def W_accuracy(Mdict, Dt=1, frames=None):
"""
Compute the error value for adjacent values of Dt
Mdict is a dictionnary of Mdata. The key correspond to the couple of parameters (W,Dt)
"""
# W=16
key = (Dt, W)
M = Mdict[key]
frames = get_frames(M, frames)
n = min(len(frames), 10)
Dtlist = range(1, 20)
dim = Mdict[key].shape()
U = np.zeros(dim[:-1] + (n, len(Dtlist)))
Ux = np.zeros(dim[:-1] + (n, len(Dtlist)))
Uy = np.zeros(dim[:-1] + (n, len(Dtlist)))
for i, Dt in enumerate(Dtlist):
key = (Dt, W)
Ux[..., i] = np.sqrt(access.get(Mdict[key], 'Ux', frames[0], Dt=n))
Uy[..., i] = np.sqrt(access.get(Mdict[key], 'Uy', frames[0], Dt=n))
U[..., i] = np.sqrt(access.get(Mdict[key], 'E', frames[0], Dt=n))
for i, Dt in enumerate(Dtlist[:-1]):
std_moy_Ux, std_Ux = compare(Ux, U, start=i, n=2, b=2)
std_moy_Uy, std_Uy = compare(Uy, U, start=i, n=2, b=2)
std_moy_U = (std_moy_Ux + std_moy_Ux) / 2
print(std_moy_U)
return std_moy_U
def accuracy(var, M, frames=None, **kwargs):
frames = get_frames(M, frames)
n = min(len(frames), 10)
Ux = access.get(M, 'Ux', frames[0], Dt=n)
Uy = access.get(M, 'Uy', frames[0], Dt=n)
U = np.sqrt(access.get(M, 'E', frames[0], Dt=n))
dim = Ux.shape
d = len(dim)
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(M, frame, scale=True, display=True, W=None):
"""
Ux = access.get(M,'Ux',frame)
Uy = access.get(M,'Uy',frame)
U = np.asarray([Ux,Uy])
dim = U.shape
N = np.prod(dim[1:])
d = len(dim)
U = np.transpose(U,tuple(range(1,d))+(0,))
U = np.sqrt(np.sum(np.power(U,2),axis=d-1)) #velocity modulus
"""
U = np.sqrt(access.get(M, 'E', frame))
N = np.prod(U.shape)
if scale:
Umin, Umax = bounds(M)
else:
Umin, Umax = bounds_pix(W)
if N == 0:
#print(U.shape)
N = np.prod(U.shape[:-1])
r = len(np.where(np.logical_and(U > Umin, U < Umax))[0]) * 100. / N
if display:
print("Percentage of good values (velocity test) : " + str(r) + " %")
return r
def Dt_accuracy(Mdict, W=32, frames=None):
"""
Compute the error value for adjacent values of Dt
Mdict is a dictionnary of Mdata. The key correspond to the couple of parameters (W,Dt)
"""
key = (1, W)
M = Mdict[key]
frames = get_frames(M, frames)
n = min(len(frames), 10)
Dtlist = range(1, 20)
dim = Mdict[key].shape()
U = np.zeros(dim[:-1] + (n, len(Dtlist)))
Ux = np.zeros(dim[:-1] + (n, len(Dtlist)))
Uy = np.zeros(dim[:-1] + (n, len(Dtlist)))
dU = np.zeros(dim[:-1] + (2, 2, n, len(Dtlist)))
dU2 = np.zeros(dim[:-1] + (2, 2, n, len(Dtlist)))
for i, Dt in enumerate(Dtlist):
key = (Dt, W)
Ux[..., i] = access.get(Mdict[key], 'Ux', frames[0], Dt=n)
Uy[..., i] = access.get(Mdict[key], 'Uy', frames[0], Dt=n)
U[..., i] = np.sqrt(access.get(Mdict[key], 'E', frames[0], Dt=n))
dU[..., i] = access.get(Mdict[key], 'dU', frames[0], Dt=n)
dU_norm = np.sqrt(np.sum(np.power(dU[..., i], 2), axis=(2, 3)))
dU2[..., i] = np.transpose(np.tile(dU_norm, (2, 2, 1, 1, 1)),
(2, 3) + (0, 1, 4))
for i, Dt in enumerate(Dtlist[:-1]):
std_moy_Ux, std_Ux = compare(Ux, U, start=i, n=2, b=2)
std_moy_Uy, std_Uy = compare(Uy, U, start=i, n=2, b=2)
std_moy_U = (std_moy_Ux + std_moy_Ux) / 2
std_moy_dU, std_dU = compare(dU, dU2, start=i, n=2, b=2)
print(std_moy_U, std_moy_dU)
return std_moy_U
def pdf(M,field,frame,Dt=10,Dx=1024,label='ko-',fignum=1,a=15.,norm=True,sign=1):
import stephane.manager.access as access
Up = access.get(M,field,frame,Dt=Dt)
limits = [(0,Dx),(0,Dx)]
Up = sign*access.get_cut(M,field,limits,frame,Dt=Dt)
figs = distribution(Up,normfactor=1,a=a,label=label,fignum=fignum,norm=norm)
return figs
def accuracy(M, frames):
"""
from a serie of adjacent frames compute :
the ratio of measurements in the boundaries (Umin, Umax).
Compute the noise level on the velocity field by averaging over adjacent frames in time (hypothesis of well resolved dynamics)
the ratio of measurements within the shear limit (dUmax)
Compute the velocity gradient noise level using the same time-averaging technic
"""
for frame in frames:
Ux = access.get(M, 'Ux', frame)
def v_accuracy(M, frames=None, display=True, **kwargs):
frames = get_frames(M, frames)
n = min(len(frames), 10)
Ux = access.get(M, 'Ux', frames[0], Dt=n)
Uy = access.get(M, 'Uy', frames[0], Dt=n)
U = np.sqrt(access.get(M, 'E', frames[0], Dt=n))
dim = Ux.shape
d = len(dim)
std_moy_Ux, std_Ux = compare(Ux, U, n=10, b=3)
std_moy_Uy, std_Uy = compare(Uy, U, n=10, b=3)
std_moy_U = (std_moy_Ux + std_moy_Ux) / 2
if display == True:
print('Relative error velocity : ' + str(std_moy_U * 100) + " %")
# print('Relative error along y : '+str(std_moy_Uy*100)+ " %")
return std_moy_U, std_Ux, std_Uy
def chose_axe(M, t, axes, Dt=1):
"""
Chose N axis of a Mdata set
INPUT
-----
M : Madata object
t : int
time index
axes : string list
Possible values are : 'E', 'Ux', 'Uy', 'strain', 'omega'
OUTPUT
-----
"""
data = tuple([access.get(M, ax, t, Dt=Dt) for ax in axes])
return data
def gradient(M, frame, W=32, scale=True, display=True):
dU = access.get(M, 'dU', frame)
print(dU.shape)
N = np.prod(dU.shape)
dUmin, dUmax = shear_limit(W)
r = len(np.where(np.logical_and(dU > dUmin, dU < dUmax))[0]) * 100. / N
dU_opt = shear_optimum(W)
dU_moy = np.nanstd(dU)
ropt = dU_moy / dU_opt
if display:
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, ropt
def circulation_2(M, i, fignum=1, display=False):
Omega = access.get(M, 'omega', i)
x, y = space_axis_vorticity(M)
X, Y, data, center, factor = normalize(x, y, Omega[..., 0])
dx = M.x[0, 1] - M.x[0, 0]
#print(dx)
U, d = vgradient.make_Nvec(M, i) # Z : d+1 dimension np array
nx, ny = X.shape
R_list = np.arange(1., 15., 0.5)
Gamma = []
divergence = []
for b in R_list:
# print(b)
tau = strain_tensor.strain_tensor_loc(U,
center[0],
center[1],
d=2,
b=b)
omega, enstrophy = strain_tensor.vorticity(tau, d=2, norm=False)
div = strain_tensor.divergence_2d(tau, d=2)
G = (omega[0, 0] - div[0, 0]) * np.pi * b**2 * dx**2
Gamma.append(G)
divergence.append(div[0, 0] / np.abs(omega[0, 0]))
R_list = np.asarray(R_list) * dx
if display:
graphes.graph(R_list, Gamma, fignum=fignum, label='bo')
graphes.legende('r (mm)', 'Circulation (mm^2/s)', '')
graphes.graph(R_list, divergence, fignum=fignum + 1, label='ko')
graphes.graph(R_list,
np.zeros(len(R_list)),
fignum=fignum + 1,
label='r--')
graphes.legende('r (mm)', 'Relative 2d divergence', '')
graphes.set_axis(0, 30 * dx, -0.3, 0.3)
return R_list, Gamma, center, factor
def Space(M, field, tlist, N=30, Np=10**4, norm_d=1.):
dlist = range(N)
indices = {}
Corr_d = {}
U = access.get(M, field, 0)
for d in dlist:
indices[d] = corr.d_2pts_rand(U[..., 0], d, Np)
for i in tlist:
C = np.zeros(len(dlist))
for d in dlist:
C[d] = compute(M, i, indices[d], axes=[field, field])
Corr_d[(i, 'd_' + field)] = np.asarray(dlist) * 1. / norm_d
Corr_d[(i, 'Cd_' + field)] = C
return Corr_d
def dv_accuracy(M, frames=None, display=True, **kwargs):
frames = get_frames(M, frames)
n = min(len(frames), 10)
dU = access.get(M, 'dU', frames[0], Dt=n)
dim = dU.shape
d = len(dim)
dU2 = np.sqrt(np.sum(np.power(dU, 2), axis=(d - 3, d - 2)))
#dU2 = np.reshape(np.tile(dU2,dim),dim+(n,))
d = len(dU2.shape)
# std_moy_U1 = compare(dU[...,0,0,:],n=10,b=3) #need to normalizeby dU2
std_U1 = np.nanmean([
np.nanstd(dU[..., 0, 0, slice(i, i + 3)], axis=d - 1) /
np.nanmean(dU2[..., slice(i, i + 3)], axis=d - 1) for i in range(10)
]) #standard deviation along x axis
std_U2 = np.nanmean([
np.nanstd(dU[..., 0, 1, slice(i, i + 3)], axis=d - 1) /
np.nanmean(dU2[..., slice(i, i + 3)], axis=d - 1) for i in range(10)
])
std_U3 = np.nanmean([
np.nanstd(dU[..., 1, 0, slice(i, i + 3)], axis=d - 1) /
np.nanmean(dU2[..., slice(i, i + 3)], axis=d - 1) for i in range(10)
])
std_U4 = np.nanmean([
np.nanstd(dU[..., 1, 1, slice(i, i + 3)], axis=d - 1) /
np.nanmean(dU2[..., slice(i, i + 3)], axis=d - 1) for i in range(10)
])
std_moy_dU = np.median((std_U1 + std_U2 + std_U3 + std_U4) / 4)
if display == True:
print('Relative error velocity gradient : ' + str(std_moy_dU * 100) +
" %")
return std_moy_dU, std_U1, std_U2, std_U3, std_U4
def pdf_ensemble(Mlist,field,frame,Dt=10,Dx=1024,label='r-',fignum=1,a=10.,norm=True,model=False):
import stephane.manager.access as access
U_tot = []
for M in Mlist:
pdf(M,field,frame,Dt=Dt,Dx=Dx,label='k',fignum=fignum,a=a,norm=False)
Up = access.get(M,field,frame,Dt=Dt)
# limits = [(0,Dx),(0,Dx)]
# Up = access.get_cut(M,field,limits,frame,Dt=Dt)
# if Dx is larger than the box size, just keep all the data
U_tot = U_tot + np.ndarray.tolist(Up)
N = len(Mlist)
U_tot = np.asarray(U_tot)
x,y,figs = distribution(U_tot,normfactor=N,a=a,label=label,fignum=fignum,norm=norm)
if model:
n = len(y)
b = y[n//2]
Dy = np.log((y[n//2+n//8] + y[n//2-n//8])/2./b)
a = - Dy/x[n//2+n//8]**2
P = b*np.exp(-a*x**2)
semilogy(x,P,label='b.-',fignum=fignum)
set_axis(min(x),max(x),1,max(y)*2)
if field=='omega' or field=='strain':
unit = ' (s^-1)'
elif field=='E':
unit = 'mm^2/s^2'
else:
unit = ' (mm/s)'
figs = {}
figs.update(legende(field+unit,field+' PDF',time_label(M,frame)))
return figs
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 Mplot(M,field,frame,auto_axis=False,step=1,W=None,Dt=None,fignum=1,show=False,vmin=0,vmax=0,log=False,display=False,tstamp=False,compute=False,cbar=False,colorbar=False):
import stephane.pprocess.check_piv as check
import stephane.manager.access as access
data = access.get(M,field,frame,step=1,compute=compute)
dimensions = data.shape
if field=='strain':
#tensor variable. chose the trace (2d divergence !):
data = data[...,1,1,:]+data[...,0,0,:]
#print(data)
X,Y = get_axis_coord(M)
jmin = 0
data = data[:,jmin:]
X = X[:,jmin:]
Y = Y[:,jmin:]
t = M.t[frame]
ft = M.t[frame+1]-M.t[frame]
dx = np.mean(np.diff(M.x[0,:]))
if dx==0:
dx=1
if vmin==0 and vmax==0:
if auto_axis:
std = np.sqrt(np.nanmedian(np.power(data,2)))
vmax = 10*std
vmin = -vmax
if field in ['E','enstrophy']:
vmin=0
else:
if W is None:
vmin,vmax = check.bounds(M,t0=frame)
else:
vmin,vmax = check.bounds_pix(W)
if Dt is not None:
data = data/Dt
if field in ['Ux','Uy']:
vmax = np.abs(vmax)
vmin = -np.abs(vmax)#*100
if field in ['omega']:
data = data
vmax = np.abs(vmax)/5.#*15#/5.
vmin = -np.abs(vmax)#*10#*100#vmax
if field in ['strain']:
data = data
vmax = np.abs(vmax)/20.#*15#/5.
vmin = -np.abs(vmax)#*10#*100#vmax
if field in ['E']:
#std = np.std(data[...,frame])
vmax = vmax**2
vmin = vmin**2
if field in ['enstrophy']:
vmax = (vmax/5.)**2
vmin = (vmin)**2
if log:
vmax = np.log10(vmax)
if vmin>0:
vmin = np.log10(vmin)
else:
vmin = vmax /100.
n = (X.shape[0]-dimensions[0])/2
if n!=0:
X = X[n:-n,n:-n]
Y = Y[n:-n,n:-n]
color_plot(X,Y,data[...,0],show=show,fignum=fignum,vmin=vmin,vmax=vmax,log=log,cbar=cbar)
# time_stamp(M,frame)
if colorbar==True:
plt.colorbar()
# plt.axis('equal')
if tstamp:
t = M.t[frame]
Dt = M.t[frame+1]-M.t[frame]
s = ', t = '+str(np.round(t*1000)/1000)+' s, Dt = '+str(np.round(Dt*10000)/10)+'ms'
else:
s=''
figs = {}
figs.update(legende('X (mm)','Y (mm)',field+s,display=display,cplot=True,show=show))
return figs
def smoothing(M,i,field='omega',sigma=1.):
Z = access.get(M,field,i)
#Z = Z[:,5:] #crop image
return filters.gaussian_filter(Z, sigma=sigma)[...,0]
