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Guevel Yann 2024-11-19 11:28:22 +01:00
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# *-* coding: utf-8 *-*
# In this file, you will find any numerical data for GHG and their uncertainties if available.
#====================================================
# GHG DATAs
#-------------------------------
GasDATAlib = {}
#-------------------------------
#-------------------------------
# C02
#-------------------------------
GasDATAlib['CO2'] = {
'MolarMass' : { 'Comments': 'Masse molaire',
'value' : 44.0095 ,
'units' : 'g/mol' ,
'distrib' : 'uniform' ,
'delta' : 0.0014 ,
'source' : 'https://fr.wikipedia.org/wiki/Dioxyde_de_carbone'
},
'PRG100' : { 'Comments': 'Pouvoir de réchauffement global à 100 ans (dynGWP100)',
'value' : 1 ,
},
'A_RadFor' : { 'Comments': 'Forçage radiatif (Attention parfois en ppm plutot que kg)',
'distrib' : 'normal' ,
'value' : 1.71e-15 ,
'units' : 'W*m^{-2}*kg^{-1}' ,
'sigma' : 1.2505e-16,
'source' : 'Forster et al., 2021 - 1.645sigma'
},
'coeffIRF' : { 'Comments': 'I(t) = a_0 + sum_i=1^K a_i exp(-t/taui), t de 0 à 1000 ans. sum a_i=1. Correlation : a1,a2,a3,tau1,tau2,tau3.',
'distrib' : 'manual',
'values' : {
'K' : 3 ,
'a0' : 0.2173 ,
'a1' : 0.2240 , 'tau1' : 394.400 ,
'a2' : 0.2824 , 'tau2' : 36.540 ,
'a3' : 0.2763 , 'tau3' : 4.304
},
'models' : {
'1' : {'name' : 'Bern3D-LPJ (reference)',
'K' : 3 ,
'a0' : 1.85357e-09, 'a1' : 5.15004e-01, 'a2' : 2.63095e-01, 'a3' : 2.21901e-01,
'tau1' : 1.95501e+03, 'tau2' : 4.58309e+01, 'tau3' : 3.87140e+00},
'2' : {'name' : 'Bern3D-LPJ (ensemble median)',
'K' : 3 ,
'a0' : 2.79585e-01, 'a1' : 2.38190e-01, 'a2' : 2.38194e-01, 'a3' : 2.44030e-01,
'tau1' : 2.76166e+02, 'tau2' : 3.84449e+01, 'tau3' : 4.92789e+00},
'3' : {'name' : 'Bern2.5D-LPJ',
'K' : 3 ,
'a0' : 2.36182e-01, 'a1' : 9.86550e-02, 'a2' : 3.85051e-01, 'a3' : 2.80112e-01,
'tau1' : 2.32060e+02,'tau2' : 5.85029e+01, 'tau3' : 2.58649e+00},
'4' : {'name' : 'CLIMBER2-LPJ',
'K' : 3 ,
'a0' : 2.31808e-01, 'a1' : 2.15532e-01, 'a2' : 6.00716e-02, 'a3' : 4.92588e-01,
'tau1' : 2.72635e+02, 'tau2' : 2.72635e+02, 'tau3' : 6.69203e+00},
'5' : {'name' : 'DCESS',
'K' : 3 ,
'a0' : 2.15938e-01, 'a1' : 2.91205e-01, 'a2' : 2.41039e-01, 'a3' : 2.51819e-01,
'tau1' : 3.79917e+02, 'tau2' : 3.63133e+01, 'tau3' : 3.39821e+00},
'6' : {'name' : 'GENIE (ensemble median) ',
'K' : 3 ,
'a0' : 2.14468e-01, 'a1' : 2.49017e-01, 'a2' : 1.92417e-01, 'a3' : 3.44097e-01,
'tau1' : 2.70068e+02, 'tau2' : 3.93231e+01, 'tau3' : 4.30551e+00},
'7': {'name' : 'LOVECLIM',
'K' : 3 ,
'a0' : 8.20680e-09, 'a1' : 3.60847e-01, 'a2' : 4.48756e-01, 'a3' : 1.90397e-01,
'tau1' : 1.59237e+03, 'tau2' : 2.17546e+01, 'tau3' : 2.31171e+00},
'8': {'name' : 'MESMO',
'K' : 3 ,
'a0' : 2.84833e-01, 'a1' : 2.93789e-01, 'a2' : 2.38243e-01, 'a3' : 1.83135e-01,
'tau1' : 4.54262e+02, 'tau2' : 2.50004e+01, 'tau3' : 2.01397e+00},
'9': {'name' : 'UVic2.9',
'K' : 3 ,
'a0' : 3.18612e-01, 'a1' : 1.74787e-01, 'a2' : 1.92120e-01, 'a3' : 3.14480e-01,
'tau1' : 3.04635e+02, 'tau2' : 2.65602e+01, 'tau3' : 3.80036e+00},
'10': {'name' : 'ACC2',
'K' : 3 ,
'a0' : 1.77855e-01, 'a1' : 1.65350e-01, 'a2' : 3.79637e-01, 'a3' : 2.77157e-01,
'tau1' : 3.86187e+02, 'tau2' : 3.68939e+01, 'tau3' : 3.72353e+00},
'11': {'name' : 'Bern-SAR',
'K' : 3 ,
'a0' : 1.99428e-01, 'a1' : 1.76160e-01, 'a2' : 3.45213e-01, 'a3' : 2.79198e-01,
'tau1' : 3.33144e+02, 'tau2' : 3.96896e+01, 'tau3' : 4.10990e+00},
'12': {'name' : 'MAGICC6 (ensemble median)',
'K' : 3 ,
'a0' : 2.05148e-01, 'a1' : 2.53261e-01, 'a2' : 3.31761e-01, 'a3' : 2.09831e-01,
'tau1' : 5.96072e+02, 'tau2' : 2.19675e+01, 'tau3' : 2.99543e+00},
'13': {'name' : 'TOTEM2',
'K' : 3 ,
'a0' : 1.19535e-05, 'a1' : 2.03149e-01, 'a2' : 6.99459e-01, 'a3' : 9.73804e-02,
'tau1' : 8.57652e+04, 'tau2' : 1.11772e+02, 'tau3' : 1.90248e-02}
},
'source' : 'Joos 2013 with year > 500'
}
}
#-------------------------------
# CH4
#-------------------------------
GasDATAlib['CH4'] = {
'MolarMass' : { 'Comments': 'Masse molaire',
'value' : 16.0425 ,
'units' : 'g/mol' ,
'distrib' : 'uniform' ,
'delta' : 0.0011 ,
'source' : 'https://fr.wikipedia.org/wiki/M%C3%A9thane'
},
'PRG100' : { 'Comments': 'Pouvoir de réchauffement global à 100 ans (GWP100)',
'value' : 27.9 ,
'source' : 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07_SM.pdf'
},
'A_RadFor' : { 'Comments': 'Forçage radiatif (Attention parfois en ppm plutot que kg)',
'value' : 1.9990134e-13 ,
'units' : 'W*m^{-2}*kg^{-1}' ,
'distrib' : 'normal' ,
'sigma' : 2.9847158e-14,
'source' : 'Forster et al., 2021 - 1.645sigma'
},
'alphaCO2' : { 'Comments': 'Oxidized CH4 to net production of atmospheric CO2',
'value' : 0.75 ,
'distrib' : 'uniform' ,
'delta' : 0.25,
'source' : 'Forster et al., 2021'
},
'coeffIRF' : { 'Comments': 'I(t) = a_0 + sum_i=1^K a_i exp(-t/taui), t de 0 à 1000 ans',
'values' : {'K' : 1 ,
'a0' : 0.0 ,
'a1' : 1.0 , 'tau1' : 11.8 },
'distrib' : 'normal',
'sigmadic': {'tau1' : 1.094224924 },
'source' : '(Forster et al., 2021)'
}
}
#-------------------------------
# N2O
#-------------------------------
GasDATAlib['N2O'] = {
'MolarMass' : { 'Comments': 'Masse molaire',
'value' : 44.0128 ,
'units' : 'g/mol' ,
'distrib' : 'uniform' ,
'delta' : 0.0007 ,
'source' : 'https://fr.wikipedia.org/wiki/Protoxyde_d%27azote'
},
'PRG100' : { 'Comments': 'Pouvoir de réchauffement global à 100 ans (GWP100)',
'value' : 273.0 ,
'source' : 'https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07_SM.pdf'
},
'A_RadFor' : { 'Comments': 'Forçage radiatif (Attention parfois en ppm plutot que kg)',
'distrib' : 'normal' ,
'value' : 3.5621803e-13 ,
'units' : 'W*m^{-2}*kg^{-1}' ,
'distrib' : 'normal' ,
'sigma' : 8.5071609e-14,
'source' : 'Forster et al., 2021 - 1.645sigma'
},
'coeffIRF' : { 'Comments': 'I(t) = a_0 + sum_i=1^K a_i exp(-t/taui), t de 0 à 1000 ans',
'distrib' : 'normal',
'values' : {'K' : 1 ,
'a0' : 0.0 ,
'a1' : 1.0 , 'tau1' : 109.0 },
'sigmadic': {'tau1' : 6.079027356 },
'source' : 'Canadell et al. (2021)'
},
}
#-------------------------------
# OH
#-------------------------------
GasDATAlib['OH'] = {
'MolarMass' : { 'Comments': 'Masse molaire',
'value' : 17.0073 ,
'units' : 'g/mol' ,
'distrib' : 'uniform' ,
'delta' : 0.0004 ,
'source' : 'https://fr.wikipedia.org/wiki/Hydroxyde'
},
'coeffIRF' : { 'Comments': 'I(t) = a_0 + sum_i=1^K a_i exp(-t/taui), t de 0 à 1000 ans',
'distrib' : 'normal',
'values' : {'K' : 1 ,
'a0' : 0.0 ,
'a1' : 1.0 , 'tau1' : 9.7 },
'sigmadic': {'tau1' : 1.1 },
'source' : 'Gasser 2017'
}
}
#-------------------------------
# C
#-------------------------------
GasDATAlib['C'] = {
'MolarMass' : { 'Comments': 'Masse molaire',
'value' : 12.011 ,
'units' : 'g/mol'
}
}
#====================================================
# Models datas
# We made a choice here for the uncertainty of the ci, di parameters of the model.
# It has been decided to use 600 set of parameters in a Monte-Carlo procedure.
# We have tried to obtain a correlation matrix using Cholesky and inverse of jacobian of the model, but it is not yet validated.
#-------------------------------
# IRFT
#-------------------------------
IRFT_DataLib = {
'ECS' : { 'Comments': '',
'value' : 0.75776594,
'units' : 'K / (W/m^2)' ,
'distrib' : 'normal',
'sigma' : 0.16812612,
'source' : 'Forster 2021'
},
'coeffTDR' : { 'Comments': 'c1,d1,d2 correlated. c2 = 1-c1.',
'values' : {'c1' : 0.58562639 ,
'c2' : 1.-0.58562639 ,
'd1' : 3.424102092311 ,
'd2' : 285.003477841911
},
'distrib' : 'manual',
'models' : {
'1' : {'name' : 'MAGICC7', 'c1': 0.632241768107763 , 'c2': 0.367758231892237 , 'd1': 3.82386950507148 , 'd2': 389.900781087468 },
'2' : {'name' : 'MAGICC7', 'c1': 0.47651030677388 , 'c2': 0.52348969322612 , 'd1': 5.6215994122556 , 'd2': 319.061154334058 },
'3' : {'name' : 'MAGICC7', 'c1': 0.588559698303091 , 'c2': 0.411440301696909 , 'd1': 6.30506076996332 , 'd2': 371.9310573597 },
'4' : {'name' : 'MAGICC7', 'c1': 0.640567580563668 , 'c2': 0.359432419436332 , 'd1': 2.98642018118141 , 'd2': 260.696311318439 },
'5' : {'name' : 'MAGICC7', 'c1': 0.578261474349108 , 'c2': 0.421738525650892 , 'd1': 7.04529766938455 , 'd2': 330.485390077445 },
'6' : {'name' : 'MAGICC7', 'c1': 0.590424817810548 , 'c2': 0.409575182189452 , 'd1': 2.31397040718796 , 'd2': 332.389761171534 },
'7' : {'name' : 'MAGICC7', 'c1': 0.651985609754824 , 'c2': 0.348014390245176 , 'd1': 2.86077870934596 , 'd2': 237.095172544455 },
'8' : {'name' : 'MAGICC7', 'c1': 0.638346178386973 , 'c2': 0.361653821613027 , 'd1': 6.4484222282771 , 'd2': 291.970329699021 },
'9' : {'name' : 'MAGICC7', 'c1': 0.649193133479151 , 'c2': 0.350806866520849 , 'd1': 7.17146839958581 , 'd2': 198.783405788516 },
'10' : {'name' : 'MAGICC7', 'c1': 0.525107153890915 , 'c2': 0.474892846109085 , 'd1': 7.62309609297798 , 'd2': 429.23573508845 },
'11' : {'name' : 'MAGICC7', 'c1': 0.568473494547009 , 'c2': 0.431526505452991 , 'd1': 3.87864565839856 , 'd2': 361.380421679158 },
'12' : {'name' : 'MAGICC7', 'c1': 0.66552837484991 , 'c2': 0.33447162515009 , 'd1': 3.46888955155108 , 'd2': 273.144217049346 },
'13' : {'name' : 'MAGICC7', 'c1': 0.676547154701208 , 'c2': 0.323452845298792 , 'd1': 7.1687543895878 , 'd2': 246.411285530349 },
'14' : {'name' : 'MAGICC7', 'c1': 0.671969153305233 , 'c2': 0.328030846694767 , 'd1': 3.45997440753028 , 'd2': 206.831006938584 },
'15' : {'name' : 'MAGICC7', 'c1': 0.576123934161629 , 'c2': 0.423876065838371 , 'd1': 2.76558766630595 , 'd2': 135.944896033756 },
'16' : {'name' : 'MAGICC7', 'c1': 0.769136990312664 , 'c2': 0.230863009687337 , 'd1': 3.57210960390788 , 'd2': 274.366300556365 },
'17' : {'name' : 'MAGICC7', 'c1': 0.634082897526365 , 'c2': 0.365917102473635 , 'd1': 7.1172793166239 , 'd2': 269.907235190897 },
'18' : {'name' : 'MAGICC7', 'c1': 0.622839362119914 , 'c2': 0.377160637880086 , 'd1': 2.66352285394658 , 'd2': 258.253040070431 },
'19' : {'name' : 'MAGICC7', 'c1': 0.589101099336831 , 'c2': 0.410898900663169 , 'd1': 3.78102171100329 , 'd2': 239.097169295607 },
'20' : {'name' : 'MAGICC7', 'c1': 0.6238828893578 , 'c2': 0.3761171106422 , 'd1': 6.600151114628 , 'd2': 232.911784121573 },
'21' : {'name' : 'MAGICC7', 'c1': 0.647603075866261 , 'c2': 0.352396924133739 , 'd1': 5.47377927212362 , 'd2': 228.457045620848 },
'22' : {'name' : 'MAGICC7', 'c1': 0.626456860281712 , 'c2': 0.373543139718288 , 'd1': 6.03946466411415 , 'd2': 330.618608137511 },
'23' : {'name' : 'MAGICC7', 'c1': 0.636646979928147 , 'c2': 0.363353020071853 , 'd1': 7.94985770122328 , 'd2': 282.470763471812 },
'24' : {'name' : 'MAGICC7', 'c1': 0.663662559190617 , 'c2': 0.336337440809383 , 'd1': 3.85569345223402 , 'd2': 372.872771870098 },
'25' : {'name' : 'MAGICC7', 'c1': 0.587218917177877 , 'c2': 0.412781082822123 , 'd1': 3.36528894125964 , 'd2': 289.065693368163 },
'26' : {'name' : 'MAGICC7', 'c1': 0.616483197071491 , 'c2': 0.383516802928509 , 'd1': 1.83388317798649 , 'd2': 131.656583418221 },
'27' : {'name' : 'MAGICC7', 'c1': 0.676969643494841 , 'c2': 0.32303035650516 , 'd1': 5.21296082165822 , 'd2': 214.977812581241 },
'28' : {'name' : 'MAGICC7', 'c1': 0.735401632976097 , 'c2': 0.264598367023903 , 'd1': 4.13770197156748 , 'd2': 220.52693874936 },
'29' : {'name' : 'MAGICC7', 'c1': 0.692528111641875 , 'c2': 0.307471888358125 , 'd1': 3.12533515508762 , 'd2': 230.335686943678 },
'30' : {'name' : 'MAGICC7', 'c1': 0.578586208503603 , 'c2': 0.421413791496397 , 'd1': 6.98379269372093 , 'd2': 133.224555765284 },
'31' : {'name' : 'MAGICC7', 'c1': 0.666628771890031 , 'c2': 0.333371228109969 , 'd1': 3.16640720823426 , 'd2': 249.08652319974 },
'32' : {'name' : 'MAGICC7', 'c1': 0.57295925736029 , 'c2': 0.427040742639711 , 'd1': 4.39000383223875 , 'd2': 434.790214665508 },
'33' : {'name' : 'MAGICC7', 'c1': 0.605346080721354 , 'c2': 0.394653919278646 , 'd1': 2.70629958899932 , 'd2': 220.677154327203 },
'34' : {'name' : 'MAGICC7', 'c1': 0.510374898438643 , 'c2': 0.489625101561357 , 'd1': 4.37806742174467 , 'd2': 548.420102139577 },
'35' : {'name' : 'MAGICC7', 'c1': 0.70736343681524 , 'c2': 0.29263656318476 , 'd1': 3.69971641733383 , 'd2': 215.703668498468 },
'36' : {'name' : 'MAGICC7', 'c1': 0.689864228632141 , 'c2': 0.310135771367859 , 'd1': 4.76922742661194 , 'd2': 213.518836982346 },
'37' : {'name' : 'MAGICC7', 'c1': 0.563334450422736 , 'c2': 0.436665549577264 , 'd1': 2.8208992651919 , 'd2': 320.73336249832 },
'38' : {'name' : 'MAGICC7', 'c1': 0.671854799049124 , 'c2': 0.328145200950876 , 'd1': 3.32473996963507 , 'd2': 333.678515333307 },
'39' : {'name' : 'MAGICC7', 'c1': 0.617896106525012 , 'c2': 0.382103893474988 , 'd1': 6.02234306852562 , 'd2': 341.9919317971 },
'40' : {'name' : 'MAGICC7', 'c1': 0.673757095321954 , 'c2': 0.326242904678046 , 'd1': 1.90609118815092 , 'd2': 266.204797716508 },
'41' : {'name' : 'MAGICC7', 'c1': 0.647008411561823 , 'c2': 0.352991588438177 , 'd1': 5.99724013106657 , 'd2': 230.054330884948 },
'42' : {'name' : 'MAGICC7', 'c1': 0.678558393680003 , 'c2': 0.321441606319997 , 'd1': 5.86176785049258 , 'd2': 229.458840694695 },
'43' : {'name' : 'MAGICC7', 'c1': 0.65269819615743 , 'c2': 0.34730180384257 , 'd1': 2.9335439459129 , 'd2': 325.412125701192 },
'44' : {'name' : 'MAGICC7', 'c1': 0.713887076737018 , 'c2': 0.286112923262982 , 'd1': 2.82433797707 , 'd2': 208.475671410828 },
'45' : {'name' : 'MAGICC7', 'c1': 0.704549273255415 , 'c2': 0.295450726744586 , 'd1': 2.71934906957265 , 'd2': 242.194229171043 },
'46' : {'name' : 'MAGICC7', 'c1': 0.719945738078425 , 'c2': 0.280054261921575 , 'd1': 3.04164384860224 , 'd2': 230.138386357371 },
'47' : {'name' : 'MAGICC7', 'c1': 0.637844380105683 , 'c2': 0.362155619894317 , 'd1': 3.50896131914876 , 'd2': 396.893773205207 },
'48' : {'name' : 'MAGICC7', 'c1': 0.687061107254471 , 'c2': 0.31293889274553 , 'd1': 5.29755156935227 , 'd2': 219.156035720426 },
'49' : {'name' : 'MAGICC7', 'c1': 0.698232763074995 , 'c2': 0.301767236925005 , 'd1': 2.61862252045183 , 'd2': 190.421975376027 },
'50' : {'name' : 'MAGICC7', 'c1': 0.626855876911374 , 'c2': 0.373144123088626 , 'd1': 4.01753146337519 , 'd2': 301.516168975059 },
'51' : {'name' : 'MAGICC7', 'c1': 0.708893958765378 , 'c2': 0.291106041234622 , 'd1': 4.15922121222197 , 'd2': 259.426934108479 },
'52' : {'name' : 'MAGICC7', 'c1': 0.758486176652179 , 'c2': 0.241513823347821 , 'd1': 3.19435843056883 , 'd2': 212.174721440208 },
'53' : {'name' : 'MAGICC7', 'c1': 0.712076467840429 , 'c2': 0.287923532159571 , 'd1': 2.9422879032204 , 'd2': 238.720258942513 },
'54' : {'name' : 'MAGICC7', 'c1': 0.583397325131424 , 'c2': 0.416602674868576 , 'd1': 3.08895220676643 , 'd2': 275.418876744372 },
'55' : {'name' : 'MAGICC7', 'c1': 0.67187189036558 , 'c2': 0.32812810963442 , 'd1': 3.20179293305284 , 'd2': 243.785163786455 },
'56' : {'name' : 'MAGICC7', 'c1': 0.621278890077846 , 'c2': 0.378721109922154 , 'd1': 4.76709070679057 , 'd2': 256.940325201313 },
'57' : {'name' : 'MAGICC7', 'c1': 0.527098571618698 , 'c2': 0.472901428381302 , 'd1': 4.17200570837442 , 'd2': 592.859566730206 },
'58' : {'name' : 'MAGICC7', 'c1': 0.588039835227702 , 'c2': 0.411960164772298 , 'd1': 2.4516936913602 , 'd2': 295.337065831528 },
'59' : {'name' : 'MAGICC7', 'c1': 0.606089200620326 , 'c2': 0.393910799379674 , 'd1': 2.50158551541092 , 'd2': 245.380170311118 },
'60' : {'name' : 'MAGICC7', 'c1': 0.538574549895507 , 'c2': 0.461425450104493 , 'd1': 6.66446934172044 , 'd2': 411.980932479886 },
'61' : {'name' : 'MAGICC7', 'c1': 0.649041152972744 , 'c2': 0.350958847027256 , 'd1': 2.86970464088908 , 'd2': 127.638250069349 },
'62' : {'name' : 'MAGICC7', 'c1': 0.549634452215066 , 'c2': 0.450365547784934 , 'd1': 3.50489617486999 , 'd2': 336.105811569808 },
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},
'source' : 'IPCC Github'
}
}
#-------------------------------
# Gasser used a specific model in the AGWP/AGTP Carbon cycle feedback
#-------------------------------
Gasser_CCF_Model = {
#Gasser 2017 - CCF
'gamma' : 3.015,
'aS1' : 0.6368,
'aS2' : 0.3322,
'aS3' : 0.0310,
'tauS1' : 2.376,
'tauS2' : 30.14,
'tauS3' : 490.1
}

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# *-* coding: utf-8 *-*
# Definition of :
# - ImpulseRF
# - RadiativeForcing
# - IRFT
# - AGWP
# - AGTP
# - - - - - - - - - - - - - - - - - -
import numpy as np
# - - - - - - - - - - - - - - - - - -
from .Metrics_oxyd_ccf import *
# - - - - - - - - - - - - - - - - - -
#------------------------------------------------------------
# IRF : Impulse response function
# I(t) = a_0 + sum_i=1^K a_i exp(-t/taui)
# Input :
# + time
# + Gas informations
# + mass of this gas
# Output :
# Response function at a given time
def ImpulseRF(GasDATA,IRFT_DATA,t,gas,mass,gasorigin):
DataG = GasDATA[gas]
f = DataG['a0']
for i in range(1,DataG['K']+1):
f+= DataG['a'+str(i)] * np.exp(-t/ DataG['tau'+str(i)])
return mass*f
#------------------------------------------------------------
# RF : Radative forcing
#------------------------------------------------------------
# RF(t) = A * I(t)
# DataG['A_RadFor'] is Radiative forcing scaling factor for the given gas database.
# Input :
# + time
# + Gas informations
# + mass of this gas
# Output :
# Radative forcing at a given time
def RadiativeForcing(GasDATA,IRFT_DATA,t,gas,mass,gasorigin):
DataG = GasDATA[gas]
return DataG['A_RadFor'] * ImpulseRF(GasDATA,TdrDATA,t, gas, mass, gasorigin)
#------------------------------------------------------------
# IRFT : Impulse response function for temperature :
#------------------------------------------------------------
# Input :
# + time
# Output :
# IRFT(t) = ECS * (c1/d1 * np.exp(-t/d1) + c2/d2 * np.exp(-t/d2))
def IRFT(IRFT_DATA,t):
ECS = IRFT_DATA['ECS']
d1 = IRFT_DATA['d1']
d2 = IRFT_DATA['d2']
return ECS * ( IRFT_DATA['c1']/d1 * np.exp(-t/d1) + IRFT_DATA['c2']/d2 * np.exp(-t/d2) )
#------------------------------------------------------------
# AGWP : Absolute global warming potential
#------------------------------------------------------------
# Integral of ImpulseRF(t) over time period
# Input :
# + time
# + Gas informations
# + mass of this gas
# Output :
# Absolute global warming potential at a given time (W m^{-2} yr kg^{-1})
#
def AGWP(GasDATA,IRFT_DATA, t,gas,mass,gasorigin):
# ---
DataG = GasDATA[gas]
AGWPfunc = DataG['a0'] * t
# ---
for i in range(1,DataG['K']+1):
taui = DataG['tau'+str(i)]
AGWPfunc+= DataG['a'+str(i)] * taui * (1.-np.exp(-t/ taui))
# --- Different formula for CH4
if gas == "CH4":
# --- CH4 DATA
alphaCO2 = DataG['alphaCO2'] # Only CH4
CH4tau1 = DataG['tau1']
A_radCH4 = DataG['A_RadFor']
MCH4 = DataG['MolarMass']
# --- CO2 DATA
DataGCO2 = GasDATA['CO2']
a0 = DataGCO2['a0']
a1 = DataGCO2['a1']
a2 = DataGCO2['a2']
a3 = DataGCO2['a3']
tau1 = DataGCO2['tau1']
tau2 = DataGCO2['tau2']
tau3 = DataGCO2['tau3']
A_RadFor_CO2 = DataGCO2['A_RadFor']
MCO2 = DataGCO2['MolarMass']
# --- OH DATA
tauOH = GasDATA['OH']['tau1']
# --- C data
MC = GasDATA['C']['MolarMass']
# --- IRFT data
IRFT_ECS = IRFT_DATA['ECS']
IRFT_c1 = IRFT_DATA['c1']
IRFT_c2 = IRFT_DATA['c2']
IRFT_d1 = IRFT_DATA['d1']
IRFT_d2 = IRFT_DATA['d2']
# --- Gasser CCF suppl.Model
# GASS_gamma = Gasser_CCF_Model['gamma']
# GASS_aS1 = Gasser_CCF_Model['aS1']
# GASS_aS2 = Gasser_CCF_Model['aS2']
# GASS_aS3 = Gasser_CCF_Model['aS3']
# GASS_tauS1 = Gasser_CCF_Model['tauS1']
# GASS_tauS2 = Gasser_CCF_Model['tauS2']
# GASS_tauS3 = Gasser_CCF_Model['tauS3']
GASS_gamma = 3.015
GASS_aS1 = 0.6368
GASS_aS2 = 0.3322
GASS_aS3 = 0.0310
GASS_tauS1 = 2.376
GASS_tauS2 = 30.14
GASS_tauS3 = 490.1
# - - - -
# Carbon cycle feedback
Add_AGWP_CCF = GasserCCFDAGWPNonCO2(t, CH4tau1, IRFT_ECS, IRFT_c1, IRFT_d1, IRFT_c2, IRFT_d2, A_radCH4, a0,a3,a2,a1,tau3,tau2,tau1 ,A_RadFor_CO2, GASS_gamma , GASS_aS1, GASS_aS2, GASS_aS3, GASS_tauS1, GASS_tauS2, GASS_tauS3, MCO2, MC)
# - - - -
# CH4 oxydation (CH4 -> CO2)
Add_AGWP_Oxyd = GasserDAGWPCH4toCO2(t, a0, a1, a2, a3, tau1, tau2, tau3, A_RadFor_CO2 , tauOH, alphaCO2, MCO2, MCH4 )
# - - - -
elif gas == "N2O":
# --- CO2 DATA
DataGCO2 = GasDATA['CO2']
a0 = DataGCO2['a0']
a1 = DataGCO2['a1']
a2 = DataGCO2['a2']
a3 = DataGCO2['a3']
tau1 = DataGCO2['tau1']
tau2 = DataGCO2['tau2']
tau3 = DataGCO2['tau3']
A_RadFor_CO2 = DataGCO2['A_RadFor']
MCO2 = DataGCO2['MolarMass']
# --- N2O DATA
N2Otau1 = DataG['tau1']
A_radN2O = DataG['A_RadFor']
# --- C data
MC = GasDATA['C']['MolarMass']
# --- IRFT data
IRFT_ECS = IRFT_DATA['ECS']
IRFT_c1 = IRFT_DATA['c1']
IRFT_c2 = IRFT_DATA['c2']
IRFT_d1 = IRFT_DATA['d1']
IRFT_d2 = IRFT_DATA['d2']
# --- Gasser CCF suppl.Model
# GASS_gamma = Gasser_CCF_Model['gamma']
# GASS_aS1 = Gasser_CCF_Model['aS1']
# GASS_aS2 = Gasser_CCF_Model['aS2']
# GASS_aS3 = Gasser_CCF_Model['aS3']
# GASS_tauS1 = Gasser_CCF_Model['tauS1']
# GASS_tauS2 = Gasser_CCF_Model['tauS2']
# GASS_tauS3 = Gasser_CCF_Model['tauS3']
GASS_gamma = 3.015
GASS_aS1 = 0.6368
GASS_aS2 = 0.3322
GASS_aS3 = 0.0310
GASS_tauS1 = 2.376
GASS_tauS2 = 30.14
GASS_tauS3 = 490.1
# - - - -
# Carbon cycle feedback
Add_AGWP_CCF = GasserCCFDAGWPNonCO2(t, N2Otau1, IRFT_ECS, IRFT_c1, IRFT_d1, IRFT_c2, IRFT_d2, A_radN2O, a0,a3,a2,a1,tau3,tau2,tau1, A_RadFor_CO2, GASS_gamma , GASS_aS1, GASS_aS2, GASS_aS3, GASS_tauS1, GASS_tauS2, GASS_tauS3, MCO2, MC)
# - - - -
# no oxydation
if isinstance(t,np.ndarray) :
Add_AGWP_Oxyd = np.zeros(len(t))
else :
Add_AGWP_Oxyd = 0.
# - - - -
else :
# No CCF and oxydation for other GHG considered here
if isinstance(t,np.ndarray) :
Add_AGWP_CCF = np.zeros(len(t))
Add_AGWP_Oxyd = np.zeros(len(t))
else :
Add_AGWP_CCF = 0.
Add_AGWP_Oxyd = 0.
return mass * ( DataG['A_RadFor'] * AGWPfunc + Add_AGWP_CCF + Add_AGWP_Oxyd )
#------------------------------------------------------------
# AGTP : Absolute global temperature change potential
#------------------------------------------------------------
# Integral of (RF(t) * delayed temperature response) over time period
# Input :
# + time
# + Gas informations
# + mass of this gas
# REQUIRES
# Time delayed response coefficients
# Output :
# Absolute global temperature change potential at a given time
#
def AGTP( GasDATA,IRFT_DATA,t,gas,mass,gasorigin):
# ---
DataG = GasDATA[gas]
# ---
ECS = IRFT_DATA['ECS']
azero = DataG['a0']
AGTPfunc = azero * ECS * IRFT_DATA['c1'] * (1.-np.exp(-t/IRFT_DATA['d1']))
AGTPfunc += azero * ECS * IRFT_DATA['c2'] * (1.-np.exp(-t/IRFT_DATA['d2']))
for j in [1,2] :
for i in range(1,DataG['K']+1): # boucle de 1 à "K"
ai = DataG['a'+str(i)]
taui = DataG['tau'+str(i)]
cj = IRFT_DATA['c'+str(j)]
dj = IRFT_DATA['d'+str(j)]
coef = ai * ECS * cj * taui / (taui-dj)
AGTPfunc+= coef * ( np.exp(-t/taui) - np.exp(-t/dj) )
# Different formula for CH4
if gas == "CH4":
# --- CO2 DATA
DataGCO2 = GasDATA['CO2']
a0 = DataGCO2['a0']
a1 = DataGCO2['a1']
a2 = DataGCO2['a2']
a3 = DataGCO2['a3']
tau1 = DataGCO2['tau1']
tau2 = DataGCO2['tau2']
tau3 = DataGCO2['tau3']
A_RadFor_CO2 = DataGCO2['A_RadFor']
MCO2 = DataGCO2['MolarMass']
# --- CH4 DATA
alphaCO2 = DataG['alphaCO2'] # Only CH4
CH4tau1 = DataG['tau1']
A_radCH4 = DataG['A_RadFor']
MCH4 = DataG['MolarMass']
# --- OH DATA
tauOH = GasDATA['OH']['tau1']
# --- IRFT data
IRFT_ECS = IRFT_DATA['ECS']
IRFT_c1 = IRFT_DATA['c1']
IRFT_c2 = IRFT_DATA['c2']
IRFT_d1 = IRFT_DATA['d1']
IRFT_d2 = IRFT_DATA['d2']
# --- C data
MC = GasDATA['C']['MolarMass']
# --- Gasser CCF suppl.Model
# GASS_gamma = Gasser_CCF_Model['gamma']
# GASS_aS1 = Gasser_CCF_Model['aS1']
# GASS_aS2 = Gasser_CCF_Model['aS2']
# GASS_aS3 = Gasser_CCF_Model['aS3']
# GASS_tauS1 = Gasser_CCF_Model['tauS1']
# GASS_tauS2 = Gasser_CCF_Model['tauS2']
# GASS_tauS3 = Gasser_CCF_Model['tauS3']
GASS_gamma = 3.015
GASS_aS1 = 0.6368
GASS_aS2 = 0.3322
GASS_aS3 = 0.0310
GASS_tauS1 = 2.376
GASS_tauS2 = 30.14
GASS_tauS3 = 490.1
# - - - -
# Carbon cycle feedback
Add_AGTP_CCF = GasserCCFDAGTPNonCO2(t, CH4tau1, IRFT_ECS, IRFT_c1, IRFT_d1, IRFT_c2, IRFT_d2, A_radCH4, a0,a3,a2,a1,tau3,tau2,tau1, A_RadFor_CO2, GASS_gamma , GASS_aS1, GASS_aS2, GASS_aS3, GASS_tauS1, GASS_tauS2, GASS_tauS3, MCO2, MC)
# - - - -
# CH4 oxydation (CH4 -> CO2)
Add_AGTP_Oxyd = GasserDAGTPCH4toCO2(t, a0, a1, a2, a3, tau1, tau2, tau3, IRFT_ECS, IRFT_c1, IRFT_d1, IRFT_c2, IRFT_d2, A_RadFor_CO2, tauOH, alphaCO2, MCO2, MCH4 )
# - - - - - -
elif gas =="N2O":
# --- CO2 DATA
DataGCO2 = GasDATA['CO2']
a0 = DataGCO2['a0']
a1 = DataGCO2['a1']
a2 = DataGCO2['a2']
a3 = DataGCO2['a3']
tau1 = DataGCO2['tau1']
tau2 = DataGCO2['tau2']
tau3 = DataGCO2['tau3']
A_RadFor_CO2 = DataGCO2['A_RadFor']
MCO2 = DataGCO2['MolarMass']
# --- CH4 DATA
N2Otau1 = DataG['tau1']
A_radN2O = DataG['A_RadFor']
# --- IRFT data
IRFT_ECS = IRFT_DATA['ECS']
IRFT_c1 = IRFT_DATA['c1']
IRFT_c2 = IRFT_DATA['c2']
IRFT_d1 = IRFT_DATA['d1']
IRFT_d2 = IRFT_DATA['d2']
# --- C data
MC = GasDATA['C']['MolarMass']
# --- Gasser CCF suppl.Model
# GASS_gamma = Gasser_CCF_Model['gamma']
# GASS_aS1 = Gasser_CCF_Model['aS1']
# GASS_aS2 = Gasser_CCF_Model['aS2']
# GASS_aS3 = Gasser_CCF_Model['aS3']
# GASS_tauS1 = Gasser_CCF_Model['tauS1']
# GASS_tauS2 = Gasser_CCF_Model['tauS2']
# GASS_tauS3 = Gasser_CCF_Model['tauS3']
GASS_gamma = 3.015
GASS_aS1 = 0.6368
GASS_aS2 = 0.3322
GASS_aS3 = 0.0310
GASS_tauS1 = 2.376
GASS_tauS2 = 30.14
GASS_tauS3 = 490.1
# - - - -
# Carbon cycle feedback
Add_AGTP_CCF = GasserCCFDAGTPNonCO2(t, N2Otau1, IRFT_ECS, IRFT_c1, IRFT_d1, IRFT_c2, IRFT_d2, A_radN2O, a0,a3,a2,a1,tau3,tau2,tau1 , A_RadFor_CO2, GASS_gamma , GASS_aS1, GASS_aS2, GASS_aS3, GASS_tauS1, GASS_tauS2, GASS_tauS3, MCO2, MC)
# - - - - - -
# Add_AGTP_Oxyd = 0 FOR N2O
if isinstance(t,np.ndarray) :
Add_AGTP_Oxyd = np.zeros(len(t))
else :
Add_AGTP_Oxyd = 0.
# - - - -
else :
# No CCF and oxydation for other GHG considered here
if isinstance(t,np.ndarray) :
Add_AGTP_CCF = np.zeros(len(t))
Add_AGTP_Oxyd = np.zeros(len(t))
else :
Add_AGTP_CCF = 0.
Add_AGTP_Oxyd = 0.
return mass * ( DataG['A_RadFor']* AGTPfunc + Add_AGTP_CCF + Add_AGTP_Oxyd )

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# *-* coding: utf-8 *-*
# - - - - - - - - - - - - - - - - - -
import numpy as np
# - - - - - - - - - - - - - - - - - -
# - - - - - - - - - - - - - - - - - -
from .Metrics import *
# - - - - - - - - - - - - - - - - - -
########################################################################
# HOW TO EVALUATE A CURVE USING INDICATORS
#------------------------------------------------------------
# ---
# Myfunction in indicators name (Myfunction is not a string, it is directly the function name)
#
# Based on localtime
# using numpy to create the pulse on a given timeline
def doSolutioncurve( Myfunction, GasDATA,TdrDATA, timeline, pulsetime, gas, mass, gasorigin ):
localtime = timeline - pulsetime
solutioncurve = np.piecewise( localtime, [localtime < 0, localtime >= 0], [ lambda x:0. , lambda x:Myfunction(GasDATA,TdrDATA,x,gas,mass,gasorigin) ] ) # if t<0 then => "O", else => f(localtime)
return solutioncurve
########################################################################

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# *-* coding: utf-8 *-*
# - - - - - - - - - - - - - - - - - -
import math
import numpy as np
import sys
# - - - - - - - - - - - - - - - - - -
from random import random, normalvariate, randint
# - - - - - - - - - - - - - - - - - -
# - - - - - - - - - - - - - - - - - -
from .Database import *
# - - - - - - - - - - - - - - - - - -
#==========================================================================================
def GasDATAlib_to_GasDATA_values():
GasDATA={}
for ges in GasDATAlib.keys():
GasDATA[ges]={}
for variable in GasDATAlib[ges].keys():
Ggvar = GasDATAlib[ges][variable]
if variable!='coeffIRF':
GasDATA[ges][variable] = Ggvar['value']
else:
GgvarVs = Ggvar['values']
for IRFvar in GgvarVs.keys():
GasDATA[ges][IRFvar] = GgvarVs[IRFvar]
return GasDATA
def TdrDATAlib_to_TdrDATA_values():
TdrDATA={}
for variable in IRFT_DataLib.keys():
Tvar = IRFT_DataLib[variable]
if variable!='coeffTDR':
TdrDATA[variable] = Tvar['value']
else:
TvarVs = Tvar['values']
for var in TvarVs.keys():
TdrDATA[var] = TvarVs[var]
return TdrDATA
#==========================================================================================
#==========================================================================================
# -- uniform :
def uniformDraw(X, Delta):
return (X-Delta) + 2.*Delta*random()
# -- normal : normalvariate(X, S)
# -- normalcorrelated :
# mu : mean values
# L : LLt Covariant matrix Cholesky decomposition
def normalcorrelatedDraw(mu,L):
draw = np.empty(len(mu))
for i in range(len(mu)):
draw[i] = normalvariate(0, 1) # Normal draw
Y = L.dot(draw) # Correlation
return mu + Y.transpose() # Add mean values. Return an array
#==========================================================================================
#==========================================================================================
def GESlibtoDATA_draw_values(whattodraw):
GasDATA={}
for ges in GasDATAlib.keys():
GasDATA[ges]={}
for variable in GasDATAlib[ges].keys():
if variable in whattodraw[ges].keys():
GGV = GasDATAlib[ges][variable]
if whattodraw[ges][variable]:
distrib = GGV['distrib']
if variable!='coeffIRF':
mean = GGV['value']
if distrib == 'uniform':
delta = GGV['delta']
drawValue = uniformDraw(mean, delta) # DRAW
elif distrib == 'normal':
sigma = GGV['sigma']
drawValue = normalvariate(mean, sigma) # DRAW
GasDATA[ges][variable] = drawValue
elif variable=='coeffIRF' :
GGVV = GGV['values']
# - Distribution cases : normal / manual
distribIRF = GGV['distrib']
if distribIRF =="normal":
for var in GGVV.keys():
mean = GGVV[var]
# - if concerned by random or not (e.g. CH4 and N2O only tau1 is random)
if var in GGV['sigmadic'].keys():
sigma = GGV['sigmadic'][var]
GasDATA[ges][var] = normalvariate(mean, sigma) # DRAW
else:
GasDATA[ges][var] = GGVV[var]
elif distribIRF =="manual":
#coeffIRF
GGVM = GGV['models']
nbmodels = len(GGVM) # Number of recorded models in database
randi = str(randint(1,nbmodels)) # DRAW
ChosenModel = GGVM[randi]
#
for var in ChosenModel.keys():
GasDATA[ges][var] = ChosenModel[var]
else:
# seen as constant
if variable!='coeffIRF':
GasDATA[ges][variable] = GasDATAlib[ges][variable]['value']
else:
GGVV = GGV['values']
for var in GGVV.keys():
GasDATA[ges][var] = GGVV[var]
else:
# seen as constant
GasDATA[ges][variable] = GasDATAlib[ges][variable]['value']
return GasDATA
def TdrDATAlib_to_TdrDATA_draw_values():
TdrDATA={}
for variable in IRFT_DataLib.keys():
Tvar = IRFT_DataLib[variable]
if variable!='coeffTDR':
distrib = Tvar['distrib']
mean = Tvar['value']
if distrib =='normal':
sigma = Tvar['sigma']
rval = normalvariate(mean, sigma) # DRAW
elif distrib =='uniform':
delta = Tvar['delta']
rval = uniformDraw(mean, delta) # DRAW
TdrDATA[variable] = rval
else:
TvarM = Tvar['models']
nbmodels = len(TvarM) # Number of recorded models in database
randi = str(randint(1,nbmodels)) # DRAW
ChosenModel = TvarM[randi]
#
for var in ChosenModel.keys():
TdrDATA[var] = ChosenModel[var]
return TdrDATA

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# *-* coding: utf-8 *-*
# - - - - - - - - - - - - - - - - - -
import os
import matplotlib.pyplot as plt
import numpy as np
# - - - - - - - - - - - - - - - - - -
#------------------------------------------------------------
# ---
def dF_CF(mean_material, sigma, std_material):
print("dF_20 =", mean_material[20], "+/-", sigma*std_material[20])
print("dF_100 =", mean_material[100], "+/-", sigma*std_material[100])
print("dF_500 =", mean_material[500], "+/-", sigma*std_material[500], "\n")
# ---
def dT_CF(sample, mean_material, sigma, std_material):
for itime in range(sample):
if mean_material[itime] == max(mean_material):
t_max = itime
incer_up = max(mean_material+sigma*std_material) - max(mean_material)
print("dT peak =", max(mean_material), "+/-", incer_up)
print("dTpeak timing = ", t_max, "years")
print("dT_long-term =", mean_material[500+t_max], "+/-", sigma*std_material[500+t_max])
for itime in range(sample):
if mean_material[itime] == min(mean_material):
if itime == 0:
print ("no dT negative")
break
else:
t_min = itime
incer_down= min(mean_material-sigma*std_material) - min(mean_material)
print ("dT negative =", min(mean_material), "+/-", incer_down)
print("dT negative timing = ", t_min, "years")

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#!/bin/env python3
# *-* coding: utf-8 *-*
# USAGE : python3 script.py
#
# Work in progress version, not yet a user friendly version.
#
# - - - - - - - - - - - - - - - - - -
import os
import matplotlib.pyplot as plt
import numpy as np
# - - - - - - - - - - - - - - - - - -
# - - - - - - - - - - - - - - - - - -
from modules.Database import *
from modules.Metrics import *
# - - - - - - - - - - - - - - - - - -
from modules.OrganiseCurves import *
from modules.Utils import *
# - - - - - - - - - - - - - - - - - -
from modules.Uncertainty import *
# - - - - - - - - - - - - - - - - - -
###################################################################################
# - - - - - - - - - - - - - - - - - - - -
# USER CHOICE
# Time line
tmin = 0. # USER CHOICE
tmax = 1000. # USER CHOICE
# - - - - - - - - - - - - - - - - - - - -
# USER CHOICE
#
# ImpulseRF : "kg"
# RadiativeForcing : "W/m^2"
# AGWP : "W/m^2*yr/kg"
# AGTP : "K/kg"
indicator = AGWP # USER CHOICE
# ------- Monte Carlo -----
# False : nominal value are used
# True : random value are computed using information in Database
# USER CHOICEs
whattodraw={}
whattodraw['CO2'] = {
'MolarMass' : False ,
'A_RadFor' : True ,
'coeffIRF' : True
}
whattodraw['CH4'] = {
'MolarMass' : False ,
'A_RadFor' : True ,
'alphaCO2' : True ,
'coeffIRF' : True
}
whattodraw['N2O'] = {
'MolarMass' : False ,
'A_RadFor' : True ,
'coeffIRF' : True
}
whattodraw['OH'] = {
'coeffIRF' : True
}
whattodraw['C'] = {
'MolarMass' : False
}
# ------- ONE SHOT -----
# GasDATA and TdrDATA are filled with constant values
GasDATA = GasDATAlib_to_GasDATA_values()
TdrDATA = TdrDATAlib_to_TdrDATA_values()
#=======================================================================
# Do not modify
sample=int((tmax-tmin)+1) # Every year
timeline = np.linspace(tmin,tmax,sample) # [ 0. 1. 2. ... 998. 999. 1000.]
#=======================================================================
NBDRAW = 50 # USER CHOICE (Higher value (e.g. 5000) -> ensuer statistical convergence of mean and dispersion)
# Stock initialisation
StoreCO2=np.empty([NBDRAW,sample]) # data in line
StoreCH4=np.empty([NBDRAW,sample]) # data in line
StoreN2O=np.empty([NBDRAW,sample]) # data in line
# En dur pour test : idéalement il faut passer par le catalogue
for i in range(NBDRAW):
# Draw
GasDATA = GESlibtoDATA_draw_values(whattodraw) # COMMENT IF CONSTANT / UNCOMMENT IF RANDOM AND ADD GasDATA = GasDATAlib_to_GasDATA_values() BEFORE LOOP
TdrDATA = TdrDATAlib_to_TdrDATA_draw_values() # COMMENT IF CONSTANT / UNCOMMENT IF RANDOM AND ADD TdrDATA = TdrDATAlib_to_TdrDATA_values() BEFORE LOOP
# Compute and store
# doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, PULSE_TIME, 'GHG', GHG_MASS, GHG_origin)
StoreCO2[i] = doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, 0., 'CO2', 1. ,'')
StoreCH4[i] = doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, 0., 'CH4', 1. ,'')
StoreN2O[i] = doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, 0., 'N2O', 1. ,'')
# Ici on a toute les coubres
#=======================================================================
# ANALYSIS
# 3 - At each time do a stats on xmean and sigma
meanCO2 = np.empty(sample)
stdCO2 = np.empty(sample)
meanCH4 = np.empty(sample)
stdCH4 = np.empty(sample)
meanN2O = np.empty(sample)
stdN2O = np.empty(sample)
for itime in range(sample):
meanCO2[itime] = np.mean(StoreCO2[:,itime])
stdCO2[itime] = np.std(StoreCO2[:,itime])
meanCH4[itime] = np.mean(StoreCH4[:,itime])
stdCH4[itime] = np.std(StoreCH4[:,itime])
meanN2O[itime] = np.mean(StoreN2O[:,itime])
stdN2O[itime] = np.std(StoreN2O[:,itime])
#=======================================================================
# VISUALISATION
# 4 - Create Mean plot, -/+ (1,2?) sigma plots
# ---
plt.clf()
alpha = 0.25 # Transparence : 1 opaque / 0 rien
plt.figure(figsize=(10,6)) # carré ou rectangle...
plt.yscale("log") # Y en log ou pas
# --- STEP BY STEP
fuseau_sigma = 1
plt.plot(timeline, meanCO2,'-' , label="mean $CO_2$", color="grey",zorder=2)
plt.fill_between(timeline, meanCO2-fuseau_sigma*stdCO2, meanCO2+fuseau_sigma*stdCO2, alpha=alpha, label="$\\sigma_{CO_2}$", color="grey",zorder=1)
plt.plot(timeline, meanCH4,'-' , label="mean $CH_4$", color="blue",zorder=2)
plt.fill_between(timeline, meanCH4-fuseau_sigma*stdCH4, meanCH4+fuseau_sigma*stdCH4, alpha=alpha, label="$\\sigma_{CH_4}$", color="blue",zorder=1)
plt.plot(timeline, meanN2O,'-' , label="mean $N_2O$", color="orange",zorder=2)
plt.fill_between(timeline, meanN2O-fuseau_sigma*stdN2O, meanN2O+fuseau_sigma*stdN2O, alpha=alpha, label="$\\sigma_{N_2O}$", color="orange",zorder=1)
# ---
plt.xlabel("time (year)")
plt.ylabel("AGWP (W.yr.m$^{-2}$.kg$^{-1}$)")
plt.title("a)", loc='left')
# --- LEGEND
plt.legend(bbox_to_anchor=(0.5, 0.1), loc="lower center", ncol=3, prop={'size': 12})
# Plot the Data
#plt.show()
plt.savefig("FIGURE_01_111_AGWP.pdf", bbox_inches='tight')
#=======================================================================
#4B - print information by VLAD
print("CO2")
dF_CF(meanCO2, 1, stdCO2)

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#!/bin/env python3
# *-* coding: utf-8 *-*
# USAGE : python3 script.py
#
# Work in progress version, not yet a user friendly version.
#
# - - - - - - - - - - - - - - - - - -
import os
import matplotlib.pyplot as plt
import numpy as np
# - - - - - - - - - - - - - - - - - -
# - - - - - - - - - - - - - - - - - -
from modules.Database import *
from modules.Metrics import *
# - - - - - - - - - - - - - - - - - -
from modules.OrganiseCurves import *
from modules.Utils import *
# - - - - - - - - - - - - - - - - - -
from modules.Uncertainty import *
# - - - - - - - - - - - - - - - - - -
###################################################################################
# - - - - - - - - - - - - - - - - - - - -
# USER CHOICE
# Time line
tmin = 0. # USER CHOICE
tmax = 1000. # USER CHOICE
# - - - - - - - - - - - - - - - - - - - -
# USER CHOICE
#
# ImpulseRF : "kg"
# RadiativeForcing : "W/m^2"
# AGWP : "W/m^2*yr/kg"
# AGTP : "K/kg"
indicator = AGTP
# ------- Monte Carlo -----
# False : nominal value are used
# True : random value are computed using information in Database
# USER CHOICEs
whattodraw={}
whattodraw['CO2'] = {
'MolarMass' : False ,
'A_RadFor' : True ,
'coeffIRF' : True
}
whattodraw['CH4'] = {
'MolarMass' : False ,
'A_RadFor' : True ,
'alphaCO2' : True ,
'coeffIRF' : True
}
whattodraw['N2O'] = {
'MolarMass' : False ,
'A_RadFor' : True ,
'coeffIRF' : True
}
whattodraw['OH'] = {
'coeffIRF' : True
}
whattodraw['C'] = {
'MolarMass' : False
}
# ------- ONE SHOT -----
# GasDATA and TdrDATA are filled with values
GasDATA = GasDATAlib_to_GasDATA_values()
TdrDATA = TdrDATAlib_to_TdrDATA_values()
#=======================================================================
# Do not modify
sample=int((tmax-tmin)+1) # Every year
timeline = np.linspace(tmin,tmax,sample) # [ 0. 1. 2. ... 998. 999. 1000.]
#=======================================================================
NBDRAW = 50 # USER CHOICE (Higher value (e.g. 5000) -> ensuer statistical convergence of mean and dispersion)
# Stock initialisation
StoreCO2=np.empty([NBDRAW,sample]) # data in line
StoreCH4=np.empty([NBDRAW,sample]) # data in line
StoreN2O=np.empty([NBDRAW,sample]) # data in line
# En dur pour test : idéalement il faut passer par le catalogue
for i in range(NBDRAW):
# Draw
GasDATA = GESlibtoDATA_draw_values(whattodraw) # COMMENT IF CONSTANT / UNCOMMENT IF RANDOM AND ADD GasDATA = GasDATAlib_to_GasDATA_values() BEFORE LOOP
TdrDATA = TdrDATAlib_to_TdrDATA_draw_values() # COMMENT IF CONSTANT / UNCOMMENT IF RANDOM AND ADD TdrDATA = TdrDATAlib_to_TdrDATA_values() BEFORE LOOP
# Compute and store
# doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, PULSE_TIME, 'GHG', GHG_MASS)
StoreCO2[i] = doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, 0., 'CO2', 1. ,'')
StoreCH4[i] = doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, 0., 'CH4', 1. ,'')
StoreN2O[i] = doSolutioncurve( indicator, GasDATA,TdrDATA, timeline, 0., 'N2O', 1. ,'')
# Ici on a toute les coubres
#=======================================================================
# ANALYSIS
# 3 - At each time do a stats on xmean and sigma
meanCO2 = np.empty(sample)
stdCO2 = np.empty(sample)
meanCH4 = np.empty(sample)
stdCH4 = np.empty(sample)
meanN2O = np.empty(sample)
stdN2O = np.empty(sample)
for itime in range(sample):
meanCO2[itime] = np.mean(StoreCO2[:,itime])
stdCO2[itime] = np.std(StoreCO2[:,itime])
meanCH4[itime] = np.mean(StoreCH4[:,itime])
stdCH4[itime] = np.std(StoreCH4[:,itime])
meanN2O[itime] = np.mean(StoreN2O[:,itime])
stdN2O[itime] = np.std(StoreN2O[:,itime])
#=======================================================================
# VISUALISATION
# 4 - Create Mean plot, -/+ (1,2?) sigma plots
# ---
plt.clf()
alpha = 0.25 # Transparence : 1 opaque / 0 rien
plt.figure(figsize=(10,6)) # carré ou rectangle...
plt.yscale("log") # Y en log ou pas
# --- STEP BY STEP
fuseau_sigma = 1
plt.plot(timeline, meanCO2,'-' , label="mean $CO_2$", color="grey",zorder=2)
plt.fill_between(timeline, meanCO2-fuseau_sigma*stdCO2, meanCO2+fuseau_sigma*stdCO2, alpha=alpha, label="$\\sigma_{CO_2}$", color="grey",zorder=1)
plt.plot(timeline, meanCH4,'-' , label="mean $CH_4$", color="blue",zorder=2)
plt.fill_between(timeline, meanCH4-fuseau_sigma*stdCH4, meanCH4+fuseau_sigma*stdCH4, alpha=alpha, label="$\\sigma_{CH_4}$", color="blue",zorder=1)
plt.plot(timeline, meanN2O,'-' , label="mean $N_2O$", color="orange",zorder=2)
plt.fill_between(timeline, meanN2O-fuseau_sigma*stdN2O, meanN2O+fuseau_sigma*stdN2O, alpha=alpha, label="$\\sigma_{N_2O}$", color="orange",zorder=1)
# ---
plt.xlabel("time (year)")
plt.ylabel("AGTP (K.kg$^{-1}$)")
plt.title("b)", loc='left')
# --- LEGEND
plt.legend(loc='upper right', fancybox=True, ncol = 3, prop={'size': 12})
# --- GRID
#plt.grid()
#plt.grid(axis="y")
# Plot the Data
#plt.show()
plt.savefig("FIGURE_01_111_AGTP.pdf", bbox_inches='tight') # __logscale
#-------------------------------
#=======================================================================
#4B - print information by VLAD
print ("\n", "CO2")
dT_CF(sample, meanCO2, 1, stdCO2)
print ("\n", "CH4")
dT_CF(sample, meanCH4, 1, stdCH4)
print ("\n", "N2O")
dT_CF(sample, meanN2O, 1, stdN2O)
#-------------------------------