def graph(data_in_y, data_in_x):
data_in_y = np.array(list(map(float, data_in_y.split())))
data_in_x = np.array(list(map(float, data_in_x.split())))
main_graphic = plt.figure('Test Graphic', figsize=(8, 5))
ax = plt.subplot()
main_graphic.subplots_adjust(left=0.07, right=0.96, top=0.95, bottom=0.05)
ax.plot(data_in_x, data_in_y, '.', markerfacecolor='black', markeredgecolor='black', markersize=7)
interpolation_function = interpolation.scipy_rbf(data_in_x, data_in_y)
extreme = optimization.scipy_minimize(interpolation_function)
ax.plot(np.arange(data_in_x[0], data_in_x[-1], 0.01),
interpolation_function[0](np.arange(data_in_x[0], data_in_x[-1], 0.01)))
plt.plot(extreme[0], interpolation_function[0](extreme[0]), 'xg')
plt.plot(extreme[1], interpolation_function[0](extreme[1]), 'xr')
plt.ylim((np.array(plt.ylim())[0]-np.array(plt.xlim())[1]*0.05, np.array(plt.xlim())[1]))
plt.show()
2.0, 1.3, 0.5507688285032871, 0.9572689686335257, 0.6022617125298197,
0.32973987362833, 0.682238940178396, 1.3, 2.1
])
data_in_x = np.linspace(0, np.size(data_in_y), np.size(data_in_y))
data_in_x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
main_graphic = plt.figure('Graphic', figsize=(8, 5))
ax = plt.subplot()
main_graphic.subplots_adjust(left=0.07, right=0.96, top=0.95, bottom=0.05)
ax.plot(data_in_x,
data_in_y,
'.',
markerfacecolor='black',
markeredgecolor='black',
markersize=7)
interpolation_function = interpolation.scipy_rbf(data_in_x, data_in_y)
extreme = optimization.scipy_minimize(interpolation_function)
ax.plot(
np.arange(data_in_x[0], data_in_x[-1], 0.01),
interpolation_function[0](np.arange(data_in_x[0], data_in_x[-1], 0.01)))
plt.plot(extreme[0], interpolation_function[0](extreme[0]), 'xg')
plt.plot(extreme[1], interpolation_function[0](extreme[1]), 'xr')
areas = flooding.step_by_step_flooding(extreme, interpolation_function, v)
for i in areas:
plt.plot([i.x_filling_left, i.x_filling_right], [
interpolation_function[0](i.x_filling_left), interpolation_function[0](
i.x_filling_right)
def calculate(data_in_y, data_in_x, t, v,
check_evaporation, temperature, wind, moisture,
check_filtartion, k, u, extended=None):
def draw_line(a1, a2, color=None, linewidth=1):
plt.plot(a1, a2, linewidth=linewidth, color=color)
def draw_fill(xr1):
y2 = np.empty(0)
if xr1.size == 0:
return
for point in np.linspace(xr1[0], xr1[-1], 1000):
y2 = np.append(y2, interpolation_function[0](xr1[0]))
plt.fill_between(np.linspace(xr1[0], xr1[-1], 1000),
interpolation_function[0](np.linspace(xr1[0], xr1[-1], 1000)),
y2,
color=(0.0, 0.0, 1.0), alpha=0.6)
def draw_filtration_data(xr, yr):
color = (0.3, 0.0, 0.5, 0.8)
xr1 = None
i = 1
while i < np.size(xr):
if abs(xr[i] - xr[i - 1]) > abs(xr[1] - xr[0]) + 0.1:
xr1 = xr[:i]
xr2 = xr[i:]
yr1 = yr[:i]
yr2 = yr[i:]
draw_filtration_data(xr2, yr2)
i += 1
if xr1 is None:
xr1 = xr[:]
yr1 = yr[:]
draw_line(xr1, interpolation_function[0](xr1) + yr1, color=color)
draw_fill(xr1)
data_in_y = np.array(list(map(float, data_in_y.split())))
data_in_x = np.array(list(map(float, data_in_x.split())))
t = float(t)
v = float(v) / 1000.0
temperature = float(temperature)
wind = float(wind)
moisture = float(moisture)
k = float(k)
u = float(u)
if extended:
if len(extended[0]) >= 0:
tau = float(extended[0])
else:
tau = 1.0 / 24.0
if len(extended[1]) >= 0:
intervals = int(extended[1])
else:
intervals = 100
if len(extended[2]) >= 0:
intervals_down = int(extended[2])
else:
intervals_down = 200
if len(extended[3]) >= 0:
hy = float(extended[3])
else:
hy = 1
else:
tau = 1.0 / 24.0
intervals = 100
intervals_down = 200
hy = 1
main_graphic = plt.figure('Graphic', figsize=(8, 5))
ax = plt.subplot()
main_graphic.subplots_adjust(left=0.07, right=0.96, top=0.95, bottom=0.05)
ax.plot(data_in_x, data_in_y, '.', markerfacecolor='black', markeredgecolor='black', markersize=7)
interpolation_function = interpolation.scipy_rbf(data_in_x, data_in_y)
extreme = optimization.scipy_minimize(interpolation_function)
draw_line(np.arange(data_in_x[0], data_in_x[-1], 0.01),
interpolation_function[0](np.arange(data_in_x[0], data_in_x[-1], 0.01)))
plt.plot(extreme[0], interpolation_function[0](extreme[0]), 'xg')
plt.plot(extreme[1], interpolation_function[0](extreme[1]), 'xr')
areas = flooding.step_by_step_flooding(extreme, interpolation_function, v)
for area in areas:
draw_line([area.x_filling_left, area.x_filling_right],
[interpolation_function[0](area.x_filling_left),
interpolation_function[0](area.x_filling_right)], color=(0.0, 0.0, 1.0, 0.4))
if check_evaporation:
for area in areas:
evaporation_rate = evaporation.empiric_evaporation(t=t, Vb=wind, T=temperature, u=moisture)
area.S_target -= ((area.x_filling_right - area.x_filling_left) * evaporation_rate) / (30.0 / tau)
area.S_fill = 0.0
area.x_filling_left = area.x_filling_right = area.min
areas = flooding.step_by_step_flooding(extreme, interpolation_function, v, area=areas)
for area in areas:
draw_line([area.x_filling_left, area.x_filling_right],
[interpolation_function[0](area.x_filling_left),
interpolation_function[0](area.x_filling_right)], color=(0.0, 0.0, 1.0, 0.8))
if check_filtartion:
filtration_data = []
for area in areas:
filtration_data.append(filtration.spring_filtering(area, interpolation_function[0],
k=k, mu=u, tm=t, tau=tau, hy=hy,
intervals=intervals,
intervals_down=intervals_down))
for area in filtration_data:
xr = area[0][-1][:]
yr = area[1][-1][:]
draw_filtration_data(xr, yr)
plt.ylim((np.array(plt.ylim())[0]-np.array(plt.xlim())[1]*0.05, np.array(plt.xlim())[1]))
plt.show()