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#!/usr/bin/env julia
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using DelimitedFiles
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using CUDA
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function heat1d_ftcs_cpu(diffusivity, xlength, left_boundary,
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                         right_boundary, init_fn,
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                         dt, nsteps, dx, print_at_steps, outf,
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                         psource_x, psource_value)
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    npoints = ceil(Int, xlength / dx) + 1
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    T1 = zeros(Float32, npoints)
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    T2 = similar(T1)
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    xs = range(0.0, xlength, length=npoints)
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    c = diffusivity * dt / dx^2
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    psource_offset = ceil(Int, psource_x / xlength * npoints)
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    psource_dt = psource_value * dt
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    if c > 0.5
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        println("c = ", c)
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        return -1
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    end
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    T1[1] = left_boundary
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    T1[end] = right_boundary
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    T2[1] = left_boundary
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    T2[end] = right_boundary
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    T1[2:end-1] = init_fn.(xs[2:end-1])
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    # first line of output is x values, for plotting
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    writedlm(outf, xs', ',')
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    # output initial condition state
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    writedlm(outf, T1', ',')
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    for j in 1:2:nsteps
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        for i in 2:npoints-1
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            T2[i] = T1[i] + c * (T1[i+1] - 2.0 * T1[i] + T1[i-1])
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            if i == psource_offset
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                T2[i] += psource_dt
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            end
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        end
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        for i in 2:npoints-1
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            T1[i] = T2[i] + c * (T2[i+1] - 2.0 * T2[i] + T2[i-1])
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            if i == psource_offset
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                T1[i] += psource_dt
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            end
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        end
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        if mod(j+1, print_at_steps) == 0
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            writedlm(outf, T1', ',')
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        end
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    end
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end
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function gpu_ftcs_step!(T, Told, c, psource_offset, psource_dt)
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    start_idx = (blockIdx().x - 1) * blockDim().x + threadIdx().x + 1
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    stride = gridDim().x * blockDim().x
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    for i in start_idx:stride:length(T)-1
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        @inbounds Ti = Told[i] + c * (Told[i+1] - 2.0 * Told[i] + Told[i-1])
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        if i == psource_offset
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            Ti += psource_dt
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        end
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        T[i] = Ti
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    end
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end
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function heat1d_ftcs_gpu(diffusivity, xlength, left_boundary,
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                         right_boundary, init_fn,
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                         dt, nsteps, dx, print_at_steps, outf,
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                         psource_x, psource_value)
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    npoints = Int(ceil(xlength / dx)) + 1
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    T = zeros(Float32, npoints)
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    xs = range(0.0, xlength, length=npoints)
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    T1_d = CuArray{Float32}(undef, length(T))
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    T2_d = similar(T1_d)
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    c = diffusivity * dt / dx^2
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    psource_offset = ceil(Int, psource_x / xlength * npoints)
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    psource_dt = psource_value * dt
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    if c > 0.5
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        println("c = ", c)
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        return -1
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    end
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    T[1] = left_boundary
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    T[end] = right_boundary
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    T[2:end-1] = init_fn.(xs[2:end-1])
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    # first line of output is x values, for plotting
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    writedlm(outf, xs', ',')
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    # output initial condition state
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    writedlm(outf, T', ',')
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    copyto!(T1_d, T)
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    NTHREADS = 128
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    NBLOCKS = ceil(Int, (length(T)-2) / NTHREADS)
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    for j in 1:2:nsteps
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        CUDA.@sync begin
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            @cuda threads=NTHREADS blocks=NBLOCKS gpu_ftcs_step!(T2_d, T1_d, c,
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                                                                 psource_offset,
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                                                                 psource_dt)
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        end
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        CUDA.@sync begin
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            @cuda threads=NTHREADS blocks=NBLOCKS gpu_ftcs_step!(T1_d, T2_d, c,
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                                                                 psource_offset,
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                                                                 psource_dt)
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        end
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        if mod(j+1, print_at_steps) == 0
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            copyto!(T, T1_d)
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            writedlm(outf, T', ',')
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        end
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    end
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end
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function gaussian1d(x)
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    return exp(-(5.0*(x-0.5))^2)
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end
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function test_cpu()
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    open("out/heat1d_cpu.txt", "w") do io
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        heat1d_ftcs_cpu(0.001, 1.0, 0.0, 0.0, gaussian1d,
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                        0.0004, 2^20, 2^-10, 2^13, io, 0.2, 1.0)
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    end
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end
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function test_gpu()
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    CUDA.allowscalar(false)
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    open("out/heat1d_gpu.txt", "w") do io
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        heat1d_ftcs_gpu(0.001, 1.0, 0.0, 0.0, gaussian1d,
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                        0.0004, 2^20, 2^-10, 2^13, io, 0.2, 1.0)
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    end
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end
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#@time test_cpu()
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test_gpu()
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#@time test_gpu()
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@ -0,0 +1,36 @@
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#!/usr/bin/env python3
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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fig, ax = plt.subplots()
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data_path = sys.argv[1]
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f = open(data_path)
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# first line is x values
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line = f.readline().strip()
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if ',' in line:
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    sep = ','
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else:
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    sep = ' '
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x = np.fromstring(line, sep=sep)
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# second line is y values at time 0
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y = np.fromstring(f.readline().strip(), sep=sep)
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graph, = ax.plot(x, y)
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def animate(line):
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    y = np.fromstring(line.strip(), sep=sep)
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    print(np.max(y))
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    graph.set_ydata(y)
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    return graph,
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ani = animation.FuncAnimation(fig, animate, f, interval=100, blit=True,
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                              repeat=False)
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plt.show()
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