diff --git a/environment.yml b/environment.yml
index ab5ba874..18675b46 100644
--- a/environment.yml
+++ b/environment.yml
@@ -37,3 +37,4 @@ dependencies:
   - signac-dashboard
   - signac-flow=0.20.0
   - unyt
+  - camelot-py
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/.gitignore b/reproducibility_project/src/analysis/Comparison_Figures/.gitignore
new file mode 100644
index 00000000..b5682685
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/.gitignore
@@ -0,0 +1,2 @@
+*.pdf
+*.ipynb
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 41 MPa_data.csv
new file mode 100644
index 00000000..a094d9a9
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 41 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,659.22,1.25,-16.759,0.061,temp: 98.0,0.0011377211445378135
+1,DL_POLY(BS),173.0,590.63,1.91,-14.366,0.086,temp: 173.0,0.026344163920897275
+2,DL_POLY(BS),248.0,523.63,2.48,-12.241,0.102,temp: 248.0,0.0375724383875568
+3,DL_POLY(BS),298.0,478.16,3.03,-10.908,0.113,temp: 298.0,0.09396659999071405
+4,GROMACS(BS),98.0,659.22,0.05,-16.759,0.002,temp: 98.0,0.0011377211445378135
+5,GROMACS(BS),173.0,590.66,0.07,-14.365,0.002,temp: 173.0,0.03142482410564051
+6,GROMACS(BS),248.0,523.73,0.11,-12.245,0.003,temp: 248.0,0.056677068076155676
+7,GROMACS(BS),298.0,478.13,0.15,-10.908,0.004,temp: 298.0,0.08768665395172569
+8,GROMACS(KL),98.0,660.02,0.09,-16.787,0.003,temp: 98.0,0.12249464322959451
+9,GROMACS(KL),173.0,590.8,0.1,-14.371,0.003,temp: 173.0,0.05513457163446153
+10,GROMACS(KL),248.0,524.04,0.08,-12.256,0.003,temp: 248.0,0.1159014201107883
+11,GROMACS(KL),298.0,477.99,0.12,-10.905,0.003,temp: 298.0,0.05838023910314501
+12,LAMMPS(AA),98.0,659.17,0.85,-16.758,0.032,temp: 98.0,-0.006447086485789009
+13,LAMMPS(AA),173.0,590.47,1.17,-14.361,0.037,temp: 173.0,-0.0007526903977525743
+14,LAMMPS(AA),248.0,523.55,1.88,-12.241,0.053,temp: 248.0,0.02228873463667335
+15,LAMMPS(AA),298.0,477.87,6.77,-10.904,0.061,temp: 298.0,0.033260454947215315
+16,LAMMPS(KL),98.0,658.96,0.8,-16.758,0.03,temp: 98.0,-0.03830327853310648
+17,LAMMPS(KL),173.0,590.44,1.2,-14.366,0.039,temp: 173.0,-0.005833350582495811
+18,LAMMPS(KL),248.0,523.3,1.86,-12.238,0.054,temp: 248.0,-0.025472839584812983
+19,LAMMPS(KL),298.0,477.43,2.22,-10.893,0.059,temp: 298.0,-0.05884542029118957
+20,ms2(KL),98.0,658.92,0.13,-16.748,0.003,temp: 98.0,-0.04437112463737139
+21,ms2(KL),173.0,590.09,0.13,-14.347,0.003,temp: 173.0,-0.06510771940455799
+22,ms2(KL),248.0,522.71,0.16,-12.215,0.003,temp: 248.0,-0.1381901547475051
+23,ms2(KL),298.0,477.15,0.2,-10.88,0.003,temp: 298.0,-0.11745824998836282
+24,ms2(PB),98.0,659.09,0.05,-16.744,0.002,temp: 98.0,-0.018582778694284333
+25,ms2(PB),173.0,590.26,0.07,-14.344,0.002,temp: 173.0,-0.03631731169099373
+26,ms2(PB),248.0,523.07,0.11,-12.219,0.003,temp: 248.0,-0.06941348786856216
+27,ms2(PB),298.0,477.06,0.14,-10.871,0.004,temp: 298.0,-0.13629808810530414
+28,Tinker(AA),98.0,659.1,1.2,,,temp: 98.0,-0.01706581716822242
+29,Tinker(AA),173.0,590.5,1.4,,,temp: 173.0,0.004327969786990662
+30,Tinker(AA),248.0,523.4,1.4,,,temp: 248.0,-0.006368209896214106
+31,Tinker(AA),298.0,477.9,1.4,,,temp: 298.0,0.03954040098619179
+32,TOWHEE(BS),98.0,,,,,temp: 98.0,
+33,TOWHEE(BS),173.0,590.42,0.3,-14.362,0.01,temp: 173.0,-0.009220457372343888
+34,TOWHEE(BS),248.0,523.47,0.37,-12.238,0.011,temp: 248.0,0.007005030885811624
+35,TOWHEE(BS),298.0,477.71,0.35,-10.897,0.01,temp: 298.0,-0.00023259059402823293
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 5 MPa_data.csv
new file mode 100644
index 00000000..14b9bf40
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 5 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,646.13,1.36,-16.529,0.062,temp: 98.0,-0.01005888315445661
+1,DL_POLY(BS),173.0,567.39,2.08,-13.866,0.086,temp: 173.0,0.050353256503779954
+2,DL_POLY(BS),248.0,477.8,3.32,-11.194,0.011,temp: 248.0,0.08355424392720442
+3,DL_POLY(BS),298.0,394.04,4.86,-9.009,0.131,temp: 298.0,1.9725340449024766
+4,GROMACS(BS),98.0,646.48,0.05,-16.542,0.002,temp: 98.0,0.04410433383113176
+5,GROMACS(BS),173.0,567.56,0.09,-13.87,0.003,temp: 173.0,0.08033009792432234
+6,GROMACS(BS),248.0,478.4,0.16,-11.207,0.004,temp: 248.0,0.20923472225778816
+7,GROMACS(BS),298.0,393.74,0.26,-9.001,0.006,temp: 298.0,1.8948978652926096
+8,GROMACS(KL),98.0,647.03,0.16,-16.56,0.006,temp: 98.0,0.12921796052275805
+9,GROMACS(KL),173.0,567.23,0.14,-13.86,0.005,temp: 173.0,0.02213975869620946
+10,GROMACS(KL),248.0,475.32,2.9,-11.14,0.067,temp: 248.0,-0.43592506650590834
+11,GROMACS(KL),298.0,354.88,25.0,-8.205,0.5,temp: 298.0,-8.16157526683842
+12,LAMMPS(AA),98.0,646.18,0.83,-16.531,0.029,temp: 98.0,-0.0023212807279515252
+13,LAMMPS(AA),173.0,567.03,1.57,-13.855,0.046,temp: 173.0,-0.013127113563268688
+14,LAMMPS(AA),248.0,477.72,2.99,-11.192,0.079,temp: 248.0,0.0667968468164623
+15,LAMMPS(AA),298.0,392.15,7.16,-8.968,0.154,temp: 298.0,1.4834261133603233
+16,LAMMPS(KL),98.0,645.96,0.85,-16.532,0.03,temp: 98.0,-0.03636673140459149
+17,LAMMPS(KL),173.0,567.12,1.63,-13.865,0.05,temp: 173.0,0.002742978953498484
+18,LAMMPS(KL),248.0,477.61,3.22,-11.193,0.082,temp: 248.0,0.04375542578918445
+19,LAMMPS(KL),298.0,392.37,6.47,-8.977,0.146,temp: 298.0,1.5403593117408971
+20,ms2(KL),98.0,645.93,0.12,-16.523,0.003,temp: 98.0,-0.04100929286051213
+21,ms2(KL),173.0,566.68,0.13,-13.843,0.003,temp: 173.0,-0.07484414001734542
+22,ms2(KL),248.0,477.24,0.29,-11.178,0.005,temp: 248.0,-0.033747535848014217
+23,ms2(KL),298.0,376.01,18.57,-9.209,0.044,temp: 298.0,-2.693400349650347
+24,ms2(PB),98.0,645.95,0.07,-16.513,0.002,temp: 98.0,-0.03791425188989251
+25,ms2(PB),173.0,566.75,0.09,-13.838,0.003,temp: 173.0,-0.06250073472652207
+26,ms2(PB),248.0,476.76,0.2,-11.16,0.005,temp: 248.0,-0.13429191851249073
+27,ms2(PB),298.0,391.04,0.47,-8.93,0.01,temp: 298.0,1.1961722488038384
+28,Tinker(AA),98.0,645.9,1.2,,,temp: 98.0,-0.045651854316415184
+29,Tinker(AA),173.0,567.0,1.3,,,temp: 173.0,-0.018417144402184397
+30,Tinker(AA),248.0,478.3,1.6,,,temp: 248.0,0.1882879758693635
+31,Tinker(AA),298.0,392.7,2.0,,,temp: 298.0,1.6257591093117432
+32,TOWHEE(BS),98.0,,,,,temp: 98.0,
+33,TOWHEE(BS),173.0,567.18,0.35,-13.862,0.01,temp: 173.0,0.0133230406313299
+34,TOWHEE(BS),248.0,477.46,0.28,-11.187,0.007,temp: 248.0,0.01233530620652958
+35,TOWHEE(BS),298.0,390.83,0.82,-8.938,0.019,temp: 298.0,1.1418269230769242
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 70 MPa_data.csv
new file mode 100644
index 00000000..a9dab7b0
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Ethane at 70 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,668.53,1.15,-16.907,0.059,temp: 98.0,0.020571744895408837
+1,DL_POLY(BS),173.0,605.46,1.83,-14.659,0.086,temp: 173.0,0.03304365066253271
+2,DL_POLY(BS),248.0,546.57,2.3,-12.733,0.102,temp: 248.0,0.03396062194712044
+3,DL_POLY(BS),298.0,508.63,2.53,-11.57,0.11,temp: 298.0,0.011360781272193598
+4,GROMACS(BS),98.0,668.48,0.04,-16.904,0.002,temp: 98.0,0.013091110387996718
+5,GROMACS(BS),173.0,605.51,0.06,-14.66,0.002,temp: 173.0,0.0413045633281565
+6,GROMACS(BS),248.0,546.79,0.08,-12.738,0.002,temp: 248.0,0.07422531144127438
+7,GROMACS(BS),298.0,508.83,0.1,-11.575,0.003,temp: 298.0,0.05068656259899953
+8,GROMACS(KL),98.0,669.11,0.09,-16.925,0.004,temp: 98.0,0.10734710518147442
+9,GROMACS(KL),173.0,605.44,0.08,-14.659,0.003,temp: 173.0,0.029739285596283197
+10,GROMACS(KL),248.0,546.66,0.05,-12.737,0.002,temp: 248.0,0.05043254037653854
+11,GROMACS(KL),298.0,508.8,0.15,-11.576,0.004,temp: 298.0,0.04478769539998367
+12,LAMMPS(AA),98.0,668.25,0.68,-16.896,0.026,temp: 98.0,-0.021319808346133038
+13,LAMMPS(AA),173.0,605.29,0.97,-14.654,0.032,temp: 173.0,0.004956547599374272
+14,LAMMPS(AA),248.0,546.39,1.35,-12.727,0.04,temp: 248.0,0.001016785088242621
+15,LAMMPS(AA),298.0,508.67,1.79,-11.573,0.05,temp: 298.0,0.019225937537559256
+16,LAMMPS(KL),98.0,668.09,0.79,-16.898,0.03,temp: 98.0,-0.045257838769868816
+17,LAMMPS(KL),173.0,605.19,1.09,-14.657,0.037,temp: 173.0,-0.0115652777318733
+18,LAMMPS(KL),248.0,546.26,1.38,-12.729,0.041,temp: 248.0,-0.02277598597649322
+19,LAMMPS(KL),298.0,508.34,1.82,-11.568,0.053,temp: 298.0,-0.045661601651682274
+20,ms2(KL),98.0,668.03,0.1,-16.887,0.002,temp: 98.0,-0.05423460017878037
+21,ms2(KL),173.0,604.92,0.1,-14.639,0.002,temp: 173.0,-0.056174206126298094
+22,ms2(KL),248.0,545.82,0.12,-12.71,0.002,temp: 248.0,-0.10330536496482193
+23,ms2(KL),298.0,508.27,0.15,-11.559,0.003,temp: 298.0,-0.05942562511606379
+24,ms2(PB),98.0,668.25,0.06,-16.885,0.002,temp: 98.0,-0.021319808346133038
+25,ms2(PB),173.0,604.97,0.06,-14.633,0.002,temp: 173.0,-0.04791329346065552
+26,ms2(PB),248.0,546.12,0.09,-12.711,0.003,temp: 248.0,-0.0483989702000533
+27,ms2(PB),298.0,508.28,0.11,-11.552,0.003,temp: 298.0,-0.05745933604972517
+28,Tinker(AA),98.0,668.4,1.0,,,temp: 98.0,0.001122095176120322
+29,Tinker(AA),173.0,605.3,1.3,,,temp: 173.0,0.006608730132499029
+30,Tinker(AA),248.0,546.4,1.5,,,temp: 248.0,0.0028469982470668547
+31,Tinker(AA),298.0,508.7,1.5,,,temp: 298.0,0.025124804736575117
+32,TOWHEE(BS),98.0,,,,,temp: 98.0,
+33,TOWHEE(BS),173.0,,,,,temp: 173.0,
+34,TOWHEE(BS),248.0,546.45,0.3,-12.731,0.009,temp: 248.0,0.011998064041208829
+35,TOWHEE(BS),298.0,508.63,0.35,-11.571,0.01,temp: 298.0,0.011360781272193598
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 41 MPa_data.csv
new file mode 100644
index 00000000..ddcbb4f1
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 41 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,700.76,1.13,-20.95,0.073,temp: 98.0,0.061875886491074734
+1,DL_POLY(BS),173.0,636.87,1.44,-17.87,0.084,temp: 173.0,-0.012908695401899867
+2,DL_POLY(BS),248.0,577.39,2.24,-15.18,0.124,temp: 248.0,-0.036165043407683485
+3,DL_POLY(BS),298.0,538.79,2.56,-13.507,0.135,temp: 298.0,0.07078469894833354
+4,GROMACS(BS),98.0,698.96,0.05,-20.944,0.002,temp: 98.0,-0.19514702662565478
+5,GROMACS(BS),173.0,636.83,0.09,-17.879,0.003,temp: 173.0,-0.019188601273082816
+6,GROMACS(BS),248.0,577.22,0.13,-15.178,0.003,temp: 248.0,-0.06559723298945075
+7,GROMACS(BS),298.0,538.37,0.17,-13.51,0.004,temp: 298.0,-0.00722292846411696
+8,GROMACS(KL),98.0,699.09,0.08,-20.944,0.001,temp: 98.0,-0.1765842606783356
+9,GROMACS(KL),173.0,636.68,0.1,-17.878,0.002,temp: 173.0,-0.042738248290054584
+10,GROMACS(KL),248.0,577.26,0.19,-15.182,0.003,temp: 248.0,-0.0586720119113925
+11,GROMACS(KL),298.0,538.78,0.06,-13.522,0.003,temp: 298.0,0.06892737448613385
+12,LAMMPS(AA),98.0,700.41,0.76,-20.965,0.034,temp: 98.0,0.011899208940595042
+13,LAMMPS(AA),173.0,636.96,1.03,-17.875,0.04,temp: 173.0,0.0012210928082796244
+14,LAMMPS(AA),248.0,577.47,1.37,-15.176,0.056,temp: 248.0,-0.02231460125154729
+15,LAMMPS(AA),298.0,538.31,1.81,-13.506,0.063,temp: 298.0,-0.01836687523733624
+16,LAMMPS(KL),98.0,701.19,1.08,-20.982,0.038,temp: 98.0,0.12327580462452634
+17,LAMMPS(KL),173.0,636.92,1.31,-17.873,0.041,temp: 173.0,-0.005058813062921176
+18,LAMMPS(KL),248.0,577.88,1.9,-15.174,0.054,temp: 248.0,0.048668914798608874
+19,LAMMPS(KL),298.0,538.05,2.27,-13.495,0.066,temp: 298.0,-0.06665731125457053
+20,ms2(KL),98.0,700.59,0.13,-20.961,0.004,temp: 98.0,0.037601500252277736
+21,ms2(KL),173.0,636.63,0.17,-17.858,0.003,temp: 173.0,-0.05058813062903327
+22,ms2(KL),248.0,577.83,0.2,-15.171,0.003,temp: 248.0,0.04001238845103606
+23,ms2(KL),298.0,537.84,0.26,-13.49,0.004,temp: 298.0,-0.10566112496078522
+24,ms2(PB),98.0,700.91,0.08,-20.97,0.002,temp: 98.0,0.08329446258413283
+25,ms2(PB),173.0,637.1,0.08,-17.867,0.003,temp: 173.0,0.023200763357437804
+26,ms2(PB),248.0,577.74,0.13,-15.171,0.003,temp: 248.0,0.024430641025385297
+27,ms2(PB),298.0,538.45,0.16,-13.493,0.004,temp: 298.0,0.007635667233501709
+28,Tinker(AA),98.0,700.5,1.0,,,temp: 98.0,0.02475035459643639
+29,Tinker(AA),173.0,637.2,1.5,,,temp: 173.0,0.03890052803541303
+30,Tinker(AA),248.0,577.9,1.9,,,temp: 248.0,0.052131525337638006
+31,Tinker(AA),298.0,538.6,2.2,,,temp: 298.0,0.03549553416651824
+32,TOWHEE(BS),98.0,700.53,0.38,-20.966,0.017,temp: 98.0,0.02903406981504476
+33,TOWHEE(BS),173.0,637.38,0.3,-17.881,0.012,temp: 173.0,0.06716010445575417
+34,TOWHEE(BS),248.0,577.7,0.34,-15.176,0.012,temp: 248.0,0.017505419947327044
+35,TOWHEE(BS),298.0,538.49,0.36,-13.504,0.014,temp: 298.0,0.015064965082300485
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 5 MPa_data.csv
new file mode 100644
index 00000000..36d60e31
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 5 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,686.72,1.08,-20.678,0.073,temp: 98.0,-0.016015607937936212
+1,DL_POLY(BS),173.0,,,,,temp: 173.0,
+2,DL_POLY(BS),248.0,539.59,2.49,-14.142,0.121,temp: 248.0,-0.10901738313027194
+3,DL_POLY(BS),298.0,482.48,3.35,-11.957,0.142,temp: 298.0,0.1312078733947854
+4,GROMACS(BS),98.0,684.91,0.08,-20.643,0.003,temp: 98.0,-0.2795451567345902
+5,GROMACS(BS),173.0,614.12,0.1,-17.301,0.003,temp: 173.0,-0.14532195418394378
+6,GROMACS(BS),248.0,539.42,0.23,-14.139,0.006,temp: 248.0,-0.14048843901506433
+7,GROMACS(BS),298.0,481.18,0.36,-11.946,0.01,temp: 298.0,-0.13858687506196793
+8,GROMACS(KL),98.0,685.21,0.2,-20.653,0.005,temp: 98.0,-0.2358662259948048
+9,GROMACS(KL),173.0,614.43,0.23,-17.311,0.004,temp: 173.0,-0.09491657706840037
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+35,TOWHEE(BS),298.0,482.11,0.52,-11.944,0.015,temp: 298.0,0.05442013729555533
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 70 MPa_data.csv
new file mode 100644
index 00000000..5db4df54
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS Propane at 70 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,710.05,0.93,-21.146,0.069,temp: 98.0,0.07172084618069652
+1,DL_POLY(BS),173.0,650.92,1.17,-18.201,0.083,temp: 173.0,0.00034139829915918503
+2,DL_POLY(BS),248.0,597.74,1.88,-15.702,0.125,temp: 248.0,-0.06241849370799316
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+5,GROMACS(BS),173.0,650.76,0.08,-18.2,0.002,temp: 173.0,-0.02423927923990043
+6,GROMACS(BS),248.0,597.85,0.11,-15.703,0.003,temp: 248.0,-0.0440273303833144
+7,GROMACS(BS),298.0,564.93,0.14,-14.198,0.004,temp: 298.0,0.023804090368974844
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+29,Tinker(AA),173.0,651.0,1.4,,,temp: 173.0,0.012631737068697723
+30,Tinker(AA),248.0,598.3,1.7,,,temp: 248.0,0.03120924685398708
+31,Tinker(AA),298.0,564.9,1.8,,,temp: 298.0,0.018492433840363615
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+34,TOWHEE(BS),248.0,598.44,0.24,-15.708,0.01,temp: 248.0,0.05461618199450115
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 41 MPa_data.csv
new file mode 100644
index 00000000..8d13126e
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 41 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,769.75,1.11,-27.164,0.087,temp: 98.0,-0.08278659087992593
+1,DL_POLY(BS),173.0,710.15,1.69,-22.914,0.131,temp: 173.0,-0.026904132130574843
+2,DL_POLY(BS),248.0,654.46,1.9,-19.12,0.162,temp: 248.0,-0.07718811336981712
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 5 MPa_data.csv
new file mode 100644
index 00000000..04c169eb
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 5 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
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+2,DL_POLY(BS),248.0,625.09,2.38,-18.174,0.158,temp: 248.0,-0.33129536487666855
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+29,Tinker(AA),173.0,692.9,1.8,,,temp: 173.0,0.04251257329408784
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 70 MPa_data.csv
new file mode 100644
index 00000000..067703bf
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS iso-Butane at 70 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
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+5,GROMACS(BS),173.0,724.06,0.12,-23.29,0.003,temp: 173.0,0.22701475595911969
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+29,Tinker(AA),173.0,721.8,1.6,,,temp: 173.0,-0.08582265164310486
+30,Tinker(AA),248.0,671.6,2.2,,,temp: 248.0,0.019029495718374374
+31,Tinker(AA),298.0,640.0,2.0,,,temp: 298.0,0.02952260215688342
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+33,TOWHEE(BS),173.0,722.1,0.42,-23.26,0.02,temp: 173.0,-0.04429556213837518
+34,TOWHEE(BS),248.0,669.63,0.84,-19.586,0.291,temp: 248.0,-0.27435568609605077
+35,TOWHEE(BS),298.0,639.72,0.32,-17.431,0.013,temp: 298.0,-0.01424031398155595
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 41 MPa_data.csv
new file mode 100644
index 00000000..45731bc8
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 41 MPa_data.csv	
@@ -0,0 +1,29 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,765.5,1.02,-26.539,0.086,temp: 98.0,0.3701247859981984
+1,DL_POLY(BS),173.0,705.61,1.49,-22.549,0.136,temp: 173.0,0.07597983596460639
+2,DL_POLY(BS),248.0,652.16,1.96,-18.304,0.18,temp: 248.0,0.08616172533920213
+3,DL_POLY(BS),298.0,617.27,2.12,-15.708,0.205,temp: 298.0,0.11283543635108545
+4,GROMACS(BS),98.0,761.35,0.06,-27.21,0.003,temp: 98.0,-0.1740110962511683
+5,GROMACS(BS),173.0,705.08,0.09,-22.547,0.005,temp: 173.0,0.0008104515836331746
+6,GROMACS(BS),248.0,651.89,0.11,-18.363,0.006,temp: 248.0,0.0447251704050759
+7,GROMACS(BS),298.0,617.05,0.15,-15.724,0.007,temp: 298.0,0.07715441541049226
+8,GROMACS(KL),98.0,761.28,0.19,-27.306,0.011,temp: 98.0,-0.1831892918553811
+9,GROMACS(KL),173.0,704.56,0.1,-22.556,0.018,temp: 173.0,-0.07294064252601827
+10,GROMACS(KL),248.0,651.76,0.04,-18.299,0.008,temp: 248.0,0.02477423654790336
+11,GROMACS(KL),298.0,616.8,0.11,-15.719,0.015,temp: 298.0,0.03660780070527772
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+13,ms2(KL),173.0,706.25,0.13,-22.325,0.024,temp: 173.0,0.16675041333031262
+14,ms2(KL),248.0,652.14,0.17,-18.306,0.009,temp: 248.0,0.08309235089963982
+15,ms2(KL),298.0,616.95,0.18,-15.685,0.009,temp: 298.0,0.06093576952842119
+16,ms2(PB),98.0,763.13,0.08,-27.372,0.004,temp: 98.0,0.05937730625578669
+17,ms2(PB),173.0,704.96,0.09,-22.647,0.004,temp: 173.0,-0.016209031672437754
+18,ms2(PB),248.0,652.13,0.14,-18.323,0.007,temp: 248.0,0.08155766367985864
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+21,Tinker(AA),173.0,703.4,1.6,,,temp: 173.0,-0.2374623140013598
+22,Tinker(AA),248.0,648.9,1.8,,,temp: 248.0,-0.41414630830990995
+23,Tinker(AA),298.0,613.9,2.1,,,temp: 298.0,-0.4337329298752072
+24,TOWHEE(BS),98.0,762.31,0.34,-27.187,0.01,temp: 98.0,-0.04813869939349292
+25,TOWHEE(BS),173.0,705.66,0.42,-22.584,0.031,temp: 173.0,0.08307128732129589
+26,TOWHEE(BS),248.0,652.21,0.45,-18.316,0.024,temp: 248.0,0.09383516143812538
+27,TOWHEE(BS),298.0,617.38,0.35,-15.697,0.014,temp: 298.0,0.13067594682138206
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 5 MPa_data.csv
new file mode 100644
index 00000000..552b0957
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 5 MPa_data.csv	
@@ -0,0 +1,29 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,753.48,1.15,-26.246,0.088,temp: 98.0,0.09545805633733052
+1,DL_POLY(BS),173.0,687.41,1.61,-22.064,0.134,temp: 173.0,0.047197272122416714
+2,DL_POLY(BS),248.0,624.43,2.28,-17.399,0.175,temp: 248.0,0.0757835931955047
+3,DL_POLY(BS),298.0,579.14,3.03,-14.426,0.208,temp: 298.0,0.047136401888430775
+4,GROMACS(BS),98.0,749.93,0.06,-26.919,0.003,temp: 98.0,-0.3761389019097422
+5,GROMACS(BS),173.0,686.63,0.1,-22.057,0.004,temp: 173.0,-0.06632568196939567
+6,GROMACS(BS),248.0,624.24,0.15,-17.408,0.006,temp: 248.0,0.04533278384505513
+7,GROMACS(BS),298.0,578.71,0.26,-14.447,0.009,temp: 298.0,-0.02714661888859554
+8,GROMACS(KL),98.0,749.6,0.08,-27.019,0.004,temp: 98.0,-0.4199774923946708
+9,GROMACS(KL),173.0,686.72,0.15,-22.021,0.006,temp: 173.0,-0.05322687957418147
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+11,GROMACS(KL),298.0,579.4,0.19,-14.445,0.009,temp: 298.0,0.09205171677686895
+12,ms2(KL),98.0,762.18,0.07,-23.551,0.005,temp: 98.0,1.2512027145766043
+13,ms2(KL),173.0,688.11,0.18,-21.8,0.029,temp: 173.0,0.14907684630738674
+14,ms2(KL),248.0,624.55,0.19,-17.394,0.008,temp: 248.0,0.09501568331158484
+15,ms2(KL),298.0,579.47,0.23,-14.411,0.012,temp: 298.0,0.10414430155453445
+16,ms2(PB),98.0,751.24,0.07,-27.05,0.004,temp: 98.0,-0.20211298210588832
+17,ms2(PB),173.0,687.12,0.11,-22.12,0.005,temp: 173.0,0.004990019960080029
+18,ms2(PB),248.0,624.24,0.16,-17.454,0.008,temp: 248.0,0.04533278384505513
+19,ms2(PB),298.0,579.56,0.2,-14.416,0.008,temp: 298.0,0.11969191055436483
+20,Tinker(AA),98.0,751.1,1.2,,,temp: 98.0,-0.22071117200858759
+21,Tinker(AA),173.0,685.9,1.7,,,temp: 173.0,-0.1725715236194316
+22,Tinker(AA),248.0,621.0,2.2,,,temp: 248.0,-0.4739336492890902
+23,Tinker(AA),298.0,576.3,2.4,,,temp: 298.0,-0.44347703766222424
+24,TOWHEE(BS),98.0,751.8,0.6,-26.993,0.037,temp: 98.0,-0.12772022249509116
+25,TOWHEE(BS),173.0,687.71,0.35,-22.054,0.025,temp: 173.0,0.09085994677312524
+26,TOWHEE(BS),248.0,625.0,0.29,-17.438,0.016,temp: 248.0,0.1671360212468899
+27,TOWHEE(BS),298.0,579.49,0.51,-14.425,0.02,temp: 298.0,0.10759932577671896
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 70 MPa_data.csv
new file mode 100644
index 00000000..836c2080
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLS n-Butane at 70 MPa_data.csv	
@@ -0,0 +1,29 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,773.75,0.99,-26.728,0.086,temp: 98.0,0.13810877868947308
+1,DL_POLY(BS),173.0,717.44,1.56,-22.965,0.134,temp: 173.0,0.052794839254665014
+2,DL_POLY(BS),248.0,669.19,1.76,-18.789,0.184,temp: 248.0,0.0743454714421554
+3,DL_POLY(BS),298.0,638.6,2.05,-16.361,0.202,temp: 298.0,0.12475837863384596
+4,GROMACS(BS),98.0,770.17,0.06,-27.405,0.003,temp: 98.0,-0.32521197016702114
+5,GROMACS(BS),173.0,717.03,0.09,-22.857,0.004,temp: 173.0,-0.004382967787181831
+6,GROMACS(BS),248.0,669.04,0.11,-18.85,0.006,temp: 248.0,0.05191364816218198
+7,GROMACS(BS),298.0,638.14,0.13,-16.346,0.007,temp: 298.0,0.05263594071625247
+8,GROMACS(KL),98.0,769.98,0.16,-27.515,0.009,temp: 98.0,-0.3498016188493411
+9,GROMACS(KL),173.0,716.63,0.06,-22.932,0.016,temp: 173.0,-0.06016619416945713
+10,GROMACS(KL),248.0,668.81,0.09,-18.82,0.011,temp: 248.0,0.017518185799574176
+11,GROMACS(KL),298.0,637.8,0.12,-16.382,0.004,temp: 298.0,-0.0006719481793608897
+12,ms2(KL),98.0,781.89,0.11,-23.977,0.006,temp: 98.0,1.1915810959218234
+13,ms2(KL),173.0,717.9,0.18,-22.673,0.022,temp: 173.0,0.11694554959427446
+14,ms2(KL),248.0,669.2,0.13,-18.816,0.009,temp: 248.0,0.07584092632748471
+15,ms2(KL),298.0,638.05,0.15,-16.351,0.012,temp: 298.0,0.03852502894976291
+16,ms2(PB),98.0,770.68,0.05,-27.492,0.003,temp: 98.0,-0.2592081763355113
+17,ms2(PB),173.0,717.01,0.09,-23.014,0.004,temp: 173.0,-0.007172129106293218
+18,ms2(PB),248.0,669.12,0.1,-18.853,0.005,temp: 248.0,0.06387728724483334
+19,ms2(PB),298.0,638.34,0.13,-16.314,0.007,temp: 298.0,0.08399352241955864
+20,Tinker(AA),98.0,771.4,1.0,,,temp: 98.0,-0.1660263497498452
+21,Tinker(AA),173.0,715.7,1.5,,,temp: 173.0,-0.1898621955082476
+22,Tinker(AA),248.0,666.3,1.9,,,temp: 248.0,-0.35784099041841905
+23,Tinker(AA),298.0,635.3,2.0,,,temp: 298.0,-0.3926417194705989
+24,TOWHEE(BS),98.0,770.91,0.74,-27.394,0.045,temp: 98.0,-0.2294417595095334
+25,TOWHEE(BS),173.0,717.72,0.76,-22.915,0.041,temp: 173.0,0.09184309772225614
+26,TOWHEE(BS),248.0,669.19,0.3,-18.828,0.015,temp: 248.0,0.0743454714421554
+27,TOWHEE(BS),298.0,638.4,0.35,-16.35,0.022,temp: 298.0,0.0934007969305398
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 41 MPa_data.csv
new file mode 100644
index 00000000..974fc5c6
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 41 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
+1,DL_POLY(BS),173.0,,,,,temp: 173.0,
+2,DL_POLY(BS),248.0,530.89,0.47,-11.366,0.166,temp: 248.0,2.3879397631024886
+3,DL_POLY(BS),298.0,486.69,6.41,-9.826,0.192,temp: 298.0,3.19846906049721
+4,GROMACS(BS),98.0,663.92,0.09,-16.498,0.002,temp: 98.0,0.9955567110534355
+5,GROMACS(BS),173.0,597.04,0.11,-13.81,0.002,temp: 173.0,2.008977804010499
+6,GROMACS(BS),248.0,529.0,0.14,-11.352,0.003,temp: 248.0,2.0234326031404204
+7,GROMACS(BS),298.0,481.76,0.2,-9.791,0.004,temp: 298.0,2.1531045523539323
+8,GROMACS(KL),98.0,657.37,1.5,-16.409,0.019,temp: 98.0,-0.0008297458350381748
+9,GROMACS(KL),173.0,593.43,2.9,-13.776,0.029,temp: 173.0,1.3921809229431008
+10,GROMACS(KL),248.0,533.52,4.7,-11.398,0.036,temp: 248.0,2.8951640121502367
+11,GROMACS(KL),298.0,489.68,5.5,-9.846,0.034,temp: 298.0,3.832473092819402
+12,Ind.MC(PB),98.0,663.88,0.07,-16.487,0.003,temp: 98.0,0.9894719082632824
+13,Ind.MC(PB),173.0,595.02,0.1,-13.771,0.004,temp: 173.0,1.6638449231916268
+14,Ind.MC(PB),248.0,527.92,0.12,-11.33,0.004,temp: 248.0,1.8151427974478007
+15,Ind.MC(PB),298.0,482.02,0.13,-9.775,0.004,temp: 298.0,2.2082353377732513
+16,LAMMPS(AA),98.0,647.39,0.8,-16.265,0.029,temp: 98.0,-1.5189880419796267
+17,LAMMPS(AA),173.0,572.69,1.16,-13.569,0.037,temp: 173.0,-2.151411130613055
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+19,LAMMPS(AA),298.0,463.26,2.82,-9.678,0.06,temp: 298.0,-1.7696628717131297
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+23,LAMMPS(KL),298.0,447.63,6.63,-9.594,0.159,temp: 298.0,-5.083871241343841
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+27,ms2(KL),298.0,460.38,0.56,-9.617,0.005,temp: 298.0,-2.380342340973298
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+29,ms2*(KL),173.0,595.41,0.09,-13.79,0.003,temp: 173.0,1.730479489290318
+30,ms2*(KL),248.0,528.03,0.09,-11.344,0.003,temp: 248.0,1.8363574998794583
+31,ms2*(KL),298.0,482.1,0.12,-9.788,0.003,temp: 298.0,2.22519865636382
+32,ms2(PB),98.0,650.41,0.05,-16.291,0.002,temp: 98.0,-1.0595854313226505
+33,ms2(PB),173.0,591.39,0.07,-13.728,0.002,temp: 173.0,1.0436308848884013
+34,ms2(PB),248.0,526.25,0.12,-11.303,0.004,temp: 248.0,1.4930650423490475
+35,ms2(PB),298.0,473.44,0.17,-9.706,0.005,temp: 298.0,0.3889194189356657
+36,NAMD(FM),98.0,647.95,3.14,-16.27,0.084,temp: 98.0,-1.4338008029173965
+37,NAMD(FM),173.0,571.53,4.72,-13.558,0.13,temp: 173.0,-2.349606250291235
+38,NAMD(FM),248.0,498.61,6.68,-11.133,0.176,temp: 248.0,-3.8376110959322376
+39,NAMD(FM),298.0,452.69,7.93,-9.61,0.198,temp: 298.0,-4.010941340490904
+40,Tinker(AA),98.0,654.1,0.9,,,temp: 98.0,-0.4982623739305058
+41,Tinker(AA),173.0,580.9,0.8,,,temp: 173.0,-0.7486680853046696
+42,Tinker(AA),248.0,508.9,1.2,,,temp: 248.0,-1.8530721139165263
+43,Tinker(AA),298.0,457.5,6.2,,,temp: 298.0,-2.991021810233467
+44,TOWHEE(BS),98.0,688.06,0.79,-17.543,0.034,temp: 98.0,4.667735194914185
+45,TOWHEE(BS),173.0,595.09,0.31,-13.786,0.01,temp: 173.0,1.6758049735170417
+46,TOWHEE(BS),248.0,528.0,0.6,-11.343,0.017,temp: 248.0,1.8305716719435576
+47,TOWHEE(BS),298.0,482.12,0.61,-9.789,0.018,temp: 298.0,2.229439486011456
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 5 MPa_data.csv
new file mode 100644
index 00000000..344226e0
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 5 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
+1,DL_POLY(BS),173.0,,,,,temp: 173.0,
+2,DL_POLY(BS),248.0,492.13,7.15,-10.439,0.198,temp: 248.0,4.969818484312014
+3,DL_POLY(BS),298.0,418.9,10.13,-8.216,0.263,temp: 298.0,29.599455490471065
+4,GROMACS(BS),98.0,652.5,0.14,-16.298,0.003,temp: 98.0,1.378836929439482
+5,GROMACS(BS),173.0,580.91,0.18,-13.409,0.003,temp: 173.0,3.4305211821814665
+6,GROMACS(BS),248.0,483.17,0.27,-10.327,0.006,temp: 248.0,3.0586779856237936
+7,GROMACS(BS),298.0,402.0,0.43,-7.968,0.01,temp: 298.0,24.370926491213588
+8,GROMACS(KL),98.0,645.03,1.9,-16.185,0.029,temp: 98.0,0.21822403769555
+9,GROMACS(KL),173.0,570.61,3.8,-13.282,0.049,temp: 173.0,1.5966151241406954
+10,GROMACS(KL),248.0,483.35,6.3,-10.33,0.081,temp: 248.0,3.0970714331420854
+11,GROMACS(KL),298.0,375.37,12.0,-7.538,0.2,temp: 298.0,16.13212606220608
+12,Ind.MC(PB),98.0,650.48,0.08,-16.246,0.003,temp: 98.0,1.0649898020870439
+13,Ind.MC(PB),173.0,572.09,0.12,-13.278,0.004,temp: 173.0,1.8601278392766554
+14,Ind.MC(PB),248.0,483.25,0.18,-10.311,0.005,temp: 248.0,3.075741740076364
+15,Ind.MC(PB),298.0,399.5,0.4,-7.91,0.009,temp: 298.0,23.59747545582047
+16,LAMMPS(AA),98.0,632.39,0.99,-15.982,0.035,temp: 98.0,-1.7456510562325938
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 70 MPa_data.csv
new file mode 100644
index 00000000..8f815da0
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Ethane at 70 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
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+2,DL_POLY(BS),248.0,561.46,6.51,-11.885,0.162,temp: 248.0,3.294371604135743
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+5,GROMACS(BS),173.0,610.99,0.12,-14.09,0.002,temp: 173.0,1.7231619605754094
+6,GROMACS(BS),248.0,550.6,0.13,-11.838,0.003,temp: 248.0,1.2964075895649534
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+10,GROMACS(KL),248.0,556.08,3.7,-11.863,0.018,temp: 248.0,2.3045883261992026
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 41 MPa_data.csv
new file mode 100644
index 00000000..b9a3b41a
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 41 MPa_data.csv	
@@ -0,0 +1,49 @@
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+6,GROMACS(BS),248.0,590.46,0.13,-13.427,0.004,temp: 248.0,2.148198445616026
+7,GROMACS(BS),298.0,546.13,0.18,-11.306,0.005,temp: 298.0,1.387245141857318
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+9,GROMACS(KL),173.0,646.15,3.0,-16.739,0.035,temp: 173.0,1.6873375290248134
+10,GROMACS(KL),248.0,596.03,2.4,-13.473,0.018,temp: 248.0,3.1117954129670315
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 5 MPa_data.csv
new file mode 100644
index 00000000..b60aa8be
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 5 MPa_data.csv	
@@ -0,0 +1,49 @@
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+45,TOWHEE(BS),173.0,623.71,0.61,-16.174,0.025,temp: 173.0,1.674316039378601
+46,TOWHEE(BS),248.0,549.25,0.39,-12.397,0.015,temp: 248.0,2.0063887478797158
+47,TOWHEE(BS),298.0,491.51,0.49,-9.78,0.015,temp: 298.0,2.7547038327526154
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 70 MPa_data.csv
new file mode 100644
index 00000000..b89a53f5
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER Propane at 70 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
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+2,DL_POLY(BS),248.0,602.24,4.54,-13.897,0.188,temp: 248.0,0.6310606609149138
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+4,GROMACS(BS),98.0,712.49,0.06,-20.53,0.002,temp: 98.0,0.8137244939305266
+5,GROMACS(BS),173.0,660.25,0.1,-17.056,0.003,temp: 173.0,1.519915739623151
+6,GROMACS(BS),248.0,608.58,0.11,-13.924,0.003,temp: 248.0,1.6904405171021541
+7,GROMACS(BS),298.0,573.08,0.15,-11.971,0.004,temp: 298.0,1.2977141964925147
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+9,GROMACS(KL),173.0,660.15,2.6,-17.062,0.026,temp: 173.0,1.5045397584433484
+10,GROMACS(KL),248.0,612.98,2.4,-13.943,0.012,temp: 248.0,2.4256568210806733
+11,GROMACS(KL),298.0,581.04,1.9,-11.992,0.006,temp: 298.0,2.704725093756549
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+34,ms2(PB),248.0,595.18,0.13,-13.854,0.003,temp: 248.0,-0.5486273177415442
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+41,Tinker(AA),173.0,656.2,1.1,,,temp: 173.0,0.897188501841297
+42,Tinker(AA),248.0,595.4,1.2,,,temp: 248.0,-0.5118665025426135
+43,Tinker(AA),298.0,562.0,2.4,,,temp: 298.0,-0.6607884092468952
+44,TOWHEE(BS),98.0,717.61,0.48,-20.614,0.024,temp: 98.0,1.5381785485964514
+45,TOWHEE(BS),173.0,659.06,0.4,-17.051,0.016,temp: 173.0,1.336941563583534
+46,TOWHEE(BS),248.0,605.57,0.39,-13.904,0.016,temp: 248.0,1.1874857273350299
+47,TOWHEE(BS),298.0,571.9,0.31,-11.973,0.013,temp: 298.0,1.0891372041845166
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 41 MPa_data.csv
new file mode 100644
index 00000000..26ded822
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 41 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
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+2,DL_POLY(BS),248.0,666.51,4.21,-16.44,0.235,temp: 248.0,0.20182886265487396
+3,DL_POLY(BS),298.0,626.77,5.03,-13.417,0.282,temp: 298.0,-0.411397307578755
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+5,GROMACS(BS),173.0,721.03,0.13,-21.232,0.004,temp: 173.0,0.09231431288306309
+6,GROMACS(BS),248.0,665.83,0.18,-16.499,0.004,temp: 248.0,0.09959897319094949
+7,GROMACS(BS),298.0,630.23,0.22,-13.507,0.005,temp: 298.0,0.13836826083674217
+8,GROMACS(KL),98.0,782.52,0.47,-26.482,0.014,temp: 98.0,0.7869553457580419
+9,GROMACS(KL),173.0,723.85,0.27,-21.299,0.007,temp: 173.0,0.48378252691345747
+10,GROMACS(KL),248.0,666.62,0.28,-16.526,0.005,temp: 248.0,0.21836605065639442
+11,GROMACS(KL),298.0,630.58,0.37,-13.523,0.005,temp: 298.0,0.1939803848094111
+12,Ind.MC(PB),98.0,777.7,0.1,-26.234,0.006,temp: 98.0,0.1661493283188106
+13,Ind.MC(PB),173.0,720.35,0.08,-21.16,0.005,temp: 173.0,-0.002082277734186513
+14,Ind.MC(PB),248.0,664.65,0.11,-16.42,0.006,temp: 248.0,-0.07779995264353032
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+41,Tinker(AA),173.0,716.6,0.9,,,temp: 173.0,-0.5226517112852336
+42,Tinker(AA),248.0,665.6,2.9,,,temp: 248.0,0.06502121646049916
+43,Tinker(AA),298.0,632.8,2.9,,,temp: 298.0,0.5467201425788745
+44,TOWHEE(BS),98.0,779.97,0.47,-26.4,0.027,temp: 98.0,0.4585206269883257
+45,TOWHEE(BS),173.0,720.44,0.66,-21.212,0.03,temp: 173.0,0.010411388671043036
+46,TOWHEE(BS),248.0,664.98,0.64,-16.48,0.032,temp: 248.0,-0.028188388638968928
+47,TOWHEE(BS),298.0,628.84,0.35,-13.478,0.014,temp: 298.0,-0.08249131722612647
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 5 MPa_data.csv
new file mode 100644
index 00000000..b4b4cc13
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 5 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
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+2,DL_POLY(BS),248.0,637.54,4.96,-15.543,0.244,temp: 248.0,-0.18435380273751842
+3,DL_POLY(BS),298.0,592.91,6.04,-12.255,0.311,temp: 298.0,-0.0980075541639427
+4,GROMACS(BS),98.0,772.17,0.07,-26.23,0.003,temp: 98.0,0.9159084590543968
+5,GROMACS(BS),173.0,704.59,0.15,-20.722,0.004,temp: 173.0,0.20562402447105174
+6,GROMACS(BS),248.0,640.41,0.16,-15.638,0.004,temp: 248.0,0.2649841283509526
+7,GROMACS(BS),298.0,593.49,0.24,-12.269,0.007,temp: 298.0,-0.00028082393745132683
+8,GROMACS(KL),98.0,771.26,0.31,-26.206,0.009,temp: 98.0,0.7969793674065261
+9,GROMACS(KL),173.0,706.09,0.43,-20.768,0.011,temp: 173.0,0.4189515426542599
+10,GROMACS(KL),248.0,640.36,0.48,-15.652,0.01,temp: 248.0,0.2571559413981989
+11,GROMACS(KL),298.0,595.3,0.64,-12.312,0.012,temp: 298.0,0.3046939721141549
+12,Ind.MC(PB),98.0,767.35,0.12,-26.001,0.007,temp: 98.0,0.28597634724917803
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+15,Ind.MC(PB),298.0,591.7,0.17,-12.165,0.008,temp: 298.0,-0.30188573273987146
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+41,Tinker(AA),173.0,703.0,2.7,,,temp: 173.0,-0.020503144803153323
+42,Tinker(AA),248.0,639.6,4.2,,,temp: 248.0,0.13816749971623604
+43,Tinker(AA),298.0,603.0,4.4,,,temp: 298.0,1.6021005630519738
+44,TOWHEE(BS),98.0,768.59,0.58,-26.113,0.035,temp: 98.0,0.44803357103309654
+45,TOWHEE(BS),173.0,703.11,0.62,-20.681,0.034,temp: 173.0,-0.004859126803049458
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+47,TOWHEE(BS),298.0,592.81,0.77,-12.255,0.031,temp: 298.0,-0.11485699040989215
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 70 MPa_data.csv
new file mode 100644
index 00000000..335ea0c4
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER iso-Butane at 70 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
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+2,DL_POLY(BS),248.0,681.35,4.22,-16.988,0.222,temp: 248.0,-0.07968010637050871
+3,DL_POLY(BS),298.0,649.48,4.57,-14.135,0.269,temp: 298.0,-0.13262242587558934
+4,GROMACS(BS),98.0,789.47,0.05,-26.616,0.003,temp: 98.0,0.7083307723797471
+5,GROMACS(BS),173.0,734.71,0.1,-21.599,0.003,temp: 173.0,0.38838997496132655
+6,GROMACS(BS),248.0,683.36,0.13,-17.026,0.003,temp: 248.0,0.21508740369949106
+7,GROMACS(BS),298.0,650.81,0.15,-14.162,0.004,temp: 298.0,0.07188519895285526
+8,GROMACS(KL),98.0,789.61,0.71,-26.629,0.022,temp: 98.0,0.7261897997121748
+9,GROMACS(KL),173.0,734.11,0.29,-21.598,0.007,temp: 173.0,0.30640792219904067
+10,GROMACS(KL),248.0,683.31,0.36,-17.04,0.006,temp: 248.0,0.20775487857336164
+11,GROMACS(KL),298.0,651.65,0.7,-14.185,0.005,temp: 298.0,0.20104790937083222
+12,Ind.MC(PB),98.0,786.37,0.11,-26.435,0.007,temp: 98.0,0.3128808814473753
+13,Ind.MC(PB),173.0,731.84,0.09,-21.468,0.005,temp: 173.0,-0.003757510751593444
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+41,Tinker(AA),173.0,729.3,0.9,,,temp: 173.0,-0.35081486744526785
+42,Tinker(AA),248.0,681.6,1.4,,,temp: 248.0,-0.04301748073991156
+43,Tinker(AA),298.0,656.2,1.6,,,temp: 298.0,0.9006792574681914
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+46,TOWHEE(BS),248.0,681.94,0.51,-16.48,0.032,temp: 248.0,0.006843690117705245
+47,TOWHEE(BS),298.0,649.74,0.77,-14.142,0.037,temp: 298.0,-0.09264349169859937
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 41 MPa_data.csv
new file mode 100644
index 00000000..74ac954b
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 41 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
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+2,DL_POLY(BS),248.0,661.01,4.42,-15.626,0.242,temp: 248.0,0.06585172009235274
+3,DL_POLY(BS),298.0,626.74,5.03,-12.357,0.295,temp: 298.0,0.11168068110573022
+4,GROMACS(BS),98.0,773.49,0.07,-26.49,0.003,temp: 98.0,0.47590424759301814
+5,GROMACS(BS),173.0,717.81,0.09,-20.801,0.006,temp: 173.0,0.7165206654475078
+6,GROMACS(BS),248.0,662.05,0.13,-15.668,0.006,temp: 248.0,0.22329031525565196
+7,GROMACS(BS),298.0,628.42,0.16,-12.429,0.007,temp: 298.0,0.3800337837388039
+8,GROMACS(KL),98.0,768.71,1.6,-26.302,0.043,temp: 98.0,-0.1450149915742521
+9,GROMACS(KL),173.0,719.3,2.0,-21.353,0.042,temp: 173.0,0.9255838100004086
+10,GROMACS(KL),248.0,667.41,1.2,-16.607,0.02,temp: 248.0,1.0347046134049935
+11,GROMACS(KL),298.0,629.48,0.43,-13.48,0.008,temp: 298.0,0.5493518127811149
+12,Ind.MC(PB),98.0,768.37,0.08,-26.274,0.005,temp: 98.0,-0.18918079519703304
+13,Ind.MC(PB),173.0,714.07,0.12,-21.306,0.006,temp: 173.0,0.19175814153621762
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+42,Tinker(AA),248.0,661.2,1.3,,,temp: 248.0,0.09461454036258014
+43,Tinker(AA),298.0,626.7,2.1,,,temp: 298.0,0.1052913215192341
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+46,TOWHEE(BS),248.0,662.41,0.57,-15.662,0.033,temp: 248.0,0.2777882905044903
+47,TOWHEE(BS),298.0,627.19,0.4,-12.397,0.023,temp: 298.0,0.18356097645388436
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 5 MPa_data.csv
new file mode 100644
index 00000000..ecf5be9a
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 5 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
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+2,DL_POLY(BS),248.0,637.12,4.75,-14.826,0.262,temp: 248.0,0.5340027324739749
+3,DL_POLY(BS),298.0,588.35,6.5,-11.139,0.322,temp: 298.0,-0.14624059831356792
+4,GROMACS(BS),98.0,763.68,0.09,-26.284,0.004,temp: 98.0,0.6954783121525125
+5,GROMACS(BS),173.0,700.97,0.1,-20.498,0.004,temp: 173.0,0.751838272724327
+6,GROMACS(BS),248.0,637.72,0.17,-14.808,0.007,temp: 248.0,0.6286794050623203
+7,GROMACS(BS),298.0,591.73,0.31,-11.202,0.011,temp: 298.0,0.4274072418796839
+8,GROMACS(KL),98.0,756.89,1.8,-26.004,0.054,temp: 98.0,-0.19982115587010418
+9,GROMACS(KL),173.0,705.02,1.8,-20.909,0.044,temp: 173.0,1.3339529780676782
+10,GROMACS(KL),248.0,641.98,0.91,-15.769,0.019,temp: 248.0,1.3008837804395463
+11,GROMACS(KL),298.0,594.25,1.2,-12.292,0.026,temp: 298.0,0.8550973475858927
+12,Ind.MC(PB),98.0,759.16,0.11,-28.588,0.006,temp: 98.0,0.09949103741580664
+13,Ind.MC(PB),173.0,696.86,0.14,-25.176,0.007,temp: 173.0,0.16109964582032493
+14,Ind.MC(PB),248.0,632.68,0.14,-21.964,0.006,temp: 248.0,-0.16660464467976369
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+29,ms2*(KL),173.0,708.52,0.07,-17.822,0.006,temp: 173.0,1.837015069105148
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+39,NAMD(FM),298.0,572.33,6.38,-11.762,0.313,temp: 298.0,-2.8651276988744834
+40,Tinker(AA),98.0,757.1,1.3,,,temp: 98.0,-0.17213148160135852
+41,Tinker(AA),173.0,696.6,1.8,,,temp: 173.0,0.12372931905754278
+42,Tinker(AA),248.0,633.8,1.9,,,temp: 248.0,0.010125144151808546
+43,Tinker(AA),298.0,590.5,2.2,,,temp: 298.0,0.2186537379040297
+44,TOWHEE(BS),98.0,761.37,0.89,-26.183,0.047,temp: 98.0,0.39089189519637013
+45,TOWHEE(BS),173.0,698.22,0.55,-20.341,0.039,temp: 173.0,0.3565752011948865
+46,TOWHEE(BS),248.0,635.65,0.68,-14.78,0.033,temp: 248.0,0.30204488463253293
+47,TOWHEE(BS),298.0,590.99,0.64,-11.19,0.034,temp: 298.0,0.30181570290246135
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 70 MPa_data.csv
new file mode 100644
index 00000000..f8f1aad5
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/OPLSAMBER n-Butane at 70 MPa_data.csv	
@@ -0,0 +1,49 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,,,,,temp: 98.0,
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+2,DL_POLY(BS),248.0,678.41,4.07,-16.149,0.243,temp: 248.0,0.13468684386287905
+3,DL_POLY(BS),298.0,649.1,4.73,-13.069,0.29,temp: 298.0,0.41290831579042614
+4,GROMACS(BS),98.0,781.06,0.11,-26.649,0.004,temp: 98.0,0.3704460890542576
+5,GROMACS(BS),173.0,729.76,0.09,-21.185,0.004,temp: 173.0,0.6623369159147012
+6,GROMACS(BS),248.0,679.32,0.13,-16.146,0.006,temp: 248.0,0.26900468267410443
+7,GROMACS(BS),298.0,649.26,0.14,-13.058,0.006,temp: 298.0,0.43765961039915097
+8,GROMACS(KL),98.0,776.73,1.8,-26.485,0.048,temp: 98.0,-0.1859823947569703
+9,GROMACS(KL),173.0,730.62,2.2,-21.663,0.044,temp: 173.0,0.7809644232427104
+10,GROMACS(KL),248.0,683.17,0.79,-17.09,0.013,temp: 248.0,0.8372724622599935
+11,GROMACS(KL),298.0,648.4,0.42,-14.101,0.009,temp: 298.0,0.3046214018772266
+12,Ind.MC(PB),98.0,776.2,0.1,-28.883,0.006,temp: 98.0,-0.25409026921885036
+13,Ind.MC(PB),173.0,726.09,0.11,-26.049,0.006,temp: 173.0,0.156100925340536
+14,Ind.MC(PB),248.0,676.27,0.12,-23.398,0.006,temp: 248.0,-0.18118148037447773
+15,Ind.MC(PB),298.0,645.96,0.12,-21.82,0.005,temp: 298.0,-0.0728358409058927
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+29,ms2*(KL),173.0,737.28,0.08,-18.631,0.008,temp: 173.0,1.6996379102247166
+30,ms2*(KL),248.0,684.1,0.11,-15.255,0.019,temp: 248.0,0.9745423414846496
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+42,Tinker(AA),248.0,679.2,1.3,,,temp: 248.0,0.2512924401935042
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+46,TOWHEE(BS),248.0,679.13,0.56,-16.154,0.024,temp: 248.0,0.24096029874648042
+47,TOWHEE(BS),298.0,648.04,0.51,-13.04,0.03,temp: 298.0,0.24893098900758254
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 41 MPa_data.csv
new file mode 100644
index 00000000..8c0febb5
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 41 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
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+2,DL_POLY(BS),248.0,517.67,1.34,-11.05,0.08,temp: 248.0,0.08463853545259423
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+5,GROMACS(BS),173.0,589.84,0.07,-13.125,0.002,temp: 173.0,0.06804965174034523
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+7,GROMACS(BS),298.0,468.73,0.16,-9.75,0.004,temp: 298.0,0.12056513721404713
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+30,Tinker(AA),248.0,517.4,1.6,,,temp: 248.0,0.032437611302903534
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+35,TOWHEE(BS),298.0,467.84,0.49,-9.731,0.012,temp: 298.0,-0.06953855355062512
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 5 MPa_data.csv
new file mode 100644
index 00000000..1908fd34
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 5 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,648.9,1.01,-15.23,0.051,temp: 98.0,0.045225149294378805
+1,DL_POLY(BS),173.0,562.59,1.39,-12.577,0.068,temp: 173.0,0.07075476359409438
+2,DL_POLY(BS),248.0,459.96,1.78,-9.834,0.079,temp: 248.0,0.30554660373198056
+3,DL_POLY(BS),298.0,337.81,2.52,-7.091,0.094,temp: 298.0,16.417070322222436
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+5,GROMACS(BS),173.0,562.67,0.09,-12.578,0.002,temp: 173.0,0.08498477191468354
+6,GROMACS(BS),248.0,460.02,0.18,-9.835,0.004,temp: 248.0,0.3186310736776805
+7,GROMACS(BS),298.0,333.74,0.45,-7.01,0.009,temp: 298.0,15.014455017135422
+8,GROMACS(KL),98.0,648.64,0.09,-15.223,0.003,temp: 98.0,0.005139221510720881
+9,GROMACS(KL),173.0,562.15,0.46,-12.567,0.01,temp: 173.0,-0.007510282169226788
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+29,Tinker(AA),173.0,562.1,1.4,,,temp: 173.0,-0.016404037369595004
+30,Tinker(AA),248.0,459.9,1.5,,,temp: 248.0,0.2924621337862806
+31,Tinker(AA),298.0,336.2,3.6,,,temp: 298.0,15.862227412839117
+32,TOWHEE(BS),98.0,648.53,0.53,-15.22,0.017,temp: 98.0,-0.011820209474675554
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+34,TOWHEE(BS),248.0,459.49,0.74,-9.825,0.017,temp: 248.0,0.20305158915734134
+35,TOWHEE(BS),298.0,327.11,0.27,-6.899,0.052,temp: 298.0,12.729605023836427
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 70 MPa_data.csv
new file mode 100644
index 00000000..8c541fe4
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Ethane at 70 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,673.44,0.91,-15.612,0.05,temp: 98.0,0.004784949518766969
+1,DL_POLY(BS),173.0,606.29,1.09,-13.426,0.069,temp: 173.0,0.029514207149402383
+2,DL_POLY(BS),248.0,543.93,1.15,-11.569,0.081,temp: 248.0,0.039849430459663174
+3,DL_POLY(BS),298.0,503.98,1.37,-10.458,0.09,temp: 298.0,0.02624249387491994
+4,GROMACS(BS),98.0,673.45,0.05,-15.613,0.002,temp: 98.0,0.006269933852180901
+5,GROMACS(BS),173.0,606.56,0.06,-13.433,0.002,temp: 173.0,0.07406049495874861
+6,GROMACS(BS),248.0,544.21,0.09,-11.576,0.002,temp: 248.0,0.09134715597680206
+7,GROMACS(BS),298.0,504.0,0.11,-10.457,0.003,temp: 298.0,0.03021194672994529
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+9,GROMACS(KL),173.0,606.29,0.08,-13.427,0.002,temp: 173.0,0.029514207149402383
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+17,LAMMPS(KL),173.0,605.92,1.16,-13.421,0.035,temp: 173.0,-0.03153070577452138
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 41 MPa_data.csv
new file mode 100644
index 00000000..2bd47883
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 41 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
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+35,TOWHEE(BS),298.0,565.04,0.33,-13.13,0.011,temp: 298.0,0.03245661141173518
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 5 MPa_data.csv
new file mode 100644
index 00000000..428ecffe
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 5 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,728.3,1.1,-20.392,0.072,temp: 98.0,0.1133295047714482
+1,DL_POLY(BS),173.0,648.6,2.0,-16.978,0.102,temp: 173.0,-0.20907305248365196
+2,DL_POLY(BS),248.0,568.23,3.07,-13.806,0.124,temp: 248.0,-0.025413606444880818
+3,DL_POLY(BS),298.0,502.05,7.94,-11.516,0.204,temp: 298.0,-0.294584933139153
+4,GROMACS(BS),98.0,725.55,0.07,-20.353,0.002,temp: 98.0,-0.2646900697694299
+5,GROMACS(BS),173.0,649.34,0.12,-16.995,0.003,temp: 173.0,-0.09521969765608027
+6,GROMACS(BS),248.0,567.75,0.21,-13.8,0.005,temp: 248.0,-0.10986497555405893
+7,GROMACS(BS),298.0,503.05,0.41,-11.544,0.01,temp: 298.0,-0.09598834900040021
+8,GROMACS(KL),98.0,724.93,0.21,-20.34,0.003,temp: 98.0,-0.34991630112046485
+9,GROMACS(KL),173.0,649.19,0.14,-16.996,0.004,temp: 173.0,-0.11829808039139507
+10,GROMACS(KL),248.0,568.26,0.31,-13.808,0.005,temp: 248.0,-0.020135395875562186
+11,GROMACS(KL),298.0,503.58,0.32,-11.561,0.009,temp: 298.0,0.009267840593133368
+12,LAMMPS(AA),98.0,728.02,0.88,-20.391,0.036,temp: 98.0,0.07484023900001709
+13,LAMMPS(AA),173.0,650.09,1.3,-17.002,0.047,temp: 173.0,0.02017221602051125
+14,LAMMPS(AA),248.0,568.47,2.33,-13.817,0.073,temp: 248.0,0.016812078109708233
+15,LAMMPS(AA),298.0,503.85,4.33,-11.56,0.125,temp: 298.0,0.06288891831060431
+16,LAMMPS(KL),98.0,728.83,1.23,-20.412,0.038,temp: 98.0,0.18618418641024748
+17,LAMMPS(KL),173.0,650.71,1.86,-17.007,0.053,temp: 173.0,0.11556286465982761
+18,LAMMPS(KL),248.0,568.45,2.84,-13.805,0.076,temp: 248.0,0.013293271063495815
+19,LAMMPS(KL),298.0,504.14,4.54,-11.55,0.11,temp: 298.0,0.12048192771083538
+20,ms2(KL),98.0,727.7,0.12,-20.38,0.004,temp: 98.0,0.03085250668981457
+21,ms2(KL),173.0,650.15,0.23,-16.99,0.005,temp: 173.0,0.029403569114630174
+22,ms2(KL),248.0,568.13,0.31,-13.788,0.006,temp: 248.0,-0.043007641675962924
+23,ms2(KL),298.0,504.5,0.54,-11.54,0.009,temp: 298.0,0.19197669800078912
+24,ms2(PB),98.0,727.88,0.07,-20.385,0.002,temp: 98.0,0.05559560611430154
+25,ms2(PB),173.0,650.14,0.13,-16.991,0.003,temp: 173.0,0.027865010265610352
+26,ms2(PB),248.0,568.8,0.2,-13.795,0.005,temp: 248.0,0.07487239437225318
+27,ms2(PB),298.0,503.23,0.32,-11.529,0.008,temp: 298.0,-0.06024096385542334
+28,Tinker(AA),98.0,728.2,1.2,,,temp: 98.0,0.09958333842451966
+29,Tinker(AA),173.0,651.0,1.6,,,temp: 173.0,0.16018107128143738
+30,Tinker(AA),248.0,568.5,2.5,,,temp: 248.0,0.022090288679026866
+31,Tinker(AA),298.0,503.9,2.8,,,temp: 298.0,0.07281874751753291
+32,TOWHEE(BS),98.0,727.87,0.49,-20.387,0.021,temp: 98.0,0.05422098947960868
+33,TOWHEE(BS),173.0,650.41,0.46,-17.005,0.017,temp: 173.0,0.0694060991891805
+34,TOWHEE(BS),248.0,568.78,0.31,-13.811,0.009,temp: 248.0,0.07135358732604076
+35,TOWHEE(BS),298.0,503.5,0.53,-11.537,0.002,temp: 298.0,-0.0066198861379636945
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 70 MPa_data.csv
new file mode 100644
index 00000000..1a6fc7ff
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE Propane at 70 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,750.94,1.01,-20.832,0.071,temp: 98.0,0.04396424241615848
+1,DL_POLY(BS),173.0,687.02,3.67,-17.869,0.124,temp: 173.0,-0.02296025612004679
+2,DL_POLY(BS),248.0,628.96,1.98,-15.348,0.122,temp: 248.0,-0.09283608958859124
+3,DL_POLY(BS),298.0,593.07,2.34,-13.815,0.134,temp: 298.0,0.04329637210139121
+4,GROMACS(BS),98.0,749.61,0.06,-20.815,0.002,temp: 98.0,-0.1332249770187066
+5,GROMACS(BS),173.0,686.55,0.09,-17.869,0.002,temp: 173.0,-0.09135594864664907
+6,GROMACS(BS),248.0,629.23,0.12,-15.356,0.003,temp: 248.0,-0.049947934132267675
+7,GROMACS(BS),298.0,592.68,0.15,-13.829,0.003,temp: 298.0,-0.022491621870871004
+8,GROMACS(KL),98.0,749.33,0.01,-20.814,0.007,temp: 98.0,-0.17052797058393657
+9,GROMACS(KL),173.0,686.74,0.09,-17.872,0.003,temp: 173.0,-0.06370662613588862
+10,GROMACS(KL),248.0,629.48,0.24,-15.357,0.005,temp: 248.0,-0.010236679080113567
+11,GROMACS(KL),298.0,592.78,0.36,-13.828,0.004,temp: 298.0,-0.00562290546772734
+12,LAMMPS(AA),98.0,750.93,0.71,-20.843,0.029,temp: 98.0,0.042631992645957635
+13,LAMMPS(AA),173.0,687.23,0.92,-17.876,0.038,temp: 173.0,0.007599521391842861
+14,LAMMPS(AA),248.0,629.6,1.36,-15.343,0.043,temp: 248.0,0.00882472334492113
+15,LAMMPS(AA),298.0,592.8,1.62,-13.821,0.052,temp: 298.0,-0.0022491621871024423
+16,LAMMPS(KL),98.0,751.04,0.84,-20.836,0.034,temp: 98.0,0.05728674011801551
+17,LAMMPS(KL),173.0,687.36,1.19,-17.873,0.037,temp: 173.0,0.02651747889919918
+18,LAMMPS(KL),248.0,629.67,1.86,-15.348,0.047,temp: 248.0,0.019943874759514167
+19,LAMMPS(KL),298.0,592.51,1.9,-13.816,0.052,temp: 298.0,-0.05116843975620181
+20,ms2(KL),98.0,750.72,0.12,-20.819,0.004,temp: 98.0,0.014654747472042729
+21,ms2(KL),173.0,687.61,0.12,-17.861,0.002,temp: 173.0,0.06289816641334722
+22,ms2(KL),248.0,629.51,0.16,-15.333,0.003,temp: 248.0,-0.005471328473859408
+23,ms2(KL),298.0,592.58,0.25,-13.805,0.003,temp: 298.0,-0.039360338273995495
+24,ms2(PB),98.0,750.74,0.06,-20.828,0.002,temp: 98.0,0.017319247012414135
+25,ms2(PB),173.0,687.23,0.08,-17.865,0.003,temp: 173.0,0.007599521391842861
+26,ms2(PB),248.0,629.75,0.11,-15.338,0.003,temp: 248.0,0.032651476376209985
+27,ms2(PB),298.0,592.96,0.16,-13.811,0.004,temp: 298.0,0.024740784057935093
+28,Tinker(AA),98.0,751.3,1.2,,,temp: 98.0,0.09192523414287407
+29,Tinker(AA),173.0,687.3,1.6,,,temp: 173.0,0.01778611389579505
+30,Tinker(AA),248.0,629.9,1.7,,,temp: 248.0,0.056478229407498845
+31,Tinker(AA),298.0,592.8,1.9,,,temp: 298.0,-0.0022491621871024423
+32,TOWHEE(BS),98.0,750.88,0.62,-20.827,0.003,temp: 98.0,0.03597074379502912
+33,TOWHEE(BS),173.0,687.56,0.42,-17.878,0.016,temp: 173.0,0.05562202891050769
+34,TOWHEE(BS),248.0,629.8,0.38,-15.352,0.014,temp: 248.0,0.04059372738663358
+35,TOWHEE(BS),298.0,593.14,0.2,-13.823,0.008,temp: 298.0,0.055104473583578346
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 41 MPa_data.csv
new file mode 100644
index 00000000..26bfdedd
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 41 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,774.64,1.21,-23.984,0.087,temp: 98.0,-0.13393285594985677
+1,DL_POLY(BS),173.0,710.34,1.6,-19.904,0.122,temp: 173.0,-0.06377850101764823
+2,DL_POLY(BS),248.0,650.01,2.04,-16.265,0.153,temp: 248.0,-0.05501160881620485
+3,DL_POLY(BS),298.0,611.16,2.4,-13.993,0.174,temp: 298.0,-0.0007272092617488972
+4,GROMACS(BS),98.0,784.33,0.06,-24.201,0.002,temp: 98.0,1.1152954186368562
+5,GROMACS(BS),173.0,713.67,0.09,-19.962,0.003,temp: 173.0,0.40471210572222843
+6,GROMACS(BS),248.0,651.72,0.18,-16.283,0.004,temp: 248.0,0.2079165463643738
+7,GROMACS(BS),298.0,612.35,0.2,-14.013,0.005,temp: 298.0,0.19398307056838393
+8,GROMACS(KL),98.0,780.97,1.2,-24.148,0.029,temp: 98.0,0.6821264813188636
+9,GROMACS(KL),173.0,713.68,0.26,-19.976,0.007,temp: 173.0,0.40611898442114563
+10,GROMACS(KL),248.0,652.06,0.56,-16.301,0.006,temp: 248.0,0.26019465909032313
+11,GROMACS(KL),298.0,612.39,0.25,-14.022,0.006,temp: 298.0,0.2005279539240124
+12,LAMMPS(AA),98.0,772.86,2.23,-23.937,0.065,temp: 98.0,-0.36340925726712225
+13,LAMMPS(AA),173.0,710.82,1.41,-19.912,0.05,temp: 173.0,0.003751676530446132
+14,LAMMPS(AA),248.0,650.06,2.28,-16.266,0.068,temp: 248.0,-0.04732365106239392
+15,LAMMPS(AA),298.0,610.79,2.82,-13.989,0.074,temp: 298.0,-0.061267380301367996
+16,LAMMPS(KL),98.0,772.61,2.07,-23.94,0.053,temp: 98.0,-0.3956390889128061
+17,LAMMPS(KL),173.0,709.13,1.43,-19.89,0.049,temp: 173.0,-0.2340108235868014
+18,LAMMPS(KL),248.0,650.4,1.95,-16.27,0.059,temp: 248.0,0.004954461663572873
+19,LAMMPS(KL),298.0,611.32,2.84,-13.995,0.073,temp: 298.0,0.025452324160802165
+20,ms2(KL),98.0,773.4,0.09,-23.953,0.004,temp: 98.0,-0.2937928209124498
+21,ms2(KL),173.0,710.07,0.23,-19.896,0.005,temp: 173.0,-0.10176422588844729
+22,ms2(KL),248.0,649.41,0.34,-16.251,0.004,temp: 248.0,-0.14726710186202346
+23,ms2(KL),298.0,609.98,0.39,-13.97,0.005,temp: 298.0,-0.19380126825295602
+24,ms2(PB),98.0,773.12,0.11,-23.939,0.003,temp: 98.0,-0.3298902323556122
+25,ms2(PB),173.0,710.06,0.11,-19.899,0.003,temp: 173.0,-0.10317110458738059
+26,ms2(PB),248.0,649.68,0.13,-16.246,0.004,temp: 248.0,-0.10575212999140945
+27,ms2(PB),298.0,610.69,0.19,-13.972,0.004,temp: 298.0,-0.07762958869043916
+28,Tinker(AA),98.0,774.6,0.9,,,temp: 98.0,-0.13908962901316146
+29,Tinker(AA),173.0,709.5,2.0,,,temp: 173.0,-0.18195631172681334
+30,Tinker(AA),248.0,649.9,2.1,,,temp: 248.0,-0.07192511587460637
+31,Tinker(AA),298.0,611.0,2.6,,,temp: 298.0,-0.026906742684281355
+32,TOWHEE(BS),98.0,774.58,0.77,-23.982,4.946,temp: 98.0,-0.14166801554481384
+33,TOWHEE(BS),173.0,709.87,0.76,-19.888,0.025,temp: 173.0,-0.1299017998668253
+34,TOWHEE(BS),248.0,650.07,0.66,-16.268,0.025,temp: 248.0,-0.04578605951161425
+35,TOWHEE(BS),298.0,610.8,0.51,-13.982,0.021,temp: 298.0,-0.059631159462460885
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 5 MPa_data.csv
new file mode 100644
index 00000000..69e6b4e9
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 5 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,757.2,,1.0,-23.574,0.083,temp: 98.0,-0.653388963237033
+1,DL_POLY(BS),173.0,686.82,,1.82,-19.268,0.123,temp: 173.0,-0.4142037573162327
+2,DL_POLY(BS),248.0,613.36,,2.7,-15.197,0.158,temp: 248.0,-0.40108471493758674
+3,DL_POLY(BS),298.0,559.21,,3.33,-12.468,0.172,temp: 298.0,-0.4273428179697816
+4,GROMACS(BS),98.0,774.12,,0.1,-23.993,0.003,temp: 98.0,1.5665590805321497
+5,GROMACS(BS),173.0,693.84,,0.11,-19.394,0.004,temp: 173.0,0.6036645191224823
+6,GROMACS(BS),248.0,618.36,,0.2,-15.27,0.005,temp: 248.0,0.41082766347853444
+7,GROMACS(BS),298.0,563.58,,0.29,-12.533,0.008,temp: 298.0,0.35077722975016706
+8,GROMACS(KL),98.0,768.61,,1.8,-23.872,0.047,temp: 98.0,0.8436327376735088
+9,GROMACS(KL),173.0,695.58,,0.24,-19.452,0.024,temp: 173.0,0.8559566560175502
+10,GROMACS(KL),248.0,618.71,,0.37,-15.297,0.012,temp: 248.0,0.4676615299676666
+11,GROMACS(KL),298.0,564.01,,0.78,-12.553,0.012,temp: 298.0,0.42734281796974105
+12,LAMMPS(AA),98.0,759.68,,2.56,-23.632,0.067,temp: 98.0,-0.32800650764912664
+13,LAMMPS(AA),173.0,688.71,,1.87,-19.299,0.057,temp: 173.0,-0.14016229827504148
+14,LAMMPS(AA),248.0,615.51,,2.98,-15.227,0.079,temp: 248.0,-0.05196239221865834
+15,LAMMPS(AA),298.0,561.96,,3.51,-12.502,0.103,temp: 298.0,0.0623208276205746
+16,LAMMPS(KL),98.0,758.51,,2.4,-23.61,0.065,temp: 98.0,-0.48151355322890554
+17,LAMMPS(KL),173.0,687.97,,1.85,-19.29,0.059,temp: 173.0,-0.24745895419593325
+18,LAMMPS(KL),248.0,616.33,,2.61,-15.239,0.077,temp: 248.0,0.08119123784159366
+19,LAMMPS(KL),298.0,560.69,,4.09,-12.478,0.105,temp: 298.0,-0.163814746888423
+20,ms2(KL),98.0,760.46,,0.29,-23.649,0.008,temp: 98.0,-0.22566847726259243
+21,ms2(KL),173.0,689.04,,0.32,-19.302,0.006,temp: 173.0,-0.09231378955357386
+22,ms2(KL),248.0,614.49,,0.44,-15.198,0.009,temp: 248.0,-0.21759251741554408
+23,ms2(KL),298.0,560.3,,0.47,-12.461,0.009,temp: 298.0,-0.23325795480852773
+24,ms2(PB),98.0,759.3,,0.13,-23.621,0.004,temp: 98.0,-0.37786349681179104
+25,ms2(PB),173.0,688.48,,0.15,-19.295,0.004,temp: 173.0,-0.17351125889910468
+26,ms2(PB),248.0,614.42,,0.19,-15.199,0.005,temp: 248.0,-0.22895929071337792
+27,ms2(PB),298.0,562.5,,0.28,-12.5,0.007,temp: 298.0,0.15847296166376532
+28,Tinker(AA),98.0,760.4,,1.0,,,temp: 98.0,-0.2335406334461789
+29,Tinker(AA),173.0,687.8,,1.8,,,temp: 173.0,-0.2721081859615538
+30,Tinker(AA),248.0,616.1,,2.7,,,temp: 248.0,0.04384326843444913
+31,Tinker(AA),298.0,560.9,,3.5,,,temp: 298.0,-0.1264222503160823
+32,TOWHEE(BS),98.0,761.34,,0.45,-23.675,0.024,temp: 98.0,-0.11021018657010571
+33,TOWHEE(BS),173.0,688.85,,0.41,-19.304,0.018,temp: 173.0,-0.11986293093865878
+34,TOWHEE(BS),248.0,615.19,,0.8,-15.214,0.028,temp: 248.0,-0.10392478443727975
+35,TOWHEE(BS),298.0,561.34,,0.59,-12.485,0.021,temp: 298.0,-0.04807606702161561
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 70 MPa_data.csv
new file mode 100644
index 00000000..20e3f06b
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE iso-Butane at 70 MPa_data.csv	
@@ -0,0 +1,37 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,785.18,1.4,-24.203,0.09,temp: 98.0,0.1031257879631787
+1,DL_POLY(BS),173.0,724.47,1.62,-20.267,0.125,temp: 173.0,-0.04568303028723616
+2,DL_POLY(BS),248.0,669.88,2.13,-16.823,0.159,temp: 248.0,-0.07143768760679588
+3,DL_POLY(BS),298.0,636.27,2.32,-14.712,0.176,temp: 298.0,0.019562259510832836
+4,GROMACS(BS),98.0,792.44,0.04,-24.359,0.002,temp: 98.0,1.0287080661931682
+5,GROMACS(BS),173.0,726.88,0.09,-20.296,0.003,temp: 173.0,0.28682197874972126
+6,GROMACS(BS),248.0,671.92,0.14,-16.846,0.003,temp: 248.0,0.23287691667647648
+7,GROMACS(BS),298.0,636.34,0.15,-14.713,0.004,temp: 298.0,0.030566030485687474
+8,GROMACS(KL),98.0,788.74,0.84,-24.299,0.023,temp: 98.0,0.5569925800428991
+9,GROMACS(KL),173.0,727.34,0.25,-20.322,0.004,temp: 173.0,0.3502876651219265
+10,GROMACS(KL),248.0,671.92,0.26,-16.854,0.006,temp: 248.0,0.23287691667647648
+11,GROMACS(KL),298.0,637.34,0.37,-14.729,0.002,temp: 298.0,0.18776275869778428
+12,LAMMPS(AA),98.0,782.98,2.04,-24.143,0.054,temp: 98.0,-0.17735369028832054
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+14,LAMMPS(AA),248.0,669.86,1.96,-16.827,0.054,temp: 248.0,-0.07442116411937433
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 41 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 41 MPa_data.csv
new file mode 100644
index 00000000..f2831288
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 41 MPa_data.csv	
@@ -0,0 +1,29 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,790.76,1.15,-24.349,0.087,temp: 98.0,0.25247311360566377
+1,DL_POLY(BS),173.0,725.28,1.69,-20.738,0.131,temp: 173.0,0.08023040121114045
+2,DL_POLY(BS),248.0,665.52,2.0,-16.533,0.173,temp: 248.0,0.03242340239589599
+3,DL_POLY(BS),298.0,627.2,2.41,-13.889,0.195,temp: 298.0,0.12794227317612056
+4,GROMACS(BS),98.0,787.17,0.09,-25.443,0.003,temp: 98.0,-0.20266672462318894
+5,GROMACS(BS),173.0,723.94,0.12,-20.776,0.028,temp: 173.0,-0.10467406153099328
+6,GROMACS(BS),248.0,664.71,0.22,-16.442,0.026,temp: 248.0,-0.0893253999780901
+7,GROMACS(BS),298.0,626.49,0.1,-13.914,0.032,temp: 298.0,0.014595909952334389
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+9,GROMACS(KL),173.0,724.29,0.1,-20.766,0.004,temp: 173.0,-0.056378119769997785
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+23,Tinker(AA),298.0,625.1,2.1,,,temp: 298.0,-0.20730753354210665
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diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 5 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 5 MPa_data.csv
new file mode 100644
index 00000000..971c9fe7
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 5 MPa_data.csv	
@@ -0,0 +1,29 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,778.06,1.19,-24.037,0.088,temp: 98.0,0.1612829071381915
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+6,GROMACS(BS),248.0,632.69,0.22,-15.546,0.02,temp: 248.0,-0.07129979986414814
+7,GROMACS(BS),298.0,580.98,0.23,-12.491,0.018,temp: 298.0,0.005655788699726238
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+9,GROMACS(KL),173.0,704.02,0.11,-20.234,0.004,temp: 173.0,-0.09548232766047178
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+23,Tinker(AA),298.0,579.4,2.7,,,temp: 298.0,-0.2663138766005417
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+27,TOWHEE(BS),298.0,581.78,0.72,-12.515,0.03,temp: 298.0,0.14336194834542013
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 70 MPa_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 70 MPa_data.csv
new file mode 100644
index 00000000..677d9461
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/TraPPE n-Butane at 70 MPa_data.csv	
@@ -0,0 +1,29 @@
+,Program(Group),T / K,ρ / kg m−3,δρ / kg m−3,u / kJ mol−1,δu / kJ mol−1,statepoint,Relative_Error
+0,DL_POLY(BS),98.0,800.31,1.0,-24.553,0.085,temp: 98.0,0.20569985350592634
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+2,DL_POLY(BS),248.0,684.46,1.97,-17.082,0.183,temp: 248.0,0.03444985666771471
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+20,Tinker(AA),98.0,797.8,1.2,,,temp: 98.0,-0.10857374876356794
+21,Tinker(AA),173.0,737.4,1.7,,,temp: 173.0,-0.08362175094653954
+22,Tinker(AA),248.0,683.2,1.9,,,temp: 248.0,-0.14970028624699236
+23,Tinker(AA),298.0,648.6,2.2,,,temp: 298.0,-0.27193371670568206
+24,TOWHEE(BS),98.0,798.03,1.46,-25.588,0.082,temp: 98.0,-0.0797757692727354
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+26,TOWHEE(BS),248.0,684.85,0.59,-17.045,0.029,temp: 248.0,0.09144871042702722
+27,TOWHEE(BS),298.0,650.72,0.57,-14.595,0.023,temp: 298.0,0.05403529427270891
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/aggregate_summary_all.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/aggregate_summary_all.csv
new file mode 100644
index 00000000..4d826bdc
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/aggregate_summary_all.csv
@@ -0,0 +1,57 @@
+,molecule,engine,temperature,pressure,ensemble,forcefield_name,cutoff_style,long_range_correction,r_cut,pressure-avg,pressure-std,pressure-sem,temperature-avg,temperature-std,temperature-sem,potential_energy-avg,potential_energy-std,potential_energy-sem,density-avg,density-std,density-sem
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+9,pentaneUA-constrain_bonds,gromacs,372.0,1402.0,NPT,trappe-ua,hard,energy_pressure,1.4,14.357145017690527,3.1597071853930756,0.7899267963482689,371.96957083362616,0.1266716916298173,0.03166792290745433,-2425.9794197016618,6.719675700234463,1.6799189250586157,0.5419011696532579,0.00032131570087705043,8.032892521926261e-05
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+19,benzeneUA,cassandra,450.0,2260.0,NPT,benzene-ua,hard,energy_pressure,1.4,2235.812474062985,116.1144865920127,29.028621648003174,450.0,0.0,0.0,-8279.860781446354,10.994613044795699,2.7486532611989247,0.6984355226084895,0.0009722288849456309,0.00024305722123640772
+20,pentaneUA-flexible_bonds,hoomd,372.0,1402.0,NPT,trappe-ua,hard,energy_pressure,1.4,1857.8986043278728,1029.4136806403035,257.35342016007587,370.07282272345594,0.06753494307570201,0.016883735768925504,-598.348686676936,18.29546292690224,4.57386573172556,0.5444344331662561,0.0016273073789420784,0.0004068268447355196
+21,ethanolAA,gomc,300.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,189.33884519343806,160.73121090962596,40.18280272740649,300.0,0.0,0.0,-1153163.4855680207,4151.372410961057,1037.8431027402642,0.7913484722631563,0.0013261621823414586,0.00033154054558536466
+22,methaneUA,lammps-VU,140.0,1318.0,NPT,trappe-ua,hard,energy_pressure,1.4,1.3727125266159015,0.1451022781692951,0.03627556954232378,140.0187312234737,0.06909822894426526,0.017274557236066315,-5699.011530679262,0.8883769467881574,0.22209423669703934,0.37597173850147536,5.600563041526528e-05,1.400140760381632e-05
+23,pentaneUA-constrain_bonds,hoomd,372.0,1402.0,NPT,trappe-ua,hard,energy_pressure,1.4,1371.946960694772,400.5795327139803,100.14488317849508,371.88057372250626,0.16990409362951864,0.04247602340737966,-2426.950978718094,2.7282250208703003,0.6820562552175751,0.5419898727782437,0.00030186004689047055,7.546501172261764e-05
+24,ethanolAA,gromacs,280.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,-0.1880251244876936,14.8946603224094,3.72366508060235,279.98895524102625,0.26090072711825657,0.06522518177956414,-7045.887545749052,22.953775808713953,5.738443952178488,0.8167763941860889,0.00023989817952917825,5.997454488229456e-05
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+26,ethanolAA,cassandra,300.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,1306.5241974025141,326.08413793394254,81.52103448348564,300.0,0.0,0.0,-9618.100656297396,23.642645476418732,5.910661369104683,0.7912818525394791,0.0008829528154467213,0.00022073820386168033
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+28,waterSPCE,cassandra,280.0,101.325,NPT,spce,hard,energy_pressure,0.9,911.4224853985197,380.33294699059564,95.08323674764891,280.0,0.0,0.0,-52704.462343776046,37.35925345726298,9.339813364315745,1.006219489195677,0.001951130264662797,0.00048778256616569926
+29,waterSPCE,gromacs,300.0,101.325,NPT,spce,hard,energy_pressure,0.9,2.078137808115704,5.0032406767093,1.250810169177325,300.03317599214233,0.10531469548206666,0.026328673870516666,-51379.23613949193,5.093489371990191,1.2733723429975476,0.997872222479935,0.00010611411405899197,2.6528528514747992e-05
+30,waterSPCE,gomc,300.0,101.325,NPT,spce,hard,energy_pressure,0.9,311.771610490875,506.6753819510162,126.66884548775406,300.0,0.0,0.0,-6173561.208366301,2376.258731989235,594.0646829973088,0.9978894387415365,0.001193495558743343,0.00029837388968583574
+31,ethanolAA,hoomd,320.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,139.141174437068,620.2447448690797,155.06118621726992,318.0518146368627,0.0585342039959654,0.01463355099899135,-3638.707472577408,10.776813654689468,2.694203413672367,0.7737540395695532,0.0005696384060432825,0.00014240960151082062
+32,pentaneUA-constrain_bonds,mcccs,372.0,1402.0,NPT,trappe-ua,hard,energy_pressure,1.4,1405.6732120732916,62.34335707970884,15.58583926992721,372.0,0.0,0.0,-2417.163109475033,4.0531456026239985,1.0132864006559996,0.5410886453415661,0.0003461206838981674,8.653017097454186e-05
+33,benzeneUA,gromacs,450.0,2260.0,NPT,benzene-ua,hard,energy_pressure,1.4,598.4628613056768,54.792266845144866,13.698066711286216,449.9749758293657,0.07962156893655552,0.01990539223413888,-4763.000351206376,35.14706410030546,8.786766025076364,0.6994750481091541,0.005320356104800141,0.0013300890262000352
+34,benzeneUA,gomc,450.0,2260.0,NPT,benzene-ua,hard,energy_pressure,1.4,2261.3261238725845,113.57359149858733,28.393397874646833,450.0,0.0,0.0,-996248.5051899063,1104.6892172880082,276.17230432200205,0.6986877138757969,0.0008168704743399857,0.00020421761858499643
+35,ethanolAA,hoomd,300.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,18.19520656087098,561.3429378651543,140.33573446628859,298.18270927920105,0.031193472226197085,0.007798368056549271,-5356.528356876066,12.728565429690816,3.182141357422704,0.795539037556105,0.0006699188454743484,0.0001674797113685871
+36,ethanolAA,cassandra,280.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,1100.9015507314693,355.4507643063722,88.86269107659305,280.0,0.0,0.0,-10989.091105359375,39.15544808085992,9.78886202021498,0.8134909356063542,0.0013345960311544694,0.00033364900778861734
+37,ethanolAA,gromacs,300.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,0.48879268702858414,18.88738372413123,4.721845931032807,299.9842745059775,0.33296690752590247,0.08324172688147562,-5383.192305854824,30.97299823399313,7.743249558498283,0.7956309742518117,0.0003515280944911218,8.788202362278045e-05
+38,waterSPCE,gomc,320.0,101.325,NPT,spce,hard,energy_pressure,0.9,265.9610005581678,422.41258608670194,105.60314652167548,320.0,0.0,0.0,-6018771.941017188,2242.8561646967696,560.7140411741924,0.9861967911254947,0.0008218002333186185,0.00020545005832965462
+39,methaneUA,cassandra,140.0,1318.0,NPT,trappe-ua,hard,energy_pressure,1.4,1292.1047136521488,31.592770246951666,7.8981925617379165,140.0,0.0,0.0,-5695.189620521354,2.915553296563871,0.7288883241409677,0.3757235620453125,0.0001751962404427804,4.37990601106951e-05
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+41,ethanolAA,gomc,280.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,73.82676037295047,229.41972307688044,57.35493076922011,280.0,0.0,0.0,-1316410.1096084896,4920.244044398107,1230.0610110995267,0.8131313624791041,0.0014130531794989858,0.00035326329487474646
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+43,benzeneUA,mcccs,450.0,2260.0,NPT,benzene-ua,hard,energy_pressure,1.4,2194.916566386264,62.893504691808054,15.723376172952014,450.0,0.0,0.0,-6025.762650336912,11.953522211499461,2.9883805528748653,0.6983239878468144,0.0007810274199452238,0.00019525685498630594
+44,pentaneUA-flexible_bonds,lammps-VU,372.0,1402.0,NPT,trappe-ua,hard,energy_pressure,1.4,928.9680976970318,2861.1272486888683,715.2818121722171,371.9753474023654,0.20884629272470256,0.05221157318117564,-452.67791782923,15.456058650662392,3.864014662665598,0.5432271222473475,0.0018561358209731838,0.00046403395524329596
+45,ethanolAA,lammps-VU,300.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,-7.467739908959722,269.38193292433175,67.34548323108294,300.015323409767,0.06284411712385393,0.015711029280963483,-4854.502013981132,7.479035537092114,1.8697588842730286,0.7954329437061574,0.0002927443340395005,7.318608350987512e-05
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+47,benzeneUA,lammps-VU,450.0,2260.0,NPT,benzene-ua,hard,energy_pressure,1.4,2275.7208916034388,190.05164979065881,47.512912447664704,450.5924355773069,0.2650670841539167,0.06626677103847918,-8268.001035948888,3.8592596056358226,0.9648149014089556,0.6976587115413384,0.00031912413669267124,7.978103417316781e-05
+48,pentaneUA-constrain_bonds,cassandra,372.0,1402.0,NPT,trappe-ua,hard,energy_pressure,1.4,1348.1548122757595,70.96284002873008,17.74071000718252,372.0,0.0,0.0,-2423.008360798958,8.612346519187353,2.1530866297968383,0.541189253940625,0.0008495282868671981,0.00021238207171679952
+49,ethanolAA,gomc,320.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,187.7324407434529,235.43716134138515,58.85929033534629,320.0,0.0,0.0,-982220.6171556511,3957.009782240972,989.252445560243,0.7680116167513229,0.0011735365258760796,0.0002933841314690199
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+51,ethanolAA,hoomd,280.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,-213.60666293220967,622.6881733188677,155.67204332971693,278.280373216568,0.048639019260606924,0.012159754815151731,-7019.56063054578,15.701777553793786,3.9254443884484465,0.8162973288982891,0.0006679099141502698,0.00016697747853756744
+52,ethanolAA,mcccs,320.0,101.325,NPT,oplsaa,hard,energy_pressure,1.0,237.10278940694064,130.13928396070077,32.53482099017519,320.0,0.0,0.0,-8229.879840892247,20.393359453562393,5.098339863390598,0.7687308005306762,0.0008008656849902207,0.00020021642124755517
+53,waterSPCE,mcccs,280.0,101.325,NPT,spce,hard,energy_pressure,0.9,492.521711559375,274.4140746706369,68.60351866765923,0.0,0.0,0.0,-52748.07641691869,41.68320678915052,10.42080169728763,1.006562398238529,0.0017102567726938519,0.00042756419317346297
+54,methaneUA,mcccs,140.0,1318.0,NPT,trappe-ua,hard,energy_pressure,1.4,1285.4831025069893,37.20743432388115,9.301858580970288,140.0,0.0,0.0,-5695.738792702148,3.029363327288005,0.7573408318220013,0.37575936057731646,0.0001837137385027993,4.592843462569983e-05
+55,methaneUA,gromacs,140.0,1318.0,NPT,trappe-ua,hard,energy_pressure,1.4,13.588645423936484,1.0591647556941834,0.26479118892354586,140.00376846643206,0.051552152017493635,0.012888038004373409,-5698.862840276335,1.6907945007949114,0.42269862519872786,0.3759555197390835,5.592110954235338e-05,1.3980277385588345e-05
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/csvs/job_density_data.csv b/reproducibility_project/src/analysis/Comparison_Figures/csvs/job_density_data.csv
new file mode 100644
index 00000000..0d95213c
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/csvs/job_density_data.csv
@@ -0,0 +1,897 @@
+,associated_work,MCorMD,engine,molecule,replicate,temperature,density,density-std,forcefield
+0,MoSDeF,MD,gromacs,ethanolAA,14,320.0,0.7735207800292969,0.010110613077917021,oplsaa
+1,MoSDeF,MD,hoomd,waterSPCE,7,280.0,1.0068064283735252,0.0072941553581096965,spce
+2,MoSDeF,MD,hoomd,waterSPCE,10,300.0,0.9976709290832094,0.007521107927439588,spce
+3,MoSDeF,MD,gromacs,pentaneUA-flexible_bonds,4,372.0,0.5346973692820622,0.021955527372966032,trappe-ua
+4,MoSDeF,MC,gomc,ethanolAA,13,300.0,0.7926841606760833,0.0068209388038109,oplsaa
+5,MoSDeF,MC,gomc,pentaneUA-constrain_bonds,1,372.0,0.5412649296850833,0.011499652233530945,trappe-ua
+6,MoSDeF,MC,gomc,pentaneUA-constrain_bonds,7,372.0,0.54136970455525,0.01183206654864145,trappe-ua
+7,MoSDeF,MC,cassandra,methaneUA,0,140.0,0.3756420617758333,0.004490899501958551,trappe-ua
+8,MoSDeF,MD,lammps-VU,methaneUA,12,140.0,0.3759078714789572,0.004477798031083108,trappe-ua
+9,MoSDeF,MD,lammps-VU,waterSPCE,6,300.0,0.9981839407503689,0.007603404003640751,spce
+10,MoSDeF,MC,cassandra,waterSPCE,5,300.0,0.9953463570116666,0.007538525073838185,spce
+11,MoSDeF,MC,mcccs,methaneUA,7,140.0,0.3758223487533516,0.004322838137520545,trappe-ua
+12,MoSDeF,MD,gromacs,benzeneUA,6,450.0,0.6955600068492275,0.0222127345822273,benzene-ua
+13,MoSDeF,MD,lammps-VU,ethanolAA,1,300.0,0.7953839208956944,0.007293262637195991,oplsaa
+14,MoSDeF,MD,lammps-VU,pentaneUA-flexible_bonds,14,372.0,0.5476375393162778,0.011462411498066408,trappe-ua
+15,MoSDeF,MC,mcccs,waterSPCE,10,280.0,1.0064909798767026,0.006536842264955913,spce
+16,MoSDeF,MC,gomc,waterSPCE,10,280.0,1.00866087245825,0.006396357909430115,spce
+17,MoSDeF,MD,hoomd,ethanolAA,1,300.0,0.7936346984068231,0.0072882006762512675,oplsaa
+18,MoSDeF,MD,gromacs,ethanolAA,10,280.0,0.816633294977106,0.009312414312709884,oplsaa
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+20,MoSDeF,MC,gomc,pentaneUA-constrain_bonds,12,372.0,0.5404184629258333,0.011770658750962205,trappe-ua
+21,MoSDeF,MD,hoomd,benzeneUA,5,450.0,0.6991885617545183,0.0136234770284215,benzene-ua
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+23,MoSDeF,MC,gomc,ethanolAA,3,320.0,0.76909183965425,0.007300385874546468,oplsaa
+24,MoSDeF,MD,gromacs,waterSPCE,4,280.0,1.0066576437343528,0.007125314250470776,spce
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+28,MoSDeF,MC,cassandra,ethanolAA,5,300.0,0.7903367767541668,0.007528735606963092,oplsaa
+29,MoSDeF,MD,lammps-VU,waterSPCE,4,300.0,0.9983137311286927,0.00776929597398238,spce
+30,MoSDeF,MD,lammps-VU,methaneUA,13,140.0,0.37597791136701714,0.0045050993878547226,trappe-ua
+31,MoSDeF,MC,mcccs,waterSPCE,8,280.0,1.0073116083686788,0.0069345809778629565,spce
+32,MoSDeF,MC,gomc,waterSPCE,13,320.0,0.9852482428944168,0.007292960621740566,spce
+33,MoSDeF,MC,mcccs,benzeneUA,3,450.0,0.6990807010392238,0.013545605214452496,benzene-ua
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+36,MoSDeF,MD,gromacs,pentaneUA-flexible_bonds,2,372.0,0.5337417271596461,0.023704938179353027,trappe-ua
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+840,MoSDeF,MC,cassandra,pentaneUA-constrain_bonds,2,372.0,0.541206320445,0.012200984600943314,trappe-ua
+841,MoSDeF,MD,hoomd,pentaneUA-flexible_bonds,12,372.0,0.5171071925241328,0.013065014450195762,trappe-ua
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+862,MoSDeF,MD,gromacs,methaneUA,3,140.0,0.37600154714060346,0.004333925086611504,trappe-ua
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+864,MoSDeF,MD,hoomd,methaneUA,9,140.0,0.37596484289558874,0.004459901308959986,trappe-ua
+865,MoSDeF,MC,mcccs,benzeneUA,8,450.0,0.6972902939439799,0.013888524213779615,benzene-ua
+866,MoSDeF,MC,cassandra,waterSPCE,12,320.0,0.9868125940716667,0.007282151520928618,spce
+867,MoSDeF,MC,cassandra,waterSPCE,9,300.0,1.0014169668275,0.00751946918939084,spce
+868,MoSDeF,MC,mcccs,waterSPCE,12,280.0,1.0074296680654204,0.0074685679851110185,spce
+869,MoSDeF,MD,gromacs,pentaneUA-constrain_bonds,2,372.0,0.5416883111334466,0.012814938231782837,trappe-ua
+870,MoSDeF,MD,gromacs,ethanolAA,6,300.0,0.7953123145961363,0.009261391430074843,oplsaa
+871,MoSDeF,MD,lammps-VU,pentaneUA-flexible_bonds,12,372.0,0.5415355195635146,0.010594220027621554,trappe-ua
+872,MoSDeF,MC,mcccs,waterSPCE,10,320.0,0.98786798296719,0.007428463459725297,spce
+873,MoSDeF,MC,cassandra,pentaneUA-constrain_bonds,0,372.0,0.5399878586216666,0.011981435954573497,trappe-ua
+874,MoSDeF,MD,lammps-VU,ethanolAA,6,320.0,0.7735832144345469,0.00787427061685085,oplsaa
+875,MoSDeF,MD,lammps-VU,ethanolAA,11,300.0,0.7957597323403903,0.007490821848282488,oplsaa
+876,MoSDeF,MD,gromacs,ethanolAA,3,320.0,0.7734236114208515,0.010399028185077725,oplsaa
+877,MoSDeF,MD,hoomd,waterSPCE,11,320.0,0.9862186127672562,0.00784632642188575,spce
+878,MoSDeF,MD,lammps-VU,waterSPCE,15,280.0,1.006765840313812,0.007026731788947236,spce
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+885,MoSDeF,MD,hoomd,waterSPCE,9,300.0,0.9976323666371488,0.0076002456068711725,spce
+886,MoSDeF,MD,gromacs,pentaneUA-flexible_bonds,11,372.0,0.5341181621601054,0.02145768280403366,trappe-ua
+887,MoSDeF,MD,lammps-VU,waterSPCE,4,320.0,0.9865380604990422,0.007816415041526656,spce
+888,MoSDeF,MC,mcccs,pentaneUA-constrain_bonds,1,372.0,0.540638173800088,0.012222063236672222,trappe-ua
+889,MoSDeF,MC,cassandra,ethanolAA,1,300.0,0.7920879047183335,0.007390358337742578,oplsaa
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--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/generate_comparison_plots.py
@@ -0,0 +1,1063 @@
+"""Functionality to generate figure plots to compare MoSDeF to RR relative errors."""
+import warnings
+
+warnings.filterwarnings("ignore")
+import copy
+import os
+import re
+import shutil
+from collections import Counter
+
+# Plot settings and imports
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+matplotlib.use("pdf")
+import matplotlib.gridspec as gridspec
+import matplotlib.ticker as mtick
+import numpy as np
+from matplotlib import rc
+from matplotlib.ticker import (
+    AutoMinorLocator,
+    FormatStrFormatter,
+    MaxNLocator,
+    MultipleLocator,
+    NullFormatter,
+    ScalarFormatter,
+    StrMethodFormatter,
+)
+
+rc("font", **{"family": "serif", "serif": ["Times"]})
+rc("text", usetex=True)
+
+# plot settings
+ms = 8  # markersize
+xtickfs = 15  # xtickfontsize
+xlabelfs = 18  # xlabelfontsize
+ylabelfs = 20  # ylabelfontsize
+ytickfs = 14  # ytickfontsize
+titlefs = 14  # title size
+legendfs = 9
+alpha = 0.2
+
+
+# equation to calculate RE for a pandas series
+def _calculate_relative_error(ser):
+    dataArray = ser.to_numpy()
+    mean = np.nanmean(dataArray)
+    return (dataArray - mean) / mean * 100
+
+
+# 2 Load RR data
+def _read_rr_csv(model):
+    df = pd.read_csv(f"csvs/{model}_data.csv", index_col=0)
+    pressure = float(model.split(" ")[3])
+    df.insert(len(df.columns) - 2, "pressure", np.full(len(df.index), pressure))
+    return df
+
+
+# 4 filter RR data
+def _filter_rr_data(df, model, remove_MC=True):
+    keep_labelsList = ["Program(Group)", "T / K", "ρ / kg m−3", "pressure"]
+    new_labelsList = ["engine", "temperature", "density", "pressure"]
+    df = df[keep_labelsList]
+    df.columns = new_labelsList
+    MCList = ["TOWHEE(BS)", "Ind.MC(PB)" "ms2*(KL)"]
+
+    n_rows = len(df.index)
+    df.insert(1, "replicate", np.full(n_rows, 0), True)
+    df.insert(4, "density-std", np.full(n_rows, 0.0), True)
+    df.insert(5, "forcefield", np.full(n_rows, model.split()[0]), True)
+    df.insert(1, "molecule", np.full(n_rows, model.split()[1]), True)
+    df.insert(0, "associated_work", np.full(n_rows, "RR"), True)
+    df.insert(
+        1,
+        "MCorMD",
+        list(map(lambda x: "MC" if x in MCList else "MD", df["engine"])),
+    )
+
+    return df
+
+
+def _load_all_rr_data():
+    rr_moleculeList = [
+        "OPLS Ethane at 5 MPa",
+        "OPLS Ethane at 41 MPa",
+        "OPLS Ethane at 70 MPa",
+        "TraPPE Ethane at 5 MPa",
+        "TraPPE Ethane at 41 MPa",
+        "TraPPE Ethane at 70 MPa",
+        "OPLSAMBER Ethane at 5 MPa",
+        "OPLSAMBER Ethane at 41 MPa",
+        "OPLSAMBER Ethane at 70 MPa",
+        "OPLS Propane at 5 MPa",
+        "OPLS Propane at 41 MPa",
+        "OPLS Propane at 70 MPa",
+        "TraPPE Propane at 5 MPa",
+        "TraPPE Propane at 41 MPa",
+        "TraPPE Propane at 70 MPa",
+        "OPLSAMBER Propane at 5 MPa",
+        "OPLSAMBER Propane at 41 MPa",
+        "OPLSAMBER Propane at 70 MPa",
+        "OPLS n-Butane at 5 MPa",
+        "OPLS n-Butane at 41 MPa",
+        "OPLS n-Butane at 70 MPa",
+        "TraPPE n-Butane at 5 MPa",
+        "TraPPE n-Butane at 41 MPa",
+        "TraPPE n-Butane at 70 MPa",
+        "OPLSAMBER n-Butane at 5 MPa",
+        "OPLSAMBER n-Butane at 41 MPa",
+        "OPLSAMBER n-Butane at 70 MPa",
+        "OPLS iso-Butane at 5 MPa",
+        "OPLS iso-Butane at 41 MPa",
+        "OPLS iso-Butane at 70 MPa",
+        "TraPPE iso-Butane at 5 MPa",
+        "TraPPE iso-Butane at 41 MPa",
+        "TraPPE iso-Butane at 70 MPa",
+        "OPLSAMBER iso-Butane at 5 MPa",
+        "OPLSAMBER iso-Butane at 41 MPa",
+        "OPLSAMBER iso-Butane at 70 MPa",
+    ]
+    hasse_dfList = []
+    for molecule in rr_moleculeList:
+        hasseDF = _read_rr_csv(molecule)
+        hasse_dfList.append(_filter_rr_data(hasseDF, molecule, remove_MC=False))
+    return hasse_dfList
+
+
+def _mask_df(df):
+    # mask = ((df["molecule"] == "Ethane") & (df["forcefield"] == "TraPPE") #testing masks to check for outliers
+    #       & (df["temperature"] == 298.0) & (df["engine"] == "ms2(KL)")
+    #        & (df["pressure"] == 5)
+    #       )
+    # df = df[~mask]
+    mask = (df["molecule"] == "Ethane") & (df["engine"] == "ms2(KL)")
+    df = df[~mask]
+    mask = (df["molecule"] == "Ethane") & (df["engine"] == "GROMACS(KL)")
+    df = df[~mask]
+    # mask = ((df["molecule"] == "Ethane") & (df["forcefield"] == "TraPPE")
+    #    & (df["temperature"] == 298.0) & (df["engine"] == "GROMACS(KL)")
+    #    & (df["pressure"] == 5)
+    #   )
+    df = df[~mask]
+    df = df[
+        ~(
+            (df["temperature"] == 298.0)
+            & (df["forcefield"] == "OPLSAMBER")
+            & (df["molecule"] == "Ethane")
+        )
+    ]
+    df = df[~(df["molecule"] == "pentaneUA-flexible_bonds")]
+    df = df[~((df["engine"] == "gromacs") & (df["molecule"] == "benzeneUA"))]
+
+    return df
+
+
+def _transform_ff_names(df):
+    df["forcefield"] = df["forcefield"].transform(
+        lambda x: "TraPPE" if "ua" in x else x
+    )
+    df["forcefield"] = df["forcefield"].transform(
+        lambda x: x.upper() if x == "oplsaa" else x
+    )
+    df["forcefield"] = df["forcefield"].transform(
+        lambda x: "SPC/E" if x == "spce" else x
+    )
+    df["ff-mol"] = df[["molecule", "forcefield"]].agg("-".join, axis=1)
+    df["ff-mol"] = df["ff-mol"].transform(
+        lambda x: re.sub("([a-zA-Z])", lambda y: y.groups()[0].upper(), x, 1)
+        if "Butane" not in x
+        else x
+    )
+
+
+def _sort_plotting_labels(df, x_label, y_label):
+    # Prepare colors
+    sortmolDict = {
+        "Ethane": 0,
+        "Propane": 1,
+        "n-Butane": 2,
+        "iso-Butane": 3,
+        "methaneUA": 4,
+        "pentaneUA-constrain_bonds": 5,
+        "pentaneUA-flexible_bonds": 9,
+        "benzeneUA": 6,
+        "waterSPCE": 7,
+        "ethanolAA": 8,
+    }
+    sortffmolDict = {
+        "Ethane-TraPPE": 1,
+        "Propane-TraPPE": 2,
+        "n-Butane-TraPPE": 3,
+        "iso-Butane-TraPPE": 4,
+        "Ethane-OPLS": 5,
+        "Propane-OPLS": 6,
+        "n-Butane-OPLS": 7,
+        "iso-Butane-OPLS": 8,
+        "Ethane-OPLSAMBER": 9,
+        "Propane-OPLSAMBER": 10,
+        "n-Butane-OPLSAMBER": 11,
+        "iso-Butane-OPLSAMBER": 12,
+        "MethaneUA-TraPPE": 13,
+        "PentaneUA-constrain_bonds-TraPPE": 14,
+        "BenzeneUA-TraPPE": 16,
+        "WaterSPCE-SPC/E": 17,
+        "EthanolAA-OPLSAA": 18,
+    }
+    if x_label == "molecule":
+        df["sortedindex"] = df["molecule"].apply(
+            func=lambda x: sortmolDict.get(x)
+        )
+    elif x_label == "ff-mol":
+        df["sortedindex"] = df[x_label].apply(
+            func=lambda x: sortffmolDict.get(x)
+        )
+    df = df.sort_values(by=["sortedindex"], ascending=True).copy()
+    color_labels = list(Counter(df[x_label].to_numpy()).keys())
+    palette = sns.color_palette("colorblind", n_colors=len(color_labels))
+    color_map = dict(zip(color_labels, palette))
+
+    xtick_labelsList = copy.deepcopy(color_labels)
+    for i in range(len(color_labels)):
+        for filter_label in [
+            "AA",
+            "SPCE",
+            "UA",
+            "-constrain_bonds",
+            "-flexible_bonds",
+        ]:
+            if filter_label in color_labels[i]:
+                xtick_labelsList[i] = xtick_labelsList[i].replace(
+                    filter_label, ""
+                )
+    return color_labels, xtick_labelsList, color_map
+
+
+def _plot_params(color_labels):
+    """Generate summary plots for the aggregate project."""
+    fig, ax = plt.subplots(
+        1,
+        1,
+        sharex=False,
+        sharey=False,
+        figsize=(len(color_labels) * 0.5 + 3, 4),
+    )
+    trans = plt.gca().transData
+    x_ticks = np.arange(len(color_labels))
+    ax.set_xticks(x_ticks)
+    ax.set_axisbelow(True)
+    ax.grid(visible=True, which="major", axis="y", alpha=0.5, color="gray")
+    ax.set_xticks([], minor=True)
+    ax.set_yticks([], minor=True)
+
+    return fig, ax
+
+
+def _plot_scatter(df, fig, ax, color_labels, color_map, x_label, y_label):
+    x_ticks = np.arange(len(color_labels))
+    for i, molecule in enumerate(color_labels):
+        series = df.loc[df[x_label] == molecule][y_label]
+        ydata = series[series.notnull()]
+        if len(x_ticks) > 1:
+            spread = (x_ticks[1] - x_ticks[0]) * 0.25
+        else:
+            spread = 0.25
+        offsets = np.linspace(-1 * spread, spread, len(ydata))
+        xdata = np.full(len(ydata), x_ticks[i]) + offsets
+        ax.scatter(
+            x=xdata,
+            y=ydata,
+            color=color_map[molecule],
+            label=molecule,
+            s=np.full(len(ydata.index), 50),
+            edgecolors="black",
+            marker="o",
+            alpha=0.6,
+        )
+        # ax.axvline(x=6.5, linestyle="--", color="k")
+
+
+def _plot_violin(df, fig, ax, color_labels, color_map, x_label, y_label):
+    x_ticks = np.arange(len(color_labels))
+    for i, molecule in enumerate(color_labels):
+        series = df.loc[df[x_label] == molecule][y_label]
+        ydata = series[series.notnull()]
+        if len(x_ticks) > 1:
+            spread = (x_ticks[1] - x_ticks[0]) * 0.25
+        else:
+            spread = 0.25
+        print(
+            f"Maximum deviation is: {max(abs(ydata)):.2f} for molecule {color_labels[i]}"
+        )
+        offsets = np.linspace(-1 * spread, spread, len(ydata))
+        xdata = np.full(len(ydata), x_ticks[i]) + offsets
+        ax.violinplot([ydata], positions=[i], showextrema=True)
+
+
+def _plot_box(df, fig, ax, color_labels, color_map, x_label, y_label):
+    x_ticks = np.arange(len(color_labels))
+    for i, molecule in enumerate(color_labels):
+        series = df.loc[df[x_label] == molecule][y_label]
+        ydata = series[series.notnull()]
+        if len(x_ticks) > 1:
+            spread = (x_ticks[1] - x_ticks[0]) * 0.25
+        else:
+            spread = 0.25
+        offsets = np.linspace(-1 * spread, spread, len(ydata))
+        xdata = np.full(len(ydata), x_ticks[i]) + offsets
+        ax.boxplot([ydata], positions=[i])
+
+
+import textwrap
+
+
+def _wrap_labels(ax, width, break_long_words=False, rotation=45):
+    labels = []
+    for label in ax.get_xticklabels():
+        text = label.get_text()
+        labels.append(
+            textwrap.fill(text, width=width, break_long_words=break_long_words)
+        )
+    if rotation > 0:
+        ax.set_xticklabels(
+            labels,
+            rotation_mode="anchor",
+            rotation=rotation,
+            ha="right",
+            size=xtickfs,
+        )
+    else:
+        ax.set_xticklabels(
+            labels,
+            rotation_mode="anchor",
+            rotation=rotation,
+            ha="center",
+            size=xtickfs,
+        )
+
+
+def _general_plot_formatting(
+    df,
+    ax,
+    splitby,
+    figdir,
+    combMCMD,
+    xtick_labelsList,
+    color_labels,
+    average_replicas,
+    ymax=None,
+    rotation=0,
+    wrap_labels=True,
+):
+    # tick params
+    ax.tick_params(axis="y", labelsize=ytickfs)
+    ax.tick_params(axis="x", labelsize=xtickfs)
+    ax.set_xlim([-0.5, len(color_labels) - 0.5])
+    if ymax is None:
+        maxy = max(np.abs(df["Relative_Error"])) * 1.05
+    else:
+        maxy = ymax
+    ax.set_ylim([-maxy, maxy])
+    ax.yaxis.set_major_locator(plt.MaxNLocator(3))
+    x_ticks = np.arange(len(color_labels))
+    ax.set_xticks(x_ticks)
+    ax.set_axisbelow(True)
+    ax.grid(visible=True, which="major", axis="y", alpha=0.5, color="gray")
+    # ax.axhline(y = 0, color = 'black', alpha=0.5, linestyle = '--', zorder=-1)
+    ax.tick_params(which="both", width=1)
+    ax.set_xticks([], minor=True)
+    ax.tick_params(which="minor", length=4, axis="y", bottom=True)
+    ax.yaxis.set_minor_locator(AutoMinorLocator(2))
+
+    if splitby == "associated_work":
+        ax.set_xticklabels(
+            xtick_labelsList, ha="center", rotation_mode="anchor", size=15
+        )
+    elif figdir == "MoSDeF":
+        xtick_labelsList = list(
+            map(lambda lab: "-\n".join(lab.split("-")), xtick_labelsList)
+        )
+        ax.set_xticklabels(xtick_labelsList, size=xlabelfs, rotation=rotation)
+    else:
+        ax.set_xticklabels(
+            xtick_labelsList,
+            size=xlabelfs,
+            rotation_mode="anchor",
+            rotation=rotation,
+            ha="right",
+        )
+    if wrap_labels:
+        _wrap_labels(ax, width=12, break_long_words=True, rotation=rotation)
+    ax.set_ylabel(r"$100\times\Delta\rho / \rho$", size=ylabelfs)
+
+
+def _save_single_scatter_labels(
+    fig, ax, combMCMD, average_replicas, figdir, splitby, plot_type
+):
+    if combMCMD:
+        MCMDStr = "MCandMD"
+    else:
+        MCMDStr = "MCorMD"
+
+    if average_replicas:
+        avgStr = "_avg"
+    else:
+        avgStr = ""
+    fname = (
+        f"figures/{figdir}/{figdir}_{splitby}_{MCMDStr}{avgStr}_{plot_type}.pdf"
+    )
+    print(f"Plotting {fname}")
+    fig.tight_layout()
+    fig.savefig(fname, dpi=900)
+
+
+def single_plot_comparisons(
+    df, splitby, combMCMD, plot_type, average_replicas=False, figdir="combined"
+):
+    """Plot deviations split into splitby groupings on xaxis and compared to their statepoint and/or MD/MC means."""
+    if average_replicas:
+        df = copy.deepcopy(
+            df.groupby(
+                [
+                    "molecule",
+                    "forcefield",
+                    "engine",
+                    "MCorMD",
+                    "associated_work",
+                    "temperature",
+                    "pressure",
+                ]
+            )["density"]
+            .mean()
+            .reset_index()
+        )
+
+    df = _mask_df(df)
+
+    if combMCMD:
+        groupREList = ["molecule", "temperature", "forcefield", "pressure"]
+    else:
+        groupREList = [
+            "MCorMD",
+            "molecule",
+            "temperature",
+            "forcefield",
+            "pressure",
+        ]
+    df["Relative_Error"] = df.groupby(groupREList)["density"].transform(
+        _calculate_relative_error
+    )
+
+    _transform_ff_names(df)
+
+    x_label = splitby  #'molecule', "forcefield", "associated_work"
+    y_label = "Relative_Error"
+
+    color_labels, xtick_labelsList, color_map = _sort_plotting_labels(
+        df, x_label, y_label
+    )
+    fig, ax = _plot_params(color_labels)
+
+    if plot_type == "scatter":
+        _plot_scatter(df, fig, ax, color_labels, color_map, x_label, y_label)
+    elif plot_type == "violin":
+        _plot_violin(df, fig, ax, color_labels, color_map, x_label, y_label)
+    else:
+        return
+
+    _general_plot_formatting(
+        df,
+        ax,
+        splitby,
+        figdir,
+        groupMCMD,
+        xtick_labelsList,
+        color_labels,
+        average_replicas,
+    )  # formatting for all plots
+    _save_single_scatter_labels(
+        fig, ax, groupMCMD, average_replicas, figdir, splitby, plot_type
+    )
+    plt.close()
+
+    return ax
+
+
+def _plot_stack_params(stack):
+    """Initialize subplots with shared x axis."""
+    fig = plt.figure(figsize=(9, 6))
+    gs = fig.add_gridspec(len(stack), hspace=0)
+    stackax = gs.subplots(sharex=True, sharey=False)
+
+    return fig, stackax
+
+
+def _plot_stack_params_multix(stack, hspace=0.5):
+    """Initialize subplots with shared x axis."""
+    fig = plt.figure(figsize=(9, 9))
+    gs = fig.add_gridspec(len(stack), hspace=hspace)
+    stackax = gs.subplots(sharex=False, sharey=False)
+
+    return fig, stackax
+
+
+def stack_plot_comparisons(df, stack, ymax=None):
+    """Stack multiple plots on each other to generate a single figure."""
+    # Initialize Figure
+    fig, axs = _plot_stack_params(stack)
+
+    # Iterate through stack to generate plot
+    for ax, sta in zip(axs, stack):
+        if sta["figdir"] in ["MoSDeF", "RR"]:
+            stackDF = df.loc[df["associated_work"] == sta["figdir"]].copy()
+        else:
+            stackDF = df.copy()
+
+        if sta["average_replicas"]:
+            stackDF = copy.deepcopy(
+                stackDF.groupby(
+                    [
+                        "molecule",
+                        "forcefield",
+                        "engine",
+                        "MCorMD",
+                        "associated_work",
+                        "temperature",
+                        "pressure",
+                    ]
+                )["density"]
+                .mean()
+                .reset_index()
+            )
+
+        stackDF = _mask_df(stackDF)
+
+        if sta["combMCMD"]:
+            groupREList = ["molecule", "temperature", "forcefield", "pressure"]
+        else:
+            groupREList = [
+                "MCorMD",
+                "molecule",
+                "temperature",
+                "forcefield",
+                "pressure",
+            ]
+        stackDF["Relative_Error"] = stackDF.groupby(groupREList)[
+            "density"
+        ].transform(_calculate_relative_error)
+        # print(stackDF.loc[stackDF["Relative_Error"].abs() > 3])
+
+        _transform_ff_names(stackDF)
+        x_label = sta["splitby"]  #'molecule', "forcefield", "associated_work"
+        y_label = "Relative_Error"
+        color_labels, xtick_labelsList, color_map = _sort_plotting_labels(
+            stackDF, x_label, y_label
+        )
+
+        if sta["plot_type"] == "scatter":
+            _plot_scatter(
+                stackDF, fig, ax, color_labels, color_map, x_label, y_label
+            )
+        elif sta["plot_type"] == "violin":
+            _plot_violin(
+                stackDF, fig, ax, color_labels, color_map, x_label, y_label
+            )
+        else:
+            return
+            # color_labels = list(Counter(df[x_label].to_numpy()).keys())
+
+        _general_plot_formatting(
+            stackDF,
+            ax,
+            sta["splitby"],
+            sta["figdir"],
+            sta["combMCMD"],
+            xtick_labelsList,
+            color_labels,
+            sta["average_replicas"],
+            ymax=ymax,
+            rotation=sta.get("rotation", 0),
+            wrap_labels=sta.get("wrap_labels", True),
+        )  # format all plots
+    # remove extra labels
+    axs[0].set_ylabel("")
+    axs[2].set_ylabel("")
+
+    fig_names = []
+    for sta in stack:
+        fig_names.append("-".join(list(map(str, sta.values()))))
+    fname = f"figures/Stacked_Figures/{'_'.join(fig_names)}.pdf"
+    print(f"Plotting {fname}")
+    fig.tight_layout()
+    fig.savefig(fname, dpi=500, bbox_inches="tight", overwrite=True)
+
+
+def stack_plot_multix(stack):
+    """Stack multiple plots on each other with differing xaxis labels on each."""
+    # Initialize Figure
+    fig, axs = _plot_stack_params_multix(stack)
+
+    # Iterate through stack to generate plot
+    for ax, sta in zip(axs, stack):
+        ax.axvline(
+            x=sta["seperator_line_pos"],
+            color="black",
+            alpha=0.75,
+            linestyle="--",
+            zorder=-2,
+        )
+        # generate filteredDF
+        if sta["figdir"] in ["MoSDeF", "RR"]:
+            df = sta["df"]
+            stackDF = df.loc[df["associated_work"] == sta["figdir"]].copy()
+        else:
+            df = sta["df"]
+            stackDF = df.copy()
+
+        if sta["average_replicas"]:
+            stackDF = copy.deepcopy(
+                stackDF.groupby(
+                    [
+                        "molecule",
+                        "forcefield",
+                        "engine",
+                        "MCorMD",
+                        "associated_work",
+                        "temperature",
+                        "pressure",
+                    ]
+                )["density"]
+                .mean()
+                .reset_index()
+            )
+
+        stackDF = _mask_df(stackDF)
+
+        if sta["combMCMD"]:
+            groupREList = ["molecule", "temperature", "forcefield", "pressure"]
+        else:
+            groupREList = [
+                "MCorMD",
+                "molecule",
+                "temperature",
+                "forcefield",
+                "pressure",
+            ]
+        stackDF["Relative_Error"] = stackDF.groupby(groupREList)[
+            "density"
+        ].transform(_calculate_relative_error)
+        print(
+            "Number of larger values: ",
+            len(stackDF.loc[stackDF["Relative_Error"].abs() > 0.70].index),
+        )
+        print(
+            "Number of smaller values: ",
+            len(stackDF.loc[stackDF["Relative_Error"].abs() < 0.70].index),
+        )
+        # print(stackDF.loc[stackDF["Relative_Error"].abs() > 0.50])
+
+        _transform_ff_names(stackDF)
+        x_label = sta["splitby"]  #'molecule', "forcefield", "associated_work"
+        y_label = "Relative_Error"
+        color_labels, xtick_labelsList, color_map = _sort_plotting_labels(
+            stackDF, x_label, y_label
+        )
+
+        if sta["plot_type"] == "scatter":
+            _plot_scatter(
+                stackDF, fig, ax, color_labels, color_map, x_label, y_label
+            )
+        elif sta["plot_type"] == "violin":
+            _plot_violin(
+                stackDF, fig, ax, color_labels, color_map, x_label, y_label
+            )
+        else:
+            return
+            color_labels = list(Counter(df[x_label].to_numpy()).keys())
+
+        _general_plot_formatting(
+            stackDF,
+            ax,
+            sta["splitby"],
+            sta["figdir"],
+            sta["combMCMD"],
+            xtick_labelsList,
+            color_labels,
+            sta["average_replicas"],
+            ymax=sta["ymax"],
+        )  # format all plots
+    axs[0].set_ylabel("")
+    axs[2].set_ylabel("")
+
+    fname = f"figures/Stacked_Figures/{'molecule_stack'}-{sta['figdir']}.pdf"
+    print(f"Plotting {fname}")
+    fig.tight_layout()
+    fontsize = 20
+    axs[2].text(3.65, 5.8, "MoSDeF", fontdict={"size": fontsize})
+    axs[2].text(3.65, 29.7, "MoSDeF", fontdict={"size": fontsize})
+    axs[2].text(3.65, 53.8, "MoSDeF", fontdict={"size": fontsize})
+    axs[2].text(3.1, 5.8, "RR", fontdict={"size": fontsize})
+    axs[2].text(3.1, 29.7, "RR", fontdict={"size": fontsize})
+    axs[2].text(0.8, 53.8, "RR", fontdict={"size": fontsize})
+    fig.savefig(fname, dpi=500, bbox_inches="tight", overwrite=True)
+
+
+def looper_for_plotting_single_data(df):
+    """Seperate out plotting data from source specific dataframes."""
+    for filter_method in ["MoSDeF", "RR", None]:
+        if filter_method:
+            df = copy.deepcopy(densityDF)
+            df = df.loc[df["associated_work"] == filter_method]
+        for use_replica_avg in (True, False):
+            for splitby in ["ff-mol", "forcefield", "associated_work"]:
+                for combMCMDMCMD in [True, False]:
+                    for plot_type in ["scatter", "violin", "box"]:
+                        if filter_method is None:
+                            single_plot_comparisons(
+                                df.copy(),
+                                splitby=splitby,
+                                combMCMD=combMCMD,
+                                plot_type=plot_type,
+                                average_replicas=use_replica_avg,
+                                figdir="combined",
+                            )
+                        else:
+                            single_plot_comparisons(
+                                df.copy(),
+                                splitby=splitby,
+                                combMCMD=combMCMDMCMD,
+                                plot_type=plot_type,
+                                average_replicas=use_replica_avg,
+                                figdir=filter_method,
+                            )
+                        df = copy.deepcopy(densityDF)
+
+
+def plot_rr_mosdef_mosdef_avg(fullDF):
+    """Generate the overall averages for intrawork sources and get a total distribution."""
+    ylabelfs = 20
+
+    splitby = "associated_work"
+    combMCMD = False
+    plot_type = "violin"
+    average_replicas = [False, False, True]
+    figdir = "combined"
+    df = fullDF.copy()
+    df = _mask_df(df)
+    groupREList = [
+        "MCorMD",
+        "molecule",
+        "temperature",
+        "forcefield",
+        "pressure",
+    ]
+    df["Relative_Error"] = df.groupby(groupREList)["density"].transform(
+        _calculate_relative_error
+    )
+    _transform_ff_names(df)
+    x_label = splitby  #'molecule', "forcefield", "associated_work"
+    y_label = "Relative_Error"
+
+    # labels should be Round Robin, MoSDeF, MoSDeF-Averaged
+    # color_labels, xtick_labelsList, color_map = _sort_plotting_labels(df, x_label, y_label)
+    color_labels = ["RR", "MoSDeF", "MoSDeF-Averaged"]
+    dfs = list(
+        map(
+            lambda x: df.loc[df["associated_work"] == x].copy(),
+            color_labels[:2],
+        )
+    )
+
+    # generate final plotting df
+    df3 = fullDF.copy()
+    df = (
+        df.groupby(
+            [
+                "molecule",
+                "forcefield",
+                "engine",
+                "MCorMD",
+                "associated_work",
+                "temperature",
+                "pressure",
+            ]
+        )["density"]
+        .mean()
+        .reset_index()
+    )
+    df = _mask_df(df)
+    groupREList = [
+        "MCorMD",
+        "molecule",
+        "temperature",
+        "forcefield",
+        "pressure",
+    ]
+    df["Relative_Error"] = df.groupby(groupREList)["density"].transform(
+        _calculate_relative_error
+    )
+    dfs.append(df.loc[df["associated_work"] == "MoSDeF"])
+    fig, ax = _plot_params(color_labels)
+
+    if plot_type == "scatter":
+        _plot_scatter(
+            stackDF, fig, ax, color_labels, color_map, x_label, y_label
+        )
+    elif plot_type == "violin":
+        x_ticks = np.arange(len(color_labels))
+        for i, iDF in enumerate(dfs):
+            series = iDF[y_label]
+            ydata = series[series.notnull()]
+            if len(x_ticks) > 1:
+                spread = (x_ticks[1] - x_ticks[0]) * 0.25
+            else:
+                spread = 0.25
+            print(
+                f"Maximum deviation is: {max(abs(ydata)):.2f} for grouping {color_labels[i]}"
+            )
+            offsets = np.linspace(-1 * spread, spread, len(ydata))
+            xdata = np.full(len(ydata), x_ticks[i]) + offsets
+            ax.violinplot([ydata], positions=[i], showextrema=True)
+
+    xtick_labelsList = ["Round Robin", "MoSDeF", "MoSDeF-Averaged"]
+    _general_plot_formatting(
+        dfs[0],
+        ax,
+        splitby,
+        figdir,
+        combMCMD,
+        xtick_labelsList,
+        color_labels,
+        average_replicas,
+        ymax=8,
+    )  # format all plots
+    ax.set_ylabel(r"$100\times\Delta\rho / \rho$", size=ylabelfs)
+
+    fname = f"figures/Stacked_Figures/{'Grouped-Comparison'}-{plot_type}.pdf"
+    print(f"Plotting {fname}")
+    fig.tight_layout()
+    fig.savefig(fname, dpi=500, bbox_inches="tight", overwrite=True)
+
+
+def print_errors_for_text(df):
+    """Print out relative errors for discussion and conclusion in the manuscript."""
+    df = _mask_df(df)
+    # std error FF RR
+    df_RR = df.loc[(df["associated_work"] == "RR")].copy()
+    groupREList = [
+        "MCorMD",
+        "molecule",
+        "temperature",
+        "forcefield",
+        "pressure",
+    ]
+    df_RR["Relative_Error"] = df_RR.groupby(groupREList)["density"].transform(
+        _calculate_relative_error
+    )
+    RR_sim_std = df_RR["Relative_Error"].std()
+    print(
+        f"Round Robin STD Error is {df_RR['Relative_Error'].std()}, Max Error is {df_RR['Relative_Error'].abs().max()}"
+    )
+
+    # std error FF MoSDeF
+    df_mosdef = df.loc[(df["associated_work"] == "MoSDeF")].copy()
+    groupREList = [
+        "MCorMD",
+        "molecule",
+        "temperature",
+        "forcefield",
+        "pressure",
+    ]
+    df_mosdef["Relative_Error"] = df_mosdef.groupby(groupREList)[
+        "density"
+    ].transform(_calculate_relative_error)
+    mosdef_sim_std = df_mosdef["Relative_Error"].std()
+    print(
+        f"MoSDeF STD Error is {df_mosdef['Relative_Error'].std()}, Max Error is {df_mosdef['Relative_Error'].abs().max()}"
+    )
+
+    # std error FF MoSDeF average of 16
+    groupREList = [
+        "MCorMD",
+        "molecule",
+        "temperature",
+        "forcefield",
+        "pressure",
+    ]
+    df_mosdef_avg = df.loc[(df["associated_work"] == "MoSDeF")].copy()
+    df_mosdef_avg = copy.deepcopy(
+        df_mosdef_avg.groupby(
+            [
+                "molecule",
+                "forcefield",
+                "engine",
+                "MCorMD",
+                "associated_work",
+                "temperature",
+                "pressure",
+            ]
+        )["density"]
+        .mean()
+        .reset_index()
+    )
+    df_mosdef_avg["Relative_Error"] = df_mosdef_avg.groupby(groupREList)[
+        "density"
+    ].transform(_calculate_relative_error)
+    mosdef_avg_std = df_mosdef_avg["Relative_Error"].std()
+    print(
+        f"MoSDeF Best Practices STD Error is {df_mosdef_avg['Relative_Error'].std()}, Max Error is {df_mosdef_avg['Relative_Error'].abs().max()}"
+    )
+
+    print(f"Comparable MoSDeF Decrease: {RR_sim_std/mosdef_sim_std}")
+    print(f"Best Practices MoSDeF Decrease: {RR_sim_std/mosdef_avg_std}")
+    mean = df_mosdef_avg["Relative_Error"].mean()
+    length_RE = len(df_mosdef_avg["Relative_Error"])
+    print(
+        f"MoSDeF CI for all errors: {mean} +- {mosdef_avg_std/np.sqrt(length_RE)*1.96}"
+    )
+
+
+if __name__ == "__main__":
+    mosdefDF = pd.read_csv("csvs/job_density_data.csv", index_col=0)
+    mosdefDF.insert(
+        len(mosdefDF.columns),
+        "pressure",
+        np.full(len(mosdefDF.index), 0.101325),
+    )
+
+    hasse_dfList = _load_all_rr_data()
+
+    # 4 combine data into one dataframe
+    densityDF = mosdefDF.loc[
+        mosdefDF["molecule"] != "pentaneUA-flexible_bonds"
+    ].copy()
+    densityDF = pd.concat([densityDF, *hasse_dfList], ignore_index=True)
+    df = copy.deepcopy(densityDF)
+
+    # generate individual scatter and violin plots
+    # looper_for_plotting_single_data(df)
+
+    # generate stacked plots
+
+    stack1 = [
+        {
+            "figdir": "MoSDeF",
+            "splitby": "ff-mol",
+            "combMCMD": True,
+            "plot_type": "violin",
+            "average_replicas": False,
+        },
+        {
+            "figdir": "MoSDeF",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "violin",
+            "average_replicas": False,
+        },
+        {
+            "figdir": "MoSDeF",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "violin",
+            "average_replicas": True,
+        },
+    ]
+
+    stack_plot_comparisons(df, stack1, ymax=0.8)
+
+    stack2 = [
+        {
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": True,
+            "plot_type": "scatter",
+            "average_replicas": False,
+            "rotation": 45,
+            "wrap_labels": False,
+        },
+        {
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "scatter",
+            "average_replicas": False,
+            "rotation": 45,
+            "wrap_labels": False,
+        },
+    ]
+
+    # stack_plot_comparisons(df, stack2) # try other plotting
+
+    stack3 = [
+        {
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": True,
+            "plot_type": "violin",
+            "average_replicas": False,
+            "rotation": 45,
+            "wrap_labels": False,
+        },
+        {
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "violin",
+            "average_replicas": False,
+            "rotation": 45,
+            "wrap_labels": False,
+        },
+    ]
+
+    # stack_plot_comparisons(df, stack3)
+
+    # compare TraPPE -> all
+    df1 = copy.deepcopy(df).loc[
+        df["forcefield"].isin(["TraPPE", "trappe-ua", "benzene-ua"])
+    ]
+    df2 = copy.deepcopy(df).loc[df["forcefield"].isin(["OPLS", "spce"])]
+    df3 = copy.deepcopy(df).loc[df["forcefield"].isin(["OPLSAMBER", "oplsaa"])]
+    stack_mol_by_mol_basis = [  # Add text above MoSDeF and RR sections
+        {
+            "df": df1,
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "violin",
+            "average_replicas": False,
+            "ymax": 2,
+            "seperator_line_pos": 3.5,
+        },
+        {
+            "df": df2,
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "violin",
+            "average_replicas": False,
+            "ymax": 2,
+            "seperator_line_pos": 3.5,
+        },
+        {
+            "df": df3,
+            "figdir": "combined",
+            "splitby": "ff-mol",
+            "combMCMD": False,
+            "plot_type": "violin",
+            "average_replicas": False,
+            "ymax": 8,
+            "seperator_line_pos": 3.5,
+        },
+    ]
+
+    # Compare TraPPE -> TraPPE
+    # compare OPLS -> SPC/E
+    # compare OPLS AMBER -> OPLS Ethanol
+    stack_plot_multix(stack_mol_by_mol_basis)
+
+    plot_rr_mosdef_mosdef_avg(densityDF.copy())
+
+    print_errors_for_text(densityDF.copy())
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/generate_densities.py b/reproducibility_project/src/analysis/Comparison_Figures/generate_densities.py
new file mode 100644
index 00000000..5d0ce339
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/generate_densities.py
@@ -0,0 +1,210 @@
+"""Generate CSV from job docs for job densities."""
+import os
+
+import camelot
+import numpy as np
+import pandas as pd
+import signac
+
+updated_masses = {
+    "methaneUA": 16.043,
+    "pentaneUA-flexible_bonds": 72.151,
+    "pentaneUA-constrain_bonds": 72.151,
+    "pentaneUA": 72.151,
+    "benzeneUA": 78.114,
+    "waterSPCE": 18.015,  # 18.015324,
+    "ethanolAA": 46.069,  # 46.068672,
+}
+
+
+def load_mosdef_jobs():
+    """Load MoSDeF densities from job docs."""
+    # Load by molecule, temperature, forcefield
+    densitiesDict = {}
+    p = signac.get_project("./")
+    sampled_jobsDict = {}
+    for i, job in enumerate(p):
+        if not job.isfile("signac_statepoint.json"):
+            continue
+        filter_list = [
+            (
+                job.sp.engine == "mcccs"
+                and job.sp.mass != updated_masses[job.sp.molecule]
+            ),
+            job.sp.engine == "lammps-UD",
+            job.sp.ensemble != "NPT",
+            job.sp.molecule == "pentaneUA",
+        ]
+        if np.any(filter_list):
+            continue
+
+        print(f"{i/len(p)*100:.1f}% done")
+        try:
+            sampled_jobsDict[job] = (
+                job.doc["npt_density_avg"],
+                job.doc["npt_density_std"],
+            )
+            # job.doc['npt/sampling_results']["density"]
+        except:
+            return job.id, job.doc, job.sp
+    return sampled_jobsDict
+
+
+headers = [
+    "associated_work",
+    "MCorMD",
+    "engine",
+    "molecule",
+    "replicate",
+    "temperature",
+    "density",
+    "density-std",
+    "forcefield",
+]
+MCMDDict = dict()
+for engines, enginetype in list(
+    zip(
+        [("cassandra", "gomc", "mcccs"), ("lammps-VU", "gromacs", "hoomd")],
+        ("MC", "MD"),
+    )
+):
+    for engine in engines:
+        MCMDDict.update({engine: enginetype})
+
+
+def generate_mosdef_job_csv():
+    """Create a csv of all data for mosdef jobs based on statepoint to read into dataframes."""
+    if os.path.exists("csvs/job_density_data.csv"):
+        print("Density data is already generated at csvs/job_density_data.csv.")
+        return
+    job_densityDict = load_mosdef_jobs()
+    row_list = []
+    for job in job_densityDict:
+        inputs = [
+            "MoSDeF",
+            MCMDDict[job.sp.engine],
+            job.sp.engine,
+            job.sp.molecule,
+            job.sp.replica,
+            job.sp.temperature,
+            job_densityDict[job][0],
+            job_densityDict[job][1],
+            job.sp.forcefield_name,
+        ]
+        row_list.append(dict((key, val) for key, val in zip(headers, inputs)))
+    df = pd.DataFrame(row_list)
+    df.to_csv("csvs/job_density_data.csv")
+
+
+def _generate_csv_from_PDF(molecule):
+    """Methods to generate dataframe from PDF supplied for paper SI."""
+    page = molecule_pageDictionary[molecule]
+
+    # Read remote pdf into a list of DataFrame
+    link = "Hasse_SI.pdf"  # must have access locally for this python file to load it.
+    tables = camelot.read_pdf(link, flavor="stream", pages=page, edge_tol=500)
+    if len(tables) == 1:
+        table = tables[0]
+        molecule_nameStr = table.df[1][0]
+        if molecule in [
+            "OPLS Ethane at 5 MPa",
+            "OPLS Propane at 5 MPa",
+            "OPLS n-Butane at 5 MPa",
+            "OPLS iso-Butane at 5 MPa",
+        ]:
+            df = table.df[table.df.columns[1:]][2:]
+            df.columns = np.arange(len(df.columns))
+        elif "AMBER" in molecule:
+            df = table.df[table.df.columns[:]][2:]
+        else:
+            df = table.df[table.df.columns[:]][1:]
+        row_start = 1 + np.where(df[0] == "Program(Group)")[0][0]
+        headers = df.iloc[row_start - 1]
+        newDF = pd.DataFrame(df.values[row_start:], columns=headers)
+    else:
+        # need to combine table0 and table1 parts
+        df = pd.concat((tables[0].df, tables[1].df), axis=0)
+        header_ids = np.where(df[0] == "Program(Group)")[0]
+        if len(header_ids) == 2:
+            row_start = 1 + header_ids[0]
+            row_end = header_ids[1] - 2
+            headers = df.iloc[row_start - 1]
+            newDF = pd.DataFrame(df.values[row_start:row_end], columns=headers)
+        elif len(header_ids) == 1:
+            row_start = 1 + header_ids[0]
+            headers = df.iloc[row_start - 1]
+            newDF = pd.DataFrame(df.values[row_start:], columns=headers)
+
+    for i in range(len(newDF.index)):
+        headerInt = int(4 * np.floor(i / 4))
+        header = newDF.iloc[headerInt]["Program(Group)"]
+        newDF.iloc[i]["Program(Group)"] = header
+
+    # we will change the data type
+    # of id column to str by giving
+    # the dict to the astype method
+    columns = list(newDF.columns[1:])
+
+    for c in columns:
+        newDF[c] = pd.to_numeric(newDF[c], errors="coerce")
+    newDF["statepoint"] = "temp: " + newDF["T / K"].astype(str)
+    newDF["Relative_Error"] = newDF.groupby("statepoint")[
+        "ρ / kg m−3"
+    ].transform(_calculate_relative_error)
+    newDF.to_csv(f"csvs/{molecule}_data.csv")
+
+
+def _calculate_relative_error(ser):
+    """Calculate relative density error for all engines at that statepoint."""
+    dataArray = ser.to_numpy()
+    mean = np.nanmean(dataArray)
+    return (dataArray - mean) / mean * 100
+
+
+if __name__ == "__main__":
+    generate_mosdef_job_csv()
+
+    molecule_pageDictionary = {
+        "OPLS Ethane at 5 MPa": "8",
+        "OPLS Ethane at 41 MPa": "9",
+        "OPLS Ethane at 70 MPa": "10",
+        "TraPPE Ethane at 5 MPa": "11",
+        "TraPPE Ethane at 41 MPa": "12",
+        "TraPPE Ethane at 70 MPa": "13",
+        "OPLSAMBER Ethane at 5 MPa": "14,15",
+        "OPLSAMBER Ethane at 41 MPa": "15,16",
+        "OPLSAMBER Ethane at 70 MPa": "16,17",
+        "OPLS Propane at 5 MPa": "18",
+        "OPLS Propane at 41 MPa": "19",
+        "OPLS Propane at 70 MPa": "20",
+        "TraPPE Propane at 5 MPa": "21",
+        "TraPPE Propane at 41 MPa": "22",
+        "TraPPE Propane at 70 MPa": "23",
+        "OPLSAMBER Propane at 5 MPa": "24-25",
+        "OPLSAMBER Propane at 41 MPa": "25-26",
+        "OPLSAMBER Propane at 70 MPa": "26-27",
+        "OPLS n-Butane at 5 MPa": "28",
+        "OPLS n-Butane at 41 MPa": "29",
+        "OPLS n-Butane at 70 MPa": "30",
+        "TraPPE n-Butane at 5 MPa": "31",
+        "TraPPE n-Butane at 41 MPa": "32",
+        "TraPPE n-Butane at 70 MPa": "33",
+        "OPLSAMBER n-Butane at 5 MPa": "34,35",
+        "OPLSAMBER n-Butane at 41 MPa": "35,36",
+        "OPLSAMBER n-Butane at 70 MPa": "36,37",
+        "OPLS iso-Butane at 5 MPa": "38",
+        "OPLS iso-Butane at 41 MPa": "39",
+        "OPLS iso-Butane at 70 MPa": "40",
+        "TraPPE iso-Butane at 5 MPa": "41",
+        "TraPPE iso-Butane at 41 MPa": "42",
+        "TraPPE iso-Butane at 70 MPa": "43",
+        "OPLSAMBER iso-Butane at 5 MPa": "44,45",
+        "OPLSAMBER iso-Butane at 41 MPa": "45,46",
+        "OPLSAMBER iso-Butane at 70 MPa": "46,47",
+    }
+    for molecule in molecule_pageDictionary:
+        print(f"Generating CSV for: {molecule}")
+        print("################################")
+        print("################################")
+        _generate_csv_from_PDF(molecule)
+    print("\n\nCompleted Generating all CSVs in the directory csvs/\n\n")
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/generate_scatter_plots.py b/reproducibility_project/src/analysis/Comparison_Figures/generate_scatter_plots.py
new file mode 100644
index 00000000..4316fe9c
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/generate_scatter_plots.py
@@ -0,0 +1,163 @@
+"""Analyze csvs in ./csvs in order to create scatter plots of the density differences found in the SI of 10.1021/acs.jctc.7b00489."""
+
+import camelot
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+np.random.seed(0)
+import matplotlib.transforms as transforms
+
+
+def _create_scatter_plot(summary_df, molecule, title, savefig=False):
+    """Generate summary files for the aggregate project."""
+    plt.rcParams.update(
+        {
+            "axes.titlesize": 20,
+            "axes.labelsize": 20,
+            "axes.titleweight": "bold",
+            "axes.labelweight": "bold",
+            "font.weight": "bold",
+            "xtick.labelsize": 20,
+            "ytick.labelsize": 20,
+        }
+    )
+    sns.set_style("whitegrid")
+    fig = plt.figure(figsize=(10, 10))
+    offset = lambda p: transforms.ScaledTranslation(
+        p / 72, 0, plt.gcf().dpi_scale_trans
+    )
+    trans = plt.gca().transData
+
+    x_label = "T / K"
+    y_labels = ["ρ / kg m−3", "Relative_Error"]
+    y_label = y_labels[1]
+
+    color_labels = summary_df["Program(Group)"].unique()
+    palette = sns.color_palette("colorblind", n_colors=len(color_labels))
+    color_map = dict(zip(color_labels, palette))
+    offsets = np.linspace(-12, 12, len(color_labels))
+    x_ticks = np.arange(len(color_labels))
+    for offseti, my_engine in zip(offsets, color_labels):
+        series = summary_df.loc[summary_df["Program(Group)"] == my_engine]
+        data = series[series.notnull()]
+        ax = plt.scatter(
+            x=data[x_label] + offseti,
+            y=data[y_label],
+            color=color_map[my_engine],
+            label=my_engine,
+            s=np.full(len(data.index), 40),
+            edgecolors="black",
+        )
+    plt.legend(color_labels)
+    plt.xlabel(x_label)
+    plt.ylabel(y_label)
+    val_min = summary_df[y_label].min()
+    val_max = summary_df[y_label].max()
+
+    # plt.ylim(val_min - .2 * val_min, val_max + .2 * val_max)
+    plt.title(title, y=1.0, pad=15)
+    plt.tight_layout()
+    if savefig:
+        plt.savefig(f"figures/{molecule}_summary_scatter.pdf")
+    return fig
+
+
+def generate_REPlot_by_molecule(molecule, savefig=True):
+    """Compare errors in density for engines in csvs."""
+    if "AMBER" in molecule:
+        return  # only look at molecules with opls and trappe forcefield
+    newDF = pd.read_csv(f"csvs/{molecule}_data.csv", index_col=0)
+    title = f"SI Data from Hans Hasse for {molecule}"
+    x = _create_scatter_plot(newDF, molecule, title, savefig=savefig)
+
+
+def _generate_csv_from_PDF(molecule):
+    """Methods to generate dataframe from PDF supplied for paper SI."""
+    page = molecule_pageDictionary[molecule]
+
+    # Read remote pdf into a list of DataFrame
+    link = "Hasse_SI.pdf"  # must have access locally for this python file to load it.
+    tables = camelot.read_pdf(link, flavor="stream", pages=page)
+
+    if "AMBER" in molecule:
+        return
+
+    table = tables[0]
+    molecule_nameStr = table.df[1][0]
+    if molecule in [
+        "OPLS Ethane at 5 MPa",
+        "OPLS Propane at 5 MPa",
+        "OPLS n-Butane at 5 MPa",
+    ]:
+        df = table.df[table.df.columns[1:]][2:]
+        df.columns = np.arange(len(df.columns))
+    elif "AMBER" in molecule:
+        df = table.df[table.df.columns[:]][2:]
+    else:
+        df = table.df[table.df.columns[:]][1:]
+    row_start = 1 + np.where(df[0] == "Program(Group)")[0][0]
+    headers = df.iloc[row_start - 1]
+    newDF = pd.DataFrame(df.values[row_start:], columns=headers)
+    for i in range(len(newDF.index)):
+        headerInt = int(4 * np.floor(i / 4))
+        header = newDF.iloc[headerInt]["Program(Group)"]
+        newDF.iloc[i]["Program(Group)"] = header
+
+    # we will change the data type
+    # of id column to str by giving
+    # the dict to the astype method
+    columns = list(newDF.columns[1:])
+
+    for c in columns:
+        newDF[c] = pd.to_numeric(newDF[c], errors="coerce")
+    newDF["statepoint"] = "temp: " + newDF["T / K"].astype(str)
+    newDF["Relative_Error"] = newDF.groupby("statepoint")[
+        "ρ / kg m−3"
+    ].transform(_calculate_relative_error)
+    newDF.to_csv(f"csvs/{molecule}_data.csv")
+
+
+def _calculate_relative_error(ser):
+    """Calculate relative density error for all engines at that statepoint."""
+    dataArray = ser.to_numpy()
+    mean = np.nanmean(dataArray)
+    return (dataArray - mean) / mean * 100
+
+
+if __name__ == "__main__":
+    molecule_pageDictionary = {
+        "OPLS Ethane at 5 MPa": "8",
+        "OPLS Ethane at 41 MPa": "9",
+        "OPLS Ethane at 70 MPa": "10",
+        "TraPPE Ethane at 5 MPa": "11",
+        "TraPPE Ethane at 41 MPa": "12",
+        "TraPPE Ethane at 70 MPa": "13",
+        "OPLSAMBER Ethane at 5 MPa": "14,15",
+        "OPLSAMBER Ethane at 41 MPa": "15,16",
+        "OPLSAMBER Ethane at 70 MPa": "16,17",
+        "OPLS Propane at 5 MPa": "18",
+        "OPLS Propane at 41 MPa": "19",
+        "OPLS Propane at 70 MPa": "20",
+        "TraPPE Propane at 5 MPa": "21",
+        "TraPPE Propane at 41 MPa": "22",
+        "TraPPE Propane at 70 MPa": "23",
+        "OPLSAMBER Propane at 5 MPa": "24-25",
+        "OPLSAMBER Propane at 41 MPa": "25-26",
+        "OPLSAMBER Propane at 70 MPa": "26-27",
+        "OPLS n-Butane at 5 MPa": "28",
+        "OPLS n-Butane at 41 MPa": "29",
+        "OPLS n-Butane at 70 MPa": "30",
+        "TraPPE n-Butane at 5 MPa": "31",
+        "TraPPE n-Butane at 41 MPa": "32",
+        "TraPPE n-Butane at 70 MPa": "33",
+        "OPLSAMBER n-Butane at 5 MPa": "34,35",
+        "OPLSAMBER n-Butane at 41 MPa": "35,36",
+        "OPLSAMBER n-Butane at 70 MPa": "36,37",
+    }
+    for molecule in molecule_pageDictionary:
+        print(f"Generating Plot for: {molecule}")
+        print("################################")
+        print("################################")
+        generate_REPlot_by_molecule(molecule, savefig=True)
diff --git a/reproducibility_project/src/analysis/Comparison_Figures/plotting.py b/reproducibility_project/src/analysis/Comparison_Figures/plotting.py
new file mode 100644
index 00000000..aebe5061
--- /dev/null
+++ b/reproducibility_project/src/analysis/Comparison_Figures/plotting.py
@@ -0,0 +1,160 @@
+"""Utility methods for plotting data."""
+import pathlib
+import textwrap
+
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.typing as npt
+import scipy
+
+from reproducibility_project.src.analysis.equilibration import is_equilibrated
+
+
+def plot_data_with_t0_line(
+    filename: str,
+    a_t: npt.ArrayLike,
+    vline_scale: float = 1.1,
+    threshold_fraction: float = 0.0,
+    threshold_neff: int = 1,
+    overwrite: bool = False,
+    title: str = None,
+    data_plt_kwargs: dict = None,
+    vline_plt_kwargs: dict = None,
+) -> None:
+    """Plot data with a vertical line at beginning of equilibration.
+
+    Parameters
+    ----------
+    a_t : numpy.typing.ArrayLike
+        1-D time dependent data
+    vline_scale : float, optional, default=1.1
+        Scale the min and max components of the vertical line.
+    threshold_fraction : float, optional, default=0.0
+        Threshold_fraction to error out on if threshold fraction of data is not equilibrated.
+    threshold_neff : int, optional, default=1
+        Threshold amount of uncorrelated samples to error out on if threshold amount of data is not equilibrated.
+    overwrite : bool, optional, default=False
+        Do not write to filename if a file already exists with the same name.
+        Set to True to overwrite exisiting files.
+    title : str, optional
+        Set the title of the plot
+    data_plt_kwargs : dict, optional, default={}
+        Pass in a dictionary of keyword arguments to plot the data.
+    vline_plt_kwargs : dict, optional, default={}
+        Pass in a dictionary of keyword arguments for the
+    """
+    path = pathlib.Path(filename)
+    if path.is_file() and not overwrite:
+        raise FileExistsError(
+            f"Cannot write {path.name}, file already exists at: {path.absolute()}. Set overwrite=True to replace file."
+        )
+
+    _, t0, g, Neff = is_equilibrated(
+        a_t,
+        threshold_fraction=threshold_fraction,
+        threshold_neff=threshold_neff,
+        nskip=1,
+    )
+
+    ymin = np.min(a_t) - np.min(a_t) * (np.abs(1 - vline_scale))
+    ymax = np.max(a_t) + np.max(a_t) * (np.abs(1 - vline_scale))
+
+    if data_plt_kwargs is None:
+        data_plt_kwargs = {}
+    if vline_plt_kwargs is None:
+        vline_plt_kwargs = {}
+    for key, val in {
+        "color": "b",
+        "linestyle": "-",
+        "label": "Property",
+    }.items():
+        if data_plt_kwargs.get(key) is None:
+            data_plt_kwargs[key] = val
+
+    for key, val in {
+        "colors": "r",
+        "linestyles": "--",
+        "label": "t_0={0:d}\ng={1:.2f}\nNeff={2:d}",
+    }.items():
+        if vline_plt_kwargs.get(key) is None:
+            if key == "label":
+                vline_plt_kwargs[key] = val.format(t0, g, int(Neff))
+            else:
+                vline_plt_kwargs[key] = val
+
+    fig, ax = plt.subplots()
+
+    line1 = ax.plot(a_t, **data_plt_kwargs)
+    vline1 = ax.vlines(x=t0, ymin=ymin, ymax=ymax, **vline_plt_kwargs)
+
+    ax.legend(loc="best")
+
+    if title is None:
+        plt.suptitle("Property")
+    else:
+        plt.suptitle(title)
+
+    plt.tight_layout()
+    fig.savefig(
+        str(path.absolute()),
+    )
+    plt.close()
+
+
+def mean_confidence_interval(m, se, confidence=0.95):
+    """Calculate mean confidence_interval."""
+    se = np.array(se)
+    n = 16
+    h = se * scipy.stats.t.ppf((1 + confidence) / 2.0, n - 1)
+    return h
+
+
+def wrap_labels(ax, width, break_long_words=False):
+    """Wrap labels if string is too long."""
+    labels = []
+    for label in ax.get_xticklabels():
+        text = label.get_text()
+        labels.append(
+            textwrap.fill(text, width=width, break_long_words=break_long_words)
+        )
+    ax.set_xticklabels(labels, rotation=0)
+
+
+# Colors
+symbols = {}
+symbols["cassandra"] = "o"
+symbols["mcccs"] = "^"
+symbols["gomc"] = "s"
+symbols["gromacs"] = "x"
+symbols["hoomd"] = "v"
+symbols["lammps-VU"] = "D"
+# symbols[ "LAMMPS-UD"] = ">"
+
+colors = {}
+colors["cassandra"] = "#009392"  # (23/256, 109/256, 156/256)
+colors["mcccs"] = "#39B1B5"  # (194/256, 135/256, 32/256)
+colors["gomc"] = "#9CCB86"  # (21/256, 138/256, 106/256)
+colors["gromacs"] = "#E9E29C"  # (186/256, 97/256, 26/256)
+colors["hoomd"] = "#EEB479"  # (193/256, 130/256, 181/256)
+colors["lammps-VU"] = "#E88471"  # (188/256, 146/256, 110/256)
+colors["fix"] = "#FF7F00"
+colors["flex"] = "#007FFF"
+# colors_dict = {all_engine_molecule: all_engine_colors,
+#                pentane_fixed: pentane_fixed_colors,
+#                pentane_flexible: pentane_flexible_colors}
+
+pretty_names = {
+    "mcccsflex": "MCCCS-MN-FlexOH",
+    "mcccsfix": "MCCCS-MN-FixOH",
+    "lammps-VUflex": "LAMMPS-FlexOH",
+    "lammps-VUfix": "LAMMPS-FixOH",
+    "cassandra": "Cassandra",
+    "mcccs": "MCCCS-MN",
+    "gomc": "GOMC",
+    "gromacs": "GROMACS",
+    "hoomd": "HOOMD-Blue",
+    "lammps-VU": "LAMMPS",
+}
+
+
+figsize = (9, 6)
diff --git a/reproducibility_project/src/analysis/matplotlibrc b/reproducibility_project/src/analysis/matplotlibrc
new file mode 100644
index 00000000..b8ceac0b
--- /dev/null
+++ b/reproducibility_project/src/analysis/matplotlibrc
@@ -0,0 +1,627 @@
+### MATPLOTLIBRC FORMAT
+
+# This is a sample matplotlib configuration file - you can find a copy
+# of it on your system in
+# site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
+# there, please note that it will be overwritten in your next install.
+# If you want to keep a permanent local copy that will not be
+# overwritten, place it in the following location:
+# unix/linux:
+#     $HOME/.config/matplotlib/matplotlibrc or
+#     $XDG_CONFIG_HOME/matplotlib/matplotlibrc (if $XDG_CONFIG_HOME is set)
+# other platforms:
+#     $HOME/.matplotlib/matplotlibrc
+#
+# See http://matplotlib.org/users/customizing.html#the-matplotlibrc-file for
+# more details on the paths which are checked for the configuration file.
+#
+# This file is best viewed in a editor which supports python mode
+# syntax highlighting. Blank lines, or lines starting with a comment
+# symbol, are ignored, as are trailing comments.  Other lines must
+# have the format
+#    key : val # optional comment
+#
+# Colors: for the color values below, you can either use - a
+# matplotlib color string, such as r, k, or b - an rgb tuple, such as
+# (1.0, 0.5, 0.0) - a hex string, such as ff00ff - a scalar
+# grayscale intensity such as 0.75 - a legal html color name, e.g., red,
+# blue, darkslategray
+
+#### CONFIGURATION BEGINS HERE
+
+# The default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo
+# MacOSX Qt4Agg Qt5Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG
+# Template.
+# You can also deploy your own backend outside of matplotlib by
+# referring to the module name (which must be in the PYTHONPATH) as
+# 'module://my_backend'.
+backend      : TkAgg
+
+# If you are using the Qt4Agg backend, you can choose here
+# to use the PyQt4 bindings or the newer PySide bindings to
+# the underlying Qt4 toolkit.
+#backend.qt4 : PyQt4        # PyQt4 | PySide
+
+# Note that this can be overridden by the environment variable
+# QT_API used by Enthought Tool Suite (ETS); valid values are
+# "pyqt" and "pyside".  The "pyqt" setting has the side effect of
+# forcing the use of Version 2 API for QString and QVariant.
+
+# The port to use for the web server in the WebAgg backend.
+# webagg.port : 8888
+
+# If webagg.port is unavailable, a number of other random ports will
+# be tried until one that is available is found.
+# webagg.port_retries : 50
+
+# When True, open the webbrowser to the plot that is shown
+# webagg.open_in_browser : True
+
+# When True, the figures rendered in the nbagg backend are created with
+# a transparent background.
+# nbagg.transparent : False
+
+# if you are running pyplot inside a GUI and your backend choice
+# conflicts, we will automatically try to find a compatible one for
+# you if backend_fallback is True
+#backend_fallback: True
+
+#interactive  : False
+#toolbar      : toolbar2   # None | toolbar2  ("classic" is deprecated)
+#timezone     : UTC        # a pytz timezone string, e.g., US/Central or Europe/Paris
+
+# Where your matplotlib data lives if you installed to a non-default
+# location.  This is where the matplotlib fonts, bitmaps, etc reside
+#datapath : /home/jdhunter/mpldata
+
+
+### LINES
+# See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more
+# information on line properties.
+lines.linewidth   : 0.8     # line width in points
+#lines.linestyle   : -       # solid line
+#lines.color       : C0      # has no affect on plot(); see axes.prop_cycle
+#lines.marker      : None    # the default marker
+lines.markeredgewidth  : 0.8     # the line width around the marker symbol
+#lines.markersize  : 6            # markersize, in points
+#lines.dash_joinstyle : miter        # miter|round|bevel
+#lines.dash_capstyle : butt          # butt|round|projecting
+#lines.solid_joinstyle : miter       # miter|round|bevel
+#lines.solid_capstyle : projecting   # butt|round|projecting
+#lines.antialiased : True         # render lines in antialiased (no jaggies)
+
+# The three standard dash patterns.  These are scaled by the linewidth.
+#lines.dashed_pattern : 2.8, 1.2
+#lines.dashdot_pattern : 4.8, 1.2, 0.8, 1.2
+#lines.dotted_pattern : 1.1, 1.1
+#lines.scale_dashes : True
+
+markers.fillstyle: none # full|left|right|bottom|top|none
+
+### PATCHES
+# Patches are graphical objects that fill 2D space, like polygons or
+# circles.  See
+# http://matplotlib.org/api/artist_api.html#module-matplotlib.patches
+# information on patch properties
+patch.linewidth        : 0.8        # edge width in points.
+#patch.facecolor        : C0
+#patch.edgecolor        : black   # if forced, or patch is not filled
+#patch.force_edgecolor  : False   # True to always use edgecolor
+#patch.antialiased      : True    # render patches in antialiased (no jaggies)
+
+### HATCHES
+#hatch.color     : k
+#hatch.linewidth : 1.0
+
+### Boxplot
+#boxplot.notch       : False
+#boxplot.vertical    : True
+#boxplot.whiskers    : 1.5
+#boxplot.bootstrap   : None
+#boxplot.patchartist : False
+#boxplot.showmeans   : False
+#boxplot.showcaps    : True
+#boxplot.showbox     : True
+#boxplot.showfliers  : True
+#boxplot.meanline    : False
+
+#boxplot.flierprops.color           : 'k'
+#boxplot.flierprops.marker          : 'o'
+#boxplot.flierprops.markerfacecolor : 'none'
+#boxplot.flierprops.markeredgecolor : 'k'
+#boxplot.flierprops.markersize      : 6
+#boxplot.flierprops.linestyle       : 'none'
+#boxplot.flierprops.linewidth       : 1.0
+
+#boxplot.boxprops.color     : 'k'
+#boxplot.boxprops.linewidth : 1.0
+#boxplot.boxprops.linestyle : '-'
+
+#boxplot.whiskerprops.color     : 'k'
+#boxplot.whiskerprops.linewidth : 1.0
+#boxplot.whiskerprops.linestyle : '-'
+
+#boxplot.capprops.color     : 'k'
+#boxplot.capprops.linewidth : 1.0
+#boxplot.capprops.linestyle : '-'
+
+#boxplot.medianprops.color     : 'C1'
+#boxplot.medianprops.linewidth : 1.0
+#boxplot.medianprops.linestyle : '-'
+
+#boxplot.meanprops.color           : 'C2'
+#boxplot.meanprops.marker          : '^'
+#boxplot.meanprops.markerfacecolor : 'C2'
+#boxplot.meanprops.markeredgecolor : 'C2'
+#boxplot.meanprops.markersize      :  6
+#boxplot.meanprops.linestyle       : 'none'
+#boxplot.meanprops.linewidth       : 1.0
+
+### FONT
+#
+# font properties used by text.Text.  See
+# http://matplotlib.org/api/font_manager_api.html for more
+# information on font properties.  The 6 font properties used for font
+# matching are given below with their default values.
+#
+# The font.family property has five values: 'serif' (e.g., Times),
+# 'sans-serif' (e.g., Helvetica), 'cursive' (e.g., Zapf-Chancery),
+# 'fantasy' (e.g., Western), and 'monospace' (e.g., Courier).  Each of
+# these font families has a default list of font names in decreasing
+# order of priority associated with them.  When text.usetex is False,
+# font.family may also be one or more concrete font names.
+#
+# The font.style property has three values: normal (or roman), italic
+# or oblique.  The oblique style will be used for italic, if it is not
+# present.
+#
+# The font.variant property has two values: normal or small-caps.  For
+# TrueType fonts, which are scalable fonts, small-caps is equivalent
+# to using a font size of 'smaller', or about 83%% of the current font
+# size.
+#
+# The font.weight property has effectively 13 values: normal, bold,
+# bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
+# 400, and bold is 700.  bolder and lighter are relative values with
+# respect to the current weight.
+#
+# The font.stretch property has 11 values: ultra-condensed,
+# extra-condensed, condensed, semi-condensed, normal, semi-expanded,
+# expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
+# property is not currently implemented.
+#
+# The font.size property is the default font size for text, given in pts.
+# 10 pt is the standard value.
+#
+#font.family         : sans-serif
+#font.style          : normal
+#font.variant        : normal
+#font.weight         : medium
+#font.stretch        : normal
+# note that font.size controls default text sizes.  To configure
+# special text sizes tick labels, axes, labels, title, etc, see the rc
+# settings for axes and ticks. Special text sizes can be defined
+# relative to font.size, using the following values: xx-small, x-small,
+# small, medium, large, x-large, xx-large, larger, or smaller
+#font.size           : 10.0
+#font.serif          : DejaVu Serif, Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
+#font.sans-serif     : DejaVu Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
+#font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive
+#font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy
+#font.monospace      : DejaVu Sans Mono, Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+
+### TEXT
+# text properties used by text.Text.  See
+# http://matplotlib.org/api/artist_api.html#module-matplotlib.text for more
+# information on text properties
+
+#text.color          : black
+
+### LaTeX customizations. See http://wiki.scipy.org/Cookbook/Matplotlib/UsingTex
+#text.usetex         : True # use latex for all text handling. The following fonts
+                              # are supported through the usual rc parameter settings:
+                              # new century schoolbook, bookman, times, palatino,
+                              # zapf chancery, charter, serif, sans-serif, helvetica,
+                              # avant garde, courier, monospace, computer modern roman,
+                              # computer modern sans serif, computer modern typewriter
+                              # If another font is desired which can loaded using the
+                              # LaTeX \usepackage command, please inquire at the
+                              # matplotlib mailing list
+#text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for handling
+                            # unicode strings.
+#text.latex.preamble :  # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
+                            # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
+                            # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
+                            # preamble is a comma separated list of LaTeX statements
+                            # that are included in the LaTeX document preamble.
+                            # An example:
+                            # text.latex.preamble : \usepackage{bm},\usepackage{euler}
+                            # The following packages are always loaded with usetex, so
+                            # beware of package collisions: color, geometry, graphicx,
+                            # type1cm, textcomp. Adobe Postscript (PSSNFS) font packages
+                            # may also be loaded, depending on your font settings
+
+#text.dvipnghack : None      # some versions of dvipng don't handle alpha
+                             # channel properly.  Use True to correct
+                             # and flush ~/.matplotlib/tex.cache
+                             # before testing and False to force
+                             # correction off.  None will try and
+                             # guess based on your dvipng version
+
+#text.hinting : auto   # May be one of the following:
+                       #   'none': Perform no hinting
+                       #   'auto': Use FreeType's autohinter
+                       #   'native': Use the hinting information in the
+                       #             font file, if available, and if your
+                       #             FreeType library supports it
+                       #   'either': Use the native hinting information,
+                       #             or the autohinter if none is available.
+                       # For backward compatibility, this value may also be
+                       # True === 'auto' or False === 'none'.
+#text.hinting_factor : 8 # Specifies the amount of softness for hinting in the
+                         # horizontal direction.  A value of 1 will hint to full
+                         # pixels.  A value of 2 will hint to half pixels etc.
+
+#text.antialiased : True # If True (default), the text will be antialiased.
+                         # This only affects the Agg backend.
+
+# The following settings allow you to select the fonts in math mode.
+# They map from a TeX font name to a fontconfig font pattern.
+# These settings are only used if mathtext.fontset is 'custom'.
+# Note that this "custom" mode is unsupported and may go away in the
+# future.
+#mathtext.cal : cursive
+#mathtext.rm  : serif
+#mathtext.tt  : monospace
+#mathtext.it  : serif:italic
+#mathtext.bf  : serif:bold
+#mathtext.sf  : sans
+#mathtext.fontset : dejavusans # Should be 'dejavusans' (default),
+                               # 'dejavuserif', 'cm' (Computer Modern), 'stix',
+                               # 'stixsans' or 'custom'
+#mathtext.fallback_to_cm : True  # When True, use symbols from the Computer Modern
+                                 # fonts when a symbol can not be found in one of
+                                 # the custom math fonts.
+
+#mathtext.default : it # The default font to use for math.
+                       # Can be any of the LaTeX font names, including
+                       # the special name "regular" for the same font
+                       # used in regular text.
+
+### AXES
+# default face and edge color, default tick sizes,
+# default fontsizes for ticklabels, and so on.  See
+# http://matplotlib.org/api/axes_api.html#module-matplotlib.axes
+#axes.facecolor      : white   # axes background color
+#axes.edgecolor      : black   # axes edge color
+axes.linewidth      : 0.8     # edge linewidth
+#axes.grid           : False   # display grid or not
+#axes.titlesize      : large   # fontsize of the axes title
+#axes.titlepad       : 6.0     # pad between axes and title in points
+axes.labelsize      : 12.0 # fontsize of the x any y labels
+#axes.labelpad       : 4.0     # space between label and axis
+#axes.labelweight    : normal  # weight of the x and y labels
+axes.labelcolor     : black
+#axes.axisbelow      : 'line'  # draw axis gridlines and ticks below
+                               # patches (True); above patches but below
+                               # lines ('line'); or above all (False)
+
+#axes.formatter.limits : -7, 7 # use scientific notation if log10
+                               # of the axis range is smaller than the
+                               # first or larger than the second
+#axes.formatter.use_locale : False # When True, format tick labels
+                                   # according to the user's locale.
+                                   # For example, use ',' as a decimal
+                                   # separator in the fr_FR locale.
+#axes.formatter.use_mathtext : False # When True, use mathtext for scientific
+                                     # notation.
+#axes.formatter.useoffset      : True    # If True, the tick label formatter
+                                         # will default to labeling ticks relative
+                                         # to an offset when the data range is
+                                         # small compared to the minimum absolute
+                                         # value of the data.
+#axes.formatter.offset_threshold : 4     # When useoffset is True, the offset
+                                         # will be used when it can remove
+                                         # at least this number of significant
+                                         # digits from tick labels.
+
+# axes.spines.left   : True   # display axis spines
+# axes.spines.bottom : True
+# axes.spines.top    : True
+# axes.spines.right  : True
+
+
+#axes.unicode_minus  : True    # use unicode for the minus symbol
+                               # rather than hyphen.  See
+                               # http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
+#axes.prop_cycle    : cycler('color',
+#                            ['1f77b4', 'ff7f0e', '2ca02c', 'd62728',
+#                              '9467bd', '8c564b', 'e377c2', '7f7f7f',
+#                              'bcbd22', '17becf'])
+                                            # color cycle for plot lines
+                                            # as list of string colorspecs:
+                                            # single letter, long name, or
+                                            # web-style hex
+#axes.autolimit_mode : data # How to scale axes limits to the data.
+                            # Use "data" to use data limits, plus some margin
+                            # Use "round_number" move to the nearest "round" number
+#axes.xmargin        : .05  # x margin.  See `axes.Axes.margins`
+#axes.ymargin        : .05  # y margin See `axes.Axes.margins`
+
+#polaraxes.grid      : True    # display grid on polar axes
+#axes3d.grid         : True    # display grid on 3d axes
+
+### DATES
+# These control the default format strings used in AutoDateFormatter.
+# Any valid format datetime format string can be used (see the python
+# `datetime` for details).  For example using '%%x' will use the locale date representation
+# '%%X' will use the locale time representation and '%%c' will use the full locale datetime
+# representation.
+# These values map to the scales:
+#    {'year': 365, 'month': 30, 'day': 1, 'hour': 1/24, 'minute': 1 / (24 * 60)}
+
+# date.autoformatter.year     : %Y
+# date.autoformatter.month    : %Y-%m
+# date.autoformatter.day      : %Y-%m-%d
+# date.autoformatter.hour     : %m-%d %H
+# date.autoformatter.minute   : %d %H:%M
+# date.autoformatter.second   : %H:%M:%S
+# date.autoformatter.microsecond   : %M:%S.%f
+
+### TICKS
+# see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick
+xtick.top            : True # draw ticks on the top side
+xtick.bottom         : True   # draw ticks on the bottom side
+xtick.major.size     : 6      # major tick size in points
+xtick.minor.size     : 3      # minor tick size in points
+xtick.major.width    : 0.8    # major tick width in points
+xtick.minor.width    : 0.4    # minor tick width in points
+#xtick.major.pad      : 3.5      # distance to major tick label in points
+#xtick.minor.pad      : 3.4      # distance to the minor tick label in points
+#xtick.color          : k      # color of the tick labels
+xtick.labelsize      : 10 # fontsize of the tick labels
+xtick.direction      : in # direction: in, out, or inout
+xtick.minor.visible  : True # visibility of minor ticks on x-axis
+xtick.major.top      : True   # draw x axis top major ticks
+xtick.major.bottom   : True   # draw x axis bottom major ticks
+xtick.minor.top      : True   # draw x axis top minor ticks
+xtick.minor.bottom   : True   # draw x axis bottom minor ticks
+
+ytick.left           : True   # draw ticks on the left side
+ytick.right          : True # draw ticks on the right side
+ytick.major.size     : 6      # major tick size in points
+ytick.minor.size     : 3      # minor tick size in points
+ytick.major.width    : 0.8    # major tick width in points
+ytick.minor.width    : 0.4    # minor tick width in points
+#ytick.major.pad      : 3.5      # distance to major tick label in points
+#ytick.minor.pad      : 3.4      # distance to the minor tick label in points
+#ytick.color          : k      # color of the tick labels
+ytick.labelsize      : 10 # fontsize of the tick labels
+ytick.direction      : in # direction: in, out, or inout
+ytick.minor.visible  : True # visibility of minor ticks on y-axis
+ytick.major.left     : True   # draw y axis left major ticks
+ytick.major.right    : True   # draw y axis right major ticks
+ytick.minor.left     : True   # draw y axis left minor ticks
+ytick.minor.right    : True   # draw y axis right minor ticks
+
+
+### GRIDS
+#grid.color       :   b0b0b0    # grid color
+#grid.linestyle   :   -         # solid
+#grid.linewidth   :   0.8       # in points
+#grid.alpha       :   1.0       # transparency, between 0.0 and 1.0
+
+### Legend
+legend.loc           : best
+legend.frameon       : False # if True, draw the legend on a background patch
+legend.framealpha    : 1.0      # legend patch transparency
+#legend.facecolor     : inherit  # inherit from axes.facecolor; or color spec
+#legend.edgecolor     : 0.8      # background patch boundary color
+#legend.fancybox      : True     # if True, use a rounded box for the
+                                 # legend background, else a rectangle
+#legend.shadow        : False    # if True, give background a shadow effect
+#legend.numpoints     : 1        # the number of marker points in the legend line
+#legend.scatterpoints : 1        # number of scatter points
+#legend.markerscale   : 1.0      # the relative size of legend markers vs. original
+legend.fontsize      : 8
+# Dimensions as fraction of fontsize:
+#legend.borderpad     : 0.4      # border whitespace
+#legend.labelspacing  : 0.5      # the vertical space between the legend entries
+#legend.handlelength  : 2.0      # the length of the legend lines
+#legend.handleheight  : 0.7      # the height of the legend handle
+#legend.handletextpad : 0.8      # the space between the legend line and legend text
+#legend.borderaxespad : 0.5      # the border between the axes and legend edge
+#legend.columnspacing : 2.0      # column separation
+
+### FIGURE
+# See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure
+#figure.titlesize : large      # size of the figure title (Figure.suptitle())
+#figure.titleweight : normal   # weight of the figure title
+figure.figsize   : 3.25, 3.25 # figure size in inches
+figure.dpi       : 300      # figure dots per inch
+#figure.facecolor : white   # figure facecolor; 0.75 is scalar gray
+#figure.edgecolor : white   # figure edgecolor
+#figure.autolayout : False  # When True, automatically adjust subplot
+                            # parameters to make the plot fit the figure
+#figure.max_open_warning : 20  # The maximum number of figures to open through
+                               # the pyplot interface before emitting a warning.
+                               # If less than one this feature is disabled.
+
+# The figure subplot parameters.  All dimensions are a fraction of the
+#figure.subplot.left    : 0.125  # the left side of the subplots of the figure
+figure.subplot.right   : 0.98    # the right side of the subplots of the figure
+#figure.subplot.bottom  : 0.11    # the bottom of the subplots of the figure
+figure.subplot.top     : 0.98    # the top of the subplots of the figure
+#figure.subplot.wspace  : 0.2    # the amount of width reserved for blank space between subplots,
+                                 # expressed as a fraction of the average axis width
+#figure.subplot.hspace  : 0.2    # the amount of height reserved for white space between subplots,
+                                 # expressed as a fraction of the average axis height
+
+
+### IMAGES
+#image.aspect : equal             # equal | auto | a number
+#image.interpolation  : nearest   # see help(imshow) for options
+#image.cmap   : viridis           # A colormap name, gray etc...
+#image.lut    : 256               # the size of the colormap lookup table
+#image.origin : upper             # lower | upper
+#image.resample  : True
+#image.composite_image : True     # When True, all the images on a set of axes are
+                                  # combined into a single composite image before
+                                  # saving a figure as a vector graphics file,
+                                  # such as a PDF.
+
+### CONTOUR PLOTS
+#contour.negative_linestyle : dashed # dashed | solid
+#contour.corner_mask        : True   # True | False | legacy
+
+### ERRORBAR PLOTS
+errorbar.capsize : 3             # length of end cap on error bars in pixels
+
+### HISTOGRAM PLOTS
+#hist.bins : 10                   # The default number of histogram bins.
+                                  # If Numpy 1.11 or later is
+                                  # installed, may also be `auto`
+
+### SCATTER PLOTS
+#scatter.marker : o               # The default marker type for scatter plots.
+
+### Agg rendering
+### Warning: experimental, 2008/10/10
+#agg.path.chunksize : 0           # 0 to disable; values in the range
+                                  # 10000 to 100000 can improve speed slightly
+                                  # and prevent an Agg rendering failure
+                                  # when plotting very large data sets,
+                                  # especially if they are very gappy.
+                                  # It may cause minor artifacts, though.
+                                  # A value of 20000 is probably a good
+                                  # starting point.
+### SAVING FIGURES
+#path.simplify : True   # When True, simplify paths by removing "invisible"
+                        # points to reduce file size and increase rendering
+                        # speed
+#path.simplify_threshold : 0.1  # The threshold of similarity below which
+                                # vertices will be removed in the simplification
+                                # process
+#path.snap : True # When True, rectilinear axis-aligned paths will be snapped to
+                  # the nearest pixel when certain criteria are met.  When False,
+                  # paths will never be snapped.
+#path.sketch : None # May be none, or a 3-tuple of the form (scale, length,
+                    # randomness).
+                    # *scale* is the amplitude of the wiggle
+                    # perpendicular to the line (in pixels).  *length*
+                    # is the length of the wiggle along the line (in
+                    # pixels).  *randomness* is the factor by which
+                    # the length is randomly scaled.
+
+# the default savefig params can be different from the display params
+# e.g., you may want a higher resolution, or to make the figure
+# background white
+#savefig.dpi         : figure   # figure dots per inch or 'figure'
+#savefig.facecolor   : white    # figure facecolor when saving
+#savefig.edgecolor   : white    # figure edgecolor when saving
+savefig.format      : pdf # png, ps, pdf, svg
+#savefig.bbox        : standard # 'tight' or 'standard'.
+                                # 'tight' is incompatible with pipe-based animation
+                                # backends but will workd with temporary file based ones:
+                                # e.g. setting animation.writer to ffmpeg will not work,
+                                # use ffmpeg_file instead
+#savefig.pad_inches  : 0.1      # Padding to be used when bbox is set to 'tight'
+#savefig.jpeg_quality: 95       # when a jpeg is saved, the default quality parameter.
+#savefig.directory   : ~        # default directory in savefig dialog box,
+                                # leave empty to always use current working directory
+#savefig.transparent : False    # setting that controls whether figures are saved with a
+                                # transparent background by default
+
+# tk backend params
+#tk.window_focus   : False    # Maintain shell focus for TkAgg
+
+# ps backend params
+#ps.papersize      : letter   # auto, letter, legal, ledger, A0-A10, B0-B10
+#ps.useafm         : False    # use of afm fonts, results in small files
+#ps.usedistiller   : False    # can be: None, ghostscript or xpdf
+                                          # Experimental: may produce smaller files.
+                                          # xpdf intended for production of publication quality files,
+                                          # but requires ghostscript, xpdf and ps2eps
+#ps.distiller.res  : 6000      # dpi
+#ps.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)
+
+# pdf backend params
+#pdf.compression   : 6 # integer from 0 to 9
+                       # 0 disables compression (good for debugging)
+#pdf.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)
+
+# svg backend params
+#svg.image_inline : True       # write raster image data directly into the svg file
+#svg.fonttype : 'path'         # How to handle SVG fonts:
+#    'none': Assume fonts are installed on the machine where the SVG will be viewed.
+#    'path': Embed characters as paths -- supported by most SVG renderers
+#    'svgfont': Embed characters as SVG fonts -- supported only by Chrome,
+#               Opera and Safari
+#svg.hashsalt : None           # if not None, use this string as hash salt
+                               # instead of uuid4
+
+# docstring params
+#docstring.hardcopy = False  # set this when you want to generate hardcopy docstring
+
+# Set the verbose flags.  This controls how much information
+# matplotlib gives you at runtime and where it goes.  The verbosity
+# levels are: silent, helpful, debug, debug-annoying.  Any level is
+# inclusive of all the levels below it.  If your setting is "debug",
+# you'll get all the debug and helpful messages.  When submitting
+# problems to the mailing-list, please set verbose to "helpful" or "debug"
+# and paste the output into your report.
+#
+# The "fileo" gives the destination for any calls to verbose.report.
+# These objects can a filename, or a filehandle like sys.stdout.
+#
+# You can override the rc default verbosity from the command line by
+# giving the flags --verbose-LEVEL where LEVEL is one of the legal
+# levels, e.g., --verbose-helpful.
+#
+# You can access the verbose instance in your code
+#   from matplotlib import verbose.
+#verbose.level  : silent      # one of silent, helpful, debug, debug-annoying
+#verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr
+
+# Event keys to interact with figures/plots via keyboard.
+# Customize these settings according to your needs.
+# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')
+
+#keymap.fullscreen : f, ctrl+f       # toggling
+#keymap.home : h, r, home            # home or reset mnemonic
+#keymap.back : left, c, backspace    # forward / backward keys to enable
+#keymap.forward : right, v           #   left handed quick navigation
+#keymap.pan : p                      # pan mnemonic
+#keymap.zoom : o                     # zoom mnemonic
+#keymap.save : s                     # saving current figure
+#keymap.quit : ctrl+w, cmd+w         # close the current figure
+#keymap.grid : g                     # switching on/off a grid in current axes
+#keymap.yscale : l                   # toggle scaling of y-axes ('log'/'linear')
+#keymap.xscale : L, k                # toggle scaling of x-axes ('log'/'linear')
+#keymap.all_axes : a                 # enable all axes
+
+# Control location of examples data files
+#examples.directory : ''   # directory to look in for custom installation
+
+###ANIMATION settings
+#animation.html : 'none'           # How to display the animation as HTML in
+                                   # the IPython notebook. 'html5' uses
+                                   # HTML5 video tag.
+#animation.writer : ffmpeg         # MovieWriter 'backend' to use
+#animation.codec : h264            # Codec to use for writing movie
+#animation.bitrate: -1             # Controls size/quality tradeoff for movie.
+                                   # -1 implies let utility auto-determine
+#animation.frame_format: 'png'     # Controls frame format used by temp files
+#animation.ffmpeg_path: 'ffmpeg'   # Path to ffmpeg binary. Without full path
+                                   # $PATH is searched
+#animation.ffmpeg_args: ''         # Additional arguments to pass to ffmpeg
+#animation.avconv_path: 'avconv'   # Path to avconv binary. Without full path
+                                   # $PATH is searched
+#animation.avconv_args: ''         # Additional arguments to pass to avconv
+#animation.mencoder_path: 'mencoder'
+                                   # Path to mencoder binary. Without full path
+                                   # $PATH is searched
+#animation.mencoder_args: ''       # Additional arguments to pass to mencoder
+#animation.convert_path: 'convert' # Path to ImageMagick's convert binary.
+                                   # On Windows use the full path since convert
+                                   # is also the name of a system tool.