def shear_limit_M(M, W, Dt, type=1, **kwargs):
"""
Test the shear criterion : dU/W < 0.1
"""
values = access.get(M, 'strain', frame)
M, field = vgradient.compute(M,
'strain',
step=1,
filter=False,
Dt=1,
rescale=False,
type=type,
compute=False)
values = getattr(M, field) #/W
dUmin, dUmax = check.shear_limit_M(M, W)
xbin, n = graphes.hist(values,
normalize=False,
num=200,
range=(-0.5, 0.5),
**kwargs) #xfactor = Dt
maxn = max(n) * 1.2
graphes.graph([dUmin, dUmin], [0, maxn], label='r-', **kwargs)
graphes.graph([dUmax, dUmax], [0, maxn], label='r-', **kwargs)
graphes.legende('', '', '')
def velocity_profile(M,
xlines,
ylines,
display=True,
start=0,
end=10000,
label='k^'):
nx, ny, nt = M.shape()
nt = min(nt, end)
U = np.sqrt(M.Ux[:, :, start:nt]**2 + M.Uy[:, :, start:nt]**2)
label = ['k^', 'rx', 'bo']
Dt = 10
t = M.t[start + Dt:nt - Dt]
Ut = []
for i in ylines:
for j in xlines:
Ut.append(basics.smooth(
U[i, j],
Dt)) #[np.mean(S.Uy[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
# std_U=[np.std(U[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
if display:
graphes.graph(t, Ut[-1])
graphes.legende('t (ms)', 'V (m/s)', '')
#return a list of time series, for each element in xlines and ylines
return t, Ut
def vertical_profile(S, xlines, Dt, start=0):
nx, ny, nt = S.shape()
y = S.y[:, 0]
for i in range(start, nt, Dt):
Ux = np.mean(np.mean(S.Ux[:, xlines, i:i + Dt], axis=1), axis=1)
Uy = np.mean(np.mean(S.Uy[:, xlines, i:i + Dt], axis=1), axis=1)
#standard deviation computation
std_Ux = np.sqrt(
np.mean(np.mean(abs(S.Ux[:, xlines, i:i + Dt] - Ux)**2, axis=1),
axis=1))
std_Uy = np.sqrt(
np.mean(np.mean(abs(S.Uy[:, xlines, i:i + Dt] - Uy)**2, axis=1),
axis=1))
print(std_Ux)
plt.subplot(121)
graphes.graph(y, Ux, std_Ux)
graphes.legende('z (m)', 'V (m/s)', 'Ux')
plt.subplot(122)
graphes.graph(y, Uy, std_Uy)
graphes.legende('z (m)', 'V (m/s)', 'Uy')
plt.draw()
raw_input()
def test_bound(dataList, W, Dt, **kwargs):
maxn = 0
Umin, Umax = bounds_pix(W)
ratio = []
for data in dataList:
# values = np.asarray(data['u'])**2+np.asarray(data['v']**2)
values = np.sqrt(np.asarray(data['u'])**2 + np.asarray(data['v'])**2)
r = len(np.where(np.logical_and(
values > Umin, values < Umax))[0]) * 100. / len(data['u'])
ratio.append(r)
xbin, n = graphes.hist(values,
normalize=False,
num=200,
range=(0., 2 * Umax),
**kwargs) #xfactor = Dt
maxn = max([maxn, max(n) * 1.2])
ratio = np.nanmean(np.asarray(ratio))
graphes.graph([Umin, Umin], [0, maxn], label='r-', **kwargs)
graphes.graph([Umax, Umax], [0, maxn], label='r-', **kwargs)
graphes.set_axis(0, Umax * 1.2, 0, maxn)
title = 'Dt = ' + str(Dt) + ', W = ' + str(W) + 'pix'
fig = graphes.legende('U (pix)', 'Histogram of U', title)
# graphes.set_axis(0,1.5,0,maxn)
return ratio, fig
def fit_core_circulation(M, fignum=1, display=False):
R, Gamma, center, factor = compute_circulation_2(M,
fignum=fignum,
display=display)
factor = np.max(Gamma)
Gamma = Gamma / factor
#fit_core_circulation(R_list,Gamma,M,fignum=fignum,display=False)
nx, ny, nt = M.shape()
a0 = (1, np.max(Gamma))
fun = circ_gaussian
res = opt.minimize(distance_fit, a0, args=(fun, R, Gamma))
cond = ((center[0] > 5) and (center[0] < nx - 5) and (center[1] > 5)
and (center[1] < ny - 5))
if cond:
lc = np.sqrt(res.x[0])
G0 = factor * res.x[1]
else:
lc = None
G0 = None
if display:
graphes.graph(R, factor * Gamma, fignum=1, label='ko')
graphes.graph(R, factor * fun(res.x, R), fignum=1, label='r')
return lc, G0, center
def velocity_profile_xy(S, xlines, ylines, display=False, label='k^'):
nx, ny, nt = S.shape()
label = ['k^', 'rx', 'bo']
t = S.t
Dt = 5
Uxt = []
Uyt = []
for i in ylines:
for j in xlines:
# std_Ux=[np.std(S.Ux[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
# std_Uy=[np.std(S.Uy[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
Uxt.append(
basics.smooth(S.Ux[i, j], Dt)
) #(-1)**i*(-1)**j* [(-1)**i*(-1)**j*np.mean(S.Ux[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
Uyt.append(basics.smooth(
S.Uy[i, j],
Dt)) #[np.mean(S.Uy[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
if display:
# plt.subplot(211)
graphes.graph(t[Dt:-Dt], Uxt[-1]) #,std_Ux)
graphes.legende('t (ms)', 'V (m/s)', 'Ux')
# plt.subplot(212)
graphes.graph(t[Dt:-Dt], Uyt[-1]) #,std_Uy)
graphes.legende('t (ms)', 'V (m/s)', 'Uy')
return t, Uxt, Uyt
def spatial_corr(data, N=1024, Dt=10):
Cxx = np.zeros((N / 2, Dt))
d = np.arange(N / 2)
figs = {}
for p in range(3):
for k in range(Dt):
key = data.keys()[k]
Z = np.asarray(data[key])
Ex = np.nanmean(np.power(Z[:N / 2, 0, 0, p], 2))
Cxx[:, k] = np.nanmean(np.asarray([[
Z[i, 0, 0, p] * Z[i + j, 0, 0, p] / Ex for i in range(N / 2)
] for j in range(N / 2)]),
axis=1)
#print(Cxx[0,:])
C = np.nanmean(Cxx, axis=1)
graphes.graph(d, C, fignum=1)
graphes.set_axis(0, N / 4, -1, 1.5)
figs.update(graphes.legende('d', 'C', ''))
graphes.save_figs(figs,
savedir='./Corr_functions/',
suffix='',
prefix='',
frmt='pdf',
dpi=300)
def fit_core_size(x, y, Z, fignum=1, display=False):
"""
Find the half width of a gaussian bump
INPUT
-----
x : 2d np array
spatial coordinates (columns)
y : 2d np array
spatial coordinates (lines)
Z : 2d np array
data to be fitted (typically vorticity field )
fignum : int
figure number for the output. Default 1
display : bool
OUTPUT
-----
a : float
parameter of the gaussian bump
center : 2 elements np array
center coordinates
"""
ny, nx = Z.shape
X, Y, data, center, factor = normalize(x, y, Z)
R, theta = Smath.cart2pol(X, Y)
a0 = 1
fun = gaussian
res = opt.minimize(distance_fit, a0, args=(fun, R, data))
cond = ((center[0] > 5) and (center[0] < nx - 5) and (center[1] > 5)
and (center[1] < ny - 5))
if cond:
a = np.sqrt(res.x)
else:
a = None
if display:
figs = {}
graphes.graph(R, factor * data, fignum=3, label='ko')
graphes.graph(R,
factor * gaussian(res.x, R),
label='r.',
fignum=fignum + 2)
graphes.set_axis(0, 20, 0, factor * 1.1)
figs.update(graphes.legende('r (mm)', 'Vorticity s^{-1})', ''))
graphes.cla(fignum=fignum)
graphes.color_plot(X, Y, factor * data, fignum=fignum + 3)
graphes.colorbar()
figs.update(
graphes.legende('X (mm)',
'Y (mm)',
'Vorticity',
display=False,
cplot=True))
return a, center, figs
else:
return a, center
def display_profile(x, V, label='k', axe=2, fignum=0):
x = np.asarray(x)
z = x[:, axe]
labels = ['Ux', 'Uy', 'Uz']
for i in range(3):
graphes.graph(z, V[:, i], fignum=-i + 3 + fignum, label=label)
graphes.legende(labels[i][1] + ' (au)', 'V ', labels[i])
def plot(p,tmin,tmax,label='',c=True,fignum=0):
"""
Plot the position of the vortex as a function time.
pos is a dictionnary obtained from track.position
tmin :
minimum index
tmax :
maximum index
"""
figs = {}
keys = ['Xmax','Xmin','Ymin','Ymax']
subplot = {'Xmax':121,'Xmin':121,'Ymax':122,'Ymin':122}
fig1 = graphes.set_fig(fignum+1)
fig1.set_size_inches(10,4)
accurate = {key:None for key in keys}
for key in keys:
# print(p[key][tmin:tmax])
if c:
p[key][tmin:tmax],accurate[key] = correct(p[key][tmin:tmax],a=5.)
else:
accurate[key]=True
if 'Y' in key:
#print('invert !')
if np.nanmean(p[key])<0:
p[key] = -np.asarray(p[key]) #X axis is inverted !
if accurate[key]:
graphes.set_fig(fignum+1,subplot[key])
graphes.graph(p['t'],p[key],fignum=fignum+1,label=label)
figs.update(graphes.legende('Time (s)',key[0]+' position (mm)',''))
if 'Y' in key:
graphes.set_axis(0.05,p['t'][tmax],0,100)
else:
graphes.set_axis(0.05,p['t'][tmax],-50,50)
p['d']=np.sqrt((np.asarray(p['Xmin'])-np.asarray(p['Xmax']))**2+(np.asarray(p['Ymin'])-np.asarray(p['Ymax']))**2)
graphes.graph(p['t'],p['d'][tmin:tmax],fignum=fignum+2,label=label)
graphes.set_axis(0,p['t'][tmax],0,50)
figs.update(graphes.legende('Time (s)','Distance (mm)',''))
if accurate['Xmin'] and accurate['Ymin']:
graphes.graph(p['Ymin'][tmin:tmax],p['Xmin'][tmin:tmax],fignum=fignum+3,label=label)
figs.update(graphes.legende('X position (mm)','Y position (mm)',''))
graphes.set_axis(0,60,-50,50)
if accurate['Xmax'] and accurate['Ymax']:
graphes.graph(p['Ymax'][tmin:tmax],p['Xmax'][tmin:tmax],fignum=fignum+3,label=label)
graphes.graph(p['t'],p['Gammamax'],fignum=fignum+4,label=label)
figs.update(graphes.legende('Time (s)','Circulation (mm^2/s)',''))
graphes.set_axis(p['t'][tmin],p['t'][tmax],0,5*10**4)
return figs,accurate
def spatial_average(M, indices=None):
figs = {}
fields, names, vmin, vmax, labels, units = std_fields()
for j, field in enumerate(fields):
Y_moy = np.nanmean(getattr(M, field), axis=(0, 1))
graphes.graph(M.t, Y_moy, label=labels[j], fignum=j + 1)
#graphes.set_axis(0,5,0,18000)
figs.update(
graphes.legende('Time (s)', names[j] + ' (' + units[j] + ')', ''))
return figs
def compute_Ct(M,
tlist=[],
axes=['Ux', 'Ux'],
p=1,
display=False,
label='ko',
fignum=1):
display_part = False
if tlist == []:
t0 = 20
Dt = 50
dimensions = M.shape()
tlist = range(t0, dimensions[2] - t0, Dt)
tau = np.zeros(len(tlist))
tf = np.zeros(len(tlist))
for i, t in enumerate(tlist):
X, Y, Yerr = stat_corr_t(M, t, axes=axes, p=1, display=False)
try:
popt, pcurv = scipy.optimize.curve_fit(fitting.exp, np.abs(X), Y)
except ValueError:
print("NaN values encountered, fit skipped")
X, Y, Yerr = stat_corr_t(M, t, axe='E', p=1, display=True)
# input()
pcurv = []
popt = [1]
except RuntimeError:
print(
"Fitting did not converge, arbitrarly chosen to previous value"
)
pcurv = []
if i == 0:
popt = [1]
else:
popt = [tau[i - 1]]
tf[i] = M.t[t]
tau[i] = -1 / popt[0]
# print(str(M.t[t]) + ' : ' +str(tau[i]))
if display_part:
texp = np.abs(X)
graphes.set_fig(1)
graphes.errorbar(texp, Y, texp * 0, Yerr, fignum=0, label='ko')
graphes.graph(texp, exp(texp, -1 / tau[i]), fignum=1, label='r')
graphes.legende('$t/u^{2m}$', '$C_t$', '$m=1/2$')
if display:
graphes.graphloglog(tf, tau, fignum=fignum, label=label)
graphes.legende('t (s)', 't_c', graphes.title(M))
return tf, tau
def spectrum_1d(M, indices=None, norm_factor=1):
Fourier.compute_spectrum_1d(M, Dt=3)
S_k = np.nanmean(M.S_k[..., indices], axis=1) / norm_factor
graphes.graphloglog(M.k, S_k, label='^-', fignum=1)
k0 = 0.1
i = np.argmin(np.abs(M.k - k0))
A = S_k[i] * k0**(5. / 3)
# print('Total energy : '+np.sum(M.k*S_k))
graphes.graph(M.k, A * M.k**(-5. / 3), label='r--')
figs = graphes.legende('$k$ (mm$^{-1}$)', 'E (m/s^2)', '')
return figs
def from_circulation_2(M, fignum=1, display=True):
# R_list,Gamma,center,factor = compute_circulation_2(M,fignum=fignum)
lc, G0, center = fit_core_circulation(M, fignum=fignum, display=True)
nx, ny, nt = M.shape()
for i in range(nt):
R_list, Gamma, center, factor = circulation_2(M, i)
graphes.graph(R_list, Gamma * factor, fignum=fignum, label='k^')
graphes.legende('r (bmm)', 'Circulation (mm^2/s)', '')
graphes.set_axis(0, 12., -7000, 500)
return None
def artificial_1d():
#just to test derivative : works perfectly
dx = 0.001
p = 5
x = np.arange(0, 1, dx)
y = np.power(x, p)
dy_num = tangent(y, d=1, step=1, cyclic=False) / dx
dy_th = p * np.power(x[3:-3], p - 1)
graphes.graph(x, dy_num)
graphes.graph(x, dy_th)
graphes.set_axis(0, 1, -1, p + 1)
def from_vorticity(M, fignum=1, display=True):
ny, nx, nt = M.shape()
field = 'omega'
M.get(field)
# M,field = vgradient.compute(M,'vorticity',filter=True)
Z = getattr(M, field)
x, y = space_axis_vorticity(M)
a = []
indices = range(0, nt, 1)
x0 = []
y0 = []
index = []
for i in indices:
sigma, center = fit_core_size(x, y, Z[..., i], display=False)
if sigma is not None:
a.append(sigma)
index.append(i)
x0.append(center[0])
y0.append(center[1])
lc = np.nanmedian(a)
std_lc = np.nanstd(a)
#plot example
error = 0.5
if len(index) > 0 and std_lc / lc < error:
i_example = index[len(index) // 2]
print('Indice : ' + str(i_example))
sigma, center, figs = fit_core_size(x,
y,
Z[..., i_example],
fignum=fignum,
display=True)
t = np.asarray([M.t[i] for i in index])
graphes.graph(t, a, label='k', fignum=fignum + 1)
# graphes.graph(t,x0,label='r.',fignum=3)
# graphes.graph(t,y0,label='b.',fignum=3)
title = os.path.basename(M.dataDir)
graphes.set_axis(np.min(t), np.max(t), 0., 7.)
figs.update(graphes.legende('t (s)', 'Core size', title,
display=False))
save_graphes(M, figs, method='vorticity')
return lc, std_lc
def isotropy(M, label='k^--', display=True, fignum=1):
step = 1
tl = M.t[0:None:step]
N = 50
display_part = False
Anisotropy = np.zeros(len(tl))
Meanflow = np.zeros(len(tl))
for i, t in enumerate(tl):
print(i * 100 / len(tl))
rho, Phi = angles(M, i)
theta, U_moy, U_rms = angular_distribution(M, i)
# t,U_moy,U_rms = time_window_distribution(M,i,Dt=40)
if display_part:
graphes.hist(Phi, fignum=1, num=N)
graphes.legende('Phi', 'PDF', '')
graphes.graph(theta, U_moy, fignum=3, label='k^')
graphes.legende('$\theta$', '$U^p$',
'Angular fluctation distribution')
graphes.graph(theta, U_rms, fignum=4, label='ro')
graphes.legende('$\theta$', '$U^p$', 'Angular average flow')
Anisotropy[i] = np.std(U_rms) / np.nanmean(U_rms)
Meanflow[i] = np.std(U_moy) / np.nanmean(U_rms)
graphes.semilogx(tl,
Anisotropy,
label='ro',
fignum=fignum,
subplot=(1, 2, 1))
graphes.legende('Time (s)', 'I', 'Anisotropy' + graphes.set_title(M))
graphes.set_axes(10**-2, 10**4, 0, 2)
graphes.semilogx(tl,
Meanflow,
label='k^',
fignum=fignum,
subplot=(1, 2, 2))
graphes.legende('Time (s)', '<U>', 'Average flow')
graphes.set_axes(10**-2, 10**4, 0, 4)
def positions(M,i,field='omega',display=False,sigma=1.,**kwargs):
"""
find the maximum position of the vortex core at a given instant, using the field specified in argument
"""
imin,imax,jmin,jmax,Zfilt,X,Y = position_index(M,i,field=field,display=display,sigma=sigma,**kwargs)
deltax_max,deltay_max = SubPix2DGauss(Zfilt/np.max(Zfilt),imax,jmax,SubPixOffset=0)
deltax_min,deltay_min = SubPix2DGauss(Zfilt/np.min(Zfilt),imin,jmin,SubPixOffset=0)
# print(deltax_max)
# print(deltax_min)
# print(deltay_min)
# print(deltax_max)
#if deltax_max == np.nan or deltax_min==np.nan:
# return np.nan,np.nan,np.nan,np.nan
#else:
xmin = X[imin,jmin]+deltax_min
xmax = X[imax,jmax]+deltax_max
ymin = Y[imin,jmin]-deltay_min
ymax = Y[imax,jmax]-deltay_max
# print(Zfilt.shape)
# print(Y.shape)
# print(X.shape)
if display:
graphes.color_plot(X[:,5:],Y[:,5:],Zfilt/np.min(Zfilt),**kwargs)
graphes.graph([xmin+10],[ymin],label='bo',**kwargs)
graphes.graph([xmax+10],[ymax],label='ro',**kwargs)
# graphes.color_plot(X[:,5:],Y[:,5:],Zfilt/np.max(Zfilt),fignum=2)
#input()
# t.append(M.t[i])
# Xmin.append(M.x[imin,jmin])
# Xmax.append(M.x[imax,jmax])
# Ymin.append(M.y[imin,jmin])
# Ymax.append(M.y[imax,jmax])
return xmin,xmax,ymin,ymax
def multi_plane_measurements(Dt, N):
ti = 500
n = 13
# Dtlist=[2,4,6,8,10,20,30,50,70,100,150,200,500]
m = len(Mlist)
t = np.zeros((n, m))
Ux_rms = np.zeros((n, m))
Uy_rms = np.zeros((n, m))
Uxt_rms = np.zeros((n, m))
Uyt_rms = np.zeros((n, m))
Zlist = [M.param.Zplane for M in Mlist]
Dtlist = [Dt]
for M in Mlist:
j = Mlist.index(M)
print(j)
for i in range(n):
t0 = (i + 1) * N + ti
mid = t0 + Dt / 2
t[i] = M.t[mid]
# Sdata_measure.velocity_distribution(M_1000fps,t0,t0+Dt)
if i == 0:
fig = j * 2 + 1
plt.figure(fig)
# Ux_rms[i,j],Uy_rms[i,j],Uxt_rms[i,j],Uyt_rms[i,j]=Sdata_measure.velocity_distribution(M,t0,t0+Dt)
Dir = M.fileDir + 'Velocity_Distribution' + '/'
file = graphes.set_title(M, 'Dt=' + str(Dt / 10) + ' ms' + 'pdf_U')
filename = Dir + file
print(Dir)
print(filename)
# graphes.save_fig(fig,filename,Dir)
U_rms = (Ux_rms + Uy_rms) / 2
Ut_rms = (Uxt_rms + Uyt_rms) / 2
graphes.graph(Zlist, U_rms[0, :], -1, 'o')
graphes.graph(Zlist, Ut_rms[0, :], 0, '^')
graphes.graph(Zlist, U_rms[0, :], -1, '+--')
for i in range(1, n):
graphes.graph(Zlist, (U_rms[i, :] * t[i] / t[0]), 0, '+--')
graphes.set_axes(-110, 50, 0, 1)
graphes.legende('$Z (mm)$', '$(t/t_0) <Urms_{xi}>_{Dt,x,y}$', '')
file = graphes.set_title(M, 'U_rms_vs_t')
filename = Dir + file
def mean_profile(S, i, j, direction='v', label='k^', display=False):
#mean profile along the whole field : average on one direction only ! (and small windows on the other direction ?)
nx, ny, nt = S.shape()
Ux = S.m.Ux
Uy = S.m.Uy
#remove the data out of the PIV bounds
# Ux,Uy=fix_PIV(S)
U = np.sqrt(Ux**2 + Uy**2)
# V=np.reshape(U,(nx*ny,nt))
#median is not so affected by peak values, but standard deviation definetely !
#histogramm between vmin and vmax, and remove values out of bound (set to NaN)
U_moy = []
U_std = []
t = S.m.t
Dt = 2
if direction == 'v':
#average along the horizontal direction
U_moy = [
np.mean(np.mean(U[j - Dt:j + Dt, :, k], axis=0), axis=0)
for k in range(nt)
]
print('horizontal average')
else:
#average along the vertical direction
U_moy = [
np.mean(np.mean(U[:, i - Dt:i + Dt, k], axis=0), axis=0)
for k in range(nt)
]
print('vertical average')
print(np.shape(U_moy))
if display:
#U_moy=np.mean(V[np.invert(np.isnan(V))],axis=0)
print('Number of frames : ' + str(len(S.m.t)))
graphes.graph(t, U_moy, label)
graphes.legende('t (ms)', '<V>_{x,y} (m/s)', '')
return U_moy, U_std
def movie_spectrum(M,
field,
alpha=[-5. / 3],
Dt=10,
fignum=1,
start=0,
stop=0):
#switch_field(field)
if not hasattr(M, field):
M, field = vgradient.compute(M, field)
if not hasattr(M, 'S_' + field):
Y = getattr(M, field)
Y_k, k = spectrum_1d(Y, M, display=False, Dt=Dt)
print(Y_k.shape)
else:
Y_k = getattr(M, 'S_' + field)
k = getattr(M, 'k_' + field)
step = max(Dt / 2, 1)
N, nt = Y_k.shape
if stop == 0:
tax = range(start, nt, step)
else:
tax = range(start, stop, step)
figs = {}
for i, t in enumerate(tax):
#graphes.cla(fignum)
graphes.graph(k, Y_k[:, t], label='k-', fignum=fignum)
#graphes.graphloglog(k,Y_k[:,t],label='k-',fignum=fignum)
add_theory(k, Y_k[:, t], alpha, fignum=fignum)
# graphes.set_axis(10**-2,10**0,10**-4,10**2)
figs.update(graphes.legende('k (mm^-1)', 'E_k (mm^3/s^-2)', ''))
graphes.save_graphes(M,
figs,
prefix='Movie_Spectrum_' + field + '/',
suffix='_' + str(t))
def tangent_test():
N = 100
path = generate_vortex(1, N)
dV = tangent(path) * N / (2 * np.pi)
print(np.mean(norm(dV)))
print(np.std(norm(dV)))
indices = np.arange(0, 100, 10)
for i in indices:
print(dV[i, :], path[i, :])
# graphes.graph(path[:,0],path[:,1],label='r')
graphes.graph(np.arange(N), path[:, 0])
graphes.graph(np.arange(N), np.sum(dV * path, axis=1))
# vfield.plot(path[:,0],path[:,1],dV)
# graphes.set_axis(-1.1,1.1,-1.5,1.5)
graphes.legende('x', 'y', '')
def horizontal_profile(S, ylines, Dt, start=0):
nx, ny, nt = S.shape()
x = S.x[0, :]
for i in range(start, nt, Dt):
Ux = np.mean(np.mean(S.Ux[ylines, :, i:i + Dt], axis=0), axis=1)
Uy = np.mean(np.mean(S.Uy[ylines, :, i:i + Dt], axis=0), axis=1)
std_Ux = np.std(np.std(S.Ux[ylines, :, i:i + Dt], axis=0), axis=1)
std_Uy = np.std(np.std(S.Uy[ylines, :, i:i + Dt], axis=0), axis=1)
plt.subplot(121)
graphes.graph(x, Ux, 0, std_Ux)
graphes.legende('x (m)', 'V (m/s)', 'Ux')
plt.subplot(122)
graphes.graph(x, Uy, 0, std_Uy)
graphes.legende('x (m)', 'V (m/s)', 'Uy')
plt.draw()
raw_input()
def display_corr_vs_t(M,
dlist,
indices,
step=100,
Dt=1,
label='-',
display=False,
fignum=1):
tref, d, Cxx, Cyy, Cxy, CEE = correlation_functions(M,
dlist,
indices,
Dt=Dt)
#Display successive correlations functions
times = range(0, len(tref) - 3 * Dt, step)
times = range(0, len(tref), step)
if display:
for t in times:
graphes.graph(d, Cxx[:, t] / Cxx[0, t], fignum=fignum)
graphes.set_axis(0, max(d), -1, 1.5)
graphes.legende('d (mm)', 'C_{xx}', '')
graphes.graph(d, Cyy[:, t] / Cyy[0, t], fignum=fignum + 1)
graphes.set_axis(0, max(d), -1, 1.5)
graphes.legende('d (mm)', 'C_{yy}', '')
graphes.graph(d, CEE[:, t] / CEE[0, t], fignum=fignum + 2)
graphes.set_axis(0, max(d), -1, 1.5)
graphes.legende('d (m)', 'C_{E}', '')
return tref, d, Cxx, Cyy, Cxy, CEE
def time_corr(data, param, N=800):
keys = data.keys()
figs = {}
for p in range(3):
print(p)
keys = np.sort(keys)
[data[key] for key in keys]
t = np.asarray([param[key]['t0'] for key in keys[0:N / 2]])
C = np.zeros(N / 2)
Dt = np.zeros(N / 2)
for i in range(N / 2):
if i % 100 == 0:
print(i)
C[i] = np.nanmean(np.asarray([
data[key1][..., p] * data[keys[i + j]][..., p]
for j, key1 in enumerate(keys[0:N / 2])
]),
axis=(0, 1, 2))
Dt[i] = np.nanmean(
np.asarray([
param[keys[j + i]]['t0'] - param[keys[j]]['t0']
for j in range(N / 2)
]))
# print(Dt)
graphes.graph(Dt, C / C[0], fignum=4)
graphes.set_axis(0, 400, -1, 1.1)
figs.update(graphes.legende('t', 'C', 'JHTD corr functions'))
graphes.save_figs(figs,
savedir='./Corr_functions/',
suffix='',
prefix='',
frmt='pdf',
dpi=300)
def test_Dt(W):
figs = {}
ratio = []
Dt_list = range(1, 11)
for i, Dt in enumerate(Dt_list):
print('Dt : ' + str(Dt))
Dir = '/Users/stephane/Documents/Experiences_local/PIV_tests/Database/Turbulence/PIV_data/'
base = '_2016_03_01_PIV_sv_vp_zoom_zoom_X10mm_M24mm_fps10000_n14000_beta500m_H1180mm_S150mm_1'
Dirbase = Dir + 'PIVlab_ratio2_W' + str(W) + 'pix_Dt_' + str(Dt) + base
fileList = glob.glob(Dirbase + '/*.txt')
dataList = get_data(fileList)
r, fig = test_bound(dataList, W, Dt, fignum=1 + i)
figs.update(fig)
ratio.append(r)
graphes.graph(Dt_list, ratio, fignum=11, label='ko')
graphes.graph([0, 11], [100, 100], label='r-', fignum=11)
graphes.set_axis(0, 11, 0, 110.)
figs.update(
graphes.legende('Dt (# frame)', 'Percentage of good measurements', ''))
def Test_dv(M, frames=None, W=32, display=True, scale=True, type=1, **kwargs):
frames = get_frames(M, frames)
r = 0.
ropt = 0.
dU = access.get(M,
'dU',
frames[0],
Dt=len(frames),
compute=False,
rescale=False,
type=type)
for frame in frames:
r0, ropt0 = gradient(M, frame, display=False, W=W, scale=scale)
r += r0
ropt += ropt0
R = r / len(frames)
Ropt = ropt / len(frames)
if display:
import stephane.display.graphes as graphes
dUmin, dUmax = shear_limit(W)
xbin, n = graphes.hist(dU,
normalize=False,
num=200,
range=(-0.5, 0.5),
**kwargs) #xfactor = Dt
maxn = max(n) * 1.2
graphes.graph([dUmin, dUmin], [0, maxn], label='r-', **kwargs)
graphes.graph([dUmax, dUmax], [0, maxn], label='r-', **kwargs)
graphes.legende('', '', '')
print("Percentage of good values (gradient test) : " + str(R) + " %")
print("ratio measured shear / optimal value : " +
str(Ropt)) #greater than 1 start to be bad
return R
def compare_profil(S1, S2):
#### DEPRECIATED : move Sdata.subset_index to a new module of smart data indexing
#S1 and S2 must be the same length, and the same x and y dimensions
#compare velocity measurement obtained with two different frame rate
#norm and direction
Ux1, Uy1 = fix_PIV(S1)
Ux2, Uy2 = fix_PIV(S2)
U1, theta1 = Smath.cart2pol(Ux1, Uy1)
U2, theta2 = Smath.cart2pol(Ux2, Uy2)
print(U2.shape)
nx, ny, nt = S1.shape()
indices1, indices2, nt = subset_index(S1, S2)
#locate a subset of identical im_index
#indices correspond of the number of the U rows, not to the im_index valeurs
# indices,nt=subset_index(S1,S2)
U1 = np.reshape(U1[:, :, indices1], nx * ny * nt)
U2 = np.reshape(U2[:, :, indices2], nx * ny * nt)
#if U1 and U2 have not the same length,
#a fraction can be selected using the attr im_index of each one (to use the same list of images)
#random extract
N = 2000
ind = random.sample(xrange(nx * ny * nt), N)
graphes.graph(U1[ind], U2[ind])
xlabel = 'V (m/s) at ' + str(S1.timescale * S1.fps) + ' fps'
ylabel = 'V (m/s) at ' + str(S2.timescale * S2.fps) + ' fps'
graphes.legende(xlabel, ylabel, '')
bounded_velocity(S1, True, 5, 'v')
bounded_velocity(S2, True, 5, 'h')
raw_input()
def example():
R = 2
Gamma = 5
N = 10**3
path = generate_vortex(R, N) #generation of a vortex ring
eps = 0.02
X = [np.asarray([0., 0., z])
for z in np.arange(-10, 10 + eps, eps)] #axis to look at
start = time.time()
V = velocity_from_line([path], X, Gamma)
end = time.time()
print("Time elapsed : " + str(end - start) + "s")
print("Number of computed values :" + str(np.shape(X)[0]))
V_th = B_along_axis(X, R, Gamma)
x = np.asarray(X)
display_profile(x, V, label='ks')
graphes.graph(x[:, 2], V_th, label='r.-', fignum=1)
def divergence(S, xlines, ylines, display=False):
"""
DEPRECIATED : was used to compute the asymetry of the flow
"""
#xlines list of tuple ??
#take two points (symmetric in respect to X=0?), and mesure the relative distance between each
#as a function of time
#relative means in respect to the spatial averaged velocity at the time
t = S.t
Dt = 10
ny, nx, nt = S.shape()
U_moy, U_std = mean_profile(S, label='k^', display=False)
n = len(xlines)
Ds = np.zeros((ny, nx, nt - 2 * Dt)) #first element is centered !
for i in ylines:
for pair in xlines:
dx = S.Ux[i,
pair[0], :] + S.Ux[i,
pair[1], :] #antisymetric x profile ?
dy = S.Uy[i, pair[0], :] - S.Uy[i, pair[1], :]
D, phi = Smath.cart2pol(dx, dy)
#Divide by the global average velocity at each time !!
Ds[ylines.index(i), pair[0], :] = smooth(D,
Dt) #=smooth(D/U_moy,Dt)
Ds[ylines.index(i), pair[1], :] = Ds[i, pair[0], :]
if display:
graphes.graph(t[Dt:-Dt], Ds)
graphes.legende(
't (ms)', 'V (m/s)',
'Velocity difference between two symetric points')
return Ds
