--Diffuse/diffuse_explicit_profiling.py at master · ltt1598/--Diffuse · GitHub

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import taichi as ti
ti.init(arch = ti.cuda, kernel_profiler=True)

# control
paused = False
save_images = False

# problem setting
n = 256
scatter = 4
res = n * scatter

# physical parameters
# time-integration related
h = 1e-3    # time-step size
substep = 1 # number of substeps
dx = 1      # finite difference step size (in space)
# heat-source related
t_max = 300 # max temperature (in Celsius)
t_min = 0   # min temperature
heat_center = (n//2, n//2)
heat_radius = 5.1
k = 50.0 # rate of heat diffusion

# temperature now and temperature next_time
t_n = ti.field(ti.f32, shape = (n,n))
t_np1 = ti.field(ti.f32, shape = (n,n))
# visualization
pixels = ti.Vector.field(3, ti.f32, shape = (res, res))
# pixels = ti.Vector.field(3, ti.f32)
# ti.root.dense(ti.ij, n).dense(ti.ij, scatter).place(pixels)

@ti.kernel
def init():
    for i,j in t_n:
        if (float(i)-heat_center[0])**2 + (float(j)-heat_center[1])**2 <= heat_radius**2:
            t_n[i, j] = t_max # source
            t_np1[i, j] = t_max # source
        else:
            t_n[i, j] = t_min
            t_np1[i, j] = t_min

@ti.kernel
def update_source():
    for i,j in t_n:
        if (float(i)-heat_center[0])**2 + (float(j)-heat_center[1])**2 <= heat_radius**2:
            t_np1[i, j] = t_max

@ti.kernel
def diffuse(dt: ti.f32):
    c = dt * k / dx**2
    for i,j in t_n:
        t_np1[i,j] = t_n[i,j]
        if i-1 >= 0:
            t_np1[i, j] += c * (t_n[i-1, j] - t_n[i, j])
        if i+1 < n:
            t_np1[i, j] += c * (t_n[i+1, j] - t_n[i, j])
        if j-1 >= 0:
            t_np1[i, j] += c * (t_n[i, j-1] - t_n[i, j])
        if j+1 < n:
            t_np1[i, j] += c * (t_n[i, j+1] - t_n[i, j])

def update_source_and_commit():
    update_source()
    t_n.copy_from(t_np1)

@ti.func
def get_color(v, vmin, vmax):
    c = ti.Vector([1.0, 1.0, 1.0]) # white

    if v < vmin:
        v = vmin
    if v > vmax:
        v = vmax
    dv = vmax - vmin

    if v < (vmin + 0.25 * dv):
        c[0] = 0
        c[1] = 4 * (v-vmin) / dv
    elif v < (vmin + 0.5 * dv):
        c[0] = 0
        c[2] = 1 + 4 * (vmin + 0.25*dv -v) / dv
    elif v < (vmin + 0.75*dv):
        c[0] = 4 * (v - vmin -0.5 * dv) / dv
        c[2] = 0
    else:
        c[1] = 1 + 4 * (vmin + 0.75 * dv - v) / dv
        c[2] = 0

    return c

@ti.kernel
def temperature_to_color(t: ti.template(), color: ti.template(), tmin: ti.f32, tmax: ti.f32):
    for i,j in t:
        for k,l in ti.ndrange(scatter, scatter):
            color[i*scatter+k,j*scatter+l] = get_color(t[i,j], tmin, tmax)

# GUI
my_gui = ti.GUI("Diffuse", (res, res))

init()
i = 0

# warm start the kernels
diffuse(0)
update_source_and_commit()
temperature_to_color(t_np1, pixels, t_min, t_max)
ti.clear_kernel_profile_info() # clear resutls

# while my_gui.running:
for debug_iter in range(100):

    for e in my_gui.get_events(ti.GUI.PRESS):
        if e.key == ti.GUI.ESCAPE:
            exit()
        elif e.key == ti.GUI.SPACE:
            paused = not paused
        elif e.key == 'i':
            save_images = not save_images
            print(f"Exporting images to images\output_{i:05}.png")
        elif e.key == 'r':
            init()
            i = 0

    if not paused:
        for sub in range(substep):
            diffuse(h/substep)
            update_source_and_commit()

    temperature_to_color(t_np1, pixels, t_min, t_max)
    my_gui.set_image(pixels)
    if save_images and not paused:
        my_gui.show(f"images\output_{i:05}.png")
        i += 1
    else:
        my_gui.show()

ti.print_kernel_profile_info('count')