0.2523</Q><I unit="1/cm">0.0598948998661915</I><Idev unit="1/cm">0.00121759188786928</Idev><Qdev unit="1/A">0.01531</Qdev><Qmean unit="1/A">0.2523</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2559</Q><I unit="1/cm">0.0598726139874623</I><Idev unit="1/cm">0.00278897820945736</Idev><Qdev unit="1/A">0.01551</Qdev><Qmean unit="1/A">0.2559</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2595</Q><I unit="1/cm">0.0598865763133905</I><Idev unit="1/cm">0.00590043083256296</Idev><Qdev unit="1/A">0.01571</Qdev><Qmean unit="1/A">0.2596</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2631</Q><I unit="1/cm">0.0598714989657985</I><Idev unit="1/cm">0.00506789735621128</Idev><Qdev unit="1/A">0.01592</Qdev><Qmean unit="1/A">0.2632</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2667</Q><I unit="1/cm">0.0598796938486174</I><Idev unit="1/cm">0.00413814983082576</Idev><Qdev unit="1/A">0.01612</Qdev><Qmean unit="1/A">0.2668</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2703</Q><I unit="1/cm">0.0598695847941254</I><Idev unit="1/cm">0.000570358074717744</Idev><Qdev unit="1/A">0.01632</Qdev><Qmean unit="1/A">0.2704</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2739</Q><I unit="1/cm">0.0598746609102166</I><Idev unit="1/cm">0.00514749477354896</Idev><Qdev unit="1/A">0.01652</Qdev><Qmean unit="1/A">0.274</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2775</Q><I unit="1/cm">0.0598710946052492</I><Idev unit="1/cm">0.00233949375949796</Idev><Qdev unit="1/A">0.01673</Qdev><Qmean unit="1/A">0.2775</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2811</Q><I unit="1/cm">0.0598651661820699</I><Idev unit="1/cm">0.0013068816502873</Idev><Qdev unit="1/A">0.01693</Qdev><Qmean unit="1/A">0.2811</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2847</Q><I unit="1/cm">0.059869579181067</I><Idev unit="1/cm">0.00145840607426022</Idev><Qdev unit="1/A">0.01713</Qdev><Qmean unit="1/A">0.2847</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2882</Q><I unit="1/cm">0.0598647053691857</I><Idev unit="1/cm">0.00645270241890912</Idev><Qdev unit="1/A">0.01733</Qdev><Qmean unit="1/A">0.2882</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2918</Q><I unit="1/cm">0.0598626946445512</I><Idev unit="1/cm">0.00743014395660804</Idev><Qdev unit="1/A">0.01753</Qdev><Qmean unit="1/A">0.2918</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2953</Q><I unit="1/cm">0.0598615272479092</I><Idev unit="1/cm">0.00159526300663046</Idev><Qdev unit="1/A">0.01773</Qdev><Qmean unit="1/A">0.2953</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.2988</Q><I unit="1/cm">0.0598641396420646</I><Idev unit="1/cm">0.00532105445563836</Idev><Qdev unit="1/A">0.01793</Qdev><Qmean unit="1/A">0.2989</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3024</Q><I unit="1/cm">0.0598612295806549</I><Idev unit="1/cm">0.00362076758305341</Idev><Qdev unit="1/A">0.01813</Qdev><Qmean unit="1/A">0.3024</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3059</Q><I unit="1/cm">0.0598553782115866</I><Idev unit="1/cm">0.000694342700495228</Idev><Qdev unit="1/A">0.01833</Qdev><Qmean unit="1/A">0.3059</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3094</Q><I unit="1/cm">0.0598611284593141</I><Idev unit="1/cm">0.000164906970711582</Idev><Qdev unit="1/A">0.01853</Qdev><Qmean unit="1/A">0.3094</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3129</Q><I unit="1/cm">0.0598564807267493</I><Idev unit="1/cm">0.000292255202139402</Idev><Qdev unit="1/A">0.01873</Qdev><Qmean unit="1/A">0.3129</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3164</Q><I unit="1/cm">0.0598574055729553</I><Idev unit="1/cm">0.00173368799879996</Idev><Qdev unit="1/A">0.01893</Qdev><Qmean unit="1/A">0.3164</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3199</Q><I unit="1/cm">0.0598565118552629</I><Idev unit="1/cm">0.00656001236562596</Idev><Qdev unit="1/A">0.01912</Qdev><Qmean unit="1/A">0.3199</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3234</Q><I unit="1/cm">0.059854469989597</I><Idev unit="1/cm">0.00145280150316772</Idev><Qdev unit="1/A">0.01932</Qdev><Qmean unit="1/A">0.3234</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3268</Q><I unit="1/cm">0.0598540229496195</I><Idev unit="1/cm">0.00037971264686968</Idev><Qdev unit="1/A">0.01952</Qdev><Qmean unit="1/A">0.3269</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3303</Q><I unit="1/cm">0.0598554486681863</I><Idev unit="1/cm">0.000238660576570818</Idev><Qdev unit="1/A">0.01972</Qdev><Qmean unit="1/A">0.3303</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3338</Q><I unit="1/cm">0.0598527397416005</I><Idev unit="1/cm">0.00604866634070472</Idev><Qdev unit="1/A">0.01991</Qdev><Qmean unit="1/A">0.3338</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3372</Q><I unit="1/cm">0.0598517990703868</I><Idev unit="1/cm">0.00241504403863614</Idev><Qdev unit="1/A">0.02011</Qdev><Qmean unit="1/A">0.3372</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3406</Q><I unit="1/cm">0.0598536588848801</I><Idev unit="1/cm">0.00486344183607404</Idev><Qdev unit="1/A">0.0203</Qdev><Qmean unit="1/A">0.3407</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3441</Q><I unit="1/cm">0.0598500192805346</I><Idev unit="1/cm">0.00299483243382103</Idev><Qdev unit="1/A">0.0205</Qdev><Qmean unit="1/A">0.3441</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3475</Q><I unit="1/cm">0.0598510137107766</I><Idev unit="1/cm">0.00582814456851036</Idev><Qdev unit="1/A">0.0207</Qdev><Qmean unit="1/A">0.3475</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3509</Q><I unit="1/cm">0.0598506733448819</I><Idev unit="1/cm">0.0034360032765413</Idev><Qdev unit="1/A">0.02089</Qdev><Qmean unit="1/A">0.3509</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3543</Q><I unit="1/cm">0.0598513080913984</I><Idev unit="1/cm">0.00375553714507095</Idev><Qdev unit="1/A">0.02108</Qdev><Qmean unit="1/A">0.3543</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3577</Q><I unit="1/cm">0.0598469570341251</I><Idev unit="1/cm">0.00145158303366345</Idev><Qdev unit="1/A">0.02128</Qdev><Qmean unit="1/A">0.3577</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3611</Q><I unit="1/cm">0.0598505009672979</I><Idev unit="1/cm">0.00265475137899194</Idev><Qdev unit="1/A">0.02147</Qdev><Qmean unit="1/A">0.3611</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3645</Q><I unit="1/cm">0.0598482977039411</I><Idev unit="1/cm">0.00674573004841572</Idev><Qdev unit="1/A">0.02166</Qdev><Qmean unit="1/A">0.3645</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3678</Q><I unit="1/cm">0.0598486839631768</I><Idev unit="1/cm">0.00111160831046708</Idev><Qdev unit="1/A">0.02186</Qdev><Qmean unit="1/A">0.3678</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3712</Q><I unit="1/cm">0.0598477463604065</I><Idev unit="1/cm">0.00303739914850977</Idev><Qdev unit="1/A">0.02205</Qdev><Qmean unit="1/A">0.3712</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3745</Q><I unit="1/cm">0.059848268321775</I><Idev unit="1/cm">0.00364757844838747</Idev><Qdev unit="1/A">0.02224</Qdev><Qmean unit="1/A">0.3745</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3779</Q><I unit="1/cm">0.0598469126316892</I><Idev unit="1/cm">0.00247959277349808</Idev><Qdev unit="1/A">0.02243</Qdev><Qmean unit="1/A">0.3779</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3812</Q><I unit="1/cm">0.0598471198875677</I><Idev unit="1/cm">0.00371599954529285</Idev><Qdev unit="1/A">0.02262</Qdev><Qmean unit="1/A">0.3812</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3845</Q><I unit="1/cm">0.0598462693918775</I><Idev unit="1/cm">0.00453922346919508</Idev><Qdev unit="1/A">0.02281</Qdev><Qmean unit="1/A">0.3845</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3878</Q><I unit="1/cm">0.0598466342642868</I><Idev unit="1/cm">0.006779525782308</Idev><Qdev unit="1/A">0.023</Qdev><Qmean unit="1/A">0.3878</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3911</Q><I unit="1/cm">0.0598456023046037</I><Idev unit="1/cm">0.0072376973498202</Idev><Qdev unit="1/A">0.02319</Qdev><Qmean unit="1/A">0.3911</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3944</Q><I unit="1/cm">0.0598462561762734</I><Idev unit="1/cm">0.00259365235024266</Idev><Qdev unit="1/A">0.02338</Qdev><Qmean unit="1/A">0.3944</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.3977</Q><I unit="1/cm">0.0598449850147436</I><Idev unit="1/cm">0.00205279235695875</Idev><Qdev unit="1/A">0.02357</Qdev><Qmean unit="1/A">0.3977</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+			<Idata><Q unit="1/A">0.401</Q><I unit="1/cm">0.0598461884817662</I><Idev unit="1/cm">0.00379297785107319</Idev><Qdev unit="1/A">0.02375</Qdev><Qmean unit="1/A">0.401</Qmean><Shadowfactor>1</Shadowfactor></Idata>
+
+		</SASdata>
+
+		<SASsample>
+
+			<ID></ID>
+
+			<thickness unit="mm">1</thickness>
+
+			<transmission>0.327</transmission>
+
+		</SASsample>
+
+		<SASinstrument>
+
+			<name></name>
+
+			<SASsource>
+
+				<radiation>neutron</radiation>
+
+				<wavelength unit="A">5</wavelength>
+
+			</SASsource>
+
+			<SAScollimation>
+
+			</SAScollimation>
+
+			<SASdetector>
+
+				<name></name>
+
+			</SASdetector>
+
+		</SASinstrument>
+
+		<SASnote>
+
+			free form description of processing
+
+		</SASnote>
+
+	</SASentry>
+
+	<SASentry>
+
+		<Title>latex particles 0.5micron diameter in D2O slit</Title>
+
+		<Run>latex_usans</Run>
+
+		<SASdata>
+
+			<Idata><Q unit="1/A">7.7457e-05</Q><I unit="1/cm">8432.04</I><Idev unit="1/cm">153.745</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">8.3007e-05</Q><I unit="1/cm">8298.38</I><Idev unit="1/cm">152.01</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">8.8557e-05</Q><I unit="1/cm">8091.51</I><Idev unit="1/cm">149.908</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">9.4107e-05</Q><I unit="1/cm">7947.7</I><Idev unit="1/cm">148.184</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">9.9657e-05</Q><I unit="1/cm">8174.04</I><Idev unit="1/cm">149.824</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000105207</Q><I unit="1/cm">7890.52</I><Idev unit="1/cm">147.061</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000110757</Q><I unit="1/cm">8067.04</I><Idev unit="1/cm">74.9155</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000121857</Q><I unit="1/cm">7995.24</I><Idev unit="1/cm">74.0244</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000132957</Q><I unit="1/cm">7969.3</I><Idev unit="1/cm">73.7818</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000144057</Q><I unit="1/cm">7953.56</I><Idev unit="1/cm">73.6532</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000155157</Q><I unit="1/cm">7896.35</I><Idev unit="1/cm">73.3392</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000166257</Q><I unit="1/cm">7877.91</I><Idev unit="1/cm">73.1936</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000177357</Q><I unit="1/cm">7769.46</I><Idev unit="1/cm">72.6739</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000188457</Q><I unit="1/cm">7746.93</I><Idev unit="1/cm">72.5899</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000199557</Q><I unit="1/cm">7820.8</I><Idev unit="1/cm">72.9063</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000210657</Q><I unit="1/cm">7656.83</I><Idev unit="1/cm">72.1007</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000221757</Q><I unit="1/cm">7777.33</I><Idev unit="1/cm">72.6551</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000232857</Q><I unit="1/cm">7613.01</I><Idev unit="1/cm">71.8768</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000243957</Q><I unit="1/cm">7498.28</I><Idev unit="1/cm">71.316</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000255057</Q><I unit="1/cm">7494.75</I><Idev unit="1/cm">71.2836</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000266157</Q><I unit="1/cm">7266.92</I><Idev unit="1/cm">70.182</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000277257</Q><I unit="1/cm">7292.37</I><Idev unit="1/cm">57.4236</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000305007</Q><I unit="1/cm">7214.55</I><Idev unit="1/cm">57.0526</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000332757</Q><I unit="1/cm">7103.18</I><Idev unit="1/cm">56.5915</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000360507</Q><I unit="1/cm">6883.81</I><Idev unit="1/cm">55.7205</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000388257</Q><I unit="1/cm">6818.14</I><Idev unit="1/cm">55.4232</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000416007</Q><I unit="1/cm">6591.98</I><Idev unit="1/cm">54.4991</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000443757</Q><I unit="1/cm">6420.5</I><Idev unit="1/cm">53.7756</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000471507</Q><I unit="1/cm">6120.15</I><Idev unit="1/cm">52.5042</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000499257</Q><I unit="1/cm">5950.12</I><Idev unit="1/cm">51.7582</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000527007</Q><I unit="1/cm">5792.76</I><Idev unit="1/cm">51.0591</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000554757</Q><I unit="1/cm">5524.39</I><Idev unit="1/cm">43.2058</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000610257</Q><I unit="1/cm">5127.92</I><Idev unit="1/cm">41.599</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000665757</Q><I unit="1/cm">4753.49</I><Idev unit="1/cm">40.0733</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000721257</Q><I unit="1/cm">4355.25</I><Idev unit="1/cm">38.3487</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000776757</Q><I unit="1/cm">3878.25</I><Idev unit="1/cm">36.2076</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000832257</Q><I unit="1/cm">3471.25</I><Idev unit="1/cm">34.2637</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000887757</Q><I unit="1/cm">3022.77</I><Idev unit="1/cm">31.9597</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000943257</Q><I unit="1/cm">2600.33</I><Idev unit="1/cm">29.6463</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000998757</Q><I unit="1/cm">2309.11</I><Idev unit="1/cm">27.9459</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00105426</Q><I unit="1/cm">1975.45</I><Idev unit="1/cm">25.8603</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00110976</Q><I unit="1/cm">1701.57</I><Idev unit="1/cm">21.4532</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00122076</Q><I unit="1/cm">1156.72</I><Idev unit="1/cm">17.7038</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00133176</Q><I unit="1/cm">748.659</I><Idev unit="1/cm">14.2685</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00144276</Q><I unit="1/cm">465.515</I><Idev unit="1/cm">11.2799</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00155376</Q><I unit="1/cm">252.307</I><Idev unit="1/cm">8.35705</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00166476</Q><I unit="1/cm">157.822</I><Idev unit="1/cm">6.66382</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00177576</Q><I unit="1/cm">117.146</I><Idev unit="1/cm">5.78352</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00188676</Q><I unit="1/cm">93.1465</I><Idev unit="1/cm">5.19397</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00199776</Q><I unit="1/cm">103.864</I><Idev unit="1/cm">5.46342</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00210876</Q><I unit="1/cm">128.147</I><Idev unit="1/cm">6.0333</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00221976</Q><I unit="1/cm">135.96</I><Idev unit="1/cm">6.20529</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00233076</Q><I unit="1/cm">138.712</I><Idev unit="1/cm">6.26599</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00244176</Q><I unit="1/cm">126.272</I><Idev unit="1/cm">5.99155</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00255276</Q><I unit="1/cm">114.46</I><Idev unit="1/cm">5.72081</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00266376</Q><I unit="1/cm">74.2613</I><Idev unit="1/cm">4.67786</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00277476</Q><I unit="1/cm">72.3794</I><Idev unit="1/cm">4.62347</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00288576</Q><I unit="1/cm">47.3385</I><Idev unit="1/cm">3.82544</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00299676</Q><I unit="1/cm">36.0379</I><Idev unit="1/cm">3.40491</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00310776</Q><I unit="1/cm">30.3854</I><Idev unit="1/cm">3.17364</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00321876</Q><I unit="1/cm">24.7429</I><Idev unit="1/cm">2.92505</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00332976</Q><I unit="1/cm">28.5101</I><Idev unit="1/cm">3.09348</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00344076</Q><I unit="1/cm">27.9635</I><Idev unit="1/cm">3.06924</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00355176</Q><I unit="1/cm">33.3649</I><Idev unit="1/cm">3.29833</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00366276</Q><I unit="1/cm">35.237</I><Idev unit="1/cm">3.37337</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00377376</Q><I unit="1/cm">29.0365</I><Idev unit="1/cm">3.11578</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00388476</Q><I unit="1/cm">32.5455</I><Idev unit="1/cm">3.26424</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00399576</Q><I unit="1/cm">23.1284</I><Idev unit="1/cm">2.84982</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00410676</Q><I unit="1/cm">23.6642</I><Idev unit="1/cm">2.87489</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00421776</Q><I unit="1/cm">19.3575</I><Idev unit="1/cm">2.66567</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00432876</Q><I unit="1/cm">12.8971</I><Idev unit="1/cm">2.31662</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00443976</Q><I unit="1/cm">12.8958</I><Idev unit="1/cm">2.31647</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00455076</Q><I unit="1/cm">9.66256</I><Idev unit="1/cm">2.12012</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00466176</Q><I unit="1/cm">9.66792</I><Idev unit="1/cm">2.1208</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00477276</Q><I unit="1/cm">9.39793</I><Idev unit="1/cm">2.10354</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00488376</Q><I unit="1/cm">13.7138</I><Idev unit="1/cm">2.36412</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00499476</Q><I unit="1/cm">14.2495</I><Idev unit="1/cm">2.39427</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00510576</Q><I unit="1/cm">9.12935</I><Idev unit="1/cm">2.08632</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00521676</Q><I unit="1/cm">11.822</I><Idev unit="1/cm">2.25339</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00532776</Q><I unit="1/cm">10.7551</I><Idev unit="1/cm">2.18935</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00543876</Q><I unit="1/cm">11.8258</I><Idev unit="1/cm">2.25385</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00554976</Q><I unit="1/cm">8.04703</I><Idev unit="1/cm">2.01492</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+		</SASdata>
+
+		<SASsample>
+
+			<ID></ID>
+
+			<thickness unit="mm">1</thickness>
+
+			<transmission>0.84031</transmission>
+
+		</SASsample>
+
+		<SASinstrument>
+
+			<name></name>
+
+			<SASsource>
+
+				<radiation>neutron</radiation>
+
+				<wavelength unit="A">2.38</wavelength>
+
+			</SASsource>
+
+			<SAScollimation>
+
+			</SAScollimation>
+
+			<SASdetector>
+
+				<name></name>
+
+			</SASdetector>
+
+		</SASinstrument>
+
+		<SASnote>
+
+			free form description of processing
+
+		</SASnote>
+
+	</SASentry>
+
+</SASroot>
Index: sansinvariant/test/latex_smeared_slit.xml
===================================================================
--- sansinvariant/test/latex_smeared_slit.xml	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/latex_smeared_slit.xml	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,232 @@
+<?xml version="1.0"?>
+
+<?xml-stylesheet type="text/xsl" href="cansasxml-html.xsl" ?>
+
+<SASroot version="1.0"
+
+    xmlns="cansas1d/1.0"
+
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+
+    xsi:schemaLocation="cansas1d/1.0 http://svn.smallangles.net/svn/canSAS/1dwg/trunk/cansas1d.xsd"
+
+    >
+
+
+	<SASentry>
+
+		<Title>latex particles 0.5micron diameter in D2O slit</Title>
+
+		<Run>latex_usans</Run>
+
+		<SASdata>
+
+			<Idata><Q unit="1/A">7.7457e-05</Q><I unit="1/cm">8432.04</I><Idev unit="1/cm">153.745</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">8.3007e-05</Q><I unit="1/cm">8298.38</I><Idev unit="1/cm">152.01</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">8.8557e-05</Q><I unit="1/cm">8091.51</I><Idev unit="1/cm">149.908</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">9.4107e-05</Q><I unit="1/cm">7947.7</I><Idev unit="1/cm">148.184</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">9.9657e-05</Q><I unit="1/cm">8174.04</I><Idev unit="1/cm">149.824</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000105207</Q><I unit="1/cm">7890.52</I><Idev unit="1/cm">147.061</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000110757</Q><I unit="1/cm">8067.04</I><Idev unit="1/cm">74.9155</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000121857</Q><I unit="1/cm">7995.24</I><Idev unit="1/cm">74.0244</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000132957</Q><I unit="1/cm">7969.3</I><Idev unit="1/cm">73.7818</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000144057</Q><I unit="1/cm">7953.56</I><Idev unit="1/cm">73.6532</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000155157</Q><I unit="1/cm">7896.35</I><Idev unit="1/cm">73.3392</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000166257</Q><I unit="1/cm">7877.91</I><Idev unit="1/cm">73.1936</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000177357</Q><I unit="1/cm">7769.46</I><Idev unit="1/cm">72.6739</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000188457</Q><I unit="1/cm">7746.93</I><Idev unit="1/cm">72.5899</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000199557</Q><I unit="1/cm">7820.8</I><Idev unit="1/cm">72.9063</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000210657</Q><I unit="1/cm">7656.83</I><Idev unit="1/cm">72.1007</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000221757</Q><I unit="1/cm">7777.33</I><Idev unit="1/cm">72.6551</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000232857</Q><I unit="1/cm">7613.01</I><Idev unit="1/cm">71.8768</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000243957</Q><I unit="1/cm">7498.28</I><Idev unit="1/cm">71.316</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000255057</Q><I unit="1/cm">7494.75</I><Idev unit="1/cm">71.2836</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000266157</Q><I unit="1/cm">7266.92</I><Idev unit="1/cm">70.182</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000277257</Q><I unit="1/cm">7292.37</I><Idev unit="1/cm">57.4236</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000305007</Q><I unit="1/cm">7214.55</I><Idev unit="1/cm">57.0526</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000332757</Q><I unit="1/cm">7103.18</I><Idev unit="1/cm">56.5915</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000360507</Q><I unit="1/cm">6883.81</I><Idev unit="1/cm">55.7205</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000388257</Q><I unit="1/cm">6818.14</I><Idev unit="1/cm">55.4232</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000416007</Q><I unit="1/cm">6591.98</I><Idev unit="1/cm">54.4991</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000443757</Q><I unit="1/cm">6420.5</I><Idev unit="1/cm">53.7756</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000471507</Q><I unit="1/cm">6120.15</I><Idev unit="1/cm">52.5042</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000499257</Q><I unit="1/cm">5950.12</I><Idev unit="1/cm">51.7582</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000527007</Q><I unit="1/cm">5792.76</I><Idev unit="1/cm">51.0591</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000554757</Q><I unit="1/cm">5524.39</I><Idev unit="1/cm">43.2058</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000610257</Q><I unit="1/cm">5127.92</I><Idev unit="1/cm">41.599</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000665757</Q><I unit="1/cm">4753.49</I><Idev unit="1/cm">40.0733</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000721257</Q><I unit="1/cm">4355.25</I><Idev unit="1/cm">38.3487</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000776757</Q><I unit="1/cm">3878.25</I><Idev unit="1/cm">36.2076</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000832257</Q><I unit="1/cm">3471.25</I><Idev unit="1/cm">34.2637</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000887757</Q><I unit="1/cm">3022.77</I><Idev unit="1/cm">31.9597</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000943257</Q><I unit="1/cm">2600.33</I><Idev unit="1/cm">29.6463</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.000998757</Q><I unit="1/cm">2309.11</I><Idev unit="1/cm">27.9459</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00105426</Q><I unit="1/cm">1975.45</I><Idev unit="1/cm">25.8603</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00110976</Q><I unit="1/cm">1701.57</I><Idev unit="1/cm">21.4532</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00122076</Q><I unit="1/cm">1156.72</I><Idev unit="1/cm">17.7038</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00133176</Q><I unit="1/cm">748.659</I><Idev unit="1/cm">14.2685</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00144276</Q><I unit="1/cm">465.515</I><Idev unit="1/cm">11.2799</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00155376</Q><I unit="1/cm">252.307</I><Idev unit="1/cm">8.35705</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00166476</Q><I unit="1/cm">157.822</I><Idev unit="1/cm">6.66382</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00177576</Q><I unit="1/cm">117.146</I><Idev unit="1/cm">5.78352</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00188676</Q><I unit="1/cm">93.1465</I><Idev unit="1/cm">5.19397</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00199776</Q><I unit="1/cm">103.864</I><Idev unit="1/cm">5.46342</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00210876</Q><I unit="1/cm">128.147</I><Idev unit="1/cm">6.0333</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00221976</Q><I unit="1/cm">135.96</I><Idev unit="1/cm">6.20529</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00233076</Q><I unit="1/cm">138.712</I><Idev unit="1/cm">6.26599</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00244176</Q><I unit="1/cm">126.272</I><Idev unit="1/cm">5.99155</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00255276</Q><I unit="1/cm">114.46</I><Idev unit="1/cm">5.72081</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00266376</Q><I unit="1/cm">74.2613</I><Idev unit="1/cm">4.67786</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00277476</Q><I unit="1/cm">72.3794</I><Idev unit="1/cm">4.62347</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00288576</Q><I unit="1/cm">47.3385</I><Idev unit="1/cm">3.82544</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00299676</Q><I unit="1/cm">36.0379</I><Idev unit="1/cm">3.40491</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00310776</Q><I unit="1/cm">30.3854</I><Idev unit="1/cm">3.17364</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00321876</Q><I unit="1/cm">24.7429</I><Idev unit="1/cm">2.92505</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00332976</Q><I unit="1/cm">28.5101</I><Idev unit="1/cm">3.09348</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00344076</Q><I unit="1/cm">27.9635</I><Idev unit="1/cm">3.06924</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00355176</Q><I unit="1/cm">33.3649</I><Idev unit="1/cm">3.29833</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00366276</Q><I unit="1/cm">35.237</I><Idev unit="1/cm">3.37337</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00377376</Q><I unit="1/cm">29.0365</I><Idev unit="1/cm">3.11578</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00388476</Q><I unit="1/cm">32.5455</I><Idev unit="1/cm">3.26424</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00399576</Q><I unit="1/cm">23.1284</I><Idev unit="1/cm">2.84982</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00410676</Q><I unit="1/cm">23.6642</I><Idev unit="1/cm">2.87489</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00421776</Q><I unit="1/cm">19.3575</I><Idev unit="1/cm">2.66567</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00432876</Q><I unit="1/cm">12.8971</I><Idev unit="1/cm">2.31662</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00443976</Q><I unit="1/cm">12.8958</I><Idev unit="1/cm">2.31647</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00455076</Q><I unit="1/cm">9.66256</I><Idev unit="1/cm">2.12012</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00466176</Q><I unit="1/cm">9.66792</I><Idev unit="1/cm">2.1208</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00477276</Q><I unit="1/cm">9.39793</I><Idev unit="1/cm">2.10354</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00488376</Q><I unit="1/cm">13.7138</I><Idev unit="1/cm">2.36412</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00499476</Q><I unit="1/cm">14.2495</I><Idev unit="1/cm">2.39427</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00510576</Q><I unit="1/cm">9.12935</I><Idev unit="1/cm">2.08632</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00521676</Q><I unit="1/cm">11.822</I><Idev unit="1/cm">2.25339</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00532776</Q><I unit="1/cm">10.7551</I><Idev unit="1/cm">2.18935</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00543876</Q><I unit="1/cm">11.8258</I><Idev unit="1/cm">2.25385</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+			<Idata><Q unit="1/A">0.00554976</Q><I unit="1/cm">8.04703</I><Idev unit="1/cm">2.01492</Idev><dQl unit="1/A">0.117</dQl></Idata>
+
+		</SASdata>
+
+		<SASsample>
+
+			<ID></ID>
+
+			<thickness unit="mm">1</thickness>
+
+			<transmission>0.84031</transmission>
+
+		</SASsample>
+
+		<SASinstrument>
+
+			<name></name>
+
+			<SASsource>
+
+				<radiation>neutron</radiation>
+
+				<wavelength unit="A">2.38</wavelength>
+
+			</SASsource>
+
+			<SAScollimation>
+
+			</SAScollimation>
+
+			<SASdetector>
+
+				<name></name>
+
+			</SASdetector>
+
+		</SASinstrument>
+
+		<SASnote>
+
+			free form description of processing
+
+		</SASnote>
+
+	</SASentry>
+
+</SASroot>
Index: sansinvariant/test/linefittest.txt
===================================================================
--- sansinvariant/test/linefittest.txt	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/linefittest.txt	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,10 @@
+1	3	1
+2	6	1
+3	8	1
+4	11	2
+5	13	2
+6	15	2
+7	17	3
+8	20	3
+9	22	3
+10	27	6
Index: sansinvariant/test/linefittest_no_weight.txt
===================================================================
--- sansinvariant/test/linefittest_no_weight.txt	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/linefittest_no_weight.txt	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,10 @@
+1	3	
+2	6	
+3	8	
+4	11	
+5	13	
+6	15	
+7	17	
+8	20	
+9	22	
+10	27	
Index: sansinvariant/test/utest_data_handling.py
===================================================================
--- sansinvariant/test/utest_data_handling.py	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/utest_data_handling.py	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,674 @@
+"""
+This software was developed by the University of Tennessee as part of the
+Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
+project funded by the US National Science Foundation. 
+
+See the license text in license.txt
+
+copyright 2010, University of Tennessee
+"""
+import unittest
+import numpy, math
+from DataLoader.loader import  Loader
+from DataLoader.data_info import Data1D
+from sans.invariant import invariant
+    
+class TestLinearFit(unittest.TestCase):
+    """
+        Test Line fit 
+    """
+    def setUp(self):
+        x = numpy.asarray([1.,2.,3.,4.,5.,6.,7.,8.,9.])
+        y = numpy.asarray([1.,2.,3.,4.,5.,6.,7.,8.,9.])
+        dy = y/10.0
+        
+        self.data = Data1D(x=x,y=y,dy=dy)
+        
+    def test_fit_linear_data(self):
+        """ 
+            Simple linear fit
+        """
+        
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        #a,b = fit.fit()
+        p, dp = fit.fit()
+
+        # Test results
+        self.assertAlmostEquals(p[0], 1.0, 5)
+        self.assertAlmostEquals(p[1], 0.0, 5)
+
+    def test_fit_linear_data_with_noise(self):
+        """ 
+            Simple linear fit with noise
+        """
+        import random, math
+        
+        for i in range(len(self.data.y)):
+            self.data.y[i] = self.data.y[i]+.1*(random.random()-0.5)
+            
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        p, dp = fit.fit()
+
+        # Test results
+        self.assertTrue(math.fabs(p[0]-1.0)<0.05)
+        self.assertTrue(math.fabs(p[1])<0.1)        
+        
+    def test_fit_with_fixed_parameter(self):
+        """
+            Linear fit for y=ax+b where a is fixed.
+        """
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        p, dp = fit.fit(power=-1.0)
+
+        # Test results
+        self.assertAlmostEquals(p[0], 1.0, 5)
+        self.assertAlmostEquals(p[1], 0.0, 5)
+
+    def test_fit_linear_data_with_noise_and_fixed_par(self):
+        """ 
+            Simple linear fit with noise
+        """
+        import random, math
+        
+        for i in range(len(self.data.y)):
+            self.data.y[i] = self.data.y[i]+.1*(random.random()-0.5)
+            
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        p, dp = fit.fit(power=-1.0)
+
+        # Test results
+        self.assertTrue(math.fabs(p[0]-1.0)<0.05)
+        self.assertTrue(math.fabs(p[1])<0.1)        
+        
+
+
+class TestInvariantCalculator(unittest.TestCase):
+    """
+        Test main functionality of the Invariant calculator
+    """
+    def setUp(self):
+        self.data = Loader().load("latex_smeared_slit.xml")
+        
+    def test_initial_data_processing(self):
+        """ 
+            Test whether the background and scale are handled properly 
+            when creating an InvariantCalculator object
+        """
+        length = len(self.data.x)
+        self.assertEqual(length, len(self.data.y))
+        inv = invariant.InvariantCalculator(self.data)
+        
+        self.assertEqual(length, len(inv._data.x))
+        self.assertEqual(inv._data.x[0], self.data.x[0])
+
+        # Now the same thing with a background value
+        bck = 0.1
+        inv = invariant.InvariantCalculator(self.data, background=bck)
+        self.assertEqual(inv._background, bck)
+        
+        self.assertEqual(length, len(inv._data.x))
+        self.assertEqual(inv._data.y[0]+bck, self.data.y[0])
+        
+        # Now the same thing with a scale value
+        scale = 0.1
+        inv = invariant.InvariantCalculator(self.data, scale=scale)
+        self.assertEqual(inv._scale, scale)
+        
+        self.assertEqual(length, len(inv._data.x))
+        self.assertAlmostEqual(inv._data.y[0]/scale, self.data.y[0],7)
+        
+    
+    def test_incompatible_data_class(self):
+        """
+            Check that only classes that inherit from Data1D are allowed as data.
+        """
+        class Incompatible():
+            pass
+        self.assertRaises(ValueError, invariant.InvariantCalculator, Incompatible())
+        
+    def test_error_treatment(self):
+        x = numpy.asarray(numpy.asarray([0,1,2,3]))
+        y = numpy.asarray(numpy.asarray([1,1,1,1]))
+        
+        # These are all the values of the dy array that would cause
+        # us to set all dy values to 1.0 at __init__ time.
+        dy_list = [ [], None, [0,0,0,0] ]
+        
+        for dy in dy_list:
+            data = Data1D(x=x, y=y, dy=dy)
+            inv = invariant.InvariantCalculator(data)
+            self.assertEqual(len(inv._data.x), len(inv._data.dy))
+            self.assertEqual(len(inv._data.dy), 4)
+            for i in range(4):
+                self.assertEqual(inv._data.dy[i],1)
+                
+    def test_qstar_low_q_guinier(self):
+        """
+            Test low-q extrapolation with a Guinier
+        """
+        inv = invariant.InvariantCalculator(self.data)
+        
+        # Basic sanity check
+        _qstar = inv.get_qstar()
+        qstar, dqstar = inv.get_qstar_with_error()
+        self.assertEqual(qstar, _qstar)
+        
+        # Low-Q Extrapolation
+        # Check that the returned invariant is what we expect given
+        # the result we got without extrapolation
+        inv.set_extrapolation('low', npts=10, function='guinier')
+        qs_extr, dqs_extr = inv.get_qstar_with_error('low')
+        delta_qs_extr, delta_dqs_extr = inv.get_qstar_low()
+        
+        self.assertEqual(qs_extr, _qstar+delta_qs_extr)
+        self.assertEqual(dqs_extr, math.sqrt(dqstar*dqstar + delta_dqs_extr*delta_dqs_extr))
+        
+        # We don't expect the extrapolated invariant to be very far from the
+        # result without extrapolation. Let's test for a result within 10%.
+        self.assertTrue(math.fabs(qs_extr-qstar)/qstar<0.1)
+        
+        # Check that the two results are consistent within errors
+        # Note that the error on the extrapolated value takes into account
+        # a systematic error for the fact that we may not know the shape of I(q) at low Q.
+        self.assertTrue(math.fabs(qs_extr-qstar)<dqs_extr)
+        
+    def test_qstar_low_q_power_law(self):
+        """
+            Test low-q extrapolation with a power law
+        """
+        inv = invariant.InvariantCalculator(self.data)
+        
+        # Basic sanity check
+        _qstar = inv.get_qstar()
+        qstar, dqstar = inv.get_qstar_with_error()
+        self.assertEqual(qstar, _qstar)
+        
+        # Low-Q Extrapolation
+        # Check that the returned invariant is what we expect given
+        inv.set_extrapolation('low', npts=10, function='power_law')
+        qs_extr, dqs_extr = inv.get_qstar_with_error('low')
+        delta_qs_extr, delta_dqs_extr = inv.get_qstar_low()
+        
+        # A fit using SansView gives 0.0655 for the value of the exponent
+        self.assertAlmostEqual(inv._low_extrapolation_function.power, 0.0655, 3)
+        
+        if False:
+            npts = len(inv._data.x)-1
+            import matplotlib.pyplot as plt
+            plt.loglog(inv._data.x[:npts], inv._data.y[:npts], 'o', label='Original data', markersize=10)
+            plt.loglog(inv._data.x[:npts], inv._low_extrapolation_function.evaluate_model(inv._data.x[:npts]), 'r', label='Fitted line')
+            plt.legend()
+            plt.show()        
+        
+        self.assertEqual(qs_extr, _qstar+delta_qs_extr)
+        self.assertEqual(dqs_extr, math.sqrt(dqstar*dqstar + delta_dqs_extr*delta_dqs_extr))
+        
+        # We don't expect the extrapolated invariant to be very far from the
+        # result without extrapolation. Let's test for a result within 10%.
+        self.assertTrue(math.fabs(qs_extr-qstar)/qstar<0.1)
+        
+        # Check that the two results are consistent within errors
+        # Note that the error on the extrapolated value takes into account
+        # a systematic error for the fact that we may not know the shape of I(q) at low Q.
+        self.assertTrue(math.fabs(qs_extr-qstar)<dqs_extr)
+        
+    def test_qstar_high_q(self):
+        """
+            Test high-q extrapolation
+        """
+        inv = invariant.InvariantCalculator(self.data)
+        
+        # Basic sanity check
+        _qstar = inv.get_qstar()
+        qstar, dqstar = inv.get_qstar_with_error()
+        self.assertEqual(qstar, _qstar)
+        
+        # High-Q Extrapolation
+        # Check that the returned invariant is what we expect given
+        # the result we got without extrapolation
+        inv.set_extrapolation('high', npts=20, function='power_law')
+        qs_extr, dqs_extr = inv.get_qstar_with_error('high')
+        delta_qs_extr, delta_dqs_extr = inv.get_qstar_high()
+        
+        # From previous analysis using SansView, we expect an exponent of about 3 
+        self.assertTrue(math.fabs(inv._high_extrapolation_function.power-3)<0.1)
+        
+        self.assertEqual(qs_extr, _qstar+delta_qs_extr)
+        self.assertEqual(dqs_extr, math.sqrt(dqstar*dqstar + delta_dqs_extr*delta_dqs_extr))
+        
+        # We don't expect the extrapolated invariant to be very far from the
+        # result without extrapolation. Let's test for a result within 10%.
+        #TODO: verify whether this test really makes sense
+        #self.assertTrue(math.fabs(qs_extr-qstar)/qstar<0.1)
+        
+        # Check that the two results are consistent within errors
+        self.assertTrue(math.fabs(qs_extr-qstar)<dqs_extr)
+                
+    def test_qstar_full_q(self):
+        """
+            Test high-q extrapolation
+        """
+        inv = invariant.InvariantCalculator(self.data)
+        
+        # Basic sanity check
+        _qstar = inv.get_qstar()
+        qstar, dqstar = inv.get_qstar_with_error()
+        self.assertEqual(qstar, _qstar)
+        
+        # High-Q Extrapolation
+        # Check that the returned invariant is what we expect given
+        # the result we got without extrapolation
+        inv.set_extrapolation('low',  npts=10, function='guinier')
+        inv.set_extrapolation('high', npts=20, function='power_law')
+        qs_extr, dqs_extr = inv.get_qstar_with_error('both')
+        delta_qs_low, delta_dqs_low = inv.get_qstar_low()
+        delta_qs_hi,  delta_dqs_hi = inv.get_qstar_high()
+        
+        self.assertAlmostEqual(qs_extr, _qstar+delta_qs_low+delta_qs_hi, 8)
+        self.assertEqual(dqs_extr, math.sqrt(dqstar*dqstar + delta_dqs_low*delta_dqs_low \
+                                             + delta_dqs_hi*delta_dqs_hi))
+        
+        # We don't expect the extrapolated invariant to be very far from the
+        # result without extrapolation. Let's test for a result within 10%.
+        #TODO: verify whether this test really makes sense
+        #self.assertTrue(math.fabs(qs_extr-qstar)/qstar<0.1)
+        
+        # Check that the two results are consistent within errors
+        self.assertTrue(math.fabs(qs_extr-qstar)<dqs_extr)
+        
+        def _check_values(to_check, reference, tolerance=0.05):
+            self.assertTrue( math.fabs(to_check-reference)/reference < tolerance, msg="Tested value = "+str(to_check) )
+            
+        # The following values should be replaced by values pulled from IGOR
+        # Volume Fraction:
+        v, dv = inv.get_volume_fraction_with_error(1, None)
+        _check_values(v, 1.88737914186e-15)
+
+        v_l, dv_l = inv.get_volume_fraction_with_error(1, 'low')
+        _check_values(v_l, 1.94289029309e-15)
+
+        v_h, dv_h = inv.get_volume_fraction_with_error(1, 'high')
+        _check_values(v_h, 6.99440505514e-15)
+        
+        v_b, dv_b = inv.get_volume_fraction_with_error(1, 'both')
+        _check_values(v_b, 6.99440505514e-15)
+        
+        # Specific Surface:
+        s, ds = inv.get_surface_with_error(1, 1, None)
+        _check_values(s, 3.1603095786e-09)
+
+        s_l, ds_l = inv.get_surface_with_error(1, 1, 'low')
+        _check_values(s_l, 3.1603095786e-09)
+
+        s_h, ds_h = inv.get_surface_with_error(1, 1, 'high')
+        _check_values(s_h, 3.1603095786e-09)
+        
+        s_b, ds_b = inv.get_surface_with_error(1, 1, 'both')
+        _check_values(s_b, 3.1603095786e-09)
+        
+        
+    def test_bad_parameter_name(self):
+        """
+            The set_extrapolation method checks that the name of the extrapolation
+            function and the name of the q-range to extrapolate (high/low) is 
+            recognized.
+        """
+        inv = invariant.InvariantCalculator(self.data)
+        self.assertRaises(ValueError, inv.set_extrapolation, 'low', npts=4, function='not_a_name')
+        self.assertRaises(ValueError, inv.set_extrapolation, 'not_a_range', npts=4, function='guinier')
+        self.assertRaises(ValueError, inv.set_extrapolation, 'high', npts=4, function='guinier')
+    
+    
+class TestGuinierExtrapolation(unittest.TestCase):
+    """
+        Generate a Guinier distribution and verify that the extrapolation
+        produce the correct ditribution.
+    """
+    
+    def setUp(self):
+        """
+            Generate a Guinier distribution. After extrapolating, we will
+            verify that we obtain the scale and rg parameters
+        """
+        self.scale = 1.5
+        self.rg = 30.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x])
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        
+    def test_low_q(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        
+        self.assertEqual(inv._low_extrapolation_npts, 20)
+        self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier)
+        
+        # Data boundaries for fiiting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        inv._fit(model=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+        self.assertAlmostEqual(self.scale, inv._low_extrapolation_function.scale, 6)
+        self.assertAlmostEqual(self.rg, inv._low_extrapolation_function.radius, 6)
+    
+
+class TestPowerLawExtrapolation(unittest.TestCase):
+    """
+        Generate a power law distribution and verify that the extrapolation
+        produce the correct ditribution.
+    """
+    
+    def setUp(self):
+        """
+            Generate a power law distribution. After extrapolating, we will
+            verify that we obtain the scale and m parameters
+        """
+        self.scale = 1.5
+        self.m = 3.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.pow(q ,-1.0*self.m) for q in x])                
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        
+    def test_low_q(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='power_law')
+        
+        self.assertEqual(inv._low_extrapolation_npts, 20)
+        self.assertEqual(inv._low_extrapolation_function.__class__, invariant.PowerLaw)
+        
+        # Data boundaries for fitting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        inv._fit(model=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+        
+        self.assertAlmostEqual(self.scale, inv._low_extrapolation_function.scale, 6)
+        self.assertAlmostEqual(self.m, inv._low_extrapolation_function.power, 6)
+        
+class TestLinearization(unittest.TestCase):
+    
+    def test_guinier_incompatible_length(self):
+        g = invariant.Guinier()
+        data_in = Data1D(x=[1], y=[1,2], dy=None)
+        self.assertRaises(AssertionError, g.linearize_data, data_in)
+        data_in = Data1D(x=[1,1], y=[1,2], dy=[1])
+        self.assertRaises(AssertionError, g.linearize_data, data_in)
+    
+    def test_linearization(self):
+        """
+            Check that the linearization process filters out points
+            that can't be transformed
+        """
+        x = numpy.asarray(numpy.asarray([0,1,2,3]))
+        y = numpy.asarray(numpy.asarray([1,1,1,1]))
+        g = invariant.Guinier()
+        data_in = Data1D(x=x, y=y)
+        data_out = g.linearize_data(data_in)
+        x_out, y_out, dy_out = data_out.x, data_out.y, data_out.dy
+        self.assertEqual(len(x_out), 3)
+        self.assertEqual(len(y_out), 3)
+        self.assertEqual(len(dy_out), 3)
+        
+    def test_allowed_bins(self):
+        x = numpy.asarray(numpy.asarray([0,1,2,3]))
+        y = numpy.asarray(numpy.asarray([1,1,1,1]))
+        dy = numpy.asarray(numpy.asarray([1,1,1,1]))
+        g = invariant.Guinier()
+        data = Data1D(x=x, y=y, dy=dy)
+        self.assertEqual(g.get_allowed_bins(data), [False, True, True, True])
+
+        data = Data1D(x=y, y=x, dy=dy)
+        self.assertEqual(g.get_allowed_bins(data), [False, True, True, True])
+
+        data = Data1D(x=dy, y=y, dy=x)
+        self.assertEqual(g.get_allowed_bins(data), [False, True, True, True])
+    
+class TestDataExtraLow(unittest.TestCase):
+    """
+        Generate a Guinier distribution and verify that the extrapolation
+        produce the correct ditribution. Tested if the data generated by the 
+        invariant calculator is correct
+    """
+    
+    def setUp(self):
+        """
+            Generate a Guinier distribution. After extrapolating, we will
+            verify that we obtain the scale and rg parameters
+        """
+        self.scale = 1.5
+        self.rg = 30.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x])
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        
+    def test_low_q(self):
+        """
+            Invariant with low-Q extrapolation with no slit smear
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=10, function='guinier')
+        
+        self.assertEqual(inv._low_extrapolation_npts, 10)
+        self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier)
+        
+        # Data boundaries for fiiting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        inv._fit(model=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+        self.assertAlmostEqual(self.scale, inv._low_extrapolation_function.scale, 6)
+        self.assertAlmostEqual(self.rg, inv._low_extrapolation_function.radius, 6)
+        
+        qstar = inv.get_qstar(extrapolation='low')
+        test_y = inv._low_extrapolation_function.evaluate_model(x=self.data.x)
+        for i in range(len(self.data.x)):
+            value  = math.fabs(test_y[i]-self.data.y[i])/self.data.y[i]
+            self.assert_(value < 0.001)
+            
+class TestDataExtraLowSlitGuinier(unittest.TestCase):
+    """
+        for a smear data, test that the fitting go through 
+        real data for atleast the 2 first points
+    """
+    
+    def setUp(self):
+        """
+            Generate a Guinier distribution. After extrapolating, we will
+            verify that we obtain the scale and rg parameters
+        """
+        self.scale = 1.5
+        self.rg = 30.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x])
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        self.npts = len(x)-10
+        
+    def test_low_q(self):
+        """
+            Invariant with low-Q extrapolation with slit smear
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=self.npts, function='guinier')
+        
+        self.assertEqual(inv._low_extrapolation_npts, self.npts)
+        self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier)
+        
+        # Data boundaries for fiiting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        inv._fit(model=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+      
+        
+        qstar = inv.get_qstar(extrapolation='low')
+
+        test_y = inv._low_extrapolation_function.evaluate_model(x=self.data.x[:inv._low_extrapolation_npts])
+        self.assert_(len(test_y) == len(self.data.y[:inv._low_extrapolation_npts]))
+        
+        for i in range(inv._low_extrapolation_npts):
+            value  = math.fabs(test_y[i]-self.data.y[i])/self.data.y[i]
+            self.assert_(value < 0.001)
+            
+    def test_low_data(self):
+        """
+            Invariant with low-Q extrapolation with slit smear
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=self.npts, function='guinier')
+        
+        self.assertEqual(inv._low_extrapolation_npts, self.npts)
+        self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier)
+        
+        # Data boundaries for fiiting
+        qmin = inv._data.x[0]
+        qmax = inv._data.x[inv._low_extrapolation_npts - 1]
+        
+        # Extrapolate the low-Q data
+        inv._fit(model=inv._low_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._low_extrapolation_power)
+      
+        
+        qstar = inv.get_qstar(extrapolation='low')
+        #Compution the y 's coming out of the invariant when computing extrapolated
+        #low data . expect the fit engine to have been already called and the guinier
+        # to have the radius and the scale fitted
+        data_in_range = inv.get_extra_data_low(q_start=self.data.x[0], 
+                                               npts = inv._low_extrapolation_npts) 
+        test_y = data_in_range.y
+        self.assert_(len(test_y) == len(self.data.y[:inv._low_extrapolation_npts]))
+        for i in range(inv._low_extrapolation_npts):
+            value  = math.fabs(test_y[i]-self.data.y[i])/self.data.y[i]
+            self.assert_(value < 0.001)    
+       
+            
+class TestDataExtraHighSlitPowerLaw(unittest.TestCase):
+    """
+        for a smear data, test that the fitting go through 
+        real data for atleast the 2 first points
+    """
+    
+    def setUp(self):
+        """
+            Generate a Guinier distribution. After extrapolating, we will
+            verify that we obtain the scale and rg parameters
+        """
+        self.scale = 1.5
+        self.m = 3.0
+        x = numpy.arange(0.0001, 0.1, 0.0001)
+        y = numpy.asarray([self.scale * math.pow(q ,-1.0*self.m) for q in x])                
+        dy = y*.1
+        self.data = Data1D(x=x, y=y, dy=dy)
+        self.npts = 20
+        
+    def test_high_q(self):
+        """
+            Invariant with high-Q extrapolation with slit smear
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='high', npts=self.npts, function='power_law')
+        
+        self.assertEqual(inv._high_extrapolation_npts, self.npts)
+        self.assertEqual(inv._high_extrapolation_function.__class__, invariant.PowerLaw)
+        
+        # Data boundaries for fiiting
+        xlen = len(self.data.x)
+        start =  xlen - inv._high_extrapolation_npts
+        qmin = inv._data.x[start]
+        qmax = inv._data.x[xlen-1]
+        
+        # Extrapolate the high-Q data
+        inv._fit(model=inv._high_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._high_extrapolation_power)
+      
+        
+        qstar = inv.get_qstar(extrapolation='high')
+        
+        test_y = inv._high_extrapolation_function.evaluate_model(x=self.data.x[start: ])
+        self.assert_(len(test_y) == len(self.data.y[start:]))
+        
+        for i in range(len(self.data.x[start:])):
+            value  = math.fabs(test_y[i]-self.data.y[start+i])/self.data.y[start+i]
+            self.assert_(value < 0.001)
+            
+    def test_high_data(self):
+        """
+            Invariant with low-Q extrapolation with slit smear
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='high', npts=self.npts, function='power_law')
+        
+        self.assertEqual(inv._high_extrapolation_npts, self.npts)
+        self.assertEqual(inv._high_extrapolation_function.__class__, invariant.PowerLaw)
+        
+        # Data boundaries for fiiting
+        xlen = len(self.data.x)
+        start =  xlen - inv._high_extrapolation_npts
+        qmin = inv._data.x[start]
+        qmax = inv._data.x[xlen-1]
+        
+        # Extrapolate the high-Q data
+        inv._fit(model=inv._high_extrapolation_function,
+                          qmin=qmin,
+                          qmax=qmax,
+                          power=inv._high_extrapolation_power)
+      
+        qstar = inv.get_qstar(extrapolation='high')
+       
+        data_in_range= inv.get_extra_data_high(q_end = max(self.data.x),
+                                               npts = inv._high_extrapolation_npts) 
+        test_y = data_in_range.y
+        self.assert_(len(test_y) == len(self.data.y[start:]))
+        temp = self.data.y[start:]
+        
+        for i in range(len(self.data.x[start:])):
+            value  = math.fabs(test_y[i]- temp[i])/temp[i]
+            self.assert_(value < 0.001)                
+                      
Index: sansinvariant/test/utest_inv_mapper.py
===================================================================
--- sansinvariant/test/utest_inv_mapper.py	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/utest_inv_mapper.py	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,54 @@
+"""
+   Implementation of the use-case from a usage perspective. 
+"""
+
+import unittest
+import numpy
+from DataLoader.loader import  Loader
+from sans.invariant import invariant
+from sans.invariant.invariant_mapper import InvariantMapper
+
+N_INVARIANT = 10
+CONTRAST = 2.6e-6
+POROD_CONSTANT = 2.0
+
+class TestInvPolySphere(unittest.TestCase):
+    """
+        Test iteration through unsmeared data for invariant computation
+    """
+    def setUp(self):
+        self.list_of_invariant = []
+        self.list_of_contrast = []
+        self.list_of_porod_const = []
+        #Define an invariant mapper
+        self.mapper = InvariantMapper()
+        for i in range(N_INVARIANT):
+            data = Loader().load("PolySpheres.txt")
+            # Create invariant object. Background and scale left as defaults.
+            inv = invariant.InvariantCalculator(data=data)
+            self.list_of_invariant.append(inv)
+            self.list_of_contrast.append(CONTRAST)
+            self.list_of_porod_const.append(POROD_CONSTANT)
+        
+    def test_use_case_1(self):
+        """
+        Invariant without extrapolation
+        """
+        qstar_list = map(self.mapper.get_qstar, self.list_of_invariant)
+        
+        for qstar in qstar_list:
+            self.assertAlmostEquals(qstar, 7.48959e-5, 2)
+        
+        v_list = map(self.mapper.get_volume_fraction_with_error,
+                         self.list_of_invariant, self.list_of_contrast)
+        for v, dv in v_list:
+            self.assertAlmostEquals(v, 0.005644689, 4)
+            
+        s_list = map(self.mapper.get_surface_with_error,
+                         self.list_of_invariant,
+                         self.list_of_contrast,
+                         self.list_of_porod_const)
+        for s, ds in s_list:
+            self.assertAlmostEquals(s , 941.7452, 3)
+        
+   
Index: sansinvariant/test/utest_use_cases.py
===================================================================
--- sansinvariant/test/utest_use_cases.py	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
+++ sansinvariant/test/utest_use_cases.py	(revision ad4053a35ebb020c5b26adb27098e74140045aca)
@@ -0,0 +1,373 @@
+"""
+   Implementation of the use-case from a usage perspective. 
+  
+"""
+#TODO: there's no test for smeared extrapolation
+import unittest
+import numpy
+from DataLoader.loader import  Loader
+from sans.invariant import invariant
+
+class Data1D:
+    pass
+    
+class TestLineFit(unittest.TestCase):
+    """
+        Test Line fit 
+    """
+    def setUp(self):
+        self.data = Loader().load("linefittest.txt")
+        
+    def test_fit_line_data(self):
+        """ 
+            Fit_Test_1: test linear fit, ax +b, without fixed
+        """
+        
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        
+        ##Without holding
+        p, dp = fit.fit(power=None)
+
+        # Test results
+        self.assertAlmostEquals(p[0], 2.3983,3)
+        self.assertAlmostEquals(p[1], 0.87833,3)
+
+
+    def test_fit_line_data_fixed(self):
+        """ 
+            Fit_Test_2: test linear fit, ax +b, with 'a' fixed
+        """
+        
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        
+        #With holding a = -power =4
+        p, dp = fit.fit(power=-4)
+
+        # Test results
+        self.assertAlmostEquals(p[0], 4)
+        self.assertAlmostEquals(p[1], -4.0676,3)
+        
+class TestLineFitNoweight(unittest.TestCase):
+    """
+        Test Line fit without weight(dy data)
+    """
+    def setUp(self):
+        self.data = Loader().load("linefittest_no_weight.txt")
+        
+    def test_fit_line_data_no_weight(self):
+        """ 
+            Fit_Test_1: test linear fit, ax +b, without fixed
+        """
+        
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        
+        ##Without holding
+        p, dp = fit.fit(power=None)
+
+        # Test results
+        self.assertAlmostEquals(p[0], 2.4727,3)
+        self.assertAlmostEquals(p[1], 0.6,3)
+
+
+    def test_fit_line_data_fixed_no_weight(self):
+        """ 
+            Fit_Test_2: test linear fit, ax +b, with 'a' fixed
+        """
+        
+        # Create invariant object. Background and scale left as defaults.
+        fit = invariant.Extrapolator(data=self.data)
+        
+        #With holding a = -power =4
+        p, dp = fit.fit(power=-4)
+
+        # Test results
+        self.assertAlmostEquals(p[0], 4)
+        self.assertAlmostEquals(p[1], -7.8,3)
+                
+class TestInvPolySphere(unittest.TestCase):
+    """
+        Test unsmeared data for invariant computation
+    """
+    def setUp(self):
+        self.data = Loader().load("PolySpheres.txt")
+        
+    def test_wrong_data(self):
+        """ test receiving Data1D not of type loader"""
+
+
+        self.assertRaises(ValueError,invariant.InvariantCalculator, Data1D())
+        
+    def test_use_case_1(self):
+        """
+            Invariant without extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        
+        # We have to be able to tell the InvariantCalculator whether we want the
+        # extrapolation or not. By default, when the user doesn't specify, we
+        # should compute Q* without extrapolation. That's what should be done in __init__.
+        
+        # We call get_qstar() with no argument, which signifies that we do NOT
+        # want extrapolation.
+        qstar = inv.get_qstar()
+        
+        # The volume fraction and surface use Q*. That means that the following 
+        # methods should check that Q* has been computed. If not, it should 
+        # compute it by calling get_qstare(), leaving the parameters as default.
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 7.48959e-5,2)
+        self.assertAlmostEquals(v, 0.005644689, 4)
+        self.assertAlmostEquals(s , 941.7452, 3)
+        
+    def test_use_case_2(self):
+        """
+            Invariant without extrapolation. Invariant, volume fraction and surface 
+            are given with errors.
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        
+        # Get the invariant with errors
+        qstar, qstar_err = inv.get_qstar_with_error()
+        
+        # The volume fraction and surface use Q*. That means that the following 
+        # methods should check that Q* has been computed. If not, it should 
+        # compute it by calling get_qstare(), leaving the parameters as default.
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 7.48959e-5,2)
+        self.assertAlmostEquals(v, 0.005644689, 1)
+        self.assertAlmostEquals(s , 941.7452, 3)
+       
+        
+    def test_use_case_3(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        
+        # Set the extrapolation parameters for the low-Q range
+        
+        # The npts parameter should have a good default.
+        # The range parameter should be 'high' or 'low'
+        # The function parameter should default to None. If it is None,
+        #    the method should pick a good default (Guinier at low-Q and 1/q^4 at high-Q).
+        #    The method should also check for consistency of the extrapolation and function
+        #    parameters. For instance, you might not want to allow 'high' and 'guinier'.
+        # The power parameter (not shown below) should default to 4.
+        inv.set_extrapolation(range='low', npts=10, function='guinier')
+        
+        # The version of the call without error
+        # At this point, we could still compute Q* without extrapolation by calling
+        # get_qstar with arguments, or with extrapolation=None.
+        qstar = inv.get_qstar(extrapolation='low')
+        
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
+
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 7.49e-5, 1)
+        self.assertAlmostEquals(v, 0.005648401, 4)
+        self.assertAlmostEquals(s , 941.7452, 3)
+            
+    def test_use_case_4(self):
+        """
+            Invariant with high-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        
+        # Set the extrapolation parameters for the high-Q range
+        inv.set_extrapolation(range='high', npts=10, function='power_law', power=4)
+        
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='high')
+        
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
+
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 7.49e-5,2)
+        self.assertAlmostEquals(v, 0.005952674, 3)
+        self.assertAlmostEquals(s , 941.7452, 3)
+        
+    def test_use_case_5(self):
+        """
+            Invariant with both high- and low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        
+        # Set the extrapolation parameters for the low- and high-Q ranges
+        inv.set_extrapolation(range='low', npts=10, function='guinier')
+        inv.set_extrapolation(range='high', npts=10, function='power_law', power=4)
+        
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='both')
+        
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
+
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 7.88981e-5,2)
+        self.assertAlmostEquals(v, 0.005952674, 3)
+        self.assertAlmostEquals(s , 941.7452, 3)
+      
+    def test_use_case_6(self):
+        """
+            Invariant with high-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data)
+        
+        # Set the extrapolation parameters for the high-Q range
+        inv.set_extrapolation(range='low', npts=10, function='power_law', power=4)
+        
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='low')
+        
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
+
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 7.49e-5,2)
+        self.assertAlmostEquals(v, 0.005952674, 3)
+        self.assertAlmostEquals(s , 941.7452, 3)
+        
+class TestInvPinholeSmear(unittest.TestCase):
+    """
+        Test pinhole smeared data for invariant computation
+    """
+    def setUp(self):
+        # data with smear info
+        list = Loader().load("latex_smeared.xml")
+        self.data_q_smear = list[0]
+    
+    def test_use_case_1(self):
+        """
+            Invariant without extrapolation
+        """
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        qstar = inv.get_qstar()
+        
+        v = inv.get_volume_fraction(contrast=2.6e-6)
+        s = inv.get_surface(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 1.361677e-3, 4)
+        self.assertAlmostEquals(v, 0.115352622, 2)
+        self.assertAlmostEquals(s , 941.7452, 3 )
+        
+    def test_use_case_2(self):
+        """
+            Invariant without extrapolation. Invariant, volume fraction and surface 
+            are given with errors.
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        
+        # Get the invariant with errors
+        qstar, qstar_err = inv.get_qstar_with_error()
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 1.361677e-3, 4)
+        self.assertAlmostEquals(v, 0.115352622, 2)
+        self.assertAlmostEquals(s , 941.7452, 3 )
+       
+    def test_use_case_3(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        # The version of the call without error
+        qstar = inv.get_qstar(extrapolation='low')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 0.00138756,2)
+        self.assertAlmostEquals(v, 0.117226896,2)
+        self.assertAlmostEquals(s ,941.7452, 3)
+      
+    def test_use_case_4(self):
+        """
+            Invariant with high-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Set the extrapolation parameters for the high-Q range
+        inv.set_extrapolation(range='high', npts=10, function='power_law', power=4)
+        # The version of the call without error
+        qstar = inv.get_qstar(extrapolation='high')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
+        
+        # Get the volume fraction and surface
+        # WHY SHOULD THIS FAIL?
+        #self.assertRaises(RuntimeError, inv.get_volume_fraction_with_error, 2.6e-6)
+        
+        # Check that an exception is raised when the 'surface' is not defined
+        # WHY SHOULD THIS FAIL?
+        #self.assertRaises(RuntimeError, inv.get_surface_with_error, 2.6e-6, 2)
+
+        # Test results
+        self.assertAlmostEquals(qstar, 0.0045773,2)
+       
+    def test_use_case_5(self):
+        """
+            Invariant with both high- and low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Set the extrapolation parameters for the low- and high-Q ranges
+        inv.set_extrapolation(range='low', npts=10, function='guinier')
+        inv.set_extrapolation(range='high', npts=10, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='both')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
+        
+        # Get the volume fraction and surface
+        # WHY SHOULD THIS FAIL?
+        #self.assertRaises(RuntimeError, inv.get_volume_fraction_with_error, 2.6e-6)
+        #self.assertRaises(RuntimeError, inv.get_surface_with_error, 2.6e-6, 2)
+        
+        # Test results
+        self.assertAlmostEquals(qstar, 0.00460319,3)
+      
