diff --git a/doc/HandwrittenNotes/2023/LectureNovember17.pdf b/doc/HandwrittenNotes/2023/LectureNovember17.pdf new file mode 100644 index 00000000..0e471f69 Binary files /dev/null and b/doc/HandwrittenNotes/2023/LectureNovember17.pdf differ diff --git a/doc/LectureNotes/_build/.doctrees/boltzmannmachines.doctree b/doc/LectureNotes/_build/.doctrees/boltzmannmachines.doctree index c325be68..bb8d5643 100644 Binary files a/doc/LectureNotes/_build/.doctrees/boltzmannmachines.doctree and b/doc/LectureNotes/_build/.doctrees/boltzmannmachines.doctree differ diff --git a/doc/LectureNotes/_build/.doctrees/cc.doctree b/doc/LectureNotes/_build/.doctrees/cc.doctree index e90cdde9..659a70b4 100644 Binary files a/doc/LectureNotes/_build/.doctrees/cc.doctree and b/doc/LectureNotes/_build/.doctrees/cc.doctree differ diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle index 17e4c4f2..2afd5ca1 100644 Binary files a/doc/LectureNotes/_build/.doctrees/environment.pickle and b/doc/LectureNotes/_build/.doctrees/environment.pickle differ diff --git a/doc/LectureNotes/_build/.doctrees/gradientmethods.doctree b/doc/LectureNotes/_build/.doctrees/gradientmethods.doctree index 5bf738cd..f62b4ef9 100644 Binary files a/doc/LectureNotes/_build/.doctrees/gradientmethods.doctree and b/doc/LectureNotes/_build/.doctrees/gradientmethods.doctree differ diff --git a/doc/LectureNotes/_build/.doctrees/notation.doctree b/doc/LectureNotes/_build/.doctrees/notation.doctree index 49000b35..20b40ee6 100644 Binary files a/doc/LectureNotes/_build/.doctrees/notation.doctree and b/doc/LectureNotes/_build/.doctrees/notation.doctree differ diff --git a/doc/LectureNotes/_build/.doctrees/resamplingmethods.doctree b/doc/LectureNotes/_build/.doctrees/resamplingmethods.doctree index f4ce191b..8ddf80c7 100644 Binary files a/doc/LectureNotes/_build/.doctrees/resamplingmethods.doctree and b/doc/LectureNotes/_build/.doctrees/resamplingmethods.doctree differ diff --git a/doc/LectureNotes/_build/.doctrees/schedule.doctree b/doc/LectureNotes/_build/.doctrees/schedule.doctree index 73c7933b..d97bdcd1 100644 Binary files a/doc/LectureNotes/_build/.doctrees/schedule.doctree and b/doc/LectureNotes/_build/.doctrees/schedule.doctree differ diff --git a/doc/LectureNotes/_build/html/_images/resamplingmethods_59_1.png b/doc/LectureNotes/_build/html/_images/resamplingmethods_59_1.png index a3a0d9d8..3dd0ea76 100644 Binary files a/doc/LectureNotes/_build/html/_images/resamplingmethods_59_1.png and b/doc/LectureNotes/_build/html/_images/resamplingmethods_59_1.png differ diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md index 6a2846e8..c5ac12f6 100644 --- a/doc/LectureNotes/_build/html/_sources/schedule.md +++ b/doc/LectureNotes/_build/html/_sources/schedule.md @@ -170,32 +170,37 @@ This course will be delivered in a hybrid mode, with online and on site lectures ### Week 46, November 13-17, 2023 - Topics to be covered - Thursday: - - Summary many-body perturbation theory and discussion of Coupled Cluster theory + - Diagram rules (whiteboard notes) + - Examples of diagrams and discussion of Pauli violating diagrams (Whiteboard notes) + - Video of lecture at https://youtu.be/rCtpX221u24 + - Whiteboard notes at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/HandwrittenNotes/2023/LectureNovember16.pdf - Friday: - - Coupled Cluster theory with doubles excitations - - Diagrammatic representation of Coupled Cluster theory -- Lecture Material: Lecture slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week46/LatexSlides/slidesweek46.pdf, handwritten notes and Shavitt and Bartlett chapter 9 + - Time-dependent perturbation theory, definitions of Schroedinger, Heisenberg and interaction picture, see slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week45/LatexSlides/pictures.pdf + - Video of lecture at https://youtu.be/8-dnWUDTnS0 + - Whiteboard notes at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/HandwrittenNotes/2023/LectureNovember17.pdf +- Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/pub/week46/pdf/slidesweek46.pdf, handwritten notes a\ +nd Shavitt and Bartlett chapter 4-6 - Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf ### Week 47, November 20-24, 2023 - Topics to be covered - Thursday: - - Coupled Cluster theory, singles and doubles excitations + - Green's function theory - Friday: - - Coupled Cluster theory and summary of course -- Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week47/LatexSlides/slidesweek47.pdf, handwritten notes and Shavitt and Bartlett chapters 9 and 10 + - Green's function theory +- Lecture Material: - Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf ### Week 48, November 27-December 1, 2023 - Topics to be covered - Thursday: - - Coupled Cluster theory, singles and doubles excitations + - Green's function theory - Friday: - - Coupled Cluster theory and summary of course -- Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week47/LatexSlides/slidesweek47.pdf, handwritten notes and Shavitt and Bartlett chapters 9 and 10 + - Green's function theory and summary of course +- Lecture Material: - Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf -### Week 50, December 19 and 21, 2023 -- Final oral exam, day to be determined +### Week 50, December 19, 2023 +- Final oral exam diff --git a/doc/LectureNotes/_build/html/boltzmannmachines.html b/doc/LectureNotes/_build/html/boltzmannmachines.html index feeed708..c1289c00 100644 --- a/doc/LectureNotes/_build/html/boltzmannmachines.html +++ b/doc/LectureNotes/_build/html/boltzmannmachines.html @@ -2455,40 +2455,40 @@

13.7. Python version for the two non-int
-
Energy: 3.270829693673645
-Energy: 3.233104652672888
+
Energy: 3.2616589395123157
+Energy: 3.250328623538633
+Energy: 3.210096688088389
+Energy: 3.2616162121215706
 

 

-
Energy: 3.247383897523189
-Energy: 3.2323935531841137
-Energy: 3.3658081999383316
-Energy: 3.201257830861303
-Energy: 3.1640313431712044
-Energy: 3.2956720397479438
-Energy: 3.245099658936495
-Energy: 3.273061876235883
-Energy: 3.1811830181358536
-Energy: 3.2619697749130916
-Energy: 3.2097609308000075
+
Energy: 3.2601523514477346
+Energy: 3.2569803619031514
+Energy: 3.27515114735846
 

 

-
Energy: 3.222733848271767
+
Energy: 3.226532626427531
+Energy: 3.2479229142962693
+Energy: 3.2405790551148295
+Energy: 3.339269450372016
+Energy: 3.292985914196608
+Energy: 3.2734831269767466
+Energy: 3.468371242428862
+Energy: 3.2814210399238766
+Energy: 3.2714197897886885
+Energy: 3.2193959772791
+Energy: 3.2912864812817935
+Energy: 3.2423803253014007
+Energy: 3.1928143709455195
+Energy: 3.2146899252942056
+Energy: 3.2999210822323337
+Energy: 3.2811059647446417
 

 

-
Energy: 3.2447619247830217
-Energy: 3.269317606376056
-Energy: 3.200534237237334
-Energy: 3.2349636044589025
-Energy: 3.340305199776341
-Energy: 3.3044665357695058
-Energy: 3.2631436797918103
-Energy: 3.2914767328754526
-Energy: 3.2451508473321633
-Energy: 3.2176548221895658
-Energy: 3.323211030424025
+
Energy: 3.221709384120671
+Energy: 3.2201117261448933
 

 

-
Energy: 3.2049106538710403
+
Energy: 3.2411631395541476
 

 

 
---------------------------------------------------------------------------
@@ -2501,22 +2501,18 @@ 
13.7. Python version for the two non-int
     237     bgradient = EDerivative[1]
 
 Input In [1], in EnergyMinimization(a, b, w)
-    177         wfold = wfnew
-    178 #print("wf new:        ", wfnew)
-    179 #print("force on 1 new:", QuantumForceNew[0,:])
-    180 #print("pos of 1 new:  ", PositionNew[0,:])
-    181 #print("force on 2 new:", QuantumForceNew[1,:])
-    182 #print("pos of 2 new:  ", PositionNew[1,:])
---> 183 DeltaE = LocalEnergy(PositionOld,a,b,w)
-    184 DerPsi = DerivativeWFansatz(PositionOld,a,b,w)
-    186 DeltaPsi[0] += DerPsi[0]
+    159     PositionNew[i,j] = PositionOld[i,j]+normalvariate(0.0,1.0)*sqrt(TimeStep)+\
+    160                            QuantumForceOld[i,j]*TimeStep*D
+    161 wfnew = WaveFunction(PositionNew,a,b,w)
+--> 162 QuantumForceNew = QuantumForce(PositionNew,a,b,w)
+    164 GreensFunction = 0.0
+    165 for j in range(Dimension):
 
-Input In [1], in LocalEnergy(r, a, b, w)
-     54         dlnpsi1 = -(r[iq,ix] - a[iq,ix]) /sig2 + sum1/sig2
-     55         dlnpsi2 = -1/sig2 + sum2/sig2**2
----> 56         locenergy += 0.5*(-dlnpsi1*dlnpsi1 - dlnpsi2 + r[iq,ix]**2)
-     58 if(interaction==True):
-     59     for iq1 in range(NumberParticles):
+Input In [1], in QuantumForce(r, a, b, w)
+     99 for ih in range(NumberHidden):
+    100     sum1 += w[:,:,ih]/(1+np.exp(-Q[ih]))
+--> 102 qforce = 2*(-(r-a)/sig2 + sum1/sig2)
+    104 return qforce
 
 KeyboardInterrupt: 
 

diff --git a/doc/LectureNotes/_build/html/cc.html b/doc/LectureNotes/_build/html/cc.html
index bbd2266c..cee4630d 100644
--- a/doc/LectureNotes/_build/html/cc.html
+++ b/doc/LectureNotes/_build/html/cc.html
@@ -1999,38 +1999,36 @@ 
7.24. Project 15: Solve the CCD equation
 

 
iter= 6 erg= -0.41154472066379555 myeps= 0.011472448299162774
 iter= 7 erg= -0.415448787715655 myeps= 0.00939722817179461
+iter= 8 erg= -0.41867711472018154 myeps= 0.007710779717883887
 

 

-
iter= 8 erg= -0.41867711472018154 myeps= 0.007710779717883887
-iter= 9 erg= -0.4213467805437514 myeps= 0.006336029956428388
+
iter= 9 erg= -0.4213467805437514 myeps= 0.006336029956428388
 iter= 10 erg= -0.4235545667262582 myeps= 0.005212518895903349
+iter= 11 erg= -0.4253804729664931 myeps= 0.004292407283064816
 

 

-
iter= 11 erg= -0.4253804729664931 myeps= 0.004292407283064816
-iter= 12 erg= -0.42689062703898156 myeps= 0.003537566713430196
-

-

-
iter= 13 erg= -0.4281396916442187 myeps= 0.002917423050500827
+
iter= 12 erg= -0.42689062703898156 myeps= 0.003537566713430196
+iter= 13 erg= -0.4281396916442187 myeps= 0.002917423050500827
 iter= 14 erg= -0.4291728542011779 myeps= 0.0024073343568810036
-iter= 15 erg= -0.4300274713630842 myeps= 0.0019873548059555165
 

 

-
iter= 16 erg= -0.4307344279442794 myeps= 0.0016412818092326651
+
iter= 15 erg= -0.4300274713630842 myeps= 0.0019873548059555165
+iter= 16 erg= -0.4307344279442794 myeps= 0.0016412818092326651
 iter= 17 erg= -0.4313192597672807 myeps= 0.0013559140004942823
-iter= 18 erg= -0.43180308142773266 myeps= 0.001120468290435161
 

 

-
iter= 19 erg= -0.4322033528891356 myeps= 0.00092611836240335
+
iter= 18 erg= -0.43180308142773266 myeps= 0.001120468290435161
+iter= 19 erg= -0.4322033528891356 myeps= 0.00092611836240335
 iter= 20 erg= -0.43253451293787193 myeps= 0.0007656268779271082
-iter= 21 erg= -0.43280850265722925 myeps= 0.0006330506856384872
 

 

-
iter= 22 erg= -0.4330351980489746 myeps= 0.0005235033844055304
+
iter= 21 erg= -0.43280850265722925 myeps= 0.0006330506856384872
+iter= 22 erg= -0.4330351980489746 myeps= 0.0005235033844055304
 iter= 23 erg= -0.4332227676000893 myeps= 0.00043296328157862334
-iter= 24 erg= -0.4333779678407642 myeps= 0.0003581175144831452
 

 

-
iter= 25 erg= -0.4335063876676202 myeps= 0.00029623514326258533
+
iter= 24 erg= -0.4333779678407642 myeps= 0.0003581175144831452
+iter= 25 erg= -0.4335063876676202 myeps= 0.00029623514326258533
 iter= 26 erg= -0.4336126503302039 myeps= 0.0002450635665329708
 

 

@@ -2046,53 +2044,51 @@ 
7.24. Project 15: Solve the CCD equation
 

 
iter= 33 erg= -0.43398696881076043 myeps= 6.506447928127211e-05
 iter= 34 erg= -0.43401033674723327 myeps= 5.384188922313506e-05
+iter= 35 erg= -0.4340296753237455 myeps= 4.455588548820297e-05
 

 

-
iter= 35 erg= -0.4340296753237455 myeps= 4.455588548820297e-05
-iter= 36 erg= -0.43404567947824346 myeps= 3.6872051156509844e-05
+
iter= 36 erg= -0.43404567947824346 myeps= 3.6872051156509844e-05
 iter= 37 erg= -0.4340589242583793 myeps= 3.0513783718287337e-05
+iter= 38 erg= -0.434069885513208 myeps= 2.525228124449486e-05
 

 

-
iter= 38 erg= -0.434069885513208 myeps= 2.525228124449486e-05
-iter= 39 erg= -0.43407895701178667 myeps= 2.08982684650241e-05
+
iter= 39 erg= -0.43407895701178667 myeps= 2.08982684650241e-05
 iter= 40 erg= -0.43408646460614353 myeps= 1.729515884276886e-05
+iter= 41 erg= -0.4340926779492775 myeps= 1.4313402297628368e-05
 

 

-
iter= 41 erg= -0.4340926779492775 myeps= 1.4313402297628368e-05
-iter= 42 erg= -0.4340978201905307 myeps= 1.1845812197343312e-05
-

-

-
iter= 43 erg= -0.43410207599766415 myeps= 9.803701407424543e-06
+
iter= 42 erg= -0.4340978201905307 myeps= 1.1845812197343312e-05
+iter= 43 erg= -0.43410207599766415 myeps= 9.803701407424543e-06
 iter= 44 erg= -0.4341055981945805 myeps= 8.113686925482528e-06
-iter= 45 erg= -0.43410851325371014 myeps= 6.715047138304284e-06
 

 

-
iter= 46 erg= -0.43411092584077954 myeps= 5.557535933304656e-06
+
iter= 45 erg= -0.43410851325371014 myeps= 6.715047138304284e-06
+iter= 46 erg= -0.43411092584077954 myeps= 5.557535933304656e-06
 iter= 47 erg= -0.4341129225755269 myeps= 4.5995745427982914e-06
-iter= 48 erg= -0.43411457514368107 myeps= 3.8067557478495853e-06
 

 

-
iter= 49 erg= -0.43411594287215505 myeps= 3.150606413873383e-06
+
iter= 48 erg= -0.43411457514368107 myeps= 3.8067557478495853e-06
+iter= 49 erg= -0.43411594287215505 myeps= 3.150606413873383e-06
 iter= 50 erg= -0.4341170748600758 myeps= 2.607563687969726e-06
-iter= 51 erg= -0.4341180117422817 myeps= 2.158127929698868e-06
 

 

-
iter= 52 erg= -0.4341187871486943 myeps= 1.7861618421804902e-06
+
iter= 51 erg= -0.4341180117422817 myeps= 2.158127929698868e-06
+iter= 52 erg= -0.4341187871486943 myeps= 1.7861618421804902e-06
 iter= 53 erg= -0.4341194289120248 myeps= 1.478310547265082e-06
-iter= 54 erg= -0.4341199600672291 myeps= 1.2235217292978426e-06
 

 

-
iter= 55 erg= -0.4341203996786241 myeps= 1.012648554028702e-06
+
iter= 54 erg= -0.4341199600672291 myeps= 1.2235217292978426e-06
+iter= 55 erg= -0.4341203996786241 myeps= 1.012648554028702e-06
 iter= 56 erg= -0.43412076352439366 myeps= 8.381210947268386e-07
-iter= 57 erg= -0.4341210646630742 myeps= 6.936744264669387e-07
 

 

-
iter= 58 erg= -0.43412131390237374 myeps= 5.741236183312693e-07
+
iter= 57 erg= -0.4341210646630742 myeps= 6.936744264669387e-07
+iter= 58 erg= -0.43412131390237374 myeps= 5.741236183312693e-07
 iter= 59 erg= -0.43412152018716577 myeps= 4.75177530791271e-07
-iter= 60 erg= -0.43412169092059416 myeps= 3.9328472169422255e-07
 

 

-
iter= 61 erg= -0.43412183222982187 myeps= 3.2550592303140516e-07
+
iter= 60 erg= -0.43412169092059416 myeps= 3.9328472169422255e-07
+iter= 61 erg= -0.43412183222982187 myeps= 3.2550592303140516e-07
 iter= 62 erg= -0.43412194918596947 myeps= 2.6940851025684355e-07
 

 

@@ -2108,23 +2104,21 @@ 
7.24. Project 15: Solve the CCD equation
 

 
iter= 69 erg= -0.4341223614365778 myeps= 7.16789278285526e-08
 iter= 70 erg= -0.43412238719145646 myeps= 5.9326308511289724e-08
+iter= 71 erg= -0.4341224085079444 myeps= 4.910248249319024e-08
 

 

-
iter= 71 erg= -0.4341224085079444 myeps= 4.910248249319024e-08
-iter= 72 erg= -0.4341224261509296 myeps= 4.064057537950126e-08
+
iter= 72 erg= -0.4341224261509296 myeps= 4.064057537950126e-08
 iter= 73 erg= -0.43412244075348105 myeps= 3.3636942196453457e-08
+iter= 74 erg= -0.4341224528395664 myeps= 2.7840267822341936e-08
 

 

-
iter= 74 erg= -0.4341224528395664 myeps= 2.7840267822341936e-08
-iter= 75 erg= -0.43412246284285466 myeps= 2.304254930229631e-08
+
iter= 75 erg= -0.43412246284285466 myeps= 2.304254930229631e-08
 iter= 76 erg= -0.43412247112227875 myeps= 1.9071632181026577e-08
+iter= 77 erg= -0.4341224779749146 myeps= 1.578502886931811e-08
 

 

-
iter= 77 erg= -0.4341224779749146 myeps= 1.578502886931811e-08
-iter= 78 erg= -0.43412248364664213 myeps= 1.306480946214148e-08
-

-

-
iter= 79 erg= -0.434122488340968 myeps= 1.0813367191382213e-08
+
iter= 78 erg= -0.43412248364664213 myeps= 1.306480946214148e-08
+iter= 79 erg= -0.434122488340968 myeps= 1.0813367191382213e-08
 Energy =  -0.4341224922263279
 

 

@@ -3442,7 +3436,7 @@ 
8.9. Exercise 20: Write a CCD code for n
 number of s.p. states: 54
 

 

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:309: NumbaWarning: 
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:309: NumbaWarning: 
 Compilation is falling back to object mode WITH looplifting enabled because Function "compute_total_Tkin" failed type inference due to: No implementation of function Function(<built-in function array>) found for signature:
  
  >>> array(array(int64, 1d, C))
@@ -3456,23 +3450,23 @@ 
8.9. Exercise 20: Write a CCD code for n
   raised from /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/typing/npydecl.py:488
 
 During: resolving callee type: Function(<built-in function array>)
-During: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (319)
+During: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (319)
 
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 319:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 319:
 <source missing, REPL/exec in use?>
 
   @jit
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:309: NumbaWarning: 
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:309: NumbaWarning: 
 Compilation is falling back to object mode WITHOUT looplifting enabled because Function "compute_total_Tkin" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 317:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 317:
 <source missing, REPL/exec in use?>
 
   @jit
 /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: Function "compute_total_Tkin" was compiled in object mode without forceobj=True, but has lifted loops.
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 316:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 316:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaWarning(warn_msg,
@@ -3481,11 +3475,11 @@ 
8.9. Exercise 20: Write a CCD code for n
 
 For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 316:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 316:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaDeprecationWarning(msg,
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:309: NumbaWarning: 
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:309: NumbaWarning: 
 Compilation is falling back to object mode WITHOUT looplifting enabled because Function "compute_total_Tkin" failed type inference due to: No implementation of function Function(<built-in function array>) found for signature:
  
  >>> array(array(int64, 1d, C))
@@ -3499,16 +3493,16 @@ 
8.9. Exercise 20: Write a CCD code for n
   raised from /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/typing/npydecl.py:488
 
 During: resolving callee type: Function(<built-in function array>)
-During: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (319)
+During: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (319)
 
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 319:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 319:
 <source missing, REPL/exec in use?>
 
   @jit
 /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: Function "compute_total_Tkin" was compiled in object mode without forceobj=True.
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 317:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 317:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaWarning(warn_msg,
@@ -3517,7 +3511,7 @@ 
8.9. Exercise 20: Write a CCD code for n
 
 For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 317:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 317:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaDeprecationWarning(msg,
@@ -3534,7 +3528,7 @@ 
8.9. Exercise 20: Write a CCD code for n
 hh number of total combinations 182
 pp number of total combinations 656
 get v_hhhh, v_pphh, v_pppp
-what a hog! 0.0584207499999998 seconds
+what a hog! 0.05801991599999923 seconds
 compute energy denominator
 

 

@@ -3543,63 +3537,63 @@ 
8.9. Exercise 20: Write a CCD code for n
 
 For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 326:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 326:
 <source missing, REPL/exec in use?>
 
   warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:511: NumbaWarning: 
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:511: NumbaWarning: 
 Compilation is falling back to object mode WITH looplifting enabled because Function "make_diagram" failed type inference due to: Use of unsupported NumPy function 'numpy.matmul' or unsupported use of the function.
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 521:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 521:
 <source missing, REPL/exec in use?>
 
-During: typing of get attribute at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (521)
+During: typing of get attribute at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (521)
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 521:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 521:
 <source missing, REPL/exec in use?>
 
   @jit
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:511: NumbaWarning: 
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:511: NumbaWarning: 
 Compilation is falling back to object mode WITHOUT looplifting enabled because Function "make_diagram" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 518:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 518:
 <source missing, REPL/exec in use?>
 
   @jit
 

 

-
that's faster 0.1893234580000005 seconds
+
that's faster 0.1893236250000001 seconds
 Initialize T2 from MBPT2
 MBPT2 correlation energy per neutron = -0.17553785744804082
 start CCD iterations ...
-time of making Hbar: 0.17643266600000018 seconds
+time of making Hbar: 0.17841224999999916 seconds
 iter= 0 Correlation energy per neutron= -0.20550292085394894 , epsilon= 1.0
-time of making Hbar: 0.0002477079999998466 seconds
+time of making Hbar: 0.0002564580000008476 seconds
 iter= 1 Correlation energy per neutron= -0.2107009003533346 , epsilon= 0.024669944412524628
-time of making Hbar: 0.00022991600000032975 seconds
+time of making Hbar: 0.00023595900000117354 seconds
 iter= 2 Correlation energy per neutron= -0.2116672343138936 , epsilon= 0.004565345050646539
-time of making Hbar: 0.00022641700000036735 seconds
+time of making Hbar: 0.00022775000000052614 seconds
 iter= 3 Correlation energy per neutron= -0.21184820867906343 , epsilon= 0.0008542643164097731
-time of making Hbar: 0.0002269160000007986 seconds
+time of making Hbar: 0.00022474999999921863 seconds
 iter= 4 Correlation energy per neutron= -0.21188227228364806 , epsilon= 0.00016076665696240967
-time of making Hbar: 0.0002237919999998894 seconds
+time of making Hbar: 0.000225874999999931 seconds
 iter= 5 Correlation energy per neutron= -0.21188870821702438 , epsilon= 3.0374121539822725e-05
-time of making Hbar: 0.00022312499999976865 seconds
+time of making Hbar: 0.00023183299999907092 seconds
 iter= 6 Correlation energy per neutron= -0.21188992798649683 , epsilon= 5.756618467265144e-06
-time of making Hbar: 0.00022620899999914457 seconds
+time of making Hbar: 0.0002274159999995362 seconds
 iter= 7 Correlation energy per neutron= -0.211890159780636 , epsilon= 1.093935364400894e-06
-time of making Hbar: 0.0002216659999998427 seconds
+time of making Hbar: 0.00022137500000063426 seconds
 iter= 8 Correlation energy per neutron= -0.21189020393325292 , epsilon= 2.0837497962657972e-07
-time of making Hbar: 0.00022162500000000307 seconds
+time of making Hbar: 0.00022020900000008226 seconds
 iter= 9 Correlation energy per neutron= -0.21189021236156144 , epsilon= 3.9776771344709894e-08
-time of making Hbar: 0.00022133299999893552 seconds
+time of making Hbar: 0.0002222910000000411 seconds
 Correlation energy per neutron=  -0.2118902139735854
-total time in seconds 1.6360875000000004
+total time in seconds 1.2425067080000005
 

 

 
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: Function "make_diagram" was compiled in object mode without forceobj=True, but has lifted loops.
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 516:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 516:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaWarning(warn_msg,
@@ -3608,21 +3602,21 @@ 
8.9. Exercise 20: Write a CCD code for n
 
 For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 516:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 516:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaDeprecationWarning(msg,
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:511: NumbaWarning: 
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:511: NumbaWarning: 
 Compilation is falling back to object mode WITHOUT looplifting enabled because Function "make_diagram" failed type inference due to: non-precise type pyobject
-During: typing of argument at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (518)
+During: typing of argument at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (518)
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 518:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 518:
 <source missing, REPL/exec in use?>
 
   @jit
 /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: Function "make_diagram" was compiled in object mode without forceobj=True.
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 518:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 518:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaWarning(warn_msg,
@@ -3631,7 +3625,7 @@ 
8.9. Exercise 20: Write a CCD code for n
 
 For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit
 
-File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py", line 518:
+File "../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py", line 518:
 <source missing, REPL/exec in use?>
 
   warnings.warn(errors.NumbaDeprecationWarning(msg,
diff --git a/doc/LectureNotes/_build/html/gradientmethods.html b/doc/LectureNotes/_build/html/gradientmethods.html
index a39954ae..9b1b72a9 100644
--- a/doc/LectureNotes/_build/html/gradientmethods.html
+++ b/doc/LectureNotes/_build/html/gradientmethods.html
@@ -1175,8 +1175,8 @@ 
11.11. Python program for 2-electrons in
 

 

 

-
1.0085748874608291 0.31779454097168824
-3.0027130057855604 -0.04763239067831826 -0.07459848329535479
+
1.0087099588144601 0.31778422796149464
+3.0040051420613456 -0.05388509404744202 -0.07939029201677261
 

 

 

@@ -1381,11 +1381,11 @@ 
11.12. Using Broyden’s algorithm in sc
 

 

 
Warning: Desired error not necessarily achieved due to precision loss.
-         Current function value: 2.997317
-         Iterations: 5
-         Function evaluations: 27
+         Current function value: 2.996914
+         Iterations: 4
+         Function evaluations: 28
          Gradient evaluations: 15
-[0.98983762 0.39892933]
+[0.97682809 0.42266524]
 

 

 

@@ -1812,11 +1812,11 @@ 
11.34. Steepest descent example
 

 

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8093/1833905057.py:16: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5279/1833905057.py:16: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 

 

-
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11f1bfb20>
+
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x124b2bc10>
 

 

 _images/gradientmethods_81_2.png
@@ -1874,7 +1874,7 @@ 
11.34. Steepest descent example
 

 

-
[<matplotlib.lines.Line2D at 0x126d32700>]
+
[<matplotlib.lines.Line2D at 0x125473850>]
 

 

 _images/gradientmethods_89_1.png
@@ -2303,8 +2303,8 @@ 
11.52. Program for stochastic gradient
 

 
Own inversion
-[[4.09072751]
- [2.99662099]]
+[[3.96691934]
+ [3.09044107]]
 

 

 
---------------------------------------------------------------------------
diff --git a/doc/LectureNotes/_build/html/notation.html b/doc/LectureNotes/_build/html/notation.html
index 6de84b56..888c233e 100644
--- a/doc/LectureNotes/_build/html/notation.html
+++ b/doc/LectureNotes/_build/html/notation.html
@@ -558,8 +558,8 @@ 
1.4. Numpy and arrays
-
[-0.38005418  1.54878832  1.17184624 -1.17842626 -1.73955345 -1.79748932
- -0.63041751 -0.15290907  0.6439199   1.88708544]
+
[-0.18181242 -0.82390948 -0.95197186  0.17706887  0.80026555 -0.45625011
+ -0.18799733 -0.34267102 -1.12100632 -0.18662716]
 

 

 

@@ -780,26 +780,26 @@ 
1.4. Numpy and arrays
-
[[0.58328247 0.03040501 0.27963406 0.44093771 0.69195793 0.40091461
-  0.06098957 0.55003012 0.45374613 0.85487081]
- [0.24583166 0.56769196 0.9061827  0.23018296 0.50207802 0.75241232
-  0.85102688 0.76319983 0.09296749 0.57715363]
- [0.07423133 0.55568732 0.2453194  0.36154513 0.29072776 0.16063197
-  0.42589086 0.70984236 0.29521469 0.43984924]
- [0.56953949 0.28967051 0.63368525 0.06160488 0.38025695 0.08289615
-  0.36472359 0.33063952 0.96579751 0.39082526]
- [0.80001674 0.59946684 0.43932877 0.02156606 0.81553478 0.23673328
-  0.54694075 0.56820081 0.29350801 0.04902982]
- [0.68777857 0.50073475 0.22497749 0.17402761 0.70692466 0.01451004
-  0.50850427 0.932051   0.32355336 0.52518412]
- [0.41802363 0.75633005 0.13045395 0.932665   0.53432834 0.9020486
-  0.1170548  0.73671788 0.80641115 0.26737294]
- [0.74038321 0.06970898 0.19702405 0.83095304 0.59079917 0.45368766
-  0.71566572 0.22448243 0.16310651 0.2402106 ]
- [0.76792881 0.18568688 0.49507077 0.85970836 0.85470974 0.14060903
-  0.44802059 0.77804606 0.73665774 0.09371713]
- [0.78131065 0.83381268 0.31639926 0.1062502  0.85522352 0.00796671
-  0.71980758 0.75492216 0.21185956 0.30261992]]
+
[[0.42668154 0.44529398 0.81431386 0.41532905 0.08538057 0.68954186
+  0.05741467 0.62343179 0.29156132 0.50311603]
+ [0.75838834 0.6090632  0.32329292 0.12567032 0.44684508 0.90291719
+  0.67409939 0.30861483 0.27160845 0.37386191]
+ [0.77853093 0.83642995 0.77942173 0.87281744 0.43413506 0.33728723
+  0.82678561 0.13855613 0.7705333  0.96473269]
+ [0.0632419  0.50224636 0.26817617 0.0563718  0.55243532 0.58035027
+  0.46833658 0.69820786 0.98841162 0.17397314]
+ [0.35748561 0.48144532 0.83970178 0.92497464 0.65877538 0.02446637
+  0.40784019 0.83621124 0.47947519 0.1382501 ]
+ [0.56973725 0.38465877 0.85538751 0.13002132 0.30504484 0.82358887
+  0.10074632 0.02949402 0.77084258 0.93282264]
+ [0.44722558 0.24534328 0.12840244 0.9423928  0.1366858  0.99570313
+  0.87400321 0.69250731 0.24558877 0.04518465]
+ [0.94924418 0.37864605 0.10082995 0.06187188 0.28347895 0.97877997
+  0.69113578 0.01888094 0.46364549 0.42022255]
+ [0.49468485 0.47782191 0.51235668 0.57404905 0.45231321 0.47933511
+  0.27193391 0.15257909 0.28908355 0.19820541]
+ [0.28773096 0.11602248 0.34159001 0.39539761 0.78975011 0.62034427
+  0.5657688  0.32520647 0.9107702  0.80607227]]
 

 

 

diff --git a/doc/LectureNotes/_build/html/resamplingmethods.html b/doc/LectureNotes/_build/html/resamplingmethods.html
index a1ca79c0..e15829ec 100644
--- a/doc/LectureNotes/_build/html/resamplingmethods.html
+++ b/doc/LectureNotes/_build/html/resamplingmethods.html
@@ -937,10 +937,10 @@ 
12.16. Jackknife code example
-
Runtime: 0.128286 sec
+
Runtime: 0.134595 sec
 Jackknife Statistics :
 original           bias      std. error
- 99.8196        99.8096        0.151352
+ 99.8991        99.8891        0.150016
 

 

 

@@ -1072,10 +1072,10 @@ 
12.22. Code example for the Bootstrap me
 

 

 

-
Runtime: 1.24256 sec
+
Runtime: 1.21746 sec
 Bootstrap Statistics :
 original           bias      std. error
- 100.137  14.9318        100.137        0.148632
+ 100.005  14.9921        100.005        0.150523
 

 

 _images/resamplingmethods_59_1.png
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index aa4855b2..9da060ab 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -366,8 +366,8 @@ 
Quantum Mechanics for Many-Particle System
     
    
    
  • 
-    
-     Week 50, December 19 and 21, 2023
+    
+     Week 50, December 19, 2023
     
    
    • 
   
@@ -473,8 +473,8 @@ 
     Contents 
    
     
    
    
  • 
-    
-     Week 50, December 19 and 21, 2023
+    
+     Week 50, December 19, 2023
     
    
    • 
   
@@ -770,18 +770,23 @@ 
    Week 46, November 13-17, 2023https://youtu.be/rCtpX221u24
    
+
  • Whiteboard notes at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/HandwrittenNotes/2023/LectureNovember16.pdf
    • 
 
 
 
  • Friday:
    
 
 
    • 
 
 
-
  • Lecture Material: Lecture slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week46/LatexSlides/slidesweek46.pdf, handwritten notes and Shavitt and Bartlett chapter 9
    • 
+
  • Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/pub/week46/pdf/slidesweek46.pdf, handwritten notes a
    
+nd Shavitt and Bartlett chapter 4-6
    • 
 
  • Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf
    • 
 
 
    
@@ -792,17 +797,17 @@ 
    Week 47, November 20-24, 2023https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week47/LatexSlides/slidesweek47.pdf, handwritten notes and Shavitt and Bartlett chapters 9 and 10
    
+
  • Lecture Material:
    • 
 
  • Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf
    • 
 
 
    
@@ -813,24 +818,24 @@ 
    Week 48, November 27-December 1, 2023https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week47/LatexSlides/slidesweek47.pdf, handwritten notes and Shavitt and Bartlett chapters 9 and 10
    
+
  • Lecture Material:
    • 
 
  • Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf
    • 
 
 
    
-
    
-
    Week 50, December 19 and 21, 2023
    
+
    
+
    Week 50, December 19, 2023
    
 
      
-
    • Final oral exam, day to be determined
      • 
+
    • Final oral exam
      • 
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 1d442e41..7399882e 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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@@ -1 +1 @@
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udi:7,style:8,substitut:14,summar:[2,11],symmetri:1,sympi:11,synchron:8,syntax:8,system:5,t:1,t_2:1,task:8,teach:10,techniqu:9,tensor:7,test:14,textbook:13,than:3,theorem:[4,11],theori:[2,4,6],thi:[8,9],thing:8,thoma:14,thouless:4,thread:8,tild:4,time:[3,9,11,14],todai:8,top:3,topic:5,train:0,transform:[1,7,9,11],transpos:14,trapezoid:8,tridiagon:14,truncat:1,two:[0,8,11,15],type:0,understand:[1,9],unitari:1,unknown:1,up:[0,9,11],upon:14,upper:14,us:[3,8,11,14],usag:1,v:1,valu:[8,15],vari:[4,7],variabl:[3,8],varianc:3,variant:8,variat:[4,15],vector:[7,14],version:0,vert:1,vmc:15,wai:2,wave:[0,4,6,7],we:[3,9,14],week:[9,10],what:[1,8,14],when:[1,3,14],which:1,why:[1,9],wick:11,wigner:11,without:14,world:8,wrap:9,write:1,yet:8}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/jupyter_execute/boltzmannmachines.ipynb b/doc/LectureNotes/_build/jupyter_execute/boltzmannmachines.ipynb
index 906c6e5c..f620e444 100644
--- a/doc/LectureNotes/_build/jupyter_execute/boltzmannmachines.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/boltzmannmachines.ipynb
@@ -2999,56 +2999,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy: 3.270829693673645\n",
-      "Energy: 3.233104652672888\n"
+      "Energy: 3.2616589395123157\n",
+      "Energy: 3.250328623538633\n",
+      "Energy: 3.210096688088389\n",
+      "Energy: 3.2616162121215706\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy: 3.247383897523189\n",
-      "Energy: 3.2323935531841137\n",
-      "Energy: 3.3658081999383316\n",
-      "Energy: 3.201257830861303\n",
-      "Energy: 3.1640313431712044\n",
-      "Energy: 3.2956720397479438\n",
-      "Energy: 3.245099658936495\n",
-      "Energy: 3.273061876235883\n",
-      "Energy: 3.1811830181358536\n",
-      "Energy: 3.2619697749130916\n",
-      "Energy: 3.2097609308000075\n"
+      "Energy: 3.2601523514477346\n",
+      "Energy: 3.2569803619031514\n",
+      "Energy: 3.27515114735846\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy: 3.222733848271767\n"
+      "Energy: 3.226532626427531\n",
+      "Energy: 3.2479229142962693\n",
+      "Energy: 3.2405790551148295\n",
+      "Energy: 3.339269450372016\n",
+      "Energy: 3.292985914196608\n",
+      "Energy: 3.2734831269767466\n",
+      "Energy: 3.468371242428862\n",
+      "Energy: 3.2814210399238766\n",
+      "Energy: 3.2714197897886885\n",
+      "Energy: 3.2193959772791\n",
+      "Energy: 3.2912864812817935\n",
+      "Energy: 3.2423803253014007\n",
+      "Energy: 3.1928143709455195\n",
+      "Energy: 3.2146899252942056\n",
+      "Energy: 3.2999210822323337\n",
+      "Energy: 3.2811059647446417\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy: 3.2447619247830217\n",
-      "Energy: 3.269317606376056\n",
-      "Energy: 3.200534237237334\n",
-      "Energy: 3.2349636044589025\n",
-      "Energy: 3.340305199776341\n",
-      "Energy: 3.3044665357695058\n",
-      "Energy: 3.2631436797918103\n",
-      "Energy: 3.2914767328754526\n",
-      "Energy: 3.2451508473321633\n",
-      "Energy: 3.2176548221895658\n",
-      "Energy: 3.323211030424025\n"
+      "Energy: 3.221709384120671\n",
+      "Energy: 3.2201117261448933\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Energy: 3.2049106538710403\n"
+      "Energy: 3.2411631395541476\n"
      ]
     },
     {
@@ -3059,8 +3059,8 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
       "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m    232\u001b[0m EnergyDerivatives2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(MaxIterations)\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28miter\u001b[39m \u001b[38;5;241m<\u001b[39m MaxIterations:\n\u001b[0;32m--> 235\u001b[0m     Energy, EDerivative \u001b[38;5;241m=\u001b[39m \u001b[43mEnergyMinimization\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43mw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    236\u001b[0m     agradient \u001b[38;5;241m=\u001b[39m EDerivative[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    237\u001b[0m     bgradient \u001b[38;5;241m=\u001b[39m EDerivative[\u001b[38;5;241m1\u001b[39m]\n",
-      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36mEnergyMinimization\u001b[0;34m(a, b, w)\u001b[0m\n\u001b[1;32m    177\u001b[0m         wfold \u001b[38;5;241m=\u001b[39m wfnew\n\u001b[1;32m    178\u001b[0m \u001b[38;5;66;03m#print(\"wf new:        \", wfnew)\u001b[39;00m\n\u001b[1;32m    179\u001b[0m \u001b[38;5;66;03m#print(\"force on 1 new:\", QuantumForceNew[0,:])\u001b[39;00m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;66;03m#print(\"pos of 1 new:  \", PositionNew[0,:])\u001b[39;00m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;66;03m#print(\"force on 2 new:\", QuantumForceNew[1,:])\u001b[39;00m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;66;03m#print(\"pos of 2 new:  \", PositionNew[1,:])\u001b[39;00m\n\u001b[0;32m--> 183\u001b[0m DeltaE \u001b[38;5;241m=\u001b[39m \u001b[43mLocalEnergy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPositionOld\u001b[49m\u001b[43m,\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43mw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m DerPsi \u001b[38;5;241m=\u001b[39m DerivativeWFansatz(PositionOld,a,b,w)\n\u001b[1;32m    186\u001b[0m DeltaPsi[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m DerPsi[\u001b[38;5;241m0\u001b[39m]\n",
-      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36mLocalEnergy\u001b[0;34m(r, a, b, w)\u001b[0m\n\u001b[1;32m     54\u001b[0m         dlnpsi1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m(r[iq,ix] \u001b[38;5;241m-\u001b[39m a[iq,ix]) \u001b[38;5;241m/\u001b[39msig2 \u001b[38;5;241m+\u001b[39m sum1\u001b[38;5;241m/\u001b[39msig2\n\u001b[1;32m     55\u001b[0m         dlnpsi2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39msig2 \u001b[38;5;241m+\u001b[39m sum2\u001b[38;5;241m/\u001b[39msig2\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[0;32m---> 56\u001b[0m         locenergy \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m-\u001b[39mdlnpsi1\u001b[38;5;241m*\u001b[39mdlnpsi1 \u001b[38;5;241m-\u001b[39m dlnpsi2 \u001b[38;5;241m+\u001b[39m \u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[43miq\u001b[49m\u001b[43m,\u001b[49m\u001b[43mix\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m)\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(interaction\u001b[38;5;241m==\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m iq1 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NumberParticles):\n",
+      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36mEnergyMinimization\u001b[0;34m(a, b, w)\u001b[0m\n\u001b[1;32m    159\u001b[0m     PositionNew[i,j] \u001b[38;5;241m=\u001b[39m PositionOld[i,j]\u001b[38;5;241m+\u001b[39mnormalvariate(\u001b[38;5;241m0.0\u001b[39m,\u001b[38;5;241m1.0\u001b[39m)\u001b[38;5;241m*\u001b[39msqrt(TimeStep)\u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m    160\u001b[0m                            QuantumForceOld[i,j]\u001b[38;5;241m*\u001b[39mTimeStep\u001b[38;5;241m*\u001b[39mD\n\u001b[1;32m    161\u001b[0m wfnew \u001b[38;5;241m=\u001b[39m WaveFunction(PositionNew,a,b,w)\n\u001b[0;32m--> 162\u001b[0m QuantumForceNew \u001b[38;5;241m=\u001b[39m \u001b[43mQuantumForce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPositionNew\u001b[49m\u001b[43m,\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43mw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    164\u001b[0m GreensFunction \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[1;32m    165\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(Dimension):\n",
+      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36mQuantumForce\u001b[0;34m(r, a, b, w)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ih \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NumberHidden):\n\u001b[1;32m    100\u001b[0m     sum1 \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m w[:,:,ih]\u001b[38;5;241m/\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m+\u001b[39mnp\u001b[38;5;241m.\u001b[39mexp(\u001b[38;5;241m-\u001b[39mQ[ih]))\n\u001b[0;32m--> 102\u001b[0m qforce \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m/\u001b[39msig2 \u001b[38;5;241m+\u001b[39m sum1\u001b[38;5;241m/\u001b[39msig2)\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m qforce\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/cc.ipynb b/doc/LectureNotes/_build/jupyter_execute/cc.ipynb
index 48138ca1..640dfd56 100644
--- a/doc/LectureNotes/_build/jupyter_execute/cc.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/cc.ipynb
@@ -2115,66 +2115,60 @@
      "output_type": "stream",
      "text": [
       "iter= 6 erg= -0.41154472066379555 myeps= 0.011472448299162774\n",
-      "iter= 7 erg= -0.415448787715655 myeps= 0.00939722817179461\n"
+      "iter= 7 erg= -0.415448787715655 myeps= 0.00939722817179461\n",
+      "iter= 8 erg= -0.41867711472018154 myeps= 0.007710779717883887\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter= 8 erg= -0.41867711472018154 myeps= 0.007710779717883887\n",
       "iter= 9 erg= -0.4213467805437514 myeps= 0.006336029956428388\n",
-      "iter= 10 erg= -0.4235545667262582 myeps= 0.005212518895903349\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter= 11 erg= -0.4253804729664931 myeps= 0.004292407283064816\n",
-      "iter= 12 erg= -0.42689062703898156 myeps= 0.003537566713430196\n"
+      "iter= 10 erg= -0.4235545667262582 myeps= 0.005212518895903349\n",
+      "iter= 11 erg= -0.4253804729664931 myeps= 0.004292407283064816\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 12 erg= -0.42689062703898156 myeps= 0.003537566713430196\n",
       "iter= 13 erg= -0.4281396916442187 myeps= 0.002917423050500827\n",
-      "iter= 14 erg= -0.4291728542011779 myeps= 0.0024073343568810036\n",
-      "iter= 15 erg= -0.4300274713630842 myeps= 0.0019873548059555165\n"
+      "iter= 14 erg= -0.4291728542011779 myeps= 0.0024073343568810036\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 15 erg= -0.4300274713630842 myeps= 0.0019873548059555165\n",
       "iter= 16 erg= -0.4307344279442794 myeps= 0.0016412818092326651\n",
-      "iter= 17 erg= -0.4313192597672807 myeps= 0.0013559140004942823\n",
-      "iter= 18 erg= -0.43180308142773266 myeps= 0.001120468290435161\n"
+      "iter= 17 erg= -0.4313192597672807 myeps= 0.0013559140004942823\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 18 erg= -0.43180308142773266 myeps= 0.001120468290435161\n",
       "iter= 19 erg= -0.4322033528891356 myeps= 0.00092611836240335\n",
-      "iter= 20 erg= -0.43253451293787193 myeps= 0.0007656268779271082\n",
-      "iter= 21 erg= -0.43280850265722925 myeps= 0.0006330506856384872\n"
+      "iter= 20 erg= -0.43253451293787193 myeps= 0.0007656268779271082\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 21 erg= -0.43280850265722925 myeps= 0.0006330506856384872\n",
       "iter= 22 erg= -0.4330351980489746 myeps= 0.0005235033844055304\n",
-      "iter= 23 erg= -0.4332227676000893 myeps= 0.00043296328157862334\n",
-      "iter= 24 erg= -0.4333779678407642 myeps= 0.0003581175144831452\n"
+      "iter= 23 erg= -0.4332227676000893 myeps= 0.00043296328157862334\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 24 erg= -0.4333779678407642 myeps= 0.0003581175144831452\n",
       "iter= 25 erg= -0.4335063876676202 myeps= 0.00029623514326258533\n",
       "iter= 26 erg= -0.4336126503302039 myeps= 0.0002450635665329708\n"
      ]
@@ -2202,93 +2196,87 @@
      "output_type": "stream",
      "text": [
       "iter= 33 erg= -0.43398696881076043 myeps= 6.506447928127211e-05\n",
-      "iter= 34 erg= -0.43401033674723327 myeps= 5.384188922313506e-05\n"
+      "iter= 34 erg= -0.43401033674723327 myeps= 5.384188922313506e-05\n",
+      "iter= 35 erg= -0.4340296753237455 myeps= 4.455588548820297e-05\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter= 35 erg= -0.4340296753237455 myeps= 4.455588548820297e-05\n",
       "iter= 36 erg= -0.43404567947824346 myeps= 3.6872051156509844e-05\n",
-      "iter= 37 erg= -0.4340589242583793 myeps= 3.0513783718287337e-05\n"
+      "iter= 37 erg= -0.4340589242583793 myeps= 3.0513783718287337e-05\n",
+      "iter= 38 erg= -0.434069885513208 myeps= 2.525228124449486e-05\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter= 38 erg= -0.434069885513208 myeps= 2.525228124449486e-05\n",
       "iter= 39 erg= -0.43407895701178667 myeps= 2.08982684650241e-05\n",
-      "iter= 40 erg= -0.43408646460614353 myeps= 1.729515884276886e-05\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter= 41 erg= -0.4340926779492775 myeps= 1.4313402297628368e-05\n",
-      "iter= 42 erg= -0.4340978201905307 myeps= 1.1845812197343312e-05\n"
+      "iter= 40 erg= -0.43408646460614353 myeps= 1.729515884276886e-05\n",
+      "iter= 41 erg= -0.4340926779492775 myeps= 1.4313402297628368e-05\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 42 erg= -0.4340978201905307 myeps= 1.1845812197343312e-05\n",
       "iter= 43 erg= -0.43410207599766415 myeps= 9.803701407424543e-06\n",
-      "iter= 44 erg= -0.4341055981945805 myeps= 8.113686925482528e-06\n",
-      "iter= 45 erg= -0.43410851325371014 myeps= 6.715047138304284e-06\n"
+      "iter= 44 erg= -0.4341055981945805 myeps= 8.113686925482528e-06\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 45 erg= -0.43410851325371014 myeps= 6.715047138304284e-06\n",
       "iter= 46 erg= -0.43411092584077954 myeps= 5.557535933304656e-06\n",
-      "iter= 47 erg= -0.4341129225755269 myeps= 4.5995745427982914e-06\n",
-      "iter= 48 erg= -0.43411457514368107 myeps= 3.8067557478495853e-06\n"
+      "iter= 47 erg= -0.4341129225755269 myeps= 4.5995745427982914e-06\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 48 erg= -0.43411457514368107 myeps= 3.8067557478495853e-06\n",
       "iter= 49 erg= -0.43411594287215505 myeps= 3.150606413873383e-06\n",
-      "iter= 50 erg= -0.4341170748600758 myeps= 2.607563687969726e-06\n",
-      "iter= 51 erg= -0.4341180117422817 myeps= 2.158127929698868e-06\n"
+      "iter= 50 erg= -0.4341170748600758 myeps= 2.607563687969726e-06\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 51 erg= -0.4341180117422817 myeps= 2.158127929698868e-06\n",
       "iter= 52 erg= -0.4341187871486943 myeps= 1.7861618421804902e-06\n",
-      "iter= 53 erg= -0.4341194289120248 myeps= 1.478310547265082e-06\n",
-      "iter= 54 erg= -0.4341199600672291 myeps= 1.2235217292978426e-06\n"
+      "iter= 53 erg= -0.4341194289120248 myeps= 1.478310547265082e-06\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 54 erg= -0.4341199600672291 myeps= 1.2235217292978426e-06\n",
       "iter= 55 erg= -0.4341203996786241 myeps= 1.012648554028702e-06\n",
-      "iter= 56 erg= -0.43412076352439366 myeps= 8.381210947268386e-07\n",
-      "iter= 57 erg= -0.4341210646630742 myeps= 6.936744264669387e-07\n"
+      "iter= 56 erg= -0.43412076352439366 myeps= 8.381210947268386e-07\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 57 erg= -0.4341210646630742 myeps= 6.936744264669387e-07\n",
       "iter= 58 erg= -0.43412131390237374 myeps= 5.741236183312693e-07\n",
-      "iter= 59 erg= -0.43412152018716577 myeps= 4.75177530791271e-07\n",
-      "iter= 60 erg= -0.43412169092059416 myeps= 3.9328472169422255e-07\n"
+      "iter= 59 erg= -0.43412152018716577 myeps= 4.75177530791271e-07\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 60 erg= -0.43412169092059416 myeps= 3.9328472169422255e-07\n",
       "iter= 61 erg= -0.43412183222982187 myeps= 3.2550592303140516e-07\n",
       "iter= 62 erg= -0.43412194918596947 myeps= 2.6940851025684355e-07\n"
      ]
@@ -2316,39 +2304,33 @@
      "output_type": "stream",
      "text": [
       "iter= 69 erg= -0.4341223614365778 myeps= 7.16789278285526e-08\n",
-      "iter= 70 erg= -0.43412238719145646 myeps= 5.9326308511289724e-08\n"
+      "iter= 70 erg= -0.43412238719145646 myeps= 5.9326308511289724e-08\n",
+      "iter= 71 erg= -0.4341224085079444 myeps= 4.910248249319024e-08\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter= 71 erg= -0.4341224085079444 myeps= 4.910248249319024e-08\n",
       "iter= 72 erg= -0.4341224261509296 myeps= 4.064057537950126e-08\n",
-      "iter= 73 erg= -0.43412244075348105 myeps= 3.3636942196453457e-08\n"
+      "iter= 73 erg= -0.43412244075348105 myeps= 3.3636942196453457e-08\n",
+      "iter= 74 erg= -0.4341224528395664 myeps= 2.7840267822341936e-08\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter= 74 erg= -0.4341224528395664 myeps= 2.7840267822341936e-08\n",
       "iter= 75 erg= -0.43412246284285466 myeps= 2.304254930229631e-08\n",
-      "iter= 76 erg= -0.43412247112227875 myeps= 1.9071632181026577e-08\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter= 77 erg= -0.4341224779749146 myeps= 1.578502886931811e-08\n",
-      "iter= 78 erg= -0.43412248364664213 myeps= 1.306480946214148e-08\n"
+      "iter= 76 erg= -0.43412247112227875 myeps= 1.9071632181026577e-08\n",
+      "iter= 77 erg= -0.4341224779749146 myeps= 1.578502886931811e-08\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "iter= 78 erg= -0.43412248364664213 myeps= 1.306480946214148e-08\n",
       "iter= 79 erg= -0.434122488340968 myeps= 1.0813367191382213e-08\n",
       "Energy =  -0.4341224922263279\n"
      ]
@@ -3767,7 +3749,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:309: NumbaWarning: \u001b[1m\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:309: NumbaWarning: \u001b[1m\n",
       "Compilation is falling back to object mode WITH looplifting enabled because Function \"compute_total_Tkin\" failed type inference due to: \u001b[1m\u001b[1m\u001b[1mNo implementation of function Function() found for signature:\n",
       " \n",
       " >>> array(array(int64, 1d, C))\n",
@@ -3781,23 +3763,23 @@
       "  raised from /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/typing/npydecl.py:488\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[1mDuring: resolving callee type: Function()\u001b[0m\n",
-      "\u001b[0m\u001b[1mDuring: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (319)\n",
+      "\u001b[0m\u001b[1mDuring: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (319)\n",
       "\u001b[0m\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 319:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 319:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  @jit\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:309: NumbaWarning: \u001b[1m\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:309: NumbaWarning: \u001b[1m\n",
       "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"compute_total_Tkin\" failed type inference due to: \u001b[1m\u001b[1mCannot determine Numba type of \u001b[0m\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 317:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 317:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\u001b[0m\n",
       "  @jit\n",
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"compute_total_Tkin\" was compiled in object mode without forceobj=True, but has lifted loops.\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 316:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 316:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
@@ -3806,11 +3788,11 @@
       "\n",
       "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 316:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 316:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:309: NumbaWarning: \u001b[1m\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:309: NumbaWarning: \u001b[1m\n",
       "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"compute_total_Tkin\" failed type inference due to: \u001b[1m\u001b[1m\u001b[1mNo implementation of function Function() found for signature:\n",
       " \n",
       " >>> array(array(int64, 1d, C))\n",
@@ -3824,16 +3806,16 @@
       "  raised from /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/typing/npydecl.py:488\n",
       "\u001b[0m\n",
       "\u001b[0m\u001b[1mDuring: resolving callee type: Function()\u001b[0m\n",
-      "\u001b[0m\u001b[1mDuring: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (319)\n",
+      "\u001b[0m\u001b[1mDuring: typing of call at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (319)\n",
       "\u001b[0m\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 319:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 319:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  @jit\n",
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"compute_total_Tkin\" was compiled in object mode without forceobj=True.\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 317:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 317:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
@@ -3842,7 +3824,7 @@
       "\n",
       "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 317:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 317:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
@@ -3867,7 +3849,7 @@
       "hh number of total combinations 182\n",
       "pp number of total combinations 656\n",
       "get v_hhhh, v_pphh, v_pppp\n",
-      "what a hog! 0.0584207499999998 seconds\n",
+      "what a hog! 0.05801991599999923 seconds\n",
       "compute energy denominator\n"
      ]
     },
@@ -3880,26 +3862,26 @@
       "\n",
       "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 326:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 326:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:511: NumbaWarning: \u001b[1m\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:511: NumbaWarning: \u001b[1m\n",
       "Compilation is falling back to object mode WITH looplifting enabled because Function \"make_diagram\" failed type inference due to: \u001b[1m\u001b[1mUse of unsupported NumPy function 'numpy.matmul' or unsupported use of the function.\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 521:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 521:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
-      "\u001b[0m\u001b[1mDuring: typing of get attribute at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (521)\u001b[0m\n",
+      "\u001b[0m\u001b[1mDuring: typing of get attribute at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (521)\u001b[0m\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 521:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 521:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  @jit\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:511: NumbaWarning: \u001b[1m\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:511: NumbaWarning: \u001b[1m\n",
       "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"make_diagram\" failed type inference due to: \u001b[1m\u001b[1mCannot determine Numba type of \u001b[0m\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 518:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 518:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\u001b[0m\n",
       "  @jit\n"
@@ -3909,33 +3891,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "that's faster 0.1893234580000005 seconds\n",
+      "that's faster 0.1893236250000001 seconds\n",
       "Initialize T2 from MBPT2\n",
       "MBPT2 correlation energy per neutron = -0.17553785744804082\n",
       "start CCD iterations ...\n",
-      "time of making Hbar: 0.17643266600000018 seconds\n",
+      "time of making Hbar: 0.17841224999999916 seconds\n",
       "iter= 0 Correlation energy per neutron= -0.20550292085394894 , epsilon= 1.0\n",
-      "time of making Hbar: 0.0002477079999998466 seconds\n",
+      "time of making Hbar: 0.0002564580000008476 seconds\n",
       "iter= 1 Correlation energy per neutron= -0.2107009003533346 , epsilon= 0.024669944412524628\n",
-      "time of making Hbar: 0.00022991600000032975 seconds\n",
+      "time of making Hbar: 0.00023595900000117354 seconds\n",
       "iter= 2 Correlation energy per neutron= -0.2116672343138936 , epsilon= 0.004565345050646539\n",
-      "time of making Hbar: 0.00022641700000036735 seconds\n",
+      "time of making Hbar: 0.00022775000000052614 seconds\n",
       "iter= 3 Correlation energy per neutron= -0.21184820867906343 , epsilon= 0.0008542643164097731\n",
-      "time of making Hbar: 0.0002269160000007986 seconds\n",
+      "time of making Hbar: 0.00022474999999921863 seconds\n",
       "iter= 4 Correlation energy per neutron= -0.21188227228364806 , epsilon= 0.00016076665696240967\n",
-      "time of making Hbar: 0.0002237919999998894 seconds\n",
+      "time of making Hbar: 0.000225874999999931 seconds\n",
       "iter= 5 Correlation energy per neutron= -0.21188870821702438 , epsilon= 3.0374121539822725e-05\n",
-      "time of making Hbar: 0.00022312499999976865 seconds\n",
+      "time of making Hbar: 0.00023183299999907092 seconds\n",
       "iter= 6 Correlation energy per neutron= -0.21188992798649683 , epsilon= 5.756618467265144e-06\n",
-      "time of making Hbar: 0.00022620899999914457 seconds\n",
+      "time of making Hbar: 0.0002274159999995362 seconds\n",
       "iter= 7 Correlation energy per neutron= -0.211890159780636 , epsilon= 1.093935364400894e-06\n",
-      "time of making Hbar: 0.0002216659999998427 seconds\n",
+      "time of making Hbar: 0.00022137500000063426 seconds\n",
       "iter= 8 Correlation energy per neutron= -0.21189020393325292 , epsilon= 2.0837497962657972e-07\n",
-      "time of making Hbar: 0.00022162500000000307 seconds\n",
+      "time of making Hbar: 0.00022020900000008226 seconds\n",
       "iter= 9 Correlation energy per neutron= -0.21189021236156144 , epsilon= 3.9776771344709894e-08\n",
-      "time of making Hbar: 0.00022133299999893552 seconds\n",
+      "time of making Hbar: 0.0002222910000000411 seconds\n",
       "Correlation energy per neutron=  -0.2118902139735854\n",
-      "total time in seconds 1.6360875000000004\n"
+      "total time in seconds 1.2425067080000005\n"
      ]
     },
     {
@@ -3944,7 +3926,7 @@
      "text": [
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"make_diagram\" was compiled in object mode without forceobj=True, but has lifted loops.\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 516:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 516:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
@@ -3953,21 +3935,21 @@
       "\n",
       "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 516:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 516:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py:511: NumbaWarning: \u001b[1m\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py:511: NumbaWarning: \u001b[1m\n",
       "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"make_diagram\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
-      "\u001b[0m\u001b[1mDuring: typing of argument at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py (518)\u001b[0m\n",
+      "\u001b[0m\u001b[1mDuring: typing of argument at /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py (518)\u001b[0m\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 518:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 518:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  @jit\n",
       "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numba/core/object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"make_diagram\" was compiled in object mode without forceobj=True.\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 518:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 518:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
@@ -3976,7 +3958,7 @@
       "\n",
       "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
       "\u001b[1m\n",
-      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8087/2479038418.py\", line 518:\u001b[0m\n",
+      "File \"../../../../../../var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5273/2479038418.py\", line 518:\u001b[0m\n",
       "\u001b[1m\u001b[0m\n",
       "\u001b[0m\n",
       "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
diff --git a/doc/LectureNotes/_build/jupyter_execute/gradientmethods.ipynb b/doc/LectureNotes/_build/jupyter_execute/gradientmethods.ipynb
index 40d94362..4b79d60e 100644
--- a/doc/LectureNotes/_build/jupyter_execute/gradientmethods.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/gradientmethods.ipynb
@@ -328,8 +328,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0085748874608291 0.31779454097168824\n",
-      "3.0027130057855604 -0.04763239067831826 -0.07459848329535479\n"
+      "1.0087099588144601 0.31778422796149464\n",
+      "3.0040051420613456 -0.05388509404744202 -0.07939029201677261\n"
      ]
     }
    ],
@@ -502,11 +502,11 @@
      "output_type": "stream",
      "text": [
       "Warning: Desired error not necessarily achieved due to precision loss.\n",
-      "         Current function value: 2.997317\n",
-      "         Iterations: 5\n",
-      "         Function evaluations: 27\n",
+      "         Current function value: 2.996914\n",
+      "         Iterations: 4\n",
+      "         Function evaluations: 28\n",
       "         Gradient evaluations: 15\n",
-      "[0.98983762 0.39892933]\n"
+      "[0.97682809 0.42266524]\n"
      ]
     }
    ],
@@ -1439,14 +1439,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8093/1833905057.py:16: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_5279/1833905057.py:16: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 3,
@@ -1598,7 +1598,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 7,
@@ -2304,8 +2304,8 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.09072751]\n",
-      " [2.99662099]]\n"
+      "[[3.96691934]\n",
+      " [3.09044107]]\n"
      ]
     },
     {
diff --git a/doc/LectureNotes/_build/jupyter_execute/notation.ipynb b/doc/LectureNotes/_build/jupyter_execute/notation.ipynb
index 50597776..0c69492d 100644
--- a/doc/LectureNotes/_build/jupyter_execute/notation.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/notation.ipynb
@@ -253,8 +253,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.38005418  1.54878832  1.17184624 -1.17842626 -1.73955345 -1.79748932\n",
-      " -0.63041751 -0.15290907  0.6439199   1.88708544]\n"
+      "[-0.18181242 -0.82390948 -0.95197186  0.17706887  0.80026555 -0.45625011\n",
+      " -0.18799733 -0.34267102 -1.12100632 -0.18662716]\n"
      ]
     }
    ],
@@ -690,26 +690,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.58328247 0.03040501 0.27963406 0.44093771 0.69195793 0.40091461\n",
-      "  0.06098957 0.55003012 0.45374613 0.85487081]\n",
-      " [0.24583166 0.56769196 0.9061827  0.23018296 0.50207802 0.75241232\n",
-      "  0.85102688 0.76319983 0.09296749 0.57715363]\n",
-      " [0.07423133 0.55568732 0.2453194  0.36154513 0.29072776 0.16063197\n",
-      "  0.42589086 0.70984236 0.29521469 0.43984924]\n",
-      " [0.56953949 0.28967051 0.63368525 0.06160488 0.38025695 0.08289615\n",
-      "  0.36472359 0.33063952 0.96579751 0.39082526]\n",
-      " [0.80001674 0.59946684 0.43932877 0.02156606 0.81553478 0.23673328\n",
-      "  0.54694075 0.56820081 0.29350801 0.04902982]\n",
-      " [0.68777857 0.50073475 0.22497749 0.17402761 0.70692466 0.01451004\n",
-      "  0.50850427 0.932051   0.32355336 0.52518412]\n",
-      " [0.41802363 0.75633005 0.13045395 0.932665   0.53432834 0.9020486\n",
-      "  0.1170548  0.73671788 0.80641115 0.26737294]\n",
-      " [0.74038321 0.06970898 0.19702405 0.83095304 0.59079917 0.45368766\n",
-      "  0.71566572 0.22448243 0.16310651 0.2402106 ]\n",
-      " [0.76792881 0.18568688 0.49507077 0.85970836 0.85470974 0.14060903\n",
-      "  0.44802059 0.77804606 0.73665774 0.09371713]\n",
-      " [0.78131065 0.83381268 0.31639926 0.1062502  0.85522352 0.00796671\n",
-      "  0.71980758 0.75492216 0.21185956 0.30261992]]\n"
+      "[[0.42668154 0.44529398 0.81431386 0.41532905 0.08538057 0.68954186\n",
+      "  0.05741467 0.62343179 0.29156132 0.50311603]\n",
+      " [0.75838834 0.6090632  0.32329292 0.12567032 0.44684508 0.90291719\n",
+      "  0.67409939 0.30861483 0.27160845 0.37386191]\n",
+      " [0.77853093 0.83642995 0.77942173 0.87281744 0.43413506 0.33728723\n",
+      "  0.82678561 0.13855613 0.7705333  0.96473269]\n",
+      " [0.0632419  0.50224636 0.26817617 0.0563718  0.55243532 0.58035027\n",
+      "  0.46833658 0.69820786 0.98841162 0.17397314]\n",
+      " [0.35748561 0.48144532 0.83970178 0.92497464 0.65877538 0.02446637\n",
+      "  0.40784019 0.83621124 0.47947519 0.1382501 ]\n",
+      " [0.56973725 0.38465877 0.85538751 0.13002132 0.30504484 0.82358887\n",
+      "  0.10074632 0.02949402 0.77084258 0.93282264]\n",
+      " [0.44722558 0.24534328 0.12840244 0.9423928  0.1366858  0.99570313\n",
+      "  0.87400321 0.69250731 0.24558877 0.04518465]\n",
+      " [0.94924418 0.37864605 0.10082995 0.06187188 0.28347895 0.97877997\n",
+      "  0.69113578 0.01888094 0.46364549 0.42022255]\n",
+      " [0.49468485 0.47782191 0.51235668 0.57404905 0.45231321 0.47933511\n",
+      "  0.27193391 0.15257909 0.28908355 0.19820541]\n",
+      " [0.28773096 0.11602248 0.34159001 0.39539761 0.78975011 0.62034427\n",
+      "  0.5657688  0.32520647 0.9107702  0.80607227]]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/resamplingmethods.ipynb b/doc/LectureNotes/_build/jupyter_execute/resamplingmethods.ipynb
index 93528091..40f0c8d8 100644
--- a/doc/LectureNotes/_build/jupyter_execute/resamplingmethods.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/resamplingmethods.ipynb
@@ -695,10 +695,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 0.128286 sec\n",
+      "Runtime: 0.134595 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      " 99.8196        99.8096        0.151352\n"
+      " 99.8991        99.8891        0.150016\n"
      ]
     }
    ],
@@ -836,15 +836,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 1.24256 sec\n",
+      "Runtime: 1.21746 sec\n",
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.137  14.9318        100.137        0.148632\n"
+      " 100.005  14.9921        100.005        0.150523\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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sEdmysDDA3192CutWXAy88grPJiSyYyyWiGzVyy8DS5fKTmH9FAVYuBD47jvZSYhIEhZLRLYoIwNYtgwoLZWdxPq5ugIPPQQsXy47CRFJwmKJyBZ99ZVY52zYMNlJbMPo0cCvvwLHj8tOQkQSsFgisjV6vTgL7oknAB8f2Wlsw5AhYqmYFStkJyEiCTh1AJGt2bcPOHMGWLlSdhLbodGI9mzVSnYSIpKAxRKRpYmJqd1+O3dWv71HD2D/fqBrV+NlIuDRR2UnICJJeBiOyJbo9WIyxXvvFf+ScX3zDTB+vOwURGRmLJaIbMnChcD99wNlZbKT2KbCQmDJEuDiRdlJiMiMWCwR2QpFEdMFBARwxm5TefppwMUFWLVKdhIiMiMWS0S24rffgKNHxRInZBpeXsCTT4qz4vR62WmIyEw4wJvIVvzjH2J5k4cekp3E9tw66P7qVSAtDbjnHqBBg8r71TTonoisGnuWiGxBWRmwY4eYPJGH4EzL2xvo2JFzWBHZEfYsEdkCR0cgNRXQ6WQnsX0q1c0eJUXhWYdEdoA9S0S24OpVMfDY01N2EvugKMAffwCXL8tOQkRmwGKJyNodPgwEBooB3mQeKpXozbt8WRRORGTTWCwRWbvly8Vhoago2UnsS3CwmHfp2jXZSYjIxFgsEVmz4mJg9Wpg1ChArZadxr40aAA4OwOZmbKTEJGJsVgismYbNwJXroiz4Mi8HByAoCAgJ4eH4ohsHM+GI7JmqanAgw8CLVrITmKfQkPF3FY8I47IprFYIrJms2YBpaWyU9iv8kOfZWWc34rIhvEwHJG1OnZMzKvkxO88UuXnA7/+ChQVyU5CRCYitViaO3cuunTpAk9PTwQEBGDIkCFITU294/2SkpIQHR0NFxcXNG3aFEuWLDFDWiILoteLw29vvSU7CXl4iPFLGRmykxCRiUgtlpKSkjB+/Hjs378fCQkJKC0tRWxsLAoLC2u8T1paGgYMGIBevXohOTkZb775Jl555RVs2LDBjMmJJMvLE2dhDRsmOwk5Oop5rrKyOIM6kY2S2n+/ffv2StdXrlyJgIAAHDx4EPfff3+191myZAnCwsIwf/58AEBkZCQOHDiAefPmYejQoaaOTGQZsrLE+mSdOslOQoCYc+nSJWDbNmDwYNlpiMjILGqwQ35+PgDA19e3xn327duH2NjYStv69euH5cuXQ6fTQX3bXDNarRZarbbiekFBAQBAp9NBZyXfAstzWktea2YRbe3sbPh2nQ5OubnQx8VBX5ucd3o8CXT/+z3V2crcUL6+cPTygrJ/P/QDBshOU8Ei3s92gO1sHjLbV6UoljFBiKIoGDx4MK5cuYLdu3fXuF/Lli0xatQovPnmmxXb9u7di549e+Ly5csIDg6utP/MmTMxa9asKo+zdu1auLm5Ge8FEJmJZ3o67vnsM/w3Ph7F/v6y49D/OOh00NtK8UdkgYqKijBs2DDk5+fDy8vLrM9tMT1LEyZMwOHDh7Fnz5477qu6bU6T8nrv9u0AEB8fj7i4uIrrBQUFCA0NRWxsrNkbu750Oh0SEhLQt2/fKj1nZFwW0dYDB955n2bN8MCIEcZ7PDPTqdVIGDMGfZctg9qWvo1v3gxkZ4vJKi2ARbyf7QDb2Txyc3OlPbdFFEsTJ07E5s2bsWvXLjRu3NjgvkFBQci8bXmB7OxsODk5wc/Pr8r+Go0GGo2myna1Wm11b2przGytpLZ1SYnh27RacQZWbfMZejzJ1Dod1Bacr85GjwbOnwcM9I7LwM8O82A7m5bMtpV6NpyiKJgwYQI2btyI//znP4iIiLjjfbp3746EhIRK23bs2IHOnTvzTUq2LzMTOHRITIJIlufhh4E9e0TBREQ2Q2qxNH78eKxZswZr166Fp6cnMjMzkZmZiRs3blTsEx8fjxG3HG4YO3Yszp8/j7i4OBw/fhwrVqzA8uXL8frrr8t4CUTmlZ0N+PtzIkpLNXgw4OoKrF0rOwkRGZHUYmnx4sXIz89Hnz59EBwcXHFZv359xT4ZGRlIT0+vuB4REYFt27YhMTERnTp1wuzZs7FgwQJOG0C2r7AQuH4dCAiQnYRq4ukpCqZvvuHiukQ2ROrX09qciLdq1aoq23r37o1Dhw6ZIBGRBcvKEj1K1YzNIwvy7LPAa6+JiUP5f0VkE7g2HJG1cHICQkLE0hpkuQYMAE6cYKFEZEP4qUtkLcLCgKZNZaegO3FwAFQq4ORJsYYfEVk9FktE1iAvT0wZQNYhORlo1QrYtUt2EiIyAhZLRJZOrweOHQMuXpSdhGqrUyegSRMx0JuIrB6LJSJLl5cHlJaKle3JOqhUwLBhwPffs0eQyAZwshYiS5eVBbi7i1m7bxUTIycP1c6zzwLvvQf89BMwZIjsNER0F1gsEVmy0lIgN1cc0iHLd3sB6+MDvPEG8Nlnlbfv3Gm2SER091gsEVmy0lIxYzcnorROnTrJTkBERsBiiciSubgAbdrITkF3Q6cTixm7u8tOQkT1xAHeRJZKqxXjlbhornU7dgw4dUp2CiK6CyyWiCxVdraYCZoTG1q3gADg6lWeFUdkxVgsEVmqrCyxZIZaLTsJ3Q1/fzGVQHa27CREVE8slogsUWEhcP0651ayBWq1KHqzsmQnIaJ6YrFEZImyssTCuVyM1TYEBQEaDcefEVkpng1HZIk8PIDwcLEoK1k/f39xISKrxGKJyBJxXiXbo9cDV64Avr6ykxBRHfFrK5GlycoC8vNlpyBjy88HjhwBCgpkJyGiOmKxRGRJSkrEnDw5ObKTkLH5+ADOzhzoTWSFWCwRWZLt28USJzwLzvaoVOL/9a+/xKzeRGQ1WCwRWZJ168SyGB4espOQKQQEiEIpIUF2EiKqAxZLRJaisBD48UcO7rZlHh7i/1elkp2EiOqgXsXSqlWrUFRUZOwsRPZNpwPeeIPFki1TqcTCyP37y05CRHVQr2IpPj4eQUFBGD16NPbu3WvsTET2yccHePttwNVVdhIytQMHgORk2SmIqJbqVSxdvHgRa9aswZUrVxATE4PWrVvjgw8+QGZmprHzEdmH/Hxg5kyuH2Yvxo0DZs+WnYKIaqlexZKjoyMeffRRbNy4ERcuXMCLL76Ib775BmFhYXj00Ufx448/Qs+V0olqb9MmYNYsMXUA2b6nnwa2beN8WkRW4q4HeAcEBKBnz57o3r07HBwccOTIEYwaNQrNmjVDYmKiESIS2YH164FevYDGjWUnIXN48klAqxUD+onI4tW7WMrKysK8efPQtm1b9OnTBwUFBdiyZQvS0tJw+fJlPP744xg5cqQxsxLZppwccSr500/LTkLm0rgxcN99YqoIIrJ49VobbtCgQfj555/RsmVLjBkzBiNGjIDvLesdubq64rXXXsOnn35qtKBENmvjRrFu2P/9n+wkZE4vvyyWP1EUTiVAZOHqVSwFBAQgKSkJ3bt3r3Gf4OBgpKWl1TsYkd3o0gX4+GNOGWBvhg2TnYCIaqlexVLv3r1xzz33VNleUlKCdevWYcSIEVCpVAgPD7/rgEQ2LypKXMh+xMSIf7Va4OrVmpe32bnTbJGIqGb1GrP0/PPPI7+asziuXbuG559//q5DEdmNH34AVqyQnYJkycsDjh8XRRMRWax6FUuKokBVzTH2ixcvwtvb+65DEdmNjz4S0waQffL3F+OV/vpLdhIiMqBOh+GioqKgUqmgUqnw4IMPwsnp5t3LysqQlpaGhx9+2OghiWzSuXPAvn3AN9/ITkKyqNWAr6+YjJTTRhBZrDoVS0OGDAEApKSkoF+/fvC4ZWV0Z2dnNGnSBEOHDjVqQCKb9c9/iqVNHn1UdhKSKSBAHIorLgZcXGSnIaJq1KlYmjFjBgCgSZMmeOqpp+DCX2yi+lu3Dhg4UKxET/bLz08M8OaqB0QWq15nw3GySaK7pCjAlClAWJjsJCSbkxMQGSk7BREZUOtiydfXFydPnoS/vz8aNGhQ7QDvcnl5eUYJR2SzVCrO2E036fVikLeXlzg0S0QWpdbF0qeffgpPT8+Knw0VS0RkgKIAr70mJiXs3Fl2GrIEigKcPAmEhgJNmshOQ0S3qXWxdOuht1GjRpkiC5FtK5+I8Pp14MAB4JdfxHgVIkdHMY1AdjYQHs7lT4gsTK2LpYKCglo/qJeXV73CENmF7GwxTqVBA9lJyJIEBABZWUBhIQf9E1mYWhdLPj4+dzz0Vj5ZZVlZ2V0HI7JJiiKKpYYNAYd6zQlLtqpBA1FEZ2ezWCKyMLUulnZyjSKiu3ftmphPh4vm0u0cHMQhOI1GdhIiuk2ti6XevXubMgeRfXBxAZo3B3x8ZCchSxQaKjsBEVWj1sXS4cOH0a5dOzg4OODw4cMG9+3QocNdByOySc7OXNaCDMvPFwvrsveRyGLUuljq1KkTMjMzERAQgE6dOkGlUkFRlCr7ccwSUQ3y88VcOhER4uwnoupkZ4v3ScOGspMQ0f/UulhKS0tDw//98qalpZksEJHNysoCcnOBZs1kJyFLFhAAXLokimsisgi1LpbCw8Or/ZmIakGnE70FQUGcQ4cM8/ISg7yzs2UnIaL/qdfacACQmpqKzz//HMePH4dKpULr1q0xceJEtGrVypj5iGzDf/4jCiaOQ6E7UanE+yQzEygtFdMJEJFU9Zro5fvvv0e7du1w8OBBdOzYER06dMChQ4fQrl07fPfdd8bOSGT9vv1WrPnF+XOoNgIDgUaNxEBvIpKuXl9ZpkyZgvj4eLzzzjuVts+YMQNvvPEGnnjiCaOEI7IZkycDyck8BEe14+EhLu7uspMQEerZs5SZmYkRI0ZU2f7cc88hMzPzrkMR2ZyOHQFfX9kpyJpotcCsWexdIrIA9SqW+vTpg927d1fZvmfPHvTq1avWj7Nr1y4MGjQIISEhUKlU2LRpk8H9ExMToVKpqlxOnDhR15dAZDwxMYYv4eFAmzayU5K1KS0FZs4Etm+XnYTI7tX6MNzmzZsrfn700Ufxxhtv4ODBg+jWrRsAYP/+/fjuu+8wa9asWj95YWEhOnbsiOeffx5Dhw6t9f1SU1MrLdbbkPORkKUqLQUuXgSaNJGdhKyNuzvQoYMY7zZ4sOw0RHat1sXSkCFDqmxbtGgRFi1aVGnb+PHjMXbs2Fo9Zv/+/dG/f//aRqgQEBAAHy4XQdYgJwfQ63kWHNXP008Ds2cD16/z5AAiiWpdLOn1elPmqJOoqCgUFxejTZs2mDZtGmJiYmrcV6vVQnvLMf+CggIAgE6ng06nM3lWYyjPaS15rVm92trZucabHHNyAB8flN3SE0qATq2u9C/VYNs2qG/cQGmPHlCCg2veb8uWajfzs8M82M7mIbN9VUp1a5ZIoFKp8MMPP1Tbg1UuNTUVu3btQnR0NLRaLVavXo0lS5YgMTER999/f7X3mTlzZrWHBteuXQs3NzdjxSeqQl1QgIeffx5HR49G2oABsuOQlWq+cSOy77kHBTyUS3auqKgIw4YNQ35+fqWhOOZQ72KpsLAQSUlJSE9PR0lJSaXbXnnllboHqUWxVJ1BgwZBpVJVGlN1q+p6lkJDQ5GTk2P2xq4vnU6HhIQE9O3bF2p+EzeperX1wIHVb9frocrNheLtbbD3yR7p1GokjBmDvsuWQc1v43fPQM8SPztMj+1sHrm5uQgODpZSLNVrnqXk5GQMGDAARUVFKCwshK+vL3JycuDm5oaAgIB6FUv11a1bN6xZs6bG2zUaDTQaTZXtarXa6t7U1pjZWtWprW/7slCJt/ed97Fjap0OarbNnV2+LJZA8fOr/vY7vFf52WEebGfTktm29Zo6YPLkyRg0aBDy8vLg6uqK/fv34/z584iOjsa8efOMndGg5ORkBBs6lk8kg1YLHD4M3LghOwnZgr/+Ai5ckJ2CyG7Vq2cpJSUFX375JRwdHeHo6AitVoumTZviww8/xMiRI/H444/X6nGuX7+O06dPV1xPS0tDSkoKfH19ERYWhvj4eFy6dAlff/01AGD+/Plo0qQJ2rZti5KSEqxZswYbNmzAhg0b6vMyiEwnOxu4coXrepFxBAQAqamiCK+mp5yITKten+RqtRqq/y3bEBgYiPT0dERGRsLb2xvp6em1fpwDBw5UOpMtLi4OADBy5EisWrUKGRkZlR6vpKQEr7/+Oi5dugRXV1e0bdsWW7duxQAOniVLk50tDpmwS56Mwd8fOHlSvK9CQ2WnIbI79SqWoqKicODAAbRs2RIxMTF4++23kZOTg9WrV6N9+/a1fpw+ffrA0PjyVatWVbo+ZcoUTJkypT6RicynqAi4do1/1Mh41GqxXA6LJSIp6jVm6b333qsYJzR79mz4+fnh5ZdfRnZ2NpYuXWrUgERWJzsbcHSseTAuUX2EhoqLZcz2QmRX6tWz1Llz54qfGzZsiG3bthktEJHVa9RInAXn6Cg7CdkSrlpAJM1djT7Nzs5GamoqVCoVWrVqxTXaiABxyKRBA9kpyBYVFIiey2bNgP+NGyUi06vXYbiCggIMHz4cjRo1Qu/evXH//fcjJCQEzz33HPLz842dkch6nD8P3HKGJ5FRlZSIhZkLC2UnIbIr9SqWXnjhBfz3v//Fli1bcPXqVeTn52PLli04cOAAxowZY+yMRNZBUYCMDKCsTHYSslW+vmI6iuxs2UmI7Eq9DsNt3boVP//8M+67776Kbf369cOyZcvw8MMPGy0ckVUpKACKi8WcOESm4OAANGwoiqWICB6KIzKTevUs+fn5wbt8GYdbeHt7owHHapC9ys4Wa8BxIC6ZUkCAKMoLCmQnIbIb9SqWpk2bhri4OGRkZFRsy8zMxN///ndMnz7daOGIrIaiiCUpAgL4bZ9My8cHaNECcHWVnYTIbtT6MFxUVFTFrN0AcOrUKYSHhyMsLAwAkJ6eDo1Gg7/++gsvvfSS8ZMSWTKVCujUSRwmITIllUpMT0FEZlPrYmnIkCEmjEFkA9zcZCcge1FWBqSliWVQeNiXyORqXSzNmDHDlDmIrJdeDyQniwG3vr6y05A9cHAAcnKA0lIWS0RmcFeTUh48eBDHjx+HSqVCmzZtEBUVZaxcRNYjL0+sBefsLDsJ2QuVCggMFHMutWwpOw2RzatXsZSdnY2nn34aiYmJ8PHxgaIoyM/PR0xMDNatW8eZvMm+ZGcD7u6Ah4fsJGRPAgLEJKh5ebKTENm8eo1GnThxIgoKCvDnn38iLy8PV65cwdGjR1FQUIBXXnnF2BmJLFdhoTgcwrmVyNzc3cUlK0t2EiKbV6+epe3bt+OXX35BZGRkxbY2bdrgiy++QGxsrNHCEVm87dvFmCUWSyRD8+ZiLUIiMql6FUt6vR7qan5B1Wo19Hr9XYcishqPPQZER3POG5KDkwATmUW9DsM98MADePXVV3H58uWKbZcuXcLkyZPx4IMPGi0ckUVTFHFWkqen7CRkz7KygNdek52CyKbVq2dp4cKFGDx4MJo0aYLQ0FCoVCqkp6ejffv2WLNmjbEzEskRE2P49osXxXiljh05azfJU1oKLFgATJ0q1o0jIqOrV7EUGhqKQ4cOISEhASdOnICiKGjTpg0eeughY+cjslxZWWK6ABZKJFPDhmKCyvXrgQkTZKchskl1LpZKS0vh4uKClJQU9O3bF3379jVFLiLLVlgo5lZq21Z2ErJ3zs7AI48AX3/NYonIROo8ZsnJyQnh4eEoKyszRR4i65CVBTg5AX5+spMQAcOHA7//Dhw/LjsJkU2q1wDvadOmIT4+HnmcDI3sVfncSlw4lyzBwIHA1q1AixaykxDZpHqNWVqwYAFOnz6NkJAQhIeHw93dvdLthw4dMko4IosVHS0WMyWyBBoNMGCA7BRENqtexdKQIUOgUqmgKIqx8xBZPr0ecHQUFyJLceUKMGwY8MYbQJ8+stMQ2ZQ6FUtFRUX4+9//jk2bNkGn0+HBBx/E559/Dn9/f1PlI7IsZWXA/v3icAdn7SZL4uMDnDoFfPUViyUiI6vTgIsZM2Zg1apVeOSRR/DMM8/gl19+wcsvv2yqbESWJycH0Ok4ESVZHpUKGDEC+P57cbYmERlNnYqljRs3Yvny5Vi6dCk+++wzbN26FZs2beKZcWQ/srIALy8ub0KW6bnngOvXgU2bZCchsil1KpYuXLiAXr16VVzv2rUrnJycKi17QmSztFogLw8IDJSdhKh6TZsCvXoBa9fKTkJkU+o0ZqmsrAzOzs6VH8DJCaWlpUYNRWSR8vPFVAEcq0SWbOlSIChIdgoim1KnYklRFIwaNQoajaZiW3FxMcaOHVtp+oCNGzcaLyGRpQgIEKu8q9WykxDVrHVr8S/PViYymjoVSyNHjqyy7bnnnjNaGCKLVVoqepVYKJE1WLZMLH/y73/LTkJkE+pULK1cudJUOYgs2/nzYrxS585cOJcsX0gIsGcP8McfspMQ2QSu1UB0J4oizoLz9mahRNYhNhYICIDDN9/ITkJkE1gsEd3JlStASQnPgiProVYDzzwDh3XroOLULkR3jcUS0Z1kZYl5lby8ZCchqr0RI6DKykKDkydlJyGyevVaG47IbigKUFQkepV4CI4sVUxM1W2KAt199yEvMhIYOFD0ju7caf5sRDaAxRKRISoVcM89PA2brI9KBbi5ifeuXi87DZFV42E4IkOKi8UfHQf+qpAVKivDg+PGQcVVFojuCv8CENVEqwX27weys2UnIaofR0cUBQTAISNDdhIiq8Ziiagm2dmiV6lBA9lJiOrtQp8+cLhyBbhxQ3YUIqvFYomoJpmZgL8/Z+0mq5bRrRsUR0dxVicR1QuLJaLqHD4MFBZybiWyemWurlACAsRC0ERULzwbjqgaqvPnxZlEvr6yoxDdtbLISDhwckqiemPPElE1lEGDgC5deBYc2QYnJzH+7upV2UmIrBL/EhDdxuPixZuDu4lsxaVLQPPmYjoMIqoTFktEt2m/bBkcn31Wdgwi42rQAMjNBTZulJ2EyOqwWCK61ZkzCPjjD+hHjJCdhMi43NyAPn2ApUtlJyGyOiyWiG7hsGIFStzdofzf/8mOQmR8L74IJCUBJ07ITkJkVVgsEZXT6eDw9de42Ls34OoqOw2R8T3+OBARARw9KjsJkVXh1AFE5fLyoHTrhvMxMQiVnYXIFDQa4NQpwNFRdhIiqyK1Z2nXrl0YNGgQQkJCoFKpsGnTpjveJykpCdHR0XBxcUHTpk2xZMkS0wcl2xITU/3l6adRduMGCpo0AQYOlJ2SyDQcHcUElSdPyk5CZDWkFkuFhYXo2LEjFi5cWKv909LSMGDAAPTq1QvJycl488038corr2DDhg0mTko2r7hYTBeg18tOQmR6Tz0lxi8RUa1IPQzXv39/9O/fv9b7L1myBGFhYZg/fz4AIDIyEgcOHMC8efMwdOhQE6Uku3D5spiHJihIdhIi0xs5Ehg2TAz0bt1adhoii2dVA7z37duH2NjYStv69euHAwcOQKfTSUpFVk+vF4vmBgaKmY6JbN3jjwN+fsCyZbKTEFkFq/rLkJmZicDbFjYNDAxEaWkpcnJyEBwcXOU+Wq0WWq224npBQQEAQKfTWU2BVZ7TWvJaPGfnSldV2dlwKimBLiwMOrUaACr+JdNgO5tHlXYu/wxxcIDDiBFwWLUKpTNnAi4ucgLaCH5Gm4fM9rWqYgkAVLctQaEoSrXby82dOxezZs2qsn3Hjh1wc3MzfkATSkhIkB3BNowbV+nqve++C03z5tg1dWrFtoQxY8ydyi6xnc2jop23bavY5t68OTqEhSF5/XoUN2woKZlt4We0aRUVFUl7bqsqloKCgpCZmVlpW3Z2NpycnODn51ftfeLj4xEXF1dxvaCgAKGhoYiNjYWXl5dJ8xqLTqdDQkIC+vbtCzW/id+92850UxUVAR4eGLBoEXRqNRLGjEHfZcug5rdEk2E7m0eVdt6ypfIOY8bgATnRbAo/o80jNzdX2nNbVbHUvXt3/Otf/6q0bceOHejcuXONb1CNRgONRlNlu1qttro3tTVmtkglJZWvBwRU2a7W6aC+fT8yOrazeVS08+2fH2VlwI4dYpB3RISccDaEn9GmJbNtpQ7wvn79OlJSUpCSkgJATA2QkpKC9PR0AKJXaMQta3SNHTsW58+fR1xcHI4fP44VK1Zg+fLleP3112XEJ2unKMDp04DErl0iqUpLgeHDgVpO30Jkr6QWSwcOHEBUVBSioqIAAHFxcYiKisLbb78NAMjIyKgonAAgIiIC27ZtQ2JiIjp16oTZs2djwYIFnDaA6icvD7h4UfzBILJHGg0wahSwapWYa4yIqiX1MFyfPn0qBmhXZ9WqVVW29e7dG4cOHTJhKrIbly8DHh6Ap6fsJETyjBkDfPwxsHGjmHuJiKqwqnmWiIxGqwVyc4HgYKCGMymJ7EKrVkCfPsDSpbKTEFksqxrgTWQ0mZmAg4OYiJLI3k2ZAhw7Jsbx8csDURUslsg+NWwIuLpyxm4iAOjfX1yIqFo8DEf2yc3t5pQBRCROdpg6lQO9iarBYonsz+nTQEaG7BRElqWoCPjgAzHQm4gqYbFE9iUzE7h0SUzGR0Q3tWzJgd5ENWCxRPZl1SoxgJUDu4mqevFFICkJSE2VnYTIorBYIvtRWgosWiQGd3NJAqKqHn8c8PNj7xLRbXgqENmPbduACxeAzp1lJyGyTBoNsGwZ0Ly57CREFoU9S2Q/Bg0C9u8Xs3YTUfUeewxo3152CiKLwmKJ7INWK8Yq3Xuv7CRElm/HDuDRRwG9XnYSIovAw3BkH55+WgzqXrJEdhIiyzd1KpCcDHTsCPj717zfzp3my0QkEXuWyPadPg38+CPHKhHVlrc34OUlJqokIhZLZAc++0x8O372WdlJiKxHaChw9SpQUCA7CZF0LJbItuXlAStWAOPGibXgiKh2/P0BFxcxkSuRneOYJbJtycmAuzvw8suykxBZF5VKjFnSaGQnIZKOxRLZtgcfFOMunJ1lJyGyPuW9sTodJ3Ilu8bDcGS7jh4FrlxhoUR0NzIzxfxkOp3sJETSsGeJbJOiAMOHA02aAD/8IDsNkWWIian7fRo0EPMtZWQAYWHGz0RkBdizRLYpMRFISQHGj5edhMi6aTRijrKLFzlJJdktFktkmz7+WCzZ8OCDspMQWb/GjYGSEiA7W3YSIil4GI5sz4kTwNatwMqV4oweIro7Hh5iKgGtVnYSIilYLJHt0enE8ibPPCM7CZHtaNuWXz7IbvEwHNme9u2Bb7/l/DBExqRSAWVlQE6O7CREZsdiiWzL2rXAzz/LTkFkm3JyxJQchYWykxCZFYslsh3FxcDkyWLRXCIyvoYNxbxlFy7ITkJkVhyzRLajUydxts7Bg/WbT4aIDHNwEGfGpaUBERGy0xCZDXuWyDYoivi26+cHuLnJTkNku4KDRdF06ZLsJERmw2KJbENCAlBUBISGyk5CZNvUaqBpU8DLS3YSIrPhYTiyDd26Aa1aAd7espMQ2b5GjWQnIDIr9iyR9VMU8S03OJjzwBCZy/XrwEsviekEiGwciyWybooC9O0LLFokOwmRfVEUYOlSYM0a2UmITI7FElm3n34C/v1voFkz2UmI7IunJ/D448CMGVwGhWweiyWyXno9MG0a0KsXEBsrOw2R/Zk9W5yF+o9/yE5CZFIslsh6bdwIJCcDc+ZwrBKRDG3aAMOHA/Pniy8vRDaKZ8OR9dq3D+jXT/QsEZEc778v5l1y4Hdvsl0slsh6ffwxx0oQyRYUJP7NzxcFk6en3DxEJsCvAmR9SkrE+m96PaDRyE5DRCUlQPv2opeJyAaxWCLrs3Il8NhjwMmTspMQESAW1332WTF2KStLdhoio2OxRNaluFicgfPMM0Dr1rLTEFG5KVPEUihz5shOQmR0LJbIuixeDGRmAjNnyk5CRLdq0EAUTEuWAOfPy05DZFQc4E3W4/p1YO5cYNQooEUL2WmI6HavvALs3w9cuyY7CZFRsVgi66FWA9OnA4MHy05CRNXx8AA2b5adgsjoeBiOrIdGA0ycCISFyU5CRIbs2wfMmiU7BZHRsFgi6/DOO0BcnOwURFQbqaliXOHBg7KTEBkFiyWyfNnZwIcfitOTicjyDR8OREYCb70lOwmRUXDMElm+6GgxZcCePUBMjOw0RFTO0O+jgwPw889AVJRYw5HIirFniSzbpUviEhoqBngTkXXw9xcDvtPTZSchumvsWSLLlpAAODkBjRvLTkJEdaFSAW3a8EsO2QT2LJFlGzUKuPdeUTARkXVxcxPFUk6OWMuRyEqxWCLLVFoKrF0LlJWxUCKyZlot0LQp8I9/yE5CVG/Si6VFixYhIiICLi4uiI6Oxu7du2vcNzExESqVqsrlxIkTZkxMZvHFF8BzzwGHD8tOQkR3Q6MBhg4VS6FkZspOQ1QvUoul9evXY9KkSXjrrbeQnJyMXr16oX///ki/w4DA1NRUZGRkVFxacOkL23LpEjBtGjB2rDiThois27x54nDcpEmykxDVi9Ri6ZNPPsHo0aPxwgsvIDIyEvPnz0doaCgWL15s8H4BAQEICgqquDg6OpopMZnFq68C7u7Ae+/JTkJExuDnB3zyCbB+PfDTT7LTENWZtMEgJSUlOHjwIKZOnVppe2xsLPbu3WvwvlFRUSguLkabNm0wbdo0xBiY60Or1UKr1VZcLygoAADodDrodLq7eAXmU57TWvLeDdXu3XDasAGlX38Nxd0d0OnMOhml7n9n7uh4Bo9JsZ3Nw2LaWacDnnoKDikp0DdqJK7bEHv6jJZJZvuqFEVRZDzx5cuX0ahRI/z666/o0aNHxfb33nsPX331FVJTU6vcJzU1Fbt27UJ0dDS0Wi1Wr16NJUuWIDExEffff3+1zzNz5kzMqmaNorVr18LNzc14L4iMQ69H4IEDyOrSRZx6TES2R68Xk1YS1UFRURGGDRuG/Px8eHl5mfW5pRdLe/fuRffu3Su2z5kzB6tXr671oO1BgwZBpVJhcw0rXVfXsxQaGoqcnByzN3Z96XQ6JCQkoG/fvlDL/oZoSunp1S+SO3Cg2SLo1GokjBmDvsuWQc1viSbDdjYPi2nnLVtu/nzuHJweewylK1cCnTpJi2RMdvMZLVlubi6Cg4OlFEvSDsP5+/vD0dERmbedHZGdnY3AwMBaP063bt2wZs2aGm/XaDTQaDRVtqvVaqt7U1tj5lo7ehS45x5gwwZg0KDKt5WUmD2OWqeDWsLz2hu2s3lIb+fY2Js/6/XAuXNQP/ig+J2/vQd5507zZjMim/6MtgAy21ZaP6izszOio6ORkJBQaXtCQkKlw3J3kpycjODgYGPHI3PS64GXXgKaNav8oUpEtsfBAWjZErh2TZz5SmQFpM72FxcXh+HDh6Nz587o3r07li5divT0dIwdOxYAEB8fj0uXLuHrr78GAMyfPx9NmjRB27ZtUVJSgjVr1mDDhg3YsGGDzJdBd2vFCmDvXiAxUczJQkS2zdsbCAkB0tLEGnIuLrITERkktVh66qmnkJubi3feeQcZGRlo164dtm3bhvDwcABARkZGpTmXSkpK8Prrr+PSpUtwdXVF27ZtsXXrVgwYMEDWS6C7lZ0tJqsbORLo3Vt2GiIyl6ZNRe+SVstiiSye9HUkxo0bh3HjxlV726pVqypdnzJlCqZMmWKGVGQ2Li7A6NGiYCIi++HkVP2YJSILxHM3SR69HvDyAj76CGjYUHYaIjI3lUr0LJ04IdaDJLJQLJZIDq0W6NpVnP1GRPZLUcTh+LNnZSchqpH0w3Bkp95/Hzh0CPjgA2DhQtlpiEgWFxcgIgI4cwaow7QxRObEniUyv717gXffBUJDxRpwRGTfGjcGPD2BkydtbikUsg0slsi8/voLePJJ4N57gSZNZKchIkugUom5l27cAH77TXYaoipYLJF56XRAdLRYfZxrQxFROU9PoFs3oGdP2UmIquBfKzKfkhIxEd2PPwKNGslOQ0SWxtlZfKGKjweqWUydSBYWS2QeCQlAZCRw4YLsJERkybRa8YXq8ceB69dlpyECwGKJzOHiRWDYMKBFC/YoEZFhHh5iSpHz54ExY8TUAkSSsVgi09LpgKeeEqcHr1nDcUpEdGeRkWLNyHXrOLUIWQTOs0TGFxNz8+czZ0TPUqdOwBNPSItERFbmySeBffuAw4dlJyFisUQm5usLuLqKVcaJiOpi3rybvdGKwnXkSBoeEyHTKCkRH24NGogz4IiI6srRURRIP/wADB7M9eNIGhZLZHxlZaLr/ORJ2UmIyBb4+gLbtgHTpslOQnaKxRIZ3+nTQFERz3wjIuPo3VusJ/nBB8CmTbLTkB1isUTG9fXXQEaGmCbAw0N2GiKyFa+9JuZeGjkSOHVKdhqyMxzgTcZz6hTw4otAUBAQHCw7DRFZo1vPpr1daSmg0QDDhwP795svE9k99iyR8TRvDixdKhbEJCIyNicnoG1bcYbtX3+JhXeJzIDFEt29/fvFwrgqFTBiBCeeJCLTUhRgwABgyBCguFh2GrID/KtGd+fQIeDhh4HFiwG9XnYaIrIHKhXw4YfA7t1iHJNWKzsR2TgWS1R/R48CsbFAq1bA5s3sUSIi84mJEZ87O3eK1QFKSmQnIhvGAd5Ue7cOvCwqApKTxWBLtVpMGEdEZC7ln0ctWwJbtwJdu4pJcG+3c6d5c5FNYrFE9ePoKJYwadlSFEtERDL4+gLdugHOzmIsE8BlUcjoWCxR3RQXi8NtGg3Qrp3sNEREolACgHPnRK93ZCSHBZBR8d1EtafVAn/8AZw4ITsJEVFVHh5iSoETJ272MhEZAYslqp3sbFEo6fVidm4iIkvTsCHQpo34vGLBREbEw3B0Z+fPA4MGidlzO3USE8IREVmigABRJB0/ziWXyGhYLNGdpaQA168DHTsCbm6y0xARGRYYKE488fYW1xWFg77prvAwHFVPUcTpuIoipgU4dgxwd5edioiodnx9xVm7f/4JREeLeeGI6onFElVVVAQ89xwwcCCwZ4/Y5uIiNxMRUX1oNGIIwb33At9+KzsNWSkWS1RZWhrQowewaZNY761XL9mJiIjqr3lzYN8+4LHHgGHDgFdf5WzfVGcsluimEyeAzp2Ba9fE4rhPPik7ERHR3XN3B1avBhYuFF8CMzNlJyIrw2KJbmrRApgwAfj9d6B9e9lpiIiMR6UCxo8HTp8GwsKA/Hxg717ZqchKsFiyd4WFwLPPAklJYjDkrFliYCQRkS0qn07g00+B++8H5s/nfEx0R5w6wJ7t2weMGSPmIzl8GPD3l52IiMg8pk0TXxYnTxbDDpYuBby8ZKciC8WeJXt05Qrwt7+JgdwajTitloUSEdkTJyfgo4+A774T06S0ayfOBCaqBnuW7JFaDRw8CCxeLHqWHnpIdiIiItOIibnzPu3aAWPHikl3S0rE3ExRUabPRlaDPUv24tdfRU/S+fPimH1ysvhwcHSUnYyISC4XF2DkSPHz6tWit/2FF8Qac0RgsWT7srLEh8B994mJ2QoLxXYH/tcTEVUxciTw+efAxo1Ay5ZiALhOJzsVScbDcLasfXsxeFulEr/0bm7i1FkiIqqek5P4nHz6aWD6dOC114CICLHsE9ktFku2pqQEOHfuZnEUGCh+0dVq2cmIiCxXTWObOncW0wzMnw+kpwP/+hfQpo1Zo5F8PBZjK/76C3j3XSA8HHjkETFviJubKJpYKBER1Y+bm+id1+mAS5eAtm3FSTE//giUlclOR2bCYsna5ecDo0cDoaHAe++JruIffxS/3EREZBxqtViMd80a4Pp1YMgQMRBcr5edjMyAh+GskV4P/PYb0K2bOLPtzBlg5kwxDYCfn+x0RES2ycFBrHjw7LNiWajUVLEtJwcdliwRX1rvuUd2SjIBFkvWJDMT+OEHcabG6dPA2bNiPFJiouxkRET24faxTcuXQ1VUhKDTp6GOjga8vYHGjYGUFDFYnGwC/ycthaGJ05ydcW9eHpwOHBDXAwLEhGl/+5t5shERUY0UHx8kLF2KAe+8A6dz58Skls8/L+Zs0mrFhUupWDUWS5ZGUYCCAiAnB8jLAzp1Apyd8VenTvC/cQNO3t6As7PslEREdAvFyQlKUJBYiPzaNTFtS0yM+Cz/80/Ax0cMk/D3F5Ngltu5U1pmqj0WS5ZCUcShtb/+Eqf/q9XiF+t/Z1ucHTgQrdPTxW1ERGS5PD0r/9ysGZCbK8aXnj4NBAUBrVuLz329npMEWwEWS+ZWVia+Zfz3v2Kl65QU8a9KJX5pAgLENw9vb57RRkRk7TQaMYapcWOxikJe3s1lpgoKRE9Uly5A1643L8HBcjNTFSyWTElRgJ49bx6vLisD9u69OTeHh4f41hETI3qSWrWSm5eIiEzHyUl8IS7n7CyKpeRkYNcuceTAw0NMhKkowMWLN/9O1HawOA/rmQSLJWO4dk0URP7+wIkTwJw5wMmTwKlTwJUr4ptF9+7i20SzZmKSMw8PnilBRGTPXF3FRMKAKI602pvr0JWvxlD+5drFRezfrp34W3LtmvjXxYWH8cxA+l/rRYsW4aOPPkJGRgbatm2L+fPno1evXjXun5SUhLi4OPz5558ICQnBlClTMHbsWNMF1OnEYrQZGUCLFmKQ3rZtYpHFU6dEUZSZCYwbB3zxhbjP2bNiOvzBg4G1aysfvw4JMV1WIiKyTiqVKHzKB39rNGIB9KIicbiusFAUU+WF0YkTNxdGLy+kIiLEkYqiIrGvs7O4ODlVHdbBHqg6kVosrV+/HpMmTcKiRYvQs2dPfPnll+jfvz+OHTuGsLCwKvunpaVhwIABGDNmDNasWYNff/0V48aNQ8OGDTF06NC6PXl+PpCUBFy9evNSUgLs2SNuP3xYVO63rjbdvr0YdH3xoiiQ3NzEGzQyUkxQVn76v7Oz+EZw7hzQsGHdG4aIiEilAtzdxeV27dsDN27cvBQV3SyIMjPFOna3Pk5wsFj+SqsVf5veekuMjfXxARo0AJ54Qux7/rz4G+bjI4owjp0FILlY+uSTTzB69Gi88MILAID58+fj559/xuLFizF37twq+y9ZsgRhYWGYP38+ACAyMhIHDhzAvHnz6l4sDR4sjhMDolJ3chLjhrp0Edt8fMQbqbwyd3YWxRFwc7AeERGRDOW9UA0aVL0tPFwURyUlNy/lPVZlZWK5lm++EZ0G+fliWEh5sTRgAHDsmPjZ2VkcHoyMFGOrMjPFURYHB3FxdBR/Jxs1EhMmz5tX+W+mszPw8stiv3//WwxLcXYW152cRMEXEiIe99y5m9udnMRj+vqapSlrQ1qxVFJSgoMHD2Lq1KmVtsfGxmLv3r3V3mffvn2IjY2ttK1fv35Yvnw5dDod1NUsGKvVaqHVaiuu5+fnAwDy3N2h69lT/KeUn5lwqyZN6viKTEcHoKioCLkA1Dw2bVJsa/NgO5sH29k8LK6dHRzEl//qeqQ8PG52CgCiGCorAx58EACgcnUFOnQAdDqoSkuBsjLoXV0BBweonJ3h4Ooq9i8rA0pKoJSWQu/gAJw/D6evv75ZnOl0gFaL0iefBBwd4RgfD4fff68UpbR1ayghIXC4eBGOJ09Wuq3sgw+gHz260ra8vLz/RVaM0Eh1pEhy6dIlBYDy66+/Vto+Z84cpWXLltXep0WLFsqcOXMqbfv1118VAMrly5ervc+MGTMUALzwwgsvvPDCiw1czpw5Y5xCpA6kD/BW3XY8VFGUKtvutH9128vFx8cjLi6u4rper0deXh78/PwMPo8lKSgoQGhoKC5cuAAvTplvUmxr82A7mwfb2TzYzuaRn5+PsLAw+Eo4PCetWPL394ejoyMyMzMrbc/OzkZgYGC19wkKCqp2fycnJ/j5+VV7H41GA41GU2mbj49P/YNL5OXlxV9EM2Fbmwfb2TzYzubBdjYPBwmHOqUdXHV2dkZ0dDQSEhIqbU9ISECPHj2qvU/37t2r7L9jxw507ty52vFKRERERHdL6ki0uLg4/OMf/8CKFStw/PhxTJ48Genp6RXzJsXHx2PEiBEV+48dOxbnz59HXFwcjh8/jhUrVmD58uV4/fXXZb0EIiIisnFSxyw99dRTyM3NxTvvvIOMjAy0a9cO27ZtQ/j/ZjTNyMhA+i1zRURERGDbtm2YPHkyvvjiC4SEhGDBggV1nzbAymg0GsyYMaPK4UQyPra1ebCdzYPtbB5sZ/OQ2c4qRZFxDh4RERGRdbCACSGIiIiILBeLJSIiIiIDWCwRERERGcBiiYiIiMgAFksSXLt2DZMmTUJ4eDhcXV3Ro0cP/H7LmjlZWVkYNWoUQkJC4ObmhocffhinTp264+NevXoV48ePR3BwMFxcXBAZGYlt27aZ8qVYNFO18/z589GqVSu4uroiNDQUkydPRnFxsSlfisXYtWsXBg0ahJCQEKhUKmzatKnS7YqiYObMmQgJCYGrqyv69OmDP//8s9I+Wq0WEydOhL+/P9zd3fHoo4/i4sWLd3zuRYsWISIiAi4uLoiOjsbu3buN+dIsjqy2njt3Lrp06QJPT08EBARgyJAhSE1NNfbLsxgy39Pl5s6dC5VKhUmTJhnhFVkmme186dIlPPfcc/Dz84Obmxs6deqEgwcP1ik/iyUJXnjhBSQkJGD16tU4cuQIYmNj8dBDD+HSpUtQFAVDhgzB2bNn8eOPPyI5ORnh4eF46KGHUFhYWONjlpSUoG/fvjh37hy+//57pKamYtmyZWjUqJEZX5llMUU7f/PNN5g6dSpmzJiB48ePY/ny5Vi/fj3i4+PN+MrkKSwsRMeOHbFw4cJqb//www/xySefYOHChfj9998RFBSEvn374tq1axX7TJo0CT/88APWrVuHPXv24Pr16xg4cCDKyspqfN7169dj0qRJeOutt5CcnIxevXqhf//+laYWsTWy2jopKQnjx4/H/v37kZCQgNLSUsTGxhr8vbBmstq53O+//46lS5eiQ4cORntNlkhWO1+5cgU9e/aEWq3GTz/9hGPHjuHjjz+u+0oeZl+Nzs4VFRUpjo6OypYtWypt79ixo/LWW28pqampCgDl6NGjFbeVlpYqvr6+yrJly2p83MWLFytNmzZVSkpKTJbdmpiqncePH6888MADlbbFxcUp9913n3FfgBUAoPzwww8V1/V6vRIUFKS8//77FduKi4sVb29vZcmSJYqiKMrVq1cVtVqtrFu3rmKfS5cuKQ4ODsr27dtrfK6uXbsqY8eOrbStdevWytSpU430aiybOdv6dtnZ2QoAJSkp6e5fiIUzdztfu3ZNadGihZKQkKD07t1befXVV436eiyVOdv5jTfeMMrnM3uWzKy0tBRlZWVwcXGptN3V1RV79uyBVqsFgEq3Ozo6wtnZGXv27KnxcTdv3ozu3btj/PjxCAwMRLt27fDee+/V6puNLTJVO9933304ePAgfvvtNwDA2bNnsW3bNjzyyCMmeBXWJS0tDZmZmYiNja3YptFo0Lt3b+zduxcAcPDgQeh0ukr7hISEoF27dhX73K6kpAQHDx6sdB8AiI2NrfE+ts5UbV2d/Px8AJCyeKlspm7n8ePH45FHHsFDDz1kmhdgJUzZzps3b0bnzp3xxBNPICAgAFFRUVi2bFmdM7JYMjNPT090794ds2fPxuXLl1FWVoY1a9bgv//9LzIyMtC6dWuEh4cjPj4eV65cQUlJCd5//31kZmYiIyOjxsc9e/Ysvv/+e5SVlWHbtm2YNm0aPv74Y8yZM8eMr85ymKqdn376acyePRv33Xcf1Go1mjVrhpiYGEydOtWMr84ylS9yfftC2IGBgRW3ZWZmwtnZGQ0aNKhxn9vl5OSgrKzM4OPaG1O19e0URUFcXBzuu+8+tGvXzgjJrYsp23ndunU4dOgQ5s6da+TU1seU7Xz27FksXrwYLVq0wM8//4yxY8filVdewddff12njCyWJFi9ejUURUGjRo2g0WiwYMECDBs2DI6OjlCr1diwYQNOnjwJX19fuLm5ITExEf3794ejo2ONj6nX6xEQEIClS5ciOjoaTz/9NN566y0sXrzYjK/MspiinRMTEzFnzhwsWrQIhw4dwsaNG7FlyxbMnj3bjK/MsqlUqkrXFUWpsu12tdmnPo9r60zV1uUmTJiAw4cP49tvv613Rltg7Ha+cOECXn31VaxZs6ZK77c9M8X7Wa/X45577sF7772HqKgovPTSSxgzZkyd/zayWJKgWbNmSEpKwvXr13HhwgX89ttv0Ol0iIiIAABER0cjJSUFV69eRUZGBrZv347c3NyK26sTHByMli1bVvpDHxkZiczMTJSUlJj8NVkiU7Tz9OnTMXz4cLzwwgto3749HnvsMbz33nuYO3cu9Hq9uV6aRQoKCgKAKt/ysrOzK74xBgUFoaSkBFeuXKlxn9v5+/vD0dHR4OPaG1O19a0mTpyIzZs3Y+fOnWjcuLGRklsXU7XzwYMHkZ2djejoaDg5OcHJyQlJSUlYsGABnJyc7G74hCnfz8HBwWjTpk2lbZGRkXU+OYTFkkTu7u4IDg7GlStX8PPPP2Pw4MGVbvf29kbDhg1x6tQpHDhwoMrtt+rZsydOnz5d6Q/2yZMnERwcDGdnZ5O9BmtgzHYuKiqCg0PlXxtHR0coigLFzpdZjIiIQFBQEBISEiq2lZSUICkpCT169AAgClS1Wl1pn4yMDBw9erRin9s5OzsjOjq60n0AICEhocb72DpTtTUgvqlPmDABGzduxH/+8x+DXx5snana+cEHH8SRI0eQkpJScencuTOeffZZpKSkGOzdtkWmfD/37NmzytQXJ0+eRHh4eN1C3vUQcaqz7du3Kz/99JNy9uxZZceOHUrHjh2Vrl27VpzJ9s9//lPZuXOncubMGWXTpk1KeHi48vjjj1d6jOHDh1c6Eyg9PV3x8PBQJkyYoKSmpipbtmxRAgIClHfffdesr82SmKKdZ8yYoXh6eirffvttxeM2a9ZMefLJJ8362mS5du2akpycrCQnJysAlE8++URJTk5Wzp8/ryiKorz//vuKt7e3snHjRuXIkSPKM888owQHBysFBQUVjzF27FilcePGyi+//KIcOnRIeeCBB5SOHTsqpaWlFfs88MADyueff15xfd26dYparVaWL1+uHDt2TJk0aZLi7u6unDt3znwv3sxktfXLL7+seHt7K4mJiUpGRkbFpaioyHwv3oxktfPtbP1sOFnt/NtvvylOTk7KnDlzlFOnTinffPON4ubmpqxZs6ZO+VksSbB+/XqladOmirOzsxIUFKSMHz9euXr1asXtn332mdK4cWNFrVYrYWFhyrRp0xStVlvpMXr37q2MHDmy0ra9e/cq9957r6LRaJSmTZsqc+bMqfQmsjemaGedTqfMnDlTadasmeLi4qKEhoYq48aNU65cuWKmVyXXzp07FQBVLuVtpNfrlRkzZihBQUGKRqNR7r//fuXIkSOVHuPGjRvKhAkTFF9fX8XV1VUZOHCgkp6eXmmf8PBwZcaMGZW2ffHFF0p4eLji7Oys3HPPPTZ/Krustq7uOQEoK1euNPErlkPme/pWtl4syWznf/3rX0q7du0UjUajtG7dWlm6dGmd86sUxc6PHRAREREZwDFLRERERAawWCIiIiIygMUSERERkQEsloiIiIgMYLFEREREZACLJSIiIiIDWCwRERERGcBiiYiIiMgAFktEJEV2djZeeuklhIWFQaPRICgoCP369cO+ffvMnuXcuXNQqVRISUkx+3MTkeVzkh2AiOzT0KFDodPp8NVXX6Fp06bIysrCv//9b+Tl5Zk1R0lJiVmfj4isD3uWiMjsrl69ij179uCDDz5ATEwMwsPD0bVrV8THx+ORRx4BAKhUKnz55ZcYOHAg3NzcEBkZiX379uH06dPo06cP3N3d0b17d5w5c6bicc+cOYPBgwcjMDAQHh4e6NKlC3755ZdKz92kSRO8++67GDVqFLy9vTFmzBhEREQAAKKioqBSqdCnTx8AQGJiIrp27Qp3d3f4+PigZ8+eOH/+vHkaiYgsBoslIjI7Dw8PeHh4YNOmTdBqtTXuN3v2bIwYMQIpKSlo3bo1hg0bhpdeegnx8fE4cOAAAGDChAkV+1+/fh0DBgzAL7/8guTkZPTr1w+DBg1Cenp6pcf96KOP0K5dOxw8eBDTp0/Hb7/9BgD45ZdfkJGRgY0bN6K0tBRDhgxB7969cfjwYezbtw8vvvgiVCqVCVqEiCxanZfeJSIygu+//15p0KCB4uLiovTo0UOJj49X/vjjj4rbASjTpk2ruL5v3z4FgLJ8+fKKbd9++63i4uJi8HnatGmjfP755xXXw8PDlSFDhlTaJy0tTQGgJCcnV2zLzc1VACiJiYn1fYlEZCPYs0REUgwdOhSXL1/G5s2b0a9fPyQmJuKee+7BqlWrKvbp0KFDxc+BgYEAgPbt21faVlxcjIKCAgBAYWEhpkyZgjZt2sDHxwceHh44ceJElZ6lzp073zGfr68vRo0aVdE79dlnnyEjI+NuXjIRWSkWS0QkjYuLC/r27Yu3334be/fuxahRozBjxoyK29VqdcXP5Ye/qtum1+sBAH//+9+xYcMGzJkzB7t370ZKSgrat29fZRC3u7t7rfKtXLkS+/btQ48ePbB+/Xq0bNkS+/fvr9+LJSKrxWKJiCxGmzZtUFhYWO/77969G6NGjcJjjz2G9u3bIygoCOfOnbvj/ZydnQEAZWVlVW6LiopCfHw89u7di3bt2mHt2rX1zkdE1olTBxCR2eXm5uKJJ57A3/72N3To0AGenp44cOAAPvzwQwwePLjej9u8eXNs3LgRgwYNgkqlwvTp0yt6nQwJCAiAq6srtm/fjsaNG8PFxQV5eXlYunQpHn30UYSEhCA1NRUnT57EiBEj6p2PiKwTiyUiMjsPDw/ce++9+PTTT3HmzBnodDqEhoZizJgxePPNN+v9uJ9++in+9re/oUePHvD398cbb7xRMZ7JECcnJyxYsADvvPMO3n77bfTq1Qvr16/HiRMn8NVXXyE3NxfBwcGYMGECXnrppXrnIyLrpFIURZEdgoiIiMhSccwSERERkQEsloiIiIgMYLFEREREZACLJSIiIiIDWCwRERERGcBiiYiIiMgAFktEREREBrBYIiIiIjKAxRIRERGRASyWiIiIiAxgsURERERkAIslIiIiIgP+H3JjATsU5NuBAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "
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diff --git a/doc/LectureNotes/_build/jupyter_execute/resamplingmethods_59_1.png b/doc/LectureNotes/_build/jupyter_execute/resamplingmethods_59_1.png
index a3a0d9d8..3dd0ea76 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/resamplingmethods_59_1.png and b/doc/LectureNotes/_build/jupyter_execute/resamplingmethods_59_1.png differ
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index 6a2846e8..c5ac12f6 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -170,32 +170,37 @@ This course will be delivered in a hybrid mode, with online and on site lectures
 ###  Week 46, November 13-17, 2023
 - Topics to be covered
   - Thursday:
-    - Summary many-body perturbation theory and discussion of Coupled Cluster theory
+    - Diagram rules (whiteboard notes)
+    - Examples of diagrams and discussion of Pauli violating diagrams (Whiteboard notes)
+    - Video of lecture at https://youtu.be/rCtpX221u24
+    - Whiteboard notes at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/HandwrittenNotes/2023/LectureNovember16.pdf
   - Friday:
-    - Coupled Cluster theory with doubles excitations
-    - Diagrammatic representation of Coupled Cluster theory
-- Lecture Material: Lecture slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week46/LatexSlides/slidesweek46.pdf, handwritten notes and Shavitt and Bartlett chapter 9
+    - Time-dependent perturbation theory, definitions of Schroedinger, Heisenberg and interaction picture, see slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week45/LatexSlides/pictures.pdf
+    - Video of lecture at https://youtu.be/8-dnWUDTnS0
+    - Whiteboard notes at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/HandwrittenNotes/2023/LectureNovember17.pdf
+- Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/pub/week46/pdf/slidesweek46.pdf, handwritten notes a\
+nd Shavitt and Bartlett chapter 4-6
 - Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf
 
 ###  Week 47, November 20-24, 2023
 - Topics to be covered
   - Thursday:
-    - Coupled Cluster theory, singles and doubles excitations
+    - Green's function theory
   - Friday:
-    - Coupled Cluster theory and summary of course
-- Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week47/LatexSlides/slidesweek47.pdf, handwritten notes and Shavitt and Bartlett chapters 9 and 10
+    - Green's function theory
+- Lecture Material: 
 - Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf
 
 ###  Week 48, November 27-December 1, 2023
 - Topics to be covered
   - Thursday:
-    - Coupled Cluster theory, singles and doubles excitations
+    - Green's function theory
   - Friday:
-    - Coupled Cluster theory and summary of course
-- Lecture Material: Slides at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/src/week47/LatexSlides/slidesweek47.pdf, handwritten notes and Shavitt and Bartlett chapters 9 and 10
+    - Green's function theory and summary of course
+- Lecture Material: 
 - Work on second midterm set at https://github.com/ManyBodyPhysics/FYS4480/blob/master/doc/Exercises/2023/SecondMidterm2023.pdf
 
 
-###  Week 50, December 19 and 21, 2023
-- Final oral exam, day to be determined
+###  Week 50, December 19, 2023
+- Final oral exam