-
Notifications
You must be signed in to change notification settings - Fork 2
/
profile.nb
3784 lines (3701 loc) · 184 KB
/
profile.nb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
(* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Mathematica 13.0' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLength[ 187859, 3776]
NotebookOptionsPosition[ 182335, 3682]
NotebookOutlinePosition[ 182743, 3698]
CellTagsIndexPosition[ 182700, 3695]
WindowFrame->Normal*)
(* Beginning of Notebook Content *)
Notebook[{
Cell[CellGroupData[{
Cell["Profile Analysis", "Title",ExpressionUUID->"2d8fb8c3-8c0c-4fac-b787-e5eb1eb711ab"],
Cell[BoxData[
RowBox[{"ClearAll", "[", "\"\<Global`*\>\"", "]"}]], "Input",
CellLabel->"In[88]:=",ExpressionUUID->"1f27ba01-10c6-4856-aa3a-b72e57ca053a"],
Cell[CellGroupData[{
Cell["Check algorithm", "Section",ExpressionUUID->"e06a1291-ffa7-41de-8dbc-89c5f4cb6300"],
Cell["\<\
Use one benchmark point from cosmoTransitions, check whether can produce \
correct bounce action by reading profile data and compute here.\
\>", "TextIndent",ExpressionUUID->"11ea3c9d-1f71-4058-ab21-b6ffd3281f8e"],
Cell[BoxData[
RowBox[{"DumpGet", "[",
RowBox[{
RowBox[{"NotebookDirectory", "[", "]"}], "<>", "\"\</output/highT.m\>\""}],
"]"}]], "Input",
CellLabel->
"In[2321]:=",ExpressionUUID->"2617aba5-5b2a-41c1-82e7-79e72444c92b"],
Cell[BoxData[
RowBox[{
RowBox[{
RowBox[{"Vpotential", "[",
RowBox[{"h_", ",", "S_"}], "]"}], "=",
RowBox[{"VhighT2d", "[",
RowBox[{"5", ",", "0.245", ",", "h", ",", "S", ",", "18.08448948742459"}],
"]"}]}], ";"}]], "Input",
CellLabel->
"In[2322]:=",ExpressionUUID->"72bc88f9-2664-48f3-b36c-c41cd9601f76"],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"FindMinimum", "[",
RowBox[{
RowBox[{"Vpotential", "[",
RowBox[{"h", ",", "S"}], "]"}], ",",
RowBox[{"{",
RowBox[{"h", ",", "S"}], "}"}]}], "]"}]], "Input",
CellLabel->
"In[2323]:=",ExpressionUUID->"c4537580-cafb-4078-9dcf-2758f53af3d4"],
Cell[BoxData[
TemplateBox[{
"FindMinimum", "lstol",
"\"The line search decreased the step size to within the tolerance \
specified by AccuracyGoal and PrecisionGoal but was unable to find a \
sufficient decrease in the function. You may need more than \\!\\(\\*RowBox[{\
\\\"MachinePrecision\\\"}]\\) digits of working precision to meet these \
tolerances.\"", 2, 2323, 301, 25392432325068669778, "Local"},
"MessageTemplate"]], "Message", "MSG",
CellLabel->
"During evaluation of \
In[2323]:=",ExpressionUUID->"81be477e-10f7-4eba-8fe9-b622eb2bd2c5"],
Cell[BoxData[
RowBox[{"{",
RowBox[{
RowBox[{"-", "1.0809174594935197`*^8"}], ",",
RowBox[{"{",
RowBox[{
RowBox[{"h", "\[Rule]", "94.09610844154533`"}], ",",
RowBox[{"S", "\[Rule]",
RowBox[{"-", "404.375838338838`"}]}]}], "}"}]}], "}"}]], "Output",
CellLabel->
"Out[2323]=",ExpressionUUID->"f25580a0-8dfe-434a-ab66-c36881b2e9fb"]
}, Open ]],
Cell[BoxData[{
RowBox[{
RowBox[{"Rdata", "=",
RowBox[{
RowBox[{"Import", "[",
RowBox[{
RowBox[{"NotebookDirectory", "[", "]"}], "<>",
"\"\</output/profile2d.csv\>\""}], "]"}], "[",
RowBox[{"[",
RowBox[{"All", ",", "1"}], "]"}], "]"}]}],
";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"hdata", "=",
RowBox[{
RowBox[{"Import", "[",
RowBox[{
RowBox[{"NotebookDirectory", "[", "]"}], "<>",
"\"\</output/profile2d.csv\>\""}], "]"}], "[",
RowBox[{"[",
RowBox[{"All", ",", "2"}], "]"}], "]"}]}],
";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"Sdata", "=",
RowBox[{
RowBox[{"Import", "[",
RowBox[{
RowBox[{"NotebookDirectory", "[", "]"}], "<>",
"\"\</output/profile2d.csv\>\""}], "]"}], "[",
RowBox[{"[",
RowBox[{"All", ",", "3"}], "]"}], "]"}]}], ";"}]}], "Input",
CellLabel->"In[91]:=",ExpressionUUID->"7b55a988-e205-4f1a-a455-a392fa8493fa"],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"ListLinePlot", "[",
RowBox[{"{",
RowBox[{"Transpose", "[",
RowBox[{"{",
RowBox[{"Rdata", ",", "hdata"}], "}"}], "]"}], "}"}], "]"}]], "Input",
CellLabel->"In[94]:=",ExpressionUUID->"5bff2031-adc2-4d62-9c01-e332d377b529"],
Cell[BoxData[
GraphicsBox[{{}, {{}, {},
{RGBColor[0.368417, 0.506779, 0.709798], AbsolutePointSize[4],
AbsoluteThickness[1.6], LineBox[CompressedData["
1:eJw1l2c4Fo7Xx+11c7tvq/QjJEJlj6zOEUmiIqsUpYySjCSKMkply0yEyMou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"]]}}, {{}, {}}},
AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
Axes->{True, True},
AxesLabel->{None, None},
AxesOrigin->{0, 0},
AxesStyle->Directive[
AbsoluteThickness[1],
GrayLevel[0], FontSize -> 14],
DisplayFunction->Identity,
Frame->{{True, True}, {True, True}},
FrameLabel->{{None, None}, {None, None}},
FrameStyle->Directive[
AbsoluteThickness[1],
GrayLevel[0], FontSize -> 14],
FrameTicks->{{Automatic, Automatic}, {Automatic, Automatic}},
FrameTicksStyle->Directive[
GrayLevel[0], FontSize -> 12],
GridLines->{None, None},
GridLinesStyle->Directive[
AbsoluteThickness[0.5],
Opacity[0.5]],
ImageSize->{351.63470458984375`, Automatic},
ImageSizeRaw->{{180}, {180}},
LabelStyle->Directive[
GrayLevel[0], FontSize -> 12],
Method->{
"OptimizePlotMarkers" -> True, "OptimizePlotMarkers" -> True,
"CoordinatesToolOptions" -> {"DisplayFunction" -> ({
Identity[
Part[#, 1]],
Identity[
Part[#, 2]]}& ), "CopiedValueFunction" -> ({
Identity[
Part[#, 1]],
Identity[
Part[#, 2]]}& )}},
PlotRange->{{0, 4.8616187394983355`}, {0, 33.91070646873663}},
PlotRangeClipping->True,
PlotRangePadding->{{
Scaled[0.02],
Scaled[0.02]}, {
Scaled[0.02],
Scaled[0.05]}},
Ticks->{Automatic, Automatic},
TicksStyle->Directive[
GrayLevel[0], FontSize -> 12]]], "Output",
CellLabel->"Out[94]=",ExpressionUUID->"6ebb675a-9525-4dc9-aee7-9ae33ee98475"]
}, Open ]],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"ListLinePlot", "[",
RowBox[{"Transpose", "[",
RowBox[{"{",
RowBox[{"Rdata", ",", "Sdata"}], "}"}], "]"}], "]"}]], "Input",
CellLabel->"In[95]:=",ExpressionUUID->"bffddef7-2e66-430a-9b51-23a5f579081b"],
Cell[BoxData[
GraphicsBox[{{}, {{}, {},
{RGBColor[0.368417, 0.506779, 0.709798], AbsolutePointSize[4],
AbsoluteThickness[1.6], LineBox[CompressedData["
1:eJw113k0Ft8bAPDXvvN6JW0IWSOkyNa9IqQksgsloq8sbUImKpSdbKXsCkWW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"]]}}, {{}, {}}},
AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948],
Axes->{True, True},
AxesLabel->{None, None},
AxesOrigin->{0, -464.2550923766489},
AxesStyle->Directive[
AbsoluteThickness[1],
GrayLevel[0], FontSize -> 14],
DisplayFunction->Identity,
Frame->{{True, True}, {True, True}},
FrameLabel->{{None, None}, {None, None}},
FrameStyle->Directive[
AbsoluteThickness[1],
GrayLevel[0], FontSize -> 14],
FrameTicks->{{Automatic, Automatic}, {Automatic, Automatic}},
FrameTicksStyle->Directive[
GrayLevel[0], FontSize -> 12],
GridLines->{None, None},
GridLinesStyle->Directive[
AbsoluteThickness[0.5],
Opacity[0.5]],
ImageSize->{359.56707763671875`, Automatic},
ImageSizeRaw->{{180}, {180}},
LabelStyle->Directive[
GrayLevel[0], FontSize -> 12],
Method->{
"OptimizePlotMarkers" -> True, "OptimizePlotMarkers" -> True,
"CoordinatesToolOptions" -> {"DisplayFunction" -> ({
Identity[
Part[#, 1]],
Identity[
Part[#, 2]]}& ), "CopiedValueFunction" -> ({
Identity[
Part[#, 1]],
Identity[
Part[#, 2]]}& )}},
PlotRange->{{
0, 4.8616187394983355`}, {-473.68038357300503`, -464.7511603343509}},
PlotRangeClipping->True,
PlotRangePadding->{{
Scaled[0.02],
Scaled[0.02]}, {
Scaled[0.05],
Scaled[0.05]}},
Ticks->{Automatic, Automatic},
TicksStyle->Directive[
GrayLevel[0], FontSize -> 12]]], "Output",
CellLabel->"Out[95]=",ExpressionUUID->"678fc5ad-c326-40a8-ba64-c838be92964a"]
}, Open ]],
Cell[BoxData[{
RowBox[{
RowBox[{"hprofile", "[", "r_", "]"}], ":=",
RowBox[{
RowBox[{"Interpolation", "[",
RowBox[{"Transpose", "[",
RowBox[{"{",
RowBox[{"Rdata", ",", "hdata"}], "}"}], "]"}], "]"}], "[", "r",
"]"}]}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"Sprofile", "[", "r_", "]"}], ":=",
RowBox[{
RowBox[{"Interpolation", "[",
RowBox[{"Transpose", "[",
RowBox[{"{",
RowBox[{"Rdata", ",", "Sdata"}], "}"}], "]"}], "]"}], "[", "r",
"]"}]}]}], "Input",
CellLabel->"In[96]:=",ExpressionUUID->"a6b3ffb1-f3ec-466f-a213-1c4a424af16b"],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"Min", "[", "hdata", "]"}]], "Input",
CellLabel->"In[98]:=",ExpressionUUID->"626550bb-9759-4917-94dd-35a695ae9d08"],
Cell[BoxData["0.01422103184869154`"], "Output",
CellLabel->"Out[98]=",ExpressionUUID->"7762fc64-7e38-4256-b123-ca4f58a55733"]
}, Open ]],
Cell[BoxData[{
RowBox[{
RowBox[{
RowBox[{"Kinh", "[", "r_", "]"}], "=",
RowBox[{
FractionBox["1", "2"],
SuperscriptBox[
RowBox[{
RowBox[{"hprofile", "'"}], "[", "r", "]"}], "2"]}]}],
";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{
RowBox[{"KinS", "[", "r_", "]"}], "=",
RowBox[{
FractionBox["1", "2"],
SuperscriptBox[
RowBox[{
RowBox[{"Sprofile", "'"}], "[", "r", "]"}], "2"]}]}],
";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"potential", "[", "r_", "]"}], ":=",
RowBox[{
RowBox[{"Vpotential", "[",
RowBox[{
RowBox[{"hprofile", "[", "r", "]"}], ",",
RowBox[{"Sprofile", "[", "r", "]"}]}], "]"}], "-",
RowBox[{"Vpotential", "[",
RowBox[{
RowBox[{"Min", "[", "hdata", "]"}], ",",
RowBox[{"Min", "[", "Sdata", "]"}]}], "]"}]}]}]}], "Input",
CellLabel->"In[99]:=",ExpressionUUID->"6be4a398-f702-4462-816a-d0c26e716567"],
Cell[CellGroupData[{
Cell[BoxData[{
RowBox[{
RowBox[{"4", "\[Pi]", " ",
RowBox[{"NIntegrate", "[",
RowBox[{
RowBox[{
SuperscriptBox["r", "2"],
RowBox[{"(",
RowBox[{
RowBox[{"Kinh", "[", "r", "]"}], "+",
RowBox[{"KinS", "[", "r", "]"}], "+",
RowBox[{"potential", "[", "r", "]"}]}], ")"}]}], ",",
RowBox[{"{",
RowBox[{"r", ",", "0", ",",
RowBox[{"Max", "[", "Rdata", "]"}]}], "}"}]}], "]"}]}],
RowBox[{"(*",
RowBox[{"2", "d", " ", "action"}], "*)"}]}], "\[IndentingNewLine]",
RowBox[{"4", "\[Pi]", " ",
RowBox[{"NIntegrate", "[",
RowBox[{
RowBox[{
SuperscriptBox["r", "2"],
RowBox[{"(",
RowBox[{
RowBox[{"Kinh", "[", "r", "]"}], "+",
RowBox[{"potential", "[", "r", "]"}]}], ")"}]}], ",",
RowBox[{"{",
RowBox[{"r", ",", "0", ",",
RowBox[{"Max", "[", "Rdata", "]"}]}], "}"}]}], "]"}],
RowBox[{"(*",
RowBox[{"1", "d", " ", "action"}], "*)"}]}]}], "Input",
CellLabel->
"In[102]:=",ExpressionUUID->"eacf16ff-95ba-4b7d-8c9f-90f20fc86182"],
Cell[BoxData["2740.5390087046917`"], "Output",
CellLabel->
"Out[102]=",ExpressionUUID->"9330beb8-edb0-4ea1-93bd-5f2e5b8352c8"],
Cell[BoxData["2538.59270314411`"], "Output",
CellLabel->
"Out[103]=",ExpressionUUID->"0ad2c8f2-16cf-4551-aaca-23dd3091fbf7"]
}, Open ]],
Cell["\<\
The error between this and python is very small. So the computation method is \
correct. \
\>", "TextIndent",ExpressionUUID->"175c7c95-e3a5-4e8a-a9bb-efdf1d164902"],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"Plot", "[",
RowBox[{
RowBox[{"{",
RowBox[{
RowBox[{"Kinh", "[", "r", "]"}], ",",
RowBox[{"KinS", "[", "r", "]"}]}], "}"}], ",",
RowBox[{"{",
RowBox[{"r", ",", "0", ",",
RowBox[{"Max", "[", "Rdata", "]"}]}], "}"}], ",",
RowBox[{"PlotRange", "\[Rule]", "Full"}]}], "]"}]], "Input",
CellLabel->
"In[104]:=",ExpressionUUID->"73fece87-574c-4694-a81c-c59a2acb7553"],
Cell[BoxData[
GraphicsBox[{{{}, {},
TagBox[
{RGBColor[0.368417, 0.506779, 0.709798], AbsolutePointSize[4],
AbsoluteThickness[1.6], Opacity[1.], LineBox[CompressedData["
1:eJwVl3c4l+8Xx+09P0ZoGZGd7BHnkIyyV6gkMlLEJ6PQIAllJESkQjYZmRWy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"]]},
Annotation[#, "Charting`Private`Tag$343507#1"]& ],
TagBox[
{RGBColor[0.880722, 0.611041, 0.142051], AbsolutePointSize[4],
AbsoluteThickness[1.6], Opacity[1.], LineBox[CompressedData["
1:eJwVV3c81t8Xf+zN8zxGmdnJiOz1fO7BQySKQiQzlJXRUqGULUmUkRGy8jVS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