From 972ee73bc81a98c6b8a70fac3fba3669b28347b9 Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:00 -0400
Subject: [PATCH 01/12] ENH: add multishell DWI test dataset

Small patch of multi-shell phantom dataset from Tristan Kuder.
Used with permission to distribute for testing (personal correspondence).
No PHI issues because this is from a phantom, and only a few voxels.
---
 .../TestData/multishell-DWI-451dir.nhdr       | 467 ++++++++++++++++++
 .../TestData/multishell-DWI-451dir.raw.gz     | Bin 0 -> 49880 bytes
 2 files changed, 467 insertions(+)
 create mode 100644 Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.nhdr
 create mode 100644 Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.raw.gz

diff --git a/Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.nhdr b/Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.nhdr
new file mode 100644
index 000000000..3199e786e
--- /dev/null
+++ b/Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.nhdr
@@ -0,0 +1,467 @@
+NRRD0005
+# Complete NRRD file format specification at:
+# http://teem.sourceforge.net/nrrd/format.html
+type: short
+dimension: 4
+space: right-anterior-superior
+sizes: 451 7 7 2
+space directions: none (-2.4904873929542752,0.001526874873563903,0.2178766031241865) (2.6429097682729909e-08,-2.4999386104169656,0.017519821531186731) (0.21788201640672655,0.017453167118519793,2.4904271887081069)
+kinds: list domain domain domain
+endian: little
+encoding: gzip
+space origin: (9.1900975709163664,98.098053174005756,9.8750159998483902)
+measurement frame: (-1,0,0) (0,-1,0) (0,0,1)
+data file: multishell-DWI-451dir.raw.gz
+modality:=DWMRI
+DWMRI_b-value:=10000
+DWMRI_gradient_0000:=0   0   0
+DWMRI_gradient_0001:=-0.0435405933728607   -0.08253993587221661   -0.127637672255725
+DWMRI_gradient_0002:=0.019808779758852785   -0.08236379602390693   -0.13350586883320079
+DWMRI_gradient_0003:=0.05080403555446169   -0.028030237301649163   -0.14708247833139243
+DWMRI_gradient_0004:=-0.07127480876410455   -0.1156972833518431   -0.08083340846799643
+DWMRI_gradient_0005:=-0.03550577181866828   -0.14896142375685542   -0.039367931921825736
+DWMRI_gradient_0006:=-0.1354225383313697   -0.08157129416774117   0.0026191112230762223
+DWMRI_gradient_0007:=-0.12138675083097808   -0.0820846816605407   -0.05939159387738341
+DWMRI_gradient_0008:=-0.07635146390615032   -0.028018593683141906   -0.13559281703437362
+DWMRI_gradient_0009:=-0.12456935077162568   -0.027843181760964994   -0.09331255939942835
+DWMRI_gradient_0010:=-0.09959284031365688   -0.1147493362544074   0.043747635951462506
+DWMRI_gradient_0011:=-0.04914768787611955   -0.14849992593393874   0.023071116963782706
+DWMRI_gradient_0012:=-0.03996209622465154   -0.08027147869389462   0.1302287227824093
+DWMRI_gradient_0013:=-0.09466969047450009   -0.08072730123066364   0.09757434194185656
+DWMRI_gradient_0014:=-0.1526104111205339   -0.027156004686520706   0.03118675185070729
+DWMRI_gradient_0015:=-0.12773979422051843   -0.026395244965366403   0.08936350306264973
+DWMRI_gradient_0016:=0.009535439920981431   -0.11414700003182758   0.1089932907713021
+DWMRI_gradient_0017:=0.005411147615978945   -0.1481464581367289   0.05498496237091392
+DWMRI_gradient_0018:=0.11064111433671167   -0.08099254652004932   0.07873214648692521
+DWMRI_gradient_0019:=0.0628949878120545   -0.08037436692862567   0.1207649838026506
+DWMRI_gradient_0020:=0.01809211645521744   0.02580870271120508   -0.15494058813121173
+DWMRI_gradient_0021:=0.04548288844650951   -0.025988992432039046   0.14918404336536828
+DWMRI_gradient_0022:=0.10594735047745904   -0.11469463852801046   0.02490579217061331
+DWMRI_gradient_0023:=0.052785443271180864   -0.14849781525152084   0.012731673421055852
+DWMRI_gradient_0024:=0.027996174799976632   -0.14878577027266218   -0.04559614484635787
+DWMRI_gradient_0025:=0.05593561942904512   -0.11577331320945371   -0.09202035694076104
+DWMRI_gradient_0026:=0.10601294465588054   -0.027857716213258322   -0.11395263435603882
+DWMRI_gradient_0027:=0.10889171205826474   -0.08198051468445483   -0.08013607128504707
+DWMRI_gradient_0028:=0.13368893953315997   -0.08156135835876928   -0.021794768479656165
+DWMRI_gradient_0029:=0.14149412627559807   -0.02685656948095772   0.06525439884888913
+DWMRI_gradient_0030:=0.15573156628933804   -0.027119798587823   0.003492198381495499
+DWMRI_gradient_0031:=-0.06164219464482649   -0.11638798730716488   -0.18070438581444667
+DWMRI_gradient_0032:=0.028078368266032265   -0.11670667624389976   -0.18865618399150996
+DWMRI_gradient_0033:=0.07207638550787794   -0.04004145530409182   -0.20785012704318945
+DWMRI_gradient_0034:=-0.10082282331018944   -0.16352816890853175   -0.11442593707636764
+DWMRI_gradient_0035:=-0.0506145355675154   -0.21065383808855018   -0.05534553360902229
+DWMRI_gradient_0036:=-0.19172071484165362   -0.11501840251434411   0.0037327451391003107
+DWMRI_gradient_0037:=-0.1716967527588114   -0.11593202609554756   -0.0841426718144883
+DWMRI_gradient_0038:=-0.10827801413964402   -0.03981492511354331   -0.1915480153768183
+DWMRI_gradient_0039:=-0.17587159737155433   -0.03938737788796415   -0.132354880921364
+DWMRI_gradient_0040:=-0.14106492090108647   -0.1622429583849325   0.06146470001793797
+DWMRI_gradient_0041:=-0.0699082131329457   -0.20994023985539517   0.03221701768689335
+DWMRI_gradient_0042:=-0.05672703605187412   -0.11373621261502757   0.1839731396790804
+DWMRI_gradient_0043:=-0.13381370700355508   -0.11435720681878117   0.13789967320190813
+DWMRI_gradient_0044:=-0.21588102020550604   -0.038091814519146394   0.04409532383698598
+DWMRI_gradient_0045:=-0.18037458026540057   -0.03775485962721007   0.12664746413264685
+DWMRI_gradient_0046:=0.013485148388621667   -0.16142823470139028   0.15413978920690635
+DWMRI_gradient_0047:=0.0072310941397070545   -0.20951155027051543   0.07779858856950933
+DWMRI_gradient_0048:=0.15645938102556564   -0.1141586697467961   0.11175087925094195
+DWMRI_gradient_0049:=0.0887530817140321   -0.11393362555728564   0.17071033435692867
+DWMRI_gradient_0050:=0.025337947765010523   0.03675848421076293   -0.21910454364116802
+DWMRI_gradient_0051:=0.06394762191979594   -0.036768840187391454   0.2110894427599275
+DWMRI_gradient_0052:=0.14970501161354594   -0.16225000808946774   0.035543539150956246
+DWMRI_gradient_0053:=0.07469633599075422   -0.2099830288798696   0.018099275527270243
+DWMRI_gradient_0054:=0.03927081229507744   -0.21043519377332578   -0.06461293466394662
+DWMRI_gradient_0055:=0.07942178819154533   -0.1635281831443905   -0.13019489882699625
+DWMRI_gradient_0056:=0.1500592224032128   -0.03935485648531481   -0.16103857874864394
+DWMRI_gradient_0057:=0.1537212066728381   -0.11583687666979026   -0.11380512916847906
+DWMRI_gradient_0058:=0.1890642624419567   -0.11530511771980552   -0.030974720297696223
+DWMRI_gradient_0059:=0.2002163334930603   -0.03776032298108295   0.0921280446638253
+DWMRI_gradient_0060:=0.22029399780670564   -0.03807782601815774   0.0045423010347048105
+DWMRI_gradient_0061:=-0.07535451657102352   -0.14269716333112586   -0.22126730900808086
+DWMRI_gradient_0062:=0.03436248554566541   -0.14284361960041161   -0.23111669250111952
+DWMRI_gradient_0063:=0.08805718583376586   -0.04882626598861433   -0.2546800608581812
+DWMRI_gradient_0064:=-0.12338394786809032   -0.2004432810414723   -0.1399960646585964
+DWMRI_gradient_0065:=-0.06180214624863405   -0.2579995963755464   -0.06794636572472271
+DWMRI_gradient_0066:=-0.23474236588963474   -0.1409893136136397   0.004247437977391619
+DWMRI_gradient_0067:=-0.2101110854993156   -0.14223799098260892   -0.10306158982671847
+DWMRI_gradient_0068:=-0.13232607485403577   -0.04894564844555471   -0.23472140728542093
+DWMRI_gradient_0069:=-0.21539933074016962   -0.0481477733897098   -0.1621262651774239
+DWMRI_gradient_0070:=-0.17255596441170093   -0.1988477943195271   0.07539230376031128
+DWMRI_gradient_0071:=-0.08548766373663456   -0.2572107663955452   0.03917252385471977
+DWMRI_gradient_0072:=-0.06947366706395741   -0.1393302485540371   0.22530090334625114
+DWMRI_gradient_0073:=-0.16400270419860832   -0.14004051677607113   0.16879506006608994
+DWMRI_gradient_0074:=-0.2644435536743765   -0.046796998772346936   0.053662402592954406
+DWMRI_gradient_0075:=-0.2209379327291914   -0.04604989519832049   0.15513169456837372
+DWMRI_gradient_0076:=0.017021872767417352   -0.19783494497138357   0.18860433722939596
+DWMRI_gradient_0077:=0.009131596117518879   -0.25669555892503426   0.09499478726009616
+DWMRI_gradient_0078:=0.19175766435225572   -0.13987625208935528   0.1366149255743419
+DWMRI_gradient_0079:=0.10874281252543104   -0.13968693687788247   0.20895589490563213
+DWMRI_gradient_0080:=0.03078200473441319   0.045332673141582604   -0.26832335447450184
+DWMRI_gradient_0081:=0.07854003089989477   -0.0450849183653179   0.2584546755396705
+DWMRI_gradient_0082:=0.18318703257612026   -0.19882913156983048   0.043697747782039026
+DWMRI_gradient_0083:=0.09130163476193216   -0.2572408137064588   0.02216260091951948
+DWMRI_gradient_0084:=0.048214371926680474   -0.2576949174435143   -0.07917518228318952
+DWMRI_gradient_0085:=0.09720007301616003   -0.20044328271857065   -0.15929419903039946
+DWMRI_gradient_0086:=0.18370984624303588   -0.048155973553261344   -0.19731117614209767
+DWMRI_gradient_0087:=0.1883155613019827   -0.14187426724158334   -0.13931599797676922
+DWMRI_gradient_0088:=0.2315556429872094   -0.14123581183858117   -0.03787393934035816
+DWMRI_gradient_0089:=0.2452171413366311   -0.04616895910749692   0.11285825816350858
+DWMRI_gradient_0090:=0.26978559612678416   -0.04676420434565768   0.005370934789536939
+DWMRI_gradient_0091:=-0.08702919249905602   -0.16484918269524795   -0.25544209769204496
+DWMRI_gradient_0092:=0.039625126432284066   -0.16489202422230043   -0.2669091013036816
+DWMRI_gradient_0093:=0.10186285485420807   -0.05625971735920944   -0.2940387757749879
+DWMRI_gradient_0094:=-0.1426045962162091   -0.23138210319663938   -0.16163616586009727
+DWMRI_gradient_0095:=-0.07127502867422089   -0.2979149116020049   -0.07852753904395476
+DWMRI_gradient_0096:=-0.27092702924056533   -0.1630139455450491   0.004999704027122781
+DWMRI_gradient_0097:=-0.24274120239060498   -0.16399530103560625   -0.1190892461805238
+DWMRI_gradient_0098:=-0.15295757190697923   -0.05635690708652583   -0.27097579020200896
+DWMRI_gradient_0099:=-0.24872621100755893   -0.055815451645712555   -0.18713606272188582
+DWMRI_gradient_0100:=-0.19944464706545803   -0.22955507331683445   0.08675424904336879
+DWMRI_gradient_0101:=-0.09884642477189538   -0.2969223070283711   0.04545906460107797
+DWMRI_gradient_0102:=-0.08029704128935039   -0.16095101934548425   0.2600906633992599
+DWMRI_gradient_0103:=-0.18932757611875436   -0.16170255161097546   0.19495474443864735
+DWMRI_gradient_0104:=-0.3053200579575673   -0.054105531478854635   0.06206651535103009
+DWMRI_gradient_0105:=-0.25520771846625817   -0.053078101982127264   0.17902998123460556
+DWMRI_gradient_0106:=0.01950918560759315   -0.22840368713964385   0.2178328396582696
+DWMRI_gradient_0107:=0.010613930480301419   -0.2963780727231232   0.10976056279159473
+DWMRI_gradient_0108:=0.22148639622943986   -0.16146454935315982   0.15771168074406472
+DWMRI_gradient_0109:=0.1256551041521829   -0.16103881447735585   0.24140690480570978
+DWMRI_gradient_0110:=0.035816442716266654   0.05227373081495207   -0.3098138766315665
+DWMRI_gradient_0111:=0.09075929336556052   -0.05203910822898737   0.29842030852165047
+DWMRI_gradient_0112:=0.21161847219019125   -0.22953844317377475   0.05029636158699937
+DWMRI_gradient_0113:=0.10546226328662543   -0.29702598098412125   0.02555927030500116
+DWMRI_gradient_0114:=0.055802539292673654   -0.2975283972897679   -0.09144904630619646
+DWMRI_gradient_0115:=0.11237105319924057   -0.2313821066502574   -0.18394310473791495
+DWMRI_gradient_0116:=0.21222204844954162   -0.055603113697575215   -0.2277500703724505
+DWMRI_gradient_0117:=0.21739462315544272   -0.16381806907937743   -0.1609447629525377
+DWMRI_gradient_0118:=0.26726424165992707   -0.16320367228868424   -0.04398164545663403
+DWMRI_gradient_0119:=0.2831373922942583   -0.05324142984762755   0.13037854842046928
+DWMRI_gradient_0120:=0.31150056452887814   -0.05409036278555934   0.006452101018890364
+DWMRI_gradient_0121:=-0.10661730950027032   -0.2017028711773885   -0.3129675854648912
+DWMRI_gradient_0122:=0.04856482018161502   -0.2020144510893584   -0.3268510796521793
+DWMRI_gradient_0123:=0.12472869386518388   -0.06911626995752577   -0.3600912420926761
+DWMRI_gradient_0124:=-0.17464631519842963   -0.2833356492451853   -0.19803934692990355
+DWMRI_gradient_0125:=-0.08741127326951775   -0.36479641968891496   -0.09634754779211196
+DWMRI_gradient_0126:=-0.33191278726221735   -0.19949363934355094   0.006016476651149365
+DWMRI_gradient_0127:=-0.2972437649792216   -0.20090735193856776   -0.1458848470404393
+DWMRI_gradient_0128:=-0.18732264085780567   -0.0689666245681091   -0.33189432263258395
+DWMRI_gradient_0129:=-0.30454943647819244   -0.06829466834932084   -0.22931525787412024
+DWMRI_gradient_0130:=-0.24428977177657205   -0.2811020092184974   0.10632109995987046
+DWMRI_gradient_0131:=-0.12109215977607642   -0.36368641885082686   0.05539752758227258
+DWMRI_gradient_0132:=-0.09827467994530596   -0.1971376986015449   0.3185574101315754
+DWMRI_gradient_0133:=-0.2319195178331428   -0.19798696785062492   0.2387771058693875
+DWMRI_gradient_0134:=-0.37393180996141284   -0.06616954319365938   0.07613541574409922
+DWMRI_gradient_0135:=-0.312583666933829   -0.06517418633077454   0.21918892554215696
+DWMRI_gradient_0136:=0.023714218549932328   -0.27970184621873023   0.26684175373880775
+DWMRI_gradient_0137:=0.012825322605173942   -0.36302283240425737   0.1343500752931172
+DWMRI_gradient_0138:=0.27121826354934536   -0.1977865919205903   0.193186764690851
+DWMRI_gradient_0139:=0.1539839780236933   -0.19739227521210348   0.2955084308045889
+DWMRI_gradient_0140:=0.04392218393741768   0.06394561885287031   -0.3794493484850035
+DWMRI_gradient_0141:=0.11107237586072549   -0.06375970242111817   0.3655101040269977
+DWMRI_gradient_0142:=0.25919354249537785   -0.28113691951248404   0.061487791420170725
+DWMRI_gradient_0143:=0.12931529779908077   -0.36370623872832186   0.03154891667291467
+DWMRI_gradient_0144:=0.06838930642552103   -0.36439417197039453   -0.11198124253038663
+DWMRI_gradient_0145:=0.13747024634142554   -0.2833425677534917   -0.22543054173666957
+DWMRI_gradient_0146:=0.25987262272626965   -0.06846227960066191   -0.2788891261904151
+DWMRI_gradient_0147:=0.26620635910067997   -0.20060874206637058   -0.19720627641865465
+DWMRI_gradient_0148:=0.3273766489886954   -0.19982677457672363   -0.05379403626052265
+DWMRI_gradient_0149:=0.3468199975713337   -0.0652836906490088   0.15954290174704477
+DWMRI_gradient_0150:=0.3815291817605719   -0.06615980878186926   0.007640133837676735
+DWMRI_gradient_0151:=-0.12319461666641202   -0.2329319746509491   -0.3613388670211172
+DWMRI_gradient_0152:=0.05610502411743374   -0.2332178050137576   -0.37744095816938905
+DWMRI_gradient_0153:=0.1440078512746057   -0.07961746098527506   -0.4158398656790959
+DWMRI_gradient_0154:=-0.2015019972088169   -0.3272225910327197   -0.2287407183891199
+DWMRI_gradient_0155:=-0.100746873597869   -0.4212779137986792   -0.11124289647508712
+DWMRI_gradient_0156:=-0.3832133960476074   -0.23043497091002552   0.006871133955507228
+DWMRI_gradient_0157:=-0.34324342585435064   -0.23193436645142718   -0.16849449687459597
+DWMRI_gradient_0158:=-0.21626450711695822   -0.07979369887356572   -0.3832265958829229
+DWMRI_gradient_0159:=-0.35164306128568185   -0.07890389375032804   -0.2648043206051414
+DWMRI_gradient_0160:=-0.28201831853963616   -0.32464034131408237   0.12277748983438137
+DWMRI_gradient_0161:=-0.13976355429000226   -0.41995940879928534   0.06403310912067042
+DWMRI_gradient_0162:=-0.11336034889214539   -0.2276450214943984   0.36786841492352296
+DWMRI_gradient_0163:=-0.26775510103027467   -0.22864031788049993   0.2757368421561591
+DWMRI_gradient_0164:=-0.43177004087322635   -0.07659134664975159   0.08779745231605111
+DWMRI_gradient_0165:=-0.36095708226934065   -0.0752547955415867   0.2530744865016578
+DWMRI_gradient_0166:=0.0275827222732003   -0.3230982777868819   0.30797190055852464
+DWMRI_gradient_0167:=0.014779249931607244   -0.41922779516328484   0.15501491781267127
+DWMRI_gradient_0168:=0.3131497852540416   -0.22839458538444177   0.22309890311898878
+DWMRI_gradient_0169:=0.17766641884128737   -0.22792600031061885   0.3412980767730684
+DWMRI_gradient_0170:=0.05070594444514501   0.07379531846907662   -0.4381588220958842
+DWMRI_gradient_0171:=0.1283602266849844   -0.0735870180369772   0.42202914377123796
+DWMRI_gradient_0172:=0.29922093226125707   -0.32468224396027323   0.07105117426267547
+DWMRI_gradient_0173:=0.14927221539110186   -0.4199951981369137   0.036357041472682095
+DWMRI_gradient_0174:=0.07886237164040218   -0.420792189401262   -0.12928516886566213
+DWMRI_gradient_0175:=0.15872141072907534   -0.32722259073921706   -0.26025542279953806
+DWMRI_gradient_0176:=0.3000737623262345   -0.07893029625425067   -0.32206480824063033
+DWMRI_gradient_0177:=0.3074020775359278   -0.23165069192128027   -0.2276881965967203
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+DWMRI_gradient_0371:=-0.27942978280096686   -0.8399976519603708   0.12776119139545458
+DWMRI_gradient_0372:=-0.22701046901229546   -0.4553816205373317   0.7355907880591819
+DWMRI_gradient_0373:=-0.5356827674458104   -0.45742739116207887   0.551184302496152
+DWMRI_gradient_0374:=-0.863622624310415   -0.15300950252966355   0.17533977231858527
+DWMRI_gradient_0375:=-0.7221169467669905   -0.1504661782981715   0.5058725385135858
+DWMRI_gradient_0376:=0.05492592392698272   -0.6462446993391064   0.6159146956347056
+DWMRI_gradient_0377:=0.029657744099444713   -0.8384854617181761   0.30993955788847893
+DWMRI_gradient_0378:=0.6264456353088292   -0.4569266550732165   0.44585185737869987
+DWMRI_gradient_0379:=0.355546798852838   -0.4559556840267565   0.6824154664653411
+DWMRI_gradient_0380:=0.10135998676218283   0.14759035285530314   -0.8763236968549545
+DWMRI_gradient_0381:=0.2567920660073086   -0.14745305287972776   0.8439878037472244
+DWMRI_gradient_0382:=0.5985738946413125   -0.6493078448074816   0.14180478189155396
+DWMRI_gradient_0383:=0.2984960407903775   -0.8400469340408746   0.07225816966207313
+DWMRI_gradient_0384:=0.157684476665634   -0.841549711499336   -0.2587076916830777
+DWMRI_gradient_0385:=0.3174128633357809   -0.6542607285644119   -0.5207609375608574
+DWMRI_gradient_0386:=0.5999440652020176   -0.15780119745261115   -0.6443336725279195
+DWMRI_gradient_0387:=0.6147218909630658   -0.4633722327390399   -0.4554153615537651
+DWMRI_gradient_0388:=0.7559864820712768   -0.46146761974923606   -0.12462766823778343
+DWMRI_gradient_0389:=0.8010944933345742   -0.15065037282948354   0.36817395176366924
+DWMRI_gradient_0390:=0.881074488697678   -0.1529683352750741   0.017561737135448122
+DWMRI_gradient_0391:=-0.2612742408846108   -0.49400341655060814   -0.7666136184109954
+DWMRI_gradient_0392:=0.118799087879937   -0.4945806363752914   -0.8007976119396271
+DWMRI_gradient_0393:=0.3053877770411869   -0.1688521002374165   -0.8821719324065792
+DWMRI_gradient_0394:=-0.42749724955423973   -0.693930821205759   -0.4854957893236185
+DWMRI_gradient_0395:=-0.21396335172952047   -0.8935827197189607   -0.23607128496078747
+DWMRI_gradient_0396:=-0.8129298222290111   -0.4888119321303101   0.014423716041429191
+DWMRI_gradient_0397:=-0.7280826446297302   -0.49197748213102516   -0.3575665869620606
+DWMRI_gradient_0398:=-0.4586242905519082   -0.16904987340420907   -0.8130719186298847
+DWMRI_gradient_0399:=-0.7458629688851396   -0.16729124244360052   -0.5618737547654493
+DWMRI_gradient_0400:=-0.5982652930552783   -0.688669911675262   0.2604082109407669
+DWMRI_gradient_0401:=-0.29637779328377106   -0.8909600114225336   0.13546404002872361
+DWMRI_gradient_0402:=-0.24074077806137884   -0.48301208490674774   0.7802199997815619
+DWMRI_gradient_0403:=-0.5681802080793281   -0.4851802780242171   0.5846121735047748
+DWMRI_gradient_0404:=-0.9160157797103989   -0.16233368919904773   0.1859110247383916
+DWMRI_gradient_0405:=-0.765913054863417   -0.15955824105286906   0.5365802871412679
+DWMRI_gradient_0406:=0.058284205356476215   -0.6854608368483011   0.6532583233299774
+DWMRI_gradient_0407:=0.03149683055643594   -0.8893362879474291   0.3287688565717387
+DWMRI_gradient_0408:=0.6644366395836395   -0.48465603045797057   0.4728980107446277
+DWMRI_gradient_0409:=0.3771464116291017   -0.48355372699875576   0.7238345264959442
+DWMRI_gradient_0410:=0.10742596779142062   0.15661570479323267   -0.9294790083175076
+DWMRI_gradient_0411:=0.27237350497383334   -0.15647611190421923   0.8951692268159746
+DWMRI_gradient_0412:=0.6348866031101201   -0.6887097934781525   0.15032586605529755
+DWMRI_gradient_0413:=0.3165475437386691   -0.891024263391925   0.07663849585289974
+DWMRI_gradient_0414:=0.16726318062989168   -0.8925892564166048   -0.2744221058550618
+DWMRI_gradient_0415:=0.33666291133761017   -0.693943487055211   -0.5523590909454107
+DWMRI_gradient_0416:=0.6363676616225468   -0.16729329530370782   -0.6834099772714621
+DWMRI_gradient_0417:=0.6520111942644069   -0.4914667160048629   -0.48305477335391184
+DWMRI_gradient_0418:=0.8018426295494818   -0.4894352910756585   -0.1322933775121033
+DWMRI_gradient_0419:=0.8496735246324469   -0.15980410270611914   0.3905350109064095
+DWMRI_gradient_0420:=0.9345368318406979   -0.1621564737852442   0.018607320854685704
+DWMRI_gradient_0421:=-0.27535635467838543   -0.5207860514247633   -0.8080598780905005
+DWMRI_gradient_0422:=0.1251986308383404   -0.5213851264351731   -0.8440869936021898
+DWMRI_gradient_0423:=0.32187259592379036   -0.177988218465993   -0.9299022669509855
+DWMRI_gradient_0424:=-0.45066365463316155   -0.7314541317749341   -0.5117393120545456
+DWMRI_gradient_0425:=-0.22549987882876316   -0.9419191293164335   -0.24887378020993775
+DWMRI_gradient_0426:=-0.8569154629569048   -0.5152350598113161   0.015123573869378392
+DWMRI_gradient_0427:=-0.7674614149833409   -0.5185952158729682   -0.3769111017009679
+DWMRI_gradient_0428:=-0.48343759755790006   -0.17822116606246616   -0.8570445176505013
+DWMRI_gradient_0429:=-0.7861782669887688   -0.17634236277511753   -0.5923065959496184
+DWMRI_gradient_0430:=-0.6305958099950751   -0.7259523946086852   0.2744850545632146
+DWMRI_gradient_0431:=-0.31244138011860934   -0.9391430059009637   0.14279635309377092
+DWMRI_gradient_0432:=-0.2537636703202753   -0.5091444742237354   0.8224207584886328
+DWMRI_gradient_0433:=-0.5989294113271988   -0.5114147119283124   0.6162293020220118
+DWMRI_gradient_0434:=-0.9655647235493465   -0.17110262736157963   0.19598126325503154
+DWMRI_gradient_0435:=-0.807356352746493   -0.16825538773056212   0.5655668344055529
+DWMRI_gradient_0436:=0.06138623917451572   -0.7225516484497474   0.6885861202221272
+DWMRI_gradient_0437:=0.03322792813563007   -0.9374595923624337   0.346504570647304
+DWMRI_gradient_0438:=0.7003968239876176   -0.5108609113336778   0.4984630560225926
+DWMRI_gradient_0439:=0.39759199296221553   -0.5097837039921421   0.7629162354258723
+DWMRI_gradient_0440:=0.11325844819092336   0.16512544011027339   -0.9797479844030157
+DWMRI_gradient_0441:=0.28711516519172425   -0.16491863622127612   0.9435924572313211
+DWMRI_gradient_0442:=0.6692036610275418   -0.7259866237857513   0.15846097990199576
+DWMRI_gradient_0443:=0.3337268480455391   -0.9392036056415041   0.08076495554129723
+DWMRI_gradient_0444:=0.1763260545684712   -0.940871255858654   -0.28925836613532024
+DWMRI_gradient_0445:=0.35488598859803666   -0.7314990022892535   -0.5822071321674274
+DWMRI_gradient_0446:=0.6707680233305371   -0.17643314620857228   -0.7203760155614486
+DWMRI_gradient_0447:=0.6872437043525724   -0.5180612835109211   -0.5092235239605204
+DWMRI_gradient_0448:=0.8452117317543049   -0.5159263534207851   -0.13941709490166473
+DWMRI_gradient_0449:=0.8956628142670163   -0.16844656131756755   0.4115991728849497
+DWMRI_gradient_0450:=0.9850878565136942   -0.17093448079953444   0.01957851432178666
diff --git a/Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.raw.gz b/Libs/MRML/Core/Testing/TestData/multishell-DWI-451dir.raw.gz
new file mode 100644
index 0000000000000000000000000000000000000000..39c23a1e473edf1ddf47497fec13ed8e538bfbdf
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z^}aY+@fwXR**kG`LK1oBv0X=6ium{4#IVxhvm|@Prz<4ItH|2_Ur}eWoHi6h(cXDt
z5D$q#Xh0G+2x0;*r?N<Sm;e8!xX<mX>|C}Br1yHbeS4y-BF8%Q$bE;@)aXTyoVX>K
zf@@uS{}TOI+*65f&Up92G*_kJZ#(X)CaNFcTjb5W3gV7dkHNHhy{l`_6r4ZUH0m&J
z<n#RhXINW51J9o3R1{^Vju4M~A56Oy8tlrAZ~j16j<D`~tV6;-Y68QC+{yw=(ntM(
znDZh|?+;9i@cquThkby}%s8DT_p2FcEAYB^1)RZMM}BUQer4GczG5)-sAv|B<L3lt
zTA*zw7>Aq~>i{B{fCz5nKpYo+MYW4MMUrWI!<`()1qnUjOEe=yHM7n~K3gA=?+K2j
z4gA|<vsd)gxEK8(&YZ${R=jtE{XCeasF%d~Eiyl1ja4<I{QplphoikO*p&^jgf;>5
z0Sx{ax5(!fk28mZ)K(ey1QX8PIpH5**)Aduw=!=`nOSh+2`t@k)*bJ;iwMGP&Jr6|
z;|o87-78@bw7GfB4K2{P+BDJQ0`D}S@xtFnZl(<WY7G)UP-|DgM>zt0Ld)OCVb-SF
z8|7JN$Wv>|fA3ARSW;vYJ(8RtRW<y*c6ob@lLZ%FVeQ3EU9m<Z(d-53(Nqw}0`qS@
zc;pE*52y}q*Q2suJnP(UAxq9{kDNr06pi@E7c%e*tbw+ncS!RJj9Y-%)oId$yc?{2
z3xXd4wYPXFO%l^yb?1ve;n4Q*ik+zk=baOO;hx0G=IH1qD%46eNO`ke<QaBoVugMC
zAE&nHLrtdFPx&_tu-n5s)i&yL*&w|qQ+?URG@<^AyuTxP?gJB_?DFfG3dd?5TRzA8
zh*?%uou=R?e3WKA<!H#6p12h#bHKA+km4HTuMIybxCr;+-nIV(LvS+HBEFjHhgz_?
zSGPUD7G_j-ondTyc-(=fbsOh=`Hau5x!F04NyWYIu?Y=USP^%}nQX}<stzQXs`IAg
zA$$1Lf!n{EHk(x6-?5Dh>z4Y|HStb~)ytV+w4~!q^FKB)p%sx!W!ext!iGHJU$swU
lK>jj#{DWzCZvu6l@#xA>X^Tn0@2*j`@(&&zo1#ov0RUabB6$D+

literal 0
HcmV?d00001

-- 
GitLab


From 9b96229924b1fbaca61c10a99d1e8265c023d28e Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:06 -0400
Subject: [PATCH 02/12] ENH: vtkNRRDWriter: don't dup 'space' field as k-v from
 node attribute

---
 Libs/vtkTeem/vtkTeemNRRDWriter.cxx | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/Libs/vtkTeem/vtkTeemNRRDWriter.cxx b/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
index 2b41c7cfd..991f7278c 100644
--- a/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
+++ b/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
@@ -279,6 +279,9 @@ void* vtkTeemNRRDWriter::MakeNRRD()
   AttributeMapType::iterator ait;
   for (ait = this->Attributes->begin(); ait != this->Attributes->end(); ++ait)
     {
+    // Don't set `space` as k-v. it is handled above, and needs to be a nrrd *field*.
+    if (ait->first == "space") { continue; }
+
     nrrdKeyValueAdd(nrrd, (*ait).first.c_str(), (*ait).second.c_str());
     }
 
-- 
GitLab


From 6d1a78814febc0a8289585c50f00681bdb5d0c74 Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:12 -0400
Subject: [PATCH 03/12] ENH: dmri multishell i/o test for MRMLNRRDStorageNode
 load path

---
 .../SlicerApp/Testing/Python/CMakeLists.txt   |  10 ++
 .../Testing/Python/DWMRIMultishellIOTests.py  | 121 ++++++++++++++++++
 Libs/vtkTeem/vtkTeemNRRDWriter.cxx            |   8 +-
 Libs/vtkTeem/vtkTeemNRRDWriter.h              |   2 +-
 4 files changed, 136 insertions(+), 5 deletions(-)
 create mode 100644 Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py

diff --git a/Applications/SlicerApp/Testing/Python/CMakeLists.txt b/Applications/SlicerApp/Testing/Python/CMakeLists.txt
index cb76e0ba1..1fbba93b5 100644
--- a/Applications/SlicerApp/Testing/Python/CMakeLists.txt
+++ b/Applications/SlicerApp/Testing/Python/CMakeLists.txt
@@ -289,6 +289,16 @@ slicer_add_python_unittest(
   TESTNAME_PREFIX nomainwindow_
   )
 
+# Test multi-shell DWI I/O
+slicer_add_python_test(
+  SCRIPT DWMRIMultishellIOTests.py
+  SLICER_ARGS --no-main-window --disable-modules
+  SCRIPT_ARGS
+    ${Slicer_SOURCE_DIR}/
+    ${Slicer_BINARY_DIR}/Testing/Temporary/
+  TESTNAME_PREFIX nomainwindow_
+  )
+
 # DCMTKPrivateDictionary test
 slicer_add_python_test(
   SCRIPT DCMTKPrivateDictTest.py
diff --git a/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py b/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
new file mode 100644
index 000000000..6c2fbc018
--- /dev/null
+++ b/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
@@ -0,0 +1,121 @@
+import sys, os, re, nose
+from nose.tools import assert_equal
+from collections import namedtuple
+
+import numpy as np
+from numpy.testing import assert_allclose
+import slicer
+
+Context = namedtuple('Ctx', ['data_dir', 'temp_dir'])
+
+#===============================================================================
+
+mrmlcore_testdata_path = "Libs/MRML/Core/Testing/TestData/"
+
+
+multishell_dwi_451 = os.path.join(mrmlcore_testdata_path, "multishell-DWI-451dir.nhdr")
+
+#================================================================================
+NRRD = namedtuple('NRRD', ['header', 'bvalue', 'gradients'])
+
+def parse_nhdr(path):
+    # TODO: NRRD b-matrix form?
+    dwmri_bval_key      = "DWMRI_b-value"
+    dwmri_grad_keybase  = "DWMRI_gradient_"
+    dwmri_grad_key_n    = "DWMRI_gradient_{:04d}"
+
+    kvdict = {}
+    grad_count = 0
+
+    with open(path, "rU") as f:
+        magic = f.readline().strip()
+        assert(magic == "NRRD0005")
+
+        while True:
+            line = f.readline()
+            # NRRD data section is separated by a newline
+            if (line == "\n") or (line == "") or (line is None):
+                break
+
+            # careful about precedence -- ":=" must match first
+            key, val = [x.strip() for x in re.split(":=|=|:", line)]
+            assert(not kvdict.has_key(key))
+            kvdict[key] = val
+
+            if key.startswith(dwmri_grad_keybase):
+                _gn = int(key[ len(dwmri_grad_keybase):None ])
+                # monotonic keys
+                assert( _gn == grad_count ) # offset
+                grad_count += 1
+
+        bvalue = float(kvdict[dwmri_bval_key])
+        grads = np.zeros((grad_count, 3))
+
+        # parse gradients
+        for i in range(0, grad_count):
+            grad_str = kvdict[dwmri_grad_key_n.format(i)]
+            grads[i] = np.fromstring(grad_str, count=3, dtype=np.float64, sep=" ")
+
+    return NRRD(header=kvdict, bvalue=bvalue, gradients=grads)
+
+
+#================================================================================
+def test_vtkMRMLNRRDStorageNode(ctx):
+    testnrrd_path = os.path.join(ctx.data_dir, multishell_dwi_451)
+
+    # load NRRD into Slicer
+    storagenode = slicer.vtkMRMLNRRDStorageNode()
+    storagenode.SetFileName(testnrrd_path)
+    dw_node = slicer.vtkMRMLDiffusionWeightedVolumeNode()
+    storagenode.ReadData(dw_node)
+
+    slicer_grads = dw_node.GetDiffusionGradients()
+    slicer_numgrads = slicer_grads.GetNumberOfTuples()
+
+    # load NRRD with direct parser
+    ext_nrrd = parse_nhdr(testnrrd_path)
+
+    # basic assertions
+    assert( len(ext_nrrd.gradients) == slicer_numgrads )
+
+
+    _ext_bval = ext_nrrd.bvalue
+    # Note: vtkDataArray.GetMaxNorm gives max for scalar array.
+    _node_bval = dw_node.GetBValues().GetMaxNorm()
+    _attr_bval = float(dw_node.GetAttribute("DWMRI_b-value"))
+    nose.tools.assert_equal(_ext_bval, _node_bval)
+    nose.tools.assert_equal(_ext_bval, _attr_bval)
+
+    # Gradients in the node attribute dictionary must exactly
+    #   match those on-disk.
+    for i in range(0, slicer_numgrads):
+        g_from_nhdr = ext_nrrd.gradients[i]
+        g_from_attr = np.fromstring(
+                        dw_node.GetAttribute("DWMRI_gradient_{:04d}".format(i)),
+                                             sep=" ", dtype=np.float64)
+
+    assert_allclose(np.linalg.norm(g_from_nhdr - g_from_attr), 0.0, atol=1e-12)
+
+    # Gradients in the node DiffusionGradients API must be
+    #   match *normalized* gradient.
+    for i in range(0, slicer_numgrads):
+        _g_tmp = ext_nrrd.gradients[i]
+        _g_tmp_norm = np.linalg.norm(_g_tmp)
+        # normalize
+        g_from_nhdr = _g_tmp if _g_tmp_norm == 0.0       \
+                             else (_g_tmp * 1/_g_tmp_norm)
+
+        g_from_node = np.array(slicer_grads.GetTuple3(i))
+        print _g_tmp, g_from_nhdr, g_from_node
+
+        assert_allclose(np.linalg.norm(g_from_nhdr - g_from_node), 0.0, atol=1e-12)
+
+
+if __name__ == '__main__':
+    # TODO make sure data paths exist
+    ctx = Context(data_dir = sys.argv[1],
+                  temp_dir = sys.argv[2])
+
+    success = test_vtkMRMLNRRDStorageNode(ctx)
+
+    sys.exit(success)
diff --git a/Libs/vtkTeem/vtkTeemNRRDWriter.cxx b/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
index 991f7278c..721659e08 100644
--- a/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
+++ b/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
@@ -23,7 +23,7 @@ vtkTeemNRRDWriter::vtkTeemNRRDWriter()
   this->IJKToRASMatrix = vtkMatrix4x4::New();
   this->MeasurementFrameMatrix = vtkMatrix4x4::New();
   this->UseCompression = 1;
-  this->DiffusionWeigthedData = 0;
+  this->DiffusionWeightedData = 0;
   this->FileType = VTK_BINARY;
   this->WriteErrorOff();
   this->Attributes = new AttributeMapType;
@@ -75,7 +75,7 @@ void vtkTeemNRRDWriter::vtkImageDataInfoToNrrdInfo(vtkImageData *in, int &kind,
 {
 
   vtkDataArray *array;
-  this->DiffusionWeigthedData = 0;
+  this->DiffusionWeightedData = 0;
   if ((array = static_cast<vtkDataArray *> (in->GetPointData()->GetScalars())))
     {
     numComp = array->GetNumberOfComponents();
@@ -102,7 +102,7 @@ void vtkTeemNRRDWriter::vtkImageDataInfoToNrrdInfo(vtkImageData *in, int &kind,
         if (numGrad == numBValues && numGrad == numComp && numGrad>6)
           {
           kind = nrrdKindList;
-          this->DiffusionWeigthedData = 1;
+          this->DiffusionWeightedData = 1;
           }
         else
           {
@@ -300,7 +300,7 @@ void* vtkTeemNRRDWriter::MakeNRRD()
     }
 
   // 2. Take care about diffusion data
-  if (this->DiffusionWeigthedData)
+  if (this->DiffusionWeightedData)
     {
     unsigned int numGrad = this->DiffusionGradients->GetNumberOfTuples();
     unsigned int numBValues = this->BValues->GetNumberOfTuples();
diff --git a/Libs/vtkTeem/vtkTeemNRRDWriter.h b/Libs/vtkTeem/vtkTeemNRRDWriter.h
index b414af1c3..5e69546f9 100644
--- a/Libs/vtkTeem/vtkTeemNRRDWriter.h
+++ b/Libs/vtkTeem/vtkTeemNRRDWriter.h
@@ -117,7 +117,7 @@ private:
   void operator=(const vtkTeemNRRDWriter&);  /// Not implemented.
   void vtkImageDataInfoToNrrdInfo(vtkImageData *in, int &nrrdKind, size_t &numComp, int &vtkType, void **buffer);
   int VTKToNrrdPixelType( const int vtkPixelType );
-  int DiffusionWeigthedData;
+  int DiffusionWeightedData;
 };
 
 #endif
-- 
GitLab


From 200e7989d6ff697d8f05c5a7e51f33b493500416 Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:19 -0400
Subject: [PATCH 04/12] ENH: minor code cleanup,
 vtkMRMLDiffusionWeightedVolumeNode

- remove unused and private vtkImageExtractComponents
- clean up constructor
---
 .../vtkMRMLDiffusionWeightedVolumeNode.cxx    | 26 ++++++-------------
 .../Core/vtkMRMLDiffusionWeightedVolumeNode.h |  2 --
 2 files changed, 8 insertions(+), 20 deletions(-)

diff --git a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
index 2f6212585..6f31695b1 100644
--- a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
+++ b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
@@ -20,7 +20,6 @@ Version:   $Revision: 1.14 $
 
 #include <vtkDoubleArray.h>
 #include <vtkImageData.h>
-#include <vtkImageExtractComponents.h>
 #include <vtkMatrix4x4.h>
 #include <vtkNew.h>
 #include <vtkObjectFactory.h>
@@ -29,12 +28,11 @@ Version:   $Revision: 1.14 $
 vtkMRMLNodeNewMacro(vtkMRMLDiffusionWeightedVolumeNode);
 
 //----------------------------------------------------------------------------
-vtkMRMLDiffusionWeightedVolumeNode::vtkMRMLDiffusionWeightedVolumeNode()
+vtkMRMLDiffusionWeightedVolumeNode::vtkMRMLDiffusionWeightedVolumeNode() :
+  DiffusionGradients(vtkDoubleArray::New()),
+  BValues(vtkDoubleArray::New())
 {
-  this->DiffusionGradients = vtkDoubleArray::New();
   this->DiffusionGradients->SetNumberOfComponents(3);
-  this->BValues = vtkDoubleArray::New();
-
   this->SetNumberOfGradientsInternal(7); //6 gradients + 1 baseline
 
   for(int i=0; i<3; i++)
@@ -44,8 +42,6 @@ vtkMRMLDiffusionWeightedVolumeNode::vtkMRMLDiffusionWeightedVolumeNode()
       this->MeasurementFrameMatrix[i][j] = (i == j) ? 1.0 : 0.0;
       }
     }
-
-  this->ExtractComponents = NULL;
 }
 
 //----------------------------------------------------------------------------
@@ -53,12 +49,6 @@ vtkMRMLDiffusionWeightedVolumeNode::~vtkMRMLDiffusionWeightedVolumeNode()
 {
   this->DiffusionGradients->Delete();
   this->BValues->Delete();
-
-  if (this->ExtractComponents)
-    {
-    this->ExtractComponents->Delete();
-    this->ExtractComponents = NULL;
-    }
 }
 
 //----------------------------------------------------------------------------
@@ -276,17 +266,17 @@ int vtkMRMLDiffusionWeightedVolumeNode::GetNumberOfGradients()
 }
 
 //----------------------------------------------------------------------------
-void vtkMRMLDiffusionWeightedVolumeNode::SetDiffusionGradient(int num,const double grad[3])
+void vtkMRMLDiffusionWeightedVolumeNode::SetDiffusionGradient(int num, const double grad[3])
 {
-  if (num < 0 || num >= this->DiffusionGradients->GetNumberOfTuples())
+  if ((num < 0) ||
+      (num >= this->DiffusionGradients->GetNumberOfTuples()))
     {
     vtkErrorMacro(<< "Gradient number is out of range. "
                      "Allocate first the number of gradients with SetNumberOfGradients");
     return;
     }
-  this->DiffusionGradients->SetComponent(num,0,grad[0]);
-  this->DiffusionGradients->SetComponent(num,1,grad[1]);
-  this->DiffusionGradients->SetComponent(num,2,grad[2]);
+  this->DiffusionGradients->SetTuple3(num, grad[0], grad[1], grad[2]);
+
   this->Modified();
 }
 
diff --git a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.h b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.h
index e287400fb..1a9e113cf 100644
--- a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.h
+++ b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.h
@@ -113,8 +113,6 @@ protected:
   vtkDoubleArray *DiffusionGradients;
   vtkDoubleArray *BValues;
 
-  vtkImageExtractComponents *ExtractComponents;
-
 };
 
 #endif
-- 
GitLab


From 28ec33a9a38c3e8579f43f9f8ad1bfdf1e57f28e Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:29 -0400
Subject: [PATCH 05/12] ENH: DiffusionWeightedVolumeNode must only accept
 unit-length gradients

---
 .../vtkMRMLDiffusionWeightedVolumeNode.cxx    | 33 ++++++++++++++++++-
 1 file changed, 32 insertions(+), 1 deletion(-)

diff --git a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
index 6f31695b1..a7e5d987d 100644
--- a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
+++ b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
@@ -24,6 +24,8 @@ Version:   $Revision: 1.14 $
 #include <vtkNew.h>
 #include <vtkObjectFactory.h>
 
+#include <vnl/vnl_double_3.h>
+
 //------------------------------------------------------------------------------
 vtkMRMLNodeNewMacro(vtkMRMLDiffusionWeightedVolumeNode);
 
@@ -265,6 +267,13 @@ int vtkMRMLDiffusionWeightedVolumeNode::GetNumberOfGradients()
   return this->DiffusionGradients->GetNumberOfTuples();
 }
 
+//------------------------------------------------------------------------------
+
+inline bool valid_grad_length(vnl_double_3 grad) {
+  // returns true if grad length is: within GRAD_EPS of 0.0 or 1.0
+  return (grad.two_norm() < 1e-6) || (fabs(1.0 - grad.two_norm()) < 1e-6);
+}
+
 //----------------------------------------------------------------------------
 void vtkMRMLDiffusionWeightedVolumeNode::SetDiffusionGradient(int num, const double grad[3])
 {
@@ -275,14 +284,36 @@ void vtkMRMLDiffusionWeightedVolumeNode::SetDiffusionGradient(int num, const dou
                      "Allocate first the number of gradients with SetNumberOfGradients");
     return;
     }
-  this->DiffusionGradients->SetTuple3(num, grad[0], grad[1], grad[2]);
 
+  vnl_double_3 tmp_grad(grad[0], grad[1], grad[2]);
+  if (!valid_grad_length(tmp_grad))
+    {
+    vtkErrorMacro(<< "vtkMRMLDiffusionWeightedVolumeNode only accepts gradient vectors with length 0.0 or 1.0!"
+                  << "  Got vector with length: " << tmp_grad.two_norm());
+    return;
+    }
+
+  this->DiffusionGradients->SetTuple3(num, grad[0], grad[1], grad[2]);
   this->Modified();
 }
 
 //----------------------------------------------------------------------------
 void vtkMRMLDiffusionWeightedVolumeNode::SetDiffusionGradients(vtkDoubleArray *grad)
 {
+  // gradients must all be length 0 (baseline) or 1.
+  vnl_double_3 tmp_grad;
+  for (int i = 0; i < grad->GetNumberOfTuples(); i++)
+    {
+    tmp_grad.copy_in(grad->GetTuple3(i));
+    double grad_norm = tmp_grad.two_norm();
+    if (!valid_grad_length(tmp_grad))
+      {
+      vtkErrorMacro(<< "vtkMRMLDiffusionWeightedVolumeNode only accepts gradient vectors with length 0.0 or 1.0!"
+                    << " Got vector with length: " << tmp_grad.two_norm());
+      return;
+      }
+    }
+
   this->DiffusionGradients->DeepCopy(grad);
   this->Modified();
 }
-- 
GitLab


From 760559e4bfd3a491fc8eb3f52e8d0efd13a7ceac Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:37 -0400
Subject: [PATCH 06/12] ENH: add vtkNRRDReader::GetHeaderKeysMap

---
 Libs/vtkTeem/vtkTeemNRRDReader.cxx | 6 ++++++
 Libs/vtkTeem/vtkTeemNRRDReader.h   | 7 ++++++-
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/Libs/vtkTeem/vtkTeemNRRDReader.cxx b/Libs/vtkTeem/vtkTeemNRRDReader.cxx
index 8fcaf526c..3424e05d0 100644
--- a/Libs/vtkTeem/vtkTeemNRRDReader.cxx
+++ b/Libs/vtkTeem/vtkTeemNRRDReader.cxx
@@ -104,6 +104,12 @@ const char* vtkTeemNRRDReader::GetHeaderKeys()
   return this->HeaderKeys.c_str();
 }
 
+//----------------------------------------------------------------------------
+const std::map<std::string,std::string> vtkTeemNRRDReader::GetHeaderKeysMap()
+{
+  return this->HeaderKeyValue;
+}
+
 //----------------------------------------------------------------------------
 std::vector<std::string> vtkTeemNRRDReader::GetHeaderKeysVector()
 {
diff --git a/Libs/vtkTeem/vtkTeemNRRDReader.h b/Libs/vtkTeem/vtkTeemNRRDReader.h
index 3908c9243..b87092007 100644
--- a/Libs/vtkTeem/vtkTeemNRRDReader.h
+++ b/Libs/vtkTeem/vtkTeemNRRDReader.h
@@ -75,7 +75,12 @@ public:
   ///
   /// Get a list of keys in the header. Preferred method to use as it
   /// supports spaces in key names.
-  std::vector<std::string> GetHeaderKeysVector();
+  const std::vector<std::string> GetHeaderKeysVector();
+
+  ///
+  /// Get a map of keys in the header. Preferred method to use as it
+  /// supports spaces in key names.
+  const std::map<std::string, std::string> GetHeaderKeysMap();
 
   ///
   /// Get a value given a key in the header
-- 
GitLab


From 63ec50b67d45e59116a3c627a370da959abcd4b2 Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:41 -0400
Subject: [PATCH 07/12] ENH: refactor
 vtkMRMLNRRDStorageNode::ParseDiffusionGradients

- Improve correctness and readability
- Raise error for unsupported DWMRI_NEX_ key
- Raise error for non-consecutive DWMRI_gradient_ keys
- Detect DWMRI_gradient_ padding width, and
-   Raise error for padding width inconsistency
---
 Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx | 225 ++++++++++++++--------
 Libs/vtkTeem/vtkTeemNRRDReader.cxx        |   2 +-
 2 files changed, 145 insertions(+), 82 deletions(-)

diff --git a/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx b/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
index ea66a57a8..86df0d6fd 100644
--- a/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
+++ b/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
@@ -32,6 +32,9 @@ Version:   $Revision: 1.6 $
 #include <vtkStringArray.h>
 #include <vtkVersion.h>
 
+// vnl includes
+#include <vnl/vnl_double_3.h>
+
 //----------------------------------------------------------------------------
 vtkMRMLNodeNewMacro(vtkMRMLNRRDStorageNode);
 
@@ -401,104 +404,164 @@ int vtkMRMLNRRDStorageNode::WriteDataInternal(vtkMRMLNode *refNode)
 }
 
 //----------------------------------------------------------------------------
-int vtkMRMLNRRDStorageNode::ParseDiffusionInformation(vtkTeemNRRDReader *reader,vtkDoubleArray *grad,vtkDoubleArray *bvalues)
+// internal
+
+const std::string dwmri_grad_tag("DWMRI_gradient_");
+const std::string dwmri_bvalue_tag("DWMRI_b-value");
+
+bool parse_gradient_key(std::string key, size_t &grad_number, size_t &gradkey_pad_width, std::string err)
+{
+  std::stringstream err_stream;
+
+  // note: Slicer no longer supports NRRDs with DWMRI_NEX_ keys. This was removed
+  //       from Dicom2Nrrd in the following commit:
+  //           Slicer3/4 SVN: r26101, git-svn: 63a18f7d6900a
+  //       and never un-commented in Dicom2Nrrd (or DWIConvert).
+  //       If such a key is found, we print an error and fail.
+  if (key.find("DWMRI_NEX_") != std::string::npos)
+    {
+    err_stream << "DWMRI_NEX_ NRRD tag is no longer supported (since SVN r6101)."
+               << " Please adjust header manually to encode repeat excitations"
+               << " as unique DWMRI_gradient_###N keys, and re-load.";
+    err = err_stream.str();
+    return false;
+    }
+
+  if (key.find(dwmri_grad_tag) == std::string::npos)
+    {
+    return false;
+    }
+  // below here key is: DWMRI_gradient_####
+
+  // we enforce the constraint that the padding of the grad key must be consistent
+  if (gradkey_pad_width == 0) {
+    gradkey_pad_width = key.size() - dwmri_grad_tag.size();
+  }
+  else if (gradkey_pad_width != key.size() - dwmri_grad_tag.size())
+    {
+    err_stream << "DWMRI NRRD gradient key-numbers must have consistent padding (####N)"
+        << " Found tag: '" << key << "' but previous key had padding: " << gradkey_pad_width;
+    err = err_stream.str();
+    return false;
+    }
+
+  // slices key string from `dwmri_grad_tag.size()` to `key.size()`.
+  //   e.g.: "DWMRI_gradient_000001" -> "000001"
+  std::string cur_grad_num_str = key.substr(dwmri_grad_tag.size(), key.size());
+  size_t cur_grad_num = atol(cur_grad_num_str.c_str());
+
+  // enforce monotonic order
+  if (cur_grad_num != grad_number)
+    {
+    err_stream << "DWMRI NRRD gradient key-numbers must be consecutive."
+               << " Found tag: '" << key << "' but previous key was: " << grad_number;
+    err = err_stream.str();
+    return false;
+    }
+
+  grad_number += 1;
+  return true;
+}
+
+//----------------------------------------------------------------------------
+int vtkMRMLNRRDStorageNode::ParseDiffusionInformation(
+        vtkTeemNRRDReader* reader,
+        vtkDoubleArray* gradients_array,
+        vtkDoubleArray* bvalues_array)
 {
-  std::string keys(reader->GetHeaderKeys());
-  std::string key,value,num;
-  std::string tag,tagnex;
-  const char *tmp;
-  vtkNew<vtkDoubleArray> factor;
-  grad->SetNumberOfComponents(3);
-  double g[3];
-  int rep;
-
-  // search for modality tag
-  key = "modality";
-  tmp = reader->GetHeaderValue(key.c_str());
-  if (tmp == NULL)
+  // Validate modality tag
+  std::string modality(reader->GetHeaderValue("modality"));
+  if (modality != "DWMRI")
     {
+    vtkErrorMacro(<< "NRRD header missing 'modality: DWMRI' tag!")
     return 0;
     }
-  if (strcmp(tmp,"DWMRI") != 0)
+
+  std::map<std::string, std::string> nrrd_keys = reader->GetHeaderKeysMap();
+
+  /*
+      Step 1: get DWMRI_b-value
+  */
+  std::string ref_bvalue_str(reader->GetHeaderValue(dwmri_bvalue_tag.c_str()));
+  if (ref_bvalue_str.empty())
     {
+    vtkErrorMacro(<< "Missing 'DWMRI_b-value' tag!")
     return 0;
     }
-  // search for tag DWMRI_gradient_
-  tag = "DWMRI_gradient_";
-  tagnex = "DWMRI_NEX_";
-  unsigned int pos = 0;
-  int gbeginpos =0;
-  int gendpos = 0;
-  pos = (unsigned int)keys.find(tag,pos);
-  while ( pos < keys.size() )
-    {
-    num = keys.substr(pos+tag.size(),4);
-    // Insert gradient
-    key = tag+num;
-    tmp = reader->GetHeaderValue(key.c_str());
-    if (tmp == NULL)
-      {
-      continue;
-      }
-    else
-      {
-      value = tmp;
-      }
-    gbeginpos = -1;
-    gendpos = 0;
-    for (int i=0 ;i<3; i++)
+  double ref_bvalue = atof(ref_bvalue_str.c_str());
+
+
+  /*
+    Step 2: loop over all keys
+      - for all DWMRI_gradient_ keys, validate
+        - consecutive
+        - consistent padding
+      - save each gradient to tmp_grads
+      - record maximum gradient length
+  */
+  vtkNew<vtkDoubleArray> tmp_grads;
+  tmp_grads->SetNumberOfComponents(3);
+  size_t grad_idx = 0;
+  size_t gradkey_pad_width = 0;
+  double max_grad_norm = 0;
+  std::string err;
+
+  std::map<std::string, std::string>::iterator nrrd_keys_iter = nrrd_keys.begin();
+  for (; nrrd_keys_iter != nrrd_keys.end(); nrrd_keys_iter++)
+    {
+    std::string key = nrrd_keys_iter->first;
+
+    std::cout << "key a: " << key << std::endl;
+
+    if (!parse_gradient_key(key, grad_idx, gradkey_pad_width, err))
       {
-      do
+      if (err.empty())
         {
-        gbeginpos++;
-        gendpos=(int)value.find(" ",gbeginpos);
+        continue;
+        }
+      else
+        {
+        vtkErrorMacro(<< err);
+        return 0;
         }
-      while(gendpos==gbeginpos);
-      g[i] = atof(value.substr(gbeginpos,gendpos).c_str());
-      gbeginpos = gendpos;
-      }
-    grad->InsertNextTuple3(g[0],g[1],g[2]);
-    factor->InsertNextValue(sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]));
-    // find repetitions of this gradient
-    key = tagnex+num;
-    tmp = reader->GetHeaderValue(key.c_str());
-    if (tmp == NULL)
-      {
-      value = "";
-      }
-    else
-      {
-      value = tmp;
-      }
-    if (value.size()>0) {
-      rep = atoi(value.c_str());
-      for (int i=0;i<rep-1;i++) {
-        grad->InsertNextTuple3(g[0],g[1],g[2]);
-        factor->InsertNextValue(sqrt( g[0]*g[0]+g[1]*g[1]+g[2]*g[2] ));
       }
-    }
-   pos = (unsigned int)keys.find(tag,pos+1);
-  }
+    std::cout << "key d: " << key << std::endl;
+    // parse the gradient vector into double[3]
+    vnl_double_3 cur_grad(0,0,0);
+    std::stringstream grad_value_stream(nrrd_keys_iter->second);
+    grad_value_stream >> cur_grad[0] >> cur_grad[1] >> cur_grad[2];
 
-  grad->Modified();
-  factor->Modified();
-  double range[2];
-  // search for tag DWMRI_b-value
-  key = "DWMRI_b-value";
-  tmp = reader->GetHeaderValue(key.c_str());
-  if (tmp == NULL)
-    {
-    return 0;
+    max_grad_norm = std::max(cur_grad.two_norm(), max_grad_norm);
+    tmp_grads->InsertNextTuple(cur_grad.data_block());
     }
-  double bval = atof(tmp);
-  factor->GetRange(range);
-  bvalues->SetNumberOfTuples(grad->GetNumberOfTuples());
-  for (int i=0; i<grad->GetNumberOfTuples();i++)
+
+  assert(grad_idx == tmp_grads->GetNumberOfTuples());
+
+  /*
+    Step 3: loop over gradients
+      - calculate each b-value based on NA-MIC DWI gradient length-encoding
+      - then normalize each gradient to unit-length
+  */
+  bvalues_array->SetNumberOfTuples(tmp_grads->GetNumberOfTuples());
+  // calculate the b-values
+  for (int i=0; i < tmp_grads->GetNumberOfTuples(); i++)
     {
+    vnl_double_3 cur_grad(0,0,0);
+    cur_grad.copy_in(tmp_grads->GetTuple3(i));
+
     // note: this is norm^2, per the NA-MIC NRRD DWI convention
     // http://wiki.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:Nrrd_format
-    bvalues->SetValue(i, bval * (pow(factor->GetValue(i)/range[1], 2)));
+    double cur_bval = ref_bvalue * pow(cur_grad.two_norm() / max_grad_norm, 2);
+    bvalues_array->SetValue(i, cur_bval);
+
+    // normalize gradient vector to unit-length
+    //   must be done *after* bvalue extraction
+    cur_grad.normalize();
+    tmp_grads->InsertTuple(i, cur_grad.data_block());
     }
+
+  // Step 4: copy tmp_grads to output
+  gradients_array->DeepCopy(tmp_grads);
   return 1;
 }
 
diff --git a/Libs/vtkTeem/vtkTeemNRRDReader.cxx b/Libs/vtkTeem/vtkTeemNRRDReader.cxx
index 3424e05d0..6ed57223a 100644
--- a/Libs/vtkTeem/vtkTeemNRRDReader.cxx
+++ b/Libs/vtkTeem/vtkTeemNRRDReader.cxx
@@ -111,7 +111,7 @@ const std::map<std::string,std::string> vtkTeemNRRDReader::GetHeaderKeysMap()
 }
 
 //----------------------------------------------------------------------------
-std::vector<std::string> vtkTeemNRRDReader::GetHeaderKeysVector()
+const std::vector<std::string> vtkTeemNRRDReader::GetHeaderKeysVector()
 {
   std::vector<std::string> keys;
   for (std::map<std::string,std::string>::iterator i = HeaderKeyValue.begin(); i != HeaderKeyValue.end(); i++)
-- 
GitLab


From d9556eef5be58b9c1a46c4152c706b013a7c1626 Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:47 -0400
Subject: [PATCH 08/12] STYLE: remove debugging output

---
 Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx b/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
index 86df0d6fd..5ca75edd0 100644
--- a/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
+++ b/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
@@ -511,8 +511,6 @@ int vtkMRMLNRRDStorageNode::ParseDiffusionInformation(
     {
     std::string key = nrrd_keys_iter->first;
 
-    std::cout << "key a: " << key << std::endl;
-
     if (!parse_gradient_key(key, grad_idx, gradkey_pad_width, err))
       {
       if (err.empty())
@@ -525,7 +523,6 @@ int vtkMRMLNRRDStorageNode::ParseDiffusionInformation(
         return 0;
         }
       }
-    std::cout << "key d: " << key << std::endl;
     // parse the gradient vector into double[3]
     vnl_double_3 cur_grad(0,0,0);
     std::stringstream grad_value_stream(nrrd_keys_iter->second);
-- 
GitLab


From cb45c5ca992d548249d602eb5ea2ea76e6a93f9c Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:51 -0400
Subject: [PATCH 09/12] STYLE: remove unused line

---
 Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
index a7e5d987d..196b38e12 100644
--- a/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
+++ b/Libs/MRML/Core/vtkMRMLDiffusionWeightedVolumeNode.cxx
@@ -305,7 +305,6 @@ void vtkMRMLDiffusionWeightedVolumeNode::SetDiffusionGradients(vtkDoubleArray *g
   for (int i = 0; i < grad->GetNumberOfTuples(); i++)
     {
     tmp_grad.copy_in(grad->GetTuple3(i));
-    double grad_norm = tmp_grad.two_norm();
     if (!valid_grad_length(tmp_grad))
       {
       vtkErrorMacro(<< "vtkMRMLDiffusionWeightedVolumeNode only accepts gradient vectors with length 0.0 or 1.0!"
-- 
GitLab


From 0650f935e37b454d8947c4ce0e96c679a571786e Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:51:59 -0400
Subject: [PATCH 10/12] ENH: use DoubleConvert from ITK to improve float-string
 representation

---
 Libs/vtkTeem/vtkTeemNRRDWriter.cxx | 58 +++++++++++++++++++-----------
 1 file changed, 38 insertions(+), 20 deletions(-)

diff --git a/Libs/vtkTeem/vtkTeemNRRDWriter.cxx b/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
index 721659e08..286afe05d 100644
--- a/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
+++ b/Libs/vtkTeem/vtkTeemNRRDWriter.cxx
@@ -9,6 +9,12 @@
 #include "vtkInformation.h"
 #include <vtkVersion.h>
 
+#include <vnl/vnl_math.h>
+#include <vnl/vnl_double_3.h>
+
+#include "itkNumberToString.h"
+
+
 class AttributeMapType: public std::map<std::string, std::string> {};
 class AxisInfoMapType : public std::map<unsigned int, std::string> {};
 
@@ -299,6 +305,10 @@ void* vtkTeemNRRDWriter::MakeNRRD()
       }
     }
 
+  // use double-conversion library (via ITK) for better
+  // float64 string representation.
+  itk::NumberToString<double> DoubleConvert;
+
   // 2. Take care about diffusion data
   if (this->DiffusionWeightedData)
     {
@@ -308,30 +318,38 @@ void* vtkTeemNRRDWriter::MakeNRRD()
     if (kind[0] == nrrdKindList && numGrad == size[0] && numBValues == size[0])
       {
       // This is diffusion Data
-      double *grad;
-      double bVal,factor;
+      vnl_double_3 grad;
+      double bVal, factor;
       double maxbVal = this->BValues->GetRange()[1];
-      char value[1024];
-      char key[1024];
-      strcpy(key,"modality");
-      strcpy(value,"DWMRI");
-      nrrdKeyValueAdd(nrrd, key, value);
-
-      strcpy(key,"DWMRI_b-value");
-      //sprintf(value,"%f",maxbVal,1024);
-      sprintf(value,"%f",maxbVal);
-      nrrdKeyValueAdd(nrrd,key, value);
-      for (unsigned int ig =0; ig< numGrad; ig++)
+
+      std::string modality_key("modality");
+      std::string modality_value("DWMRI");
+      nrrdKeyValueAdd(nrrd, modality_key.c_str(), modality_value.c_str());
+
+      std::string bval_key("DWMRI_b-value");
+      std::stringstream bval_value;
+      bval_value << DoubleConvert(maxbVal);
+      nrrdKeyValueAdd(nrrd, bval_key.c_str(), bval_value.str().c_str());
+
+      for (unsigned int ig =0; ig < numGrad; ig++)
         {
-        grad=this->DiffusionGradients->GetTuple3(ig);
+        // key
+        std::stringstream key_stream;
+        key_stream << "DWMRI_gradient_" << setfill('0') << setw(4) << ig;
+
+        // gradient value
+        grad.copy_in(this->DiffusionGradients->GetTuple3(ig));
+
         bVal = this->BValues->GetValue(ig);
-        // for multiple b-values, scale factor is `sqrt(b/b_max)`
-        // per NA-MIC DWI convention. so we take `norm^2 * b_max`
-        // to get back the original b-values.
+        // per NA-MIC DWI convention
         factor = sqrt(bVal/maxbVal);
-        sprintf(key,"%s%04d","DWMRI_gradient_",ig);
-        sprintf(value,"%f %f %f",grad[0]*factor, grad[1]*factor, grad[2]*factor);
-        nrrdKeyValueAdd(nrrd,key, value);
+        std::stringstream value_stream;
+        value_stream << std::setprecision(17) <<
+                        DoubleConvert(grad[0] * factor) << "   " <<
+                        DoubleConvert(grad[1] * factor) << "   " <<
+                        DoubleConvert(grad[2] * factor);
+
+        nrrdKeyValueAdd(nrrd, key_stream.str().c_str(), value_stream.str().c_str());
         }
       }
     }
-- 
GitLab


From cc05b9ace597887020d1fb38796a821a791ce8fc Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:52:04 -0400
Subject: [PATCH 11/12] ENH: address review comments

---
 .../SlicerApp/Testing/Python/CMakeLists.txt   |   2 +-
 .../Testing/Python/DWMRIMultishellIOTests.py  | 142 +++++++++++++-----
 Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx     |   5 +-
 Libs/vtkTeem/CMakeLists.txt                   |   2 +
 4 files changed, 110 insertions(+), 41 deletions(-)

diff --git a/Applications/SlicerApp/Testing/Python/CMakeLists.txt b/Applications/SlicerApp/Testing/Python/CMakeLists.txt
index 1fbba93b5..4e5507633 100644
--- a/Applications/SlicerApp/Testing/Python/CMakeLists.txt
+++ b/Applications/SlicerApp/Testing/Python/CMakeLists.txt
@@ -292,7 +292,7 @@ slicer_add_python_unittest(
 # Test multi-shell DWI I/O
 slicer_add_python_test(
   SCRIPT DWMRIMultishellIOTests.py
-  SLICER_ARGS --no-main-window --disable-modules
+  SLICER_ARGS --no-main-window --disable-modules --testing
   SCRIPT_ARGS
     ${Slicer_SOURCE_DIR}/
     ${Slicer_BINARY_DIR}/Testing/Temporary/
diff --git a/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py b/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
index 6c2fbc018..3f17da86e 100644
--- a/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
+++ b/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
@@ -3,11 +3,10 @@ from nose.tools import assert_equal
 from collections import namedtuple
 
 import numpy as np
-from numpy.testing import assert_allclose
+import numpy.testing
+from vtk.util import numpy_support
 import slicer
 
-Context = namedtuple('Ctx', ['data_dir', 'temp_dir'])
-
 #===============================================================================
 
 mrmlcore_testdata_path = "Libs/MRML/Core/Testing/TestData/"
@@ -19,7 +18,6 @@ multishell_dwi_451 = os.path.join(mrmlcore_testdata_path, "multishell-DWI-451dir
 NRRD = namedtuple('NRRD', ['header', 'bvalue', 'gradients'])
 
 def parse_nhdr(path):
-    # TODO: NRRD b-matrix form?
     dwmri_bval_key      = "DWMRI_b-value"
     dwmri_grad_keybase  = "DWMRI_gradient_"
     dwmri_grad_key_n    = "DWMRI_gradient_{:04d}"
@@ -60,62 +58,128 @@ def parse_nhdr(path):
 
 
 #================================================================================
-def test_vtkMRMLNRRDStorageNode(ctx):
-    testnrrd_path = os.path.join(ctx.data_dir, multishell_dwi_451)
+def normalize(vec):
+    norm = np.linalg.norm(vec)
+    if norm == 0.0:
+        return vec
+    else:
+        return vec * 1/norm
+
+
+def test_nrrd_dwi_load(first_file, second_file=None):
+    """
+    - load a DWI NRRD file into Slicer
+    - validate b values and gradient vectors against original header
+        - check the values in the vtkMRMLDiffusionWeightedVolumeNode
+        - check the values in the vtkMRMLDWVNode attribute dictionary
+    """
+    if second_file is None:
+        second_file = first_file
 
     # load NRRD into Slicer
     storagenode = slicer.vtkMRMLNRRDStorageNode()
-    storagenode.SetFileName(testnrrd_path)
+    storagenode.SetFileName(first_file)
     dw_node = slicer.vtkMRMLDiffusionWeightedVolumeNode()
     storagenode.ReadData(dw_node)
 
-    slicer_grads = dw_node.GetDiffusionGradients()
-    slicer_numgrads = slicer_grads.GetNumberOfTuples()
+    slicer_grads = numpy_support.vtk_to_numpy(dw_node.GetDiffusionGradients())
+    slicer_numgrads = slicer_grads.shape[0]
+
+    # load NRRD with pure-python parser
+    parsed_nrrd = parse_nhdr(second_file)
 
-    # load NRRD with direct parser
-    ext_nrrd = parse_nhdr(testnrrd_path)
+    ##################################
+    # 1) check the number of gradients
 
-    # basic assertions
-    assert( len(ext_nrrd.gradients) == slicer_numgrads )
+    assert( len(parsed_nrrd.gradients) == slicer_numgrads )
 
+    ##################################
+    # 2) check the node b values and gradients are correct
 
-    _ext_bval = ext_nrrd.bvalue
     # Note: vtkDataArray.GetMaxNorm gives max for scalar array.
-    _node_bval = dw_node.GetBValues().GetMaxNorm()
-    _attr_bval = float(dw_node.GetAttribute("DWMRI_b-value"))
-    nose.tools.assert_equal(_ext_bval, _node_bval)
-    nose.tools.assert_equal(_ext_bval, _attr_bval)
+    # max b value from the node
+    nose.tools.assert_equal(parsed_nrrd.bvalue, dw_node.GetBValues().GetMaxNorm())
+
+    max_parsed_grad_norm = np.max(np.apply_along_axis(np.linalg.norm, 1, parsed_nrrd.gradients))
 
-    # Gradients in the node attribute dictionary must exactly
-    #   match those on-disk.
     for i in range(0, slicer_numgrads):
-        g_from_nhdr = ext_nrrd.gradients[i]
-        g_from_attr = np.fromstring(
-                        dw_node.GetAttribute("DWMRI_gradient_{:04d}".format(i)),
-                                             sep=" ", dtype=np.float64)
+        g_parsed_raw = parsed_nrrd.gradients[i]
+        g_parsed_normed = normalize(g_parsed_raw)
+
+        bval_parsed = parsed_nrrd.bvalue * pow(np.linalg.norm(g_parsed_raw) / max_parsed_grad_norm, 2)
+        np.testing.assert_almost_equal(bval_parsed, dw_node.GetBValue(i), decimal=7,
+                                       err_msg="MRMLNode b value does not match NRRD header")
 
-    assert_allclose(np.linalg.norm(g_from_nhdr - g_from_attr), 0.0, atol=1e-12)
+        g_from_node = slicer_grads[i, :]
 
-    # Gradients in the node DiffusionGradients API must be
-    #   match *normalized* gradient.
+        # gradients stored in the vtkMRMLDiffusionWeightedVolumeNode must be *normalized*.
+        np.testing.assert_allclose(np.linalg.norm(g_parsed_normed - g_from_node), 0.0, atol=1e-15)
+
+    # b value from the node attribute dictionary
+    np.testing.assert_equal(parsed_nrrd.bvalue, float(dw_node.GetAttribute("DWMRI_b-value")))
+
+    # 3) check gradients in the node attribute dictionary
+    #    gradients must match the value on-disk.
     for i in range(0, slicer_numgrads):
-        _g_tmp = ext_nrrd.gradients[i]
-        _g_tmp_norm = np.linalg.norm(_g_tmp)
-        # normalize
-        g_from_nhdr = _g_tmp if _g_tmp_norm == 0.0       \
-                             else (_g_tmp * 1/_g_tmp_norm)
+        grad_key = "DWMRI_gradient_{:04d}".format(i)
+        parsed_gradient = np.fromstring(parsed_nrrd.header[grad_key], count=3, sep=' ', dtype=np.float64)
+        attr_gradient =   np.fromstring(dw_node.GetAttribute(grad_key), count=3, sep=' ', dtype=np.float64)
+
+        np.testing.assert_array_almost_equal(parsed_gradient, attr_gradient, decimal=12,
+                                             err_msg="NHDR gradient does not match gradient in node attribute dictionary")
+
+    return (parsed_nrrd, dw_node)
+
+def test_nrrd_dwi_roundtrip(test_nrrd_path):
+    """DWI NRRD round-trip test
+    - loads and saves a NRRD file via Slicer's I/O, twice
+    - checks the node values against the original file each time
+    """
+
+    import tempfile
+
+    # load and re-save NRRD once
+    storagenode1 = slicer.vtkMRMLNRRDStorageNode()
+    storagenode1.SetFileName(test_nrrd_path)
+    dw_node1 = slicer.vtkMRMLDiffusionWeightedVolumeNode()
+    storagenode1.ReadData(dw_node1)
+    __f_tmp_nrrd1 = tempfile.NamedTemporaryFile(suffix=".nhdr", dir=tmp_dir, delete=False)
+    tmp_nrrd1 = __f_tmp_nrrd1.name
+    storagenode1.SetFileName(tmp_nrrd1)
+    storagenode1.WriteData(dw_node1)
+
+    parsed_nrrd2, dw_node2 = test_nrrd_dwi_load(test_nrrd_path, tmp_nrrd1)
+
+    # re-save NRRD again
+    storagenode2 = slicer.vtkMRMLNRRDStorageNode()
+    __f_tmp_nrrd2 = tempfile.NamedTemporaryFile(suffix=".nhdr", dir=tmp_dir, delete=False)
+    tmp_nrrd2 = __f_tmp_nrrd2.name
+    storagenode2.SetFileName(tmp_nrrd2)
+    storagenode2.WriteData(dw_node2)
+
+    # test twice-saved file against original NRRD
+    parsed_nrrd3, dw_node3 = test_nrrd_dwi_load(test_nrrd_path, tmp_nrrd2)
+
+    print(test_nrrd_path, tmp_nrrd1, tmp_nrrd2)
+
 
-        g_from_node = np.array(slicer_grads.GetTuple3(i))
-        print _g_tmp, g_from_nhdr, g_from_node
+def run_tests(data_dir, tmp_dir):
+    # construct path to test data
+    testnrrd_path = os.path.join(data_dir, multishell_dwi_451)
 
-        assert_allclose(np.linalg.norm(g_from_nhdr - g_from_node), 0.0, atol=1e-12)
+    test_nrrd_dwi_load(testnrrd_path)
+    test_nrrd_dwi_roundtrip(testnrrd_path)
 
 
 if __name__ == '__main__':
     # TODO make sure data paths exist
-    ctx = Context(data_dir = sys.argv[1],
-                  temp_dir = sys.argv[2])
+    data_dir = sys.argv[1]
+    tmp_dir = sys.argv[2]
 
-    success = test_vtkMRMLNRRDStorageNode(ctx)
+    try:
+        run_tests(data_dir, tmp_dir)
+        exit(slicer.util.EXIT_SUCCESS)
+    except:
+        raise
 
-    sys.exit(success)
+    exit(slicer.util.EXIT_SUCCESS)
diff --git a/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx b/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
index 5ca75edd0..00c8460a5 100644
--- a/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
+++ b/Libs/MRML/Core/vtkMRMLNRRDStorageNode.cxx
@@ -433,7 +433,10 @@ bool parse_gradient_key(std::string key, size_t &grad_number, size_t &gradkey_pa
     }
   // below here key is: DWMRI_gradient_####
 
-  // we enforce the constraint that the padding of the grad key must be consistent
+  // padding is the extra zeros to give a specific digit count
+  //   0001
+  //   ^^^  <- zeros here are padding to 4 digits
+  // we enforce the constraint that the padding of the grad keys must be consistent
   if (gradkey_pad_width == 0) {
     gradkey_pad_width = key.size() - dwmri_grad_tag.size();
   }
diff --git a/Libs/vtkTeem/CMakeLists.txt b/Libs/vtkTeem/CMakeLists.txt
index 89b565038..83e4ea32c 100644
--- a/Libs/vtkTeem/CMakeLists.txt
+++ b/Libs/vtkTeem/CMakeLists.txt
@@ -87,6 +87,8 @@ set(srcs ${vtkTeem_SRCS})
 add_library(${lib_name} ${srcs})
 
 set(libs
+  itkvnl
+  ITKCommon
   ${Teem_LIBRARIES}
   ${VTK_LIBRARIES}
   )
-- 
GitLab


From 53c55d598b6d149fa984d9d668861ec0cd751859 Mon Sep 17 00:00:00 2001
From: Isaiah Norton <inorton@bwh.harvard.edu>
Date: Tue, 28 Aug 2018 17:55:08 -0400
Subject: [PATCH 12/12] STYLE: remove unnecessary print statement

---
 Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py b/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
index 3f17da86e..53aa0fa58 100644
--- a/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
+++ b/Applications/SlicerApp/Testing/Python/DWMRIMultishellIOTests.py
@@ -160,8 +160,6 @@ def test_nrrd_dwi_roundtrip(test_nrrd_path):
     # test twice-saved file against original NRRD
     parsed_nrrd3, dw_node3 = test_nrrd_dwi_load(test_nrrd_path, tmp_nrrd2)
 
-    print(test_nrrd_path, tmp_nrrd1, tmp_nrrd2)
-
 
 def run_tests(data_dir, tmp_dir):
     # construct path to test data
-- 
GitLab