def make_movie(M,Range=None,field=['E','vorticity'],Dirbase=None,Dt=1):
if Dirbase==None:
Dirbase = '/Users/stephane/Documents/Experiences_local/Accelerated_grid/PIV_data' #local saving
nx,ny,nt = M.shape()
axes = panel(M)
if Range is not None:
start,stop,step = tuple(Range)
else:
start,stop,step = tuple([0,nt,1])
frames = range(start,stop,step)
# field = ['Ux','Uy']
# field = ['E','vorticity']
figs={}
Dirname = Dirbase+'/'+M.Id.get_id()+'/'+graphes.remove_special_chars(str(field))+'/'
print(Dirname)
for i,f in enumerate(field):
if not hasattr(M,f):
M,field_n[i] = vgradient.compute(M,field[i],Dt=Dt)
field = field_n
# M,field[1] = vgradient.compute(M,field[1],Dt=Dt)
if Dt>1:
print('Smoothed data')
Dirname = Dirname + 'Smooth_Dt_'+str(int(Dt))+'/'
for i in frames:
graphes.set_fig(1)
for axe in axes:
plt.sca(axe)
plt.cla()
axes = panel(M,fignum=1)
plt.sca(axes[1])
print(i)
graphes.Mplot(M,field[0],i,fignum=1,log=True)
plt.text(-10,80,field[0],fontsize=20)
plt.sca(axes[2])
graphes.Mplot(M,field[1],i,fignum=1,log=True)
plt.text(-10,80,field[1],fontsize=20)
figs.update(graphes.legende('','','Front view',cplot=True))
graphes.save_figs(figs,savedir=Dirname,suffix='_'+str(i),dpi=100,frmt='png',display=False)
return axes
def example(M,Range=None,field=['E','vorticity']):
nx,ny,nt = M.shape()
axes = panel(M)
if Range is not None:
start,stop,step = tuple(Range)
else:
start,stop,step = tuple([0,nt,1])
frames = range(start,stop,step)
# field = ['Ux','Uy']
# field = ['E','vorticity']
figs={}
Dirbase = '/Users/stephane/Documents/Experiences_local/Accelerated_grid/PIV_data' #local saving
Dirname = Dirbase+'/'+M.Id.get_id()+'/'+graphes.remove_special_chars(str(field))+'/'
print(Dirname)
M,field[0] = vgradient.compute(M,field[0])
M,field[1] = vgradient.compute(M,field[1])
for i in frames:
graphes.set_fig(1)
for axe in axes:
plt.sca(axe)
plt.cla()
axes = panel(M,fignum=1)
plt.sca(axes[1])
graphes.Mplot(M,field[0],i,log=True)
plt.text(-10,20,field[0],fontsize=20)
plt.sca(axes[2])
graphes.Mplot(M,field[1],i,fignum=1,log=True)
plt.text(-10,20,field[1],fontsize=20)
figs.update(graphes.legende('','','Front view',cplot=True))
graphes.save_figs(figs,savedir=Dirname,suffix='_'+str(i),dpi=100,frmt='png')
return axes
def example(M, i=None):
figs = {}
if i == None:
nx, ny, nt = M.shape()
i = nt // 4
fields, names, vmin, vmax, labels, units = std_fields()
for j, field in enumerate(fields):
figs.update(
graphes.Mplot(M,
field,
i,
fignum=j + 1,
colorbar=True,
vmin=vmin[j],
vmax=vmax[j]))
### add label on colorbar
return figs
def remove_spurious(M, borne=2., display=False):
M.get('E')
nx, ny, nt = M.shape()
i = nt / 2
#only a small data chunk
#tup,error = rm_baddata(M.E[...,i-5:i+5],d_max=2,b=1,borne=borne,display=False)
#for j,t in enumerate(tup):
# tup[j] = (t[0],t[1],t[2] + i-5)
tup, error = rm_baddata(M.E, d_max=2, b=1, borne=borne, display=False)
if display:
import stephane.display.graphes as graphes
graphes.Mplot(M, 'E', i, colorbar=True)
xc, yc = [], []
for t in tup:
xc.append([M.x[t[0], t[1]]])
yc.append([M.y[t[0], t[1]]])
graphes.graph(xc, yc, label='ro')
#tup_list_y,error_y = rm_baddata(M.Uy[...,t:t+Dt],d_max=2,b=1,borne=borne,display=False)
M.Ux = replace_data(M.Ux, tup)
M.Uy = replace_data(M.Uy, tup)
return M
def make_plot(M,Range=None,color='k',field=['E','vorticity'],Dirbase=None,Dt=1,example=False,total=True,fignum=1,save=True):
if Dirbase==None:
Dirbase = '/Users/stephane/Documents/Experiences_local/Accelerated_grid/PIV_data/Test6/' #local saving
Dirbase = './Stat_avg/Panel/'+M.Id.date
axes = flex_panel(M,fignum=fignum)
# for axe in axes:
# plt.sca(axe)
#plt.cla()
frames = select_range(M,Range)
# field = ['Ux','Uy']
# field = ['E','vorticity']
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))+'/'
#Dt = 50
t_moy,E_moy = access.avg_vs_t(M,'E',frames,Dt=Dt)
t_moy,Omega_moy = access.avg_vs_t(M,'omega',frames,Dt=Dt)
t_moy,Strain_moy = access.avg_vs_t(M,'strain',frames,Dt=Dt)
t_moy,Y_pdf_moy = access.avg_vs_t(M,field[1],frames,Dt=Dt)
epsilon = scale.dissipation_rate(Omega_moy) #dissipation rate
eta = scale.K_scale(epsilon) #dissipative scale
micro_1 = np.sqrt(1./4*E_moy/Strain_moy**2)
micro_2 = np.sqrt(E_moy/(Omega_moy)**2)
Re = scale.Re(micro_1,eta)
Re_lambda_1 = scale.Re_lambda(E_moy,micro_1)
Re_lambda_2 = scale.Re_lambda(E_moy,micro_2)
L = scale.I_scale(Re,E_moy)
# plt.sca(axes[2])
# graphes.graphloglog(t_moy,E_moy,fignum=1,label=color+'o-')
#Fourier.add_theory(t_moy,E_moy,[-2.],fignum=1)
# graphes.graphloglog(t_moy,Y_pdf_moy,fignum=1,label=color+'s-')
# graphes.graphloglog(t_moy,epsilon,fignum=1,label='gv-')
#Fourier.add_theory(t_moy,epsilon,[-3.],fignum=1)
# figs.update(graphes.legende('t (s)','E (mm^2/s^2), epsilon(mm^2/s^-3)',''))
plt.sca(axes[1])
graphes.graphloglog(t_moy,eta,fignum=fignum,label=color+'o-')
graphes.graphloglog(t_moy,micro_1,fignum=fignum,label=color+'s-')
graphes.graphloglog(t_moy,micro_2,fignum=fignum,label='cp-')
graphes.graphloglog(t_moy,L,fignum=fignum,label='gv-')
figs.update(graphes.legende('t (s)','eta (mm), lambda (mm)',''))
plt.sca(axes[2])
graphes.graphloglog(t_moy,Re,fignum=fignum,label=color+'o-')
graphes.graphloglog(t_moy,Re_lambda_1,fignum=fignum,label=color+'s-')
graphes.graphloglog(t_moy,Re_lambda_2,fignum=fignum,label='cp-')
figs.update(graphes.legende('t (s)','Re , Re_lambda',''))
# print(t_moy)
# print(Y_moy)
# print(t)
# print(Y_moy)
indices = [0,1]
# indices = [1,4]
cla_axes = [axes[i] for i in indices]
if save:
graphes.save_figs(figs,savedir=Dirname,prefix='General',suffix='_vs_t',dpi=300,frmt='png',display=True)
individual=False
if example:
frames_disp =[1200]
else:
step =frames[1]-frames[0]
frames_disp = range(frames[0]+step*10,frames[-1],step*10)
if individual:
for frame in frames_disp:
#print(frame)
#print(frames)
i = frames.index(frame)
graphes.set_fig(1)
for axe in cla_axes:
plt.sca(axe)
plt.cla()
axes = flex_panel(M,fignum=1)
if total:
plt.sca(axes[0])
graphes.Mplot(M,field[0],frame,fignum=1,log=True)
plt.text(-10,80,field[0],fontsize=20)
plt.sca(axes[1])
# graphes.graphloglog(t_moy,eta,fignum=1,label='ko-')
# graphes.graphloglog(t_moy,micro,fignum=1,label='ks-')
graphes.graph([t_moy[i]],[eta[i]],fignum=1,label='ro')
graphes.graph([t_moy[i]],[micro_1[i]],fignum=1,label='rs')
graphes.graph([t_moy[i]],[micro_2[i]],fignum=1,label='rp')
graphes.graphloglog([t_moy[i]],[L[i]],fignum=1,label='rv-')
plt.sca(axes[2])
graphes.graphloglog([t_moy[i]],[Re[i]],fignum=1,label='ro-')
graphes.graphloglog([t_moy[i]],[Re_lambda_1[i]],fignum=1,label='rs-')
graphes.graphloglog([t_moy[i]],[Re_lambda_2[i]],fignum=1,label='rp-')
plt.sca(axes[3])
figs.update(graphes.pdf(M,'Ux',frame,Dt=Dt,fignum=1,label='m^'))
figs.update(graphes.pdf(M,'Uy',frame,Dt=Dt,fignum=1,label='b>'))
figs.update(graphes.legende('t (s)','Re , Re_lambda',''))
plt.sca(axes[4])
figs.update(graphes.pdf(M,field[1],frame,Dt=Dt,fignum=1,label=color+'-'))
# graphes.Mplot(M,field[1],i,fignum=1,log=True)
# plt.text(-10,80,'PDF '+field[1],fontsize=20)
# figs.update(graphes.legende('','','Front view',cplot=True))
graphes.save_figs(figs,savedir=Dirname,suffix='_'+str(frame),dpi=100,frmt='png',display=False)
return axes
def make_2dmovie(M,
name,
Range=None,
fignum=1,
local=True,
log=False,
vmin=0,
vmax=1,
filt=False):
"""
Movie of the colormap of the velocity modulus U over time
INPUT
-----
M : Mdata class instance, or any other object that contains the following fields :
methods : shape()
attributes : Ux, Uy
Sdata object
Ids object
...
name : name of the field to be plotted. example : Ux,Uy, vorticity, strain
Range : np array
fignum : int. default value is 1
Dirname : string
Directory to save the figures
log : bool. default value is True
OUTPUT
-----
None
"""
if Range is not None:
start, stop, step = tuple(Range)
else:
start, stop, step = tuple([0, M.shape()[-1], 5])
# U=np.sqrt(S.Ux**2+S.Uy**2)
#by default, pictures are save here :
if local:
Dirlocal = '/Users/stephane/Documents/Experiences_local/Accelerated_grid'
else:
Dirlocal = os.path.dirname(M.Sdata.fileCine)
Dirname = name
if filt:
Dirname = Dirname + '_filtered'
Dir = Dirlocal + '/PIV_data/' + M.Id.get_id() + '/' + Dirname + '/'
print(Dir)
fig = graphes.set_fig(fignum)
graphes.set_fig(0) #clear current figure
print('Compute ' + name)
M, field = vgradient.compute(M, name, filter=filt)
for i in range(start, stop, step):
#Z = energy(M,i)
graphes.Mplot(M,
field,
i,
fignum=fignum,
vmin=vmin,
vmax=vmax,
log=log,
auto_axis=True)
# graphes.color_plot(Xp,Yp,-Z,fignum=fignum,vmax=700,vmin=0)
if i == start:
cbar = graphes.colorbar(
) #fignum=fignum,label=name+'(mm^2 s^{-2})')
else:
print('update')
# cbar.update_normal(fig)
filename = Dir + 'V' + str(i)
graphes.save_fig(fignum, filename, frmt='png')
def single(filename, display=False):
figs = {}
savedir = '/Users/stephane/Documents/Postdoc_Chicago/Process_data/Scripts/Samples/Smoothing/'
filesave, ext = os.path.splitext(filename)
filesave = filesave + '_processed.hdf5'
M = M_manip.load_Mdata_file(filename, data=True)
M.get('E')
M.get('omega')
nx, ny, nt = M.shape()
i = nt / 2
# display raw energy
if display:
graphes.plt.close('all')
graphes.Mplot(M, 'E', i, vmin=0, vmax=50000, fignum=1, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Energy without pre-processing',
cplot=True))
#Display raw vorticity
graphes.Mplot(M, 'omega', i, vmin=0, vmax=100, fignum=2, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Vorticity without pre-processing',
cplot=True))
graphes.save_figs(figs, savedir=savedir, prefix=M.Id.get_id())
graphes.Mplot(M, 'Ux', i, vmin=-200, vmax=200, fignum=3, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Ux after pre-processing',
cplot=True))
M = cdata.remove_spurious(M, borne=1.5)
M.get('E', force=True)
M.get('omega', force=True)
if display:
graphes.Mplot(M, 'E', i, vmin=0, vmax=50000, fignum=4, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Energy after pre-processing',
cplot=True))
#Display raw vorticity
graphes.Mplot(M, 'omega', i, vmin=0, vmax=100, fignum=5, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Vorticity after pre-processing',
cplot=True))
### Gaussian filter
omega_filt = track.smoothing(M, i, field='omega', sigma=2.5)
graphes.color_plot(M.x, M.y, omega_filt, vmin=0, vmax=100, fignum=6)
graphes.plt.colorbar()
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Vorticity after gaussian smoothing',
cplot=True))
graphes.save_figs(figs, savedir=savedir, prefix=M.Id.get_id())
### Gaussian filter
# omega_filt = filters.gaussian_filter(omega, sigma=2.)#[...,0]
# graphes.color_plot(x,y,omega_filt,vmin=0,vmax=50,fignum=5)
# graphes.plt.colorbar()
# figs.update(graphes.legende('$x$ (mm)','$y$ (mm)','Vorticity after gaussian smoothing',cplot=True))
# graphes.save_figs(figs,savedir=savedir)
to_hdf5.write(M, filename=filesave, key='Mdata')
def example(filename):
figs = {}
savedir = '/Users/stephane/Documents/Postdoc_Chicago/Process_data/Scripts/Example/Smoothing/'
M = M_manip.load_Mdata_file(filename, data=True)
M.get('E')
M.get('omega')
nx, ny, nt = M.shape()
x, y = M.x, M.y
i = nt / 2
# display raw energy
graphes.Mplot(M, 'E', i, vmin=0, vmax=50000, fignum=1, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Energy without pre-processing',
cplot=True))
#Display raw vorticity
graphes.Mplot(M, 'omega', i, vmin=0, vmax=80, fignum=2, colorbar=True)
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Vorticity without pre-processing',
cplot=True))
# Look for bada data point in amplitude
E = M.E[..., i]
tup, errors = cdata.rm_baddata(E, borne=2.)
xc = []
yc = []
for t in tup:
xc.append([x[t[0], t[1]]])
yc.append([y[t[0], t[1]]])
graphes.graph(xc, yc, label='ro', fignum=2)
print(figs)
graphes.save_figs(figs, savedir=savedir)
Ux, Uy = M.Ux[..., i], M.Uy[..., i]
#replace component of the velocity
Ux = cdata.replace_data(Ux, tup)[..., 0]
Uy = cdata.replace_data(Uy, tup)[..., 0]
U = np.transpose(np.asarray([Uy, Ux]), (1, 2, 0))
dU = strain.strain_tensor_C(U, b=2.)
omega, enstrophy = strain.vorticity(dU, d=2, norm=False)
E = Ux**2 + Uy**2
graphes.color_plot(x, y, omega, vmin=0, vmax=80, fignum=4)
graphes.plt.colorbar()
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Vorticity after pre-processing',
cplot=True))
graphes.color_plot(x, y, E, vmin=0, vmax=50000, fignum=3)
graphes.plt.colorbar()
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Energy after pre-processing',
cplot=True))
### Gaussian filter
omega_filt = filters.gaussian_filter(omega, sigma=2.) #[...,0]
graphes.color_plot(x, y, omega_filt, vmin=0, vmax=80, fignum=5)
graphes.plt.colorbar()
figs.update(
graphes.legende('$x$ (mm)',
'$y$ (mm)',
'Vorticity after gaussian smoothing',
cplot=True))
graphes.save_figs(figs, savedir=savedir)