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 compare_logtime(fignum=1):
Dt = 0.1
alpha = 1.001
n = 7000
log_time_step(Dt, alpha, n)
# read time step
file = '/Users/stephane/Documents/Experiences_local/Accelerated_grid/Set_time_scale/H1180mm_S300mm_decay/param_NL_Dt.txt'
t_s, Dt = read_time_step(file)
graphes.graphloglog(t_s, Dt, fignum=fignum, label='ko')
Dir = os.path.dirname(file)
print(Dir)
params = {'Dt': Dt, 'alpha': alpha, 'n': n}
s = rw_data.gen_name(params)
file_fig = Dir + '' + '/' + 'Dt_vs_t_data_mes1' # +s
print(file_fig)
graphes.save_fig(0, file_fig)
def make_2dmovie(M, Z, start, stop, Dirname='velocity_module', vmin=-3, vmax=3):
# Movie of the velocity modulus U
# U=np.sqrt(S.Ux**2+S.Uy**2)
fignum = 5
for i in range(start, stop):
Z0 = Z[:, :, i] / 1000
print(i)
# print('<dU>_x,y = '+str(np.median(Z0)))
# print('<dU^2>_x,y = '+str(np.std(Z0)))
graphes.color_plot(M.x, M.y, np.log10(Z0), fignum=fignum, vmin=vmin, vmax=vmax)
if i == start:
graphes.colorbar(fignum=fignum, label='V (m/s)')
titre = ''
graphes.legende('x (mm)', 'y (mm)', titre)
if i == start:
input()
# graphes.vfield_plot(S)
Dir = os.path.dirname(M.Sdata.fileCine) + '/' + Dirname + '/'
print(Dir)
filename = Dir + 'V' + str(i)
graphes.save_fig(fignum, filename, fileFormat='png')
ind = 0
for skk in sk_disc:
kk = k_disc[ind]
print 'np.shape(skk) = ', np.shape(skk)
print 'np.shape(kk) = ', np.shape(kk)
todo = range(20, np.shape(skk)[1])
colors = tcmaps.cubehelix_palette(n_colors=len(todo), start=0.5, rot=-0.75, check=False)
print 'np.shape(colors) = ', np.shape(colors)
dmyi = 0
for ii in todo:
print 'Plotting energy spectrum, item =', ii
graphes.graphloglog(kk, skk[..., ii], 'k', color=colors[dmyi])
dmyi += 1
graphes.graph(mm.k, 100 * mm.k ** (-5. / 3), label='r-')
figs = graphes.legende(r'$k$ [mm$^{-1}$]', r'$E$ [mm/s$^{2}$]', r'$S(k)$ for $r <$' + str(radii[ind]) + ' pixels')
imout_dir = savedir + 'energy_spectrum_radius_sequence/' + date + '_' + str(index) + \
'_energy_spectrum_radius_sequence/'
if not os.path.exists(imout_dir):
print 'le.ensure_dir: creating dir: ', imout_dir
os.makedirs(imout_dir)
figname = imout_dir + date + '_' + str(index) + '_energy_spectrum_radius{0:03d}'.format(radii[ind])
print 'saving fig: ' + figname
graphes.save_fig(1, figname, frmt='png', dpi=300, overwrite=True)
plt.clf()
ind += 1
########################################################
# Energy spectrum (Kolmogorov scaling: -5/3)
########################################################
plt.close('all')
M = Mlist[0]
for ii in range(20, 148):
print 'Plotting energy spectrum, item =', ii
graphes.graphloglog(M.k, M.S_k[..., ii], 'k')
graphes.graph(M.k, 15 * M.k**(-5. / 3), label='r-')
figs = graphes.legende(r'$k$ [mm$^{-1}$]', r'$E$ [mm/s$^{2}$]', '')
figname = savedir + 'energy_spectrum1'
print 'saving fig: ' + figname
graphes.save_fig(1,
savedir + 'energy_spectrum1',
frmt='pdf',
dpi=300,
overwrite=False)
########################################################
# Energy spectrum as function of shell radius
########################################################
radii = [40, 80, 160, 320, 640, 1280, 2560]
sk_disc = []
k_disc = []
for rad in radii:
sk, k = Fourier.compute_spectrum_1d_within_region(M,
radius=rad,
polygon=None,
display=False,
dt=10)
def profile_1d(M, Dt=10, direction='v', start=20, fignum=1):
"""
Compute the 1d averaged profile> Averaging is performed along a Dt window in time, and along the specified axis, either 'v' or 'h'
INPUT
-----
M : Mdata object.
Dt : int. Default 10
time windows width for the averaging
direction : string. default 'v'. Only option now
start : int
starting index
fignum : int
number for the figure output
OUTPUT
-----
Ux,Uy,Ux_std,Uy_std
"""
dimensions = M.shape()
if M.param.typeplane == 'vp':
if M.param.angle == 0.:
z = M.y[:, 0]
axis = [1, 0]
if M.param.angle == 90.:
z = M.x[0, :]
axis = [0, 1]
else:
z = M.y[:, 0]
axis = [1, 0]
for i in range(start, dimensions[2], Dt):
# averaging over Dt in time, and along one dimension in space
Ux = np.nanmean(np.nanmean(M.Ux[..., i:i + Dt], axis=2), axis=axis[0])
Uy = np.nanmean(np.nanmean(M.Uy[..., i:i + Dt], axis=2), axis=axis[0])
# standard deviation computation
# print(dimensions[axis[1]])
# print(tuple(axis+[Dt]))
orientation = tuple([axis[0] + 1, axis[1] + 1] + [0])
Ux_mean = np.asarray(
np.transpose([[dict2list.to_1d_list(Ux) for k in range(dimensions[axis[0]])] for t in range(Dt)],
orientation))
Uy_mean = np.asarray(
np.transpose([[dict2list.to_1d_list(Uy) for k in range(dimensions[axis[0]])] for t in range(Dt)],
orientation))
# Uy_mean = np.asarray([[dict2list.to_1d_list(Uy) for k in range(dimensions[axis[0]])] for t in range(Dt)],tuple(axis+[Dt]))
std_Ux = np.sqrt(np.mean(np.mean(np.abs(M.Ux[..., i:i + Dt] - Ux_mean) ** 2, axis=2), axis=axis[0]))
std_Uy = np.sqrt(np.mean(np.mean(np.abs(M.Uy[..., i:i + Dt] - Uy_mean) ** 2, axis=2), axis=axis[0]))
graphes.set_fig(0) # clear current axis
graphes.graph(z, Ux, label='k^', fignum=fignum) # ,std_Ux)
graphes.graph(z, Uy, label='ro', fignum=fignum) # ,std_Ux)
graphes.set_axis(-400, -100, -100, 100)
fig = graphes.legende('z (mm)', 'V_{rms} (mm/s)', 'Ux, Uy')
filename = './Results/Mean_profile_' + M.id.get_id() + '/' + fig[fignum] + '_t_' + str(i)
# print(filename)
graphes.save_fig(fignum, filename, frmt='png')
# graphes.graph(z,Uy,std_Uy)
# graphes.legende('z (m)','V (m/s)','Uy')
# raw_input()
return Ux, Uy, std_Ux, std_Uy