text:
### A Pluto.jl notebook ###
# v0.17.3

using Markdown
using InteractiveUtils

# ╔═╡ 1a15712e-4010-11ec-02fb-f548751f3a1a
using Symbolics,ModelingToolkit, DifferentialEquations, Catalyst,OrdinaryDiffEq,Plots

# ╔═╡ 2e0898da-4a53-4f3a-9490-1752044d804c
@parameters T p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27 p28 p29 p30 p31 p32 p33 p34 p35 p36 p37 p38 p39 p40 p41 p42 p43 p44 p45 p46 p47 p48 p49 p50 p51 p52 p53 p54 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 d13 d14 d15 d16 d17 d18 d19

# ╔═╡ 0498dda1-1fb7-4283-8d84-0cc4c0394557
begin
		@variables CANO32s(T) CA2aq(T) NO3aq(T) CACL2s(T) CLaq(T) CASO4s(T) SO42aq(T) H2Og(T) K2SO4s(T) Kaq(T) KHSO4s(T) HSO4aq(T) KNO3s(T) KCLs(T) MGSO4s(T) MG2aq(T) MGNO32s(T) MGCL2s(T) Haq(T) NH3g(T) NH3aq(T) NH4aq(T) OHaq(T) HNO3g(T) Haq(T) HNO3aq(T) HCLg(T) HCLaq(T) NA2SO4s(T) NAaq(T) NH42SO4s(T) NH4CLs(T) NANO3s(T) NO3aq(T) NACLs(T) NAHSO4s(T) NH4NO3s(T) NH4HSO4s(T) NH43HSO42s(T) H2Oaq(T)

rxs = [Reaction(p1, [CANO32s], [CA2aq,NO3aq], [1], [1,2])
				Reaction(p2, [CA2aq,NO3aq], [CANO32s], [1,2], [1])
				Reaction(p3, [CACL2s], [CA2aq,CLaq], [1], [1,2])
				Reaction(p4, [CA2aq,CLaq], [CACL2s], [1,2], [1])
				Reaction(p5, [CASO4s], [CA2aq,SO42aq,H2Og], [1], [1,1,2])
				Reaction(p6, [CA2aq,SO42aq,H2Og], [CASO4s], [1,1,2], [1])
				Reaction(p7, [K2SO4s], [Kaq,SO42aq], [1], [1,2])
				Reaction(p8, [Kaq,SO42aq], [K2SO4s], [1,2], [1])
				Reaction(p9, [KHSO4s], [Kaq,HSO4aq], [1], [1,1])
				Reaction(p10, [Kaq,HSO4aq], [KHSO4s], [1,1], [1])
				Reaction(p11, [KNO3s], [Kaq,NO3aq], [1], [1,1])
				Reaction(p12, [Kaq,NO3aq], [KNO3s], [1,1], [1])
				Reaction(p13, [KCLs], [Kaq,CLaq], [1], [1,1])
				Reaction(p14, [Kaq,CLaq], [KCLs], [1,1], [1])
				Reaction(p15, [MGSO4s], [MG2aq,SO42aq], [1], [1,1])
				Reaction(p16, [MG2aq,SO42aq], [MGSO4s], [1,1], [1])
				Reaction(p17, [MGNO32s], [MG2aq,NO3aq], [1], [1,2])
				Reaction(p18, [MG2aq,NO3aq], [MGNO32s], [1,2], [1])
				Reaction(p19, [MGCL2s], [MG2aq,CLaq], [1], [1,2])
				Reaction(p20, [MG2aq,CLaq], [MGCL2s], [1,2], [1])
				Reaction(p21, [HSO4aq], [Haq,SO42aq], [1], [1,1])
				Reaction(p22, [Haq,SO42aq], [HSO4aq], [1,1], [1])
				Reaction(p23, [NH3g], [NH3aq], [1], [1])
				Reaction(p24, [NH3aq], [NH3g], [1], [1])
				Reaction(p25, [NH3aq,H2Oaq], [NH4aq,OHaq], [1,1], [1,1])
				Reaction(p26, [NH4aq,OHaq], [NH3aq,H2Oaq], [1,1], [1,1])
				Reaction(p27, [HNO3g], [Haq,NO3aq], [1], [1,1])
				Reaction(p28, [Haq,NO3aq], [HNO3g], [1,1], [1])
				Reaction(p29, [HNO3g], [HNO3aq], [1], [1])
				Reaction(p30, [HNO3aq], [HNO3g], [1], [1])
				Reaction(p31, [HCLg], [Haq,CLaq], [1], [1,1])
				Reaction(p32, [Haq,CLaq], [HCLg], [1,1], [1])
				Reaction(p33, [HCLaq], [HCLg], [1], [1])
				Reaction(p34, [HCLg], [HCLaq], [1], [1])
				Reaction(p35, [H2Oaq], [Haq,OHaq], [1], [1,1])
				Reaction(p36, [Haq,OHaq], [H2Oaq], [1,1], [1])
				Reaction(p37, [NA2SO4s], [NAaq,SO42aq], [1], [2,1])
				Reaction(p38, [NAaq,SO42aq], [NA2SO4s], [2,1], [1])
				Reaction(p39, [NH42SO4s], [NH4aq,SO42aq], [1], [2,1])
				Reaction(p40, [NH4aq,SO42aq], [NH42SO4s], [2,1], [1])
				Reaction(p41, [NH4CLs], [NH3g,SO42aq], [1], [1,1])
				Reaction(p42, [NH3g,SO42aq], [NH4CLs], [1,1], [1])
				Reaction(p43, [NANO3s], [NAaq,NO3aq], [1], [1,1])
				Reaction(p44, [NAaq,NO3aq], [NANO3s], [1,1], [1])
				Reaction(p45, [NACLs], [NAaq,CLaq], [1], [1,1])
				Reaction(p46, [NAaq,CLaq], [NACLs], [1,1], [1])
				Reaction(p47, [NAHSO4s], [NAaq,HSO4aq], [1], [1,1])
				Reaction(p48, [NAaq,HSO4aq], [NAHSO4s], [1,1], [1])
				Reaction(p49, [NH4NO3s], [NH3g,HNO3g], [1], [1,1])
				Reaction(p50, [NH3g,HNO3g], [NH4NO3s], [1,1], [1])
				Reaction(p51, [NH4HSO4s], [NH4aq,HSO4aq], [1], [1,1])
				Reaction(p52, [NH4aq,HSO4aq], [NH4HSO4s], [1,1], [1])
				Reaction(p53, [NH43HSO42s], [NH4aq,HSO4aq,SO42aq], [1], [3,1,1])
				Reaction(p54, [NH4aq,HSO4aq,SO42aq], [NH43HSO42s], [3,1,1], [1])

	]

		@named ISORROPIA = ReactionSystem(rxs, T)

end

# ╔═╡ c59e2aa6-d5cc-40a6-9826-3e12f298f300
begin

		u₀map = [CANO32s => 0.000,  CA2aq => 0.400, NO3aq => 0.000,  CACL2s => 0.000, CLaq => 0.000, CASO4s => 0.000, SO42aq => 0.000, H2Og => 0.000, K2SO4s => 0.000, Kaq => 0.330, KHSO4s => 0.000, HSO4aq => 10.000, KNO3s => 0.000, KCLs => 0.000, MGSO4s => 0.000, MG2aq => 0.000, MGNO32s => 0.000, MGCL2s => 0.000, Haq => 10.000, NH3g => 0.000, NH3aq => 3.400, NH4aq => 0.000, OHaq => 0.000, HNO3g => 0.000, Haq => 0.000, HNO3aq => 2.000, HCLg => 0.000, HCLaq => 0.000, NA2SO4s => 0.000, NAaq => 0.000, NH42SO4s => 0.000, NH4CLs => 0.000, NANO3s => 0.000, NO3aq => 0.000, NACLs => 0.000, NAHSO4s => 0.000, NH4NO3s => 0.000, NH4HSO4s => 0.000, NH43HSO42s => 0.000, H2Oaq => 12.000]

				tspan = (0.0, 360.0)

				T_ = 298.15

			parammap = [p1 => 6.067*10^(5)*exp((-11.299*((298.15/T_)-1))-(1+log((2.718),(298.15/T_))-(298.15/T_))),
						p2 => 1.648*10^(-6)*exp((-11.299*((298.15/T_)-1))+(1+log((2.718),(298.15/T_))-(298.15/T_))),
						p3 => 7.974*10^(11)*exp((-14.087*((298.15/T_)-1))-(1+log((2.718),(298.15/T_))-(298.15/T_))),
						p4 => 1.254^(-12)*exp((-14.087*((298.15/T_)-1))+(1+log((2.718),(298.15/T_))-(298.15/T_))),
						p5 => 4.319*10^(-5)*exp((((298.15/T_)-1))-(1+log((2.718),(298.15/T_))-(298.15/T_))),
						p6 => 2.315*10^(4)*exp((((298.15/T_)-1))+(1+log((2.718),(298.15/T_))-(298.15/T_))),
						p7 => 1.569*10^(-2)*exp((-9.589*((298.15/T_)-1))-(45.807(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p8 => 6.373*10^(-1)*exp((-9.589*((298.15/T_)-1))+(45.807(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p9 => 24.016*exp((-8.423*((298.15/T_)-1))-(17.964(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p10 => 4.164*10^(-2)*exp((-8.423*((298.15/T_)-1))+(17.964(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p11 => 0.872*exp((14.075*((298.15/T_)-1))-(19.388(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p12 => 1.147*exp((14.075*((298.15/T_)-1))+(19.388(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p13 => 8.680*exp((-6.167*((298.15/T_)-1))-(19.953(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p14 => 1.152*10^(-1)*exp((-6.167*((298.15/T_)-1))+(19.953(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p15 => 1.079*10^(5)*exp((36.798*((298.15/T_)-1))-((1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p16 => 9.268*10^(-6)*exp((36.798*((298.15/T_)-1))+((1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p17 => 2.507*10^(15)*exp((-8.754*((298.15/T_)-1))-((1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p18 => 3.989*10^(-16)*exp((-8.754*((298.15/T_)-1))+((1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p19 => 9.557*10^(21)*exp((-1.347*((298.15/T_)-1))-((1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p20 => 1.046*10^(-22)*exp((-1.347*((298.15/T_)-1))+((1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p21 => 1.015*10^(-2)*exp((8.85*((298.15/T_)-1))-(25.14(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p22 => 9.852*10^(1)*exp((8.85*((298.15/T_)-1))+(25.14(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p23 => 5.764*10^(1)*exp((13.79*((298.15/T_)-1))-(-5.39(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p24 => 1.735*10^(-2)*exp((13.79*((298.15/T_)-1))+(-5.39(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p25 => 1.805*10^(-5)*exp((-1.50*((298.15/T_)-1))-(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p26 => 5.540*10^(4)*exp((-1.50*((298.15/T_)-1))+(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p27 => 2.511*10^(6)*exp((29.17*((298.15/T_)-1))-(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p28 => 3.982*10^(-7)*exp((29.17*((298.15/T_)-1))+(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p29 => 2.1*10^(5)*exp((29.17*((298.15/T_)-1))-(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p30 => 4.762*10^(-6)*exp((29.17*((298.15/T_)-1))+(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p31 => 1.971*10^(6)*exp((30.20*((298.15/T_)-1))-(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p32 => 5.074*10^(-7)*exp((30.20*((298.15/T_)-1))+(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p33 => 2.5*10^(3)*exp((30.20*((298.15/T_)-1))-(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p34 => 4*10^(-4)*exp((30.20*((298.15/T_)-1))+(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p35 => 1.010*10^(-14)*exp((-22.52*((298.15/T_)-1))-(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p36 => 9.901*10^(13)*exp((-22.52*((298.15/T_)-1))+(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p37 => 4.799*10^(-1)*exp((0.98*((298.15/T_)-1))-(39.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p38 => 2.084*exp((0.98*((298.15/T_)-1))+(39.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p39 => 1.817*exp((-2.65*((298.15/T_)-1))-(38.57(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p40 => 5.504*10^(-1)*exp((-2.65*((298.15/T_)-1))+(38.57(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p41 => 1.086*10^(-16)*exp((-71*((298.15/T_)-1))-(2.4(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p42 => 9.208*10^(15)*exp((-71*((298.15/T_)-1))+(2.4(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p43 => 1.197*10^(1)*exp((-8.22*((298.15/T_)-1))-(16.01(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p44 => 8.354*10^(-2)*exp((-8.22*((298.15/T_)-1))+(16.01(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p45 => 3.766*10^(1)*exp((-1.56*((298.15/T_)-1))-(16.90(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p46 => 2.655*10^(-2)*exp((-1.56*((298.15/T_)-1))+(16.90(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p47 => 2.413*10^(4)*exp((0.79*((298.15/T_)-1))-(14.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p48 => 4.144*10^(-5)*exp((0.79*((298.15/T_)-1))+(14.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p49 => 4.199*10^(-17)*exp((-74.735*((298.15/T_)-1))-(6.025(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p50 => 2.382*10^(16)*exp((-74.735*((298.15/T_)-1))+(14.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p51 => 1.383*exp((-2.87*((298.15/T_)-1))-(15.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p52 => 7.231*10^(-1)*exp((-2.87*((298.15/T_)-1))+(15.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p53 => 2.972*10^(1)*exp((-5.19*((298.15/T_)-1))-(54.40(1+log((2.718),(298.15/T_))-(298.15/T_)))),
						p54 => 3.365*10^(-2)*exp((-5.19*((298.15/T_)-1))+(54.40(1+log((2.718),(298.15/T_))-(298.15/T_))))

					]

		parammap_ = [d1 => 0.4906exp(-509.4((1/298.15)-(1/T_))),
					d2 => 0.2830exp(-551.1((1/298.15)-(1/T_))),
					d3 => 0.9700exp(-((1/298.15)-(1/T_))),
					d4 => 0.8600exp(-((1/298.15)-(1/T_))),
					d5 => 0.9751exp(-35.6((1/298.15)-(1/T_))),
					d6 => 0.9248exp(-((1/298.15)-(1/T_))),
					d7 => 0.8426exp(-158.9((1/298.15)-(1/T_))),
					d8 => 0.8613exp(714.5((1/298.15)-(1/T_))),
					d9 => 0.5400exp(-230.2((1/298.15)-(1/T_))),
					d10 => 0.3284exp(-42.23((1/298.15)-(1/T_))),
					d11 => 0.7528exp(-25.0((1/298.15)-(1/T_))),
					d12 => 0.9300exp(-80.0((1/298.15)-(1/T_))),
					d13 => 0.7379exp(-304.0((1/298.15)-(1/T_))),
					d14 => 0.7997exp(-80.0((1/298.15)-(1/T_))),
					d15 => 0.6183exp(-852.0((1/298.15)-(1/T_))),
					d16 => 0.7710exp(-239.0((1/298.15)-(1/T_))),
					d17 => 0.4000exp(-384.0((1/298.15)-(1/T_))),
					d18 => 0.5200exp(45((1/298.15)-(1/T_))),
					d19 => 0.6900exp(-186.0((1/298.15)-(1/T_))),
					]

		function test_DRH(d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11, d12, d13, d14, d15, d16, d17, d18, d19, p1, p3, p5, p9, p7, p11, p13, p15, p17, p19, p45, p37, p43, p39, p49, p41, p51, p47, p53)
			RH = 0.700
				if RH < d1
					return p1 => 0
				elseif RH > d1
					return p1
				end
				if RH < d2
					return p3 => 0
				elseif RH > d2
					return p3 => 0
				end
				if RH < d3
					return p5 => 0
				elseif RH > d3
					return p5 => 0
				end
				if RH < d4
					return p9 => 0
				elseif RH > d4
					return p9 => 0
				end
				if RH < d5
					return p7 => 0
				elseif RH > d5
					return p7 => 0
				end
				if RH < d6
					return p11 => 0
				elseif RH > d6
					return p11 => 0
				end
				if RH < d7
					return p13 => 0
				elseif RH > d7
					return p13 => 0
				end
				if RH < d8
					return p15 => 0
				elseif RH > d8
					return p15 => 0
				end
				if RH < d9
					return p17 => 0
				elseif RH > d9
					return p17 => 0
				end
				if RH < d10
					return p19 => 0
				elseif RH > d10
					return p19 => 0
				end
				if RH < d11
					return p45 => 0
				elseif RH > d11
					return p45 => 0
				end
				if RH < d12
					return p37 => 0
				elseif RH > d12
					return p37 => 0
				end
				if RH < d13
					return p43 => 0
				elseif RH > d13
					return p43 => 0
				end
				if RH < d14
					return p39 => 0
				elseif RH > d14
					return p39 => 0
				end
				if RH < d15
					return p49 => 0
				elseif RH > d15
					return p49 => 0
				end
				if RH < d16
					return p41 => 0
				elseif RH > d16
					return p41 => 0
				end
				if RH < d17
					return p51 => 0
				elseif RH > d17
					return p51 => 0
				end
				if RH < d18
					return p47 => 0
				elseif RH > d18
					return p47 => 0
				end
				if RH < d19
					return p53 => 0
				elseif RH > d19
					return p53 => 0
				end

		end

end

# ╔═╡ 9c26ff48-9750-45e3-94c5-eac019b0f01f
begin

		oprob = ODEProblem(ISORROPIA, u₀map, tspan, parammap)

		sol = solve(oprob, Tsit5(), saveat=10.)

		plot(sol)
end

# ╔═╡ 9114e631-a740-4154-8743-55510620932a

function model(T_, RH_, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag, ah, ai, aj, ak, al, am, an)

	u₀map_ = [CANO32s => a  CA2aq => b NO3aq => c  CACL2s => d CLaq => e CASO4s => f SO42aq => g H2Og => h K2SO4s => i Kaq => j KHSO4s => k HSO4aq => l KNO3s => m KCLs => n MGSO4s => o MG2aq => p MGNO32s => q MGCL2s => r Haq => s NH3g => t NH3aq => u NH4aq => v OHaq => w HNO3g => x Haq => y HNO3aq => z HCLg => aa HCLaq => ab NA2SO4s => ac NAaq => ad NH42SO4s => ae NH4CLs => af NANO3s => ag NO3aq => ah NACLs => ai NAHSO4s => aj NH4NO3s => ak NH4HSO4s => al NH43HSO42s => am H2Oaq => an]

	parammap1 = [p1 => 6.067*10^(5)*exp((-11.299*((298.15/T_)-1))-(1+log((2.718),(298.15/T_))-(298.15/T_))),
				p2 => 1.648*10^(-6)*exp((-11.299*((298.15/T_)-1))+(1+log((2.718),(298.15/T_))-(298.15/T_))),
				p3 => 7.974*10^(11)*exp((-14.087*((298.15/T_)-1))-(1+log((2.718),(298.15/T_))-(298.15/T_))),
				p4 => 1.254^(-12)*exp((-14.087*((298.15/T_)-1))+(1+log((2.718),(298.15/T_))-(298.15/T_))),
				p5 => 4.319*10^(-5)*exp((((298.15/T_)-1))-(1+log((2.718),(298.15/T_))-(298.15/T_))),
				p6 => 2.315*10^(4)*exp((((298.15/T_)-1))+(1+log((2.718),(298.15/T_))-(298.15/T_))),
				p7 => 1.569*10^(-2)*exp((-9.589*((298.15/T_)-1))-(45.807(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p8 => 6.373*10^(-1)*exp((-9.589*((298.15/T_)-1))+(45.807(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p9 => 24.016*exp((-8.423*((298.15/T_)-1))-(17.964(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p10 => 4.164*10^(-2)*exp((-8.423*((298.15/T_)-1))+(17.964(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p11 => 0.872*exp((14.075*((298.15/T_)-1))-(19.388(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p12 => 1.147*exp((14.075*((298.15/T_)-1))+(19.388(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p13 => 8.680*exp((-6.167*((298.15/T_)-1))-(19.953(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p14 => 1.152*10^(-1)*exp((-6.167*((298.15/T_)-1))+(19.953(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p15 => 1.079*10^(5)*exp((36.798*((298.15/T_)-1))-((1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p16 => 9.268*10^(-6)*exp((36.798*((298.15/T_)-1))+((1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p17 => 2.507*10^(15)*exp((-8.754*((298.15/T_)-1))-((1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p18 => 3.989*10^(-16)*exp((-8.754*((298.15/T_)-1))+((1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p19 => 9.557*10^(21)*exp((-1.347*((298.15/T_)-1))-((1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p20 => 1.046*10^(-22)*exp((-1.347*((298.15/T_)-1))+((1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p21 => 1.015*10^(-2)*exp((8.85*((298.15/T_)-1))-(25.14(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p22 => 9.852*10^(1)*exp((8.85*((298.15/T_)-1))+(25.14(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p23 => 5.764*10^(1)*exp((13.79*((298.15/T_)-1))-(-5.39(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p24 => 1.735*10^(-2)*exp((13.79*((298.15/T_)-1))+(-5.39(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p25 => 1.805*10^(-5)*exp((-1.50*((298.15/T_)-1))-(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p26 => 5.540*10^(4)*exp((-1.50*((298.15/T_)-1))+(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p27 => 2.511*10^(6)*exp((29.17*((298.15/T_)-1))-(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p28 => 3.982*10^(-7)*exp((29.17*((298.15/T_)-1))+(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p29 => 2.1*10^(5)*exp((29.17*((298.15/T_)-1))-(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p30 => 4.762*10^(-6)*exp((29.17*((298.15/T_)-1))+(16.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p31 => 1.971*10^(6)*exp((30.20*((298.15/T_)-1))-(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p32 => 5.074*10^(-7)*exp((30.20*((298.15/T_)-1))+(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p33 => 2.5*10^(3)*exp((30.20*((298.15/T_)-1))-(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p34 => 4*10^(-4)*exp((30.20*((298.15/T_)-1))+(19.91(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p35 => 1.010*10^(-14)*exp((-22.52*((298.15/T_)-1))-(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p36 => 9.901*10^(13)*exp((-22.52*((298.15/T_)-1))+(26.92(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p37 => 4.799*10^(-1)*exp((0.98*((298.15/T_)-1))-(39.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p38 => 2.084*exp((0.98*((298.15/T_)-1))+(39.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p39 => 1.817*exp((-2.65*((298.15/T_)-1))-(38.57(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p40 => 5.504*10^(-1)*exp((-2.65*((298.15/T_)-1))+(38.57(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p41 => 1.086*10^(-16)*exp((-71*((298.15/T_)-1))-(2.4(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p42 => 9.208*10^(15)*exp((-71*((298.15/T_)-1))+(2.4(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p43 => 1.197*10^(1)*exp((-8.22*((298.15/T_)-1))-(16.01(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p44 => 8.354*10^(-2)*exp((-8.22*((298.15/T_)-1))+(16.01(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p45 => 3.766*10^(1)*exp((-1.56*((298.15/T_)-1))-(16.90(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p46 => 2.655*10^(-2)*exp((-1.56*((298.15/T_)-1))+(16.90(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p47 => 2.413*10^(4)*exp((0.79*((298.15/T_)-1))-(14.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p48 => 4.144*10^(-5)*exp((0.79*((298.15/T_)-1))+(14.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p49 => 4.199*10^(-17)*exp((-74.735*((298.15/T_)-1))-(6.025(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p50 => 2.382*10^(16)*exp((-74.735*((298.15/T_)-1))+(14.75(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p51 => 1.383*exp((-2.87*((298.15/T_)-1))-(15.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p52 => 7.231*10^(-1)*exp((-2.87*((298.15/T_)-1))+(15.83(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p53 => 2.972*10^(1)*exp((-5.19*((298.15/T_)-1))-(54.40(1+log((2.718),(298.15/T_))-(298.15/T_)))),
				p54 => 3.365*10^(-2)*exp((-5.19*((298.15/T_)-1))+(54.40(1+log((2.718),(298.15/T_))-(298.15/T_))))

	]

	parammap_1 = [d1 => 0.4906exp(-509.4((1/298.15)-(1/T_))),
					d2 => 0.2830exp(-551.1((1/298.15)-(1/T_))),
					d3 => 0.9700exp(-((1/298.15)-(1/T_))),
					d4 => 0.8600exp(-((1/298.15)-(1/T_))),
					d5 => 0.9751exp(-35.6((1/298.15)-(1/T_))),
					d6 => 0.9248exp(-((1/298.15)-(1/T_))),
					d7 => 0.8426exp(-158.9((1/298.15)-(1/T_))),
					d8 => 0.8613exp(714.5((1/298.15)-(1/T_))),
					d9 => 0.5400exp(-230.2((1/298.15)-(1/T_))),
					d10 => 0.3284exp(-42.23((1/298.15)-(1/T_))),
					d11 => 0.7528exp(-25.0((1/298.15)-(1/T_))),
					d12 => 0.9300exp(-80.0((1/298.15)-(1/T_))),
					d13 => 0.7379exp(-304.0((1/298.15)-(1/T_))),
					d14 => 0.7997exp(-80.0((1/298.15)-(1/T_))),
					d15 => 0.6183exp(-852.0((1/298.15)-(1/T_))),
					d16 => 0.7710exp(-239.0((1/298.15)-(1/T_))),
					d17 => 0.4000exp(-384.0((1/298.15)-(1/T_))),
					d18 => 0.5200exp(45((1/298.15)-(1/T_))),
					d19 => 0.6900exp(-186.0((1/298.15)-(1/T_))),
					]

		function test_DRH(d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11, d12, d13, d14, d15, d16, d17, d18, d19, p1, p3, p5, p9, p7, p11, p13, p15, p17, p19, p45, p37, p43, p39, p49, p41, p51, p47, p53)

				if RH < d1
					p1 => 0
				end
				if RH < d2
					p3 => 0
				end
				if RH < d3
					p5 => 0
				end
				if RH < d4
					p9 => 0
				end
				if RH < d5
					p7 => 0
				end
				if RH < d6
					p11 => 0
				end
				if RH < d7
					p13 => 0
				end
				if RH < d8
					p15 => 0
				end
				if RH < d9
					p17 => 0
				end
				if RH < d10
					p19 => 0
				end
				if RH < d11
					p45 => 0
				end
				if RH < d12
					p37 => 0
				end
				if RH < d13
					p43 => 0
				end
				if RH < d14
					p39 => 0
				end
				if RH < d15
					p49 => 0
				end
				if RH < d16
					p41 => 0
				end
				if RH < d17
					p51 => 0
				end
				if RH < d18
					p47 => 0
				end
				if RH < d19
					p53 => 0
				end

		end

	oprob_ = ODEProblem(ISORROPIA, u₀map_, tspan, parammap1)
	sol_ = solve(oprob_, Tsit5(), saveat=10.)
	plot(sol_)

end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83"
DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"

[compat]
Catalyst = "~10.0.0"
DifferentialEquations = "~6.19.0"
ModelingToolkit = "~6.7.1"
OrdinaryDiffEq = "~5.69.0"
Plots = "~1.25.2"
Symbolics = "~3.5.1"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised

[[AbstractAlgebra]]
deps = ["GroupsCore", "InteractiveUtils", "LinearAlgebra", "Markdown", "Random", "RandomExtensions", "SparseArrays", "Test"]
git-tree-sha1 = "94babc7413ae4247d53f8aa3786720157c9b26c3"
uuid = "c3fe647b-3220-5bb0-a1ea-a7954cac585d"
version = "0.22.2"

[[AbstractTrees]]
git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5"
uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
version = "0.3.4"

[[Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "3.3.1"

[[ArgTools]]
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"

[[ArnoldiMethod]]
deps = ["LinearAlgebra", "Random", "StaticArrays"]
git-tree-sha1 = "f87e559f87a45bece9c9ed97458d3afe98b1ebb9"
uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
version = "0.1.0"

[[ArrayInterface]]
deps = ["Compat", "IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"]
git-tree-sha1 = "265b06e2b1f6a216e0e8f183d28e4d354eab3220"
uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
version = "3.2.1"

[[ArrayLayouts]]
deps = ["FillArrays", "LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "e1ba79094cae97b688fb42d31cbbfd63a69706e4"
uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
version = "0.7.8"

[[Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"

[[BandedMatrices]]
deps = ["ArrayLayouts", "FillArrays", "LinearAlgebra", "Random", "SparseArrays"]
git-tree-sha1 = "ce68f8c2162062733f9b4c9e3700d5efc4a8ec47"
uuid = "aae01518-5342-5314-be14-df237901396f"
version = "0.16.11"

[[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"

[[Bijections]]
git-tree-sha1 = "705e7822597b432ebe152baa844b49f8026df090"
uuid = "e2ed5e7c-b2de-5872-ae92-c73ca462fb04"
version = "0.1.3"

[[BitTwiddlingConvenienceFunctions]]
deps = ["Static"]
git-tree-sha1 = "bc1317f71de8dce26ea67fcdf7eccc0d0693b75b"
uuid = "62783981-4cbd-42fc-bca8-16325de8dc4b"
version = "0.1.1"

[[BoundaryValueDiffEq]]
deps = ["BandedMatrices", "DiffEqBase", "FiniteDiff", "ForwardDiff", "LinearAlgebra", "NLsolve", "Reexport", "SparseArrays"]
git-tree-sha1 = "fe34902ac0c3a35d016617ab7032742865756d7d"
uuid = "764a87c0-6b3e-53db-9096-fe964310641d"
version = "2.7.1"

[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
version = "1.0.8+0"

[[CEnum]]
git-tree-sha1 = "215a9aa4a1f23fbd05b92769fdd62559488d70e9"
uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
version = "0.4.1"

[[CPUSummary]]
deps = ["Hwloc", "IfElse", "Static"]
git-tree-sha1 = "87b0c9c6ee0124d6c1f4ce8cb035dcaf9f90b803"
uuid = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
version = "0.1.6"

[[CSTParser]]
deps = ["Tokenize"]
git-tree-sha1 = "f9a6389348207faf5e5c62cbc7e89d19688d338a"
uuid = "00ebfdb7-1f24-5e51-bd34-a7502290713f"
version = "3.3.0"

[[Cairo_jll]]
deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
git-tree-sha1 = "f2202b55d816427cd385a9a4f3ffb226bee80f99"
uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a"
version = "1.16.1+0"

[[Catalyst]]
deps = ["AbstractAlgebra", "DataStructures", "DiffEqBase", "DiffEqJump", "DocStringExtensions", "Latexify", "LightGraphs", "MacroTools", "ModelingToolkit", "Parameters", "Reexport", "Requires", "SparseArrays", "Symbolics"]
git-tree-sha1 = "2dde79e761c00f0c5b4c60bb21b42a4f316467a2"
uuid = "479239e8-5488-4da2-87a7-35f2df7eef83"
version = "10.0.0"

[[ChainRulesCore]]
deps = ["Compat", "LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "f885e7e7c124f8c92650d61b9477b9ac2ee607dd"
uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
version = "1.11.1"

[[ChangesOfVariables]]
deps = ["LinearAlgebra", "Test"]
git-tree-sha1 = "9a1d594397670492219635b35a3d830b04730d62"
uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0"
version = "0.1.1"

[[CloseOpenIntervals]]
deps = ["ArrayInterface", "Static"]
git-tree-sha1 = "7b8f09d58294dc8aa13d91a8544b37c8a1dcbc06"
uuid = "fb6a15b2-703c-40df-9091-08a04967cfa9"
version = "0.1.4"

[[ColorSchemes]]
deps = ["ColorTypes", "Colors", "FixedPointNumbers", "Random"]
git-tree-sha1 = "a851fec56cb73cfdf43762999ec72eff5b86882a"
uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
version = "3.15.0"

[[ColorTypes]]
deps = ["FixedPointNumbers", "Random"]
git-tree-sha1 = "024fe24d83e4a5bf5fc80501a314ce0d1aa35597"
uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
version = "0.11.0"

[[Colors]]
deps = ["ColorTypes", "FixedPointNumbers", "Reexport"]
git-tree-sha1 = "417b0ed7b8b838aa6ca0a87aadf1bb9eb111ce40"
uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
version = "0.12.8"

[[Combinatorics]]
git-tree-sha1 = "08c8b6831dc00bfea825826be0bc8336fc369860"
uuid = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
version = "1.0.2"

[[CommonMark]]
deps = ["Crayons", "JSON", "URIs"]
git-tree-sha1 = "393ac9df4eb085c2ab12005fc496dae2e1da344e"
uuid = "a80b9123-70ca-4bc0-993e-6e3bcb318db6"
version = "0.8.3"

[[CommonSolve]]
git-tree-sha1 = "68a0743f578349ada8bc911a5cbd5a2ef6ed6d1f"
uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
version = "0.2.0"

[[CommonSubexpressions]]
deps = ["MacroTools", "Test"]
git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7"
uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
version = "0.3.0"

[[Compat]]
deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
git-tree-sha1 = "dce3e3fea680869eaa0b774b2e8343e9ff442313"
uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
version = "3.40.0"

[[CompilerSupportLibraries_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"

[[CompositeTypes]]
git-tree-sha1 = "d5b014b216dc891e81fea299638e4c10c657b582"
uuid = "b152e2b5-7a66-4b01-a709-34e65c35f657"
version = "0.1.2"

[[ConstructionBase]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "f74e9d5388b8620b4cee35d4c5a618dd4dc547f4"
uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
version = "1.3.0"

[[Contour]]
deps = ["StaticArrays"]
git-tree-sha1 = "9f02045d934dc030edad45944ea80dbd1f0ebea7"
uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
version = "0.5.7"

[[Crayons]]
git-tree-sha1 = "3f71217b538d7aaee0b69ab47d9b7724ca8afa0d"
uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
version = "4.0.4"

[[DEDataArrays]]
deps = ["ArrayInterface", "DocStringExtensions", "LinearAlgebra", "RecursiveArrayTools", "SciMLBase", "StaticArrays"]
git-tree-sha1 = "31186e61936fbbccb41d809ad4338c9f7addf7ae"
uuid = "754358af-613d-5f8d-9788-280bf1605d4c"
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deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"

[[ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"

[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+0"

[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

# ╔═╡ Cell order:
# ╠═1a15712e-4010-11ec-02fb-f548751f3a1a
# ╠═2e0898da-4a53-4f3a-9490-1752044d804c
# ╠═0498dda1-1fb7-4283-8d84-0cc4c0394557
# ╠═c59e2aa6-d5cc-40a6-9826-3e12f298f300
# ╠═9c26ff48-9750-45e3-94c5-eac019b0f01f
# ╠═9114e631-a740-4154-8743-55510620932a
# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002


text:
# define our own method to avoid type piracy with Base.showable
_showable(a::AbstractVector{<:MIME}, x) = any(m -> showable(m, x), a)
_showable(m, x) = showable(m, x)

"""
A vector of MIME types (or vectors of MIME types) that IJulia will try to
render. IJulia will try to render every MIME type specified in the first level
of the vector. If a vector of MIME types is specified, IJulia will include only
the first MIME type that is renderable (this allows for the expression of
priority and exclusion of redundant data).

For example, since "text/plain" is specified as a first-child of the array,
IJulia will always try to include a "text/plain" representation of anything that
is displayed. Since markdown and html are specified within a sub-vector, IJulia
will always try to render "text/markdown", and will only try to render
"text/html" if markdown isn't possible.
"""
const ijulia_mime_types = Vector{Union{MIME, AbstractVector{MIME}}}([
    MIME("text/plain"),
    MIME("image/svg+xml"),
    [MIME("image/png"),MIME("image/jpeg")],
    [
        MIME("text/markdown"),
        MIME("text/html"),
    ],
    MIME("text/latex"),
])

"""
MIME types that when rendered (via stringmime) return JSON data. See
`ijulia_mime_types` for a description of how MIME types are selected.

This is necessary to embed the JSON as is in the displaydata bundle (rather than
as stringify'd JSON).
"""
const ijulia_jsonmime_types = Vector{Union{MIME, Vector{MIME}}}([
    [[MIME("application/vnd.vegalite.v$n+json") for n in 4:-1:2]...,
    [MIME("application/vnd.vega.v$n+json") for n in 5:-1:3]...],
    MIME("application/vnd.dataresource+json"), MIME("application/vnd.plotly.v1+json")
])

register_mime(x::Union{MIME, Vector{MIME}})= push!(ijulia_mime_types, x)
register_mime(x::AbstractVector{<:MIME}) = push!(ijulia_mime_types, Vector{Mime}(x))
register_jsonmime(x::Union{MIME, Vector{MIME}}) = push!(ijulia_jsonmime_types, x)
register_jsonmime(x::AbstractVector{<:MIME}) = push!(ijulia_jsonmime_types, Vector{Mime}(x))

"""
Generate the preferred MIME representation of x.

Returns a tuple with the selected MIME type and the representation of the data
using that MIME type.
"""
function display_mimestring(mime_array::Vector{MIME}, x)
    for m in mime_array
        if _showable(m, x)
            return display_mimestring(m, x)
        end
    end
    error("No displayable MIME types in mime array.")
end

display_mimestring(m::MIME, x) = (m, limitstringmime(m, x))

# text/plain output must have valid Unicode data to display in Jupyter
function display_mimestring(m::MIME"text/plain", x)
    s = limitstringmime(m, x)
    return m, (isvalid(s) ? s : "(binary data)")
end

"""
Generate the preferred json-MIME representation of x.

Returns a tuple with the selected MIME type and the representation of the data
using that MIME type (as a `JSONText`).
"""
function display_mimejson(mime_array::Vector{MIME}, x)
    for m in mime_array
        if _showable(m, x)
            return display_mimejson(m, x)
        end
    end
    error("No displayable MIME types in mime array.")
end

display_mimejson(m::MIME, x) = (m, JSON.JSONText(limitstringmime(m, x)))

"""
Generate a dictionary of `mime_type => data` pairs for all registered MIME
types. This is the format that Jupyter expects in display_data and
execute_result messages.
"""
function display_dict(x)
    data = Dict{String, Union{String, JSONText}}()
    for m in ijulia_mime_types
        try
            if _showable(m, x)
                mime, mime_repr = display_mimestring(m, x)
                data[string(mime)] = mime_repr
            end
        catch
            if m == MIME("text/plain")
                rethrow() # text/plain is required
            end
        end
    end

    for m in ijulia_jsonmime_types
        try
            if _showable(m, x)
                mime, mime_repr = display_mimejson(m, x)
                data[string(mime)] = mime_repr
            end
        catch
        end
    end

    return data

end

ext: jl


text:
using Gen
using PyCall
using GenRedner: get_depth_renderer

plt = pyimport("matplotlib.pyplot")

const depth_renderer = get_depth_renderer(num_samples=2, print_timing=true)

function construct_simple_scene()

    vertices = vcat(

        # first foreground triangle (closest to camera)
        [-0.25, -0.25, 1.0]', # 1
        [0.25, -0.25, 1.0]', # 2
        [0.0, 0.25, 1.0]', # 3

        # second foreground triangle
        [-1, -1, 1.5]', # 4
        [1, -1, 1.5]', # 5
        [0.0, 1, 1.5]', # 6

        # two triangles forming a background
        [-2, -2, 2.0]', # 7
        [-2, 2, 2.0]', # 8
        [2, 2, 2.0]', # 9
        [2, -2, 2.0]' # 10
    )

    indices = vcat(
        [1, 2, 3]', # first triangle
        [4, 5, 6]', # second triangle
        [7, 8, 9]', # background triangle 1
        [9, 10, 7]' # background triangle 2
    )

    return (vertices, indices)
end

function construct_corrupted_scene()
    (vertices, indices) = construct_simple_scene()
    corrupted_vertices = copy(vertices)

    # offset one vertex of the front triangle by 0.1 units
    corrupted_vertices[1,:] += [-0.1, 0.0, 0.0]
    return corrupted_vertices, indices
end

# get scene and corrupted scene
vertices, indices = construct_simple_scene()
corrupted_vertices, corrupted_indices = construct_corrupted_scene()

# focal lengths
fx = 48.0
fy = 48.0

# principal point
cx = 32.0
cy = 24.0

# image resolution
width = 64
height = 48

intrinsics = [fx, fy, cx, cy]
dims = [width, height]

function forward_example()

    # run forward three times (to show variability) and plot using matplotlib
    plt.figure(figsize=(18, 27))

    for replicate in [1, 2, 3]

        trace = simulate(depth_renderer, (vertices, indices, intrinsics, dims))
        points = get_retval(trace)

        plt.subplot(3, 2, (replicate-1) * 2 + 1)
        plt.imshow(points[:,:,3], cmap="Greys", vmin=0, vmax=2.0, origin="lower")
        plt.title("depth image")

        plt.subplot(3, 2, (replicate-1) * 2 + 2, projection="3d")
        ax = plt.gca()
        ax.scatter(points[:,:,1], points[:,:,2], points[:,:,3], s=2)
        ax.scatter([0.0], [0.0], [0.0], color="red", s=10, marker="o")
        ax.quiver([0.0], [0.0], [0.0], [1.0], [0.0], [0.0], length=0.5, color="red")
        ax.quiver([0.0], [0.0], [0.0], [0.0], [1.0], [0.0], length=0.5, color="green")
        ax.quiver([0.0], [0.0], [0.0], [0.0], [0.0], [1.0], length=0.5, color="blue")
        ax.set_xlim([-2, 2])
        ax.set_ylim([-2, 2])
        ax.set_zlim([-0, 2])
        plt.title("point cloud")

    end

    plt.tight_layout()
    plt.savefig("forward.png")

end

forward_example()

struct NoiseModel <: Gen.Distribution{Matrix{Float64}} end
const noise_model = NoiseModel()

function Gen.logpdf(::NoiseModel,
        x::Matrix{Float64},
        mu::Matrix{Float64})
    diffs = (x .- mu)
    return -sum(diffs .^ 2)
end

Gen.has_argument_grads(::NoiseModel) = (true,)
Gen.has_output_grad(::NoiseModel) = false

function Gen.logpdf_grad(::NoiseModel,
        x::Matrix{Float64},
        mu::Matrix{Float64})
    diffs = x .- mu
    mu_grad = 2 * diffs
    return (nothing, mu_grad)
end

@gen function model()

    # prior on two foreground triangles
    foreground_vertices_list = []
    for i in 1:6
        v = ({:vertices => i} ~ mvnormal([0.0, 0.0, 1.0], [1000.0 0.0 0.0; 0.0 1000.0 0.0; 0.0 0.0 1000.0]))
        push!(foreground_vertices_list, v)
    end
    foreground_vertices_mat = copy(transpose(hcat(foreground_vertices_list...)))
    foreground_indices = vcat(
        [1, 2, 3]', # first triangle
        [4, 5, 6]', # second triangle
    )

    # fixed background
    background_vertices = vcat(
        # two triangles forming a background
        [-2, -2, 2.0]', # 7
        [-2, 2, 2.0]', # 8
        [2, 2, 2.0]', # 9
        [2, -2, 2.0]' # 10
    )
    background_indices = vcat(
        [7, 8, 9]', # background triangle 1
        [9, 10, 7]' # background triangle 2
    )

    points ~ depth_renderer(
        vcat(foreground_vertices_mat, background_vertices),
        vcat(foreground_indices, background_indices),
        intrinsics, dims)

    depths = points[:,:,3]
    obs ~ noise_model(depths)
end

function visualize_trace(fname, trace)
    plt.clf()
    points = trace[:points]
    plt.figure(figsize=(27, 8))

    plt.subplot(1, 5, 1)
    plt.title("observed depth image")
    plt.imshow(trace[:obs], cmap="Greys", vmin=0, vmax=2.0, origin="lower")

    plt.subplot(1, 5, 2)
    plt.title("reconstructed depth image")
    plt.imshow(points[:,:,3], cmap="Greys", vmin=0.0, vmax=2.0, origin="lower")

    plt.subplot(1, 5, 3)
    plt.title("observed .- latent diff")
    plt.imshow(trace[:obs] .- points[:,:,3], cmap="RdBu", vmin=-1.0, vmax=1.0, origin="lower")

    plt.subplot(1, 5, 4)
    plt.title("gradient with respect to depth")
    plt.imshow(logpdf_grad(noise_model, trace[:obs], points[:,:,3])[2], cmap="RdBu", vmin=-1.0, vmax=1.0, origin="lower")

    plt.subplot(1, 5, 5, projection="3d")
    plt.title("latent point cloud")
    ax = plt.gca()
    ax.scatter(points[:,:,1], points[:,:,2], points[:,:,3], s=2)
    ax.scatter([0.0], [0.0], [0.0], color="red", s=10, marker="o")
    ax.quiver([0.0], [0.0], [0.0], [1.0], [0.0], [0.0], length=0.5, color="red")
    ax.quiver([0.0], [0.0], [0.0], [0.0], [1.0], [0.0], length=0.5, color="green")
    ax.quiver([0.0], [0.0], [0.0], [0.0], [0.0], [1.0], length=0.5, color="blue")
    ax.set_xlim([-2, 2])
    ax.set_ylim([-2, 2])
    ax.set_zlim([0, 2])

    for t in 1:2
        x1, y1, z1 = trace[:vertices => indices[t,1]]
        x2, y2, z2 = trace[:vertices => indices[t,2]]
        x3, y3, z3 = trace[:vertices => indices[t,3]]
        ax.plot([x1, x2], [y1, y2], [z1, z2], color="black", linewidth=2)
        ax.plot([x3, x2], [y3, y2], [z3, z2], color="black")
        ax.plot([x1, x3], [y1, y3], [z1, z3], color="black")
    end

    (_, _, choice_grads) = choice_gradients(trace, select(:vertices))
    for v in 1:6
        x, y, z = trace[:vertices => v]
        grad = choice_grads[:vertices => v]
        grad = grad / 10
        xstop, ystop, zstop = [x, y, z] .+ grad
        ax.plot([x, xstop], [y, ystop], [z, zstop], color="purple")
    end

    plt.tight_layout()
    plt.savefig(fname)
end

using Printf: @sprintf
using Random: seed!

function do_inference()
    seed!(1) # note: this does not affect Redner's random seed

    num_latent_vertices = 6

    observed = depth_renderer(vertices, indices, intrinsics, dims)[:,:,3]
    constraints = choicemap()
    constraints[:obs] = observed

    # initialize to foreground vertices that are slightly wrong
    for i in 1:6
        constraints[:vertices => i] = corrupted_vertices[i,:]
    end
    trace, = generate(model, (), constraints)
    frame = 0

    fname = @sprintf("iter_%03d.png", frame); frame += 1
    visualize_trace(fname, trace)
    iters = 500
    for iter in 1:iters
        println("iter: $iter")

        # stochastic gradient ascent for MAP estimation
        (_, _, grads) = choice_gradients(trace, select((:vertices => v for v in 1:num_latent_vertices)...))
        constraints = choicemap()
        for v in 1:num_latent_vertices
            constraints[:vertices => v] = trace[:vertices => v] .+ (0.000001) * grads[:vertices => v]
        end

        trace, _ = update(trace, (), (), constraints)
        if iter % 10 == 0
            fname = @sprintf("iter_%03d.png", frame); frame += 1
            visualize_trace(fname, trace)
        end
        plt.clf()

       println("iter; $iter of $iters")#,  acc3: $acc3")

    end
    fname = @sprintf("iter_%03d.png", frame); frame += 1
    visualize_trace(fname, trace)
end

do_inference()


text:
using Profile

using FFTW

using Distributed
using SharedArrays

@everywhere import DelimitedFiles

@everywhere import Random
@everywhere import LinearAlgebra
@everywhere import PyPlot
@everywhere import Printf

@everywhere import Calculus
@everywhere import ForwardDiff

@everywhere include("../src/misc/declarations.jl")
@everywhere include("../src/minimax/Chebyshev.jl")
#@everywhere include("../src/minimax/APF.jl")
@everywhere include("../src/minimax/interpolators.jl")
@everywhere include("../src/minimax/eval.jl")
@everywhere include("../src/minimax/filter.jl")

@everywhere include("../src/misc/utilities.jl")
@everywhere include("../src/misc/template_signals.jl")
@everywhere include("../src/misc/bounds.jl")
@everywhere include("../src/minimax/exchange.jl")
@everywhere include("../src/minimax/exchange_strategy.jl")
@everywhere include("../src/misc/IO.jl")

@everywhere include("../src/minimax/engine.jl")
@everywhere include("../src/minimax/extrema_search.jl")

@everywhere include("../src/FIR/design.jl")
@everywhere include("../src/FIR/type_conversion.jl")

@everywhere include("../src/misc/frontend.jl")
@everywhere include("../test/visualize.jl")
@everywhere include("../test/filter_helpers.jl")

ig_num = 1
PyPlot.close("all")

Random.seed!(25)

fig_num = 1

#L = 800
L = 25

# bandpass.
# rs = 1.05664
# rp = 1.45664
# fp = 1.68496
# fs = 2.08496
# f = xx->cosinetransitionbandpassfunction(xx, BigFloat(rs),
#                                             BigFloat(rp),
#                                             BigFloat(fp),
#                                             BigFloat(fs))

# rs = 0.428319
# rp = 0.828319
# fp = 1.05664
# fs = 1.45664
# f = xx->cosinetransitionbandpassfunction(xx, BigFloat(rs), BigFloat(rp),
#                                              BigFloat(fp), BigFloat(fs))

# lowpass.
fp = 0.428319
fs = 0.828319
f = xx->cosinetransitionlowpassfunction(xx, BigFloat(fp), BigFloat(fs))

filter_type_num = 1 # symmetric, odd length.
max_iters = 50
verbose_flag = true
debug_flag = false # plot reference update at every iteration.

savefig_flag = false
plot_output_flag = true # plot mag rsp.

profile_flag = false

#opt_params_file_name = "0" # no plot between iterations.
#opt_params_file_name = "/home/roy/Documents/repos/FIRRemez/test/config_opt.txt" # plot between iterations.

h_BigFloat = Vector{BigFloat}(undef,0)
h = Vector{Float64}(undef,0)
X = Vector{BigFloat}(undef,0)

if profile_flag
    io = open("./outputs/profile_output.txt", "w")

    Profile.clear()
    Profile.init(n=15_000)
    #Profile.init()

    @profile frontendfilterdesign(filter_type_num, f,
                                L, fig_num, savefig_flag, plot_output_flag,
                                max_iters, verbose_flag, debug_flag)
    Profile.print(io);

    # using ProfileView
    # ProfileView.view()

else

    config_opt = getdefaultoptparameters(L)
    config_opt.max_iters = max_iters
    config_opt.verbose_flag = verbose_flag
    config_opt.plot_flag = debug_flag

    h_BigFloat, X, fig_num = frontendfilterdesignfast(filter_type_num, f,
                                L, fig_num, false, false,
                                config_opt)
    h = convert(Vector{Float64}, h_BigFloat)

    Printf.@printf("Order of Chebyshev is %d, length of filter is %d, FIR type %d\n", L, length(h), filter_type_num)
end

max_stars_repo_name: RoyCCWang/FIRRemez

max_issues_repo_name: RoyCCWang/FIRRemez

max_forks_repo_name: RoyCCWang/FIRRemez


text:
using OpenCL, CLBLAS, CLBlast, BenchmarkTools
@static if VERSION < v"0.7-"
    LA = Base.LinAlg
else
    using LinearAlgebra, Random, Printf
    LA = LinearAlgebra
end

CLBLAS.setup()

function run_axpy(n::Integer, T=Float32)
    srand(12345)
    y = rand(T, n)
    x = rand(T, n)
    α = rand(T)

    @printf("\nn = %d, eltype = %s\n", n, T)

    println("BLAS:")
    y_true = copy(y)
    LinAlg.BLAS.axpy!(α, x, y_true)
    yy = copy(y)
    display(@benchmark LinAlg.BLAS.axpy!($α, $x, $yy))
    println()

    for device in cl.devices()
        println("-"^70)
        @printf("Platform name   : %s\n", device[:platform][:name])
        @printf("Platform version: %s\n", device[:platform][:version])
        @printf("Device name     : %s\n", device[:name])
        @printf("Device type     : %s\n", device[:device_type])
        println()

        ctx = cl.Context(device)
        queue = cl.CmdQueue(ctx)

        println("CLBLAS:")
        y_cl = cl.CLArray(queue, y)
        x_cl = cl.CLArray(queue, x)
        CLBLAS.axpy!(α, x_cl, y_cl)
        @assert cl.to_host(y_cl) ≈ y_true
        display(@benchmark CLBLAS.axpy!($α, $x_cl, $y_cl))
        println()

        println("CLBlast:")
        y_cl = cl.CLArray(queue, y)
        x_cl = cl.CLArray(queue, x)
        CLBlast.axpy!(α, x_cl, y_cl)
        @assert cl.to_host(y_cl) ≈ y_true
        display(@benchmark CLBlast.axpy!($α, $x_cl, $y_cl))
        println()
    end
end

run_axpy(2^10, Float32)


text:
# This file is a part of LegendDataTypes.jl, licensed under the MIT License (MIT).

"""
    read_from_properties(read_property::Function, src::Any, ::Type{T}) where {T<:AbstractArrayCodec}

Create a array codec of type `T` from properties of `src`, using the
`src`-specific function `read_property(src, name::Symbol, default_value)` to
read each property required.

Returns an value of type `T`.
"""
function read_from_properties end

"""
    write_to_properties!(write_property!::Function, dest::Any, codec::AbstractArrayCodec)

Write all information required to re-create `codec` to `dest` using thes
`dest`-specific function `write_property!(dest, name::Symbol, x)`.

Typically returns `nothing`.
"""
function to_properties end

function read_from_properties(read_property::Function, src::Any, ::Type{VarlenDiffArrayCodec})
    VarlenDiffArrayCodec()
end

function write_to_properties!(write_property!::Function, dest::Any, codec::VarlenDiffArrayCodec)
    nothing
end


text:
using LatticePhysics
using Base.Test

# TEST EVERYTHING FROM HERE ON
latticephysics_testset = @testset "All LatticePhysics.jl tests" begin

    # UNITCELL STUFF
    include("unitcell_tests.jl")

    # LATTICE STUFF
    include("lattice_tests.jl")

    # MODIFICATION STUFF
    include("modification_tests.jl")

    # PLAQUETTE STUFF
    include("plaquette_tests.jl")

    # PATH STUFF
    include("path_tests.jl")

    # INTERACTION MATRIX STUFF
    include("interaction_matrix_tests.jl")

    # SVG PLOTTING STUFF
    include("SVG_plotting_tests.jl")

    # BAND STRUCTURE STUFF
    include("bandstructure_tests.jl")

    # FERMI SURFACE STUFF
    include("fermi_surface_tests.jl")

end;