def draw_trajectory(data_dict, show=True):
fig3, ax31 = graph.set_fig(fignum=3, subplot=111, figsize=(16, 10))
for domain_num in range(1, 5):
# these are lists of core positions
cxmax = data_dict["domain{0}".format(domain_num)]["cxmaxlist"]
cymax = data_dict["domain{0}".format(domain_num)]["cymaxlist"]
cxmin = data_dict["domain{0}".format(domain_num)]["cxminlist"]
cymin = data_dict["domain{0}".format(domain_num)]["cyminlist"]
core_positions = [[cxmax, cymax], [cxmin, cymin]]
# For plot
marker = ['v', '^', 's', 'o'] # makers corresponding to domains
fillstyle = ['full',
'none'] # Positive core: fill, Negative core: no fill
colors = graph.get_first_n_colors_from_color_cycle(4)
for j, core_position in enumerate(core_positions):
print core_position
cx, cy = core_position[0], core_position[1]
ax31.plot(cx,
cy,
color=colors[domain_num - 1],
marker=marker[domain_num - 1],
alpha=0.7,
fillstyle=fillstyle[j])
graph.setaxes(ax31, 0, imsize[1] * scale, 0, imsize[0] * scale)
graph.labelaxes(ax31, 'x [mm]', 'y [mm]')
ax31.invert_yaxis()
if show:
graph.show()
#time_csv = [x + 0.207 for x in time_csv]
vel_csv = [x / 6.2 * 5.3 for x in vel_csv]
#vel_csv = np.roll(vel_csv, int(len(vel_csv) * 0.237))
# Position Plot
fig1, ax1 = graph.plot(time_csv, commanded_pos_csv, label='Commanded position (software outputs)', fignum=1, subplot=211, figsize=(12, 8))
fig1, ax1 = graph.plot(time_csv, pos_csv, label='Actual position (software outputs)', fignum=1, subplot=211, figsize=(12, 8))
#fig1, ax1 = graph.plot(time_hdf5, y_pos_hdf5, label='Actual position (tracking result)', fignum=1, subplot=211, figsize=(12, 8))
fig1, ax1, color_patch1 = graph.errorfill(time_hdf5, y_pos_hdf5, fx, label='Actual position (tracking result)', fignum=1, subplot=211,
figsize=(12, 8), color='C2')
# Velocity Plot
fig1, ax2 = graph.plot(time_csv, commanded_vel_csv, label='Commanded velocity (software outputs)', fignum=1, subplot=212,
figsize=(12, 8))
fig1, ax2 = graph.plot(time_csv, vel_csv, label='Actual velocity (software outputs)', fignum=1, subplot=212, figsize=(12, 8))
fig1, ax2 = graph.plot(time_hdf5, v_hdf5, label='Actual velocity (tracking result)', fignum=1, subplot=212, figsize=(12, 8))
graph.setaxes(ax1, tmin, tmax, -0.2, span * 1.3)
graph.setaxes(ax2, tmin, tmax, -480, 150)
graph.legend(ax1, loc=1)
graph.legend(ax2, loc=4)
graph.addtext(ax1, 'Actual stroke length(tracking code): %.2f + %.2f mm' % (stroke_length_hdf5, 2*fx), option='bl', fontsize=10)
graph.addtext(ax1, 'Actual stroke length(software): %.2f mm' % stroke_length_csv, option='bl2', fontsize=10)
graph.labelaxes(ax1,'Time [s]', 'Position y [mm]')
graph.labelaxes(ax2, 'Time [s]', 'Velocity [mm/s]')
graph.suptitle(data_tracking_dir_tail)
# Draw a line where the two data were aligned
# graph.axvline(ax1, x=time_csv[vel_csv_right_ind], linestyle='--', color='k')
# graph.axvline(ax2, x=time_csv[vel_csv_right_ind], linestyle='--', color='k')
# Scatter plot where the two data (tracking code results and software outputs) were aligned
# if v_hdf5_left_ind+shift_arg >= time_hdf5.shape[0]:
savedir = resultsdir + '/time_evolution/'
filename = 'No_vortex_ring_detected'
graph.save(savedir + filename, ext='png', close=True)
continue
# Plot circulation, ring velocity, and diameter
#fig8, ax8 = graph.scatter(trForGammar, gammar, fignum=2, subplot=131,figsize=(20, 5))
fig8, ax8 = graph.errorbar(trForGammar,
gammar,
yerr=gammar_err,
fignum=2,
subplot=131,
figsize=(20, 5))
graph.labelaxes(ax8, 'Time ($s$)', 'Circulation ($mm^2/s$)')
if gammar_avg * 1.3 > np.max(gammar):
graph.setaxes(ax8, 0, time[-1], 0, gammar_avg * 1.3)
else:
graph.setaxes(ax8, 0, time[-1], 0, np.max(gammar))
text = 'Average $\Gamma$: %.1f' % gammar_avg + '$mm^2/s$'
graph.addtext(ax8,
x=(time[-1]) / 10.,
y=gammar_avg * 1.3 / 4. * 2,
text=text)
text = 'Std.: %.1f' % gammar_std + '$mm^2/s$'
graph.addtext(ax8,
x=(time[-1]) / 10.,
y=gammar_avg * 1.3 / 4.,
text=text)
fig9, ax9 = graph.scatter(trForVr,
vr,
gammas_uy_err.append(np.sqrt(np.diag(pcov))[1])
except:
lorentz_peaks_uy.append(np.nan)
gammas_uy.append(np.nan)
lorentz_peaks_uy_err.append(np.nan)
gammas_uy_err.append(np.nan)
pass
# Plotting stuff
axes = [ax11, ax12]
xlabels, ylabel = [r'$\Delta U_x$ (px/frame)', r'$\Delta U_y$ (px/frame)'], r'Probability density'
title = r'W=%dpx, $v$=%.1f px/frame (PIVLab, Original, Diff.)' % (iw, mag)
for ax, xlabel in zip(axes, xlabels):
graph.labelaxes(ax, xlabel, ylabel)
graph.setaxes(ax, -0.2, 0.2, 0, 80)
ax.legend()
graph.suptitle(title, fignum=1)
# fig1.tight_layout()
# Close hdf5 files
pivdata.close()
fdata.close()
figname = '/noisedist/pivlab_fakedata_comp_W%d_v%spxframe_multiple_passes' % (iw, mag_str_2)
graph.save(resultdir + figname)
# plt.show()
plt.close()
break
# elif args.mode == 'gradient':
# max = fs.get_float_from_str(pivdatum_loc, 'max', '_min') # max velocity
subplot=236)
# Plotting stuff
axes = [ax11, ax12, ax13, ax14, ax15, ax16]
ccs = [cc11, cc12, cc13, cc14, cc15, cc16]
cblabels = [
r'$U_x$', r'$U_x$', r'$U_x$', r'$U_y$', r'$U_y$',
r'$U_y$'
]
xlabel, ylabel = r'$X$ (px)', r'$Y$ (px)'
title = r'W=%dpx, $v$=%.1f px/frame (PIVLab, Original, Diff.)' % (
iw, mag)
for ax, cc, cblabel in zip(axes, ccs, cblabels):
graph.add_colorbar(cc, ax=ax, label=cblabel)
graph.labelaxes(ax, xlabel, ylabel)
graph.setaxes(ax, 0, xx0.shape[1], 0, xx0.shape[0])
graph.suptitle(title, fignum=1)
fig1.tight_layout()
pivdata.close()
fdata.close()
figname = '/pivlab_fakedata_comp_W%d_v%spxframe_multiple_passes' % (
iw, mag_str_2)
graph.save(resultdir + figname, ext='png')
plt.close()
break
elif args.mode == 'gradient':
max = fs.get_float_from_str(pivdatum_loc, 'max',
'_min') # max velocity
min = fs.get_float_from_str(pivdatum_loc, 'min',
# veff and vring_err
veff_list_for_vring, vr_err_list2 = fa.sort_two_arrays_using_order_of_first_array(
veff_list, vr_err_list)
# Circulation vs veff
fig1, ax1 = graph.errorbar(veff_list_for_gammar,
gammar_list1,
yerr=gammar_err_list,
fignum=1,
label=label,
fmt=fmt)
graph.labelaxes(
ax1, '|$\overline{v^2_p} / \overline{v_p} \alpha $| [mm/s]',
'$\Gamma [mm^2/s]$ ')
ax1.legend()
graph.setaxes(ax1, 0, 300, 0, 15000)
graph.save(base_dir + 'gamma_vs_Veff')
# Stroke length
fig2, ax2 = graph.plot(veff_list_for_actual_span,
actual_span_ratio_list1,
fignum=2,
marker=fmt,
label=label)
graph.labelaxes(ax2,
'|$\overline{v^2_p} / \overline{v_p}$| [mm/s]',
'$L/ L_c[mm^2/s]$ ')
ax2.legend()
graph.setaxes(ax2, 0, 300, 0, 1.5)
graph.axhline(ax2, 1)
graph.save(base_dir + 'strokelength')
fig1, ax11, cc11 = graph.color_plot(x, y, ux, vmin=-vmax, vmax=vmax, cmap=cmap, subplot=131, figsize=(20, 6))
fig1, ax12, cc12 = graph.color_plot(x, y, uy, vmin=-vmax, vmax=vmax, cmap=cmap, subplot=132, figsize=(20, 6))
fig1, ax13, cc13 = graph.color_plot(x, y, uz, vmin=-vmax, vmax=vmax, cmap=cmap, subplot=133, figsize=(20, 6))
axes = [ax11, ax12, ax13]
ccs = [cc11, cc12, cc13]
clabels = ['$u_x$ (px/unit time)', '$u_y$ (px/unit time)', '$u_z$ (px/unit time)']
for ax, cc, clabel in zip(axes, ccs, clabels):
graph.add_colorbar(cc, ax=ax, label=clabel)
graph.labelaxes(ax, '$X$ (px)', '$Y$ (px)')
graph.suptitle(r't= %.1f $ \tau_\eta$ = %.2f (unit time)' % (float(vel_keys[i][1:]) * dt / tau_eta, float(vel_keys[i][1:]) * dt), fignum=1)
#
fig2, ax2 = graph.pdf(ux, nbins=int(np.sqrt(ydim * xdim)), fignum=2, label='$u_x$ (px/unit time)')
fig2, ax2 = graph.pdf(uy, nbins=int(np.sqrt(ydim * xdim)), fignum=2, label='$u_y$ (px/unit time)')
fig2, ax2 = graph.pdf(uz, nbins=int(np.sqrt(ydim * xdim)), fignum=2, label='$u_z$ (px/unit time)')
graph.setaxes(ax2, -vmax, vmax, -0.05/100., 0.006)
graph.labelaxes(ax2, '$u_i$ (px/unit time)', 'Probability density')
graph.suptitle(r't= %.1f $ \tau_\eta$ = %.2f (unit time)' % (float(vel_keys[i][1:]) * dt / tau_eta, float(vel_keys[i][1:]) * dt), fignum=2)
ax2.legend()
filename1 = 'vel_field_t' + vel_keys[i][1:]
filename2 = 'pdf_vel_field_t' + vel_keys[i][1:]
graph.save(resultdir + filename1, fignum=1, ext='png')
graph.save(resultdir + filename2, fignum=2, ext='png')
plt.close('all')
fyle.close()
# # make movies
# movie.make_movie(resultdir + 'vel_field_t', resultdir + 'movie_vel_field', ext='png', framerate=10, option='glob')
# movie.make_movie(resultdir + 'pdf_vel_field_t', resultdir + 'movie_pdf_vel_field', ext='png', framerate=10, option='glob')
graph.legend(ax2, loc=4)
graph.addtext(ax1,
'Actual stroke length(tracking code): %.2f + %.2f mm' %
(stroke_length_hdf5, 2 * fx),
option='bl',
fontsize=10)
graph.addtext(ax1,
'Actual stroke length(software): %.2f mm' %
stroke_length_csv,
option='bl2',
fontsize=10,
alpha=0.5)
graph.labelaxes(ax1, 'Time [s]', 'Position y [mm]')
graph.labelaxes(ax2, 'Time [s]', 'Velocity [mm/s]')
graph.suptitle(data_tracking_dir_tail)
graph.setaxes(ax1, 0.15, 0.8, -0.5, 13)
graph.setaxes(ax2, 0.15, 0.8, -550, 200)
# Draw a line where the two data were aligned
# graph.axvline(ax1, x=time_csv[vel_csv_right_ind], linestyle='--', color='k')
# graph.axvline(ax2, x=time_csv[vel_csv_right_ind], linestyle='--', color='k')
# Scatter plot where the two data (tracking code results and software outputs) were aligned
# if v_hdf5_left_ind+shift_arg >= time_hdf5.shape[0]:
# fig1, ax1 = graph.scatter([time_hdf5[v_hdf5_right_ind + shift_arg - time_hdf5.shape[0]]], [v_hdf5[v_hdf5_right_ind + shift_arg - time_hdf5.shape[0]]], fignum=1, subplot=212,
# marker='x', color='C2', figsize=(12,8))
# else:
# fig1, ax1 = graph.scatter(time_hdf5[v_hdf5_right_ind+shift_arg], v_hdf5[v_hdf5_right_ind+shift_arg], fignum=1, subplot=212,
# marker='x', color='C2', figsize=(12,8))
# fig1, ax1 = graph.scatter(time_csv[vel_csv_right_ind], vel_csv[vel_csv_right_ind], fignum=1, subplot=212,
# marker='x', color='C1', figsize=(12,8))
fignum=7,
marker='o',
linestyle='-',
label=label)
fig8, ax9 = graph.plot(vp_commanded_list_for_vmin,
vmin_list,
fignum=8,
marker='o',
linestyle='-',
label=label)
plt.figure(7)
plt.legend()
graph.labelaxes(ax7, 'Commanded velocity [mm/s]',
'|$\overline{v^2_p} / \overline{v_p}$| [mm/s]')
graph.setaxes(ax7, 0, 1100, 0, 800)
# Save a figure
plotfilename = os.path.join(
tracking_settings['input_dir'],
'motor_characteristics/motor_characteristics')
graph.save(plotfilename, verbose=True)
# Save data in pkl
pickledir = os.path.join(tracking_settings['input_dir'],
'motor_characteristics/')
pickle_rw.write(vp_commanded_list_for_veff,
pickledir + 'vp_commanded_for_veff.pkl')
pickle_rw.write(veff_list, pickledir + 'veff.pkl')
plt.figure(8)
plt.legend()
graph.labelaxes(ax8, 'Commanded velocity [mm/s]', '|$v_{min}$| [mm/s]')
n_particles_list.append(float(pivlab_output[-8:-3]))
# get piv data size
pivdatasize = ux.shape
print nnon_nans_ux
if nnon_nans_ux / 4. < 200:
nbins = int(nnon_nans_ux * 0.25)
else:
nbins = 200
fig6, ax6 = graph.pdf(ux, nbins=nbins, fignum=6, label=label, alpha=0.7, linestyle=linestyles[i])
fig7, ax7 = graph.pdf(uy, nbins=nbins, fignum=7, label=label, alpha=0.7, linestyle=linestyles[i])
data.close()
plt.figure(6)
ax6.legend()
graph.labelaxes(ax6, '$U_x$ (a.u.)', 'Prob. density')
graph.setaxes(ax6, -4, 4, -0.1, 2)
filename = 'pdf_ux'
graph.save(resultdir + filename)
plt.figure(7)
ax7.legend()
graph.labelaxes(ax7, '$U_y$ (a.u.)', 'Prob. density')
graph.setaxes(ax7, -4, 4, -0.1, 2)
filename = 'pdf_uy'
graph.save(resultdir + filename)
# Number of nans as a function of density/number of particles
# Fig 8: Number of nans
uz0_coarse,
cmap=cmap,
vmin=-2,
vmax=2,
fignum=1,
subplot=224)
axes1 = [ax11, ax12, ax13, ax14]
ccs1 = [cc11, cc12, cc13, cc14]
titles1 = [
'Original $U_x$', 'Coarse-grained $U_x$', 'Coarse-grained $U_y$',
'Coarse-grained $U_z$'
]
for ax, cc, title in zip(axes1, ccs1, titles1):
graph.add_colorbar(cc, ax=ax)
graph.title(ax, title)
graph.setaxes(ax, 0, 2 * np.pi, 0, 2 * np.pi)
graph.labelaxes(ax, '$X$ (a.u.)', '$Y$ (a.u.)')
if cmap == 'RdBu':
ax.set_facecolor('k')
graph.suptitle('JHTD')
filename = 'jhtd_vel_fields_%s' % cmap
graph.save(resultdir + filename)
plt.close()
################
# PIV-processed data
################
# data architecture
pivlab_outputs = glob.glob(pivdatadir + '/*')
pivlab_output_names = [
os.path.split(filename)[1] for filename in pivlab_outputs
x=np.linspace(0,20,10) data = np.sin(x) ps = np.abs(np.fft.fft(data))**2 time_diff = np.diff(time) time_diff_avg = np.nanmean(time_diff) std = np.nanstd(time_diff) time_step = time_diff_avg freqs = np.fft.fftfreq(data.size, time_step) idx = np.argsort(freqs) ps[:]=[x / max(ps) for x in ps] fig1, ax1 = graph.plot(freqs[idx], ps[idx]) graph.setaxes(ax1, -10,10,0,1) plt.show() # # # Number of sample points # N = len(diameter) # # sample spacing # dt = 1.0 / 200.0 # t = np.linspace(0.0, N*dt, N) # x = np.sin(5.0 * 2.0*np.pi*t) + 0.5*np.sin(1.0 * 2.0*np.pi*t) # xf = fft(x) # print len(xf) # f = np.linspace(0.0, 1.0/(2.0*dt), N//2) # # # fig2, ax2 = graph.scatter(t, x) # graph.show()
basedir = "/Volumes/labshared3-1/takumi/2018_05_13_mid/freq1Hz/PIV_AnalysisResults/PIV_sv_vp_left_macro55mm_fps2000_D25p6mm_piston13p1mm_freq1Hz_v100mms_fx0p1259mmpx_setting1/time_evolution/"
marker = ['v', '^', 's', 'o'] # makers corresponding to domains
fillstyle = ['full', 'none'] # Positive core: fill, Negative core: no fill
colors = graph.get_first_n_colors_from_color_cycle(4)
for domain_num in range(1, 5):
filename = "time_evolution_data_domain{0}_before_collision.json".format(
domain_num)
filepath = basedir + filename
data = rw.read_json(filepath)
cx_max, cy_max = data["cxmaxlist"], data["cymaxlist"]
cx_min, cy_min = data["cxminlist"], data["cyminlist"]
core_positions = [[cx_max, cy_max], [cx_min, cy_min]]
fig, ax = graph.set_fig(fignum=3, subplot=111, figsize=(16, 10))
for j, core_position in enumerate(core_positions):
cx, cy = core_position[0], core_position[1]
ax.plot(cx,
cy,
color=colors[domain_num - 1],
marker=marker[domain_num - 1],
alpha=0.7,
fillstyle=fillstyle[j])
graph.setaxes(ax, 0, 127, 0, 90)
graph.labelaxes(ax, 'x [mm]', 'y [mm]')
ax.invert_yaxis()
filename = 'trajectories_1'
graph.save(basedir + filename)
graph.show()