Skip to content

Add a regresion thprediction test for a hessian PDF #1469

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
scarlehoff merged 4 commits into from
Nov 18, 2021
Merged

Add a regresion thprediction test for a hessian PDF #1469

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
b5ab199
add a regresion thprediction test for a hessian PDF
scarlehoff Nov 15, 2021
902c07b
add comments
scarlehoff Nov 15, 2021
861b7b5
transpose for nicer diff
scarlehoff Nov 15, 2021
06c9191
remove duplicate
scarlehoff Nov 18, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 15 additions & 0 deletions validphys2/src/validphys/tests/conftest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,14 @@ def data_internal_cuts_config(data_config):
config_dict.update(use_cuts='internal')
return config_dict

@pytest.fixture(scope='module')
def single_data_internal_cuts_config(data_internal_cuts_config):
"""Like data_internal_cuts_config but for a single dataset"""
config_dict = dict(data_internal_cuts_config)
config_dict.pop("dataset_inputs")
config_dict.update(dataset_input=DATA[0])
return config_dict

@pytest.fixture(scope='module')
def data_witht0_config():
config_dict = dict(
Expand Down Expand Up @@ -152,3 +160,10 @@ def hessian_data_internal_cuts_config(data_internal_cuts_config):
new_config = dict(data_internal_cuts_config)
new_config["pdf"] = HESSIAN_PDF
return new_config

@pytest.fixture(scope='module')
def hessian_single_data_internal_cuts_config(single_data_internal_cuts_config):
"""Same as single data config but with hessian PDF"""
new_config = dict(single_data_internal_cuts_config)
new_config["pdf"] = HESSIAN_PDF
return new_config
205 changes: 205 additions & 0 deletions validphys2/src/validphys/tests/regressions/test_thprediction_results.csv
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,205 @@
0 1
0 0.3552117943763733 0.0034057728480547667
1 0.36789944767951965 0.0034565727692097425
2 0.36790141463279724 0.003148130839690566
3 0.37267741560935974 0.002972088987007737
4 0.3738352656364441 0.0027645917143672705
5 0.3738477826118469 0.0025941296480596066
6 0.368956983089447 0.0037308454047888517
7 0.37043607234954834 0.0033280453644692898
8 0.3711569607257843 0.0029894893523305655
9 0.3666095733642578 0.0041872067376971245
10 0.367704302072525 0.0036759930662810802
11 0.36836308240890503 0.003261325415223837
12 0.36314621567726135 0.0034475834108889103
13 0.36330240964889526 0.003078252775594592
14 0.36288878321647644 0.0027549099177122116
15 0.353524386882782 0.004111022688448429
16 0.3527098596096039 0.0036288287956267595
17 0.3519518971443176 0.0031422381289303303
18 0.3517233729362488 0.002655594376847148
19 0.3366711437702179 0.003944032825529575
20 0.3345445692539215 0.0033962815068662167
21 0.3311925530433655 0.0028453893028199673
22 0.331525981426239 0.0024931614752858877
23 0.3117638826370239 0.0021649072878062725
24 0.30510565638542175 0.0020746977534145117
25 0.3037455677986145 0.0017220894806087017
26 0.2573825716972351 0.002879419596865773
27 0.25573989748954773 0.002484554424881935
28 0.37259605526924133 0.003434942802414298
29 0.3726899027824402 0.0031852901447564363
30 0.37587711215019226 0.0034879243467003107
31 0.37812119722366333 0.0031857299618422985
32 0.3784932792186737 0.002922946587204933
33 0.37176159024238586 0.003283943049609661
34 0.37606269121170044 0.002990243723616004
35 0.37883487343788147 0.002779746428132057
36 0.3806123435497284 0.002585142385214567
37 0.38052549958229065 0.0023965854197740555
38 0.3671613037586212 0.00438464991748333
39 0.3705226480960846 0.003777713282033801
40 0.37287604808807373 0.003359298687428236
41 0.3747601807117462 0.0029872599989175797
42 0.3757287561893463 0.0026327536907047033
43 0.36744293570518494 0.004184555262327194
44 0.3690122663974762 0.0036818122025579214
45 0.37025776505470276 0.0032254536636173725
46 0.3711031675338745 0.0027884948067367077
47 0.37081870436668396 0.0025360817089676857
48 0.3632119297981262 0.003362359246239066
49 0.363742470741272 0.0030563536565750837
50 0.3637655973434448 0.002729743719100952
51 0.3635607063770294 0.0024424695875495672
52 0.3623272180557251 0.0022313527297228575
53 0.35354122519493103 0.004769927356392145
54 0.3517196774482727 0.0042913914658129215
55 0.35175850987434387 0.0037166778929531574
56 0.3511461317539215 0.003184567205607891
57 0.3505251407623291 0.002643611980602145
58 0.3488006293773651 0.0023049621377140284
59 0.3352634906768799 0.003798414021730423
60 0.3323695659637451 0.0032677841372787952
61 0.3296094238758087 0.002749386476352811
62 0.3282954692840576 0.0023600903805345297
63 0.32501181960105896 0.0020005537662655115
64 0.3094976544380188 0.002282080939039588
65 0.3022909462451935 0.0022155328188091516
66 0.2997799813747406 0.0018577603623270988
67 0.29593950510025024 0.001640703179873526
68 0.2911166548728943 0.0015070458175614476
69 0.26420098543167114 0.0028951121494174004
70 0.24940820038318634 0.0024030348286032677
71 0.24292244017124176 0.0020290547981858253
72 0.23748834431171417 0.0017464914126321673
73 0.159316286444664 0.0013681247364729643
74 0.1461881846189499 0.001232636976055801
75 0.3771058917045593 0.005236274097114801
76 0.38309478759765625 0.004873471334576607
77 0.39133206009864807 0.004601756110787392
78 0.3952886164188385 0.004240089096128941
79 0.39831963181495667 0.0038504640106111765
80 0.3884022533893585 0.003514457494020462
81 0.3951019048690796 0.0033083148300647736
82 0.3990063965320587 0.003090003738179803
83 0.39881956577301025 0.0028390486259013414
84 0.37527137994766235 0.0039760759100317955
85 0.3822689950466156 0.0035409964621067047
86 0.38795006275177 0.0032395964954048395
87 0.39346542954444885 0.0029475747141987085
88 0.396557092666626 0.0027140332385897636
89 0.39662978053092957 0.002540596527978778
90 0.3786911070346832 0.0030098722781986
91 0.3827684819698334 0.002821766072884202
92 0.3868564963340759 0.0026072682812809944
93 0.39069247245788574 0.002439674222841859
94 0.3928397297859192 0.002310327719897032
95 0.3927120268344879 0.0021900851279497147
96 0.37507832050323486 0.003334310371428728
97 0.3780592381954193 0.0029691869858652353
98 0.38087260723114014 0.002640479477122426
99 0.38300999999046326 0.0023908272851258516
100 0.3839799463748932 0.0022043047938495874
101 0.3827150762081146 0.0020605844911187887
102 0.3721292018890381 0.0032184929586946964
103 0.37398308515548706 0.0028043834026902914
104 0.3752273619174957 0.002506538527086377
105 0.3760697841644287 0.0022786948829889297
106 0.375394731760025 0.002103489125147462
107 0.365077942609787 0.002727566286921501
108 0.3650404214859009 0.002454987494274974
109 0.3650830388069153 0.002251188736408949
110 0.3646349012851715 0.002077704994007945
111 0.36341533064842224 0.0019447640515863895
112 0.36054757237434387 0.0018321778625249863
113 0.35299310088157654 0.002968096872791648
114 0.3515250086784363 0.0026421670336276293
115 0.34945034980773926 0.0022884043864905834
116 0.34835729002952576 0.0019872949924319983
117 0.34625279903411865 0.001778074773028493
118 0.3435204327106476 0.001634285319596529
119 0.3308887481689453 0.002395152347162366
120 0.3258650302886963 0.0020014254841953516
121 0.3224591016769409 0.0017243600450456142
122 0.31937161087989807 0.0015442284056916833
123 0.314965158700943 0.0014207478379830718
124 0.29630035161972046 0.0016031641280278563
125 0.292711079120636 0.001448870520107448
126 0.28698766231536865 0.0013499791966751218
127 0.28100746870040894 0.0012766867876052856
128 0.2763378620147705 0.001230631023645401
129 0.24436494708061218 0.0017514871433377266
130 0.2341831922531128 0.0014881291426718235
131 0.2270982414484024 0.0013111353619024158
132 0.21935884654521942 0.0011871196329593658
133 0.15003065764904022 0.0011010424932464957
134 0.1306365728378296 0.0010081417858600616
135 0.1233493834733963 0.0009427779586985707
136 0.3914680778980255 0.005487227346748114
137 0.3956078588962555 0.004792842082679272
138 0.4023468792438507 0.004839753732085228
139 0.40531134605407715 0.004473244305700064
140 0.4077366590499878 0.004220102448016405
141 0.4011569619178772 0.004629521630704403
142 0.4099288284778595 0.004301422741264105
143 0.41589847207069397 0.003979190718382597
144 0.41731205582618713 0.0036797677166759968
145 0.4168106019496918 0.003375240135937929
146 0.3925975561141968 0.003536438336595893
147 0.40085166692733765 0.003325375961139798
148 0.40883705019950867 0.0031279567629098892
149 0.41556915640830994 0.0029356172308325768
150 0.416147917509079 0.0027679279446601868
151 0.4126019775867462 0.0025757024995982647
152 0.3916209042072296 0.003251207061111927
153 0.39695826172828674 0.002972051501274109
154 0.4035800099372864 0.002737200353294611
155 0.4080353081226349 0.0025628891307860613
156 0.40888693928718567 0.0024204649962484837
157 0.40560367703437805 0.0022888262756168842
158 0.3905435800552368 0.002640628954395652
159 0.39286255836486816 0.002447573933750391
160 0.39694252610206604 0.00232112524099648
161 0.39998772740364075 0.0022094878368079662
162 0.4003906548023224 0.0021090495865792036
163 0.3967117965221405 0.0020126085728406906
164 0.38426005840301514 0.002526021795347333
165 0.3850317895412445 0.0023981048725545406
166 0.38752511143684387 0.0022027932573109865
167 0.38896143436431885 0.002052024705335498
168 0.3878083825111389 0.0019546502735465765
169 0.3767269253730774 0.002486488549038768
170 0.37799620628356934 0.0022809964139014482
171 0.37877628207206726 0.002103895414620638
172 0.37828388810157776 0.0019832300022244453
173 0.37581318616867065 0.0018948095384985209
174 0.3658371865749359 0.0022419665474444628
175 0.36645886301994324 0.002097882330417633
176 0.3652689754962921 0.0019485943485051394
177 0.3639148771762848 0.0018431330099701881
178 0.36149829626083374 0.001762252883054316
179 0.34930285811424255 0.0019773179665207863
180 0.34706515073776245 0.0017673256807029247
181 0.3450043797492981 0.001632641302421689
182 0.34200072288513184 0.001547886524349451
183 0.3385894000530243 0.0014923075214028358
184 0.3235433101654053 0.0017312156269326806
185 0.3199431300163269 0.0015423191944137216
186 0.31619101762771606 0.0014274102868512273
187 0.31247416138648987 0.0013647652231156826
188 0.3079622983932495 0.0013224880676716566
189 0.29687586426734924 0.0013572542229667306
190 0.28769585490226746 0.0013351731467992067
191 0.2824299931526184 0.0012680906802415848
192 0.27677762508392334 0.0012232566950842738
193 0.2720159590244293 0.0011879632947966456
194 0.26591956615448 0.0011638551950454712
195 0.2469484955072403 0.0015061864396557212
196 0.22880302369594574 0.001311728497967124
197 0.22217845916748047 0.0011927267769351602
198 0.21592706441879272 0.0011150999926030636
199 0.20726709067821503 0.001050922553986311
200 0.15883482992649078 0.001011354848742485
201 0.13078218698501587 0.0009589289547875524
202 0.12241589277982712 0.0009089406812563539
203 0.11169078201055527 0.0008529396145604551
Loading