def mean_flow(M_log):
# compute the mean flow by averaging the flow on the 5 movies and 10 times steps (more or less depending on the time ratio !!)
Ux = []
Uy = []
M_ref = M_log[0]
# X=M_ref.x
# Y=M_ref.y
t = M_ref.t
Etot = []
for tp in range(len(t)):
Umoy, Vmoy = average(M_log, tp)
E = np.sqrt(Umoy**2 + Vmoy**2)
Etot.append(np.nanmean(E))
print(np.nanmean(E))
# graphes.color_plot(X,Y,E,fignum=3)
Ux.append(Umoy)
Uy.append(Umoy)
graphes.graph(t, Etot, fignum=-1, label='k^')
graphes.legende('$t$ (s)', '$\sqrt{<E_c>}$ (mm/s)', '')
graphes.graphloglog(t, Etot, fignum=-1, label='k^')
graphes.legende('$t$ (s)', '$\sqrt{<E_c>}$ (mm/s)', '')
return Ux, Uy
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 horizontal_profile(S, ylines, Dt, start=0):
"""
Parameters
----------
S
ylines
Dt
start
Returns
-------
"""
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 velocity_distribution(M, start, end, display=False):
# compute the distribution of velocity for Ux, Uy and U for all the individual measurements between start and end
# substract the mean flow in each point
M = cdata.rm_nan(M, 'Ux')
M = cdata.rm_nan(M, 'Uy')
(nx, ny, n) = M.shape()
nt = end - start
Ux = np.reshape(M.Ux[:, :, start:end], (nx * ny * nt, ))
Uy = np.reshape(M.Uy[:, :, start:end], (nx * ny * nt, ))
Ux_rms = np.std(Ux)
Uy_rms = np.std(Uy)
Ux_moy = np.reshape(np.mean(M.Ux[:, :, start:end], axis=2), (nx, ny, 1))
Uy_moy = np.reshape(np.mean(M.Uy[:, :, start:end], axis=2), (nx, ny, 1))
Ux_m = np.reshape(np.dot(Ux_moy, np.ones((1, 1, nt))), (nx, ny, nt))
Uy_m = np.reshape(np.dot(Uy_moy, np.ones((1, 1, nt))), (nx, ny, nt))
# Ux=np.reshape(M.Ux[:,:,start:end]-Ux_m,(nx*ny*nt,))
# Uy=np.reshape(M.Uy[:,:,start:end]-Uy_m,(nx*ny*nt,))
Ux = np.reshape(M.Ux[:, :, start:end], (nx * ny * nt, ))
Uy = np.reshape(M.Uy[:, :, start:end], (nx * ny * nt, ))
# U_s=np.zeros(len(Ux)+len(Uy))
U_s = np.concatenate((Ux, Uy))
# U=np.sqrt(Ux**2+Uy**2)
# normalized by the RMS velocity :
Uxt_rms = np.std(Ux)
Uyt_rms = np.std(Uy)
U_rms = np.std(U_s)
print('RMS velocity : ' + str(U_rms) + ' m/s')
mid = (start + end) / 2
# Normalisation by the temporal decay function
Nvec = (M.t[mid] / 100)**(-1)
Nvec = 1
if display:
print(max(U_s))
print(min(U_s))
print(U_s.shape)
print(Nvec)
# graphes.hist(Ux,Nvec,0,100,'o')
# graphes.hist(Uy,Nvec,0,100,'s')
graphes.hist(U_s, Nvec, fignum=1, num=10**4, label='o')
title = ''
# title='Z= '+str(M.param.Zplane)+' mm, t='+str(M.t[mid])+' ms'+', Dt = '+str(nt*M.ft)+' ms'
graphes.legende('$U_{x,y} (m/s)$', '$pdf(U)$', title)
# fields={'Z':'Zplane','t',}
# graphes.set_title(M,fields)
return Ux_rms, Uy_rms, Uxt_rms, Uyt_rms
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 detect_start(t, X, threshold, display=False):
# initial velocity : average on Dt points
Dt = 10
vstart = np.mean(X[:Dt])
# endwhen the motion is the stronger
indmax = np.argmax(X)
vmax = np.max(X)
vend = np.mean(X[indmax - Dt / 2:indmax + Dt / 2])
# geometrical average
lim = np.sqrt(vstart * vend)
lbound = 0.02 # in mn/s
if np.isnan(lim):
lim = lbound
# lim=2000
# print(lim)
indices = np.where(X > lim)
# print(np.shape(indices))
# np.extract : find the first indices satisfying this condition
if not (indices[0].size == 0):
ind0 = indices[0][0]
else:
ind0 = np.argmax(t)
t0 = t[ind0]
if display:
graphes.graph([t0, t0], [0, vmax], False, 'r-')
graphes.legende('t (ms)', 'U (m/s)', '')
return t0, vmax
def divergence(S, xlines, ylines, display=False):
# 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
def compare_measure(M1, M2):
indices1_t, indices2_t, nt = match.time(M1, M2)
indices1_xy, indices2_xy, nt = match.space(M1, M2)
for tup1 in indices1_xy:
Ux1 = M1.Ux[tup1[0], tup1[1], indices1_t]
Uy1 = M1.Ux[tup1[0], tup1[1], indices1_t]
tup2 = indices2_xy[indices1_xy.index(tup1)]
print(tup2[0])
print(tup2[1])
Ux2 = M2.Ux[tup2[0], tup2[1], indices2_t]
Uy2 = M2.Uy[tup2[0], tup2[1], indices2_t]
t1 = str(type(M1))
t2 = str(type(M2))
name1 = browse.get_string(t1, 'Mdata_', '.')
name2 = browse.get_string(t2, 'Mdata_', '.')
title = name1 + ' vs ' + name2
graphes.graph(Ux1, Ux2, 1, 'ro')
graphes.legende('$Ux $', '$Ux$', title)
graphes.graph(Uy1, Uy2, 2, 'ro')
graphes.legende('$Uy $', '$Uy$', title)
plt.pause(10)
def velocity_profile_xy(S, xlines, ylines, display=False, label='k^'):
nx, ny, nt = S.shape()
label = ['k^', 'rx', 'bo']
t = S.t
Dt = 5
Uxt = []
Uyt = []
for i in ylines:
for j in xlines:
# std_Ux=[np.std(S.Ux[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
# std_Uy=[np.std(S.Uy[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
Uxt.append(
basics.smooth(S.Ux[i, j], Dt)
) # (-1)**i*(-1)**j* [(-1)**i*(-1)**j*np.mean(S.Ux[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
Uyt.append(basics.smooth(
S.Uy[i, j],
Dt)) # [np.mean(S.Uy[i,j,k-Dt:k+Dt]) for k in range(Dt,nt-Dt)]
if display:
# plt.subplot(211)
graphes.graph(t[Dt:-Dt], Uxt[-1]) # ,std_Ux)
graphes.legende('t (ms)', 'V (m/s)', 'Ux')
# plt.subplot(212)
graphes.graph(t[Dt:-Dt], Uyt[-1]) # ,std_Uy)
graphes.legende('t (ms)', 'V (m/s)', 'Uy')
return t, Uxt, Uyt
def initial_shape(offset=0):
zmin = -500 + offset
zmax = 500 + offset
# Dz = 45
dz = 1
z = np.arange(zmin, zmax, dz)
Dz = 200
U0 = 2.5 * 10**3
delta = 10.
U1 = U0 * np.exp(-Dz / delta)
# delta = Dz / np.log(U0/U1)
z0 = 0 + offset
z1 = Dz + offset
U = np.zeros(len(z))
for i, zi in enumerate(z):
if zi < z0:
U[i] = U0
if zi >= z0 and zi < z1:
U[i] = U0 * np.exp((z0 - zi) / delta)
if zi >= z1:
U[i] = U0 * np.exp(-Dz / delta)
graphes.semilogy(z, U, label='r')
graphes.legende('z (mm)', 'U (mm/s)', '')
graphes.set_axis(-50, 150, 10**0, 5 * 10**3)
return z, U
def stat_corr_t(M,
t,
Dt=20,
axes=['Ux', 'Ux'],
p=1,
display=False,
label='k^',
fignum=0):
"""
Parameters
----------
M
t
Dt
axes
p
display
label
fignum
Returns
-------
X, Y, Yerr
"""
t0 = M.t[t]
tlist = range(t - Dt // 2, t + Dt // 2)
curves = []
for t in tlist:
curves.append(corr_v_t([M], t, N=20, axes=axes, p=p, display=False))
X, Y, Yerr = statP.box_average(curves, 50)
X = X[~np.isnan(X)]
Y = Y[~np.isnan(Y)]
Yerr = Yerr[~np.isnan(Yerr)]
if display:
# graphes.set_fig(1)
graphes.errorbar(np.abs(X) / t0,
Y,
X * 0,
Yerr,
fignum=fignum,
label=label)
graphes.legende('$t/u^{2m}$', '$C_t$', '$m=1/2$')
name = 'Corr_' + axes[0] + '_' + axes[1] + '_' + str(t)
filename = './Corr_functions/' + M.id.date + '/' + M.id.get_id(
) + '/' + name + '.txt'
keys = ['t', name]
List_info = [np.ndarray.tolist(X), np.ndarray.tolist(Y)]
rw_data.write_dictionnary(filename, keys, List_info, delimiter='\t')
# print(X)
# print(Y)
return X, Y, Yerr
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 ?)
Parameters
----------
S
i
j
direction
label
display
Returns
-------
"""
# 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 detect_max(t, X, display=False):
vmax = np.max(X)
indmax = np.argmax(X)
tmax = t[indmax]
if display:
graphes.graph([tmax, tmax], [0, vmax], False, 'r-')
graphes.legende('t (ms)', 'U (m/s)', '')
return tmax, vmax
def make_plot_lin(Mlist, Range=None, color='k', label=None, field=[['Ux', 'Uy'], ['omega']], Dirbase=None, Dt=1,
example=False, total=True, fignum=1, save=True):
M = Mlist[0]
if Dirbase == None:
Dirbase = '/Users/stephane/Documents/Experiences_local/Accelerated_grid/PIV_data/Test6/' # local saving
Dirbase = './Stat_avg/Panel/' + M.Id.date
axes = panel_graphs(M, subplot=[2, 3], fignum=fignum)
frames = select_range(M, Range)
figs = {}
if hasattr(M, 'Id'):
Dirname = Dirbase + '/' + M.Id.get_id() + '/' + graphes.remove_special_chars(str(field)) + '/'
else:
Dirname = Dirbase + '/JHTD_Data/' + graphes.remove_special_chars(str(field)) + '/'
print(Dirname)
if Dt > 1:
print('Smoothed data')
Dirname = Dirname + 'Smooth_Dt_' + str(int(Dt)) + '/'
figs.update(plot_scales(Mlist, axes, fignum=fignum, color=color, label=label))
plt.sca(axes[2])
frame = 1500
Dt = 1400
if label is None:
labels = ['m^', 'b>', 'ko']
else:
labels = [label, label, label]
for i, f in enumerate(field[0]): # should contain either one or two fields
figs.update(graphes.pdf_ensemble(Mlist, f, frame, Dt=Dt, fignum=fignum, label=labels[i], norm=False))
figs.update(graphes.legende(f, 'pdf of ' + f, ''))
plt.sca(axes[3])
for f in field[1]:
figs.update(graphes.pdf_ensemble(Mlist, f, frame, Dt=Dt, fignum=fignum, label=labels[2], norm=False))
figs.update(graphes.legende(f, 'pdf of ' + f, ''))
plt.sca(axes[4])
corr.corr_v_t(Mlist, frame, axes=['Ux', 'Ux'], N=200, p=1, display=True, save=False, label=labels[0], fignum=fignum)
corr.corr_v_t(Mlist, frame, axes=['Uy', 'Uy'], N=200, p=1, display=True, save=False, label=labels[1], fignum=fignum)
plt.sca(axes[5])
corr.corr_v_t(Mlist, frame, axes=['omega', 'omega'], N=200, p=1, display=True, save=False, label=labels[2],
fignum=fignum)
if save:
graphes.save_figs(figs, savedir=Dirname, prefix='General', suffix='_vs_t', dpi=300, frmt='png', display=True)
else:
return figs, Dirname
def get_cine_time(file, display=False):
c = cine.Cine(file)
# something gets wrong with the computation of the cine length
n = c.len()
print('Number of images : ' + str(n))
times = np.asarray([c.get_time(i) for i in range(n)])
if display:
graphes.graphloglog(times[1:], np.diff(times), label='k')
graphes.legende('t (s)', 'Dt (s)', file)
return times
def Sp(Mlist, t, Dt=50, axe=['Ux', 'Uy'], label='k', p=1):
# dlist=range(1,int(max(M.shape())/2),10)
dlist = [1, 2, 3, 5, 8, 10, 15, 20, 25, 50]
indices = [[] for i in range(len(dlist))]
M = Mlist[0]
dim = M.shape()
n = np.prod(dim[:-1])
N = 2 * n # 10**3
# print('Compute indices lists')
for i, d in enumerate(dlist):
indices[i] = corr.d_2pts_rand(M.x, d, N)
# print('done')
figs = {}
for i, ind in enumerate(indices):
print("d = " + str(dlist[i]))
graphes.cla(i * 2 + 1)
graphes.cla(i * 2 + 2)
C_t = []
C_n = []
# print(dlist[i])
for M in Mlist:
# print(M.shape())
Ct, Cn = diff(M, t, ind, Dt=Dt, axe=axe, p=p)
graphes.distribution(Cn, label=label, normfactor=1, fignum=i * 2 + 1, norm=False)
figs.update(graphes.legende('C_n', 'PDF C_n, d' + str(dlist[i]), ''))
graphes.distribution(Ct, label=label, normfactor=1, fignum=i * 2 + 2, norm=False)
figs.update(graphes.legende('C_t', 'PDF C_t, d' + str(dlist[i]), ''))
C_t = C_t + Ct
C_n = C_n + Cn
n_ensemble = len(Mlist)
if n_ensemble > 1:
graphes.distribution(C_n, label='r', normfactor=n_ensemble, fignum=i * 2 + 1, norm=False)
figs.update(graphes.legende('C_n', 'PDF C_n, d' + str(dlist[i]), ''))
graphes.distribution(C_t, label='r', normfactor=n_ensemble, fignum=i * 2 + 2, norm=False)
figs.update(graphes.legende('C_t', 'PDF C_t, d' + str(dlist[i]), ''))
# graphes.distribution(C_t,label='r',normfactor=len(Mlist),fignum=i+2,norm=False)
# graphes.distribution(C_t,normfactor=1,fignum=i,norm=False)
return figs
def main():
file = '/Users/stephane/Documents/Experiences_local/Accelerated_grid/Set_time_scale/Logtime_movies/setup1_Dt_fps10000_n7000_Beta_1m.cine'
times = get_cine_time(file)
Dt = (times[1:] - times[:-1]) * 1000
times = times[:-1]
print(Dt)
print(times)
graphes.graphloglog(times, Dt, fignum=1, label='b^')
graphes.legende('t (s)', 'Dt (ms)', '')
compare_logtime(1)
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 isotropy(M, label='k^--', display=True, fignum=1):
'''for each time, compute the distribution of angles (!) in horizontal plane
at a very earlier time, should be peaked along x and y directions. Then spread in box directions.
Compute that from 5000 or 10000fps movies
'''
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$', 'Angular average flow')
Anisotropy[i] = np.std(U_rms) / np.nanmean(U_rms)
Meanflow[i] = np.std(U_moy) / np.nanmean(U_rms)
graphes.semilogx(tl,
Anisotropy,
label='ro',
fignum=fignum,
subplot=(1, 2, 1))
graphes.legende('Time (s)', 'I', 'Anisotropy' + graphes.set_title(M))
graphes.set_axes(10**-2, 10**4, 0, 2)
graphes.semilogx(tl,
Meanflow,
label='k^',
fignum=fignum,
subplot=(1, 2, 2))
graphes.legende('Time (s)', '<U>', 'Average flow')
graphes.set_axes(10**-2, 10**4, 0, 4)
def time_correlation(Mlist, indices=None, display=False):
"""
Compute the spatial averaged time of velocity autocorrelation
Velocity autocorrelation functions in time are fitted by an exponential in time.
Typical time tc gives the time correlation
Parameters
----------
Mlist : list of Mdata
indices : list of int
indices of Mlist elements to process. default value process all the elements
display : bool
default value False
OUTPUT
-----
tf :
tau :
for each time in ft, timescale over which correlation is reduced to 1/e, on average
"""
if indices is None:
indices = range(len(Mlist))
labels = ['k^', 'ro', 'bp', 'c8', 'g*']
for i, indice in enumerate(indices):
label = labels[i]
M = Mlist[indice]
tf, tau = compute_Ct(M, display=False, label='ko', fignum=1)
graphes.graphloglog(tf, tau, fignum=9, label=label)
graphes.legende('$t (s)$', '$\tau (s)$', '')
# compute from the
# t_d,E = decay.decay(M,label=label)
t_d, E = Fourier.display_fft_vs_t(M, '1d', Dt=50, label=label)
Ef = np.zeros(len(tf))
for i, t in enumerate(tf):
j = np.argmin(abs(t_d - t))
# print(str(j)+ ' : '+str(E[j]) + ", " + str(tau[i]))
Ef[i] = E[j]
graphes.graphloglog(Ef, tau, fignum=10, label=label)
graphes.legende('$E (m^2/s^2)$', '$\tau (s)$', '')
return tf, tau
def log_time_step(Dt, alpha, n):
# Dt in ms
# alpha : power coefficient , close to 1. greater than 1 means increasing time step
# n : number of images
Dt_list = [Dt * alpha**i for i in range(n)]
Dt_array = np.asarray(Dt_list)
Dt_array = np.floor(Dt_array / Dt) * Dt
# graphes.graph(np.arange(1,n+1),Dt_array,label='r+')
# graphes.legende('# of image','Dt (ms)','')
t = np.cumsum(Dt_array) / 1000 # t in s
graphes.graphloglog(t, Dt_array, 1, label='r+')
graphes.legende('t (s)', 'Dt (ms)', '')
def measure_anisotropy(fileDir, t0, fpms):
fileList, n = get_fileList(fileDir, 'npz')
I = []
for name in fileList:
S = np.load(name)
# print(name)
I0 = anisotropy(S)
I.append(I0)
# default fps is 1000fps in PIV parameters files
ft = 1. / fpms
t = np.arange(t0, t0 + n * ft, ft)
graphes.graphloglog(t, U, label)
graphes.legende('t (ms)', 'I (norm.)', '')
print(str(max(t)) + ' ' + str(max(U)))
def spatial_decay(M, field='E', indices=None):
from mpl_toolkits.axes_grid.inset_locator import inset_axes
import stephane.vortex.track as track
fields, names, vmin, vmax, labels, units = std_fields()
j = fields.index(field)
Z = np.nanmean(getattr(M, field)[..., indices], axis=2)
X, Y = [], []
for i in indices:
tup = track.positions(M, i, field=field, indices=indices, step=1, sigma=10.)
X.append(tup[1])
Y.append(tup[3])
X0, Y0 = np.nanmean(X), np.nanmean(Y)
R, Theta = Smath.cart2pol(M.x - X0, M.y - Y0)
R0 = M.param.Diameter
Z_flat = np.ndarray.flatten(Z)
R_flat = np.ndarray.flatten(R)
Theta_flat = np.ndarray.flatten(Theta)
phi = np.pi / 2
C = np.mod((Theta_flat + phi + np.pi) / 2 / np.pi, 1)
cmap = matplotlib.cm.hot
color = [matplotlib.colors.rgb2hex(cmap(c)[:3]) for c in C]
fig, ax2 = graphes.set_fig(1, subplot=122)
fig.set_size_inches(20, 6)
ax2.scatter(R_flat, Z_flat, marker='o', facecolor=color, alpha=0.3, lw=0, cmap=cmap)
Rth = np.arange(10 ** 1, 10 ** 2, 1.)
# graphes.graphloglog(Rth, 10 ** 5 * (Rth / R0) ** -3.2, label='k--')
# graphes.graphloglog(Rth, 5 * 10 ** 4 * (Rth / R0) ** -4.5, label='k--')
graphes.set_axis(10 ** 0, 10 ** 2, 10 ** 2, 8 * 10 ** 4)
figs = graphes.legende('$R$ (mm)', 'Energy (mm$^2$/s$^{2}$)', 'Spatial decay')
fig, ax1 = graphes.set_fig(1, subplot=121)
graphes.color_plot(M.x, M.y, Z, fignum=1, vmin=0, vmax=40000, subplot=121)
graphes.colorbar(label=names[j] + ' (' + units[j] + ')')
figs.update(graphes.legende('X (mm)', 'Y (mm)', 'Time averaged ' + field, cplot=True))
inset_ax = inset_axes(ax2, height="50%", width="50%", loc=3)
inset_ax.pcolormesh(M.x / 10 - 10, M.y / 10 - 10, Theta, cmap=cmap)
inset_ax.axis('off')
return figs
def spectrum_2d(M, indices=None):
Fourier.compute_spectrum_2d(M, Dt=3) # smooth on 3 time step.
S_E = np.nanmean(M.S_E[..., indices], axis=2)
graphes.color_plot(M.kx, M.ky, S_E, log=True, fignum=1)
graphes.colorbar(label='$E_k$')
figs = graphes.legende('$k_x$ (mm)', '$k_y$ (mm)', 'Energy Spectrum (log)')
return figs
def distribution(sigma, n, display=False):
n_p = 1
theta, N = theta_axis(n, N=None)
r0 = 1
base = np.asarray([[r0 * np.cos(k), r0 * np.sin(k), 0] for k in theta])
paths = []
for p in range(n_p):
# print(p)
t = noise(base, sigma, n)
paths.append(t.paths[0])
# h = helicity(t)
# graphes.hist(h,fignum=2)
if p < 3:
savename = './Random_path/Tests/Examples/sigma_' + str(
round(sigma * 1000)) + 'm_n' + str(n) + '_' + str(p + 1)
save(t, prefix=savename)
t_tot = tangle.Tangle(paths)
if display:
figs = radial_density(t_tot)
figs.update(graphes.legende('R', 'PDF(R)', ''))
# graphes.save_figs(figs,prefix='Random_path/Tests/R_Distributions/',suffix='_sigma_'+str(round(sigma*1000))+'m',dpi=300,display=True,frmt='png')
return t
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):
yy = getattr(M, field)
yy_k, k = spectrum_1d(yy, M, display=False, Dt=Dt)
print(yy_k.shape)
else:
yy_k = getattr(M, 'S_' + field)
k = getattr(M, 'k_' + field)
step = max(Dt / 2, 1)
N, nt = yy_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, yy_k[:, t], label='k-', fignum=fignum)
# graphes.graphloglog(k,yy_k[:,t],label='k-',fignum=fignum)
add_theory(k, yy_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 display_fft(m, i, tag):
# to be replaced by m.z
if hasattr(m.Sdata.param, 'Zplane'):
Z = m.Sdata.param.Zplane / 10
else:
Z = -10
title = '$Z$ = ' + str(Z) + ' cm, $t$ = ' + str(m.t[i]) + ' ms'
Dir = m.fileDir + 'FFT_vs_t_part_' + tag + '_' + m.id.get_id() + '/'
if tag == '1d':
graphes.legende('$k$ (mm$^{-1}$)', '$E_k$ (a.u.)', title)
if tag == '2d':
graphes.legende('$k_x$ (mm$^{-1}$)', '$k_y$ (mm$^{-1}$)', title)
return None
def test_Dt(W):
"""
Parameters
----------
W :
Returns
-------
"""
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_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 spatial_corr(data, N=1024, Dt=10, outdir='/Users/npmitchell/Dropbox/Soft_Matter/turbulence/jhtd/'):
"""Compute spatial correlation
Parameters
----------
data
N
Dt
Returns
-------
"""
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=outdir + 'corr_functions/', suffix='', prefix='', frmt='pdf', dpi=300)
