def pivlab2hdf5(dirbase):
parentdir = os.path.dirname(dirbase)
# glob directories generated by a pivlab code
datadirs = glob.glob(dirbase + '/*')
for datadir in datadirs:
print 'Processing %s' % datadir
datafiles = glob.glob(datadir + '/*.txt')
for i, datafile in enumerate(datafiles):
if i % 100 == 0:
print '%d / %d' % (i, len(datafiles))
data = np.loadtxt(datafile, delimiter=',', skiprows=3)
xx, yy = data[:, 0], data[:, 1]
ux, uy = data[:, 2], data[:, 3]
omega = data[:, 4]
if i == 0:
delta_y = np.diff(yy)[0]
delta_x = delta_y
ncols = int((np.max(xx) - np.min(xx)) / delta_x) + 1
nrows = int((np.max(yy) - np.min(yy)) / delta_y) + 1
shape_temp = (ncols, nrows)
xgrid, ygrid = xx.reshape(shape_temp).T, yy.reshape(shape_temp).T
ux_grid, uy_grid, omega_grid = ux.reshape(shape_temp).T, uy.reshape(shape_temp).T, omega.reshape(
shape_temp).T
if i == 0:
uxdata = np.zeros((nrows, ncols, len(datafiles)))
uydata = np.zeros((nrows, ncols, len(datafiles)))
omegadata = np.zeros((nrows, ncols, len(datafiles)))
uxdata[..., i] = ux_grid
uydata[..., i] = uy_grid
omegadata[..., i] = omega_grid
savedata = {}
savedata['x'] = xgrid
savedata['y'] = ygrid
savedata['ux'] = uxdata
savedata['uy'] = uydata
savedata['omega'] = omegadata
hdf5path = parentdir + '/hdf5data/' + os.path.split(datadir)[1]
rw.write_hdf5_dict(hdf5path, savedata)
print '... Done'
subplot=122,
figsize=(16, 8))
graph.labelaxes(ax31, r'$U_x$ (px/frame)', 'Probability density')
graph.labelaxes(ax32, r'$U_y$ (px/frame)', 'Probability density')
fig3.tight_layout()
filename = 'lamb_oseen_vortex_ux_uy'
filepath = os.path.join(savedir + filename)
graph.save(filepath, fignum=1)
filename = 'lamb_oseen_vortex_omegaz'
filepath = os.path.join(savedir + filename)
graph.save(filepath, fignum=2)
filename = 'lamb_oseen_vortex_ux_uy_pdf'
filepath = os.path.join(savedir + filename)
graph.save(filepath, fignum=3)
# Output velocity/vorticity field in hdf5
data = {}
data['x'] = xgrid # px
data['y'] = ygrid # px
data['ux'] = ugrid # px/frame
data['uy'] = vgrid # px/frame
data['omega'] = omega #1/frame
hdf5dir = os.path.join(filedir, 'hdf5data/')
filename = dirname
rw.write_hdf5_dict(hdf5dir + filename, data)
graph.show()
data['sigma_uy'] = sigmas_uy
data['mu_ux_gauss'] = gauss_peaks_ux
data['mu_uy_gauss'] = gauss_peaks_uy
data['gamma_ux'] = gammas_ux
data['gamma_uy'] = gammas_uy
data['mu_ux_lorentz'] = lorentz_peaks_ux
data['mu_uy_lorentz'] = lorentz_peaks_uy
data['sigma_ux_err'] = sigmas_ux_err
data['sigma_uy_err'] = sigmas_uy_err
data['mu_ux_gauss_err'] = gauss_peaks_ux_err
data['mu_uy_gauss_err'] = gauss_peaks_uy_err
data['gamma_ux_err'] = gammas_ux_err
data['gamma_uy_err'] = gammas_uy_err
data['mu_ux_lorentz_err'] = lorentz_peaks_ux_err
data['mu_uy_lorentz_err'] = lorentz_peaks_uy_err
rw.write_hdf5_dict(datafilename, data)
else:
print 'Noise fit results already exist!'
data = rw.read_hdf5(datafilepath)
iws, disps = np.array(data['inter_wid_width']), np.array(data['displacement'])
sigmas_ux, sigmas_uy, gauss_peaks_ux, gauss_peaks_uy = np.array(data['sigma_ux']), np.array(data['sigma_uy']), np.array(data['mu_ux_gauss']), np.array(data['mu_uy_gauss'])
gammas_ux, gammas_uy, lorentz_peaks_ux, lorentz_peaks_uy = np.array(data['gamma_ux']), np.array(data['gamma_uy']), np.array(data['mu_ux_lorentz']), np.array(data['mu_uy_lorentz'])
sigmas_ux_err, sigmas_uy_err, gauss_peaks_ux_err, gauss_peaks_uy_err = np.array(data['sigma_ux_err']), np.array(data['sigma_uy_err']), np.array(data['mu_ux_gauss_err']), np.array(data['mu_uy_gauss_err'])
gammas_ux_err, gammas_uy_err, lorentz_peaks_ux_err, lorentz_peaks_uy_err = np.array(data['gamma_ux_err']), np.array(data['gamma_uy_err']), np.array(data['mu_ux_lorentz_err']), np.array(data['mu_uy_lorentz_err'])
# Now make a heat map
iw_temp, disp_temp = np.linspace(0, 80, 81), np.linspace(0, 80, 81)
iw_grid, disp_grid = np.meshgrid(iw_temp, disp_temp)
sigma_ux_int = griddata(np.array(zip(iws, disps)), np.abs(np.array(sigmas_ux)), (iw_grid, disp_grid), method='nearest')
label=r'$\bar{E}_{2D}=\frac{1}{2}(\bar{U_x}^2)$',
option='scientific')
graph.labelaxes(ax, 'X (px)', 'Y (px)')
fig.tight_layout()
filename = '/time_avg_energy_%s/im%05d' % (args.mode, i)
graph.save(args.dir + filename, ext='png', close=True)
import library.tools.rw_data as rw
savedata = {}
savedata['x'] = xx
savedata['y'] = yy
savedata['ux'] = grid_ux
savedata['uy'] = grid_uy
savedata['energy'] = grid_e
filepath = args.dir + '/grid_data_%s' % args.mode
rw.write_hdf5_dict(filepath, savedata)
# plt.show()
else:
# Load setup data and figure out how to merge data
laser_pos = np.empty(ndata)
x0s = np.empty(ndata)
zz2_top, zz2_bottom = np.empty(ndata), np.empty(ndata)
z0s = np.empty(ndata)
dthetas = np.empty(ndata)
# make a list of dictionaries, Each dictionary has a different id
dummy_func = lambda x: copy.deepcopy(x)
setups = [dummy_func({}) for i in range(((ndata)))]
for i in range(ndata):
# print pos, deltax, pts[indices], i, j, k
eei[j][i][k] = np.nanmean(energy_ravelled[indices])
# print indices, eei[i][j][k]
# break
# distances, indices = tree.query(pos, k=3) # get 2 nearest neighbors
# print distances
import library.tools.rw_data as rw
savedata = {}
savedata['x'] = xxi
savedata['y'] = yyi
savedata['z'] = zzi
savedata['energy'] = eei
rw.write_hdf5_dict(dir + '/interp_data_1p0_test_deltaz_%.1f_-30' % deltaz,
savedata)
## Does not work.
# from scipy.interpolate import LinearNDInterpolator
# int_func = LinearNDInterpolator(pts, data)
## griddata does not work
#
# # width, height, depth = ux.shape
#
# # Read a sample setup file to extract scale
# # Load setup file
# setup_str = open(setup_files[0], 'rt').read()
# setup = {}
# dummy = {}
# exec ("from math import *", dummy)
180.)
slope = -(args.max - args.min) / np.sqrt(
(args.width * np.cos(args.theta * np.pi / 180.))**2 +
(args.height * np.sin(args.theta * np.pi / 180.))**2)
rgrid = xgrid * np.cos(args.theta * np.pi / 180.) + ygrid * np.sin(
args.theta * np.pi / 180.)
vel_mag = np.ones((args.height, args.width)) * args.max + slope * rgrid
ugrid, vgrid = vel_mag * np.cos(
args.theta * np.pi / 180.), vel_mag * np.sin(args.theta * np.pi / 180.)
# ugrid = np.ones((args.height, args.width)) * args.mag * np.cos(args.theta * np.pi / 180.)
# vgrid = np.ones((args.height, args.width)) * args.mag * np.sin(args.theta * np.pi / 180.)
# print ugrid.shape, xgrid.shape
vel = np.stack((ugrid, vgrid))
omega = vec.curl(vel)
# # Debugging purpose
# fig1 = plt.figure(1)
# ax1 = fig1.add_subplot(111)
# ax1.quiver(xgrid, ygrid, ugrid, vgrid)
# plt.show()
data = {}
data['x'] = xgrid
data['y'] = ygrid
data['ux'] = ugrid
data['uy'] = vgrid
data['omega'] = omega #for unidirectional flow, vorticity is zero
datapath = args.resultdir + filename
rw.write_hdf5_dict(datapath, data)
yy,
n_bins,
option=option)
else:
_, DLL = compute_struc_func(ux0,
ux0_noise,
xx,
yy,
n_bins,
option=option)
rr_scaled = rr / eta
Dxx_scaled = DLL / ((epsilon * rr)**(2. / 3))
fig, ax = graph.plot(rr_scaled, Dxx_scaled, label=labels[i], alpha=0.9)
fig, ax = graph.scatter(rr_scaled, Dxx_scaled, alpha=0.9)
data['rr_scaled_' + option] = rr_scaled
data['Dxx_scaled_' + option] = Dxx_scaled
data['rr_' + option] = rr
data['Dxx_' + option] = DLL
graph.tosemilogx(ax)
ax.legend(loc=2)
graph.labelaxes(ax, '$r/\eta$', r'$D_{xx}/(\epsilon r)^{2/3}$')
#graph.setaxes(ax, 1, 5000, -0.2, 4)
graph.axhband(ax, 2 * 0.85, 2 * 1.15, 1, 5000)
graph.save(savedir + 'jhtd_struc_func_scaled_white_noise_budget_test')
# graph.show()
datafile = 'jhtd_struc_func_scaled_white_noise_budget_test_data'
rw.write_hdf5_dict(savedir + datafile, data)
# 'bins_ux_jhtd_c', 'hist_ux_jhtd_c', 'bins_uy_jhtd_c', 'hist_uy_jhtd_c',
# 'bins_ux_diff_c', 'hist_ux_diff_c', 'bins_uy_diff_c', 'hist_uy_diff_c',
# 'bins_ux_jhtd_avg', 'hist_ux_jhtd_avg', 'bins_uy_jhtd_avg', 'hist_uy_jhtd_avg',
# 'bins_ux_diff_avg', 'hist_ux_diff_avg', 'bins_uy_diff_avg', 'hist_uy_diff_avg']
# for dataname, content in zip(datanames, histdata_list):
# histdata[dataname] = content
#
# datafilepath = os.path.join(parentdir, 'pdf_data_local/' + resultsubdirname)
# rw.write_hdf5_dict(datafilepath, histdata)
#
err_data = {}
datanames = ['ux_mean', 'uy_mean', 'ux_center_temp', 'uy_center_temp',
'ux_err_mean_temp', 'uy_err_mean_temp', 'ux_err_center_temp', 'uy_err_center_temp']
for dataname, content in zip(datanames, errdata_list):
err_data[dataname] = content
datafilepath = os.path.join(parentdir, 'err_data/' + resultsubdirname)
rw.write_hdf5_dict(datafilepath, err_data)
except:
piv_data.close()
continue
piv_data.close()
jhtd_data.close()
print 'Done'
