def plot_vortex(a, vortices):
outfile = open('../results/vortices.dat', 'w')
outfile.write('radius gamma x_index y_index u_c v_c dist corr\n')
for i, line in enumerate(vortices):
#print(line)
outfile.write("{0} {1} {2} {3} {4} {5} {6} {7} \n".format(
line[0], line[1], line[2], line[3], line[4], line[5], line[6],
line[7]))
print('r:', line[0], 'gamma:', line[1], 'x:', line[2], 'y', line[3],
'dist', line[6], 'corr', line[7])
dx = a.dx[5] - a.dx[4]
dy = a.dy[5] - a.dy[4]
x_index, y_index, u_data, v_data = tools.window(
a, round(line[2] / dx, 0), round(line[3] / dy, 0), line[6])
u_model, v_model = fitting.velocity_model(line[0], line[1], line[2],
line[3], line[4], line[5],
x_index, y_index)
corr = fitting.correlation_coef(u_data, v_data, u_model, v_model)
plot_fit(x_index, y_index, u_data, v_data, u_model, v_model, line[2],
line[3], line[0], line[1], line[4], line[5], corr, i, 1)
corr = fitting.correlation_coef(u_data - line[4], v_data - line[5],
u_model - line[4], v_model - line[5])
plot_fit(x_index, y_index, u_data - line[4], v_data - line[5],
u_model - line[4], v_model - line[5], line[2], line[3],
line[0], line[1], line[4], line[5], corr, i, 2)
def test_correlation_coef_1_1_0_1(self):
# Different u and v compared with u=0 and v=v
result_calc = fitting.correlation_coef(sample_field, sample_field,
sample_field * 0.0,
sample_field)
np.testing.assert_almost_equal(result_calc, 0.5)
def test_oseen(coreR, gamma, dist,xdrift,ydrift):
print('|*|coreR:',coreR,'Gamma',gamma,'xdrift',xdrift,'ydrift',ydrift,'|*|')
model = [[],[],[],[],[],[]]
model[0] = coreR
model[1] = gamma
coreRori = model[0]
gammaori = model[1]
X = np.linspace(-1,1,dist)
Y = np.linspace(-1,1,dist)
X, Y = np.meshgrid(X,Y)
fxCenter = 0.0
fyCenter = 0.0
u_conv = 0.0 #flipped with v, fix later
v_conv = 0.0
Uw, Vw = fitting.velocity_model(coreR, gamma, fxCenter, fyCenter, u_conv, v_conv, X+xdrift, Y+ydrift)
Uw = Uw + u_conv
Vw = Vw + v_conv
# NOISE
Uw = np.random.normal(Uw,0.3)
Vw = np.random.normal(Vw,0.3)
model = fitting.fit(coreR, gamma, X, Y, fxCenter, fyCenter, Uw, Vw, u_conv, v_conv)
print('coreR:',model[0],'error(%):',(1-(model[0])/coreRori)*100)
print('gamma:',model[1],'error(%):',(1-(model[1])/gammaori)*100)
print('fxCenter:',model[2])
print('fyCenter:',model[3])
#print('u_conv:',model[4])
#print('v_conv:',model[5])
#print('xCenter:', fxCenter)
#print('yCenter:',fyCenter)
uMod, vMod = fitting.velocity_model(model[0], model[1], model[2], model[3],u_conv,v_conv, X, Y)#, model[4], model[5])
corr = fitting.correlation_coef(Uw,Vw,uMod,vMod)
print('correlation:',corr)
print('---')
plot.plot_corr(X, Y, Uw, Vw, uMod, vMod, model[0], corr)
def test_oseen(coreR, gamma, dist,xdrift,ydrift,u_conv,v_conv):
print('coreR:',coreR,'Gamma',gamma,'xdrift',xdrift,
'ydrift',ydrift,'u_conv',u_conv,'v_conv',v_conv)
model = [[],[],[],[],[],[]]
model[0] = coreR
model[1] = gamma
coreRori = model[0]
gammaori = model[1]
x_index = np.linspace(-1,1,dist)
y_index = np.linspace(-1,1,dist)
x_index, y_index = np.meshgrid(x_index, y_index)
x_real = 0.0
y_real = 0.0
model[4] = u_conv
model[5] = v_conv
u_data, v_data = fitting.velocity_model(coreR, gamma, x_real, y_real,
u_conv, v_conv, x_index+xdrift, y_index+ydrift)
u_data = u_data + u_conv
v_data = v_data + v_conv
# NOISE
u_data = np.random.normal(u_data,0.3)
v_data = np.random.normal(v_data,0.3)
model = fitting.fit(coreR, gamma, x_index, y_index, x_real, y_real, u_data, v_data, u_conv, v_conv,0)
print('coreR:',model[0],'error(%):',(1-(model[0])/coreRori)*100)
print('gamma:',model[1],'error(%):',(1-(model[1])/gammaori)*100)
print('x_real:',model[2])
print('y_real:',model[3])
u_model, v_model = fitting.velocity_model(model[0], model[1], model[2], model[3],
model[4],model[5], x_index, y_index)
corr = fitting.correlation_coef(u_data,v_data,u_model,v_model)
print('correlation:',corr)
print('---')
plot.plot_fit_test(x_index, y_index, u_data, v_data, u_model, v_model, model[2], model[3], model[0], model[1], model[4],model[5], corr)
def test_correlation_coef_2_05_2_05(self):
# Different u and v compared with the same u and v
result_calc = fitting.correlation_coef(sample_field * 2.0,
sample_field * 0.5,
sample_field * 2.0,
sample_field * 0.5)
np.testing.assert_almost_equal(result_calc, 1.0)
def test_correlation_coef_1_1_1_1(self):
# Exact same u and v compared with exact same u and v
result_calc = fitting.correlation_coef(sample_field,
sample_field,
sample_field,
sample_field)
np.testing.assert_almost_equal(result_calc, 1.0)
def test_oseen(core_radius, gamma, dist, xdrift, ydrift, u_advection,
v_advection):
print('core_radius:', core_radius, 'Gamma', gamma, 'xdrift', xdrift,
'ydrift', ydrift, 'u_advection', u_advection, 'v_advection',
v_advection)
model = [[], [], [], [], [], []]
model[0] = core_radius
model[1] = gamma
core_radius_ori = model[0]
gamma_ori = model[1]
x_index = np.linspace(-1, 1, dist)
y_index = np.linspace(-1, 1, dist)
x_index, y_index = np.meshgrid(x_index, y_index)
x_real = 0.0
y_real = 0.0
model[4] = u_advection
model[5] = v_advection
u_data, v_data = fitting.velocity_model(core_radius, gamma, x_real,
y_real, u_advection,
v_advection, x_index + xdrift,
y_index + ydrift)
u_data = u_data + u_advection
v_data = v_data + v_advection
# NOISE
u_data = np.random.normal(u_data, 0.3)
v_data = np.random.normal(v_data, 0.3)
model = fitting.fit(core_radius, gamma, x_index, y_index, x_real,
y_real, u_data, v_data, u_advection, v_advection,
0)
print('core_radius:', model[0], 'error(%):',
(1 - (model[0]) / core_radius_ori) * 100)
print('gamma:', model[1], 'error(%):',
(1 - (model[1]) / gamma_ori) * 100)
print('x_real:', model[2])
print('y_real:', model[3])
u_model, v_model = fitting.velocity_model(model[0], model[1], model[2],
model[3], model[4], model[5],
x_index, y_index)
corr = fitting.correlation_coef(u_data, v_data, u_model, v_model)
print('correlation:', corr)
print('---')
fitting.plot_fit(x_index, y_index, u_data, v_data, u_model, v_model,
model[2], model[3], model[0], model[1], model[4],
model[5], corr, 0, 0, '.', 0, 'png')
vortices[i][5], vortices[i][1] -
vortices[i][5]:vortices[i][1] +
vortices[i][5]]
X, Y, Uw, Vw = tools.window(a, vortices[i][0], vortices[i][1],
vortices[i][5])
uMod, vMod = fitting.velocity_model(vortices[i][3], vortices[i][2],
vortices[i][6], vortices[i][7],
vortices[i][8], vortices[i][9],
X, Y)
plot.plot_quiver(X, Y, Uw, Vw, swirlingw)
elif args.plot_x == 'fit':
for i in range(len(vortices)):
print('xC:', vortices[i][0], 'yC:', vortices[i][1], 'vort:',
vortices[i][2], 'mesh', vortices[i][5], 'corr',
vortices[i][4], 'coreR', vortices[i][3])
X, Y, Uw, Vw = tools.window(a, vortices[i][0], vortices[i][1],
vortices[i][5])
uMod, vMod = fitting.velocity_model(vortices[i][3], vortices[i][2],
vortices[i][6], vortices[i][7],
vortices[i][8], vortices[i][9],
X, Y)
corr = fitting.correlation_coef(Uw, Vw, uMod, vMod)
plot.plot_corr(X, Y, Uw, Vw, uMod, vMod, vortices[i][3],
vortices[i][4])
elif args.plot_x == 'radius':
plot.plot_radius(vortices)
else:
print('no plot')
