text:
########################
# Initial parameters
########################
# Echo 0-1
rho_Echo01_u,rho_Echo01_v = GLOQ.EchoParityForwardSolve(u0_echo_01,v0_echo_01,
				 2.0*pi.*p_initial[1:3],omr_echo_01,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 0,
				 TC_01,t_echo_01,N_states)
initial_echo_01 = GLOQ.get_population(rho_Echo01_u)

# Echo 1-2
rho_Echo12_u,rho_Echo12_v = GLOQ.EchoParityForwardSolve(u0_echo_12,v0_echo_12,
				 2.0*pi.*p_initial[1:3],omr_echo_12,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 1,
				 TC_12,t_echo_12,N_states)
initial_echo_12 = GLOQ.get_population(rho_Echo12_u)

# Echo 2-3
rho_Echo23_u,rho_Echo23_v = GLOQ.EchoParityForwardSolve(u0_echo_23,v0_echo_23,
				 2.0*pi.*p_initial[1:3],omr_echo_23,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 2,
				 TC_23,t_echo_23,N_states)
initial_echo_23 = GLOQ.get_population(rho_Echo23_u)

# Ramsey 0-1
rho_Ramsey01_u,rho_Ramsey01_v = GLOQ.RamseyParityForwardSolve(u0_ramsey_01,v0_ramsey_01,
				 2.0*pi.*p_optim[1:3],omr_ramsey_01,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 0,
				 TC_01,t_ramsey_01,N_states)
initial_ramsey_01 = GLOQ.get_population(rho_Ramsey01_u)

# Ramsey 1-2
rho_Ramsey12_u,rho_Ramsey12_v = GLOQ.RamseyParityForwardSolve(u0_ramsey_12,v0_ramsey_12,
				 2.0*pi.*p_initial[1:3],omr_ramsey_12,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 1,
				 TC_12,t_ramsey_12,N_states)
initial_ramsey_12 = GLOQ.get_population(rho_Ramsey12_u)

# Ramsey 2-3
rho_Ramsey23_u,rho_Ramsey23_v = GLOQ.RamseyParityForwardSolve(u0_ramsey_23,v0_ramsey_23,
				 2.0*pi.*p_initial[1:3],omr_ramsey_23,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 2,
				 TC_23,t_ramsey_23,N_states)
initial_ramsey_23 = GLOQ.get_population(rho_Ramsey23_u)

# T1 0-1
rho_T101_u,rho_T101_v = GLOQ.T1ParityForwardSolve(u0_t1_01,v0_t1_01,
				 2.0*pi.*p_initial[1:3],omr_t1_01,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 0,
				 TC_01,t_t1_01,N_states)
initial_t1_01 = GLOQ.get_population(rho_T101_u)

# T1 1-2
rho_T112_u,rho_T112_v = GLOQ.T1ParityForwardSolve(u0_t1_12,v0_t1_12,
				 2.0*pi.*p_initial[1:3],omr_t1_12,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 1,
				 TC_12,t_t1_12,N_states)
initial_t1_12 = GLOQ.get_population(rho_T112_u)

# T1 2-3
rho_T123_u,rho_T123_v = GLOQ.T1ParityForwardSolve(u0_t1_23,v0_t1_23,
				 2.0*pi.*p_initial[1:3],omr_t1_23,
				 2.0*pi.*p_initial[4:6],
				 p_initial[7:9],p_initial[10:12],
				 2,
				 TC_23,t_t1_23,N_states)
initial_t1_23 = GLOQ.get_population(rho_T123_u)

# Echo 0-1
initial_e01_error = plot(t_ramsey_01./1000.0,initial_echo_01-data_echo_01,
		 size=(1200,600),legend=:outerright,
		 title = "Intial error of Echo 0-1",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_e01_error)

# Echo 1-2
initial_e12_error = plot(t_echo_12./1000.0,initial_echo_12-data_echo_12,
		 size=(1200,600),legend=:outerright,
		 title = "Intial error of Echo 1-2",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_e12_error)

# Echo 2-3
initial_e23_error = plot(t_echo_23./1000.0,initial_echo_23-data_echo_23,
		 size=(1200,600),legend=:outerright,
		 title = "Intial error of Echo 2-3",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_e23_error)

# Ramsey 0-1
initial_r01_error = plot(t_ramsey_01./1000.0,initial_ramsey_01-data_ramsey_01,
		 size=(1200,600),legend=:outerright,
		 title = "Initial error of Ramsey 0-1",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_r01_error)

# Ramsey 1-2
initial_r12_error = plot(t_ramsey_12./1000.0,initial_ramsey_12-data_ramsey_12,
				   size=(1200,600),legend=:outerright,
		   		   title="Initial error of Ramsey 1-2",
		 	   	   label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_r12_error)

# Ramsey 2-3
initial_r23_error = plot(t_ramsey_23./1000.0,initial_ramsey_23-data_ramsey_23,
				   size=(1200,600),legend=:outerright,
		   		   title="Initial error of Ramsey 2-3",
		 	   	   label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_r23_error)

# T1 0-1
initial_t01_error = plot(t_t1_01./1000.0,initial_t1_01-data_t1_01,
		 size=(1200,600),legend=:outerright,
		 title = "Intial error of T1 0-1",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_t01_error)

# T1 1-2
initial_t12_error = plot(t_t1_12./1000.0,initial_t1_12-data_t1_12,
		 size=(1200,600),legend=:outerright,
		 title = "Intial error of T1 1-2",
		 		 	   	   label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_t12_error)

# T1 2-3
initial_t23_error = plot(t_t1_12./1000.0,initial_t1_23-data_t1_23,
		 size=(1200,600),legend=:outerright,
		 title = "Intial error of T1 2-3",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(initial_t23_error)

########################
# Optimized parameters
########################
# Echo 0-1
rho_Echo01_u,rho_Echo01_v = GLOQ.EchoParityForwardSolve(u0_echo_01,v0_echo_01,
				 2.0*pi.*p_optim[1:3],omr_echo_01,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 0,
				 TC_01,t_echo_01,N_states)
optim_echo_01 = GLOQ.get_population(rho_Echo01_u)

# Echo 1-2
rho_Echo12_u,rho_Echo12_v = GLOQ.EchoParityForwardSolve(u0_echo_12,v0_echo_12,
				 2.0*pi.*p_optim[1:3],omr_echo_12,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 1,
				 TC_12,t_echo_12,N_states)
optim_echo_12 = GLOQ.get_population(rho_Echo12_u)

# Echo 2-3
rho_Echo23_u,rho_Echo23_v = GLOQ.EchoParityForwardSolve(u0_echo_23,v0_echo_23,
				 2.0*pi.*p_optim[1:3],omr_echo_23,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 2,
				 TC_23,t_echo_23,N_states)
optim_echo_23 = GLOQ.get_population(rho_Echo23_u)

# Ramsey 0-1
rho_Ramsey01_u,rho_Ramsey01_v = GLOQ.RamseyParityForwardSolve(u0_ramsey_01,v0_ramsey_01,
				 2.0*pi.*p_optim[1:3],omr_ramsey_01,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 0,
				 TC_01,t_ramsey_01,N_states)
optim_ramsey_01 = GLOQ.get_population(rho_Ramsey01_u)

# Ramsey 1-2
rho_Ramsey12_u,rho_Ramsey12_v = GLOQ.RamseyParityForwardSolve(u0_ramsey_12,v0_ramsey_12,
				 2.0*pi.*p_optim[1:3],omr_ramsey_12,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 1,
				 TC_12,t_ramsey_12,N_states)
optim_ramsey_12 = GLOQ.get_population(rho_Ramsey12_u)

# Ramsey 2-3
rho_Ramsey23_u,rho_Ramsey23_v = GLOQ.RamseyParityForwardSolve(u0_ramsey_23,v0_ramsey_23,
				 2.0*pi.*p_optim[1:3],omr_ramsey_23,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 2,
				 TC_23,t_ramsey_23,N_states)
optim_ramsey_23 = GLOQ.get_population(rho_Ramsey23_u)

# T1 0-1
rho_T101_u,rho_T101_v = GLOQ.T1ParityForwardSolve(u0_t1_01,v0_t1_01,
				 2.0*pi.*p_optim[1:3],omr_t1_01,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 0,
				 TC_01,t_t1_01,N_states)
optim_t1_01 = GLOQ.get_population(rho_T101_u)

# T1 1-2
rho_T112_u,rho_T112_v = GLOQ.T1ParityForwardSolve(u0_t1_12,v0_t1_12,
				 2.0*pi.*p_optim[1:3],omr_t1_12,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 1,
				 TC_12,t_t1_12,N_states)
optim_t1_12 = GLOQ.get_population(rho_T112_u)

# T1 2-3
rho_T123_u,rho_T123_v = GLOQ.T1ParityForwardSolve(u0_t1_23,v0_t1_23,
				 2.0*pi.*p_optim[1:3],omr_t1_23,
				 2.0*pi.*p_optim[4:6],
				 p_optim[7:9],p_optim[10:12],
				 2,
				 TC_23,t_t1_23,N_states)
optim_t1_23 = GLOQ.get_population(rho_T123_u)

# Echo 0-1
fig_e01 = plot(t_echo_01./1000.0,data_echo_01,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="Echo 0-1",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"])
plot!(fig_e01,t_echo_01./1000.0,optim_echo_01,legend=:outerright,
		  label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim"],
          legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_e01)

fig_e01_error = plot(t_ramsey_01./1000.0,optim_echo_01-data_echo_01,
		 size=(1200,600),legend=:outerright,
		 title = "Error of Echo 0-1",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
         legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_e01_error)

# Echo 1-2
fig_e12 = plot(t_echo_12./1000.0,data_echo_12,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="Echo 1-2",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_e12,t_echo_12./1000.0,optim_echo_12,legend=:outerright,label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_e12)

fig_e12_error = plot(t_echo_12./1000.0,optim_echo_12-data_echo_12,
		 size=(1200,600),legend=:outerright,
		 title = "Error of Echo 1-2",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
         legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_e12_error)

# Echo 2-3
fig_e23 = plot(t_echo_23./1000.0,data_echo_23,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="Echo 2-3",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_e23,t_echo_23./1000.0,optim_echo_23,legend=:outerright,label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_e23)

fig_e23_error = plot(t_echo_23./1000.0,optim_echo_23-data_echo_23,
		 size=(1200,600),legend=:outerright,
		 title = "Error of Echo 2-3",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
         legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_e23_error)

# Ramsey 0-1
fig_r01 = plot(t_ramsey_01./1000.0,data_ramsey_01,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="Ramsey 0-1",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_r01,t_ramsey_01./1000.0,optim_ramsey_01,legend=:outerright,
		  label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_r01)

fig_r01_error = plot(t_ramsey_01./1000.0,optim_ramsey_01-data_ramsey_01,
		 size=(1200,600),legend=:outerright,
		 title = "Error of Ramsey 0-1",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
         legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_r01_error)

# Ramsey 1-2
fig_r12 = plot(t_ramsey_12./1000.0,data_ramsey_12,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="Ramsey 1-2",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_r12,t_ramsey_12./1000.0,optim_ramsey_12,legend=:outerright,
		  label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_r12)

fig_r12_error = plot(t_ramsey_12./1000.0,optim_ramsey_12-data_ramsey_12,
				   size=(1200,600),legend=:outerright,
		   		   title="Error of Ramsey 1-2",
		 	   	   label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
                   legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_r12_error)

# Ramsey 2-3
fig_r23 = plot(t_ramsey_23./1000.0,data_ramsey_23,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="Ramsey 2-3",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_r23,t_ramsey_23./1000.0,optim_ramsey_23,legend=:outerright,
		  label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_r23)

fig_r23_error = plot(t_ramsey_23./1000.0,optim_ramsey_23-data_ramsey_23,
				   size=(1200,600),legend=:outerright,
		   		   title="Error of Ramsey 2-3",
		 	   	   label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
                   legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_r23_error)

# T1 0-1
fig_t01 = plot(t_t1_01./1000.0,data_t1_01,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="T1 0-1",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_t01,t_t1_01./1000.0,optim_t1_01,legend=:outerright,
		  label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_t01)

fig_t01_error = plot(t_t1_01./1000.0,optim_t1_01-data_t1_01,
		 size=(1200,600),legend=:outerright,
		 title = "Error of T1 0-1",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"])
display(fig_t01_error)

# T1 1-2
fig_t12 = plot(t_t1_12./1000.0,data_t1_12,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="T1 1-2",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_t12,t_t1_12./1000.0,optim_t1_12,legend=:outerright,
		  label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_t12)

fig_t12_error = plot(t_t1_12./1000.0,optim_t1_12-data_t1_12,
		 size=(1200,600),legend=:outerright,
		 title = "Error of T1 1-2",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
         legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_t12_error)

# T1 2-3
fig_t23 = plot(t_t1_23./1000.0,data_t1_23,line=(:dash),
		   size=(1200,600),legend=:outerright,
		   title="T1 2-3",
		   label=[L"$\rho_{00}$, data" L"$\rho_{11}$, data" L"$\rho_{22}$, data" L"$\rho_{33}$, data"],
           legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
plot!(fig_t23,t_t1_23./1000.0,optim_t1_23,legend=:outerright,label=[L"$\rho_{00}$, sim" L"$\rho_{11}$, sim" L"$\rho_{22}$, sim" L"$\rho_{33}$, sim"])
display(fig_t23)

fig_t23_error = plot(t_t1_23./1000.0,optim_t1_23-data_t1_23,
		 size=(1200,600),legend=:outerright,
		 title = "Error of T1 2-3",
		 label=[L"$\rho_{00}$" L"$\rho_{11}$" L"$\rho_{22}$" L"$\rho_{33}$"],
         legendfontsize=15,xtickfontsize=15,ytickfontsize=15,titlefontsize=18)
display(fig_t23_error)


text:
using Base.Test
import InfiniteVector

@testset "RangeInfiniteVector tests" begin
    for rep = 1:10
        a, b = sort(rand(-100:100, 2))
        vals = rand(length(a:b))
        def = rand()
        v = InfiniteVector.RangeInfiniteVector(a:b, def, vals)

        for i = a:b
            @test v[i] == vals[i - a + 1]
        end

        for r = 1 : 10
            i = rand(Int)
            if i < a || i > b
                @test v[i] == def
            else
                @test v[i] == vals[i - a + 1]
            end
        end

        i = rand(a:b)
        newv = rand()
        v[i] = newv
        @test v[i] == newv

        @test_throws BoundsError v[a - 1] = 1.
        @test_throws BoundsError v[b + 10] = 1.
    end
end

ext: jl


text:
#=
These functions are used to display a ROOTFile is a tree-like fashion
by using `AbstractTrees` printing functions. We customize what the children
of ROOTFile and a TTree is, and how to print the final `node`.
=#
function children(f::ROOTFile)
    ch = Vector{TTree}()
    for k in keys(f)
        lock(f.fobj)
        try
            push!(ch, f[k])
        catch
        finally
            unlock(f.fobj)
        end
    end
    ch
end
function children(t::TTree)
    ks = keys(t)
    if length(ks) < 2
        return ks
    elseif length(ks) > 7
        return vcat(first(ks, 3), "⋮", ks[end-2:end])
    else
        return ks
    end
end
printnode(io::IO, t::TTree) = print(io, t.fName)
printnode(io::IO, f::ROOTFile) = print(io, f.filename)

function Base.show(io::IO, tree::LazyTree)
    _hs = _make_header(tree)
    _ds = displaysize(io)
    PrettyTables.pretty_table(
        io,
        tree;
        header=_hs,
        alignment=:l,
        vlines=[1],
        hlines=[:header],
        crop_num_lines_at_beginning=2,
        row_number_alignment=:l,
        row_number_column_title="Row",
        show_row_number=true,
        compact_printing=false,
        formatters=(v, i, j) -> _treeformat(v, _ds[2] ÷ min(5, length(_hs[1]))),
        display_size=(min(_ds[1], 40), min(_ds[2], 160)),
    )
end
_symtup2str(symtup, trunc=15) = collect(first.(string.(symtup), trunc))
function _make_header(t)
    pn = propertynames(t)
    header = _symtup2str(pn)
    subheader = _symtup2str(Tables.columntype.(Ref(t), pn))
    (header, subheader)
end
function _treeformat(val, trunc)
    s = if val isa Vector{T} where T<:Integer
        string(Int.(val))
    elseif val isa Vector{T} where T<:AbstractFloat
        string(round.(Float64.(val); sigdigits=3))
    else
        string(val)
    end
    first(s, trunc)
end


text:
## Parameter containers

export OptParams, SimpleParams

abstract OptParams

## Default parameter container

type SimpleParams<:OptParams
	stepsizerule::StepSizeRule
	maxiters::Int
end
SimpleParams() = SimpleParams(ConstantStepSize(), 10)

## Default stopping rule (stop if decrease is too small)
stop(params::OptParams, objval, oldobjval, args...; kwargs...) =
	stop(params.stepsizerule) && (oldobjval - objval)/objval < 1e-6 ? true : false

max_stars_repo_name: madeleineudell/FirstOrderOptimization


text:
using DataFrames
using CSV
using FileIO
using Statistics

include("experiments/experimentalutils.jl")

resultsFolder = mainfolder * "experiments/twostagesvae_scoretest_mmdpval/"
files = readdir(resultsFolder)

results = []
for f in files
    if isfile(resultsFolder * f) && occursin(".csv", f)
        push!(results, DataFrame(CSV.read(resultsFolder * f)[1, :]))
    end
end
results = vcat(results...)

using Plots
using StatsPlots
plotlyjs()

# p1 = plot()
# for (i, n) in enumerate(names(results)[7:13])
#     scatter!(p1, ones(4) .* i, results[n], ylim = (0,1), label = String(n))
# end

# p2 = plot()
# for i in 1:4
#     scatter!(p2, 1:7, [results[i, c] for c in 7:13], ylim = (0,1))
# end

# for n in names(results)[7:end]
#     @df results boxplot(:dataset, n, size = [700, 700])
# end
pp = []
for n in names(results)[7:13]
    i = 1
    p = plot(title = String(n))
    for d in unique(results[:dataset])
        data = vec(results[results[:dataset] .== d, n])
        scatter!(p, ones(size(data)) .* i, data, ylim = (0,1), label = d)
        # scatter!(p, ones(size(data)) .* i, data, zcolor = vec(results[results[:dataset] .== d, 14]), ylim = (0,1), label = d)
        i += 1
    end
    push!(pp, p)
end
push!(pp, plot())
plot(pp..., layout = (2,4), size = (1200, 700))

# aggres = []
# for d in unique(results[:dataset])
#     ddf = results[results[:dataset] .== d, :]
#     mean_auc_pxv = mean(ddf[:auc_pxv])
#     mean_auc_pz = mean(ddf[:auc_pz])
#     push!(aggres, DataFrame(dataset = d, auc_pxv = mean_auc_pxv, auc_pz = mean_auc_pz))
# end
# aggres = vcat(aggres...)


text:
# ---
# title: 684. Redundant Connection
# id: problem684
# author: Indigo
# date: 2021-01-13
# difficulty: Medium
# categories: Tree, Union Find, Graph
# link: <https://leetcode.com/problems/redundant-connection/description/>
# hidden: true
# ---
#
# In this problem, a tree is an **undirected** graph that is connected and has
# no cycles.
#
# The given input is a graph that started as a tree with N nodes (with distinct
# values 1, 2, ..., N), with one additional edge added. The added edge has two
# different vertices chosen from 1 to N, and was not an edge that already
# existed.
#
# The resulting graph is given as a 2D-array of `edges`. Each element of `edges`
# is a pair `[u, v]` with `u < v`, that represents an **undirected** edge
# connecting nodes `u` and `v`.
#
# Return an edge that can be removed so that the resulting graph is a tree of N
# nodes. If there are multiple answers, return the answer that occurs last in
# the given 2D-array. The answer edge `[u, v]` should be in the same format,
# with `u < v`.
#
# **Example 1:**
#
#
#
#     Input: [[1,2], [1,3], [2,3]]
#     Output: [2,3]
#     Explanation: The given undirected graph will be like this:
#       1
#      / \
#     2 - 3
#
#
# **Example 2:**
#
#
#
#     Input: [[1,2], [2,3], [3,4], [1,4], [1,5]]
#     Output: [1,4]
#     Explanation: The given undirected graph will be like this:
#     5 - 1 - 2
#         |   |
#         4 - 3
#
#
# **Note:**
#
# * The size of the input 2D-array will be between 3 and 1000.
# * Every integer represented in the 2D-array will be between 1 and N, where N is the size of the input array.
#
#
#
# **Update (2017-09-26):**
# We have overhauled the problem description + test cases and specified clearly
# the graph is an **_undirected_** graph. For the **_directed_** graph follow up
# please see **[Redundant Connection
# II](https://leetcode.comhttps://leetcode.com/problems/redundant-connection-ii/description/)** ).
# We apologize for any inconvenience caused.
#
#
## @lc code=start
using LeetCode

function UFS()
    function findRoot(u::Int, farther::Vector{Int})
        return (u == farther[u]) ? (u) : (farther[u] = findRoot(farther[u], farther))
    end

    function merge(u::Int, v::Int, farther::Vector{Int})
        u = findRoot(u, farther)
        v = findRoot(v, farther)
        (u == v) || (farther[u] = v)
        nothing
    end

    function issameRoot(u::Int, v::Int, farther::Vector{Int})
        return findRoot(u, farther) == findRoot(v, farther)
    end

    findRoot, merge, issameRoot
end

function findRedundantConnection(edges::Vector{Vector{Int}})::Vector{Int}
    farther = collect(1:1000)
    findRoot, merge, issameRoot = UFS()
    for edge in edges
        if issameRoot(edge..., farther)
            return edge
        end
        merge(edge..., farther)
    end
end
## @lc code=end


text:
#import Base.eltype, Base.size

abstract type AbstractDomain{N} end
abstract type Domain{N}  <: AbstractDomain{N} end
abstract type UnstructuredDomain{N} <: Domain{N} end
abstract type StructuredDomain{N, H} <: Domain{N} where H end

dims(::AbstractDomain{N}) where {N} = N
################################################################################
################################################################################
#RGDomain

struct RGDomain{N, H, L} <: StructuredDomain{N, H}
    _shape::NTuple{N, UInt}
    _lengths::NTuple{N, T} where T
    RGDomain(shape...;
             T = Float64,
             harmonic = false,
             lengths = harmonic ? T.(shape) : ones(T, length(shape))) = begin
        lengths = tuple(T.(lengths)...)
        new{length(shape), harmonic, lengths}(shape, lengths)
    end
end

shape(domain::RGDomain) = size(domain)
#size(domain::RGDomain) = domain._shape
size(domain::RGDomain) = Int.(domain._shape)
length(domain::RGDomain) = prod(size(domain))
lengths(::RGDomain{N, H, L}) where {N, H, L} = L
isharmonic(::RGDomain{N, H, L}) where {N, H, L} = H
distances(domain::RGDomain) = lengths(domain) ./ size(domain)

function getcodomain(domain::RGDomain)
    return RGDomain(size(domain)...; lengths = size(domain) ./ lengths(domain), harmonic = !isharmonic(domain))
end

################################################################################
################################################################################
#Power Domain

struct PowerDomain{N, Dom} <: StructuredDomain{N, true} where Dom <: RGDomain{_N, true, _L} where {_N, _L}
    _codomain::StructuredDomain{N, true}
    _pindices::Array{UInt32, N}
    _kvec::Array{T, 1} where T <: Real
    PowerDomain(domain::RGDomain{N, true, L}, T = Float64) where {N, L} = begin
        kvec, karr = get_k_vals(size(domain), distances(domain), T)
        pindices = get_pindices(size(domain), kvec, karr)
        new{N, typeof(domain)}(domain, pindices, kvec)
    end
end

function PowerDomain(shape::Vararg{Integer}; T = Float64, lengths = T.(shape))
    @assert length(shape) == length(lengths)
    distances = lengths ./ shape
    domain = RGDomain(shape...; T = T, lengths = lengths, harmonic = true)
    return PowerDomain(domain, T)
end

#Get all unique k-values and an array with the corresponding k-values
function get_k_vals(shape, distances, T)
    C = CartesianIndices(div.(shape, 2) .+1)
    k_vec = Array{T}(undef, length(C))
    k_arr = similar(C, T)
    for (i, I) in enumerate(C)
        k = sqrt(sum(((Tuple(I) .- 1) .*distances).^2))
        k_vec[i] = k
        k_arr[I] = k
    end
    unique!(k_vec)
    sort!(k_vec)
    return k_vec, k_arr
end

#Get an array of the same size as the domain with each value being the index of the corresponding value in k-vector
function get_pindices(shape, kvec, karr)
    C = CartesianIndices(shape)
    pindex = similar(C, UInt32)
    p_part = indexin(karr, kvec)
    small_s = Tuple(shape)./2 .+ 1
    for I in C
        pindex[I] = p_part[CartesianIndex(Int.(small_s .- abs.(Tuple(I).-small_s)))]
    end
    return pindex
end

size(dom::PowerDomain) = size(dom._kvec)
dims(::PowerDomain) = 1
codomain(dom::PowerDomain) = dom._codomain

################################################################################
################################################################################
#DomainTuple

struct DomainTuple{N} <: AbstractDomain{N}
    domains::Tuple{Vararg{<:Domain}}
    DomainTuple(domains...) = begin
        N = mapreduce(dims, +, domains)
        new{N}(domains)
    end
end

domains(dom::DomainTuple) = dom.domains
shape(dom::DomainTuple) = map(size, domains(dom))
size(dom::DomainTuple) = tuple(mapreduce(x -> [size(x)...], vcat, domains(dom))...)
length(dom::DomainTuple) = prod(size(dom))
#length(dom::DomainTuple) = prod(length(domains))


text:
"""
$(TYPEDEF)
"""
abstract type PnmlException <: Exception end

"""
$(TYPEDEF)
$(TYPEDFIELDS)

Use exception to allow dispatch and additional data presentation to user.
"""
struct MissingIDException <: PnmlException
    msg::String
    node::XMLNode
end

"""
$(TYPEDEF)

$(TYPEDFIELDS)
"""
struct MalformedException <: PnmlException
    msg::String
    node::XMLNode
end

Base.showerror(exc::MissingIDException) = showerror(stderr, exc)
function Base.showerror(io::IO, exc::MissingIDException)
    print(io, "MissingIDException: ", exc.msg)
end

Base.showerror(exc::MalformedException) = showerror(stderr, exc)
function Base.showerror(io::IO, exc::MalformedException)
    print(io, "MalformedException: ", exc.msg)
end


text:
import DataFrames
import StatsModels

function MutableDataFrame2ClassificationKnetTransformer(
        feature_names::AbstractVector,
        label_names::AbstractVector{Symbol},
        label_levels::AbstractDict,
        index::Integer;
        transposefeatures::Bool = true,
        transposelabels::Bool = false,
        )
    dffeaturecontrasts = FeatureContrastsNotYetGenerated()
    result = MutableDataFrame2ClassificationKnetTransformer(
        feature_names,
        label_names,
        label_levels,
        index,
        transposefeatures,
        transposelabels,
        dffeaturecontrasts,
        )
    return result
end

"""
"""
function get_underlying(
        x::MutableDataFrame2ClassificationKnetTransformer;
        saving::Bool = false,
        loading::Bool = false,
        )
    result = x.dffeaturecontrasts
    return result
end

"""
"""
function get_underlying(
        x::MutableDataFrame2RegressionKnetTransformer;
        saving::Bool = false,
        loading::Bool = false,
        )
    result = x.dffeaturecontrasts
    return result
end

"""
"""
function set_feature_contrasts!(
        x::MutableDataFrame2ClassificationKnetTransformer,
        feature_contrasts::AbstractFeatureContrasts,
        )
    x.dffeaturecontrasts = feature_contrasts
    return nothing
end

"""
"""
function set_feature_contrasts!(
        x::MutableDataFrame2RegressionKnetTransformer,
        contrasts::AbstractFeatureContrasts,
        )
    x.dffeaturecontrasts = contrasts
    return nothing
end

"""
"""
function fit!(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        training_features_df::DataFrames.AbstractDataFrame,
        training_labels_df::DataFrames.AbstractDataFrame,
        tuning_features_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing,
        tuning_labels_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing;
        kwargs...
        )
    result = transform(
        transformer,
        training_features_df,
        training_labels_df,
        tuning_features_df,
        tuning_labels_df;
        kwargs...
        )
    return result
end

"""
"""
function fit!(
        transformer::MutableDataFrame2RegressionKnetTransformer,
        training_features_df::DataFrames.AbstractDataFrame,
        training_labels_df::DataFrames.AbstractDataFrame,
        tuning_features_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing,
        tuning_labels_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing;
        kwargs...
        )
    result = transform(
        transformer,
        training_features_df,
        training_labels_df,
        tuning_features_df,
        tuning_labels_df;
        kwargs...
        )
    return result
end

"""
"""
function predict(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        features_df::DataFrames.AbstractDataFrame;
        kwargs...
        )
    return transform(transformer, features_df)
end

"""
"""
function transform(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        positive_class::AbstractString;
        kwargs...
        )
    if length(transformer.label_names) == 0
        error("length(transformer.label_names) == 0")
    elseif length(transformer.label_names) == 1
        label_1 = transformer.label_names[1]
        levels_1 = transformer.label_levels[label_1]
        labelstring2intmap_1 = getlabelstring2intmap(
            levels_1,
            transformer.index,
            )
        transformed_positive_class = labelstring2intmap_1[positive_class]
    else
        error("unsupported behavior")
        # training_labels_array = Array{Int}(
        #     size(training_labels_df, 1),
        #     length(transformer.label_names),
        #     )
        # for j = 1:length(transformer.label_names)
        #     label_j = transformer.label_names[j]
        #     levels_j = transformer.label_levels[label_j]
        #     labelstring2intmap_j = getlabelstring2intmap(
        #         levels_j,
        #         transformer.index,
        #         )
        #     training_labels_array[:, j] =
        #         [labelstring2intmap_j[y] for y in labels_df[label_j]]
        # end
    end
    return transformed_positive_class
end

"""
"""
function predict(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        features_df::DataFrames.AbstractDataFrame,
        positive_class::AbstractString,
        varargs...;
        kwargs...
        )
    result = (transform(transformer, features_df),
              transform(transformer, positive_class),
              varargs...)
    return result
end

"""
"""
function predict_proba(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        features_df::DataFrames.AbstractDataFrame;
        kwargs...
        )
    return transform(transformer, features_df)
end

"""
"""
function predict(
        transformer::MutableDataFrame2RegressionKnetTransformer,
        features_df::DataFrames.AbstractDataFrame;
        kwargs...
        )
    result = transform(
        transformer,
        features_df;
        kwargs...
        )
    return result
end

"""
"""
function predict_proba(
        transformer::MutableDataFrame2RegressionKnetTransformer,
        features_df::DataFrames.AbstractDataFrame;
        kwargs...
        )
    result = transform(
        transformer,
        features_df;
        kwargs...
        )
    return result
end

"""
"""
function transform(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        training_features_df::DataFrames.AbstractDataFrame,
        training_labels_df::DataFrames.AbstractDataFrame,
        tuning_features_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing,
        tuning_labels_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing;
        kwargs...
        )
    if is_nothing(tuning_features_df) &&
            is_nothing(tuning_labels_df)
        has_tuning_data = false
    elseif !is_nothing(tuning_features_df) &&
            !is_nothing(tuning_labels_df)
        has_tuning_data = true
    else
        error(
            string(
                "Either both tuning_features_df ",
                "and tuning_labels_df ",
                "must be defined, or neither can be defined."
                )
            )
    end
    if length(transformer.label_names) == 0
        error("length(transformer.label_names) == 0")
    elseif length(transformer.label_names) == 1
        label_1 = transformer.label_names[1]
        levels_1 = transformer.label_levels[label_1]
        labelstring2intmap_1 = getlabelstring2intmap(
            levels_1,
            transformer.index,
            )
        training_labels_array =
            [labelstring2intmap_1[y] for y in training_labels_df[label_1]]
    else
        training_labels_array = Array{Int}(
            size(training_labels_df, 1),
            length(transformer.label_names),
            )
        for j = 1:length(transformer.label_names)
            label_j = transformer.label_names[j]
            levels_j = transformer.label_levels[label_j]
            labelstring2intmap_j = getlabelstring2intmap(
                levels_j,
                transformer.index,
                )
            training_labels_array[:, j] =
                [labelstring2intmap_j[y] for y in labels_df[label_j]]
        end
    end
    my_formula = transformer.dffeaturecontrasts.formula_without_intercept
    my_schema = transformer.dffeaturecontrasts.schema_without_intercept
    my_formula = StatsModels.apply_schema(my_formula, my_schema)
    response, training_features_array = StatsModels.modelcols(my_formula,
                                                 training_features_df)
    if transformer.transposefeatures
        training_features_array = transpose(training_features_array)
    end
    if transformer.transposelabels
        training_labels_array = transpose(training_labels_array)
    end
    training_features_array = convert(Array, training_features_array)
    training_labels_array = convert(Array, training_labels_array)
    if has_tuning_data
        tuning_features_array, tuning_labels_array = transform(
            transformer,
            tuning_features_df,
            tuning_labels_df;
            kwargs...
            )
        return training_features_array,
            training_labels_array,
            tuning_features_array,
            tuning_labels_array
    else
        return training_features_array,
            training_labels_array
    end
end

"""
"""
function transform(
        transformer::MutableDataFrame2ClassificationKnetTransformer,
        features_df::DataFrames.AbstractDataFrame;
        kwargs...
        )
    my_formula = transformer.dffeaturecontrasts.formula_without_intercept
    my_schema = transformer.dffeaturecontrasts.schema_without_intercept
    my_formula = StatsModels.apply_schema(my_formula, my_schema)
    response, featuresarray = StatsModels.modelcols(my_formula,
                                                    features_df)
    if transformer.transposefeatures
        featuresarray = transpose(featuresarray)
    end
    return featuresarray
end

"""
"""
function transform(
        transformer::MutableDataFrame2RegressionKnetTransformer,
        training_features_df::DataFrames.AbstractDataFrame,
        training_labels_df::DataFrames.AbstractDataFrame,
        tuning_features_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing,
        tuning_labels_df::Union{Nothing, DataFrames.AbstractDataFrame} =
            nothing;
        kwargs...
        )
    if is_nothing(tuning_features_df) &&
            is_nothing(tuning_labels_df)
        has_tuning_data = false
    elseif !is_nothing(tuning_features_df) &&
            !is_nothing(tuning_labels_df)
        has_tuning_data = true
    else
        error(
            string(
                "Either both tuning_features_df ",
                "and tuning_labels_df ",
                "must be defined, or neither can be defined.",
                )
            )
    end
    training_labels_array = hcat(
        [
            training_labels_df[label] for label in transformer.label_names
            ]...
        )
    my_formula = transformer.dffeaturecontrasts.formula_without_intercept
    my_schema = transformer.dffeaturecontrasts.schema_without_intercept
    my_formula = StatsModels.apply_schema(my_formula, my_schema)
    response, training_features_array = StatsModels.modelcols(my_formula,
                                                 training_features_df)
    if transformer.transposefeatures
        training_features_array = transpose(training_features_array)
    end
    if transformer.transposelabels
        training_labels_array = transpose(training_labels_array)
    end
    training_features_array = convert(Array, training_features_array)
    training_labels_array = convert(Array, training_labels_array)
    if has_tuning_data
        tuning_features_array, tuning_labels_array = transform(
            transformer,
            tuning_features_df,
            tuning_labels_df;
            kwargs...
            )
        return training_features_array,
            training_labels_array,
            tuning_features_array,
            tuning_labels_array
    else
        return training_features_array,
            training_labels_array
    end
end

"""
"""
function transform(
        transformer::MutableDataFrame2RegressionKnetTransformer,
        features_df::DataFrames.AbstractDataFrame,
        kwargs...
        )
    my_formula = transformer.dffeaturecontrasts.formula_without_intercept
    my_schema = transformer.dffeaturecontrasts.schema_without_intercept
    my_formula = StatsModels.apply_schema(my_formula, my_schema)
    response, featuresarray = StatsModels.modelcols(my_formula,
                                                    features_df)
    if transformer.transposefeatures
        featuresarray = transpose(featuresarray)
    end
    return featuresarray
end


text:
@testset "reshape" begin
    p = randn(2,3)
    x = Var(p)
    y = reshape(x, 6)
    @test reshape(p, 6) == y.data
    gradient!(y)
    @test size(x) == size(x.grad)
end

@testset "matmul" begin
    w = Var([1 2 3; 4 5 6])
    x = Var(transpose(w.data))
    y = w * x
    @test y.data == [14 32; 32 77]

    x = randn(3, 2)
    w = randn(2, 3)
    f = x -> x * Var(w)
    @test gradcheck(f, x)

    x_data = randn(10, 1)
    w_data = randn(1, 5)
    f = w -> matmul(Var(x_data), w)
    @test gradcheck(f, w_data)
end


text:
function _checkinput(x::BitArray{2})
  return size(x, 1) == 4 && all(sum(x, dims=1) .== 1)
end

function rand(::Type{T}, w::W, n::Int64; checkinput::Bool=true) where {T <: GeneticSeq, W<: Weights}
  if checkinput && length(w) != 4
    throw(ErrorException("Invalid sampling weights for $T generation"))
  end
  x = BitArray(fill(0, (4, n)))
  @simd for i = 1:n
    @inbounds x[sample(1:4, w), i] = 1
  end
  return T(x, checkinput=false)
end

function rand(::Type{T}, wv::Vector{W}; checkinput::Bool=true) where {T <: GeneticSeq, W<: Weights}
  len = length(wv)
  x = BitArray(fill(0, (4, len)))
  @simd for i = 1:len
    if checkinput && length(wv[i]) != 4
      throw(ErrorException("Invalid sampling weights for $(i)th nt in $T generation"))
    end
    @inbounds x[sample(1:4, wv[i]), i] = 1
  end
  return T(x, checkinput=false)
end

function length(x::T) where {T <: GeneticSeq}
  return size(x.data, 2)
end

function show(io::IO, x::T) where {T <: GeneticSeq}
  len = length(x)
  println(io, "$(len)nt DNA sequence")
  if len <= 26
    print(io, prod([onehotinv(T, x.data[:, i]) for i=1:len]))
  else
    print(io, prod([onehotinv(T, x.data[:, i]) for i=1:13]) *
              "..." *
              prod([onehotinv(T, x.data[:, i]) for i=len-13:len]))
  end
end

function getindex(x::T, i::Int64) where {T <: GeneticSeq}
  return T(x.data[:, i])
end

function setindex!(x::T, a::BitArray{1}, i::Int64) where {T <: GeneticSeq}
  if length(a) == 4 && sum(a) == 1
    return x.data[:, i] = a
  else
    throw(ErrorException("Invalid input"))
  end
end

function setindex!(x::T, a::Char, i::Int64) where {T <: GeneticSeq}
  return x.data[:, i] = onehot(T, a)
end

function ==(x::T, y::T) where {T <: GeneticSeq}
  return x.data == y.data
end


text:
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: kinesis_analytics
using AWS.Compat
using AWS.UUIDs

"""
    add_application_cloud_watch_logging_option(application_name, cloud_watch_logging_option, current_application_version_id)
    add_application_cloud_watch_logging_option(application_name, cloud_watch_logging_option, current_application_version_id, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Adds a CloudWatch log stream to monitor application configuration errors. For more
information about using CloudWatch log streams with Amazon Kinesis Analytics applications,
see Working with Amazon CloudWatch Logs.

# Arguments
- `application_name`: The Kinesis Analytics application name.
- `cloud_watch_logging_option`: Provides the CloudWatch log stream Amazon Resource Name
  (ARN) and the IAM role ARN. Note: To write application messages to CloudWatch, the IAM role
  that is used must have the PutLogEvents policy action enabled.
- `current_application_version_id`: The version ID of the Kinesis Analytics application.

"""
add_application_cloud_watch_logging_option(ApplicationName, CloudWatchLoggingOption, CurrentApplicationVersionId; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationCloudWatchLoggingOption", Dict{String, Any}("ApplicationName"=>ApplicationName, "CloudWatchLoggingOption"=>CloudWatchLoggingOption, "CurrentApplicationVersionId"=>CurrentApplicationVersionId); aws_config=aws_config)
add_application_cloud_watch_logging_option(ApplicationName, CloudWatchLoggingOption, CurrentApplicationVersionId, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationCloudWatchLoggingOption", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CloudWatchLoggingOption"=>CloudWatchLoggingOption, "CurrentApplicationVersionId"=>CurrentApplicationVersionId), params)); aws_config=aws_config)

"""
    add_application_input(application_name, current_application_version_id, input)
    add_application_input(application_name, current_application_version_id, input, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Adds a streaming source to your Amazon Kinesis application. For conceptual information, see
Configuring Application Input.  You can add a streaming source either when you create an
application or you can use this operation to add a streaming source after you create an
application. For more information, see CreateApplication. Any configuration update,
including adding a streaming source using this operation, results in a new version of the
application. You can use the DescribeApplication operation to find the current application
version.  This operation requires permissions to perform the
kinesisanalytics:AddApplicationInput action.

# Arguments
- `application_name`: Name of your existing Amazon Kinesis Analytics application to which
  you want to add the streaming source.
- `current_application_version_id`: Current version of your Amazon Kinesis Analytics
  application. You can use the DescribeApplication operation to find the current application
  version.
- `input`: The Input to add.

"""
add_application_input(ApplicationName, CurrentApplicationVersionId, Input; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationInput", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "Input"=>Input); aws_config=aws_config)
add_application_input(ApplicationName, CurrentApplicationVersionId, Input, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationInput", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "Input"=>Input), params)); aws_config=aws_config)

"""
    add_application_input_processing_configuration(application_name, current_application_version_id, input_id, input_processing_configuration)
    add_application_input_processing_configuration(application_name, current_application_version_id, input_id, input_processing_configuration, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Adds an InputProcessingConfiguration to an application. An input processor preprocesses
records on the input stream before the application's SQL code executes. Currently, the only
input processor available is AWS Lambda.

# Arguments
- `application_name`: Name of the application to which you want to add the input processing
  configuration.
- `current_application_version_id`: Version of the application to which you want to add the
  input processing configuration. You can use the DescribeApplication operation to get the
  current application version. If the version specified is not the current version, the
  ConcurrentModificationException is returned.
- `input_id`: The ID of the input configuration to add the input processing configuration
  to. You can get a list of the input IDs for an application using the DescribeApplication
  operation.
- `input_processing_configuration`: The InputProcessingConfiguration to add to the
  application.

"""
add_application_input_processing_configuration(ApplicationName, CurrentApplicationVersionId, InputId, InputProcessingConfiguration; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationInputProcessingConfiguration", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "InputId"=>InputId, "InputProcessingConfiguration"=>InputProcessingConfiguration); aws_config=aws_config)
add_application_input_processing_configuration(ApplicationName, CurrentApplicationVersionId, InputId, InputProcessingConfiguration, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationInputProcessingConfiguration", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "InputId"=>InputId, "InputProcessingConfiguration"=>InputProcessingConfiguration), params)); aws_config=aws_config)

"""
    add_application_output(application_name, current_application_version_id, output)
    add_application_output(application_name, current_application_version_id, output, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Adds an external destination to your Amazon Kinesis Analytics application. If you want
Amazon Kinesis Analytics to deliver data from an in-application stream within your
application to an external destination (such as an Amazon Kinesis stream, an Amazon Kinesis
Firehose delivery stream, or an AWS Lambda function), you add the relevant configuration to
your application using this operation. You can configure one or more outputs for your
application. Each output configuration maps an in-application stream and an external
destination.  You can use one of the output configurations to deliver data from your
in-application error stream to an external destination so that you can analyze the errors.
For more information, see Understanding Application Output (Destination).   Any
configuration update, including adding a streaming source using this operation, results in
a new version of the application. You can use the DescribeApplication operation to find the
current application version. For the limits on the number of application inputs and outputs
you can configure, see Limits. This operation requires permissions to perform the
kinesisanalytics:AddApplicationOutput action.

# Arguments
- `application_name`: Name of the application to which you want to add the output
  configuration.
- `current_application_version_id`: Version of the application to which you want to add the
  output configuration. You can use the DescribeApplication operation to get the current
  application version. If the version specified is not the current version, the
  ConcurrentModificationException is returned.
- `output`: An array of objects, each describing one output configuration. In the output
  configuration, you specify the name of an in-application stream, a destination (that is, an
  Amazon Kinesis stream, an Amazon Kinesis Firehose delivery stream, or an AWS Lambda
  function), and record the formation to use when writing to the destination.

"""
add_application_output(ApplicationName, CurrentApplicationVersionId, Output; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationOutput", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "Output"=>Output); aws_config=aws_config)
add_application_output(ApplicationName, CurrentApplicationVersionId, Output, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationOutput", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "Output"=>Output), params)); aws_config=aws_config)

"""
    add_application_reference_data_source(application_name, current_application_version_id, reference_data_source)
    add_application_reference_data_source(application_name, current_application_version_id, reference_data_source, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Adds a reference data source to an existing application. Amazon Kinesis Analytics reads
reference data (that is, an Amazon S3 object) and creates an in-application table within
your application. In the request, you provide the source (S3 bucket name and object key
name), name of the in-application table to create, and the necessary mapping information
that describes how data in Amazon S3 object maps to columns in the resulting in-application
table.  For conceptual information, see Configuring Application Input. For the limits on
data sources you can add to your application, see Limits.   This operation requires
permissions to perform the kinesisanalytics:AddApplicationOutput action.

# Arguments
- `application_name`: Name of an existing application.
- `current_application_version_id`: Version of the application for which you are adding the
  reference data source. You can use the DescribeApplication operation to get the current
  application version. If the version specified is not the current version, the
  ConcurrentModificationException is returned.
- `reference_data_source`: The reference data source can be an object in your Amazon S3
  bucket. Amazon Kinesis Analytics reads the object and copies the data into the
  in-application table that is created. You provide an S3 bucket, object key name, and the
  resulting in-application table that is created. You must also provide an IAM role with the
  necessary permissions that Amazon Kinesis Analytics can assume to read the object from your
  S3 bucket on your behalf.

"""
add_application_reference_data_source(ApplicationName, CurrentApplicationVersionId, ReferenceDataSource; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationReferenceDataSource", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "ReferenceDataSource"=>ReferenceDataSource); aws_config=aws_config)
add_application_reference_data_source(ApplicationName, CurrentApplicationVersionId, ReferenceDataSource, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("AddApplicationReferenceDataSource", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "ReferenceDataSource"=>ReferenceDataSource), params)); aws_config=aws_config)

"""
    create_application(application_name)
    create_application(application_name, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Creates an Amazon Kinesis Analytics application. You can configure each application with
one streaming source as input, application code to process the input, and up to three
destinations where you want Amazon Kinesis Analytics to write the output data from your
application. For an overview, see How it Works.  In the input configuration, you map the
streaming source to an in-application stream, which you can think of as a constantly
updating table. In the mapping, you must provide a schema for the in-application stream and
map each data column in the in-application stream to a data element in the streaming
source. Your application code is one or more SQL statements that read input data, transform
it, and generate output. Your application code can create one or more SQL artifacts like
SQL streams or pumps. In the output configuration, you can configure the application to
write data from in-application streams created in your applications to up to three
destinations.  To read data from your source stream or write data to destination streams,
Amazon Kinesis Analytics needs your permissions. You grant these permissions by creating
IAM roles. This operation requires permissions to perform the
kinesisanalytics:CreateApplication action.   For introductory exercises to create an Amazon
Kinesis Analytics application, see Getting Started.

# Arguments
- `application_name`: Name of your Amazon Kinesis Analytics application (for example,
  sample-app).

# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"ApplicationCode"`: One or more SQL statements that read input data, transform it, and
  generate output. For example, you can write a SQL statement that reads data from one
  in-application stream, generates a running average of the number of advertisement clicks by
  vendor, and insert resulting rows in another in-application stream using pumps. For more
  information about the typical pattern, see Application Code.  You can provide such series
  of SQL statements, where output of one statement can be used as the input for the next
  statement. You store intermediate results by creating in-application streams and pumps.
  Note that the application code must create the streams with names specified in the Outputs.
  For example, if your Outputs defines output streams named ExampleOutputStream1 and
  ExampleOutputStream2, then your application code must create these streams.
- `"ApplicationDescription"`: Summary description of the application.
- `"CloudWatchLoggingOptions"`: Use this parameter to configure a CloudWatch log stream to
  monitor application configuration errors. For more information, see Working with Amazon
  CloudWatch Logs.
- `"Inputs"`: Use this parameter to configure the application input. You can configure your
  application to receive input from a single streaming source. In this configuration, you map
  this streaming source to an in-application stream that is created. Your application code
  can then query the in-application stream like a table (you can think of it as a constantly
  updating table). For the streaming source, you provide its Amazon Resource Name (ARN) and
  format of data on the stream (for example, JSON, CSV, etc.). You also must provide an IAM
  role that Amazon Kinesis Analytics can assume to read this stream on your behalf. To create
  the in-application stream, you need to specify a schema to transform your data into a
  schematized version used in SQL. In the schema, you provide the necessary mapping of the
  data elements in the streaming source to record columns in the in-app stream.
- `"Outputs"`: You can configure application output to write data from any of the
  in-application streams to up to three destinations. These destinations can be Amazon
  Kinesis streams, Amazon Kinesis Firehose delivery streams, AWS Lambda destinations, or any
  combination of the three. In the configuration, you specify the in-application stream name,
  the destination stream or Lambda function Amazon Resource Name (ARN), and the format to use
  when writing data. You must also provide an IAM role that Amazon Kinesis Analytics can
  assume to write to the destination stream or Lambda function on your behalf. In the output
  configuration, you also provide the output stream or Lambda function ARN. For stream
  destinations, you provide the format of data in the stream (for example, JSON, CSV). You
  also must provide an IAM role that Amazon Kinesis Analytics can assume to write to the
  stream or Lambda function on your behalf.
- `"Tags"`: A list of one or more tags to assign to the application. A tag is a key-value
  pair that identifies an application. Note that the maximum number of application tags
  includes system tags. The maximum number of user-defined application tags is 50. For more
  information, see Using Tagging.
"""
create_application(ApplicationName; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("CreateApplication", Dict{String, Any}("ApplicationName"=>ApplicationName); aws_config=aws_config)
create_application(ApplicationName, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("CreateApplication", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName), params)); aws_config=aws_config)

"""
    delete_application(application_name, create_timestamp)
    delete_application(application_name, create_timestamp, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Deletes the specified application. Amazon Kinesis Analytics halts application execution and
deletes the application, including any application artifacts (such as in-application
streams, reference table, and application code). This operation requires permissions to
perform the kinesisanalytics:DeleteApplication action.

# Arguments
- `application_name`: Name of the Amazon Kinesis Analytics application to delete.
- `create_timestamp`:  You can use the DescribeApplication operation to get this value.

"""
delete_application(ApplicationName, CreateTimestamp; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplication", Dict{String, Any}("ApplicationName"=>ApplicationName, "CreateTimestamp"=>CreateTimestamp); aws_config=aws_config)
delete_application(ApplicationName, CreateTimestamp, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplication", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CreateTimestamp"=>CreateTimestamp), params)); aws_config=aws_config)

"""
    delete_application_cloud_watch_logging_option(application_name, cloud_watch_logging_option_id, current_application_version_id)
    delete_application_cloud_watch_logging_option(application_name, cloud_watch_logging_option_id, current_application_version_id, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Deletes a CloudWatch log stream from an application. For more information about using
CloudWatch log streams with Amazon Kinesis Analytics applications, see Working with Amazon
CloudWatch Logs.

# Arguments
- `application_name`: The Kinesis Analytics application name.
- `cloud_watch_logging_option_id`: The CloudWatchLoggingOptionId of the CloudWatch logging
  option to delete. You can get the CloudWatchLoggingOptionId by using the
  DescribeApplication operation.
- `current_application_version_id`: The version ID of the Kinesis Analytics application.

"""
delete_application_cloud_watch_logging_option(ApplicationName, CloudWatchLoggingOptionId, CurrentApplicationVersionId; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationCloudWatchLoggingOption", Dict{String, Any}("ApplicationName"=>ApplicationName, "CloudWatchLoggingOptionId"=>CloudWatchLoggingOptionId, "CurrentApplicationVersionId"=>CurrentApplicationVersionId); aws_config=aws_config)
delete_application_cloud_watch_logging_option(ApplicationName, CloudWatchLoggingOptionId, CurrentApplicationVersionId, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationCloudWatchLoggingOption", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CloudWatchLoggingOptionId"=>CloudWatchLoggingOptionId, "CurrentApplicationVersionId"=>CurrentApplicationVersionId), params)); aws_config=aws_config)

"""
    delete_application_input_processing_configuration(application_name, current_application_version_id, input_id)
    delete_application_input_processing_configuration(application_name, current_application_version_id, input_id, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Deletes an InputProcessingConfiguration from an input.

# Arguments
- `application_name`: The Kinesis Analytics application name.
- `current_application_version_id`: The version ID of the Kinesis Analytics application.
- `input_id`: The ID of the input configuration from which to delete the input processing
  configuration. You can get a list of the input IDs for an application by using the
  DescribeApplication operation.

"""
delete_application_input_processing_configuration(ApplicationName, CurrentApplicationVersionId, InputId; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationInputProcessingConfiguration", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "InputId"=>InputId); aws_config=aws_config)
delete_application_input_processing_configuration(ApplicationName, CurrentApplicationVersionId, InputId, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationInputProcessingConfiguration", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "InputId"=>InputId), params)); aws_config=aws_config)

"""
    delete_application_output(application_name, current_application_version_id, output_id)
    delete_application_output(application_name, current_application_version_id, output_id, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Deletes output destination configuration from your application configuration. Amazon
Kinesis Analytics will no longer write data from the corresponding in-application stream to
the external output destination. This operation requires permissions to perform the
kinesisanalytics:DeleteApplicationOutput action.

# Arguments
- `application_name`: Amazon Kinesis Analytics application name.
- `current_application_version_id`: Amazon Kinesis Analytics application version. You can
  use the DescribeApplication operation to get the current application version. If the
  version specified is not the current version, the ConcurrentModificationException is
  returned.
- `output_id`: The ID of the configuration to delete. Each output configuration that is
  added to the application, either when the application is created or later using the
  AddApplicationOutput operation, has a unique ID. You need to provide the ID to uniquely
  identify the output configuration that you want to delete from the application
  configuration. You can use the DescribeApplication operation to get the specific OutputId.

"""
delete_application_output(ApplicationName, CurrentApplicationVersionId, OutputId; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationOutput", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "OutputId"=>OutputId); aws_config=aws_config)
delete_application_output(ApplicationName, CurrentApplicationVersionId, OutputId, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationOutput", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "OutputId"=>OutputId), params)); aws_config=aws_config)

"""
    delete_application_reference_data_source(application_name, current_application_version_id, reference_id)
    delete_application_reference_data_source(application_name, current_application_version_id, reference_id, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Deletes a reference data source configuration from the specified application configuration.
If the application is running, Amazon Kinesis Analytics immediately removes the
in-application table that you created using the AddApplicationReferenceDataSource
operation.  This operation requires permissions to perform the
kinesisanalytics.DeleteApplicationReferenceDataSource action.

# Arguments
- `application_name`: Name of an existing application.
- `current_application_version_id`: Version of the application. You can use the
  DescribeApplication operation to get the current application version. If the version
  specified is not the current version, the ConcurrentModificationException is returned.
- `reference_id`: ID of the reference data source. When you add a reference data source to
  your application using the AddApplicationReferenceDataSource, Amazon Kinesis Analytics
  assigns an ID. You can use the DescribeApplication operation to get the reference ID.

"""
delete_application_reference_data_source(ApplicationName, CurrentApplicationVersionId, ReferenceId; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationReferenceDataSource", Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "ReferenceId"=>ReferenceId); aws_config=aws_config)
delete_application_reference_data_source(ApplicationName, CurrentApplicationVersionId, ReferenceId, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DeleteApplicationReferenceDataSource", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "CurrentApplicationVersionId"=>CurrentApplicationVersionId, "ReferenceId"=>ReferenceId), params)); aws_config=aws_config)

"""
    describe_application(application_name)
    describe_application(application_name, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Returns information about a specific Amazon Kinesis Analytics application. If you want to
retrieve a list of all applications in your account, use the ListApplications operation.
This operation requires permissions to perform the kinesisanalytics:DescribeApplication
action. You can use DescribeApplication to get the current application versionId, which you
need to call other operations such as Update.

# Arguments
- `application_name`: Name of the application.

"""
describe_application(ApplicationName; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DescribeApplication", Dict{String, Any}("ApplicationName"=>ApplicationName); aws_config=aws_config)
describe_application(ApplicationName, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DescribeApplication", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName), params)); aws_config=aws_config)

"""
    discover_input_schema()
    discover_input_schema(params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Infers a schema by evaluating sample records on the specified streaming source (Amazon
Kinesis stream or Amazon Kinesis Firehose delivery stream) or S3 object. In the response,
the operation returns the inferred schema and also the sample records that the operation
used to infer the schema.  You can use the inferred schema when configuring a streaming
source for your application. For conceptual information, see Configuring Application Input.
Note that when you create an application using the Amazon Kinesis Analytics console, the
console uses this operation to infer a schema and show it in the console user interface.
This operation requires permissions to perform the kinesisanalytics:DiscoverInputSchema
action.

# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"InputProcessingConfiguration"`: The InputProcessingConfiguration to use to preprocess
  the records before discovering the schema of the records.
- `"InputStartingPositionConfiguration"`: Point at which you want Amazon Kinesis Analytics
  to start reading records from the specified streaming source discovery purposes.
- `"ResourceARN"`: Amazon Resource Name (ARN) of the streaming source.
- `"RoleARN"`: ARN of the IAM role that Amazon Kinesis Analytics can assume to access the
  stream on your behalf.
- `"S3Configuration"`: Specify this parameter to discover a schema from data in an Amazon
  S3 object.
"""
discover_input_schema(; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DiscoverInputSchema"; aws_config=aws_config)
discover_input_schema(params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("DiscoverInputSchema", params; aws_config=aws_config)

"""
    list_applications()
    list_applications(params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Returns a list of Amazon Kinesis Analytics applications in your account. For each
application, the response includes the application name, Amazon Resource Name (ARN), and
status. If the response returns the HasMoreApplications value as true, you can send another
request by adding the ExclusiveStartApplicationName in the request body, and set the value
of this to the last application name from the previous response.  If you want detailed
information about a specific application, use DescribeApplication. This operation requires
permissions to perform the kinesisanalytics:ListApplications action.

# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"ExclusiveStartApplicationName"`: Name of the application to start the list with. When
  using pagination to retrieve the list, you don't need to specify this parameter in the
  first request. However, in subsequent requests, you add the last application name from the
  previous response to get the next page of applications.
- `"Limit"`: Maximum number of applications to list.
"""
list_applications(; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("ListApplications"; aws_config=aws_config)
list_applications(params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("ListApplications", params; aws_config=aws_config)

"""
    list_tags_for_resource(resource_arn)
    list_tags_for_resource(resource_arn, params::Dict{String,<:Any})

Retrieves the list of key-value tags assigned to the application. For more information, see
Using Tagging.

# Arguments
- `resource_arn`: The ARN of the application for which to retrieve tags.

"""
list_tags_for_resource(ResourceARN; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("ListTagsForResource", Dict{String, Any}("ResourceARN"=>ResourceARN); aws_config=aws_config)
list_tags_for_resource(ResourceARN, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("ListTagsForResource", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ResourceARN"=>ResourceARN), params)); aws_config=aws_config)

"""
    start_application(application_name, input_configurations)
    start_application(application_name, input_configurations, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Starts the specified Amazon Kinesis Analytics application. After creating an application,
you must exclusively call this operation to start your application. After the application
starts, it begins consuming the input data, processes it, and writes the output to the
configured destination.  The application status must be READY for you to start an
application. You can get the application status in the console or using the
DescribeApplication operation. After you start the application, you can stop the
application from processing the input by calling the StopApplication operation. This
operation requires permissions to perform the kinesisanalytics:StartApplication action.

# Arguments
- `application_name`: Name of the application.
- `input_configurations`: Identifies the specific input, by ID, that the application starts
  consuming. Amazon Kinesis Analytics starts reading the streaming source associated with the
  input. You can also specify where in the streaming source you want Amazon Kinesis Analytics
  to start reading.

"""
start_application(ApplicationName, InputConfigurations; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("StartApplication", Dict{String, Any}("ApplicationName"=>ApplicationName, "InputConfigurations"=>InputConfigurations); aws_config=aws_config)
start_application(ApplicationName, InputConfigurations, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("StartApplication", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "InputConfigurations"=>InputConfigurations), params)); aws_config=aws_config)

"""
    stop_application(application_name)
    stop_application(application_name, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Stops the application from processing input data. You can stop an application only if it is
in the running state. You can use the DescribeApplication operation to find the application
state. After the application is stopped, Amazon Kinesis Analytics stops reading data from
the input, the application stops processing data, and there is no output written to the
destination.  This operation requires permissions to perform the
kinesisanalytics:StopApplication action.

# Arguments
- `application_name`: Name of the running application to stop.

"""
stop_application(ApplicationName; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("StopApplication", Dict{String, Any}("ApplicationName"=>ApplicationName); aws_config=aws_config)
stop_application(ApplicationName, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("StopApplication", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName), params)); aws_config=aws_config)

"""
    tag_resource(resource_arn, tags)
    tag_resource(resource_arn, tags, params::Dict{String,<:Any})

Adds one or more key-value tags to a Kinesis Analytics application. Note that the maximum
number of application tags includes system tags. The maximum number of user-defined
application tags is 50. For more information, see Using Tagging.

# Arguments
- `resource_arn`: The ARN of the application to assign the tags.
- `tags`: The key-value tags to assign to the application.

"""
tag_resource(ResourceARN, Tags; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("TagResource", Dict{String, Any}("ResourceARN"=>ResourceARN, "Tags"=>Tags); aws_config=aws_config)
tag_resource(ResourceARN, Tags, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("TagResource", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ResourceARN"=>ResourceARN, "Tags"=>Tags), params)); aws_config=aws_config)

"""
    untag_resource(resource_arn, tag_keys)
    untag_resource(resource_arn, tag_keys, params::Dict{String,<:Any})

Removes one or more tags from a Kinesis Analytics application. For more information, see
Using Tagging.

# Arguments
- `resource_arn`: The ARN of the Kinesis Analytics application from which to remove the
  tags.
- `tag_keys`: A list of keys of tags to remove from the specified application.

"""
untag_resource(ResourceARN, TagKeys; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("UntagResource", Dict{String, Any}("ResourceARN"=>ResourceARN, "TagKeys"=>TagKeys); aws_config=aws_config)
untag_resource(ResourceARN, TagKeys, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("UntagResource", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ResourceARN"=>ResourceARN, "TagKeys"=>TagKeys), params)); aws_config=aws_config)

"""
    update_application(application_name, application_update, current_application_version_id)
    update_application(application_name, application_update, current_application_version_id, params::Dict{String,<:Any})

 This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only
supports SQL applications. Version 2 of the API supports SQL and Java applications. For
more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation.
Updates an existing Amazon Kinesis Analytics application. Using this API, you can update
application code, input configuration, and output configuration.  Note that Amazon Kinesis
Analytics updates the CurrentApplicationVersionId each time you update your application.
This operation requires permission for the kinesisanalytics:UpdateApplication action.

# Arguments
- `application_name`: Name of the Amazon Kinesis Analytics application to update.
- `application_update`: Describes application updates.
- `current_application_version_id`: The current application version ID. You can use the
  DescribeApplication operation to get this value.

"""
update_application(ApplicationName, ApplicationUpdate, CurrentApplicationVersionId; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("UpdateApplication", Dict{String, Any}("ApplicationName"=>ApplicationName, "ApplicationUpdate"=>ApplicationUpdate, "CurrentApplicationVersionId"=>CurrentApplicationVersionId); aws_config=aws_config)
update_application(ApplicationName, ApplicationUpdate, CurrentApplicationVersionId, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = kinesis_analytics("UpdateApplication", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("ApplicationName"=>ApplicationName, "ApplicationUpdate"=>ApplicationUpdate, "CurrentApplicationVersionId"=>CurrentApplicationVersionId), params)); aws_config=aws_config)


text:
module Vis
push!(LOAD_PATH,".")

using ImageView, GtkReactive, Colors

export vis, vis_col, vis2

function vis2(mri_,mask1, mask2, thresh=0.1)
  mri = Gray.(copy(mri_)/ maximum(mri_))
  mriseg1 = RGBA.(mri)
  mriseg1[mask1 .> thresh] += RGBA(0.,0.,1.,0.5)
  mriseg2 = RGBA.(mri)
  mriseg2[mask2 .> thresh] += RGBA(0.,0.,1.,0.5)
  zr, slicedata = roi(mri, (1,2))
  gd = imshow_gui((200, 200), slicedata, (1,2))
  imshow(gd["frame"][1,1], gd["canvas"][1,1], mriseg1, nothing, zr, slicedata)
  imshow(gd["frame"][1,2], gd["canvas"][1,2], mriseg2, nothing, zr, slicedata)
  showall(gd["window"])
end

function vis(mri_, mask, thresh=0.1)
  mri = Gray.(copy(mri_)/ maximum(mri_))
  mriseg = RGBA.(mri)
  mriseg[mask .> thresh] += RGBA(0.,0.,1.,0.5)
  zr, slicedata = roi(mri, (1,2))
  gd = imshow_gui((200, 200), slicedata, (1,2))
  imshow(gd["frame"][1,1], gd["canvas"][1,1], mri, nothing, zr, slicedata)
  imshow(gd["frame"][1,2], gd["canvas"][1,2], mriseg, nothing, zr, slicedata)
  showall(gd["window"])
end

function vis_col(mri_ ,mask,thresh = 0.1)
  mri = Gray.(copy(mri_)/ maximum(mri_))
  mriseg = RGB.(mri)
  cm = colormap("Oranges", 255)
  mriseg[mask .> thresh] = cm[Int.(ceil.(255*mask[mask .> thresh]))]
  zr, slicedata = roi(mri, (1,2))
  gd = imshow_gui((200, 200), slicedata, (1,2))
  imshow(gd["frame"][1,1], gd["canvas"][1,1], mri, nothing, zr, slicedata)
  imshow(gd["frame"][1,2], gd["canvas"][1,2], mriseg, nothing, zr, slicedata)
  showall(gd["window"])
end

end


text:
module WordVectorSketch

end


text:
mutable struct gps_satellite_info_list_t <: LCMType
    utime::Int64
    num_sats::Int32
    sat_info::Vector{gps_satellite_info_t}
end

@lcmtypesetup(gps_satellite_info_list_t,
    sat_info => (num_sats,),
)


text:
abstract type Face{N,T} end
Base.broadcastable(f::Face) = Ref(f)
Base.@propagate_inbounds function Base.getindex(f::Face, i::Int)
    getfield(f, :points)[i]
end

max_stars_repo_name: KyleVaughn/MOCNeutronTransport

max_issues_repo_name: KyleVaughn/MOCNeutronTransport

max_forks_repo_name: KyleVaughn/MOCNeutronTransport


text:
using DelimitedFiles
using Printf

function check_valid_old(line)
    range = split(line[1], "-")
    r1, r2 = parse(Int64, range[1]) , parse(Int64, range[2])
    letter = line[2][1]
    passwd = line[3]

    num_letter = 0
    # Loop over password letters
    for c in passwd
        if c == letter
            num_letter += 1
        end
    end

    if (num_letter >= r1) && (num_letter <= r2)
        return true
    else
        return false
    end

end

function check_valid_new(line)
    range = split(line[1], "-")
    r1, r2 = parse(Int64, range[1]) , parse(Int64, range[2])
    letter = line[2][1]
    passwd = line[3]
    # println(passwd[r1], " ", passwd[r2])
    if xor((passwd[r1] == letter), (passwd[r2] == letter))
        return true
    else
        return false
    end
end

function count_true(list,method)
    num_false = 0

    if method == "old"
        check_valid = check_valid_old
    elseif method == "new"
        check_valid = check_valid_new
    end

    for i in 1:size(list)[1]
        if check_valid(list[i,:]) == true
            num_false += 1
        end
    end

    return num_false
end

list = readdlm("./input.dat")
println("Old: ",count_true(list, "old"))
println("New: ",count_true(list, "new"))